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Demographic Patterns of Voter Turnout

Pune Municipal Corporation Elections 2017

Manasi Phadke (manasi.phadke@gmail.com) is a PhD scholar at the Gokhale Institute of Politics and Economics, Pune. Rajas Parchure (rajasparchure@gmail.com) is RBI Professor of Finance and Officiating Director, Gokhale Institute of Politics and Economics, Pune.

Municipal corporation elections in Maharashtra have been characterised by low voter turnouts. Local body administrations are interested in creating voter awareness programmes targeting the “rare” or “intermittent” voters. The article seeks to identify the social characteristics of such voters.

An earlier version of this paper was presented at the 7th Annual International Conference on Political Science, Sociology and International Relations (PSSIR 2017).

The authors thank the State Election Commission of Maharashtra for its support to the study. They are grateful to J S Saharia, Commissioner, State Election Commission of Maharashtra for inputs at the research and report-writing stages. The authors are grateful to the anonymous referees for their comments.

The study was carried out by the Gokhale Institute of Politics and Economics at the initiative of the State Election Commission, Maharashtra.

Municipal corporation elections in 10 major cities of Maharashtra were held on 21 February 2017. The elections in various municipal corporations of Maharashtra held between 2014 and 2017 are the fifth round of elections being conducted by the State Election Commission of Maharashtra (SECM), which was formed after the passage of the 73rd and 74th constitutional amendments in 1992. In the second, third and fourth round of elections (R2, R3 and R4 respectively) held in 2002, 2007, and 2012, the average voter turnout percentage across all 27 municipal corporations in Maharashtra was seen to be only around 56% (Parchure et al 2016a: 21). Thus, a major concern in municipal corporation elections in Maharashtra has been the poor voter turnout percentage.

Within Maharashtra, elections to the other local bodies have shown higher voter turnout percentages in the same rounds of elections. The average voter turnout in municipal councils and nagar panchayats in R2, R3 and R4 stood at 70.4%, whereas that in zilla parishad elections in the same rounds stood at 68.74% (Parchure et al 2016b: 8–9).

Literature documents the effects of low voter participation in elections. The act of voting can be regarded as one of the most important acts of a citizen’s engagement with democracy. Indeed for most citizens, it might be the only act by which they express their preferences for the kind of governance they desire. High voter turnouts indicate both the sign and the substance of the vitality of democracy. Conversely, low voter turnout signifies voter apathy and mistrust of political representatives. Low and declining voter turnout implies either (i) that political parties are failing to represent citizen interests adequately, (ii) that elected representatives are failing to deliver on their promises, or (iii) that citizens are losing faith in elections as the mechanism by which they can achieve representation. Whatever the reason, democratic countries devote substantial resources on electoral processes at multiple levels of governance in the hope that citizens will effectively exercise their mandates to participate in them and will achieve the representation of their preferences.

There is another equally important dimension of voter turnout. That is the “representativeness” of the turnout. If voters belonging to groups having distinct and well-defined characteristics
(by ethnicity, gender, sex, age, income, education, occupation, etc) are found to participate at rates that are significantly lower than the proportion that they represent in the voting-eligible population, then that increases the likelihood that their preferences will not be reflected in the election results and therefore, going a step further, that their interest, may be entirely overlooked by the elected representatives. The inequality of representation tends to normally favour the most influential class of citizens (Lijphart 1997). And this negates the fundamental principle of democracy; a democratically elected government is expected to translate public preferences of the entire society into public policies. Elections are the process by which citizens choose to whom they will give the right to raise and deploy public resources. Low voter turnouts and/or unrepresentative voter turnouts mean that citizens have been unable to convey their preferences regarding the public policies which they wish to be realised and thereby lead to misallocation of scarce public resources.

In India, after the passage of the 73rd and 74th constitutional amendments, local bodies have been given a constitutional existence. Going ahead, it is expected that local bodies will be the most effective mechanisms of ensuring effective service delivery at the grass-roots levels. However, as the data trends exhibit, if voters stay away from the process of electing representatives to the urban local bodies in Maharashtra, the process of ensuring effective service deliveries would be seriously undermined. Against the backdrop of such discussions, it becomes extremely important to have targeted voter awareness drives to increase political participation of the citizens.

