Tag: karnataka

Start the schools already

Irrespective of when you open the schools, there will be a second wave at that point in time. So we might as well reopen sooner rather than later and put children (and parents of young children) out of their misery.

OK, I admit I have a personal interest in this one. Being a double income, single kid, no nanny, nuclear family, we have been incredibly badly hit by the school shutdown for the last nine months. The wife and I have been effectively working at 50% capacity since March, been incredibly stressed out, and have no time for anything.

And now that I’ve begun a “proper job”, her utilisation has dropped well below 50%. This can’t last for long.

Then again, this post is not being driven solely by personal agendas or interests. The more perceptive of you might know that on my twitter account, I publish a bunch of graphs every morning, based on the statistics put out by covid19india.org . And every day, even when I don’t log into twitter, I go and take a look at the graphs to see what’s happening in the country.

And the message is clear – the pandemic is dying down in India. It is a pretty consistent trend. The Levitt Model might not really be true (my old friend’s comment that it is “random curve fitting” when I first came across it holds true, I would think), but it gives a great picture of how the pandemic has been performing in India. This is the graph I put out today.

In most states in India, the Levitt measure is incredibly close to 1, indicating that the pandmic is all but over. However, you might notice that the decline in this metric is not monotoniuc.

However, if you look at the Delhi numbers on the top right, notice how nicely the Levitt metric shows the three “waves” of the disease in the city. And you can see here that the third wave in Delhi is all but over. And while you see the clear effect of Delhi’s third wave in the Levitt metric, you can also see that it coincided with a second wave in Haryana, and a (barely noticeable) second wave in Uttar Pradesh and Rajasthan.

This wave was due to increased pollution, primarily on the account of crop burning in Punjab and Haryana in October-November. The reason the second waves in Uttar Pradesh and Rajasthan (as seen in terms of the Levitt measures) were small is that they are rather large states, and the areas affected by the bad pollution was fairly small.

And along with this, consider the serosurveys in Karnataka (both the government one and the IDFC-sponsored one), which estimated that the number of actual infections in the state are higher than the official counts of infections by a factor of 40 to 100 (we had initially assumed 10-20 for this factor). In other words, an overwhelmingly large number of cases in India are “asymptomatic” (which is to say that the people are, for all practical purposes, “unaffected”).

In other words, we know cases only when someone is affected badly enough to get themselves tested, or has a family member affected badly enough to get themselves tested. And what happened in Delhi and surrounding states in October-November was that with higher pollution, everyone who got affected got affected more severely than they would have otherwise.

Some people who might have otherwise been unaffected showed symptoms and got themselves tested. Some people who might have not been affected seriously enough ended up in hospital. Pollution meant that some people who might have recovered in hospital ended up dying. And as the crops finished burning and pollution levels dropped, you can see the Levitt metric dropping as well.

And lest you argue that I’m making an argument based on a mostly discredited metric, here is the actual number of known cases in the most affected states in the country. The graph is a Loess smoothing, and the points can be seen here.

See the precipitous decline in Delhi (green line) and Karnataka (orange) and Andhra Pradesh (pink) in the last couple of months. The pandemic has pretty much burnt through in most states. We can start relaxing, and opening schools.

You might be tempted to ask, “but won’t there be a second wave when schools reopen?”. That is a very fair concern, since people who have so far been extremely conservative might relatively relax when the schools open. The counterpoint to that is, “irrespective of when you open the schools, there will be a second wave at that point in time“.

It doesn’t matter if we reopen the schools now, or in April, or in August, or in next December. There will always be a few vestigial (possibly unaffected) cases going around, and there will be a spike in known cases at that point. And by quickly dialling up and down, we can control that.

So I hereby strongly urge the state governments (especially looking at you, Government of Karnataka) to permit schools to reopen. A few vocal and overly conservative parents should not be able to hold the rest of the country (or state) to ransom.

69 is the answer

The IDFC-Duke-Chicago survey that concluded that 50% of Bangalore had covid-19 in late June only surveyed 69 people in the city. 

