# **1 Introduction**

The estimation of labor supply elasticities plays a critical role in the design of tax policy and transfer programs. An early literature using annual wages and hours worked from salaried workers finds very small wage-elasticities.<sup>1</sup> These low elasticities could be the result of the limited ability of workers to freely choose the number of hours worked and the fact that wage changes are rarely transitory.

A more recent literature uses data from sectors where workers have discretion over the number of hours worked. By focusing on self-employment occupations (taxi drivers, vendors, etc.), researchers have re-examined whether individuals substitute leisure intertemporally to maximize their overall earnings (see for example, Camerer et al., 1997; Chou, 2002; Farber, 2005, 2008; Fehr and Goette, 2007 and more recently Dupas et al., 2015; Stafford, 2015).

While most of these studies focus on the decision of the choice of hours worked (intensive margin), an alternative and important margin is whether to work at all (extensive margin). In many occupations, individuals cannot decide the number of hours worked due to technological constraints, but they have full discretion about whether or not they work on a particular day. The few studies that have simultaneously analyzed both labor decision margins have typically found much larger labor supply elasticities on the participation margin compared to the hour margin (Heckman, 1993).<sup>2</sup> While empirically larger in magnitude, the main reason why the extensive margin of labor supply has been understudied is data availability, as most studies only include short employment spells which hinders the ability to study the extensive margin. For instance, the data used by Henry S. Farber in the influential studies on taxi driver behavior in New York City (Farber, 2005, 2008) comprises a non-random sample of the shifts worked. There is limited information on whether drivers worked during the previous days or whether they perceived earnings from alternative occupations. Given the data limitations, Farber restricts attention to the study of the amount of hours worked within a shift. However, he recognizes that a complete analysis of labor supply would need to examine both the hours and participation margins.

In this paper, we use a novel dataset of the daily labor participation of 249 South Indian boat owners during 7 years, which yields over 260,000 boat owner–day observations. Our data

> 1See Blundell and MaCurdy (1999) for a comprehensive review of this literature.

> 2Fehr and Goette (2007) find that an experimental wage increase makes Swiss bike messengers sign up for more shifts or days of work, but it also reduces the revenue per shift. The first effect dwarfs the latter so that overall labor supply increases. Similarly, Dupas et al. (2015) find a positive wage elasticity when the measure of labor supply is days worked, but conditional on attending work, the hours-wage elasticity is negative. Using a sample of lobster fishermen in Florida, Stafford (2015) also finds a positive and large wage-participation elasticity and a smaller but still positive wage-hours elasticity.

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