5.4 Duration of Firms on the e-Commerce Platform

FIGURE 5.4 Duration of Firms on the e-Commerce Platform

Sources: Administrative data on transactions and business and entrepreneur characteristics associated with a partner firm website; data at CPhS (Consumer Pyramids household Survey) (dashboard), Consumer Pyramidsdx, Centre for monitoring indian Economy, mumbai, https://consumerpyramidsdx.cmie.com/. Notes: The figure shows the kaplan-meier estimates for the survival functions of firms on the platform.

first signing up (in this case, for more than 1 month, more than 2 months, and so on, up to more than 49 months). Data on 49 months of transactions are available, and this is the maximum amount of time during which firms may be observed on the platform. The probability that a firm remains on the platform for at least one month is about 91 percent, meaning that about 9 percent of firms exit within a month of joining the platform. After the first six months, this probability drops to 70 percent. The probability of staying on the platform for more than one year is 60 percent; only about 50 percent of firms remain active on the platform for more than two years. Accordingly, the figure highlights a relatively high rate of churn and exit among firms, especially during the first months on the platform. Figure 5.4, panel b, shows that formal firms tend to remain active on the platform longer than informal ones. However, the difference in the survival probability between formal and informal firms is not statistically significant once one controls for other characteristics of the firm and entrepreneur (gender, education, age, years in business, and business type), which implies that the differential survival probabilities are the result of factors other than business size or formality alone.

MONTHLY SALES, REVENUE, AND NUMBER OF PRODUCTS

One of the main questions examined in this study is whether there are meaningful differences in the extent to which formal and informal businesses can grow their sales and revenues by using online sales channels. To answer this question, the analysis uses a series of regressions of the form:

yi,t = α + β1 · formali + β2 · experiencei,t + γ · Xi + λt + ∈i,t, (5.1)

where yi,t, is an outcome of interest such as sales, sales revenue, or its month-on-month growth rate; and Xi is a matrix of controls that includes entrepreneur and business characteristics, such as the age, education, and gender of the business owner and the age and type of the business. The variable formal takes a value of 1 if firm i is formal according to the size-based definition described above and 0 otherwise; experience is the number of months firm i has been active on the platform until period t; and λt are time fixed effects. The main parameters of interest are β1, which, for each outcome variable, measures the difference between formal and informal businesses, and β2, which measures the effect of an additional month of experience on the platform on the outcome variable. The error term ∈i,t is assumed to be correlated for observations within the same firm and standard errors are therefore clustered at the firm level.

Sales

The analysis first examines the relationship between business formality, online experience, and sales. Table 5.4, columns 1 to 3, present the results with monthly sales as the dependent variable. The results show that formal businesses have, on average, more monthly sales relative to informal businesses. In particular, a formal business is