The Political Economy of Transport Improvements in the Aftermath of the Glorious Revolution

The Political Economy of Transport Improvements in the

Dan Bogart Department of Economics, UC Irvine 3151 Social Science Plaza Irvine CA 92697-5100 [email protected] This Draft Dec. 2013

Abstract

Britain after the Glorious Revolution provides a revealing and important context to study the role of lobbying and party politics in transport improvements. This paper focuses on river navigation projects authorized by acts of parliament. The empirical analysis shows that the extension of navigation to a city depended on the political connections of likely supporters and opponents. Political connections are also found to be more significant under Tory majorities compared to Whig majorities. The paper offers new evidence on the role of parties in Britain’s development. It also provides a new framework for studying support and opposition to transport improvements. JEL Code: N43, P16, D72 Keywords: Contests, Rent-Seeking, Lobbying, Transport, Political Parties, Glorious Revolution

1 I would like to thank Robert Oandasan, Larry Bush, Thomas Wheeler, Amanda Compton, and Alina Shiotsu for providing valuable research assistance. I also thank Leigh Shaw Taylor for sharing population data and Stergios Skepardis, John Wallis, Steve Nafziger, David Chambers, Latika Chaudhary, and Gary Richardson for helpful comments on earlier drafts. Also I thank participants at the Caltech Early modern group 2012, the 2012 ISNIE meetings, the Western Economic Association meetings, and seminar participants at UC Irvine, Lund University, Oxford University, University of Edinburgh, Cambridge University, University of Arizona, UCLA, USC, University of Maryland, Yale, and the Institute for Historical Research for their comments. Lastly I thank the UC Irvine Council on Research, Computing, and Libraries for grant support. All errors are my own.

I. Introduction

Transportation improvements expand markets and change the location of economic activity in fundamental ways. Two good examples are the construction of railroads and inter- state highways and their effects on urbanization (Atack et. al. 2010, Baum-Snow 2007, Duranton and Turner 2012). In these cases, and many others, economists have studied the economy’s reaction to transport improvements, but the questions of where and when a transport improvement is implemented are rarely studied even though it greatly impacts economic activity.

This paper provides theory and evidence from Britain’s early transportation revolution and sheds new light on how lobbying and party politics influence transportation improvements. It studies the controversial extension of river navigation in Britain from 1690 to 1740. This period is of special importance because it covers the aftermath of the Glorious Revolution of 1688 and

Britain’s transition to political stability. Britain’s political institutions changed after 1688 with the monarchy being weakened and parliament coming to play an active role in government.

Parliament’s rise occurred in the context of an intense rivalry between two political parties, the

Whigs and Tories. Each party was vying for control of the House of Commons and devoted considerable resources to mobilizing their supporters during elections and legislative sessions. 2

In the decades after the Glorious Revolution there were renewed efforts to improve the transportation network. Britain was fortunate to have many navigable rivers, like the Thames or

Severn, but large areas in the interior remained distant from inland water transport (Willan

1964). The problem could be addressed by clearing obstructions and building locks on unnavigable rivers, but such investments required large upfront costs and clear powers of eminent domain. After 1688 it was increasingly common for acts of Parliament to grant a

2 For the historical literature on Britain’s parties see Holmes (1987), Horrowitz (1977), Harris (1993), Speck (1970).

1 navigation company legal rights to undertake river improvements and collect tolls (Bogart 2011).

River navigation projects were promoted by city officials and local business interests through parliamentary bills. If the bill was successfully enacted then the project could go forward, generating potentially large benefits to the company and users. The problem for navigation supporters was the stiff opposition from vested interests. Neighboring cities and landowners petitioned for the rejection of river navigation bills on the grounds they diverted trade or damaged property. Many opposition groups succeeded in stopping or slowing navigation improvement. They did so with the help of Members of Parliament (MPs) in the House of

Commons who made the key decisions regarding bills.

This paper models the process by which navigation projects were proposed, opposed and approved in the House of Commons. The model features a ‘contest’ between promoters and opponents who try to persuade the Commons to accept or reject a proposal. The Commons is assumed to have incomplete information on whether the proposed project will generate benefits and for whom. The Commons relies on evidence from the promoter and opposition to make its decision, but it is biased to one side. The bias depends on which party has the majority in the

Commons and whether the majority party is strongly represented near the location of the promoter and the opposition. The model predicts that the identity of the majority party and the geographic distribution of majority party strength influenced the probability that a location got a navigation act.

