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Electoral Studies 21 (2002) 69–89
www.elsevier.com/locate/electstud
Predicting the 1998 Indian parliamentary
election
Rajeeva L. Karandikar a, Clive Payne b,*
, Yogendra Yadav c
a
Indian Statistical Institute, Delhi, India
b
Nuffield College, New Road, Oxford, OX1 1NF, UK
c
Centre for the Study of Developing Societies, Delhi, India
Abstract
An account is given of the methods used to predict the results of the 1998 Indian parliamen-
tary election by a team from the Centre for the Study of Developing Societies, Delhi. The
special features of the Indian party and electoral system and the election-night context as they
relate to the problem of election forecasting are discussed. The methods developed for the
polling of the Indian electorate and for forecasting the composition of the parliament from the
sequence of results declared are presented. 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Indian general election; Prediction; Opinion polls
1. Introduction
Forecasting the composition of parliament from opinion polls and on election night
when results are declared, are integral parts of the media coverage of elections in
all the major democracies. In the last fifteen years, the Indian media has also taken
to serious election forecasting. But the Indian electoral and party system and its
‘election-night’ context has many particular features which present the forecaster
with both special problems and opportunities. We first give a brief description of
the Indian context and review the particular problems of election forecasting in the
Indian context. We then describe the forecasting methods developed by a team from
the Centre for the Study of Developing Societies, Delhi (CSDS — two of the authors
were members and the third author was a consultant), for the opinion poll conducted
* Corresponding author. Tel.: +44-1865-278713; fax: +44-1865-278725.
E-mail address: clive.payne@nuf.ox.ac.uk (C. Payne).
0261-3794/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved.
PII: S 0 2 6 1 - 3 7 9 4 ( 0 0 ) 0 0 0 4 2 - 170 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 for India Today — a leading news weekly in India — and for the live television coverage of the results of the Indian general election in 1998 on Doordarshan, India’s public terrestrial channel. Special attention is given here to comparing the Indian forecasting problems with those encountered in other plurality systems, particularly those of the UK. We conclude with a description of a new method developed for ‘election-night’ forecasting which performed well in the live TV programme. 2. Election forecasting in plurality systems The main aim of election forecasting is to predict the number of seats won by each party in the parliament; a subsidiary aim is to also predict the vote shares for the parties. Election forecasting has three main stages, each of which uses different types of data source and presents different methodological problems: (1) Prediction before the election using voter-intention polls — ‘poll projection’. Here the task is to convert predicted vote shares from a national opinion poll into predicted seats in the legislature. This can be a challenging problem in some plurality systems. A standard approach is to estimate changes in vote share for each party from the poll and apply these uniformly in constituencies to predict the winning party. In Section 4 we discuss opinion polling in India while Section 5 sets out the method developed for converting vote shares into seats which is specially designed to cope with particular features of the Indian elections, especially the complex and changing party system within a federal structure. Section 6.1 discusses the perform- ance of the CSDS–India Today projections based on a pre-election poll. (2) Prediction immediately after the polling stations have closed — a ‘prior fore- cast’. A frequently used approach is to conduct an exit poll of voters as they leave polling stations to estimate changes in vote shares. Often the exit polling data is supplemented with data obtained from opinion polls conducted as close as possible to polling day. Another possibility is a ‘post-poll’ where a poll is conducted on the day or the days after the election but before results are declared, asking voters how they had actually voted. Such polls are feasible in India where there is a gap between voting and the declaration of results with the staggered elections that take place over a period of two weeks. Section 6.2 describes the conduct and performance of the post poll conducted by CSDS for the 1998 election results TV programme. (3) Predictions made during the sequence of election results as they are declared — the ‘results-based forecast’. This may involve fitting statistical models for the changes in party shares using the subset of results declared and then using the coef- ficients so estimated to produce predicted changes in vote share, and hence winning parties, in the undeclared constituencies. An alternative and simpler approach is to define types of constituency and use declared results for each type to estimate the mean changes in vote share which are then applied to undeclared seats of the relevant type to predict the winning party. This result-based prediction may also include prior information obtained from exit and opinion polls. The special issue of the New Zea- land Statistician (Morton, 1990), gives descriptions of election-night forecasting in other countries while Brown et al. (1999) deal with election-night forecasting in the
R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 71 1997 UK general election in particular. Section 5.4 describes the method developed for the Indian ‘election-night’ context and Section 6.3 presents its performance dur- ing the results sequence. The method and data sources used for these tasks are determined by features of the electoral and party system to which we now turn. 3. The Indian context The Indian Parliament consists of two houses — the Upper House or the Rajya Sabha (council of states) and the Lower House or Lok Sabha (house of the people). It follows the standard conventions of a parliamentary system where the party or a coalition enjoying the support of a majority of members in the lower house, the Lok Sabha, forms the government and its leader holds the office of Prime Minister and forms the Cabinet, which runs the national government. The term of the Lok Sabha is five years but it can be dissolved earlier on the advice of the Prime Minister. This happened in 1997, when the Lok Sabha was dissolved after only 18 months, thus paving the way for the mid-term elections in 1998. The following features of the parliamentary elections have a direct bearing on the challenge of forecasting. 