Who are the city voters who stay away from the election process? Can we identify those voters or voter communities who are likely to be regular, intermittent and rare? Such an exercise would have immense value in terms of driving targeted voter awareness programmes by the SECM as well as by the local administration in the cities so as to increase the voter turnout percentage in the next election. A methodology was hence created to identify which voters are rare, intermittent and regular; and a voter survey was carried out prior to the elections in the Pune Municipal Corporation (PMC). The survey created some interesting insights on attributes of rare, intermittent and regular voters; these were duly shared with the SECM and the PMC, so that targeted voter awareness drives could be created (Parchure et al 2017). Accordingly, targeted programmes based on the findings of the survey were designed and implemented by the officials in the PMC. The voter turnout percentage for Pune was 52.82% in 2007 and 50.92% in 2012. With the multiple initiatives taken by the PMC, including targeted voter awareness drives, the voter turnout percentage in the 2017 elections increased to 55.5%.

This paper presents the methodology used to identify the rare, intermittent and regular voters in Pune. It also contains the policy recommendations submitted to the PMC so as to increase voter turnout in the elections.

Design of the Survey

Pune, with a population of 3.1 million, is the second largest city in Maharashtra. Known traditionally for its strengths in automobiles and the engineering industry, the city in the past one decade has shown tremendous growth in the information technology (IT) and education services. It is now recognised as one of the IT hubs in India.

This remarkable growth also, however, implies a tremendous growth in in-migration and an accompanying pressure on civic infrastructure. Villages at the fringe of the city were brought inside PMC limits over a period of time to create planned urban spaces for the burgeoning population. Thus, Pune grew in size laterally, with new areas being added to its circumference. In its present avatar, Pune can be distinctly divided into “Old Pune” and “New Pune.” Most of the Old Pune wards lie in the centre of the city, and are characterised by high population density, crowded streets and haphazard urban planning. Old Pune mostly consists of people who have been staying in Pune for more than about 20 years. These wards are also dotted with slums and pockets of low-income groups. The people in Old Pune wards are registered as voters with the PMC and engage with the polity actively since their wards do not really offer civic amenities of a level they would like. This automatically implies that the voter turnout percentage in Old Pune wards is higher than New Pune wards. Wards are created by municipal corporations so that the population in each ward is similar; due to the high population
density in Old Pune, the wards in Old Pune are smaller in area.

New Pune wards are mostly seen in the outer ring areas of the city. These wards are bigger in size area-wise and possess better and modern urban amenities. Most of the people living in these areas are recent additions to the population of Pune. Many of them are young, highly educated, high salaried and highly mobile IT professionals.

However, the availability of better amenities and planning has also attracted people living in Old Pune towards the more far-flung areas. Thus, the newer wards show mixed population profiles. People who have always stayed in Pune and speak the local language Marathi, coexist with people who have recently migrated to Pune and speak other Indian languages.

In the new wards, the first issue is that many people have not even registered themselves as voters with the PMC. Even those who have registered do not necessarily identify with local issues since they are relatively new to the city. The high income classes are not necessarily dependent on the PMC for some services such as water. For example, if water supply is disrupted, they have the financial muscle to order water tankers for their residences. Many of the residents in these wards are highly mobile and may not be in Pune on the day of elections. All of this has implied that the voter turnout percentage in the new wards has been quite low.

A voter study in Pune would necessarily need to account for these factors before creating a sampling plan that is representative of the population in the city. The next section elucidates the sampling plan that was created for Pune.

The two-stage sampling plan: The study was carried out in December 2016, two months prior to the PMC elections. Ward-level data on voter turnout percentage from PMC 2012 elections was used to create a robust sampling plan for the survey. As per the 2012 PMC database, there were 76 wards in Pune. It was decided to choose six wards for the study. A two-stage sampling method was used to select the number of voters for the study.