The predictions are tested using data on the diffusion of river navigation acts across cities in England and Wales. There is no readily available data to study political-economic outcomes across British cities and so this paper makes one contribution by developing such data.

Previously unused sources provide data on the population size and economic characteristics of

2 all cities around the year 1670. Cities located on waterways are geo-coded yielding information on their elevation and distance to the coast or nearest navigation head. Another data source adds the location of all constituencies in the House of Commons and the party affiliation of their MPs between 1690 and 1740, providing geographic measures of majority party strength. Lastly, legislative sources show which navigation projects got approved through acts of parliament, their location, and which groups supported and opposed river bills through petitions.

The location of petitioning groups is important as it identifies where support and opposition was most likely. The data speak strongly in showing that supporters were generally

‘upstream’ from a project, meaning they were located upland and away from the coast or navigation head. By contrast, opponents were generally ‘downstream’ from a project. The patterns are consistent with economic geography forces which suggest that areas with poor transport and are distant from large markets tend to gain more from transport improvements than areas that already have good transport and are close to large markets. Downstream areas are also more prone to suffer property damage from navigation improvements as they alter water-flows.

The main part of the empirical analysis focuses on which cities got a river act within their jurisdiction in each of the 14 parliaments covering the period from 1690 to 1740. The estimating equation is a ‘reduced-form’ of the contest model and contains the characteristics of the candidate city and the characteristics of upstream and downstream cities from the candidate city.

One of the main variables of interest is the number of upstream and downstream majority party

MPs. The identifying assumption is that the number of majority party MPs is exogenous conditional on the inclusion of city characteristics and either city random effects or city fixed effects. One justification is that economic interests could not easily predict which party would have a majority and thus they could not easily select majority party MPs.

The estimates show that economic and geographic characteristics of cities matter in sensible ways. For example, cities in less rugged areas, with greater market potential, and with manufacturing all have higher odds of getting a river act. The probability of a river act is also found to be significantly lower if it had more downstream majority party MPs. Greater upstream majority party MPs increases the probability but only under Tory majorities. The effect of downstream and upstream majority party MPs was generally lower under Whig majorities.

The party effects are consistent with the Tory party employing a ‘core-strategy’ where it was biased to approving or rejecting bills depending on whether supporters and opponents were closer to their core as measured by Tory party representation. Core-strategies are common in models of redistributive politics (Cox and Mcubbins 1986) and have been documented in many empirical settings. Core-strategies are thought to be most compelling in contexts where parties need to coordinate and mobilize voters (Cox 2008, 2009). Both of these factors were relevant in this setting as the Whigs and Tories were engaged in an intense competition. As an extension, I find that the electoral consequences of river navigation bills were greater for the Tories than the

Whigs, which helps to explain why targeting was more prominent under Tory majorities.

The paper is related to the literature analyzing rent-seeking contests where groups exert effort to win a ‘policy-prize’ (Appelbaum and Katz 1986, Nitzan 1994, Stein 2002, Konrad

2009). Rent-seeking contests have been applied to issues like licensing, patent approval, and the enforcement of property rights (e.g. Skeperdas and Vaidya 2012). This paper is the first to apply the rent-seeking contest model to transportation and economic history questions.

The paper also relates to the diverse literature on political barriers to entry. Cross-country studies have documented a relationship between political institutions and the regulation of entry

(e.g. Djankov et. al. 2002). Here I provide a carefully documented example of the patterns shown across countries. The analytical and empirical approach also helps in identifying whether a society is limited or open access in the framework of North, Wallis, and Weingast (2009). The literature to date has struggled to develop metrics for determining the degree of access. My estimation strategy provides a new metric as it identifies the quantitative effects of political connections on entry.

The political economy of transport improvements is another related literature. A number of works document a relationship between transportation infrastructure spending (or projects) and political connections of various forms. (Lee 2003, Levitt and Synder 2005, Knight 2008,

Curto Grao et. al. 2012, Albouy 2013, Burgess et. al. 2013). The analysis here is novel in that it sheds light on support and opposition to transport improvements and their relationship to economics and politics. Supporters and opponents are present in many transportation contexts and their role has been largely ignored in the literature to date.

