Abstract
Is corruption influenced by economic growth? Are legal institutions such as the ‘Right to Information Act (RTI) 2005’ in India effective in curbing corruption? Using a panel dataset covering 20 Indian states for the years 2005 and 2008 we estimate the effects of growth and law on corruption. Accounting for endogeneity, omitted fixed factors, and other nationwide changes we find that economic growth reduces overall corruption as well as corruption in banking, land administration, education, electricity, and hospitals. Growth reduces bribes but has little impact on corruption perception. In contrast the RTI Act reduces both corruption experience and corruption perception.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Note that Treisman (2000) in Table 1 (p. 411) reports a correlation of 0.87 between Business International and Transparency International macro cross-country data.
Note that Huntington (1968) disagrees with this view. He argues that rapid modernization induces normative confusion at a time when the emerging economic elites are striving for power and influence. As a result growth increases corruption.
Note that the official would never present these objections in writing.
Note that the Kolmogorov–Smirnov tests reported in Table 2 indicates that the distribution of corruption across states have changed over the two time periods. Forces such as economic growth may be driving these changes.
More on this in the section ‘Empirical strategy and data’.
Ades and Di Tella (1999), Rose-Ackerman (1999), Leite and Weidmann (1999), Dabla-Norris (2000) are other important contributions in this literature. Bardhan (1997) provides an excellent survey of the early contributions.
Note that we also use GDP per capita growth rate in Table 3 and our results are robust.
Note that the survey asks some additional questions. However, they are not common over the two time periods in our study. Therefore we are not including them here.
Note that the India Corruption Study only reports these macro percentages and the underlying micro data is not reported.
The model predicts that corruption in Bihar should have declined by 18.5% points due to the RTI Act. However, the actual decline is 30% points. Therefore, the predicted decline is 62% of the actual.
According to our estimates, economic growth reduced corruption perception only in education.
References
Acemoglu, D, Johnson, S and Robinson, J . 2001: The colonial origins of comparative development: An empirical investigation. American Economic Review 91 (5): 1369–1401.
Ades, A and Di Tella, R . 1999: Rents, competition, and corruption. American Economic Review 89 (4): 982–993.
Bardhan, P . 1997: Corruption and development: A review of issues. Journal of Economic Literature 35 (3): 1320–1346.
Besley, T and Burgess, R . 2000: Land reform, poverty reduction and growth: Evidence from India. Quarterly Journal of Economics 115 (2): 389–430.
Besley, T and Burgess, R . 2004: Can labor regulation hinder economic performance? Evidence from India. Quarterly Journal of Economics 119 (1): 91–134.
Bhattacharyya, S . 2009: Unbundled institutions, human capital and growth. Journal of Comparative Economics 37: 106–120.
Bhattacharyya, S and Hodler, R . 2010: Natural resources, democracy and corruption. European Economic Review 54: 608–621.
Dabla-Norris, E . 2000: A game – theoretic analysis of corruption in bureaucracies, IMF Working Paper No. WP/00/106.
Fan, S, Lin, C and Treisman, D . 2009: Political decentralization and corruption: Evidence from around the world. Journal of Public Economics 93: 14–34.
Hall, R and Jones, C . 1999: Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics 114 (1): 83–116.
Hausman, J, Stock, J and Yogo, M . 2005: Asymptotic properties of the Hahn-Hausman test for weak instruments. Economics Letters 89 (3): 333–342.
Hendry, D, Johansen, S and Santos, C . 2004): Selecting a regression saturated by indicators. University of Oxford, Unpublished manuscript.
Huntington, SP . 1968: Political order in changing societies. Yale University Press: New Haven.
Isham, J, Pritchett, L, Woolcock, M and Busby, G . 2005: The varieties of resource experience: Natural resource export structures and the political economy of economic growth. World Bank Economic Review 19: 141–174.
Knack, S and Keefer, P . 1995: Institutions and economic performance: Cross-country tests using alternative institutional measures. Economics and Politics 7: 207–227.
Leite, C and Weidmann, J . 1999: Does Mother Nature corrupt? Natural resources, corruption and economic growth, IMF Working Paper No. WP/99/85.
Lipset, SM . 1960: Political man: The social bases of politics. Anchor Books: Garden City.
Mauro, P . 1995: Corruption and growth. Quarterly Journal of Economics 110: 681–712.
Olken, B . 2007: Monitoring corruption: Evidence from a field experiment in Indonesia. Journal of Political Economy 115 (2): 200–249.
Olken, B . 2009: Corruption perceptions vs. corruption reality. Journal of Public Economics 93: 950–964.
Rodrik, D, Subramanian, A and Trebbi, F . 2004: Institutions rule: The primacy of institutions over geography and integration in economic development. Journal of Economic Growth 9: 131–165.
Rose-Ackerman, S . 1999: Corruption and government: Causes, consequences and reform. Cambridge University Press: Cambridge.
Staiger, D and Stock, J . 1997: Instrumental variables regression with weak instruments. Econometrica 65: 557–586.
Stock, J and Yogo, M . 2005: Testing for weak instruments in linear IV regression. In: Andrews, D and Stock, J (eds) Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. Cambridge University Press: Cambridge, pp. 80–108.
Treisman, D . 2000: The causes of corruption: A cross-national study. Journal of Public Economics 76: 399–457.
Acknowledgements
We gratefully acknowledge comments by and discussions with Paul Burke, Ranjan Ray, Takashi Kurosaki, Peter Warr, and Conference and seminar participants at the Australian National University, University of Oxford, and ISI Delhi. We thank the editor, Josef Brada, and an anonymous referee for their comments. We also thank Rodrigo Taborda for excellent research assistance. All remaining errors are our own.
