1. Donald Bruce, Xiaowen Liu, and Matthew Murray Center for Business and Economic Research and Department of Economics The University of Tennessee, Knoxville Conference on Subnational Government Competition The University of Tennessee April 25, 2014 State Tax Policy and Entrepreneurship

2. States have a long history of using income and sales tax policy to compete for mobile entrepreneurs and/or encourage new ones – 2012: Kansas removes income tax on pass- through income – 2014: Missouri considers 25% deduction of pass- through income – Other states considering income tax repeal, with small business promotion as one selling point Empirical literature has not generally supported this at the state level Policy Background

4. Intensive-margin indicators of success – Most prior work is on extensive margin – Policy makers care more about success Dynamic panel regression – Most prior work uses fixed effects – Underlying trends matter Expanded specification and time period – 1978-2009 Contributions

5. Nonfarm Proprietors’ Income (NFPI) – Per capita – As a % of total state personal income – As a % of national NFPI Nonfarm Proprietors’ Employment (NFPE) – As a % of total state employment – As a % of national NFPE Nonfarm Proprietors’ Productivity – NFPI/NFPE Measures of Performance

6. Empirical Approach

7. Addresses time series issues in panel data Model transformed to first-difference – Removes state fixed effect Inclusion of lagged Y raises endogeneity concern; external instruments not required Arellano-Bover (1995) / Blundell-Bond (1998) approach used as an alternative AB 101

8. Policy – Sales tax rate – Top marginal personal income tax (PIT) rate – Top marginal corporate income tax (CIT) rate – Sales factor weight in CIT apportionment – Per capita state government expenditures – Tax Amnesty programs Economic/Demographic – Unemployment rate – % of population aged 65 and older – Crime rate – % female – Agricultural, Manufacturing % of GSP – Nonfarm job growth – Population density Independent Variables

9. NFPI and NFPE, 1978-2009

10. NFPI/E Shares and Productivity

11. Summary Statistics Variable 19782009 NFPI ($1,000)9,666,38117,900,000 NFPI per capita2,1892,745 NFPI as a share of total income (%)8.367.06 State share of national NFPI (%)22 NFPE (1,000)256,572708,628 NFPE as a share of total employment (%)12.2120.05 State share of national NFPE22 Nonfarm proprietors’ productivity35,78423,247

12. Summary Statistics Variable 19782009 Sales tax rate3.545.07 Top PIT rate6.905.47 Top CIT rate5.956.56 Expenditures per capita2.885.55 Sales factor weight32.357.2 Unemployment rate5.628.45 Age > 6410.7213.16 Crime rate4.392.94 Female percentage51.0250.58 Nonfarm job growth5.72-4.18 Agriculture share of GSP3.901.61 Manufacturing share of GSP20.9111.10 Population density153193 Amnesty00.14

13. AB Results NFPINFPE Per Capita As a Share of Total Income State Share of National NFPI As a Share of Total Employment State Share of National NFPE Productivity Sales tax rate –0.0090.009–0.017–0.0190.004–0.052 (0.016)(0.042)(0.014)(0.016)(0.024)(0.193) Top PIT rate 0.0060.028–0.0010.001–0.005–0.030 (0.011)(0.029)(0.006)(0.012)(0.004)(0.138) Top CIT rate –0.009–0.0200.007–0.004–0.003–0.173 (0.019)(0.047) (0.012)(0.005)(0.188) Expend. per capita 0.0510.1040.0440.0630.00040.760*** (0.045)(0.077)(0.032)(0.050)(0.016)(0.186) Sales factor weight –0.001*–0.0010.00020.001–0.0002–0.011** (0.001) (0.0002)(0.005)

14. Main result echoes Bruce & Deskins (2012): taxes generally don’t matter – Higher sales factor weight  lower NFPI per capita and lower NFP productivity – Higher state gov’t. expend. per capita  higher NFP productivity Other controls matter – Higher unemployment  higher NFPI/E shares – Older population  lower NFPE share; higher NFP productivity – Higher NF job growth  higher NFPI/E shares; lower NFP productivity – Lower crime rate  higher NFPI per capita – Higher pop. Density  higher NFPE share – Lower GSP shares in manufacturing or agriculture  higher NFPI Lags always matter (dynamic specification is important) Discussion

15. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Sales tax rate 0.065 Top PIT rate –0.046 Top CIT rate –0.025 Expend. per capita –0.028 Sales factor weight –0.001

16. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Arellano-Bond Sales tax rate 0.065 –0.019 Top PIT rate –0.046 0.001 Top CIT rate –0.025 –0.004 Expend. per capita –0.028 0.062 Sales factor weight –0.001 0.001

17. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Arellano-Bond Simple Replication Sales tax rate 0.065 –0.0190.043 Top PIT rate –0.046 0.001–0.015 Top CIT rate –0.025 –0.004–0.079** Expend. per capita –0.028 0.0620.051 Sales factor weight –0.001 0.0010.003

18. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Arellano-Bond Simple Replication Simple Replication and Different Controls Sales tax rate 0.065 –0.0190.043 0.019 Top PIT rate –0.046 0.001–0.015 0.023 Top CIT rate –0.025 –0.004–0.079** –0.086*** Expend. per capita –0.028 0.0620.051 0.096 Sales factor weight –0.001 0.0010.003 0.004***

19. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Arellano-Bond Simple Replication Simple Replication and Different Controls Simple Replication and Different Time Sales tax rate 0.065 –0.0190.043 0.019–0.216*** Top PIT rate –0.046 0.001–0.015 0.023–0.010 Top CIT rate –0.025 –0.004–0.079** –0.086***–0.062** Expend. per capita –0.028 0.0620.051 0.0960.038 Sales factor weight –0.001 0.0010.003 0.004***0.002

20. Comparison to Bruce & Deskins (2012) Employment stock Bruce & Deskins Arellano-Bond Simple Replication Simple Replication and Different Controls Simple Replication and Different Time Simple Replication and Arellano-Bond Sales tax rate 0.065 –0.0190.043 0.019–0.216***–0.046 Top PIT rate –0.046 0.001–0.015 0.023–0.0100.117*** Top CIT rate –0.025 –0.004–0.079** –0.086***–0.062**0.002 Expend. per capita –0.028 0.0620.051 0.0960.0380.003 Sales factor weight –0.001 0.0010.003 0.004***0.0020.008**

21. Simply adding (1) variables or (2) years of data or (3) estimating an AB model would have generated misleading significance The combination of these three updates is important The additional years of data (2003-2009) are enough to drive the importance of a dynamic estimation approach Discussion