3.1. The electorate In the 1998 elections the total electorate was a little above 600 million, the largest in any democratic election in the world. The range of social, ethnic, cultural, religious, linguistic and regional diversity that characterises this electorate is unmatched within any single nation-state in modern times. The Indian Constitution recognises 15 languages as official languages, while the last Census had counted more than 700 dialects. The last grand anthropological exercise of enumerating the social communities in terms of caste/tribe produced a count of more than 4000 (Singh, 1992). To make matters worse for the pollsters, the social cleavages relevant to the elec- toral politics do not follow any standard pattern all over the country. More often than not the politically salient division is that of caste or a cluster of castes brought together by local context. But in many cases the religious, class or regional-linguistic cleavage cuts across and over-rides the caste cleavage. These patterns vary so much across the different states of the Indian union that one cannot apply any nation-wide social classification for purposes of sampling, weighting for differential non-response and projections. These features present survey designers with the challenge of choosing a sample which does justice to such a complex pattern of diversity. The sampling problem is actually more difficult than it appears, for reliable information about most of these social attributes is either not available at all or is not available at the level of the sampling units. The regular decennial Census of India does not record caste except in the case of Scheduled Castes and Tribes. While the religion of the ‘head of the household’ is recorded, the religious composition of the population is not disclosed at the level of a locality, let alone for households. Carrying out an opinion poll or
72 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 any other survey itself is a gigantic task. An average constituency covers more than 6000 km2 (compared to about 350 in Britain). It is not unusual for a field researcher to take a full day to travel from one location to another within the same parliamentary constituency. The vast geographical spread and poor transport communication makes a door-to-door survey very time consuming and expensive. However, the households with telephones represent a very small proportion (about 3–4%) of the population, and are mostly confined to the urban elite. Therefore a survey based on randomly generated telephone numbers is completely ruled out. Besides, a large number of the electors are still illiterate or near illiterate, thus ruling out a certain range of questions and some techniques of interviewing. 3.2. The FPTP electoral system India follows the first-past-the-post or plurality system of elections which makes seats prediction more difficult than in any system based on proportional represen- tation. The Lok Sabha has 543 elected members each representing a constituency (or ‘seat’) where they have to obtain a plurality of the valid votes cast. The consti- tution provides for a regular redrawing of the constituency boundaries after every decade to maintain constituencies with similar sizes of electorate. However, a consti- tutional amendment resulted in freezing the delimitation from 1990 until 2001. The migration of population to big cities and towns and the differential rates of population growth in this period have resulted by now in a considerable variation in the size of constituencies (McMillan, 2000; Sivaramakrishnan, 1997). In 1998, while the average number of electors per constituency was 1.12 million, there were 12 constitu- encies with less than 0.5 million electors and 21 with more than 1.5 million. 3.3. The state as the effective political arena One of the most striking developments of what has been described as India’s ‘third electoral system’ in the last decade has been the emergence of the state as the effec- tive unit of political choice and electoral competition (Yadav, 1999). While India has always been a federal polity — a ‘union of states’ as the constitution describes it — it is only recently that the states have become such a decisive political unit rendering the national election into no more than a simultaneous election held in all the states. The country is divided into 25 States and 6 ‘Union Territories’ directly administered by the central government. There is a huge variation in the size of the states. Some of the states are very big (like Uttar Pradesh, which accounts for 16% of seats in the Lok Sabha) while others have only one member in the Parliament. Each state has a legislative Assembly, whose members are elected directly, also by the plurality system. The assembly constituency (or ‘segment’) is a basic unit in the Indian electoral system and several (usually between 5 and 8) assembly segments together make one Lok Sabha constituency. Initially, in 1952 and 1957, the elections to all the state legislative assemblies and the Lok Sabha were held concurrently. Since then most state assembly elections have been held at a time different from the Lok Sabha elections. Unlike the UK, this hierarchical structure of state assembly
R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 73 segments contained within parliamentary constituencies, means that assembly-level voting data can be used to provide good estimates of recent voting behaviour at the parliamentary level and, in particular, can provide a useful basis for sample selection for polling exercises. 3.4. The counting process There are three major differences between the counting process followed in India and the typical ‘election night’ of the UK. First, the counting usually begins 36 hours after the voting is over, so as to allow one day for transportation of the ballot boxes from remote areas. So, strictly speaking, there is no Indian equivalent of the UK ‘election night’. The counting begins on the morning of the day after voting and then continues uninterrupted until it is over. Secondly, the entire counting process takes much longer. The completion of the count can take from 12 hours in the smaller constituencies to up to 48 hours in some of the large constituencies where counting has been traditionally a slow process. Thirdly, because the completion takes so long, partial counts are officially declared. All the ballot boxes from the various polling stations within a given assembly segment are collected at one place and the ballot papers are mixed (after verifying the number of ballots from each polling station against the record filed by the election officer of the polling station) so as to avoid detection of the voting pattern within each polling district. Subsequently, they are randomly divided into 10 to 12 lots and each lot is taken up in turn for counting. The results of a lot (or ‘round’) are announced and then the next lot is counted and so on. This procedure is followed simultaneously for each assembly segment of every parliamentary seat. 3.5. The party system On the face of it, India in the 1990s looks like an extreme example of multi-party fragmentation. No less than 40 parties were represented in the Lok Sabha after the 1998 elections. But the value of the Effective Number of Political Parties index was 6.9 in the same year. The figure drops sharply to 3.0 if we look at the state level average in the same election and to 2.7 at the level of the parliamentary constituency (Heath, 1999). The system is best described as ‘multiple bipolarities’: the party sys- tem in most of the states has evolved into a two-party competition, but the pairing varies from state to state. There are, to be sure, exceptions to the biparty norm. The largest state of Uttar Pradesh witnessed effective three-way and often four-way con- tests in the 1998 elections. West Bengal witnessed a four-way contest. There were many states like Karnataka, Kerala, Tamil Nadu and Orissa where there were more than two major political parties in the electoral fray, but they formed a coalition. Looked at from the vantage point of the national election, it appears to be like a multi-party system. No single party exists as a viable electoral force across all the states. To make matters worse for the psephologists, these parties split, merge and split again producing a very confusing picture at times. Between the 1996 and 1998 Lok Sabha elections major splits took place in the states of Bihar, Karnataka, Orissa
