That Anna’s proposal for Lokpal is deeply flawed is inarguable. Whether Anna is also a bigoted RSS sympathizer, if not their agent, propelled by foreign money, as Roy would have us believe, is more in doubt. Since the debate about the latter point is rendered moot by the overwhelming support that Anna seems to enjoy, I focus on some important, though very well-tread and long understood, questions around corruption raised by Roy in her polemical screed.

Corruption is ubiquitous in India. Ration shops (considerable adulteration, the skim sold off), government employment schemes (ghost employees), admission to government schools (bribes must be paid to the principal), allocation of telecom and mining licenses (bribes paid for getting licenses for cheaper than what a fair auction would fetch), ultrasound clinics providing prenatal gender identification (bribes paid to police to keep these running) etc. are but a few examples of this widespread practice.

That corruption has serious negative consequences is also not in doubt. The poor get lower quality produce, if anything at all, as a result of corruption in ration shops. Inadequate public goods (e.g. canals) result from public’s money, and some intended beneficiaries denied benefit, as a result of ghost employees in government employment schemes. Sex-selective abortions result from continued operation of prenatal ultrasound clinics. And considerable loss in government revenue (which can be used to provide public goods) results from corruption in granting of licenses.

On occasion, corruption may increase welfare of those most in need. For example, if some laws are arrayed against the poor, and if the poor can pay a nominal bribe to circumvent the law, corruption may benefit the poor. The overall impact of corruption on the poor is still likely to be heavily negative, if only because the loss to the public exchequer via the widely suspected significantly greater corruption among the rich is expected to be far greater. There also exists some empirical evidence to support the claim that corruption causes poverty (Gupta et al., 2002). However, an argument can be made to not enforce anti-corruption laws in some spheres, if successful attempts to amend the law that warrants circumvention can’t be mounted.

In all, the case for reducing corruption is strong. However schemes of solving corruption by creating a bureaucracy to go after the corrupt may be upended by bureaucrats going rogue. Stories of the almost limitless power of a ‘Vigilance Commissioner’ to harass and extort are almost legend.

‘Who shall mind the minders?’ is one of the central questions in institutional design. The traditional solution to the problem has been to institute a system of checks and balances to supplement accountability via ‘free and fair’ elections (which themselves need a functioning institutional framework). The system only works within limits, though innovative institutional designs to solve the problem can be thought off. The only other fruitful direction for reducing corruption has been to increase transparency (via RTI, post-facto disclosures of all bids in an auction, etc.), and via increased automation (cutting out the middle men, keeping bids blind from the committee so as to prevent certain kinds of collusion, etc.) – something the government is slowly and unevenly (depending on vested interests) working towards.

Corruption in enforcement is harder to tackle. Agents sent to enforce pollution laws have been known to extort from factory owners by threatening them with falsely implicating them with deliberately adulterated samples. There automating testing, and scrambling identity of source during analysis, may prove useful.

Bibliography-
Gupta, Sanjeev, Hamid Davoodi and Rosa Alonso-Terme. 2002. Does corruption affect income inequality and poverty? Economics of Governance. 3: 23–45

Law is the most popular degree at the Capitol Hill (it has been the case for a long time) – nearly 52% of the senators, and 36% of congressional representatives have a degree in law. There are some differences across parties and across houses, with Republicans likelier to have a law degree than Democrats in the Senate (58% to 48%), and the reverse holding true for the Congress – where more Democrats have law degrees than Republicans (40% to 32%). Less than 10% of members of congress have a degree in the natural sciences or engineering. Nearly 8% have a degree from Harvard, making Harvard’s the largest alumni contingent at the Capitol. Yale is a distant second with less than half the number that went to Harvard.

Now let’s do a thought experiment: Imagine a couple about to have a kid. Also assume that the couple doesn’t engage in sex-selection. Two things can happen – the couple can have a son or a daughter. It is possible that having a daughter persuades the parent to change his or her policy preferences towards a direction that is perceived as more congenial to women. It is also possible that having a son has the opposite impact – persuading parents to adopt more male congenial political preferences. Overall, it is possible that gender of the child makes a difference to parents’ policy preferences. With panel data one can identify both movements. With cross-sectional data, one can only identify the difference between those who had a son, and those who had a daughter.

Let’s test this using cross-sectional data from Jennings and Stoker’s “Study of Political Socialization: Parent-Child Pairs Based on Survey of Youth Panel and Their Offspring, 1997″.

Let’s assume that a couple’s partisan affiliation doesn’t impact the gender of their kid.

Number of kids, however, is determined by personal choice, which in turn may be impacted by ideology, income, etc. For example, it is likely that conservatives have more kids as they are less likely to believe in contraception, etc. This is also supported by the data. (Ideology is a post-treatment variable. This may not matter if impact of having a daughter is same in magnitude as impact of having a son, and if there are similar numbers of each across people.)

