They also prefer cheques as they can make late payments through post-dated cheques and other deferred instruments. A payment through a cheque is not final and the availability of funds to the payee takes days. Cheques carry considerable credit risk and the payment can be assured only with a cheque guarantee scheme. Although cheque returns is considered a criminal offence, banks in many countries are reluctant to take appropriate steps to deal with cheque returns, due to fear of recovery of loans and advances from customers. 3.3 Automation of cheque clearing: The cheque clearing process and its modernization have been taking place in many countries. Asia, Singapore, India and Sri Lanka have changed their existing laws and introduced cheque imaging and truncation systems, which have changed the whole process of cheque clearing and reduced processing time and transportation costs. The Sri Lankan experience shows that banks modernized their backoffice systems to accept electronically presented cheque information and link branches to the central cheque clearing center at LankaClear to provide end-to-end cheque processing, by crediting the proceeds to customers’ accounts on t+1 or t+2. This has had a significant impact in the rural and outstation areas in Sri Lanka, where businesses waited for 7 days or more to get cheques cleared. The system has also helped to reduce geographical barriers between regions and districts. Small businesses have begun to roll over finances better and faster. However, although it is advantageous to customers, the inefficiencies inbuilt in paperbased instruments continue to remain.

“Inflation Expectations and Behavior: Do Survey Respondents Act on Their Beliefs?” Federal Reserve Bank of New York Staff Reports, no. 509. Branch, W. A. 2004. “The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations.” The Economic Journal 114 (July): 592–621. Bruine de Bruin, W., W. van der Klaauw, J. S. Downs, B. Fischhoff, G. Topa, and O. Armantier. 2012. “The Effect of Question Wording on Consumers’ Reported Inflation Expectations.” Journal of Economic Psychology 33, no. 4 (August): 749–57. Bruine de Bruin, W., W. van der Klaauw, and G. Topa. 2011. “Expectations of Inflation: The Biasing Effect of Thoughts about Specific Prices.” Journal of Economic Psychology 32, no. 5 (October): 834–45. Carroll, C. D. 2003. “Macroeconomic Expectations of Households and Professional Forecasters.” Quarterly Journal of Economics, 118, no. 1 (February): 269–298. Engelberg, J., C. F. Manski, and J. Williams. 2009. “Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters”, Journal of Business and Economic Statistics 27, no. 1 (February): 30–41. Evans, G. W., and S. Honkapohja. 2001. Learning and Expectations in Macroeconomics. Princeton, New Jersey: Princeton University Press. Madeira, C., and B. Zafar. 2012. “Heterogeneous Inflation Expectations, Learning, and Market Outcomes.” Federal Reserve Bank of New York Staff Reports, no. 536, revised June 2014. Malmendier, U., and S. Nagel. 2013. “Learning from Inflation Experiences.” University of California, Berkeley, working paper. Mankiw, N. G., and R. Reis. 2002. “Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve,” The Quarterly Journal of Economics, 117, no. 4, (November): 1295–328.