Speed Limit Laws in America: the Role of Geography, Mobility and Ideology

Transportation Research Part A 46 (2012) 337–347

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Transportation Research Part A

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Speed limit laws in America: The role of geography, mobility and ideology ⇑ Daniel Albalate a, Germà Bel a,b, a Universitat de Barcelona, Research Group on Governments and Markets & Institut de Recerca en Economia Aplicada (GiM-IREA), Spain b Barcelona Graduate School of Economics, Spain article info abstract

Article history: Speed limits had been centralized at the federal level since 1974, until decisions were Received 12 April 2011 devolved to the states in 1995. However, the centralization debate has reemerged in recent Received in revised form 19 September years. This paper conducts the first econometric analysis of the determinants of speed limit 2011 laws and State reactions after the repeal. By using mobility, geographic and political vari- Accepted 11 October 2011 ables, our results suggest that geography – which reflects private mobility needs and social preferences –, is one of the main factors influencing speed limit laws, together with political ideology. Furthermore, we identify the presence of regional and time diffusion effects. Keywords: By presenting first evidence on policy determinants, we provide a better understanding of Transport policy Speed limits the formulation of the heterogeneity of speed limits in US and offer implications for the Decentralization debate on centralization and decentralization of transport policy. Transportation Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction

National speed limit reform has been, and still is, one of the most controversial debates in the transport sector in the Uni- ted States. Although full devolution of the regulation of speed limits was enacted in 1995, the debate as to whether national laws should once more regulate this issue remains very much alive. Safety advocates, insurance companies and trucking associations are currently lobbying for a return to a lower national speed limit, as are citizen platforms concerned by safety outcomes. At the political level, Hillary Clinton endorsed a return to a 55-mph speed limit during a speech to the National Press Club in Washington DC in 2006. In July 2008, Senator John Warner (Republican-Virginia) made similar calls for the reintroduction of a 55-mph limit to reduce energy consumption and gas costs. More recently, representative Jackie Speier (Democrat- California) made proposals in a Congressional bill for a national speed limit of 60 mph on freeways in urban areas and 65 mph in less populated areas, as a means of saving gas. Indeed, the current direction being taken by Obama’s administration on environmental issues, together with the concern to reduce CO2 emissions and energy consumption, are likely to trigger further debate on the reduction of speed limits and the recentralization of this policy. Over the last 30 years, the debate has focused primarily on two aspects of the policy: (1) there is the need to establish who is responsible for establishing speed limit laws, which leads to the debate regarding policy centralization (federal government) vs. decentralization (states); and (2) there is the need to establish the optimal speed limit, and here the discussion seems to be dictated by social preferences that differ greatly across the country owing to markedly different valuations of private mobility and safety outcomes. Ashenfelter and Greenstone (2004) and Ashenfelter (2006) have highlighted this trade-off between private mobility and safety in their attempts to estimate the value of a statistical life. Similarly, Haight (1994) has emphasized the need to evaluate mobility beneﬁts and safety consequences in a uniﬁed context, what has been

⇑ Corresponding author at: Barcelona Graduate School of Economics, Universitat de Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain. Tel.: +34 934031131/934021946; fax: +34 934024573. E-mail addresses: [email protected] (D. Albalate), [email protected] (G. Bel).

0965-8564/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra.2011.10.002 338 D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 somehow addressed by Delhaye (2006) in her theoretical model on expected accident costs by considering mobility use and speed. However, much of the controversy has focused exclusively on the safety effects of speed limit laws, and virtually no atten- tion has been paid to the policy drivers of speed limit regulation. Most of the literature has sought to estimate the effect of speed limit changes on road safety. However, this literature delivers mixed results – perhaps reflecting the ideological prej- udices identified by Haight (1998) –, providing both parties with fresh empirical evidence to uphold their point of view.1 Yet, no significant efforts have been made to look beyond the debate on road safety in an attempt at understanding the confronta- tion itself. This paper contributes to the literature by being the first study – to our knowledge – to identify the geographic and political determinants of State reactions after the repeal, and consequently, of the formulation of speed limit policies, rather than simply attempting to predict or evaluate their safety impacts. We examine the American speed limit debate to estimate how different valuations of the trade-off between private mobility and safety can shape transportation laws and policy making. Our work is of multidisciplinary character, because factors related to economics, politics, geography and mobility are considered. Hence, we contribute as well to the literature by including within our analysis geographical and ideological factors as determinants of speed limits enactments and of the stances adopted in the centralization–devolution debate. Our results suggest that geography, which has an obvious impact on private mobility needs and its valuation against safety considerations, is the main factor influencing speed limit laws. We also find evidence of the role played by political ideology: the share of Republican voters in a State is significantly associated with higher speed limits. Finally, we highlight regional clusters and time dependence effects as significant determinants of speed limits. The rest of this paper is organized as follows. Next, we briefly review the history and politics of speed limit reforms in the US. In the third section, we identify the main differences between states with high and low speed limits in terms of their geographic, historic and mobility variables. Section 4 describes the empirical strategy and the econometric model used to estimate legal determinants. In Section 5 we present the main results obtained from our empirical analysis. Finally, we draw our main conclusions.