In Stage I, all wards in Pune were separated into low, medium and high voter turnout clusters, using simple cluster analysis. Since the research problem focuses on identification of the rare voters, the sample had to technically contain more wards from the low voter turnout cluster as compared to the high voter turnout cluster. The ratio of low voter turnout wards to high voter turnout wards was decided by studying the coefficient of variation (CV) for each cluster. The CV for high voter turnout wards was nearly half of that for the low voter turnout cluster. Hence, four wards were randomly chosen from low voter turnout and two wards were randomly chosen from high voter turnout clusters as a control group. While sampling, care was taken to see that the wards were spread in all directions within the city. Ensuring a geographical spread was important so that voters belonging to different socio-economic groups would get covered in the survey.

The low voter turnout wards chosen for the study were Kothrud, Balewadi, NIBM and Viman Nagar. Latter three of these wards lie on the outer side of Pune. The high voter turnout wards chosen for the study were Alka Talkies and Hadapsar. Alka Talkies lies in the heart of the city and is a typical, Old Pune ward. Hadapsar, though it lies on the outer side of the city, is an Old Pune ward and developed as an industrial area much before the advent of the IT sector in Pune.

Stage II of the sampling plan entailed choosing sampling sizes. For this, the number of voters in the ward was used as the basic frame to choose the sample. With 95% confidence level and 3.5% error of margin, a sample size of 300 was required in each ward. Across the six chosen wards in the sample 1,892 voters were interviewed for the study. Of these, 1,200 voters belonged to low voter turnout wards and 692 belonged to high voter turnout wards.

An examination of the voter lists revealed that the addresses of the voters were given in a very sketchy fashion, rendering it impossible to contact the voter using a systematic sampling plan. A simple right-hand rule was created to identify the household in which the survey would be conducted. A quasi-Kish grid methodology was developed to identify the respondent within the households so that gender and age biases would not occur.

The Political Interest Index

A study by the Pew Research Center (2006) focuses on why rare, intermittent and regular voters have differential voting behaviours in the United States (US). The focal point of the present study is not to ask why a certain voting behaviour is observed, but rather, to identify voter communities as rare, intermittent and regular voters, so that voter awareness programmes can be targeted effectively. We first construct a political interest index (PII) to eventually help segregate voters in Pune as rare, intermittent or regular.

How does one define “political interest”? Two components of political interest were defined. The first was the self-professed interest of the voter in local politics. The second was the participation of the voter in different political activities apart from voting. Using both these components, the PII was created.

Interest in local politics: “How interested are you in local politics?” This question was asked to the voter in the questionnaire and the voter was asked to self-assess their interest in local politics by giving a score from0 to 10, with0 indicating no interest and 10 indicating maximum possible interest.

Interestingly, 20% of the voters indicated “zero” interest in local politics. The average score for interest in local politics for Pune was seen to be 4.6. The score given by the voter themself on “interest in local politics” is treated to be Component 1 of the PII. The maximum value of this component is 10 and the minimum is0.

Participation in local politics: Does the voter’s engagement with political processes end with casting a vote? Or does the voter continually influence the political process and get influenced by it by taking part in activities such as political rallies and demonstrations, signing of petitions, candlelight marches, etc? Participation of the voter in these activities signals the readiness of the voter base to engage more deeply with the local politics and hence gives a signal of the voter being politically active. On the other hand, reluctance to participate in such activities implies that political activism of the voter is quite low.

In order to gauge the political participation quotient of the voter, the questionnaire carried the following question. “In which of the following social/political activities are you likely to participate in the future?”

(i) Sign a petition for a political cause, (ii) attend a demonstration, (iii) take part in a peace march/candlelight protest, (iv) attend a political meeting or rally, (v) volunteer for a political candidate, (vi) write a letter to a newspaper on political issues, (vii) place a call to a TV show on politics, and (viii) none of the above.

Nearly 60% of the respondents replied that they were not likely to participate in any of the activities mentioned. This showed that the basic level of involvement of the PMC voter base with the political processes was fairly weak. The most favoured political activity for participation was attending a political rally; 24% of the voters were likely to participate in a political rally. Another 14% were likely to volunteer for a political candidate and 13% of the voters were likely to sign a petition pertaining to a social or political cause.

