In recent years, debit cards have become a popular payment instrument in the United States (Evans & Schmalensee, 1999, p. 306; Weiner, 2000). Since their introduction in 1975, growth of these cards has been slow, particularly throughout the 1980s and mid-1990s. However, in the last decade, the percentage of households that use debit cards has increased dramatically: from 20% in 1995 to 37% in 1998 and to 50% in 2001 (Anguelov, Hilgert & Hogarth, 2004). Debit cards achieved the highest growth rate among forms of retail payment between 1995 and 2000 with an increase of 41.8% (Anguelov et al., 2004). In 2000, debit cards accounted for 11.6% of all retail transactions (Gerdes & Walton, 2002).
For retail transactions, consumers have several choices of payment instruments, including cash, check, credit cards, and debit cards. Each choice provides a host of desired properties that differentiate one instrument from the other. Debit cards, which became feasible and more widely available through the Visa and MasterCard network, provide the point-of-sale convenience of credit cards and yet the direct transaction properties of automatic teller machine (ATM) cards (Weiner, 2000). In addition, consumer protection for this form of payment has been enhanced by the limits now placed on the liability for lost or stolen debit cards. Unlike credit cards, debit cards use funds from the consumer’s funded bank account and do not allow consumers to borrow money, a characteristic that can discourage over-spending and, thus, discourage debt accumulation. One could argue then that consumers intentionally choose debit cards instead of credit cards in an effort to avoid debt accumulation.
Hayn (1995) and Burgstahler and Dichev (1997) find evidence that there is a ‘point of discontinuity’ in the cross-sectional distribution of earnings around zero with an unusually low concentration of firms just below zero and an unusually high concentration of firms just above zero earnings (earnings level threshold). Burgstahler and Dichev (1997) find similar breaks in the distribution for small positive and small negative earnings changes (earnings changes threshold). Degeorge, Patel, and Zeckhauser (1999) find similar results for the cross sectional distribution of analyst forecast errors (analyst forecast threshold). These studies interpret the breaks in the distributions as circumstantial evidence that earnings are managed to beat these thresholds.
Firms have incentives to beat earnings thresholds. Graham, Harvey, and Rajgopal (2005) survey 312 financial executives from public companies. They ask executives which earnings benchmarks are important to them and find that roughly two thirds or more (depending on the benchmark) of the respondents agree that all three benchmarks are important (Graham et al. 2005, Table 3). Over 80% of the executives surveyed agreed that meeting earnings benchmarks helped them to ‘build credibility with the capital market’ and ‘maintain or increase stock price’ (Graham et al. 2005, Table 4). There has developed a large literature that documents firms’ capital market incentives for beating one of the benchmarks (e.g. Barth, Elliot, and Finn 1999, Myers et al. 2007, Bartov et al. 2002, Kasznik and McNichols 2003, Brown and Caylor 2005). Matsunaga and Park (2001) suggest that CEO bonus payments give CEOs an economic incentive to beat the analyst forecast benchmark and the earnings changes benchmark. If firms have incentives to beat a threshold, then why do so many firms miss a threshold? What prevents firms just below a threshold from managing earnings to beat a threshold? I examine the characteristics of firms just above and firms just below thresholds. I focus on two specific characteristics—market sensitivity to earnings announcements and a firm’s flexibility to manage earnings.
High-expectation entrepreneurial activity represents only a small proportion of all entrepreneurial activity, yet it explains the bulk of expected new jobs by cohorts of nascent entrepreneurs and baby businesses. Depending on country and world region, only some 3% to 17% of nascent entrepreneurs and baby businesses expect to employ 20 or more employees within five years. Only some 1 % to 7 % of nascent entrepreneurs and baby businesses expect to employ 50 or more employees within five years. However, its economic potential is significant, as high-expectation entrepreneurs are responsible for up to 80% of total expected jobs by all entrepreneurs.
The rate of high-expectation entrepreneurial activity varies significantly among world regions and individual countries. The highest adult-age population-level participation rate in high-expectation entrepreneurial activity is observed for North America (Canada and USA), Anglo-Saxon countries
1 (Australia, Ireland, New Zealand, United Kingdom, and USA), and Oceania (Australia and New Zealand). For these regions, the population-level prevalence rate of high-expectation entrepreneurial activity ranges from approximately 1% to 1.6%. The lowest adult participation rate in high-expectation activity is observed for European and highly developed Asian countries (HongKong, Korea, Japan, and Singapore), where this rate is approximately 0.5%. In Spain, adult-age participation in high-expectation entrepreneurial activity (20+ expected jobs) is only approximately 0.2%.