When it comes to most things in life, the answer is 42. However, if you are trying to rationalise the IDFC-Duke-Chicago survey that found that over 50% of people in Bangalore had had covid-19 by end-June, then the answer is not 42. It is 69.

For that is the sample size that the survey used in Bangalore.

Initially I had missed this as well. However, this evening I attended half of a webinar where some of the authors of the survey spoke about the survey and the paper, and there they let the penny drop. And then I found – it’s in one small table in the paper.

The IDFC-Duke-Chicago survey only surveyed 69 people in Bangalore

The above is the table in its glorious full size. It takes effort to read the numbers. Look at the second last line. In Bangalore Urban, the ELISA results (for antibodies) were available for only 69 people.

And if you look at the appendix, you find that 52.5% of respondents in Bangalore had antibodies to covid-19 (that is 36 people). So in late June, they surveyed 69 people and found that 36 had antibodies for covid-19. That’s it.

To their credit, they didn’t highlight this result (I sort of dug through their paper to find these numbers and call the survey into question). And they mentioned in tonight’s webinar as well that their objective was to get an idea of the prevalence in the state, and not just in one particular region (even if it be as important as Bangalore).

That said, two things that they said during the webinar in defence of the paper that I thought I should point out here.

First, Anu Acharya of MapMyGenome (also a co-author of the survey) said “people have said that a lot of people we approached refused consent to be surveyed. That’s a standard of all surveying”. That’s absolutely correct. In any random survey, you will always have an implicit bias because the sort of people who will refuse to get surveyed will show a pattern.

However, in this particular case, the point to note is the extremely high number of people who refused to be surveyed – over half the households in the panel refused to be surveyed, and in a further quarter of the panel households, the identified person refused to be surveyed (despite the family giving clearance).

One of the things with covid-19 in India is that in the early days of the pandemic, anyone found having the disease would be force-hospitalised. I had said back then (not sure where) that hospitalising asymptomatic people was similar to the “precogs” in Minority Report – you confine the people because they MIGHT INFECT OTHERS.

For this reason, people didn’t want to get tested for covid-19. If you accidentally tested positive, you would be institutionalised for a week or two (and be made to pay for it, if you demanded a private hospital). Rather, unless you had clear symptoms or were ill, you were afraid of being tested for covid-19 (whether RT-PCR or antibodies, a “representative sample” won’t understand).

However, if you had already got covid-19 and “served your sentence”, you would be far less likely to be “afraid of being tested”. This, in conjunction with the rather high proportion of the panel that refused to get tested, suggests that there was a clear bias in the sample. And since the numbers for Bangalore clearly don’t make sense, it lends credence to the sampling bias.

And sample size apart, there is nothing Bangalore-specific about this bias (apart from that in some parts of the state, the survey happened after people had sort of lost their fear of testing). This further suggests that overall state numbers are also an overestimate (which fits in with my conclusion in the previous blogpost).

The other thing that was mentioned in the webinar that sort of cracked me up was the reason why the sample size was so low in Bangalore – a lockdown got announced while the survey was on, and the sampling team fled. In today’s webinar, the paper authors went off on a rant about how surveying should be classified as an “essential activity”.

In any case, none of this matters. All that matters is that 69 is the answer.

 

More on Covid-19 prevalence in Karnataka

As the old song went, “when the giver gives, he tears the roof and gives”.

Last week the Government of Karnataka released its report on the covid-19 serosurvey done in the state. You might recall that it had concluded that the number of cases had been undercounted by a factor of 40, but then some things were suspect in terms of the sampling and the weighting.

This week comes another sero-survey, this time a preprint of a paper that has been submitted to a peer reviewed journal. This survey was conducted by the IDFC Institute, a think tank, and involves academics from the University of Chicago and Duke University, and relies on the extensive sampling network of CMIE.