74 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 and West Bengal which altered the electoral equation fundamentally. These parties form various kinds of alliances — ranging from enduring coalitions to temporary and informal ‘seats adjustments’ — with one another. The AIADMK, a regional party in Tamil Nadu formed an alliance first with the Congress in 1996, then with the BJP in 1998 and then back with the Congress in the 1999 elections. Furthermore, the same party can form an alliance with a major party in one state (or even part of a state) while contesting on its own in other states (or in the remaining part of the state). In Bihar, the Congress and the RJD had an alliance in the northern and the central region but it did not extend to southern Bihar. Party loyalty and identification is highly fluid in India. The high degree of voters’ alignment that characterises the stable party systems of Western democracies is larg- ely missing in India. This results in an unusually high rate of aggregate and individual volatility across elections. Between 1977 and 1998, all the parliamentary elections had witnessed a minimum of 10 per cent aggregate volatility. Between two parlia- mentary elections, a major political party often retains only between 50 to 65 per cent of the votes it secured last time (Yadav, 1999). Some of this sharp fluctuation can take place very close to the elections, as happened in the 1998 elections. This constant state of flux renders past electoral results, except the most recent one, rather unhelpful to the task of prediction. It also makes it difficult to say which of the constituencies are ‘marginal’. 3.6. Structure of party competition in the 1998 elections An analysis of the three main blocs fighting the 1998 election serves to illustrate the nature of the party system and the challenge it poses for the task of election pre- diction. (1) The centrist Congress party, or the INC, which won the first five elections for the Lok Sabha and ruled the country for all but four years between 1952 and 1996, constituted the first bloc largely on its own. The party has undergone many splits, but each split has left only one major survivor. The main inheritor of the Congress legacy is currently called The Indian National Congress (I), the ‘I’ standing for Indira Gandhi. It was led in the 1998 election campaign by Sonia Gandhi, widow of Rajiv Gandhi who was Prime Minister from 1984 to 1989 and was assassinated in 1991 during the Lok Sabha elections. It had forged an alliance with two parties in the State of Maharashtra, the Republican Party of India (RPI) and Samajwadi Party (SP). In Bihar, in about half the constituencies, it had an alliance with Rashtriya Janata Dal (RJD). In the State of Kerala it continued its old alliance, the UDF, with two regional parties, the Muslim League and the Kerala Congress. (2) The other major bloc was led by the right-wing Bhartiya Janata Party (literally, Indian Peoples Party), or the BJP, a party that advocates a pronounced Hindu majorit- arian ideology. The BJP emerged as the single largest party in the Lok Sabha elec- tions in 1998 and led the coalition government with Atal Bihari Vajpayee as the Prime Minister, before it lost a confidence vote in April 1999 and bounced back to power in the mid-term elections held in September 1999. While in the past the BJP had few allies and had no presence in the south and the east, in 1998 it forged
R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 75 alliances with regional parties and breakaway groups in almost every state. In 1998 its partners were: Akali Dal (in Punjab), Shiv Sena (in Maharashtra), Lok Shakti (breakaway faction of Janata Dal led by Hegde in Karnataka), AIADMK (and its allies- MDMK, PMK, TRC and JP in Tamil Nadu), Biju Janata Dal (in Orissa), Telagu Desam Party (the ‘NTR’ faction led by Laxmi Parvati in Andhra Pradesh), Trinamool Congress (breakaway Congress group led by Mamata Banerjee in West Bengal), Samata Party (in Bihar), Haryana Vikas Party (in Haryana). The alliance carried no name then and was christened the ‘National Democratic Alliance’ in 1999. (3) Several left-of-centre parties, including the two communist parties and the Janata Dal (which was once a major force having had three Prime Ministers from its ranks) along with some regional parties contested the 1998 parliamentary election under the banner of the ‘United Front’. These parties had formed the United Front as a coalition after the 1996 parliamentary election and had formed the government, with the support from Congress party. The government lasted about 18 months. Besides Janata Dal and the Left Front (itself a coalition of four parties: CPI, CPI (Marxist), RSP and Forward Bloc), this decidedly more heterogeneous alliance included the following regional parties: Samajwadi Party (Uttar Pradesh), the ‘Naidu’ faction of Telagu Desam Party (in Andhra Pradesh), Assam Gana Parishad (Assam) and the TMC and DMK (in Tamil Nadu). Table 1 shows the state-by-state outcome for the major alliances in the 1998 Lok Sabha election. 3.7. The Indian forecasting challenge To summarise, making a seats forecast in an Indian election presents four kinds of challenge specific to the Indian situation: 1. The conceptual challenge of converting votes into seats in any plurality system but with the added problems of the system of ‘multiple-bipolarities’ and the practice of shifting alliances; 2. The methodological challenge of designing a representative, efficient and feasible survey for the electorate of the kind described above; 3. The operational challenge of data collection and data transmission, given the large distances and the difficult transport conditions involved; and 4. The statistical challenge of devising a model for results-based forecasting that takes into account the Indian system of partial counts. We first look at how earlier attempts to make election forecasts in India had responded to some of these challenges and then turn to our own methods used in the 1998 elections. 4. Past election forecasts in India Viewed from the perspective of the enormous challenge which faces any election forecaster in India, the performance of Indian pollsters has been very good, at least
76 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89
Table 1
Seats won by major alliances in the 1998 electiona
State Seats BJP+ INC+ UF Other
Andhra Pradesh 42 4 22 15 1
Arunchal Pradesh 2 – – – 2
Assam 14 1 10 – 3
Bihar 54 29 5 1 19
Goa 2 – 2 – –
Gujarat 26 19 7 – –
Haryana 10 2 3 – 5
Himachal Pradesh 4 2 1 – 1
Jammu & Kashmir 6 1 1 – 4
Karnataka 28 16 9 3 –
Kerala 20 – 11b 9 –
Madhya Pradesh 40 30 10 – –
Maharashtra 48 10 37c – 1
Manipur 2 – – 1 1
Meghalaya 2 – 2 – –
Mizoram 1 – – – 1
Nagaland 1 – 1 – –
Orissa 21 16 5 – –
Punjab 13 11 – 1 1
Rajasthan 25 5 18 – 2
Sikkim 1 – – – 1
Tamil Nadu 39 30 – 9
Tripura 2 – – 2 –
Uttar Pradesh 85 59 – 20 6
West Bengal 42 8 1 31 2
A & N Isls. 1 – 1 – –
Chandigarh 1 1 – – –
D & N Havelli 1 1 – – –
Daman & Diu 1 1 – – –
Delhi 7 6 1 – –
Lakshadweep 1 – 1 – –
Pondicherry 1 – – 1 –
Total 543 252 148 93 50
a
Source: CSDS Data Unit.
