Varieties of Corruption: The Organization of Rent-Sharing ...

Table C1. Politicians at Higher Levels More Likely to Respond to Distribution of Bribe

Question

Politician Type of Politician Responded Did Not Percent

Group Respond Responded

High-level* Members of Parliament 36 1 97.3

Members of the 143 8 94.8

Legislative Assembly

Mid-level* District Council 32 5 86.5

Presidents

District Council 35 6 85.4

Members

Block Council Presidents 68 49 59.1

Block Council Members 67 66 50.4

Low-level Village Council 249 313 44.3

Presidents

Village Council 489 665 42.4

Members

Total Sample 1119 1113 50.1

*This is the most conservative measure, as the lowest response rate was taken from across the two

scenarios to which high- and mid-level politicians were exposed.

Table C2. Bureaucrats at Highest Level Most Likely to Respond to Distribution of Bribe

Question

Bureaucrat Type of Bureaucrat Responded Did Not Percent

Group Respond Responded

Mid-level District Collectors 39 13 75.0

Block Development 54 137 28.3

Officers

Low-level Village Council 275 222 55.3

Secretaries

Total Sample 368 372 49.7

When the scenarios are considered independently, we see in general that respondents were

very likely to respond to the scenario if it described corruption in a government resource for which

they are likely to have direct control (tables C3 and C4). For example, 99 percent of members of

state assemblies responded to the policy scenario and district collectors had one of their highest

response rates, 85%, for the road contract scenario.

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At the same time, one scenario, that describing corruption in road contracts, was nearly

universally the scenario to which subjects were least likely to respond. This suggests that there may

be something particular about the scenario itself or the type of corruption described that makes

respondents less likely to respond. This is an issue to be explored further in future work.

Beyond these two trends, there is only minimal within respondent group variation in response

rates for the scenarios. This implies that those individuals who were comfortable responding to the

question were not strongly affected by the nature of the corruption described in the scenario itself.

Thus, there is clear variation across officials in overall willingness to respond, but this seems more

likely to be a reflection of differences in comfort levels about answering a question concerning

perceptions of corruption than willingness to comment on a type of corruption with which one may

not be very familiar.

Table C3. Percent Responding to Scenarios by Politician Type

Respondent Type of Politician Ration Land Road Policy

Group

High-level Members of Parliament 100 93 95 100

Politician Members of the 100 92 90 99

Legislative Assembly

Mid-level District Council 94 95 71 90

Politician Presidents

District Council 100 81 75 86

Members

Block Council Presidents 68 62 46 64

Block Council Members 54 60 47 48

Low-level Village Council 68 65 24 60

Politician Presidents

Village Council 58 48 16 49

Members

*Note: in each table, the cell with the lowest response rate for each type of respondent is shaded.

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Table C4. Percent Responding to Scenarios by Bureaucrat Type

Bureaucrat Type of Bureaucrat Ration Land Road Policy

Group 85 62
26 27
Mid-level District Collectors 89 69
35 58
Block Development 23 37

Officers

Low-level Village Council 66 60

Secretaries

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(Online) Appendix D – Additional Tests

In this Appendix, I present the results of t-tests used to examine within-actor difference in the
perceived sharing of rents. In each table, the columns show differences in means for pairs of
scenarios. For example, the first column shows the mean proportion of rents allocated to a particular
type of actor in the Ration scenario minus the mean proportion of rents allocated to the same actor
type in the Land Record scenario. The rows reflect the various types of actors. Thus, going down one
column allows for a comparison of two scenarios in terms of the proportion of rents allocated to all
actor types. Alternatively, looking across a single row allows for a comparison within a single actor
type of the relative perceived rents received by that actor across each of the scenario pairs. In each
table, I highlight the row(s) that reflect the type of actor(s) whose responses are provided in the table,
such that in the first table, which provides responses for mid- and high-level politicians, I highlight
the rows with responses for the allocation of rents to these types of actors. I also highlight the
column(s) for which these types of actors are hypothesized to have the best direct information, which,
in the case of mid- and high-level politicians is the comparison between corruption in the Policy and
Road Contract scenarios. Thus, I present the responses of all individuals, but emphasize in the
discussion only the perspectives of those individuals likely to be the most informed about corruption
for a given set of state resources.

Overall, the results provide further support for the findings shown in Figures 1-4. Among
mid- and high-level politician respondents (Table D1), when comparing the road contract and policy
scenarios, high-level politicians are seen to benefit more from the policy scenario, where they have
formal authority, whereas mid-level politicians are seen to benefit more from corruption in road
contracts, where they may have more indirect influence than at higher, policy-making levels. Mid-

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and high-level respondents also see the policy and road scenarios to be more profitable than each of
the other scenarios for their own groups, respectively. With regard to perceptions of benefits to other
actors, these respondents perceive the road scenario to be more beneficial than the policy scenario to
any other group.