At the broad level, this survey confirms the results of the other survey – it concludes that “Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August”. This is much higher than the overall conclusions of the state-sponsored survey, which had concluded that “about 19 million residents had been infected by mid-September”.

I like seeing two independent assessments of the same quantity. While each may have its own sources of error, and may not independently offer much information, comparing them can offer some really valuable insights. So what do we have here?

The IDFC-Duke-Chicago survey took place between June and August, and concluded that 31.5 million residents of Karnataka (out of a total population of about 70 million) have been infected by covid-19. The state survey in September had suggested 19 million residents had been infected by September.

Clearly, since these surveys measure the number of people “who have ever been affected”, both of them cannot be correct. If 31 million people had been affected by end August, clearly many more than 19 million should have been infected by mid-September. And vice versa. So, as Ravi Shastri would put it, “something’s got to give”. What gives?

Remember that I had thought the state survey numbers might have been an overestimate thanks to inappropriate sampling (“low risk” not being low risk enough, and not weighting samples)? If 20 million by mid-September was an overestimate, what do you say about 31 million by end August? Surely an overestimate? And that is not all.

If you go through the IDFC-Duke-Chicago paper, there are a few figures and tables that don’t make sense at all. For starters, check out this graph, that for different regions in the state, shows the “median date of sampling” and the estimates on the proportion of the population that had antibodies for covid-19.

Check out the red line on the right. The sampling for the urban areas for the Bangalore region was completed by 24th June. And the survey found that more than 50% of respondents in this region had covid-19 antibodies. On 24th June.

Let’s put that in context. As of 24th June, Bangalore Urban had 1700 confirmed cases. The city’s population is north of 10 million. I understand that 24th June was the “median date” of the survey in Bangalore city. Even if the survey took two weeks after that, as of 8th of July, Bangalore Urban had 12500 confirmed cases.

The state survey had estimated that known cases were 1 in 40. 12500 confirmed cases suggests about 500,000 actual cases. That’s 5% of Bangalore’s population, not 50% as the survey claimed. Something is really really off. Even if we use the IDFC-Duke-Chicago paper’s estimates that only 1 in 100 cases were reported / known, then 12500 known cases by 8th July translates to 1.25 million actual cases, or 12.5% of the city’s population (well below 50% ).

My biggest discomfort with the IDFC-Duke-Chicago effort is that it attempts to sample a rather rapidly changing variable over a long period of time. The survey went on from June 15th to August 29th. By June 15th, Karnataka had 7200 known cases (and 87 deaths). By August 29th the state had 327,000 known cases and 5500 deaths. I really don’t understand how the academics who ran the study could reconcile their data from the third week of June to the data from the third week of August, when the nature of the pandemic in the state was very very different.

And now, having looked at this paper, I’m more confident of the state survey’s estimations. Yes, it might have sampling issues, but compared to the IDFC-Duke-Chicago paper, the numbers make so much more sense. So yeah, maybe the factor of underestimation of Covid-19 cases in Karnataka is 40.

Putting all this together, I don’t understand one thing. What these surveys have shown is that

  1. More than half of Bangalore has already been infected by covid-19
  2. The true infection fatality rate is somewhere around 0.05% (or lower).

So why do we still have a (partial) lockdown?

PS: The other day on WhatsApp I saw this video of an extremely congested Chickpet area on the last weekend before Diwali. My initial reaction was “these people have lost their minds. Why are they all in such a crowded place?”. Now, after thinking about the surveys, my reaction is “most of these people have most definitely already got covid and recovered. So it’s not THAT crazy”.

Covid-19 Prevalence in Karnataka

Finally, many months after other Indian states had conducted a similar exercise, Karnataka released the results of its first “covid-19 sero survey” earlier this week. The headline number being put out is that about 27% of the state has already suffered from the infection, and has antibodies to show for it. From the press release:

Out of 7.07 crore estimated populationin Karnataka, the study estimates that 1.93 crore (27.3%) of the people are either currently infected or already had the infection in the past, as of 16 September 2020.