b
Includes 3 seats won by independent candidates.
c
Includes 4 RPI seats.
in the last decade or so. And this despite the fact that nation-wide opinion polls and
election forecasts based on these are of recent origin in India. The huge size of the
country and lack of funds required to conduct such an extensive polling exercise has
mitigated against the regular use of polls in the past. After some unsuccessful
attempts in the late 1960s and early 1970s, election forecasting in India came of age
in the parliamentary elections held in 1980 and 1984 when the English-language
magazine India Today backed the efforts of Ashok Lahiri and Prannoy Roy to under-
take a sophisticated analysis and forecast of the Indian elections. They abandonedR.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 77
the earlier attempts to use crude methods like the cube law or to discover a stable
vote–seat conversion ratio. David Butler joined the team as they went on to innovate
methodologically. Their main innovations were the development of a simplified ver-
sion of swing (denoting percentage change in vote for the then dominant party,
Congress) to measure shift in votes and a new Index of Opposition Unity (proportion
of the total votes for non-dominant parties obtained by the largest non-dominant
party candidate) to measure the extent of the split of non-Congress votes (Butler et
al., 1995). They also teamed up with MARG (Marketing and Research Group) to
come up with innovations in the construction of a sampling frame and field research
for large sample surveys to provide the first set of robust predictions of the Indian
elections. The selection of constituencies was based on demarcation of ‘swing zones’
from which a representative constituency was selected to reflect the electoral shifts
in that zone. Their election forecasts of the 1980 and the 1984 elections might appear
in retrospect to be off the mark, but they did establish the superiority of survey-
based predictions over the speculative modes which dominated earlier.
As compared to their first two efforts, the forecasts made by the same team in
the 1989 and the 1991 parliamentary elections proved more accurate, at least in
predicting the seats for the then dominant Congress party. They were much less
successful in predicting the seats for other parties. A weakness was built into their
method which divided the entire party system into Congress versus its opponents.
The method also proved rather unsuccessful in predicting the seats for the state-level
elections in three-cornered contests. The model became less useful as the Congress
slipped from its position as the single dominant party around which political compe-
tition was organised. To be fair, the method worked if the Congress was replaced
by another dominant party — such as the BJP — against which its opposition was
divided. But increasingly there was no party that occupied this position in most of
the states. For this and for other various other reasons, this pioneering team withdrew
from election forecasting after the 1996 elections. We summarise in Tables 2–5 some
of the predictions made by various agencies for the national parliament in the last
20 years.
The details of the survey methods used by the various agencies have not been dis-
closed and no post mortems were published so it has been impossible to get any
indication of the causes of any forecasting errors. Most of the market research organ-
isations which conduct opinion polls in India use quota sampling. The sample is
selected by prescribing to the interviewer a target sample profile (in terms of
rural/urban locations and male/female composition etc.). Alternatively, some agenc-
ies use the rule of thumb approach (anyone from the nth house on your left) for
randomising the choice of respondents. For this reason market research agencies
have been reluctant to provide details of the social profiles of their achieved samples
and how these compare with population characteristics.
While these pioneering predictions proved that Indian elections could be predicted
with a fair degree of accuracy, they left open the challenge of methodological
improvement and of developing a model suited to the special features of the Indian
party system in the 1990s. As for the results-based forecast during the early stages
of counting, this was never attempted in India.78 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89
Table 2
Lok Sabha seat predictions, 1984–98a, b
Year Party Forecast seats Actual seats
1984 Congress 366 415
1989 Congress 195 195
1991 Congress 233 232
BJP+ 155 124
LF/NF 105 115
1996 Congress+ 168–187 143
BJP+ 178–197 176
LF/NF 103–117 151
1998 Congress+ 134 148
BJP+ 252 252
UF 109 93
a
Source: India Today-MARG opinion polls.
b
Notes: 1996 Congress+ includes UDF in Kerela, BJP+ includes Shiv Sena in Maharashtra, NF/LF
includes JD, SP, DMK, TDP(N), AGP and CPM, CPI, RSP, FBL. 1991 BJP+ includes Shiv Sena, NF/LF
includes JD,DMK,AGP,TDP,CPM, CPI, RSP, FBL.
Table 3
1996 Exit poll vote share predictionsa, b
INC+ BJP+ LF/NF
Exit poll 31 30 24
Actual 30 24 24
a
Source: CSDS Data Unit.
Notes: INC+ = INC, UDF, AIADMK. BJP+ = BJP, Shiv Sena, Samta. LF = JD, SP, DMK, TDP(N),
b
AGP. NF = CPIM, CPI, RSP, FBL.