2. History and politics of speed limit reforms

Speed limit reform was initiated in 1974 when the national speed limit law was passed by the Nixon administration and Congress. A 55-mph limit was established nationwide and it was predicted that this would cut gasoline consumption by 2.2%. This policy responded to the 1973 oil embargo imposed by the Arab members of the Organization of Petroleum Export- ing Countries (OPEC) on countries that allied with Israel in the Yom Kippur War. The federal government carefully regulated gasoline prices and so they never rose to reflect demand, but local shortages were recorded (Yowell, 2005). The centralization of the national speed limit was initially given temporary status, and it was set to expire on 30 June 1975. However, in 1975 the regulation was given permanent status, based on the rationale that traffic injuries and fatalities had declined significantly (Csere, 1995; Forester et al., 1984; Segal, 1987). The centralization of speed limit regulation, and of the energy policy overall, was not only a response to gasoline shortages but arguably also a further step along the path to government centralization.2 In this regard, the 1974 law represented a significant change in the political status quo. Before 1974, speed limit regulation had been decentralized and was included among the powers reserved for the states.3 Speed limits dated from 1901, and before the reform there were huge disparities in state speed limits.4 From the outset, the implementation of a national speed limit was controversial and several western states opposed the measure as contravening state rights. In response to this opposition, the government chose to tie federal highway funds to the prior enactment of a 55-mph speed limit in the states and, in 1978, to the enforcement of the national limit. The end of the embargo weakened the arguments linked to energy conservation, and the main reasons for defending the national speed limit became increased road safety, and the threat of a rise in fatality rates should speed limits be raised. In- deed, the national speed limit was held up as being a major contributor to the decline in fatality rates, but the drivers’ non- compliance did undermine its political validity (Haight, 1998). Thus, when the leadership and party make-up of Congress changed, the time was ripe for a partial reform that was introduced with the Surface Transportation and Uniform Relocation Act of 1987 (Yowell, 2005).5 This law allowed states to raise their maximum speed limit to 65 mph on rural interstates. Most states immediately took advantage of this partial devolution and raised their limits, although several eastern states chose to keep the 55-mph limit.

1 See Friedman et al. (2009) and Yowell (2005) for two recent examples of opposite results when evaluating the impact of changes in speed limit laws. Nonetheless, it is worth mentioning that a causal relationship between speed and number of accidents and the severity of injuries was found by Elvik (2005) in his meta-analysis of 98 studies on speed and safety in many countries. See as well Elvik (2002). 2 President Nixon imposed controls on 15 August 1971. According to Bowman and Krause (2003), attempts at decentralization were seemingly overwhelmed by the centralizing actions of the Kennedy–Johnson era, but ostensibly gained in intensity during the Nixon and Reagan–Bush years. 3 The sole exception was the Second World War emergency speed limit of 35 mph. 4 Before the centralization of speed limits, Montana and Idaho did not have any speed limits, while other states such as Connecticut, Delaware, Rhode Island and New Jersey established a limit of in 60 mph. 5 It is worth noting that President Reagan, who agreed with the speed limit amendment (Moore, 1999) but disagreed with other provisions of the bill, vetoed the highway authorization bill. On 2 April 1987, the Congress overrode the President’s veto. See Segal (1987). D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 339

This reform did not, however, put an end to the debate on speed limit devolution. Western representatives continued to demand full powers to set higher limits, while supporters of centralization warned of fatality increases as a result of rises in speed limits. In fact, a number of states (Montana, Kansas, Nevada and Wyoming among others) passed laws that would raise their speed limits automatically when the federal cap came off. For this reason, when the newly-elected Republicans took control of both houses in 1994 and sought to devolve many functions assumed by the federal government, one of the ﬁrst powers to go was the regulation of speed limits (Yowell, 2005). Thus, the repeal of the national speed limit was provided for under the National Highway Designation Act of 1995. On November 28, 1995, President Clinton reluctantly signed the leg- islation and the repeal became effective from December 8 of that year (Palmaffy, 1996; Yowell, 2005; Kaye et al., 1995). After the repeal, 33 states raised their speed limits to 70 mph or higher on portions of their roadway systems, but at various dates as is shown in Table 1. Indeed, the political parties played an important role in establishing speed limits. The position adopted by the Democratic Party has been highly inﬂuential in developments associated with speed limit reforms. It supports lower speed limits on safety and environmental grounds. Its position on this issue and the majority it enjoyed in Congress led to the blocking

Table 1 Current interstate speed limits by state, 2011. Source: Insurance Institute for Highway Safety.