While constructing the PII, participation of the respondent in the political activities forms Component 2. The respondent is given one point for saying that they participate in any one of the given activities. Thus, if the respondent says that they are likely to participate in three of the events given above, they get a score of 3 in Component 2. Higher the engagement of the voter into the polity, higher is the score they get. The maximum value of Component 2 is seven.

Construction of the PII: The minimum value of Component 1 is0 and maximum is 10. The minimum value of Component 2 is0 and maximum is seven. The values in both the components are added to get the total score for each individual voter. This total score is then divided by 17 and multiplied by 100 to normalise the maximum value of the PII to 100. The PII constructed in this manner was further aggregated at ward level and eventually at city level. The PII for Pune, constructed in this manner, stands at 31.45, indicating a low level of political interest amongst the voters across the city.

It is interesting to note that the PII for the Old Pune wards, characterised by low and middle income groups staying in Pune for a long time and having a high voter turnout percentage, is higher than that seen for New Pune wards. We next classify the wards as low voter turnout and high voter turnout and compare the PII for the clusters (Table 1). There seems to be a positive relation between PII and voter turnout. Higher the PII, higher is the voter turnout within the ward. The next section focuses on identifying the rare, intermittent and regular voters.

Identifying the Regular, Intermittent and Rare Voter

The last two rounds of PMC elections had been held in 2012 and 2007. In order to classify voters into rare, intermittent and regular, the questionnaire carried the following questions:

(i) Did you vote in the PMC 2012 elections? Y/N

(ii) Did you vote in the PMC 2007 elections? Y/N

These questions find an echo in the existing literature on classifying voters. Multi-election data on voting often serves to identify voters correctly because voter “characteristics stay fairly constant from one election to another” (Sigelman et al 1985).

Since the eligible age for voting in India is 18, voters currently less than 28 years of age would not have been eligible to vote in the 2007 and/or 2012 elections. In the survey 141 respondents belonged to this category and hence have not been classified as rare, intermittent or regular voters. The classification has been carried out on a database of 1,751 voters (Table 2).

Those voters who responded as having voted for both elections were to be classified as regular, those who responded that they had voted in one of the elections were to be classified as intermittent and those who responded that they had not voted in both were to be classified to be rare.

However, these questions created responses with a definite, albeit anticipated bias. It has been documented in the literature on the subject that voter responses to such questions tend to overestimate voter turnout by about 10% (Sigelman et al 1985). Given that the questionnaires were designed just to elicit responses of the voters without checking the validity of their responses, nearly 55% of the voters, when probed about the past two elections, responded by answering that they had voted in both the elections. Given that the average voter turnout in the 2012 PMC elections was just 50.92%, and given that the sample was being chosen in four low voter turnout wards and only two high voter turnout wards, it was obvious that 55% of the respondents could not have voted in both the earlier elections. Thus, there was a response bias towards replying in the affirmative to the question on whether they had voted in the past two elections.

A methodology had to be created to identify those voters who may indeed have voted in both the past elections in PMC. First, those who had replied that they had not voted in both the earlier elections were classified to be rare. Also, those who had replied that they had voted only in one of the past two elections were classified to be intermittent. The assumption was that there was no bias being observed in these replies.

Next, the attributes of the rare and the intermittent voters were examined. A very interesting trend came to light. It was found that the median PTI value for the rare and intermittent voters was 29.1. Given the positive correlation between the PII and voter turnout and given the median PII for rare and intermittent categories at 29.1, it would be reasonable to conclude that regular voters would have PII values of more than 29.1.

Now, some of the voters who had claimed that they were regular and had voted in both the past elections too showed a PII value of less than 29.1. The PII value was used to identify those respondents whose replies carried a bias and these respondents were then reclassified as intermittent voters. Those voters who had replied that they had voted in both the earlier elections and had a PII value of more than 29.1 were retained within the data set as “regular” voters. Table 3 indicates the final percentage of rare, intermittent and regular voters within the sample.