To put that number in context, as of 16th September, there were a total of 485,000 confirmed cases in Karnataka (official statistics via covid19india.org), and 7536 people had died of the disease in the state.

It had long been estimated that official numbers of covid-19 cases are off by a factor of 10 or 20 – that the actual number of people who have got the disease is actually 10 to 20 times the official number. The serosurvey, assuming it has been done properly, suggests that the factor (as of September) is 40!

If the ratio has continued to hold (and the survey accurate), nearly one in two people in Karnataka have already got the disease! (as of today, there are 839,000 known cases in Karnataka)

Of course, there are regional variations, though I should mention that the smaller the region you take, the less accurate the survey will be (smaller sample size and all that). In Bangalore Urban, for example, the survey estimates that 30% of the population had been infected by mid-September. If the ratio holds, we see that nearly 60% of the population in the city has already got the disease.

The official statistics (separate from the survey) also suggest that the disease has peaked in Karnataka. In fact, it seems to have peaked right around the time the survey was being conducted, in September. In September, it was common to see 7000-1000 new cases confirmed in Karnataka each day. That number has come down to about 3000 per day now.

Now, there are a few questions we need to answer. Firstly – is this factor of 40 (actual cases to known cases) feasible? Based on this data point, it makes sense:

In May, when Karnataka had a very small number of “native cases” and was aggressively testing everyone who had returned to the state from elsewhere, a staggering 93% of currently active cases were asymptomatic. In other words, only 1 in 14 people who was affected was showing any sign of symptoms.

Then, as I might have remarked on Twitter a few times, compulsory quarantining or hospitalisation (which was in force until July IIRC) has been a strong disincentive to people from seeking medical help or getting tested. This has meant that people get themselves tested only when the symptoms are really clear, or when they need attention. The downside of this, of course, has been that many people have got themselves tested too late for help. One statistic I remember is that about 33% of people who died of covid-19 in hospitals died within 24 hours of hospitalisation.

So if only one in 14 show any symptoms, and only those with relatively serious symptoms (or with close relatives who have serious symptoms) get themselves tested, this undercount by a factor of 40 can make sense.

Then – does the survey makes sense? Is 15000 samples big enough for a state of 70 million? For starters, the population of the state doesn’t matter. Rudimentary statistics (I always go to this presentation by Rajeeva Karandikar of CMI)  tells us that the size of the population doesn’t matter. As long as the sample has been chosen randomly, all that matters for the accuracy of the survey is the size of the sample. And for a binary decision (infected / not), 15000 is good enough as long as the sample has been random.

And that is where the survey raises questions – the survey has used an equal number of low risk, high risk and medium risk samples. “High risk” have been defined as people with comorbidities. Moderate risk are people who interact a lot with a lot of people (shopkeepers, healthcare workers, etc.). Both seem fine. It’s the “low risk” that seems suspect, where they have included pregnant women and attendants of outpatient patients in hospitals.

I have a few concerns – are the “low risk” low risk enough? Doesn’t the fact that you have accompanied someone to hospital, or  gone to hospital yourself (because you are pregnant), make you higher than average risk? And then – there are an equal number of low risk, medium risk and high risk people in the sample and there doesn’t seem to be any re-weighting. This suggests to me that the medium and high risk people have been overrepresented in the sample.

Finally, the press release says:

We excluded those already diagnosed with SARS-CoV2 infection, unwilling to provide a sample for the test, or did not agree to provide informed consent

I wonder if this sort of exclusion doesn’t result in a bias in itself.

Putting all this together – that there are qual samples of low, medium and high risk, that the “low risk” sample itself contains people of higher than normal risk, and that people who have refused to participate in the survey have been excluded – I sense that the total prevalence of covid-19 in Karnataka is likely to be overstated. By what factor, it is impossible to say. Maybe our original guess that the incidence of the disease is about 20 times the number of known cases is still valid? We will never know.

Nevertheless, we can be confident that a large section of the state (may not be 50%, but maybe 40%?) has already been infected with covid-19 and unless the ongoing festive season plays havoc, the number of cases is likely to continue dipping.