State Rural Trucks Urban Change to current rural speed limit Change after repeal over 65 mph Alabama 70 70 60 05/21/96 05/21/1996 Alaska 65 65 65 01/15/88 – Arizona 75 75 65 12/08/95 12/08/95 Arkansas 70 65 65 08/19/96 08/19/96 California 70 55 65 01/08/96 01/08/96 Colorado 75 75 55–65 06/24/96 06/24/96 Connecticut 65 65 45–55 10/01/98 10/01/98 Delaware 65 65 50–55 01/17/96 01/17/96 District of Columbia – – 50 – – Florida 70 70 70 04/08/96 04/08/96 Georgia 70 70 55–65 07/01/96 07/01/96 Hawaii 55–60 55–60 50 No action – Idaho 75 65 65 05/01/96 05/01/96 Illinois 65 55 55 01/25/96 – Indiana 70 65 50–55 07/01/05 07/01/05 Iowa 70 70 55–65 07/01/05 07/01/05 Kansas 70 70 65 03/07/96 03/07/96 Kentucky 70 70 55 07/10/07 3/14/96 Louisiana 70 70 60 08/15/97 08/15/97 Maine 65 65 55 06/12/87 – Maryland 65 65 55–60 07/01/95 – Massachusetts 65 65 55 01/29/96 – Michigan 70 60 70 08/01/96 08/01/96 Minnesota 70 70 45–60 07/01/97 07/01/97 Mississippi 70 70 60–70 02/29/96 02/29/96 Missouri 70 70 55–65 03/13/96 03/13/96 Montana 75 65 65 05/28/99 12/08/95 Nebraska 75 75 60 09/01/96 09/01/96 Nevada 75 75 65 12/08/95 12/08/95 New Hampshire 65 65 55 04/16/87 – New Jersey 65 65 55 01/19/98 – New Mexico 75 75 65–75 05/15/96 05/15/96 New York 65 65 50–55 08/01/95 – North Carolina 75 75 65 08/05/96 08/05/96 North Dakota 75 75 55–75 08/01/03 07/01/96 Ohio 65 65 65 07/01/09 – Oklahoma 70–75 70–75 55–65 08/29/96 12/15/95 Oregon 65 55 55–60 06/27/87 – Pennsylvania 65 65 55 07/13/95 – Rhode Island 65 65 55 05/12/96 – South Carolina 70 70 60 04/30/99 04/30/99 South Dakota 75 75 65 04/01/96 04/01/96 Tennessee 70 70 55 03/25/98 03/25/98 Texas 70–80 70 60 02/13/08 12/08/95 Utah 80 75 65 05/01/96 05/01/96 Vermont 65 65 55 04/21/87 – Virginia 70 65 55–65 07/01/10 07/01/06 Washington 70 60 60 03/15/96 03/15/96 West Virginia 70 70 50–60 08/25/97 08/25/97 Wisconsin 65 65 55–65 06/17/87 – Wyoming 75 75 60 12/08/95 12/08/95 340 D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347

Table 2 Democrat senators favoring the repeal by electoral state (19th June, 1995).

State Democrats Democrat Senator favoring the repeal Second Democrat Senator Populationa density favoring the repeal (pop./km2) Florida 1/1 Graham (D-FL) – 83.18 Georgia 1/1 Nunn (D-GA) – 46.78 Hawaii 1/2 Inouye (D-HI) – 41.92 Louisiana 2/2 Breaux (D-LA) Johnston (D-LA) 32.33 Massachusetts 1/2 Kerry (D-MA) – 222.19 Montana 1/1 Baucus (D-MT) – 2.28 Nevada 2/2 Bryan (D-NV) Reid (D-NV) 5.34 New Mexico 1/1 Bingaman (D-NM) – 5.35 North Dakota 1/2 Conrad (D-ND) – 3.50 Vermont 1/1 Leahy (D-VT) – 23.49 Virginia 1/1 Robb (D-VA) – 59.74 Wisconsin 1/2 Feingold (D-WI) – 30.20