About 36% of Pune citizens vote regularly, and another 27% vote rarely. Thirty percent of the voters in Pune are intermittent voters. It is the rare and the intermittent voters that have to be reached through the voter awareness efforts and through innovative campaigns so as to convert them into the regular voting category. However, for the local bodies to run targeted voter awareness drives, attributes of the rare and the intermittent voters have to be identified.

Attributes of the Regular, Intermittent and Rare Voters

The problem of economics is to allocate scarce resources efficiently so as to achieve the desired objectives. In running voter awareness programmes, the municipal corporations actually face an economic problem. They have limited finances with which to create a massive amount of voter awareness. The solution of the economic problemme of municipal corporations is to run targeted voter awareness programmes so that lesser funds are spent more usefully. This section of the paper offers insights into attributes of the rare and intermittent voters of Pune, which could be used by the PMC to solve its economic problem.

Voter classification in low and high voter turnout wards: Should the PMC run voter awareness programmes with equal intensity in all wards? Or should it allocate more funds to these programmes in those wards where the voter turnout in earlier elections has been low? Data collected in our study goes to support the latter idea.

Table 4 indicates that the percentage of rare voters in low voter turnout wards is much higher as compared to the same in high voter turnout wards. Thus, the data support the idea that voter awareness programmes should be focused more in the low voter turnout areas as compared to high voter turnout wards.

Voter classification by gender: Voter classification by gender offers interesting insights. Female voters are mostly rare (30.30%) or intermittent (31.20%) voters; hence the awareness campaign needs to focus more intensively on getting the women voters to vote. Only 32.10% of the female voters from across all wards were found to be the regular voters as compared to 38.80% males (Table 5).

Voter classification by age: The middle aged population of Pune forms the most solid voter base, with 44.2% of the middle aged voters getting classified as regular voters. Similar numbers are also observed for the elderly citizens above 51 years of age. The truly worrisome voting percentages are seen in the youth of Pune, with only 23% voting regularly and 38.3% getting classified as rare voters (Table 6). This implies that in creating targeted voter awareness programmes, the PMC would have to pay special attention in getting the youth to vote.

Voter classification by education: Regularity in voting in Pune is negatively correlated to educational status; 40.6% of the illiterate voters are regular whereas only 26% of the graduate voters are regular (Table 7).

Voter classification by socio-economic groups: Using the primary survey data, voters were also classified into different socio-economic groups. In order to do so, the “New Socio-Economic Classification (SEC) System” by the Media Research User’s Council (MRUC) to classify Indian households into different socio-economic groups was used (Market Research Society Report 2011). The new SEC model uses the education level of the main earner of the family together with the number of assets owned by the family to arrive at the of the respondent. The voters belonging to socio-economic classification “A” belong to well-educated families which own reasonable number of assets. Those belonging to socio-economic classification C belong to poor families where the main earner is not well-educated. Table 8 shows the percentage of rare, intermittent and regular voters in different socio-economic groups.

Since the proportion of regular voters is the highest in the middle income groups (38.20%) and that of rare voters is very low (25.1%), one may well claim that it is this group which forms the most solid voter base in pmc limits. The proportion of rare voters in both the affluent (26.80%) and low income group category (31.10%) is found to be substantially higher.

Voter classification by native language: Marathi is the local language used in Pune. Table 9 examines the patterns in rare, intermittent and regular voters when the voters are classified as per their native language (mother tongue).

It does seem to be the case that language matters while examining voting probabilities. Marathi is the main language spoken in Pune. Nearly 39% of the Marathi-speaking population are regular voters, whereas only 23% are rare. On the other hand, for almost any other native language, one finds that the proportion of regular voters is much lower and that of rare voters is much higher. Thus, it is the newly migrated, non-Marathi population on whom the voter-awareness programmes need to be focused. The same issue of in-migration can also be examined by looking at voter classification as per the number of years of stay in Pune.

