Abstract
I show that a state representative’s political party determines transportation expenditure in the area she represents. Previous studies of this topic consider party changes through election outcomes, which may be correlated with unobservable determinants of expenditure. To overcome this issue, I identify my estimates using Ohio’s 2012 state legislative redistricting, which moved many geographic areas into districts with opposite party incumbents. The Republican party controlled the state legislature and governorship over the period I study. I find that areas moving from governing party Republican to minority party Democratic districts received $3.4M (0.18 standard deviations) less annual highway construction funding than areas remaining in Republican districts. Areas moving from a Democratic to a Republican district, on the other hand, experienced no increase in expenditure—the negative effect of moving to a different representative’s district appears to outweigh the positive effect of a majority party representative. Descriptive evidence suggests that changing representative’s party through redistricting had a different effect on construction funding than changing through an election, underlining the importance of my identification strategy.
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Notes
The state of Ohio spent $3.07 billion on highways and roads in 2014, the majority of $5.41 billion state and local spending on roads and highways in the state. This included the bulk of capital expenditure ($2.41 billion out of $3.19 billion). Throughout the US, states undertook 59% of expenditure on highways (57% in Ohio), including 73% of capital expenditure (76% in Ohio). (United States Census Bureau 2014).
States with an entirely Democratic delegation received only 17% more when Democrats controlled the Senate, House, and presidency.
I derive each intersection between new and old districts by spatially joining 2003–2012 district shapefiles and 2013–2022 district shapefiles (United States Census Bureau 2013) using ArcMap10.1 GIS software. In addition to very small areas that may have derived from measurement error, I also dropped 15 areas with population over 100 but missing control variables. More details on sample construction are in Online Appendix A.
To calculate the population of each area of intersection, I spatially joined the TIGER/Line shapefile of 2010 Ohio Census blocks (United States Census Bureau 2011) to the joined shapefiles of the 2003–2012 and the 2013–2022 districts (United States Census Bureau 2013). Then I summed the population of all Census blocks overlapping each area of intersection to calculate the entire population of each area of intersection.
Two incumbents lost in a general election, one lost his primary, and 17 incumbent representatives did not run for reelection. Another representative ran for reelection in a different district that did not overlap with her old district. Six of the representatives who did not run were term limited, one held the office in place of a different representative who had joined the governor’s cabinet, and one resigned while facing corruption charges. Thus, only seven representatives chose not to run voluntarily.
Section 2 of the Voting Rights Act also prohibits diluting racial minorities’ votes through gerrymander. As interpreted in Thornburg v. Gingles (1986), Section 2 requires that majority-minority districts must be formed when a racial minority votes as a bloc and is concentrated enough to be grouped into one district.
Details on this process are available in Online Appendix A.
From 1997 to 2012 the cut-off was $5 M. In 2012 the legislature raised it in accordance with the ODOT’s estimation of increased costs.
The two Democratic representatives who broke ranks and supported the bill represented Districts 10 and 12, located near an alternate route for the Ohio Turnpike (Interstate 90). Under the Plan, District 10 (Cleveland) benefited from two improvements to I-90 worth over $650 million, and District 12 (Warrensville Heights) benefited from $200 million of widening work on I-271.
For reference, I have summarized all variables at the house district level in Table A of the Online Appendix. Total expenditure remains almost the same between Republican and Democratic districts, but Republican districts receive more projects. This likely reflects Republican districts’ location in more rural areas (see Fig. 2) Because rural areas contain more road mileage but fewer bridges and intersections, Republican districts receive more small projects and fewer large projects.
These are new construction, intersection or interchange work, widening, miscellaneous projects, and bridge repair. Miscellaneous projects includes all projects that could not easily be classified including section improvement, intelligent vehicle systems, building demolition, and realignment or relocation.
Estimates of employment and employed population are available from the Longitudinal Employer Household Dynamics project of the Census Bureau (United States Census Bureau 2015) at the Census block level. I use years 2006–2015 which provide a lagged value for each year in my 2007–2016 sample. To calculate the employment and employed population of each area of intersection, I first spatially joined the TIGER/Line shapefile of 2010 Ohio Census blocks (United States Census Bureau 2011) to the areas of intersection formed by joined shapefiles of the 2003–2012 and the 2013–2022 districts (United States Census Bureau 2013). Then I merged the area of intersection-by-Census block pairings to the LEHD block-level data. Finally, I summed the employment and employed population of all Census blocks overlapping each area of intersection to calculate these variables, as summarized in Table 2.
Republican districts had slightly higher population in 2010 than Democratic districts. More areas shifted from Republican to Democratic districts than vice versa, despite Republicans picking up one more seat: the redistricting process needed to add population to Democratic districts.
Throughout this section, when I refer to party transitions, or to R to D, D to R, etc., I refer only to transitions during the redistricting year.
See Sect. 3.2. I control for lagged covariates, rather than contemporaneous values, to avoid problems of reverse causality. For example, better roads in an area may increase traffic or vehicle ownership among residents. In spite of this, I am concerned that local funding of road construction in particular may be determined by unobservable changes in state spending. I estimate the specifications in Columns 2, 3, 5, and 7 without local funding covariates in Online Appendix Table B1, and without locally sponsored projects in the outcome variable in Online Appendix Table B2. Results are similar to those in Table 4.
The positive coefficients on “GOP Representative, Dem. Gov.” and “Dem. Representative, GOP Gov.” suggests no advantage from belonging to the Democratic party in years with Democratic governor’s party. The single-term Democratic governor during my sample, Ted Strickland (2007–2010), likely possessed less political clout than his predecessors or successors. Governor Strickland followed 16 years of Republican governors, and faced a largely Republican-controlled Assembly (see Fig. 1). Some decision makers in the ODOT were appointed by his predecessors, including most of the TRAC: Governor Strickland appointed only two members to this committee during his term. At the same time, these estimates should not be interpreted as causal, and may reflect characteristics of the type districts selecting representatives from a different party than the governor.
The pre-2011 party change indicators, e.g. (R to D)\(\times\)(year < 2011), are not included because I am controlling for party and alignment with the governor in each year.
There are two other party change mechanisms. First, the incumbent could lose the primary, with her rival losing the general election. Among the ten incumbents who lost their primary reelection between 2007 and 2016, each candidate who beat the incumbent in the primary won the general election. Second, in 41 year-by-district observations the representative left office during her term (usually after an appointment to a preferable office, due to a scandal, or due to unexpected death). I consider the appointed replacement as the incumbent.
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I have benefited from discussions with Mike Conlin, Leslie Papke, and Ron Fisher, and from participants at the 111th Annual Conference of the National Tax Association and the University of Wisconsin Milwaukee Labor Lunch. I also thank the editor, Marko Köthenbürger, and two referees who provided thoughtful and helpful comments. None of the above bear responsibility for remaining errors.
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Melnik, W. Legislative redistricting and the partisan distribution of transportation expenditure. Econ Gov 25, 1–29 (2024). https://doi.org/10.1007/s10101-024-00308-w
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DOI: https://doi.org/10.1007/s10101-024-00308-w