Author information
Authors and Affiliations
Appendix
Appendix
Data description
Corruption [c it ]: Corruption is computed using a two-step procedure. First, an average is computed of the percentage of respondents answering yes to the questions that they have direct experience of bribing, using a middleman, perception that a department is corrupt, and perception that corruption increased over time for eight different sectors: banking, land administration, police, education, water, Public Distribution System (PDS), electricity, and hospitals. Second, these averages are also averaged over all the eight sectors to generate one observation per state and per time period. Higher value of the corruption measure implies higher corruption. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Banks [c it BANKS ]: Corruption computed in the same fashion as c it but only for the banking sector. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Land Administration [c it LAND ]: Corruption computed in the same fashion as c it but only for the land administration sector. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Police [c it POLICE ]: Corruption computed in the same fashion as c it but only for police. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Education [c it EDUC ]: Corruption computed in the same fashion as c it but only for education sector. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Water [c it WATER ]: Corruption computed in the same fashion as c it but only for the water supply sector. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in PDS [c it PDS ]: Corruption computed in the same fashion as c it but only for the public distribution system. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Electricity [c it ELEC ]: Corruption computed in the same fashion as c it but only for the electricity sector. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption in Hospitals [c it HOSP ]: Corruption computed in the same fashion as c it but only for hospitals. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption Experience Measures: Corruption experience measures are the average of answers to the questions on ‘direct experience of bribing’ and ‘using influence of a middleman’. Source: India Corruption Study 2005 and 2008, Transparency International.
Corruption Perception Measures: Corruption perception measures are the average of answers to the questions on ‘perception that a department is corrupt’ and ‘perception that corruption has increased’. Source: India Corruption Study 2005 and 2008, Transparency International.
Economic Growth [
]: Real growth rate in state GDP measured in 2009 constant prices. Source: Planning Commission, Government of India.
Log Rainfall [ln RAIN it−1 ]: Log of rainfall across states measured in millimeters. Source: Compendium of Environmental Statistics, Central Statistical Organisation, Ministry of Statistics and Programme Implementation.
Flood Area: Total area affected by flood in 1994 and 1996 measured in millions of hectares. Source: Central Water Commission, Government of India.
Flood Population: Total population affected by flood in 1994 and 1996 measured in millions. Source: Central Water Commission, Government of India.
Flood Crop Area: Total crop area affected by flood in 1994 and 1996 measured in millions of hectares. Source: Central Water Commission, Government of India.
Flood Household: Total number of households affected by flood in 1994 and 1996 measured in millions of hectares. Source: Central Water Commission, Government of India.
Literacy: Literacy rate for 2002 and 2005. Source: Selected Socioeconomic Statistics India 2006, Central Statistical Organization, Table 3.3.
Gini Coefficient: Gini coefficient urban for the periods 1999–2000 and 2004–2005. Source: Planning Commission.
Poverty Head Count Ratio: Percentage of population below poverty line (rural and urban combined). Source: Planning Commission.
Mining Share of GDP: Mining sector share of state GDP. Source: Handbook of Statistics on the Indian Economy, Reserve Bank of India.
Primary Sector Share of GDP: Primary sector share of state GDP. Source: Handbook of Statistics on the Indian Economy, Reserve Bank of India.
State Government Expenditure: State government expenditure as a proportion of state GDP. Source: Indian Public Finance Statistics, Ministry of Finance.
Newspaper Circulation: Number of registered newspapers in circulation. Source: Registrar of Newspapers, Government of India.
Telephone Exchange: Number of telephone exchanges. Source: Ministry of Information and Broadcasting, Government of India.
Sample and state codes
Andhra Pradesh (AP), Assam (AS), Bihar (BH), Chhattisgarh (CG), Delhi (DL), Gujarat (GJ), Haryana (HR), Himachal Pradesh (HP), Jammu and Kashmir (JK), Jharkhand (JH), Karnataka (KT), Kerala (KL), Madhya Pradesh (MP), Maharashtra (MH), Orissa (OS), Punjab (PJ), Rajasthan (RJ), Tamil Nadu (TN), Uttar Pradesh (UP), West Bengal (WB).
Measurement error and instrumental variable estimation
Assume that the true relationship between corruption and growth is 
. However the corruption variable has measurement error so that 
(where θ
it
is the time varying measurement error). Because of measurement error we only observe 
and not true corruption c
it
. So we estimate the model 
. While estimating this model using fixed effects we would difference the data and get 
. The parameter estimate of interest would be, 
and 
. Therefore, estimating the model using OLS would yield biased estimates. If we use rainfall instrument Z
it
that is correlated with 
but orthogonal (or uncorrelated) to Δθ
it
, Δβ
t
and Δɛ
it
then cov(Z
it
, Δθ
it
)=cov(Z
it
, Δβ
t
)=cov(Z
it
, Δɛ
it
)=0. Then we would get, 
and 
since cov(Z
it
, Δβ
t
)=cov(Z
it
, Δɛ
it
)=0. Therefore, the IV strategy could potentially remedy the measurement error problem with 2008 corruption data.
Rights and permissions
About this article
Cite this article
Bhattacharyya, S., Jha, R. Economic Growth, Law, and Corruption: Evidence from India. Comp Econ Stud 55, 287–313 (2013). https://doi.org/10.1057/ces.2013.4
Published:
Issue Date:
DOI: https://doi.org/10.1057/ces.2013.4