Table 3.
Impact of COVID-19 adjustment strategies (N = 463).
| (1) |
(2) |
|
|---|---|---|
| Businesses experiencing income loss due to COVID-19 shock | Business reporting time to recover from COVID-19 shock to be more than a month | |
| Service sector | 0.080 (0.049) | 0.060* (0.036) |
| Manufacturing sector | −0.025 (0.055) | 0.095** (0.040) |
| Direct-to-consumer | −0.084* (0.050) | −0.024 (0.037) |
| Sole proprietorship | −0.041 (0.044) | −0.039 (0.032) |
| Cash flow problems during COVID-19 | 0.191*** (0.046) | 0.359*** (0.048) |
| Zero to 5 employees | 0.039 (0.050) | −0.090*** (0.033) |
| Family business | 0.043 (0.044) | 0.038 (0.033) |
| Operated from home | 0.114*** (0.046) | −0.017 (0.033) |
| Number of years operating | 0.001 (0.001) | 0.001 (0.001) |
| Number of days closed COVID-19 | 0.002*** (0.000) | 0.001*** (0.001) |
| Female-owned business | 0.049 (0.043) | 0.018 (0.032) |
| Rural area | −0.053 (0.056) | 0.028 (0.048) |
| Changed to online sales | 0.029 (0.053) | 0.027 (0.036) |
| Changed how procured supplies | −0.014 (0.047) | 0.064* (0.038) |
| Changed how serve customers | 0.087* (0.052) | 0.204*** (0.042) |
| Increased business social media presence | −0.097* (0.049) | −0.070* (0.036) |
| Apply PPP loan | 0.032 (0.054) | 0.060* (0.033) |
| Apply EIDL loan | 0.069 (0.064) | 0.092*** (0.030) |
Standard errors in parentheses are corrected for heteroskedasticity. *, **, ****p < 0.01, 0.05, 0.001. The estimates in both columns are results of a linear probability model. The coefficients reported are probabilities. The regression also includes variables for business owner characteristics such as race, age, marital status, education, and region of residence.