Note: Average population density in 1995 was 26.7 (pop./km2). a Population: ‘‘Population Projections for States, by Age, Sex, Race, and Hispanic Origin: 1995–2025.’’ available on: http://www.census.gov/population/ projections/state/stpjpop.txt, Land area: ‘‘2000 Census of Population and Housing’’ available on http://www.census.gov/prod/cen2000/phc3-us-pt1.pdf. of moves by representatives of states in the west to introduce reforms during the 1980s, although they were unable to stop the first partial devolution in 1987 following years of well-documented non-compliance. Some years later, now with a Republican majority in both houses (for the first time since 1952), the Clinton Administration was adamant in its rejection of the repeal, but reluctantly had to accept the overturn as outlined above. Regarding the Republican Party, in his 1980 election campaign, President Reagan promised to have the national speed limit abolished, but he was to take a somewhat more relaxed attitude when he took office. Through that decade growing concern among the Republicans became evident. The national speed limit law was presented as violating state rights (Copulos, 1986), and was used as a symbol of the commitment on the part of the new Republican majority to limit federal government. Indeed, Republican support for repeal was compelling in the Senate, with only three of its 54 senators (5%) casting their vote against. By contrast, opinion was more divided among the Democrats, with 14 out of 46 (30%) voting in favor of repeal. The 65-to-35 result allowed the national speed limit to be repealed. Of greater interest than the overall distribution of votes in the Senate was the position taken by those senators that did not vote according to the expected party line: the Republicans that voted against repeal and the Democrats that voted in favor. An analysis of their home-state shows that individual decisions were probably motivated by the constituency they represented. Table 2 lists the names of these Democrat senators and the state in which they were elected. As can be seen, some of these senators represented low population density states, which were some of the first to raise the speed limit following repeal. Among the three Republicans that voted against the repeal, Sens. Chafee, Hatfield and War- ner were elected in Rhode Island, Oregon and Virginia respectively, three states with low speed limits that did not raise the limit following the repeal. However, all three states have relatively high population densities. The decisive Republican action taken in the Senate on this issue reflects the party’s role in representing those values that they felt were coming under direct ‘‘attack’’ by maintaining a low national speed limit: limited federal government and individual liberty over government control. Given its position, it was hardly surprising that the new Republican majority in the 1994 Congress should decide to adopt national speed limit repeal as a symbol of the so-called Republican Revolution com- mitted to fighting centralization.

3. Determinants of speed limit laws: descriptive statistics

Having reviewed the history of speed limit reforms in the United States and given the variety of regulations introduced over the last 15 years across the country, this section discusses the determinants of speed limit laws by drawing on descriptive statistics of the geographic, demographic, and transportation characteristics of the individual states. Table 3 presents data for the states grouped according to the speed limit applied in their territory. In this way, it should be possible to identify the primary differences between states with high and low speed limits. Indeed, these descriptive statistics reﬂect how geography, economy and private mobility needs seem to account for the speed limits that have been adopted. As can be seen, the states that adopted higher speed limits present relatively low population densities. These states tend to be large states with low levels of population, which before the centralization of the policy in 1974 already applied high speed limits. These geographical characteristics mean that citizen private mobility is an important issue in states with a low level of density. Indeed this greater need for mobility is highlighted by the fact that those residents in states with higher speed limits drive more miles per capita than their counterparts in states with low speed limits. This is consistent with results obtained by Johansson-Stenman and Martinsson (2005) that highlight that speed limit preferences are self-interested. Thus, we can expect citizens resident in these states to attach more value to the time savings achieved by using their private D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 341

Table 3 Demographic and economic characteristics of states grouped by speed limit levels.

Density Previous speed Private vehicle-miles driven Fatality rate GDP per % Border States (population/km2) limit (before per inhabitant (thousands) 1994/1995 capita ($) with high speed limit 1974) Current speed limit >65 31.1 72.97 1395 1.98 33,880 98 665 139.9 66.94 762 1.43 40,915 36 Action after repeal Reaction 1995–1997 44.8 72.09 1284 1.92 34,982 – No immediate action 103.0 68.68 977 1.55 38,747 –

Fig. 1. Maximum posted daytime speed limits on US rural interstates (maximum limit may apply only to speciﬁed segments of interstate). Source: Insurance Institute for Highway Safety http://www.iihs.org/.