Mid-level bureaucrat respondents also perceive relative benefits to their own group in line
with the expectations of the argument (Table D2). The road contract scenario is seen to offer a larger
proportion of rents for mid-level bureaucrats than either the policy or land record scenario, which
represent corruption in other resources with which they may have some familiarity. The road
scenario is also seen to provide more benefits than the ration scenario, while there are no discernable
differences for this group among the other scenario comparisons. With regard to other actors, the
only statistically significant differences perceived by this group of respondents relative to the road
contract scenario are that low-level bureaucrats should benefit more from corruption in land records
and high-level politicians should benefit more from corruption in policy-making.

Among low-level politicians, these respondents should be most familiar with corruption in
the ration and land record scenarios. Here, they perceive that they will benefit more from corruption
in land records, though the substantive difference is relatively small (Table D3). In addition, they
expect mid-level bureaucrats to receive a similarly larger benefit from land record corruption, while
low-level politicians are seen to receive a greater relative benefit in the land record scenario.

Finally, low-level bureaucrats should also be most attuned to the differences in rent-sharing
between the ration and land record scenarios. These respondents (Table D4) similarly perceive that
they will benefit substantially more from corruption in land records. In contrast, they anticipate that
middlemen will benefit more from corruption in rations, while there are no clear differences in their
expectations for other actors.

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Table D1 – Bihar, Jharkhand, and Uttar Pradesh - t-tests comparing the mean proportion of
funds allocated by the respondent to each actor type based on the four scenarios –
Respondents are mid- and high-level politicians (Difference in means is shown, with t-ratio in
parentheses).

Difference in Ration-Land Ration-Road Ration-Policy Land-Road Land-Policy Road-Policy
Means

Recipient .30** .59*** .82*** .25*** .81*** .26***
Type (7.44) (13.41) (29.72) (4.59) (22.78) (6.42)
Middleman
.00 -.01 .00 -.01 -.00 .01*
Low (.84) (-1.39) (1.04) (-1.86) (-.04) (2.00)
Politician
Mid Politician -.01 -.14*** -.01* -.13*** -.01 .13***
(-1.45) (-5.30) (-2.06) (-5.33) (-1.50) (5.12)
High
Politician -.01* -.46*** -.94*** -.44*** -.92*** -.49***
Party (-2.58) (-12.11) (-65.20) (12.52) (-51.53) (-12.67)

Low -.00 -.03** -.01* -.03** -.01* .02*
Bureaucrat (-.78) (-2.62) (-2.38) (-2.94) (-2.18) (2.03)
Mid
Bureaucrat -.35*** .06*** .08*** .42*** .43*** .01
High (-10.72) (3.78) (5.31) (12.67) (13.30) (1.66)
Bureaucrat
N -.03** -.15*** -.01 -.12*** .01 .14***
(-2.69) (-5.36) (-1.06) (-4.51) (1.24) (5.24)
-.08*** .04** .03** .10*** .06*** .01
(-5.24) (3.37) (2.88) (4.15) (4.53) (.67)
349-40426 144-334 309-403 275-367 166-354 181-365

26 The main discrepancy in the N across the tests comparing each pair of scenarios is due to the fact
that “middleman” was included as an answer category in the Bihar survey instrument only for the
Ration scenario and “high-level bureaucrat” was included only in the Ration and Land scenarios. As
a result, tests for these actor categories involving other scenarios use data only from Jharkhand and
Uttar Pradesh.

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Table D2 – Bihar, Jharkhand, & Uttar Pradesh - t-tests comparing the mean proportion of
funds allocated by the respondent to each actor type based on the four scenarios –
Respondents are Mid-level bureaucrats (Difference in means is shown, with t-ratio in
parentheses).

Difference in Ration-Land Ration-Road Ration-Policy Land-Road Land-Policy Road-Policy
Means

Recipient .64*** .54*** .59*** -.10 -.05 .05
Type (6.06) (4.58) (5.09) (-.79) (-.41) (.35)

Middleman

Low -.00 .00 -.02 .00 -.02 -.02
Politician (-.81) (.00) (-1.26) (.87) (-1.15) (-1.36)
Mid Politician -.00 .00 -.02 .00 -.01 -.02
(-.79) (.00) (-1.26) (.87) (-1.10) (-1.40)
High -.01 -.08 -.51*** -.08 -.50*** -.42***
Politician (-.81) (-1.34) (-4.55) (-1.51) (-5.56) (-3.58)
Party -.00 .00 -.01 .00 -.00 -.00
(-.81) (.00) (-.95) (.87) (-.23) (-1.02)
Low -.62*** .02 .01 .64*** .64*** -.00
Bureaucrat (-5.74) (1.47) (.88) (6.25) (6.18) (-1.00)
Mid
Bureaucrat -.00 -.50*** -.10 -.50*** -.10 .40**
High (-.82) (-4.25) (-1.47) (-5.13) (-1.70) (3.10)
Bureaucrat
N .05 .02 .02 .02 .02 -.00
(.27) (1.57) (1.32) (1.40) (1.13) (-1.02)

47-49 40-42 40-41 50-53 49-52 43-45

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