Voter classification by number of years of stay in Pune: The number of years of stay in Pune matters sharply in terms of impacting voting frequency. Nearly 42% of those who have been staying in the city for more than 10 years are regular voters. As the number of years of stay in the city increases, the proportion of regular voting also goes up (from 11.80%– 42.30%) and the rare voting declines (from 61.60% to 7.70%) (Table 10, p 48).

This perhaps also explains why voter turnout is low in both higher and lower socio-economic classifications. It is observed that most in-migration happens in the low and high income groups. The low income groups witness in-migration of day-labourers, construction workers, carpenters, odd-jobs men and women, who obviously come to the city in search of jobs. The high income groups have witnessed a high level of in-migration of IT engineers, who have flocked to the city which has rapidly developed as an IT hub. Since it is in the high and low income categories that one has witnessed maximum migration, it is in these categories that the proportion of people who may have spent less than five years in Pune could be high. Since the number of years of stay matters, one finds higher percentage of voting in the middle income groups and lower voting percentages in the high and low income groups.

Voter classification as per marital status: Out of the married voters 41.10% are regular voters, while only 25% of the same are rare. In contrast, only 11.9% are regular whereas 34.5% of the unmarried people are rare voters (Table 11). Thus, there are obvious differences in the political behaviour of married and unmarried voters, thereby pointing to the presence of an Indian “marriage gap” (Zengerle 2012). Unmarried people are also likely to be young and hence this trend again suggests that it is the youth population of the city which forms the reluctant voter base and has to be targeted through voter awareness programmes.

Probability of Being a Regular Voter

The above discussion indicates that there are certain citizen demographics that have a higher percentage of rare voters. These are:

(i) Female voters, (ii) young voters in the age group 18–35, (iii) the highly educated voters, (iv) voters belonging to the high income groups, (v) the non-Marathi-speaking voters, (vi) voters with a stay of less than 10 years in Pune, and (vii) unmarried voters.

It is hence, amongst these seven voter categories that voter awareness programmes are truly needed. In order to understand which of these voter demographics are statistically significant, a probit model was run. Voters who were classified as rare and intermittent were regrouped as “irregular” for the purpose of the probit. The regular voters were retained in their category.

A dummy variable was created to represent voter classification; the dummy was assigned value 1 if the voter was regular and0 if the voter was irregular. Dummy variables were also created for gender, marital status and number of years of stay in Pune. Male voters were assigned value 1 and female voters were assigned value 2. Similarly, married voters were assigned value 1 and unmarried voters were assigned value 2. Data on number of years of stay of the voter were already classified as less than five years, between 5 and 10 years and more than 10 years; these three categories were assigned values 1, 2 and 3 respectively.

The dependent variable is the probability of being classified as regular. The probit model checks whether the probability of being classified as regular depends on the seven demographics identified above. The probit results show that the age, gender, marital status and number of years of stay are highly significant in explaining the regularity of voting. The signs of the coefficients are along expected lines. As the age of the voter and number of years of stay increase, the probability of the voter being regular increases. That females and the unmarried voters have a lower probability of being regular voters is indicated by the negative sign on those coefficients (Table 12).

In order to interpret the results, we next carry out slope-at-mean analysis (Table 13).

The slope-at-mean analysis suggests that an increment in the age of the voter increases the probability of her voting by only 0.29%. However, as the number of years of stay increase from one category to another, the probability of voting regularly goes up by 17.8%. Clearly, the number of years of stay is highly significant in terms of determining the probability of being a regular voter. A female voter has 6.8% lesser probability of being a regular voter and unmarried voters would have 5.1% less chances of being regular.

Conclusions and Policy Suggestions

The above discussion clearly highlights those demographics that are associated with low probabilities of being regular voters. Newly migrated voters, female, unmarried and young voters tend not to vote regularly in Pune.

These four voter categories need to be primarily targeted through voter awareness programmes in Pune. It is a fairly challenging job for the local administration of the municipal corporations to come out with all-encompassing and effective voter awareness programmes. The above research, however, indicates that voter awareness programmes need to be run more intensively for these particular sections of the citizenry in Pune.