vehicles, being more dependent on private mobility.6 By contrast, citizens in relatively small dense regions tend to be greater consumers of public transport, and when they do use their private vehicles, they are more severely affected by traffic congestion in their daily commute to work. Moreover, state distances tend to be shorter given that they live in states with a smaller territorial area. Our data also show that the states that chose to raise their speed limits were, in fact, those that reported higher rates of road fatalities per miles driven before the repeal. However, these poor safety standards did not stop those reintroducing higher speed limits. Therefore, mobility needs and the ability to make time savings seem to be more highly valued social preferences of citizens resident in those states that chose to raise their speed limits. A further interesting fact is the regional nature reflected by this policy. This is illustrated by the final column in Table 3, which shows the percentage number of states that have introduced speed limits above 65 mph and which share a border with each other. Our results indicate that states with high speed limit laws tend to be surrounded by other states with similarly high speed limits (98%). By contrast, states with low speed limits only share borders on 36% of occasions with states with high speed limit laws. To further highlight this regional relationship we conducted two additional exercises: Fig. 1 shows a map of the United States in which the states are distinguished according to their speed limit laws; and Fig. 2 shows the results of a median spline regression highlighting the importance of geographical location in determining speed limits.

6 That people being dependent on car for commuting have the highest value of time for car trips is consistent with Casello (2007). 342 D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 80 80 75 75 70 70 Median spline Median spline 65 65 60 60 80 100 120 140 160 20 30 40 50 60 longit latit

Fig. 2. Median spline regressions. Relationship between speed limits and degrees of geographical longitude and latitude.

The map clearly highlights regional clusters, with the northeastern states tending to set lower speed limits, in common with the coastal states in general. By contrast, southern and western states generally have higher speed limit laws. This geographical distribution is also captured using non-parametric techniques. Median spline regressions provide interesting information, as they do not rely on any assumptions regarding functional forms. Fig. 2 shows the relationship between speed limits and the geographical longitude and latitude occupied by the states. In the case of longitude, the results show a readily identifiable inverted U-shape relationship, while we find two peak pattern in the case of latitude. The first graph indicates that states on both coasts seem to set low speed limits, while as we move westward the central states fix higher speed limits. The second shows relatively higher speeds in both north and south extremes, although we should be cautious due to the high sensibility of the models. In short, geographical characteristics seem to play a major role in the establishment of speed limit. First because of the direct impact of location in terms of urbanization and density that affect mobility needs. Second, because regional variables reflect heterogeneous social preferences and cultural behaviors also having and influence on mobility preferences (individ- uality, freedom of choice, etc.), as well as on the tradeoff between mobility and road safety across US. Other factors, including the previous speed limit in force (which points to a time-dependence effect) and economic variables such as GDP, seem to account for differences between the two groups of states. In spite of the on-going debate regarding road safety impacts, fatality rates do not seem to be a significant factor – or at least significant enough – to determine the speed limit in states with obvious private mobility needs. Regional patterns have also been identified as an important determinant, as captured in the application of various strategies.

4. Empirical strategy: parametric analysis

Following the repeal, several states chose to raise their speed limits; others decided to leave them unaltered and retain the law as it then stood. Moreover, not all the states who decided to raise the speed limit enacted the same increases. This great variety of speed limits 15 years after the repeal allows us to test the factors that might have influenced State reaction in this policy area, and in particular, to estimate the importance of several geographical and political variables that might reflect social preferences in the establishment of desired speed limits. Thus, here we seek to explain State reactions after the repeal as well as the current speed limit level in relation to the characteristics highlighted above. On one hand, we estimate an OLS model in order to explain current speed limits levels by using as regressors variables related to mobility preferences, geographical characteristics and political ideology.7 On the other, we use binary response models (logit) in order to test what attributes do explain differences in State reactions immediately after the repeal. The model identifies those states changing their speed limit in the first years following the repeal and compares their characteristics with those not reacting as a consequence of the devolution.

4.1. Dependent variables

Our empirical analysis uses two different types of dependent variables. First, we use the current speed limit in force in each state in order to explain what the determinants of the level selected by each state are. This dependent variable is denoted by Speed_limit and reports current speed limits in each State. Second, in our attempt to test factors driving State

7 We use White-corrected standard errors to address State speciﬁc characteristics that exists as the Breusch Pagan test for heteroskedasticity shows. D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 343 reactions after the repeal, we use a binary variable named Reaction that takes value 1 if the State has adopted a new speed limit law which is higher than 65 mph after the repeal. We will test State reactions within the ﬁrst 2 years after the repeal. Both models (Binary and Linear) share the same driving factors, which are described below.

4.2. Regressors

The small size of our sample based on US States force us to limit the number of independent variables. However, we introduce different groups of regressors according to mobility, geographical and political characteristics of each State.