A final related body of literature concerns Britain’s political-economic development. It has been argued that the Glorious Revolution contributed to Britain’s subsequent industrialization

(North and Weingast 1989, Acemoglu and Robinson 2012). A recent twist on this view suggests that the rise of the Whig party following the Glorious Revolution was especially important. The

Whigs are claimed to be more pro-development than the Tories by fostering a manufacturing and financial sector (Pincus 2009, Pincus and Robinson 2012, Dudley 2013) and by providing a stronger commitment to protect bondholder rights (Stasavage 2005). However, several histories cast doubt on the role of the Whigs or political parties more generally in Britain’s economic rise

5 after 1700.3 This paper is the first to rigorously analyze the role of political parties on economic outcomes of importance like transport. A counter-factual exercise at the end suggests that Whig party dominance would have been more favorable to river navigation development.

The rest of the paper is organized as follows. The second and third sections provide background and the fourth lays out a theoretical framework. Section five discusses the data and six presents the empirical results. The implications for Britain’s navigation development are discussed in section seven.

II. Background on Early Transportation Improvements in Britain

River navigation acts enabled the first significant improvement in Britain’s transport infrastructure since the middle ages. In the early 1600s, most rivers were under the authority of

Sewer Commissions. Sewer Commissions could compel landowners to cleanse waterways and could tax landowners along riverbanks to pay for upkeep, but could not tax individuals who traveled on the river and could not purchase land along a waterway or divert its course. These limitations kept commissions from improving and extending navigable waterways (Willan 1964,

Bogart and Richardson 2011). A river navigation act addressed these problems by establishing a new special purpose organization. It endowed a company of ‘undertakers’ with rights to levy tolls and purchase land necessary for the project. The tolls were subject to a price cap and there were provisions on how the project was to be carried out. There were also provisions that allowed juries to determine the price of land if companies and property owners could not come to an agreement.

3 See Griffiths, Hunt and O’Brien (1991), Harris (2000), Mokyr and Nye (2007), Mokyr (2009), and Zahedieh (2010).

Through their statutory powers, river navigation acts played a key role in the extension of inland waterways. Navigation companies built locks and dredged rivers substantially increasing the length of navigable waterways in England and Wales. Figure 1 draws on Willan (1964) to illustrate the changes. The black lines show rivers that were navigable in 1690 and the grey lines depict rivers with acts enabling improvements in their navigation by 1740. Generally acts extended navigation near the coast or on existing navigable rivers giving cities, like Manchester and Leeds, new access to waterway transport.

The diffusion across cities and across time is illustrated through the survival curves in figure

2. The data apply to all ‘major’ cities with a population above 2500 by 1750 and are drawn from

Corfield (1982). Among these cities, 45 percent were on navigable rivers or the coast in 1690.

By 1740, 56 percent of these cities were on navigable rivers or the coast (see the dark line). The increase from 45 to 56 percent sounds modest, but one should realize there were few extensions in river navigation over the previous centuries and that the skills required to construct canals were not yet developed (Willan 1964). Also notice that the share of cities where there was the authority to improve navigation was always higher than the share with river navigation (see the grey dashed line). On average it took 14 years to complete a river navigation project once approved by an act and 17 percent were not yet completed by 1740.

The extension of river navigation to a city generally improved its economic prospects.

Freight rates by inland waterway were approximately one-third the freight rates by road and many sources suggest that trade increased for a city when it was connected to the waterway network (Defoe 1724, Willan 1964, Bogart 2012). In light of the importance of waterway transport it is significant that some river navigation projects were proposed several times in

7 parliament before being approved and some were never approved at all. In fact, it was more common for river navigation bills to fail than to succeed.4

The House of Commons was the key decision-making body for river bills. They started as a petition to the House of Commons or an order for a bill by a Member of Parliament (MP) in the

Commons. The majority of petitioners were local business interests or city officials who argued that navigation projects would benefit their locality and the nation. For example, in the case of the River Avon bill in 1712, the Mayor, Aldermen, and citizens of the city of Bath argued that making the Avon navigable will employ the poor, promote the trade of Bath, train persons for sea-service, and preserve the roads and highways.5 Petitions like this were assigned to a special committee of MPs who would draft a bill to be reviewed by the entire Commons (there was no equivalent to the ‘Transportation or Public Works Committee’ found in modern legislatures).

The committees generally had around 25 MPs and included a proviso that any MPs from neighboring counties and boroughs could attend. The committees played a key role in the fate of bills within the Commons. Most failed river bills did not make it out of their committees.

