A.1 Excluding decades
Table 11: Tax level and Governor’s strength. Four degree polynomial specification on each side of the cutoff, excluding one decade at a time
Excluded decade Jump at 50% Cluster Robust-SE
60’s 0.69 (0.33)**
70’s 0.71 (0.39)*
80’s 0.79 (0.39)*
90’s 0.69 (0.40)*
00’s 0.64 (0.37)*
Note: This sample is comprised of state-years with line item veto from 1960 to 2006. We exclude one decade at a time. Each regression is run with 1369, 1342, 1342, 1346, and 1449 observations respectively . The dependent variable is the percentage of the sum of income, sales, and corporate taxes in a state divided by state GDP and shown as a percentage. The forcing variable is Governor’s strength, the percentage of seats in the state House of Representatives that belong to the same party as the Governor. The discontinuity is estimated at Governor’s strength=50%. Each row shows the results for a four-degree polynomial on each side of the cutoff. Theoretical cluster robust standard errors by state are in parenthesis.
A.2 Excluding one state at a time
Table 12: Tax level and Governor’s strength. Four degree polynomial specification on each side of the cutoff, excluding one state at a time
Excluded Jump at 50% Cluster robust-SE Excluded Jump at 50% Cluster robust-SE
AL 0.70 (0.35)* AZ 0.71 (0.35)*
CO 0.72 (0.36)* CT 0.74 (0.36)**
DE 0.73 (0.35)** FL 0.67 (0.35)*
GA 0.69 (0.35)* IA 0.65 (0.35)*
IL 0.64 (0.38)* KS 0.71 (0.36)*
KY 0.66 (0.36)* LA 0.66 (0.36)*
MA 0.57 (0.33)* MD 0.70 (0.35)*
MI 0.70 (0.38)* MO 0.65 (0.35)*
MS 0.74 (0.35)** MT 0.68 (0.37)*
ND 0.72 (0.37)* NJ 0.62 (0.35)*
NM 0.65 (0.35)* NY 0.71 (0.36)*
OH 0.71 (0.35)* OK 0.74 (0.35)**
OR 0.68 (0.36)* PA 0.97 (0.33)***
SC 0.71 (0.35)* SD 0.71 (0.36)*
TN 0.72 (0.36)* TX 0.62 (0.34)*
UT 0.69 (0.35)* VA 0.66 (0.35)*
WA 0.71 (0.37)* WI 0.55 (0.31)*
WV 0.66 (0.35)* WY 0.64 (0.36)*
Note: This sample is comprised of state-years with line item veto from 1960 to 2006. Each regression is run with 1665 observations. The exceptions are: CT with 1669 observations, as Connecticut had fours years with an independent Governor dropped; IA, WA, WV each with 1674 observations, as they adopted the line item veto in 1969. The dependent variable is the percentage of the sum of income, sales, and corporate taxes in a state divided by state GDP and shown as a percentage. The forcing variable is Governor’s strength, the percentage of seats in the state House of Representatives that belong to the same party as the Governor. The discontinuity is estimated at Governor’s strength=50%. In each entry we exclude from the sample the state in columns 1 or 3. Each row shows the results for a four-degree polynomial on each side of the cutoff. Theoretical cluster robust standard errors by state are in parenthesis.
A.2.1 Including Minnesota
Minnesota is an outlier in many ways. Firstly, until 1972 Minnesota had an officially non-partisan Legislature. We therefore, do not have data on Governor’s strength for Minnesota before 1973.
Secondly, from 1982 to 1998, Minnesota’s Governors were not the candidates chosen by their own parties in the primaries. Democrat Rudy Perpich entered and won the race for Governor in 1981 running directly against the Democratic candidate chosen in the primaries. Republican Arne Carson lost in all but one of the 87 state-district primaries, but his name was replaced in the ballot after a scandal forced the chosen Republican candidate to step down, and he became Governor in 1989 (for a detailed account of contemporary Minnesota political history see Brandl (2000)). Since our model depends on the alignment of interests between Governor and the Governor’s party in the Legislature, we have decided to treat these Governors as independents and have excluded them from the data. We did the same with Jesse Ventura, who was the independent Governor of the State from 1999 to 2002. After all these exclusion we thought best to drop Minnesota completely.