4.2.1. Mobility Private mobility needs – than can be also interpret as mobility use – are measured by the number of miles driven per capita (Miles_Driven). We expect a positive relationship between mobility needs and current speed limits. We also expect this variable to increase the probability of State reactions after the repeal. However, mobility needs are only one half for testing the tradeoff between mobility and safety. For this reason we also include the number of road fatalities per 100 million miles driven in private vehicles per inhabitant (Fatality_Rate), to verify as to whether or not safety outcomes prevent the introduction of higher speed limits. We use the overall fatality rate in a given state instead of the fatality rate in highways because we think this is better to capture overall safety concerns.8 The two variables capturing the tradeoff between private mobility needs and safety are measured in terms of data available for 1995, that is to say, just before the repeal was passed so as to avoid endogeneity problems in the estimation. In fact, time correlations across years for these variables are large. We should expect a negative relationship between the fatality rate and both the current speed limit and the probability associated with State reaction after the repeal.

4.2.2. Geography Geography may be considered as a main responsible for the need for private mobility. Therefore, we expect these variables to inﬂuence mobility needs. Thus, we expect large states (Size) with low levels of population (Population) to set higher speed limits, given their greater need for private mobility.9 Finally, the regional pattern already illustrated in the descriptive section is also introduced in our model by using the degrees of geographical longitude and latitude of the states.10 However, since non-parametric regressions showed a quadratic relationship between speed limits and longitude, we model this functional form by introducing the square value of the geographical longitude.

4.2.3. Politics and ideology Political parties have played an important role both in the repeal of the national speed limit and in the debate on speed limit levels. In order to determine whether the ideology of the state constituency is a driver of its speed limit level we include its average share of Republican voters in presidential elections since 1994 (Ideology). However, state legislators are the ones dictating this policy, therefore we could also consider the average share of Republican representatives in state Senates and houses of representatives between since 1994 (State_legislature). However, this variable is highly correlated with the ideology variable and we assume ideology is better to account for the tradeoff between individual mobility and safety.11 We expect Republican constituencies to support higher speed limits and state chambers controlled by Republican parties to set higher speed limits as well. This distinction is important because we expect democrat chambers less willing to impose high speed limits in States with traditionally republican constituencies, which is better accounted by the use of presidential voting (Ideology). In all, our standard model for testing factors driving State reactions after the repeal as well as current speed limits is the following:

Yi ¼ a0 þ b1Populationi þ b2Sizei þ b3Fatality Ratei þ b3Ideologyi þ b4State Legislature þ b5Longiti 2 þ b6ðLongitÞi þ ei ð1Þ where Yi is our dependent variable and ei the error term. However, we will present different speciﬁcations in the empirical section to complement this standard model. Table 4 summarizes the deﬁnitions and descriptive statistics (mean and standard deviation) of the variables employed in the parametric analysis.

8 Indeed, recent studies have shown a spillover effect in road safety outcomes in all roads when speed limits in highways are changed(Friedman et al., 2007; Richter et al., 2004). 9 In his analysis on the effects of the 55 speed limit on time, money and fatalities, Kamerud (1983) found it to be less favorable on the rural interstate system than on other affected systems. 10 Other works (i.e. Holguín-Veras et al., 2006) have used regional groups of states (Northeast, Southeast, West, Southwest, and Midwest) as variables to reﬂect geographical characteristics. We believe that using geographical longitude and latitude of the states provides a more precise to regional patterns. 11 The use of the state_legislature variable instead of the ideology variable provide similar results – although less statistically signiﬁcant – that are available upon request. 344 D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347

Table 4 Deﬁnition and descriptive statistics of variables employed in parametric analysis.

Variables Deﬁnition Mean Std. dev. Miles_Driven Private vehicle-miles driven per inhabitant in 1995 (thousands) 1167 0.69 Population State population in 1995 (thousands) 5244 5758 Size State area in square miles 70,747 85,987 Fatality_Rate Rate of road fatalities per 100 million miles driven 1.79 0.44 Ideology Average share of Republican votes in presidential elections 1996 and 2000 46.2 7.78 Longit Geographical degrees of longitude of the southern border of the state 90.1 17.81