Opposition was a prominent feature for many river bills. Formal opposition occurred through petitions to the Commons against bills proposing river navigation. Opposition groups used a variety of arguments including property damage, employment loss, and trade diversion. In the case of the River Avon bill, Henry Parsons stated that his six mills on the river Avon would be rendered useless to the great loss of the poor and to himself. He prayed that ‘the bill may not pass, or that such damages as the petitioner will sustain thereby may be made good to him by the undertakers.’ The Mayor, Burgesses, and Common people of the city of Bristol stated that the

4 The sources on failure will be discussed momentarily. It should also be noted that the failure rates are consistent with what Hoppit (1997) has shown for all legislation from 1690 to 1739. 5 The details of the petitions related to this bill are available in the Journals of the House of Commons, 1712.

8 bill contained clauses that may be construed to interrupt their ancient Right, and encroach upon the rights lately granted to the petitioners. Bristol had been given authority to make the Avon navigable closer to the sea by an earlier act of Parliament. The gentlemen and freeholders of the county of Somerset, living near the River Avon, stated the project will ‘be a great prejudice to all parts of the country near the Bath, by bringing of corn, and other commodities, from Wales, and other parts, where the value of lands are low.’ They were also concerned about the ‘damages and trespasses they may sustain by making the said River navigable.’

The arguments of opposition groups were countered by supporters of projects who also petitioned to the Commons. After the River Avon bill had been vigorously opposed by the groups discussed above, the freeholders, leaseholders, and occupiers of quarries near Bath submitted a petition in favor. They stated that it will ‘be a means to carry great quantities of wrought and unwrought stone from the quarries near the said River into diverse parts of this kingdom.’ The general pattern in these types of cases was for several groups to oppose the bill offering arguments against and for several groups to support the bill offering arguments in favor.

A key premise of this paper is that the amount and strength of arguments for and against a bill played a role in its rejection and approval. Secondly, supporters and opposition groups decided how much effort to expend in supporting or opposing projects based on the magnitude of their expected gains and losses. Supporters with high expected gains argued more vigorously in favor and opponents with high expected losses argued more vigorously against. Another key aspect concerns political bias. The Commons weighed the arguments of supporters and opponents differently depending on which political party was in the majority and the strength of the majority party in the districts near supporters and opponents. To better understand this aspect of the argument, a brief background on Britain’s politics is now provided.

III. Background on Politics in Britain

Politics in the aftermath of the Glorious Revolution were strongly influenced by the competition between Britain’s first political parties, the Whigs and Tories. The two differed in their policy positions with the Tories favoring privileges for the Church of England, lower taxes, and a small government debt. The Whigs generally favored religious toleration and an aggressive foreign policy based on a well-funded army. The two parties also differed in their supporters.

The Tories were generally supported by small to medium landowners known as country gentleman. The Whigs drew more support from merchants and large landowners. In terms of leadership, the Whigs were first led by a small group of party mangers known as the ‘Junto.’

They were particularly effective in mobilizing Whig MPs on key votes in the Commons. One of the fruits of their success was Whig control over the ‘Monied Companies,’ like the Bank of

England (Carruthers 1999). Robert Harley is the best known leader of the Tories. One of

Harley’s main aims was to unite the more diverse Tory party. Harley organized several challenges to the Whigs’ control over Monied Companies. Harley’s main success was the launch of the South Sea Company which was meant to challenge the Bank of England (Holmes 1987).

The Whigs and Tories were engaged in vigorous electoral competition between 1690 and

1721, a period known as the ‘Rage of Party.’ In these years, there were 11 elections and the majority party in the Commons changed 7 times. Cruickshanks, Handley, and Hayton’s (2002) estimates on the size of majority parties suggest they could be quite large as in the 1710 to 1713

Parliament with more than 60 percent of MPs being linked to the Tories. They could also be relatively small as in 1705 to 1708 when the Tories held a narrow majority close to 50 percent.

The Whigs came to dominate the Commons after 1715. The Whigs held a majority in all parliaments from 1715 to 1741 and for some decades after. One reason was the demise of the

Tories as an effective opposition party. Some Tories were associated with the failed Jacobite

Rebellion of 1715. The other reason was the emergence of Robert Walpole as the leader of the

Whigs. Walpole was the Prime Minister from 1721 to 1742 and is believed to have been especially effective in using government favors to secure a working majority in the Commons.