Hence one may conceptualize ‘treatment’ as gender of the kids, conditional on number of kids.

Understandably, we only study people who have one or more kids.

Conditional on number of kids, the more daughters respondent has, the less likely respondent is to identify herself as a Republican (b = -.342, p < .01) (when dependent variable is curtailed to Republican/Democrat dichotomous variable; the relationship holds – indeed becomes stronger – if the dependent variable is coded as an ordinal trichotomous variable: Republican, Independent, and Democrat, and an ordered multinomial estimated)

Future –

If what we observe is true then we should also see that as party stances evolve, impact of gender on policy preference of a parent should vary. One should also be able to do this cross-nationally.

Some other findings –

1. Probability of having a son (limiting to live births in the U.S.) is about .51. This ‘natural rate’ varies slightly by income – daughters are more likely to be born among lower income. However effect of income is extremely modest in the U.S., to the point of being ignorable. The live birth ratio is marginally rebalanced by the higher child mortality rate among males. As a result, among 0-21, the ratio between men and women is about equal in U.S.

   In the sample, there are significantly more daughters than sons. The female/male ratio is 1.16. This is ‘significantly’ unusual.

2. If families are less likely to have kids after the birth of a boy, number of kids will be negatively correlated with proportion sons. Among people with just one kid, number of sons is indeed greater than number of daughters, though the difference is insignificant. Overall correlation between proportion sons and number of kids is also very low (corr. = -.041).

Source: 2004 ANES. Cultural and Social Welfare Preferences are from Confirmatory Factor Analysis over relevant items.

Data are from the 2004 ANES. Social and Cultural Preferences are from Confirmatory Factor Analysis over relevant items.

Here’s how to interpret the graphs -

1) There is a large overlap in preference profiles of Rs and Ds.

2) Conditional on same preferences, there is a large gap in thermometer ratings. Without partisan bias – same-preferences should yield about the same R-D thermometer ratings. And this gap is not particularly responsive to change in preferences within parties.

This somewhat large difference in registration rates doesn’t automatically translate to (equally) wide distortions in racial distribution of the eligible population and the registered voter population. For example, while self-identified Whites constitute 62.8% of the VEP, they constitute marginally more – 64.2% of the voting eligible respondents who self-identify as having registered to vote.

Here’s the math –
Assume VEP Pop. = 100
Whites = 63/100; of these 72% register = 45
Latinos = 23/100; of these 62% register = 14
Rest = 14/100; of these 62% register = 9
New Registered Population = 45 + 14 +9 = 68
Registered: Whites = 66.2; Latinos = 20.6

Source: PPIC Survey (September 2010).
Note: CPS 2008, Secretary of State data confirm this. Voting day population estimates from Exit Poll also show no large distortions.

Some simple math:
For a two category case, say proportion category a = pa
Proportion category b = 1 – pa

Assume response rates for category a = qa, and for category b = qb = c*qa

Initial Ratio = pa/(1 -pa)
Final Ratio = pa*qa/(1-pa)*qb

Or between time 1 and 2, ratio changes by qa/qb or 1/c

T1 Diff. = pa – (1- pa) = 2pa – 1
T2 Diff. = (pa*qa – qb + pa*qb)/(pa*qa + (1-pa)*qb)
= (pa(qa + qb) – qb)/(pa(qa – qb) + qb)
= [pa*qa (1 + c) - c*qa]/[pa*qa(1-c) + c*qa]

T2 Diff. – T1 Diff. = [pa*qa (1 + c) - c*qa]/[pa*qa(1-c) + c*qa] – (2pa -1)
= [pa*qa (1 + c) - c*qa + pa*qa(1-c) + c*qa - 2pa (pa*qa(1-c) + c*qa)]/[pa*qa(1-c) + c*qa]
= [pa*qa + pa*qa*c - c*qa + pa*qa - pa*qa*c + c*qa - 2pa*pa*qa + 2pa*pa*qa*c - 2pa*c*qa]/[pa*qa(1-c) + c*qa]
= [2pa*qa - 2pa*pa*qa + 2pa*pa*qa*c - 2pa*c*qa]/[pa*qa(1-c) + c*qa]
= [2pa*qa(1- pa + pa*c -c)]/[pa*qa(1-c) + c*qa]
= [2pa*qa((1- c) - pa(1-c))]/[pa*qa(1-c) + c*qa]
= [2pa*qa(1-pa)(1-c)]/[pa*qa(1-c) + c*qa]