5. Results

Tables 5 and 6 show the main results derived from applying our empirical strategy.12 The first table displays our main results on current speed limits (OLS estimation), while the second is focused on the immediate reaction of States after the repeal (Logistic estimation). Model (1) shows that the application of private mobility (Miles_Driven) and safety outcomes (Fatality_Rate) just before repeal is both statistically significant. Despite finding the expected sign in mobility needs, we report a positive sign in the case of Fatality_Rate. In fact, these estimates suggest that there is a trade-off between private mobility and safety. States with greater needs for private mobility tend to enact higher speed limits, but the fact that they are also the ones that present the highest fatality rates does not impede these higher limits. In fact, our results seem to suggest that these states do not place a high value on traffic safety or, at least, they appear to believe that speed limits have little bearing on levels of road safety. The social appraisal of this trade off would seem a reasonable determinant of current speed limits. Once we introduce geographical characteristics to account for mobility needs in Model 2 – note that we substituted miles driven by Population and Size – they appear strongly statistically significant and showing that population density play an important role in speed limit formation. Yet, keeping both variables separately allows showing that highly populated states present lower speed limits given the higher levels of urbanization, while the opposite is the case in large states, reflecting it would seem their lower density levels and the greater distances that have to be covered, and hence the creation of more social interest for making time savings. In addition to these variables, we extended the model to include ideology in Model 3. This addition shows how ideology is also important for explaining current speed limits. We find that republican constituencies are positively correlated with high speed limits. Its associated coefficient indicates that states with high shares of Republican votes in presidential elections are strongly correlated with the setting of higher speed limits. Nonetheless, note that once we include ideology in the model the fatality rate is soundly altered by losing its statistical significance. Our interpretation of this result is that ideology is somehow reflecting the valuation between mobility needs and safety in a way that this variable is capturing preferences giving less weight on fatality outcomes. Finally, the regional variables included in Model 4 in addition to previous ones, provide interesting results.13 On the one hand, we observed that as long as we go west states use to set higher speed limits (Longit), although this trend changes along the west coast itself (Longit^2) as indicated by the non-parametric regressions run in the previous section. The rest of variables are unaltered. These determinants are also tested for State reactions immediately after the repeal. What we show in Table 6 is that the probability of increasing speed limits over 65mph after the repeal is again correlated with the tradeoff between mobility and safety. Again we find in Models 5 and 6 that Miles_Driven are statistically significant and positively correlated with the probability of having a reaction, while fatality rate is positive and statistically significant at 10%. This result seems to indicate that road safety was not a concern preventing the increase of speed limits. Also we confirm that ideology is one of the leading factors driving immediate reactions after the repeal. Finally, we find the same expected results on geographical variables reporting the non-linear relationship with speed limit increases after the repeal. A further important test was run to determine whether there were any time or regional dependence effects in the decisions made regarding speed limits. This paper argues that social preferences underlie the formulation of speed limit levels, and since it is unlikely that these values change much in such a short period of time, we would expect that those states with higher speed limits before the centralization of this measure would opt to reestablish higher speed limits after devolution. Thus, centralization can be considered a historical accident in the matching of social preferences and transport policy. Like- wise, we argue that geographical characteristics are key factors in determining speed limits. Consequently, we would also expect states with a common border to set similar speed limits. In order to account for these time and regional dependence effects we ran a model considering both time and regional dependence effects. Results are presented in the Appendix (A2).14

12 Common tests for misspecification, normality and multicollinearity were performed and gave satisfactory results: The Ramsey Reset Test did not reject the null hypothesis of not having omitted variables (p-value = 0.2811) while the linktest provide evidence of no specification error (p-value of hatsq = 0.490), the Variance Inflation Factor was below 2 (as indicated in Table 5) and the Jarque–Bera test of normality accepted the null hypothesis of being normally distributed for OLS (p-value = 0.398). The choice between Probit or Logit models provides only slight changes in the efficiency of our estimation. 13 Note that following a referee’s suggestion we dropped the fatality rate variable that was not significant in model 3. 14 We were unable to include these variables in the earlier models due to problems of collinearity. Moreover, these variables correlated highly with mostof the variables used earlier. D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347 345

Table 5 Least squares estimates of US current speed limit levels in rural interstates.

Independent variables Speed limit (OLS) (1) Speed limit (OLS) (2) Speed limit (OLS) (3) Speed limit (OLS) (4) Miles_Driven 2.3980 (2.49)** ––– Population – 0.2480 (3.64)*** 0.1353 (2.19)** 0.1452 (2.81)*** Size – 0.0740 (7.80)*** 0.0537 (6.29)*** 0.0496 (7.68)*** Fatality_Rate 2.7836 (2.13)** 1.704 (2.22)** 0.3118 (0.37) – Ideology – – 0.2708 (3.88)*** 0.2315 (3.21)*** Longit – – – 0.3285 (1.75)* Longit^2 –––0.0016 (1.99)** R2 0.32 0.64 0.76 0.78 F-test (joint signiﬁcance) 19.54*** 48.01*** 103.13*** 126.44*** VIF test of multicollinearity 1.38 1.30 1.56 42.56a

Note. Robust to heteroscedasticity t-statistics in parenthesis. Each model includes an intercept. * Significance at 10%. ** Significance at 5%. *** Significance at 1%. a Collinearity is only driven by the large correlation between longit and longit^2.

