Does easy availability of cash effect corruption? Evidence from panel of countries

Singh, Sunny and Bhattacharya, Kaushik (2016): Does easy availability of cash effect corruption? Evidence from panel of countries.

Abstract

Using annual panel data of 54 countries for the period 2005-14, we examine whether currency in circulation, both in aggregate and in large denominations, affects the level of corruption in a country. Standard panel data models suggest that the ratios of (i) aggregate currency in circulation to M1 and, (ii) large denominated banknotes to M1 are both statistically significant determinants of corruption. Tests for reverse causality within a panel Granger framework reveal uni-directional causality of corruption with the first variable, but a bi-directional one with the second one. These findings suggest that limitations of supply of banknotes of large denomination, inter alia, could be a tool to fight corruption and brings to the fore the important role of payment system, extending an earlier study by Goel & Mehrotra (2012). The results also highlight that along with government, the central bank of an economy can also play an important role in the fight against corruption.

• Item Type: MPRA Paper
• Language: English
• Keywords: Control of corruption Index; ICRG Corruption Index; Currency in circulation; Large denominated banknotes; Static panel data model; Dynamic panel data model; Panel. Granger causality
• Subjects: D - Microeconomics > D7 - Analysis of Collective Decision-Making > D73 - Bureaucracy ; Administrative Processes in Public Organizations ; Corruption; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E51 - Money Supply ; Credit ; Money Multipliers; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E58 - Central Banks and Their Policies

References

Abramo, C. W. (2008). How much do perceptions of corruption really tell us? Economics: The Open-Access, Open-Assessment E-Journal, 2, 3.

Adhikari, A., & Bhatia, K. (2010). NREGA wage payments: Can we bank on the banks? Economic and Political Weekly, 45(1), 30–37.

Aidt, T. S. (2003). Economic analysis of corruption: A survey. The Economic Journal, 113(491), F632–F652. http://doi.org/10.1046/j.0013-0133.2003.00171.x

Andersen, T. B. (2009). E-Government as an anti-corruption strategy. Information Economics and Policy, 21(3), 201–210. http://doi.org/10.1016/j.infoecopol.2008.11.003

Andrews, D. W. K., & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics, 101(1), 123–164. http://doi.org/10.1016/S0304-4076(00)00077-4

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. http://doi.org/10.2307/2297968

Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. http://doi.org/10.1016/0304-4076(94)01642-D

Baltagi, B. (2008). Econometric analysis of panel data (Vol. 1). John Wiley & Sons.

Bardhan, P. (1997). Corruption and development: A review of issues. Journal of Economic Literature, 35(3), 1320–1346.

Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169–217.

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. http://doi.org/10.1016/S0304-4076 (98)00009-8

Chowdhury, S. K. (2004). The effect of democracy and press freedom on corruption: An empirical test. Economics Letters, 85(1), 93–101. http://doi.org/10.1016/j.econlet.2004.03.024

Dreher, A., & Schneider, F. (2009). Corruption and the shadow economy: An empirical analysis. Public Choice, 144(1-2), 215–238. http://doi.org/10.1007/s11127-009-9513-0

Dreher, A., & Siemers, L.-H. R. (2009). The nexus between corruption and capital account restrictions. Public Choice, 140(1-2), 245–265. http://doi.org/10.1007/s11127-009-9423-1

Drehmann, M., Goodhart, C., & Krueger, M. (2002). The challenges facing currency usage: Will the traditional transaction medium be able to resist competition from the new technologies? Economic Policy, 17(34), 193–228.

Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. http://doi.org/10.1016/j.econmod.2012.02.014

Elbahnasawy, N. G. (2014). E-Government, internet adoption, and corruption: An empirical investigation. World Development, 57, 114–126. http://doi.org/10.1016/j.worlddev.2013.12.005

Elbahnasawy, N. G., & Revier, C. F. (2012). The determinants of corruption: Cross-country-panel-data analysis. The Developing Economies, 50(4), 311–333. http://doi.org/10.1111/j.1746-1049.2012.00177.x

Freille, S., Haque, M. E., & Kneller, R. (2007). A contribution to the empirics of press freedom and corruption. European Journal of Political Economy, 23(4), 838–862. http://doi.org/10.1016/j.ejpoleco.2007.03.002

Goel, R. K., & Mehrotra, A. N. (2012). Financial payment instruments and corruption. Applied Financial Economics, 22(11), 877–886. http://doi.org/10.1080/09603107.2011.628295

Goel, R. K., Budak, J., & Rajh, E. (2013). Bureaucratic monopoly and the nature and timing of bribes: evidence from Croatian data. Comparative Economic Studies, 55(1), 43–58. http://doi.org/10.1057/ces.2012.36

Goel, R. K., Nelson, M. A., & Naretta, M. A. (2012). The internet as an indicator of corruption awareness. European Journal of Political Economy, 28(1), 64–75. http://doi.org/10.1016/j.ejpoleco.2011.08.003

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. http://doi.org/10.2307/1913827

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: methodology and analytical issues. Hague Journal on the Rule of Law, 3(02), 220–246. http://doi.org/10.1017/S1876404511200046

Lio, M.-C., Liu, M.-C., & Ou, Y.-P. (2011). Can the internet reduce corruption? A cross-country study based on dynamic panel data models. Government Information Quarterly, 28(1), 47–53. http://doi.org/10.1016/j.giq.2010.01.005

Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2014). Building state capacity: Evidence from biometric smartcards in India (Working Paper No. 19999). National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w19999

Rogoff, K. (2002). The surprising popularity of paper currency. Finance and Development, 39(1), 56–7.

Rogoff, K., Giavazzi, F., & Schneider, F. (1998). Blessing or curse? Foreign and underground demand for Euro notes. Economic Policy, 13(26), 263–303.

Roodman, D. (2006). How to do xtabond2: An introduction to difference and system GMM in Stata. Center for Global Development Working Paper, (103). Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=982943

Roodman, D. (2009). A Note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71(1), 135–158. http://doi.org/10.1111/j.1468-0084.2008.00542.x

Rose-Ackerman, S. (1999). Corruption and government: Causes, consequences, and reform. Cambridge University Press.

Schaffer, M. E., & Stillman, S. (2011). XTOVERID: Stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor. Statistical Software Components. Retrieved from https://ideas.repec.org/c/boc/bocode/s456779.html

Schneider, F., & Enste, D. H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), 77–114.

Seldadyo, H., & de Haan, J. (2006). The determinants of corruption: A literature survey and new evidence, Working paper. University of Groningen.

Serra, D. (2006). Empirical determinants of corruption: A sensitivity analysis. Public Choice, 126(1-2), 225–256.

Svensson, J. (2005). Eight questions about corruption. The Journal of Economic Perspectives, 19(3), 19–42.

Tanzi, M. V., & Davoodi, M. H. R. (1997). Corruption, public Investment, and growth. International Monetary Fund.

Treisman, D. (2000). The causes of corruption: A cross-national study. Journal of Public Economics, 76(3), 399–457. http://doi.org/10.1016/S0047-2727 (99)00092-4

Treisman, D. (2007). What have we learned about the causes of corruption from ten years of cross-national empirical research? Annual Review of Political Science, 10(1), 211–244. http://doi.org/10.1146/annurev.polisci.10.081205.095418

Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51. http://doi.org/10.1016/j.jeconom.2004.02.005