Estimating the size of the black economy: new evidence from India

Abstract

Purpose The issue of black economy has long been debated in India and it has been one of the key targets of policy action from last four decades. The debate is further fueled by demonetization of higher currency notes in the country. In this context, the purpose of this paper is to estimate the size of black economy in India for the period 1970–2017. Design/methodology/approach A currency demand approach is adopted for this purpose. The test of structure break indicates for a break in the system; therefore, the authors employ Johansen et al. (2000) cointegration test. For estimating the empirical model, the authors utilize fully modified ordinary least squares in a cointegration framework for taking care the endogeneity problem. Findings The estimates indicate that the Indian economy has a sizable black economy. In early 1970s, when the tax rate in India was significantly higher, the estimated black economy was above 30 percent of the official GDP. A variety of economic reforms including taxation, regulation and industrial licensing have drastically reduced the size to below 15 percent of official GDP in the last two decades. In the last estimated year (2017), the black economy was 23,849bn Indian rupees at current market price (around $400bn), which was 14 percent of the official GDP. Practical implications On the basis of the findings, the authors suggest some important fiscal, administrative and regulatory reforms to curb the generation of black economy in India. Originality/value The structural breaks can induce stochastic behavior similar to an integrated process, which makes it difficult to differentiate between the lack of cointegration and a structural shift. Thus, in the present study, the authors attempt to address this issue by incorporating the issue of structural break in the analysis. Furthermore, India is a cash-based economy; therefore, it is likely that currency-based models are more suitable. The application of advanced time-series techniques is likely to yield better and robust results.