However, this is no reason to be complacent. I think Nitin Pai is  bang on here.

And I know a lot of people who have been aggressively social distancing (not even meeting people who have domestic help coming home, etc.). It is important that when they do relax, they do so in a graded manner.

Wear masks. Avoid crowded closed places. If you are going to get covid-19 anyway (and many of us have already got it, whether we know it or not), it is significantly better for you that you get a small viral load of it.

Covid-19 superspreaders in Karnataka

Through a combination of luck and competence, my home state of Karnataka has handled the Covid-19 crisis rather well. While the total number of cases detected in the state edged past 2000 recently, the number of locally transmitted cases detected each day has hovered in the 20-25 range.

Perhaps the low case volume means that Karnataka is able to give out data at a level that few others states in India are providing. For each case, the rationale behind why the patient was tested (which is usually the source where they caught the disease) is given. This data comes out in two daily updates through the @dhfwka twitter handle.

There was this research that came out recently that showed that the spread of covid-19 follows a classic power law, with a low value of “alpha”. Basically, most infected people don’t infect anyone else. But there are a handful of infected people who infect lots of others.

The Karnataka data, put out by @dhfwka  and meticulously collected and organised by the folks at covid19india.org (they frequently drive me mad by suddenly changing the API or moving data into a new file, but overall they’ve been doing stellar work), has sufficient information to see if this sort of power law holds.

For every patient who was tested thanks to being a contact of an already infected patient, the “notes” field of the data contains the latter patient’s ID. This way, we are able to build a sort of graph on who got the disease from whom (some people got the disease “from a containment zone”, or out of state, and they are all ignored in this analysis).

From this graph, we can approximate how many people each infected person transmitted the infection to. Here are the “top” people in Karnataka who transmitted the disease to most people.

Patient 653, a 34 year-old male from Karnataka, who got infected from patient 420, passed on the disease to 45 others. Patient 419 passed it on to 34 others. And so on.

Overall in Karnataka, based on the data from covid19india.org as of tonight, there have been 732 cases where a the source (person) of infection has been clearly identified. These 732 cases have been transmitted by 205 people. Just two of the 205 (less than 1%) are responsible for 79 people (11% of all cases where transmitter has been identified) getting infected.

The top 10 “spreaders” in Karnataka are responsible for infecting 260 people, or 36% of all cases where transmission is known. The top 20 spreaders in the state (10% of all spreaders) are responsible for 48% of all cases. The top 41 spreaders (20% of all spreaders) are responsible for 61% of all transmitted cases.

Now you might think this is not as steep as the “well-known” Pareto distribution (80-20 distribution), except that here we are only considering 20% of all “spreaders”. Our analysis ignores the 1000 odd people who were found to have the disease at least one week ago, and none of whose contacts have been found to have the disease.

I admit this graph is a little difficult to understand, but basically I’ve ordered people found for covid-19 in Karnataka by number of people they’ve passed on the infection to, and graphed how many people cumulatively they’ve infected. It is a very clear pareto curve.

The exact exponent of the power law depends on what you take as the denominator (number of people who could have infected others, having themselves been infected), but the shape of the curve is not in question.

Essentially the Karnataka validates some research that’s recently come out – most of the disease spread stems from a handful of super spreaders. A very large proportion of people who are infected don’t pass it on to any of their contacts.

Karnataka’s bizarre liquor license policy

Karnataka has a rather weird liquor license policy. Some twenty years ago, back when S Bangarappa was the chief minister (if I’m not wrong) the state decided to freeze the number of bars. “Growing alcoholism” was the ostensible reason. Since then, if someone has to open a bar, the license has to be purchased from an existing bar owner who will then shut down his bar. Thus, the number of bars in the state (whose population has increased manifold since) has remained constant.

