Table 2.

Differential Attrition Analysis by Treatment Status

AttritedClosedBusiness changedOwner name changedStore code changed
(1)(2)(3)(4)(5)
Panel A. Store owners
Treatment−0.0000.0130.003
(0.026)(0.023)(0.022)
R-squared0.0950.1300.000
Control mean0.0970.0690.061
Observations498498498
Panel B. Store sales
Treatment−0.013−0.0040.0090.006−0.024
(0.031)(0.015)(0.022)(0.015)(0.028)
R-squared0.0800.0870.0750.0540.063
Control mean0.1580.0340.0560.0230.045
Observations539539539539539
AttritedClosedBusiness changedOwner name changedStore code changed
(1)(2)(3)(4)(5)
Panel A. Store owners
Treatment−0.0000.0130.003
(0.026)(0.023)(0.022)
R-squared0.0950.1300.000
Control mean0.0970.0690.061
Observations498498498
Panel B. Store sales
Treatment−0.013−0.0040.0090.006−0.024
(0.031)(0.015)(0.022)(0.015)(0.028)
R-squared0.0800.0870.0750.0540.063
Control mean0.1580.0340.0560.0230.045
Observations539539539539539

Source: Data on attrition at the store-owner level come from the follow-up survey of the experiment. Data on attrition at the store level come from the administrative records of Casas de Pollo Rey (CDPR).

Note: Each column in this table presents the results from an ordinary least squares (OLS) regression of a different attrition measure on a treatment dummy. In column (1), attrition at the store-owner level is defined as a dummy for failing to complete the follow-up survey, while attrition at the store level is defined as a dummy for missing sales data in June 2022. In column (2), the outcome is a dummy for having attrited the sample because the owner closed their store. A dummy for having attrited the sample because the owner established a different business in the same location is the outcome variable in column (3). The outcome in column (4) is a dummy for attriting the sample because the name of the legal owner of the store changed to avoid personal income taxation. Finally, the outcome in column (5) is a dummy for changing the store code due to store relocation or for fiscal purposes, such as avoiding corporate or payroll taxation. Standard errors within parentheses are robust to heteroskedasticity of unknown form in panel A and are also clustered at the store-owner level in panel B. All regressions control for strata dummies.

Table 2.

Differential Attrition Analysis by Treatment Status

AttritedClosedBusiness changedOwner name changedStore code changed
(1)(2)(3)(4)(5)
Panel A. Store owners
Treatment−0.0000.0130.003
(0.026)(0.023)(0.022)
R-squared0.0950.1300.000
Control mean0.0970.0690.061
Observations498498498
Panel B. Store sales
Treatment−0.013−0.0040.0090.006−0.024
(0.031)(0.015)(0.022)(0.015)(0.028)
R-squared0.0800.0870.0750.0540.063
Control mean0.1580.0340.0560.0230.045
Observations539539539539539
AttritedClosedBusiness changedOwner name changedStore code changed
(1)(2)(3)(4)(5)
Panel A. Store owners
Treatment−0.0000.0130.003
(0.026)(0.023)(0.022)
R-squared0.0950.1300.000
Control mean0.0970.0690.061
Observations498498498
Panel B. Store sales
Treatment−0.013−0.0040.0090.006−0.024
(0.031)(0.015)(0.022)(0.015)(0.028)
R-squared0.0800.0870.0750.0540.063
Control mean0.1580.0340.0560.0230.045
Observations539539539539539

Source: Data on attrition at the store-owner level come from the follow-up survey of the experiment. Data on attrition at the store level come from the administrative records of Casas de Pollo Rey (CDPR).

Note: Each column in this table presents the results from an ordinary least squares (OLS) regression of a different attrition measure on a treatment dummy. In column (1), attrition at the store-owner level is defined as a dummy for failing to complete the follow-up survey, while attrition at the store level is defined as a dummy for missing sales data in June 2022. In column (2), the outcome is a dummy for having attrited the sample because the owner closed their store. A dummy for having attrited the sample because the owner established a different business in the same location is the outcome variable in column (3). The outcome in column (4) is a dummy for attriting the sample because the name of the legal owner of the store changed to avoid personal income taxation. Finally, the outcome in column (5) is a dummy for changing the store code due to store relocation or for fiscal purposes, such as avoiding corporate or payroll taxation. Standard errors within parentheses are robust to heteroskedasticity of unknown form in panel A and are also clustered at the store-owner level in panel B. All regressions control for strata dummies.

Close
This Feature Is Available To Subscribers Only

Sign In or Create an Account

Close

This PDF is available to Subscribers Only

View Article Abstract & Purchase Options

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Close