Table 4
1998 Seat predictionsa
Agency Source INC+ BJP+ UF
CSDS India Today 164 214 127
ORG-MARG TVI Network 165 208 146
AC Nielson Outlook I 149 238 123
AC Nielson Outlook II 172 231 118
CMS Frontline 150 230 125
CMS Asianet 150 235 115
DRS TOI 155 249 102
C-Voter Pioneer 142 268 114
Actual – 148 252 93
a
Source: The Pioneer, 17/3/1998.R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 79
Table 5
1998 Vote share predictionsa
Agency Source INC+ BJP+ UF
CSDS India Today 32 31 21
AC Nielson Outlook II 30 31 23
CMS Frontline 32 35 20
DRS TOI 27 34 21
C-Voter Pioneer 24 36 21
Actual – 25 36 22
a
Source: The Pioneer, 17/3/1998.
5. The CSDS method and model
Ideally, one would conduct a voter intention poll in each of the 543 constituencies
and predict each seat based on the sample from that constituency. However, even a
moderate sample size of 625 (required to estimate the true proportion of votes within
4% at a 95% confidence level) would require a total sample size of 340 000. This
is not feasible, as the cost would be prohibitive and it would be virtually impossible
to carry out. Even getting reliable interviewers to undertake a door-to-door survey
at this level would be unthinkable.
Thus, one has to resort to sampling parts of a subset of constituencies. In order
to make predictions in constituencies where no sampling has been done, one needs
to use the past voting data and a prediction model as set out below.
5.1. Sampling
An insistence on random sampling distinguished our approach from those used
by all other market research agencies. We decided to aim at a national representative
sample rather than follow the earlier practice of sampling only those constituencies
which were considered representatives of various ‘swing zones’. As mentioned
above, the concepts of a swing zone and representative constituencies assumed a
degree of crystallisation of voting patterns which is not obtained in the fluid political
situation in most parts of India today. We followed the principles of multi-stage
stratified random sampling for selecting the survey locations.
At the first stage we chose 20% of the parliamentary constituencies by a circular
sampling method (i.e. 1 in 5 from a list of constituencies ordered by state), with
probability proportionate to the size of the electorate (PPS). These 108 constituencies
were thus spread across all the major states in the same proportion as the number
of Lok Sabha seats in each state. Some of the small states were aggregated together
for this purpose. Since the official list of seats follows a geographical sequence,
circular sampling based on it ensured regional representation within each state. Once
the constituencies were selected, we chose two assembly segments within that con-
stituency, also by circular sampling, again with probability proportionate to the size
of the electorate in that segment. Then the list of polling stations for the sampled80 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89
segments was obtained. Again, two out of an average of one hundred polling stations
were chosen from each assembly segment in the sample by circular sampling (at
this level, the PPS procedure was not feasible as the information about the electorate
size could not be obtained at the time of sampling).
The same principle was rigorously applied to the selection of the respondents as
well. The target sample of 15 500 was distributed among various states as per their
proportion of the national electorate, so as to adjust for the variations in the numbers
of electors per seat in different states. The state sample was then divided equally
among the sampled polling booths within the state. The list of electors for these
chosen booths was obtained and from that the target sample was chosen, again via
circular sampling with a random starting point. A team of specially trained investi-
gators conducted face-to-face interviews with the selected respondents. No substi-
tution was allowed when prospective respondents could not be contacted. The CSDS
had conducted a voting intention poll in 1996 following the above method. We
decided to target the same respondents again and to add a proportionate sample of
the new voters whose names were added to the electoral register subsequently.
The final profile of the achieved sample and its comparison with the known attri-
butes of the population is reported in Table 6. Since the two match fairly closely,
there is a good reason to believe that we had obtained a national representative
sample, at any rate a better sample than used by other pollsters. We did not adjust
for any differential non-response — this is not really feasible in the Indian context
given the lack of detailed socio-demographic information about the electorate.
The same target sample was approached twice, once before the elections for the
pre-poll forecast and once in a post-poll survey held the morning after voting.
5.2. The model for state level forecasting
Given the nature of the Indian party system and the permanent political flux, the
methods used in UK election prediction, which rely on the fact that each of the two
major parties there, Conservative and Labour, has a large committed vote and that
it is mainly a contest between these two parties, cannot be used in the Indian context.