Following were some of the policy suggestions given to the PMC based on the study:

(i) In the sample, 31% of the respondents were not registered voters, even if they were eligible voters. A higher percentage of non-registered voters were seen in the new city wards of NIBM, Vimannagar and Balewadi. Most IT-savvy respondents in these areas shared informally that the process of getting registered as a voter was physically tiresome and time-consuming. If the PMC could host a permanent online election-kiosk to set up registration appointments, it may encourage people to register themselves. The election-kiosk should function permanently, not just in the annual run-up to the election.

(ii) Some mini-kiosks could also be hosted in colleges and voter registration of the youth could be tied up to the admission process of the colleges through these mini-kiosks. Of course, this will help the cause of voter registration in the medium or long run and should not be seen as a measure of increasing voter turnout in the immediate, up coming elections.

(iii) The voting percentage is low in the age group between 18 and 35 years. Voter awareness programmes need to be run more intensively in colleges; street plays, posters, and hoardings need to be put up in colleges and technical institutes.

(iv) The children (if any) of voters belonging to the 18–35 age group are likely to be quite small; they would be studying in pre-primary or primary schools. In pre-primary and primary sections, the children fill out a daily diary everyday, which they have to get duly signed from their parents. If the PMC could issue guidelines to schools to dictate a simple line such as “Vote for securing the future of your child” to children in the week prior to elections in this daily diary, the message will reach the targeted age group strongly.

(v) Hoardings or any visual art work created for voter awareness should have special emphasis on the youthful voters and there should be dedicated artwork for encouraging women voters to exercise their right to vote.

(vi) Shopping malls, retail grocery centres, ladies changing rooms in shopping malls could be used for displaying the posters urging women voters to exercise their right.

Brand ambassadors for voter awareness campaigns could be women. The newly migrated people come to Pune in search of a job. At least those who are employed in the formal, corporate sector can be reached effectively. The PMC could request corporate bodies to host voter awareness programmes on employee email networks. Human Resource departments could be requested to host small reward programmes for all employees showing the indelible ink mark on their finger the next day.

From an administration perspective, creating voter awareness prior to elections requires a two-pronged strategy. On the one hand, the local administration needs to create an all-pervasive awareness drive. On the other, it also needs to carry out targeted awareness programmes so that the intermittent voters could be encouraged to vote. Identification of demographic characteristics associated with lower probability of voting is the first step in creating effective voter awareness programmes. Some effort by the local body administrations in this direction could lead to more efficient voter awareness drives.

References

Lijphart, A (1997): “Unequal Participation: Democracy’s Unresolved Dilemma,” American Political Science Review, Vol 91, No 1, pp 1–14.

Parchure, Rajas, Manasi Phadke and Dnyandeo Talule (2016a): “Municipal Corporation Elections in Maharashtra: A Data-based Analysis,” Gokhale Institute of Politics and Economics, Pune, October, p 21.

— (2016b): “Local Body Elections in Maharashtra: A Comparative Analysis,” Gokhale Institute of Politics and Economics, Pune, October, pp 8–9.

— (2017): “Why People Do Not Vote in Municipal Corporation Elections: A Voter Survey in Pune Municipal Corporation,” Gokhale Institute of Politics and Economics, Pune, February.

Pew Research Center: US Politics and Policy (2006): “Who Votes, Who Doesn’t and Why: Regular Voters, Intermittent Voters and Those Who Don’t,” October, http://www.people-press.org/2006/10/18/who-votes-who-doesnt-and-why/2/.

Sigelman L, P W Roeder and M E Jewell et al (1985): “Voting and Nonvoting: A Multi-election Perspective,” American Journal of Political Science, Vol 29, No 4, pp 749–65.

The Market Research Society of India and Media Research Users Council (2011): “Socio-economic Classification 2011: The New SEC System,” May, http://imrbint.com/research/The-New-SEC-system-3rdMay2011.pdf.

Zengerle, Patricia (2012): “Married vs Unmarried Could Be the New Gender Gap,” October, http://blogs.reuters.com/talesfromthetrail/2012/10/24/married-v-unmarried-could-be-the-new-election-gender-gap/.

Updated On : 6th Nov, 2018

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