This is not the only funny aspect of liquor regulation in Karnataka.  Till recently, there was also the rather bizarre requirement that each bar sell a minimum “quota” of liquor each month. If the bar failed to do so, it had to pay “short lifting” fines. While this regulation (minimum “lifting” by bars) went much before the time when number of licenses was capped, the two can be seen to be related. When the number of licenses is capped, the state needs to ensure that it gets a certain fixed revenue out of excise licenses and sales. Fixing a minimum sale quantity ensures that licenses are not “wasted” by bars with low sales, and in case they are, the government doesn’t lose out on such sales.

A possible reason that this rather bizarre regulation on minimum sales was lifted is due to it becoming moot thanks to competition. When the number of liquor licenses is limited, the price increases, and thus bars which are selling lower amounts of liquor find it more profitable to cash out on their licenses than continue their business. Thus, bars that continue to have their licenses are those that continue to sell significant quantities, which makes the quotas moot.

Nevertheless, the cap on the number of bars means that the liquor scene in Karnataka is rather bizarre, the point being that there are no “middle class bars”. Here in Barcelona, where I’m currently on holiday, pretty much every restaurant and cafe has an alcohol license (at least beer and wine), and it is possible to have a drink in an “ordinary setting” at a reasonable price. A glass of beer at any of these establishments, for example (small quiet places which are seldom crowded), costs about EUR 1.80 (~Rs. 120 by today’s exchange rate).

In Karnataka, on the other hand, thanks to the limited licensing regime, a bar needs to do a certain minimum amount of business before it is viable. This has led to bars in Karnataka adopt one of two opposing routes. Some play the volume route, setting up an atmosphere where there is quick turnaround of customers (it can be argued that atmosphere is set up to ensure customers don’t stay too long) each of who consumes in significant volumes so that the bar can make significant amount of money despite charging only a small premium on the liqour.

At the other end you have the rather fancy “value players”, who make their margins on rather large markups on the liquor they sell. These are typically fine dining restaurants where people’s primary purpose is eating (rather than drinking) and which have rather low table turnover. A combination of the above two means that volumes are low, but such restaurants more than make up by means of significant markups. These markups are extended to non alcohol items also (these restaurants can afford to charge a premium since all other similar restaurants serving alcohol also charge the same premium, and presence of alcohol is a hygiene factor for such restaurants). Here is an old blog post where I argue why liquor regulations imply high.

So the question is if the government can do away with the bizarre regulations on minimum sales, why can’t they increase the number of liquor licenses? The problem is that it is a classic case of baptists and bootleggers. The baptist case is that by issuing more liquor licenses, it makes things easier for people to drink alcohol and that’s not a good thing for society. And the bootleggers are existing licenseholders, whose licenses will get devalued if their supply increases. I just realised I’ve already done another blog post addressing this topic.

Governments and agendas

Say what you may about the inefficiencies of the BS Yeddyurappa government in Karnataka, you must accept that the initial days were great. While the miners may have been allowed to prosper, which led to plunder and constant government instability (which led first to the BJP split and then the humiliating loss in these elections), the government did rather well in its first year of operation. Infrastructure (especially in Bangalore city) saw an improvement. I moved out of Bangalore in August 2008 (three months after Yeddy took power) and returned in June 2009, and there was a significant visible change in the city (for the better). Policing and law and order also seemed to improved (in those initial days of the Yeddy government).

It might be early days still, only four months since Siddaramaiah has taken charge, but I don’t see anything in this direction. Bangalore roads are all torn up and travel times have doubled (primarily due to potholes). Law and order seems broken (cases like this one and this one come to mind). The chief minister reportedly feigned illness and backed out of a meeting with industry captains at the last minute (heard this from two independent sources but can’t find a link). Any way, the industry in the state is not happy.

Typically, when a new government takes over, it wants to be seen gathering some “quick wins’. Typically the easiest problems to solve are to fix law and order – all it takes is to decision the police, and it typically improves whenever a new government comes to power. Another quick win is improvement of basic infrastructure – such as asphalting roads or improving water supply. Meeting industry leaders and making global statements also don’t take much effort, but go a long way in getting the support of the industry.