Table 6
CSDS sample and population profilea
Census 1998 Sample
Female 48 49
Rural 74 75
Hindu 82 83
Muslim 12 11
Christian 2 4
Sikh 2 2
Scheduled Castes 17 17
Scheduled Tribes 8 8
a
Source: 1991 Census and 1998 NES post-poll survey.R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 81 Thus, election results data that is more than a few years old has, in our opinion, very little information that is of use for forecasting the result of a forthcoming elec- tion. Each state has its own unique political history. Because of this, it has been observed that contiguous areas falling in different states but with similar socio-econ- omic profiles can have very different voting patterns. Thus, as far as predicting vote percentages in a given constituency is concerned, only the most recent results from that constituency and the others in the same state and opinion poll results from the same state are relevant. We cannot work with an assumption of a homogenous national swing that cuts across the state boundaries. A state is a more appropriate unit for the purposes of prediction than any analytically carved out ‘swing zone’. Therefore, we decided to predict the seats at the state level and then aggregate them to yield a national prediction. Some states are very small, with less than 5 constituencies. Here we use subjective projections of changes in shares of the vote from the most recent election which were provided by local political experts. Thus in effect, an election forecast in India amounts to 20 separate state opinion polls conducted simultaneously, yielding 20 separate forecasts that need to be added up to give a national forecast. We now discuss predicting seats within a state. From the opinion poll we can predict the percentage votes that a major party is to get in the election. However, to convert this to a seat prediction, we need to estimate the vote shares for each of the major parties contesting the election. Here we operate with the uniform regional change model that stipulates that the change of vote for a given party from the results of the previous election is uniform over a state, or within a contiguous group of states. Thus, under this model, all we need to do is to estimate the percentage of vote for each of the major parties over the state and take its difference from the average vote in the previous election. Applying the change to the actual vote share in the previous election would give the predicted vote share for each party in each constituency. The uniform regional change model is only an approximation — but one that helps us estimate the vote share for a party in each constituency. Once we have the predicted vote share for each of the major parties in each seat, we can estimate the seats to be won by each party by the method described below. 5.3. Dealing with shifting alliances and splits The existence of a stable coalition or alliance of parties does not pose a major difficulty for this method, for we can estimate the vote percentage and seats for the alliance instead of the particular party. We face a special problem, however, where a major party has split or the composition of alliances has changed, for the result from the previous election cannot then serve as a starting point for measuring change. We need, first of all, comparable results from a previous election that can form the basis for measuring the changes and superimposing the change as described above. Between 1996 and 1998, major parties in several states had split: Congress (I) in West Bengal, Janata Dal in Bihar, Orissa and Karnataka and BJP in Gujarat. Some older alliances were broken and new ones were forged. How do we take into account mergers or splits of parties or changes in alliances
82 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 in the uniform regional change model? For this purpose, we constructed simulated or ‘notional’ election results for the previous election. This was meant to capture what the results would have been if the voters had voted as in the last election but with the party competition and alliances as in the current one. Then this simulated election result is used instead of the previous election result as the initial position from which the change is estimated and applied. New alliances of parties that had fought the previous elections separately are sim- pler to handle, for their votes could simply be aggregated. Assessing the effect of a recent split, however, required the help of the local political experts. In the case of a split of a party, we could allocate its votes in some ratio across the state based on subjective input from the experts. For example in Bihar, the Janata Dal split to give birth to a new party called Rashtriya Janata Dal (RJD) which in the opinion of the experts was the dominant faction. It was felt that RJD would get most of the Janata Dal votes and for the purpose of constructing the simulated results, we gave it 80% of the Janata Dal votes. If the sphere of influence of the splinter faction varies from region to region in the state, this could be taken into account. For example, it was felt that in West Bengal, the breakaway faction of the Congress (I), called the Trinamool Congress (TC), had a lot of support in the urban area surrounding Cal- cutta, but much less outside it. Similarly, in Karnataka the experts’ judgement was that the Lok Shakti that broke away from the Janata Dal had more influence in Northern Karnataka. When it comes to the formation of new alliances or the break-up of old alliances, the starting point is again subjective assessment, for there is no clear record of the independent electoral strength of each of the alliance partners. When an existing alliance breaks, we have to subjectively assess the relative strength of the constituent parts and split the votes of the alliance in the previous election in the same way as we dealt with splits in parties. In Tamil Nadu, the 1996 Parliament election was contested by an alliance of AIAMDK and Congress (I). In 1998, they were no longer allies. In fact, AIADMK had forged an alliance with BJP and several other regional parties. It was perceived that most of the votes of this alliance in 1996 were indeed AIADMK votes. Hence, we assigned 80% of the votes of the AIADMK and the Congress (I) candidates in the 1996 actual results to AIADMK. When two or more parties come together, the vote for the coalition could be less than the sum of their votes. This could be the case if the social base of the two parties was judged to be incompatible (due to the political history of the state). The sum of their votes was suitably adjusted to account for this, by multiplying the votes of the minor party by a factor called the ‘friction’ factor, and then added to the major party votes. The friction factor itself was assigned a value (between 0 and 1) subjectively, based on local experts’ opinion. 5.4. The seats forecasting method To summarise, the steps required to make a seat prediction under the uniform regional change model are: 1. To construct the simulated initial position, i.e. what the results would have been
R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 83
at the previous election if that had been fought by the pattern of parties and
alliances fighting at the current election;
2. To conduct a voter intention poll and estimate the vote share for each of the major
parties/alliances in each state;
3. To translate predicted votes in each constituency into a seat prediction for each
of the major parties/alliances in each state and to aggregate these to give a
national prediction
We have already discussed the first and the second step. Let us now examine the
third step. The simplest method is to treat the predicted votes as actual votes and
count off the winners — the party/alliance predicted to get the highest vote share
in a constituency is predicted as the winner. However, one can see that there is a
difference between two seats where in one the forecast winner is well ahead of the
runner-up (say 46% to 34%) to another where the first and second predicted parties
are forecast to be very close (say 40% to 39%). This consideration leads to the idea
of producing probabilities that each party will win the seat. Thus in the former case
we might produce a probability of 1 for the winner, and 0 for all other parties, while
in the latter case giving probabilities close to 0.5 for the two competing parties would
seem to be a more realistic and accurate assessment of the parties’ chances of win-
ning. This probabilistic method has been used in other countries such as the UK
(Payne, 1992).
Suppose that the standard error of the estimated difference of vote percentage
between the first two parties is a. Label the party with the highest predicted vote
share as party X and the party with the second highest as party Y. We wish to assign
probabilities of winning to the two parties X and Y.
Assuming that party Y is actually going to get (U%) more votes than X, let us
calculate the probability that the survey produces a result as extreme as the observed
result, i.e. party X getting d% or more votes than party Y in the survey. This is
P( Z ⬎ (U⫹d)/ a)
where Z has the standard Normal distribution. The best case scenario for party Y
then is when U is small, say nearly zero, when party Y will win the seat by a very
small margin. The probability of this is
P(Z⬎ d / a)
So for each constituency in the country, we compute the observed difference between
the predicted vote shares for the two leading parties (d%) and assign probabilities
P(Z⬍d/a) and P(Z⬎d/a)
respectively to the leading party and the party coming second. Parties predicted to
be in third place or lower are assigned a probability of winning of zero. Then the
sum of probabilities of winning for a given party over all constituencies would give
its expected (predicted) number of seats in the parliament.84 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89
5.5. The model for results-based forecasting
In the past, the media have never attempted to forecast the composition of the
new parliament until partial counts were available from nearly all the seats. But the
procedure for declaration of results in India makes it possible to forecast using a
few partial counts. As mentioned above, a special feature of the Indian elections is
that partial counts or ‘trends’ are declared in chunks of about 10% for each assembly
segment. To make matters helpful for the forecasters, these chunks are a random
sample of the total votes cast in that segment, thanks to the procedure of ‘mixing’
votes from different boxes so as to maintain the secrecy of booth-wide voting pat-
terns. That placed us in an advantageous position of having large random samples.