The third reason why Minnesota is an outlier in our data is that it has the highest average tax revenue in our sample, i.e. 7.89%, compared to an average across the sample of 5.4%.
Nevertheless we could, in practice, include the data from Minnesota from 1972 to 1998 and from 2003 to 2006. There are many observations around the cutoff and with such high average taxes the inclusion of these outliers make our results less stable, as we can see in Table 13.
Table 13: Tax level and Governor’s strength-Including Minnesota
Method Jump at 50% Robust-SE Bootstp mean Cluster robust-SE
4-degree polynomials 0.42 (0.23)** - (0.42)
LLR(bandwidth 7) 0.53 (0.25)** 0.43 (0.39)
LLR(bandwidth 15) 0.23 (0.16) 0.22 (0.27)
Note: This sample is comprised of 1741 observations of states with line item veto from 1960 to 2006. Each observation represents a state within a year. The dependent variable is the total sum of a state’s income, sales, and corporate taxes divided by state GDP and shown as a percentage.
The forcing variable is Governor’s strength, which is the percentage of seats in the state House of Representatives that belong to the same party as the Governor. The discontinuity is estimated at Governor’s strength=50%. The first row shows the results for a four-degree polynomial on each side of the cutoff. Theoretical heteroskedastic and cluster robust standard errors by state are in parenthesis. The last two rows show the results for a local linear regression specification with a triangular kernel and different bandwidths. Theoretical heteroskedastic robust standard errors are provided together with bootstrapped cluster robust standard errors (wild bootstrap with 10,000 draws each).
A.3 Uniqueness of discontinuity
Table 14: Tax level and Governor’s strength - quartic-polynomial specification, alternative cutoff points
Cutoff point Jump Robust-SE Cluster robust-SE
45% 0.29 (0.76) (1.22)
46% 0.36 (0.68) (0.81)
47% 0.27 (0.44) (0.66)
48% 0.00 (0.30) (0.37)
49% 0.35 (0.25) (0.36)
50% 0.69 (0.21)*** (0.35)*
51% 0.35 (0.22) (0.42)
52% 0.36 (0.27) (0.34)
53% 0.27 (0.30) (0.51)
54% 0.34 (0.44) (0.80)
55% 0.66 (0.54) (0.96)
Note: This sample is comprised of 1712 observations of states with line item veto from 1960 to 2006. Each observation represents a state within a year. The dependent variable is the percentage of the sum of income, sales, and corporate taxes in a state divided by state GDP and shown as a percentage. The forcing variable is Governor’s strength, the percentage of seats in the state House of Representatives that belong to the same party as the Governor. The discontinuity is estimated at different cutoff values of Governor’s strength. Each row shows the results for a four-degree polynomial on each side of the cutoff. Theoretical heteroskedastic robust and cluster robust standard errors by state are in parenthesis.
A.4 Alternative measure: state taxes per capita
Table 15: Taxes per capita and Governor’s strength
Method Jump at 50% Robust-SE Bootstp mean Cluster robust-SE
4-degree polynomials 92.2 (46.3)** - (69.7)
LLR(bandwidth 7) 143.8 (50.8)*** 116.4 (64.1)*
LLR(bandwidth 15) 61.2 (33.8)* 58.7 (43.5)
LLR(bandwidth 16) 62.2 (32.7)* 59.6 (42.3)
Note: This sample is comprised of 1712 observations of states with line item veto from 1960 to 2006.
Each observation represents a state within a year. The dependent variable is the total sum of a state’s income, sales, and corporate taxes per capita in 1982-dollars. The forcing variable is Governor’s strength, which is the percentage of seats in the state House of Representatives that belong to the same party as the Governor. The discontinuity is estimated at Governor’s strength=50%. The first row shows the results for a four-degree polynomial on each side of the cutoff. Theoretical heteroskedastic and cluster robust standard errors by state are in parenthesis. The last two rows show the results for a local linear regression specification with a triangular kernel and different bandwidths.
Theoretical heteroskedastic robust standard errors are provided together with bootstrapped cluster robust standard errors (wild bootstrap with 10,000 draws each).