If it is all so easy, and the earlier government did that, why has the current dispensation not implemented any of it? I blame it on the Re. 1 per kg rice scheme. The problem with the current government is that it has a specific agenda – as soon as he came to power, Siddaramaiah announced this cheap rice scheme and promised to implement it in a month or two. This has resulted in two things. Firstly, the statement meant that most of the management bandwidth of the government bureaucracy went in managing and implementing this cheap rice scheme. Since the CM wanted this to be done in a certain number of days, officers probably scrambled to meet the deadline, thus not being able to pay attention to other issues.

More importantly, the government saw this cheap rice scheme as a quick win. The people will be generally happy  with the government if this is implemented, they might have reasoned, thanks to which other potential quick wins (policing, basic infra) took a backseat. I’m not a beneficiary of the Re. 1 per kg rice scheme so I can’t comment objectively but I’m not sure if the recipients of the scheme are happy that the scheme has been implemented or unhappy that other developments have taken a backseat.

The point with the Yeddy government is that it didn’t have a specific agenda – no “global” quick win scheme, thanks to which the government had to push on several fronts to try and score a win. And so they pushed on all fronts where quick wins were possible and managed to get them. (It is another matter, of course, that in the longer run they ran an increasingly unsteady ship and messed things up right royally, because of which they were (rightly) voted out in the next elections).

It is similar to the NDA government of 1999. There were no grand quick win plans, and that gave the government the bandwidth to push on several long-term fronts, including infrastructure projects such as the Golden Quadrilateral and the Pradhan Mantri Gram Sadak Yojana, further disinvestment and the new Electricity Acts. UPA1 on the other hand quickly came up with schemes such as the NREGA which served as a good vote-catching quick win (and it probably did its job given the enhanced majority the UPA got in 2009).

Thus, from the point of view of sustainable development and investment in public goods, it is possibly better off to have a government which has no specific idea. If not, the specific ideas might come in the way of other development.

More Karnataka: Averaging between ULB elections and 2008 elections

Recently I met my MLA, who is from the BJP, and told him about my analysis extrapolating from the recent urban local body elections, which gave Congress an absolute majority. He countered that the BJP has never been strong at the sub-state level so one shouldn’t read too much into these elections. So I decided to create this tool which uses a slider which you can use to decide how much importance to give to the ULB polls. A value of 0 represents the seat distribution as per the 2008 elections. A value of 1 uses an extrapolation of the ULB elections only (without using information from the 2008 polls). I hope you have fun with this tool.

You might also notice that I now give you the actual seat distribution party-wise.

Bangalore North, South, Central, Rural

I don’t know if you want to call this gerrymandering, but I just want to pictorially map out the areas of Bangalore that fall under different parliamentary constituencies.

White: Chickballapur

Black: Bangalore North

Red: Bangalore Central

Green: Bangalore South

Blue: Bangalore Rural

Source: http://openbangalore.org/
Source: http://openbangalore.org/

Karnataka – effect of swings from 2008 election

You can use the slider below to see how changes in vote share of major parties affects the seat distribution. The “base” here is the vote share in the 2008 Assembly Elections. The numbers on the sliders are in percentages.

Update
This new version uses the vote shares in various districts during the recent Urban Local Body elections to account for the BJP split. As you can see, there is a slider that allows you to indicate how much of the split of the BJP’s votes as per the recent ULB elections will reflect in the forthcoming assembly elections. The reason for this slider is due to feedback that the split of BJP votes in the local body elections may not translate directly to the assembly elections.

There is also a slider called ‘performance impact’. This is based on data from the Daksh survey where samples of voters were asked to evaluate their sitting MLA. The way to use this is that when the slider is at 0, there is no impact of the MLA’s performance on vote share in the forthcoming elections. When it is at 5, an MLA who voters are “extremely happy” with will get 5% additional votes, and an MLA who voters are extremely unhappy with gets 5% less votes than what he did last time.