Walpole also had a group of core supporters that he could rely on to maintain power.

According to some scholars the identity of the majority party in the Commons affected economic policies. For example, Stasavage (2003, 2007) argues that government bondholders were a key part of the Whig coalition and that a Tory majority posed a greater default risk.

Pincus and Robinson (2012) make a forceful argument against the Tories on a wide range of economic policies. In their view, the Tories would not have passed the wide series of acts improving Britain’s economic infrastructure. Somewhat counter to this view there are historians who argue that the Whigs and Tories did not have the sophisticated organizational structures of modern parties. Geoffrey Holmes, a leading historian of politics from 1702 to 1714, states that

“neither possessed a party machine in any strict sense, nor the regular income needed to maintain one; neither employed a system of official whips whose authority was generally recognized

(1987 p. 287).” Ron Harris (2000) takes a stronger view in analyzing Parliament’s role in allocating corporate charters through acts of parliament. Harris states that “barriers on entry into the corporate world was not created by Parliament intentionally, nor was it to any considerable degree manipulated by Parliament (2000, p. 135).”

This paper probes more deeply into these issues by examining the role of the Whig and Tory parties in influencing which river navigation projects got approved and at what point in time.

What is at stake is whether Britain’s early parties were able to implement sophisticated targeting strategies to mobilize supporters and sway swing voters and whether the Whigs and Tories played the political game differently. Another goal is to understand how lobbying and party politics work in concert to influence transportation improvements. To this end, the following section proposes a new theoretical framework which analyzes the lobbying activities of supporters and opponents of transport improvements.

IV. Theoretical framework

The model focuses on a location with the potential to improve river navigation and where there is a promoter that would like to undertake the project. The promoter has an exogenously given expected financial return b if the project is approved and completed. To get approved, the promoter needs to get an act of parliament. The act requires the introduction of a bill, the payment of a fixed fee, and the approval of a regulator which in practice was the House of

Commons. The promoter also incurs costs for gathering evidence to persuade the regulator on the merits of the project. The last key actor in the model is an opposition group. It has an exogenously given expected financial loss l if the project is approved and completed. The opposition group chooses whether to fight the project by appealing to the regulatory authority. If so they incur costs of gathering evidence to persuade the regulator that the project should be rejected. The timing is as follows: (1) the promoter decides whether to introduce a bill, (2) the opposition group decides whether to formally oppose the bill if introduced, (3) the promoter and opposition expend effort trying to persuade the regulator, who then approves or rejects the bill,

(4) and last payoffs are realized.

It is important to note that the regulator’s payoffs are represented by the bill success function,

, where is the probability that the regulator approves a bill, measures the bias

in favor of the promoter, measures the bias in favor of the opposition, and and are the amount of evidence provided by the promoter and opposition. This short-cut can be justified as follows. The regulator would like to approve valuable projects for the society or their constituents but they do not have full information on which projects are most valuable. Thus they rely on the evidence provided by supporters and opposition groups. More evidence provided by supporters is taken as a positive signal of the projects value. More evidence from the opposition is taken as a negative signal of its value. The regulator also reacts to the evidence with a degree of bias, favoring one side over the other. In such settings one can derive a family of probabilistic decision rules for the regulator which depends on the evidence provided and a set of parameters measuring the regulator’s preferences (see Skaperdas and Vaidya 2012). I use a form of the

Tullock Success function, , which is common in the literature.

Much of the rent-seeking literature makes the evidence and endogenous (see Konrad

2009). Endogenous efforts are natural in this setting as the promoter and the opposition should devote effort in producing evidence in expectation of their financial gains and losses. Likewise, the decision to introduce a river navigation bill and to oppose one in parliament is endogenous.

In the appendix, a stage game formalizes the entry and effort decisions taking the financial gains b, losses l, and the bias terms and as given. The game yields the equilibrium probability

that a bill is approved , the probability that a promoter introduces a bill, which is an

increasing function of of , and the probability that a bill is opposed which is an increasing function of . It is straightforward to show that the probability of a river bill being

13 introduced and approved increases if the bias to the promoter is greater or if the promoter’s financial gains are greater. The probability is lower if the bias to the opposition group or the opposition’s losses are greater. Moreover, the probability of opposition to a river bill increases if the bias to the opposition group is greater or the opposition’s losses are greater.

