Corruption and public investment in India

1. A Review on Corruption, Public
Investment and Growth
Paper Written by Vito Tanzi and Hamid R. Davoodi
Rahul Shakya & Rahul Yadav
MBF (PGDBF) 2012-14
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2. Summary
There has been an attempt to establish a relationship between high
levels of corruption and capital spending as a result of which there
is:
• An increase in the share of public spending in GDP
• A fall in the Average productivity of projects
• A reduction in Operations & Maintenance expenditure
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3. Data Type
The author has used indices of corruption data from two
sources:
• Business International
• Political Risk Services, Inc
The data has also been taken from IMF’s Government Finance
Statistics (GFS) for variable like:
• Public investment,
• Operations and Maintenance, and
• Other aspects of public expenditure
The data taken is ranging from 1980 to 1995 for different
variables of public expenditure for many countries like OECD
countries or developing countries which tells us that the data
taken is a Panel data set
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4. Method used to evaluate
corruption
The method used is testing the various hypotheses
between corruption on one hand and
•Public investment,
•Government revenue,
•O&M expenditures, and
•Other variables depicting the quality of infrastructure on
the other hand
Regression analysis has been used for testing the above
hypotheses for cross country data set.
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5. Variable Measure & Variable
Introduced
• Independent Variable:
o Corruption Index
o Real per Capita GDP
o Government Revenue – GDP Ratio
• Dependent Variable
o Public Investment
o Government Revenues
o O & M Expenditure
o Quality of public investment
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6. Hypothesis Tested
There are Four hypotheses used in the Paper:
1. High Corruption is associated with high public
investment.
2. High Corruption is associated with low government
revenue.
3. High Corruption is associated with Low O&M
expenditure.
4. High Corruption is associated with poor quality of
Infrastructure.
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7. • For First Hypothesis forward Step wise regression
combined with multiple regression has been used. Three
regressions have been run:
o Public investment GDP ratio with the corruption index.
o Moving on two variables have also been added one by one
which are real per capita GDP (proxy for economic
development) and government revenue to GDP.
These variable have been added to improve the model.
• For second hypothesis Government revenue – GDP ratio
on a constant and corruption index has been regressed
and moving on real per capita GDP has been added to
check for sensitivity.
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8. • For Third hypothesis two proxies are used for the O&M
expenditure which are:
o Expenditure on other goods and services expressed as a
fraction of wages and salaries.
o Wages and salaries expressed as a fraction of current
expenditure.
Each of the above proxies are regressed on a constant and a
corruption index, again the real per capita GDP has been
added to each regression to improve the results.
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9. •For Fourth Hypothesis, indicators of the quality of
infrastructure on a constant, the corruption index and real
per capita GDP have been regressed. Five indicators of the
quality of infrastructure are:
o Paved roads in good condition
o Power outages
o Telecommunication faults
o Water losses
o Railway diesel in use
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10. • Apart from the above hypotheses two additional hypothesis
has been used to reinforce the study
o Does corruption reduces the quality of infrastructure through
public investment.
o Does higher corruption reduces the productivity of public
investment.
• For the first hypothesis paved roads in good conditions as a
percentage of total paved roads on a constant, real per capita
GDP, the corruption index and two additional variables, i.e.
Public investment GDP ratio and it’s interaction with the
corruption index have been regressed.
• For the second hypothesis the same regression has been run
with including all the variable which shows that corruption
hampers the productivity of a public investment.
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11. Are Single, Multiple or Stepwise
Regression?
• All three (Single, Multiple and Stepwise) Regression are
used.
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12. Result of Hypothesis
• The conclusion of different hypotheses are the same, i.e.
corruption is associated with high public expenditure
• Just in the third hypothesis, for the first proxy which is
expenditure on other goods and services as a ratio of
wages and salaries, can be rejected at one percent
significance level for the world data only.
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13. Final Conclusion
There are many channels through which higher corruption
reduces economic growth they are:
•Corruption can reduce growth by increasing public
investment thereby reducing it’s quality.
•Corruption can decrease growth by increase in public
expenditure which is not followed by O&M expenditure.
•Corruption can reduce the growth by reducing the quality
of existing infrastructure.
•Corruption can reduce growth by lowering government
revenue needed to finance productive spending.
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14. Are dummy variables used?
• Dummy variables are not used as there no bifurcation of
data in 0’s and 1’s
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15. ANOVA or ANCOVA ?
• ANOVA models are used in the study as:
Quantitative variables are used and
Dummy variables are not used.
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16. Variable Used for Growth &
Corruption in Economy
The proxy used for growth is real per capita GDP and for
corruption is corruption index which has been taken from
two indices namely
Business international (BI)
International risk country guide (ICRG)
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17. Thank You!
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