But we still have the usual problem that the constituencies that report their trends
early may form an unrepresentative sample. It is known that some states tend to
count faster than others.
The solution to this problem was to combine the results-based forecast with a
postpoll-based prior forecast for the undeclared seats. In the beginning, before coun-
ting started, we had state-wise predicted vote shares for the major alliances based
on the post-poll survey (which we call the prior predicted share). As counting trends
become available we calculate the predicted vote shares for alliances in each state
based on the counting trends (called the posterior predicted share). If the number
of seats where trends were available was large enough, these were regarded as good
predictions on their own. Thus, the predicted vote share at any given time was a
convex combination of the prior and the posterior vote shares, i.e.
predicted vote share ⫽ P * prior ⫹ (1-P)* posterior
where P=1 if no partial count-based trend is available and P=0 if trends are available
from a large proportion of seats. We had a heuristic formula to give values of
between 0 and 1 for the in-between stages when some trends were available but not
enough to discount the prior. Once we have predicted vote shares, we could convert
these to seats just as in the case of opinion polls.
6. Review of forecasts and actual results
6.1. The opinion poll based forecast
The CSDS carried out a survey between 4th February and 8th February 1998 for
a target sample of 15 500 voters of which 8938 were interviewed. It needs to be
remembered that at the time of this opinion poll, the actual election dates were
between 8 to 20 days away. The actual voting took place mainly in three phases on
16th, 22nd and 28th February. Furthermore 36% of the respondents said that they
had not made up their mind at that time. Moreover, even those who had made up their
mind could later change their mind (and perhaps did, as the post poll results showed).
Table 7 compares the seats predicted in the CSDS-India Today opinion poll and
the actual results. While there are some large discrepancies between the predictedR.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 85
Table 7
Comparison of the opinion poll based forecast and the actual resulta, b
Alliance Predicted seats Actual seats
BJP and allies 214 251
Congress(I) and allies 164 144
United Front 127 93
a
The actual result reported here and in the following tables is different from the final result reported
in Table 1 as it excludes the seats that did not go to polls at the time of counting.
b
The definition of the Congress allies excludes the RJD with which the Congress had a partial
seats adjustment.
and actual seats, these can perhaps be explained by the gap between the date of the
survey (finished on February 8, 1998) and the dates of the actual voting, which were
February 16, February 22 and February 28 with about a third of the constituencies
voting on each day.
6.2. Post-poll based prior forecast
The CSDS also carried out a post-poll survey, when the same target respondents
contacted in the pre-poll survey were interviewed the day after they had voted. This
time we could interview 8133 respondents from the target sample. Given the large
time gap between polling and counting, the results of the post-poll survey were
available to us before the counting began. This survey formed the basis of a prior
forecast which could not be published because of the Election Commission’s ban
on forecasting before the last round of voting was completed. In any case, the main
aim of this post-poll survey was to serve as an input to the result-based forecasting
model described below.
Table 8 gives the seat prediction based on the post-poll and the actual outcome.
This prediction is quite close to the final outcome. We note that the difference
between the pre-poll and post-poll figures was most significant in the state of Tamil
Nadu, the most surprising set of results of the 1998 elections.
Table 8
Comparison of post-poll based forecast and actual results
Alliance Predicted seats Actual seats
BJP and allies 252 251
Congress (I) and allies 134 144
United Front 109 9386 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89
6.3. Results-based forecast
The counting of votes all over the country began at 8 am on March 2 (day 1).
The first significant trends (based on a partial count of about 10% of votes) started
coming in from a few constituencies around 1430 hours on day 1. It was not before
another 40 hours of actual counting that all the final results were declared. The first
prediction was made at about 1750 hours on day 1. Although we had our first predic-
tion ready within two hours after the first counting trends were available, we waited
for three hours for the figures to stabilise before actually going on air with the fore-
cast. Table 9 presents the results of these early projections.
It can be seen that initially there is a strong effect of the ‘prior’ on this forecast:
the predicted seats are nearly the same as for the post-poll based forecast. It is close
to the final outcome for BJP and allies but is an overestimate for United Front and
an underestimate for Congress and allies.This error was corrected after some more
trends were available. By 11.00 pm on the first day of counting (about 8 hours from
the time first trends came in), we had forecast a picture that was very close to the
final outcome for all the three alliances.
