The California Competes Tax Credit (CCTC) is a large-scale business incentive program that incorporates best practices from prior job creation policies. The CCTC award selection procedure combines formula-based and discretionary components. Hyman et al. (2024) leverage applicant score eligibility cutoffs in a regression discontinuity design and take advantage of rich longitudinal microdata on establishments and their parent firms to estimate the effect of CCTC awards on business activity in California. The paper finds that firms expand activity in California in response to CCTC awards. The paper also finds little evidence that these expansions come at the expense of firms’ operations in other states. The beneficiaries of the policy, in addition to the firms themselves, include newly hired workers. While the paper is not able to identify which workers are directly hired due to the subsidy, the CCTC program provides additional incentives for high poverty and high unemployment areas of the state. These areas have higher Hispanic shares and lower income, on average, relative to both California and the US as a whole.

MVPF = 5.7

The paper calculates the net cost as the sum of the cost-per-job plus the fiscal externality due to payroll increases.

The cost-per-job is the cost of CCTC associated with each subsidized job ($7,721). The paper assumes a 10% mark-up on this value to account for administrative costs, yielding a cost-per-job of $8,493.

To calculate the fiscal externality, the paper assumes an average effective tax rate of 3.06% and uses their payroll-per-worker estimate of $60,908: $60,908 x 0.0306 = $1,863.8.

The total net cost is then $8,493 – $1,863.8 = $6,629.

$6.6K
Net Cost

Upper Margin
Lower Margin

The willingness to pay is the workers’ willingness to pay for the jobs created by the program. This is computed as the ratio of the RD estimates for log firm payroll and employment effects, converted to levels, relative to an unemployed worker’s reservation wage in California.

The total payroll effect is the log payroll estimate (0.25), converted to levels, multiplied by the control mean payroll within California: (e^{0.25} – 1) \cdot \$35,340,000 = \$10,037,458

The total employment effect is the log employment estimate (0.26), converted to levels, multiplied by the control mean employment in California: (e^{0.26} – 1) \cdot 555 = 164.8

The reservation wage is assumed to be 52 weeks of the maximum amount of California unemployment insurance payments: \$450 \cdot 52 = \$23,400

The total WTP is then \frac{\$10,037,458}{164.8} – \$23,400 = \$37,508

This WTP estimate is an upper-bound with respect to the paper’s secondary dynamic RD design and because of the implicit assumption that each worker is drawn out of unemployment. Relaxing this latter assumption would result in a higher reservation wage and lower WTP.

$37.5K
WTP

Upper Margin
Lower Margin

The paper estimates the MVPF for the CCTC as $37,508/$6,629 = 5.66

5.7
MVPF

Upper Margin
Lower Margin

Hyman, Benjamin, Matthew Freedman, Shantanu Khanna, and David Neumark (2024). “Firm Responses to Hiring and Investment Subsidies: Regression Discontinuity Evidence from the California Competes Tax Credit.” Working Paper. https://static1.squarespace.com/static/5acbd8e736099b27ba4cfb36/t/66a3e0cfc107254f7b7fa7cf/1722015992568/HFKN_CCTC_July2024.pdf

- Empirical Method
- Regression Discontinuity
- Research Type
- Primary
- Peer Reviewed
- No