Table 6 Logistic estimates of the probability of reaction after the repeal (1995–1996).

Independent variables Reaction (logit) (5) Reaction (logit) (6) Miles_Driven 1.156 (1.84)* 2.418 (2.74)*** Fatality_Rate 1.9513 (1.76)* 2.096 (1.60) Ideology 0.2503 (3.51)*** 0.3212 (2.95)*** Longit – 0.6912 (2.86)*** Longit^2 – 0.0030 (2.68)** Pseudo-R2 0.35 0.51 Wald Chi2 (joint signiﬁcance) 16.22*** 14.25***

Note. Robust to heteroscedasticity z-statistics in parenthesis. Each model includes an intercept. * Significance at 10%. ** Significance at 5%. *** Significance at 1%.

Our main results tell that time and regional dependence effects seem to play an important role in the formulation of speed limits. States that tended to have higher speed limits chose to raise the levels again following repeal, and states with neighbors operating higher speed limits tend to set high speed limits themselves.

6. Concluding remarks

This paper has considered variables related to mobility, politics and geography in investigating the factors that account for the provisions established in speed limit laws. We have found that geography, ideology, and regional patterns influence speed limits and help to explain the differences that exist between the US states. Furthermore, we have also found that the territorial diversity in social preferences as regards speed limits and the trade-off between mobility and road safety influ- ences the decision as to whether speed limits should be centralized at the federal level or regulated by the states, as well as the limits that are currently in force across the country. In fact, we provide first evidence that these factors were also responsible for the immediate changes in speed limits passed by some States after the repeal. This paper contributes to the existing literature in being the first in studying determinants of speed limits as well as speed limit enactments after the devolution, whereas the literature has tended to focus almost exclusively on the impact of speed limit changes on road safety outcomes. Our results show that these impacts were not really important in the preferences of States when designing this policy. More importantly we show that ideological and mobility factors are leading determinants of speed limits and State reactions after the repeal. These results should be considered in the centralization-devolution debate. Such factors have been largely neglected in much of the previous empirical literature on speed limits, although they seem to play an important role. For this reasons our analysis and the results should provide new insights into the speed limit debate and beyond. These results have interesting policy implications in future policy debates and legislative procedures influenced by regional diversity and heterogeneous preferences across territories. Indeed, the future debate on the centralization of speed limit laws – or any other transportation policy – might be triggered by different valuations of environment, safety or/and energy consumption concerns and mobility needs. Were this to be the case, social preferences determined by regional and geographical mobility patterns, together with ideological attitudes governing the centralization–devolution discussion, are likely to shape the debate and its outcome, as this article has indicated. 346 D. Albalate, G. Bel / Transportation Research Part A 46 (2012) 337–347

Table A1 Correlation matrix for the standard speciﬁcation (Model 4).

Miles_Driven Population Size Fatality_Rate Ideology Longit Miles_Driven 1 Population 0.42 1 Size 0.17 0.09 1 Fatality_Rate 0.52 0.18 0.25 1 Ideology 0.59 0.21 0.36 0.50 1 Longit 0.15 0.10 0.51 0.31 0.29 1

Table A2 Time and regional dependence effects. Least squares estimates on current speed limits.

Independent variables Model (7) Correlation PREVIOUS (time) 0.0864 (2.10)** 0.43 BORDER (regional) 7.3748 (6.25)*** 0.68 R2 0.48 – F-test (Joint Signiﬁcance) 55.15*** –

Note. Robust to heterocedasticity t-statistics in parenthesis. Each model includes an intercept. Significance at 10%. ** Significance at 5%. *** Significance at 1%.

Acknowledgements

We gratefully acknowledge ﬁnancial support from the Spanish Ministry of Science and Innovation (ECO2009-06946) and from the Regional Government of Catalonia (SGR2009-1066). Daniel Albalate is particularly thankful for support received while he was visiting scholar at Ohio State University on Summer/Autumn 2009. Germà Bel thanks support from ICREA-Aca- demia. The paper has beneﬁted from comments and suggestions received when presented at the John Glenn School of Public Affairs (Ohio State University), and at the 2010 Simposio de Análisis Económico. We are also grateful for suggestions from Trevor Brown, Anand Desai, and Matt Potoski. Comments from anonymous reviewers have been very useful.

Appendix A

See Tables A1 and A2.

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