Modeling Financial Gains, Losses, and Bias

In applying the model it is necessary to identify the locations of promoters/supporters and opposition groups for each project. In general this is difficult, but in this case economic geography reasoning provides a way forward. Start with the fact that all rivers can be divided into upstream and downstream portions. The downstream portions empty into the coast or another navigable river, while the upstream areas feed the downstream. The downstream portions are often navigable even if only for a small part, whereas the upstream portions may not be navigable. My argument is that economic actors in the downstream areas were more likely to suffer losses from an extension in navigation. One reason is that they earn rents from their privileged location near navigable portions of river. If navigation is extended to upstream areas then the cost advantage of downstream locations is reduced and therefore their rents fall.6

Greater property damage is another reason that economic actors in downstream areas were more likely to suffer losses. Extensions in navigation altered water flows and concerns over flooding and destruction of mills in downstream areas were often expressed. With proper compensation from juries downstream areas might suffer little loss in income, but the complaints of landowners suggest that juries were slowly becoming credible in the early eighteenth century.

6 Similar patterns have been found for the extension of highways in China (see Faber 2013). Manufacturing in counties that are close to major cities tends to grow more slowly when they get highway connections compared to counties at a greater distance from major cities.

A corollary to the preceding arguments is that economic actors in upstream areas are more likely to benefit from extensions in river navigation. Their initial market access is lower because they don’t have access to cheap transport. Once the navigation is extended and their market access increases then their rents should rise. Moreover they are less likely to have property damage as flooding is less of a concern. An upstream city might also grow as traders from surrounding areas coalesce to transport their goods by water.

Below I show that being upstream or downstream is a good predictor of support and opposition to river bills. I then build on this finding and use the economic and geographic characteristics of upstream and downstream areas as determinants of the financial gains to the promoter and the losses to the opposition. Formally, let a project in location have an upstream location and a downstream location . The economic and geographic characteristics in

and determine the gains and the characteristics in determine the losses .

The bias terms and are assumed to be a function of the strength of the majority party in the vicinity of the promoter and opposition. In political science, there are a number of models which rationalize why the majority party would favor their ‘core’ groups. Cox and Mcubbins

(1986) argue that core groups are targeted to mobilize their votes. Pork barrel projects and local policies can also be targeted to swing voters, but they are thought to be difficult to sway. 7 In our setting, the Commons is assumed to favor the promoter if the majority party is strong in the upstream location and favor the opposition if the majority party is strong in the downstream location.

7 See Dixit and Londregan (1996) for an analysis of the swing voter strategy.

The identity of the majority party also affects the bias terms if the Whigs and Tories have different preferences for river navigation projects. Suppose for example that the Whigs were more favorable to river navigation projects because they favored the financial interests which were a key constituency of the Whigs. Alternatively, the Tories might disfavor navigation projects as they were damaging to some landowners, a key constituency of the Tories.

Differences in the structure and organization of the two parties might also affect to what degree each targets core supporters.

The following functional form for capture the patterns just described. Let

, where is an indicator for Whig majorities, equals the strength of the majority party in the upstream location , and equals the strength of the majority party upstream when the Whigs are in the majority and zero when the Tories are in the majority. The parameters, , , and , measure the influence of these three variables respectively. If the Whigs are more pro-navigation then . If a stronger majority party in upstream locations helps the promoter then .

If the Whigs are less likely to favor promoters when majority party strength is high upstream then . The bias to the opposition is defined similarly. Let

where now equals the strength of the majority party downstream.

Bringing together all the elements yields the probability of a river act occurring in location :

[ ( ) ) ( ) and the following three propositions:

Proposition 1: Any characteristic which increases the benefits of river navigation should increase the probability of a river act. The opposite for a characteristic that increases the loss .

Proposition 2: If the majority party is stronger upstream then a river act is more likely and if the majority party is stronger downstream then a river act is less likely.