6.4. Detailed analysis of forecast discrepancies
As has been reported earlier, the opinion poll was designed to give an all-India
prediction of seats and though our method involved predicting seats at the state level,
we refrained from publishing them as the sample size at the state level was too small
Table 9
Results-based forecast by time
Day Time BJP and allies Congress (I) and United Front
allies
1 1750 252 130 105
1 1814 255 128 101
1 1839 256 127 99
1 1908 244 132 103
1 2114 239 147 102
1 2210 238 150 99
1 2302 246 150 98
1 2350 245 147 100
2 0025 249 147 95
2 0205 249 145 98
2 0605 256 143 95
2 0756 251 144 97
2 0853 252 143 95
2 0945 251 142 96
2 1015 251 142 96
2 1720 252 140 92
– – – – –
Final 251 144 93R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 87
Table 10
Some state-level predictions
BJP+ BJP+ INC+ INC+ UF+ UF+
votes seats votes seats votes seats
ANDHRA PRADESH: Sample size: 618
Pre-poll 11.68 2 40.93 14 41.16 25
Post-poll 13.1 1 40.72 15 40.56 26
Final 19.53 4 38.46 22 38.37 15
GUJRAT: Sample size: 477
Pre-poll 44.95 14 34.68 12 5.29 0
Post-poll 46.58 16 32.89 10 2.28 0
Final 48.37 19 36.49 7 3.56 0
KARNATAKA: Sample size: 484
Pre-poll 28.62 11 40.84 15 24.15 2
Post-poll 38.51 20 32.01 6 24.67 2
Final 38.52 16 36.23 9 21.82 3
MADHYA PRADESH: Sample size: 567
Pre-poll 45.43 36 40.81 4 3.03 0
Post-poll 46.61 31 42.66 9 1.13 0
Final 46.62 30 39.40 10 2.64 0
MAHARASHTRA: Sample size: 980
Pre-poll 35.33 10 50.93 38 5.18 0
Post-poll 36.89 17 44.76 31 3.43 0
Final 42.14 10 50.41 37 3.21 1
ORISSA: Sample size: 305
Pre-poll 32.22 8 41.63 13 10.72 0
Post-poll 30.16 7 45.24 14 7.94 0
Final 48.99 16 41.04 5 6.42 3
RAJASTHAN: Sample size: 429
Pre-poll 41.62 10 49.82 10 0.88 0
Post-poll 50.88 18 44.01 7 0.20 0
Final 41.98 5 44.45 18 4.66 0
TAMIL NADU: Sample size: 626
Pre-poll 21.38 8 18.93 3 36.55 29
Post-poll 34.90 23 12.68 0 41.94 16
Final 45.08 30 4.78 0 43.42 8
UTTAR PRADESH: Sample size: 1254
Pre-poll 41.36 64 11.69 7 26.32 10
Post-poll 40.32 63 8.94 5 26.81 13
Final 37.34 59 6.02 0 29.57 20
WEST BENGAL: Sample size: 765
Pre-poll 17.26 0 22.61 1 53.13 41
Post-poll 27.34 13 15.98 0 50.69 29
Final 34.64 8 6.02 1 41.41 3388 R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 to have any measure of confidence in the predictions. Only when these were aggre- gated at the national level do they give reasonable predictions. In Table 10 we give the predicted vote shares and seat predictions for some of the major states. As we can see, while in some states the predictions were quite close, in others they are quite off the mark — this is not surprising as the predictions are based on a very small sample size at the state level. 7. Concluding observations The post-poll based predictions were very close to the actual results, although the sample size was not large enough for us to be confident of our forecasts in individual states. We consider ourselves lucky to be so close to the final aggregate outcome. The entire exercise depends crucially on the assumption that the political picture has not undergone major change since the previous election — at least in a major part of the country. As Indian general elections are currently occurring at frequent inter- vals this assumption is likely to hold for the time being at least. But we cannot infer from the successful results-based forecasts produced that our methodology for converting votes into seats is optimal and will always work well in future elections. Furthermore it is impossible to disentangle the various compo- nents of the prediction scheme and assess their relative contribution to the success (or failure) of the forecasts in the Indian context. The main components in this exer- cise were: the prior data obtained form the post-poll, the declaration order for the partial counts, the veracity of the experts’ construction of the notional results for the previous election and the method for converting votes into seats. For this election we were fortunate to have a good post-poll, at least in the aggregate, and the declar- ation order did not contain any significant biases. But the notional results are a counterfactual — it is impossible to judge their accuracy. Furthermore it is quite possible to get a good forecast when there are compensating errors in some of the components — in the UK context there have been good forecasts where errors in the poll-based prediction of vote shares have been compensated for by a poor method of converting votes into seats. And most election forecasters could tell you that if they rerun the forecast making this change here and that change there they would have got a very accurate forecast. So it is what is actually produced with the last poll before the election and in the hectic context of ‘election night’ itself that counts. Thus, like all election forecasters we were at the mercy of Lady Luck. Were we just lucky or have we developed a sound methodology for Indian election fore- casting? Only with repeated application over a series of elections, with varying polit- ical scenarios, could this question be answered. References Brown, P., Firth, D., Payne, C., 1999. Forecasting on British election night 1997. J. R. Statist. Soc. A. 162, 211–226.
R.L. Karandikar et al. / Electoral Studies 21 (2002) 69–89 89 Butler, D., Lahiri, A., Roy, P., 1995. India Decides: Elections 1952–1995. Books and Things, Delhi. Heath, O., 1999. The fractionalisation of Indian parties. Seminar 480, 66–71. McMillan, A., 2000. Delimitation, democracy and the end of constitutional freeze, Economic and Political Weekly, April 8 2000, 1271–1276. Morton, H. (Ed), 1990. Election night forecasting. The New Zealand Statistician 25, 36–84. Payne, C., 1992. Statistical methods for election forecasting in the United Kingdom, 1970–90. In: Norris, P., Crewe, I., Denver, D., Broughton, D. (Eds.) British Elections and Parties Handbook 1992. Harvester Wheatsheaf, Hemel Hempstead, pp. 138–158. Singh, K., 1992. People of India, vol 1. Anthropological Survey of India, Delhi. Sivaramakrishnan. K., 1997. Under-franchise in urban areas: freeze on delimitation of constituencies and resultant disparities. Economic and Political Weekly, Dec 20 1997, 3275–3281. Yadav, Y., 1999. Electoral politics in the times of change: India’s third electoral system. Economic and Political Weekly, Aug 21 1999, 2393–2399.
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