Diff. in response rates = qa – qb

When will diff. in response rates be greater than T2 – T1 Diff. -
qa – qb > [2pa*qa(1-pa)(1-c)]/(pa*qa – pa*qac + cqa)
qa(1-c)(pa*qa – pa*qac + cqa) > 2pa*qa(1-pa)(1-c)
qa(1-c)(pa*qa – pa*qa*c + c*qa) – 2pa*qa(1-pa)(1-c) > 0
(1-c)qa [pa*qa - pa*qa*c + c*qa - 2pa(1 -pa)] > 0
(1-c)qa[pa*qa -pa*qa*c + c.qa - 2pa + 2pa*pa] > 0
(1-c)qa[pa(qa - qa*c -2 + 2pa) - c.qa] > 0
(1- c) and qa are always greater than 0. Lets take them out.

pa.qa – pa.qa.c – 2pa + 2pa.pa – c.qa > 0
qa – qa.c – 2 + 2pa – c.qa/pa > 0 [ dividing by pa]
qa + 2pa – c.qa(1 + 1/pa) > 0
qa + 2pa > c.qa(1 + 1/pa)
(qa + 2pa)/[qa(1 + 1/pa)] > c
[pa*(qa + 2pa)]/[(pa + 1)qa] > c

When will diff. in response rates + initial diff. > T2 diff.
qa – qa*c + 2pa – 1 > [pa*qa (1 + c) - c*qa]/[pa*qa(1-c) + c*qa]
[pa*qa(1-c) + c*qa][qa - qa*c + 2pa - 1] – [pa*qa (1 + c) - c*qa] > 0
- pa*qa + pa*qa*c – c*qa + [pa*qa(1-c) + c*qa][qa - qa*c + 2pa] – pa*qa – pa*qa*c + c*qa > 0
-2pa*qa + [pa*qa(1-c) + c*qa][qa - qa*c + 2pa] > 0
-2pa*qa + [pa*qa - pa*qa*c + c*qa][qa - qa*c + 2pa] > 0
-2pa*qa + pa*qa[qa - qa*c + 2pa] – pa*qa*c[qa - qa*c + 2pa] + c*qa[qa - qa*c + 2pa] > 0
-2pa*qa + pa*qa*qa – pa*qa*qa*c + 2pa*qa*pa – pa*qa*c*qa + pa*qa*c*qa*c + 2pa*qa*c*pa + c*qa*qa – c*qa*qa*c + 2pa*c*qa> 0
-2pa*qa + pa*qa^2 – 2c*pa*qa^2 + 2qa*pa^2 + pa*c^2*qa^2 + 2pa^2*c*qa + c*qa^2 + c^2*qa^2 + 2pa*c*qa > 0
-2pa*qa + 2qa*pa^2 + 2pa*c*qa + 2pa^2*c*qa + pa*qa^2 – 2c*pa*qa^2 + pa*c^2*qa^2 + c*qa^2 + c^2*qa^2 > 0
2qa*pa(-1 + c + pa + pa*c) + pa*qa^2 (1 – 2c + c^2) + c*qa^2(1 + c) > 0
2qa*pa(-1 + c + pa(1+c)) + pa*qa^2 (1 – c)^2 + c*qa^2(1 + c) > 0
two of the terms are always 0 or more.
2qa*pa(-1 + c + pa(1+c)) > 0
-1 + c + pa(1+c) > 0
pa > (1-c)/(1 +c)

However there may be some negative normative implications as well. Iff parties have moved away from the center, and iff most people are near the center (as data shows), two things follow -
1) The average distance of each of those people from either of the parties has increased. So people’s choices have become impoverished.
2) The penalty of misclassification – for a leaner to mistakenly vote for the wrong party – has increased substantially. It may well be that while propensity of misclassification has decreased, the penalty has increased, leaving aggregate utility slightly worse off.

Secondly if government is at least partly in the business of providing public goods that require collective action (distributed costs), the split nature of constituencies and constituency based entrenched positions may very likely lead to an under-provision of public goods.

Thirdly, and something that is covered in the first point, clearer choices don’t mean the choices that are the best, or even what people want. One would hope that the choices on offer are optimal but we know that monopolies or duopolies under conditions where start up costs are high to get into the sector have a sparse record to providing something like that.

Fourthly, it also follows that given we have firm partisans, parties will stop broadening their constituencies beyond a certain point due to the law of really rapidly diminishing returns under ‘sorted’ electorate conditions. This will mean that the policy buckets shrink, and they will have larger incentives to cater to their bases.

Fifthly, legitimacy of the government is likely to be reduced among the losing camp which has reasons to believe that the ruling coalition doesn’t represent it.

However, lack of actual differences, hasn’t always meant lack of perceived differences, or affective dislike. Affective dislike, conditional on similarity, is unsurprising and typical, as history of racial and ethnic hatred will attest to; reasons for existence of such dynamics at the level of partisanship obvious – to any casual viewer of ‘cabal’ news.

A representative sample of Americans was asked whether they thought certain listed traits described Republicans and Democrats. Of these 18 traits (selfish, generous, close-minded, honest, etc.), a latent score was created (ICC here. Here’s a plot of latent affect by partisan self-identification.