Proposition 3: If the Whigs are more pro-navigation than the Tories then a river act is more likely when the Whigs are in the majority or when the Whigs are strong in downstream areas.8

V. Data and Sources

The main empirical objective is to explain the diffusion of river navigation acts across cities between 1690 and 1740 using economic, geographic, and political variables. The lack of data for most English cities around this time presents an immediate difficulty. This paper addresses this problem by building the data from various sources. Richard Blome’s Britannia provides a list of

782 cities in England and Wales and describes their economic and political features around the year 1670. Blome also provides a map of each county that includes cities, waterways, and coastal features. I use Blome in several ways. First, Blome’s list of 782 cities in England and Wales is taken as the population of all cities. Second, Blome’s description is used to classify their economic, infrastructural, and political characteristics.9 Third, Blome’s maps are used to identify which cities were inland and located near rivers or streams that could be made navigable. The sub-population of cities that could get river navigation in 1690 is clearly the most interesting for this study. This group of 419 cities is labelled Blome’s list of ‘potential adopting cities.’

8 Propositions 2 and 3 follow from the relationships between local party strength, the party in power, and the bias terms and . Once the bias changes then the equilibrium probability for the approval of a navigation bill

also changes. 9 It is necessary to supplement Blome with population data. The Cambridge Population History group has kindly provided their estimates of parish population in 1670 which I use to reconstruct the population of cities listed in Blome. The details are described in the appendix.

All the cities in Blome’s list were geo-coded to create spatial variables. The route of a river or stream is traced from each city in the potential adopter list to the coast or the head of river navigation using Google maps.10 The total route distance in miles is recorded along with the starting elevation at the city and then again at the coast or navigation head. The difference between the two gives the elevation change. The traced river route also identifies the location of nearby cities that are ‘downstream’ and ‘upstream’ from the navigation project. An example is shown in figure 3 for the city of Northampton which was located on an unnavigable portion of the river Nene. A straight line is drawn from Northampton to the city of Peterborough, the navigation head for the Nene in 1690. A perpendicular line is also shown dividing the upstream region to the southwest of Northampton and the downstream region to the northeast. In the upstream and downstream spaces there are several cities from Blome’s full list. Also shown is a

25 mile buffer zone which identifies cities that are close to the navigation project.

The next spatial variable is the market potential for each city in the potential adopter list.

Market potential is calculated in two ways. The first is meant to capture the size of nearby cities that could use the waterway network. It is referred to as ‘market potential local.’ The second,

‘market potential distant,’ captures the size of major cities that could be more easily reached if the navigation project was completed. Details are given in the appendix.

To identify where river navigation bills were tried and where they were approved as acts, I rely on rich information in the Journals of the House of Commons. The details of every river bill were entered in a spreadsheet, including petitions, orders of the House, committee reports, votes, amendments, and whether the bill became an act.11 The resulting sample consists of 67 river

10 A particularly useful program was http://bikehike.co.uk/index.php which provides a ‘course creator’ tool. 11 See Bogart (2011) for more details on this source. Not that votes are only occasionally reported. In those cases, the names of the ‘tellers’ for yes and no and the totals for each side are reported.

18 navigation bills and among these 31 became river navigation acts. Note that 23 of the 67 bills were for projects that were already introduced in an earlier parliament but had failed. The number of bills and acts in each parliament from 1690 to 1740 is shown in table 1.

The 67 river navigation bills are matched with the cities in Blome’s potential adopter list. It is required that the river navigation project cross a city’s boundary to be matched. Once the bill is matched to a potential adopter city then a number of spatial variables are created, including the number of cities with various characteristics within a certain distance. The supporters and opponents of river navigation bills are also matched with the cities in Blome’s list using the petitions reported in the Journals. Columns 3 and 4 in table 1 show the number of matched supporting and opposing petitions in each parliament. Overall there were 152 matched petitions supporting and 85 matched petitions opposing. More details on the matching are provided in the appendix.

The party affiliation of MPs near river navigation bills plays a key role in the analysis.

Political historians have identified the majority party in every parliament from 1690 to 1741 (see table 1), but there is no available source for the party affiliation of every MP. Elsewhere, I have identified whether each MP was a Whig when the Whigs were the majority party in the

Commons and whether each MP was a Tory when the Tories were the majority party for all parliaments from 1690 to 1741 (Bogart 2013). Thus a dummy variable identifies whether each

MP is affiliated with the majority party or not in every legislative session. The political classification draws on division lists which identify party affiliation directly or voting on major pieces of legislation associated with the leaders of the two parties.

The party-MP data are used to measure the number of majority party MPs across constituencies (counties and municipal boroughs) represented in the House of Commons for every parliament. As an example Figure 4 maps party classifications for the 1708 parliament when the Whigs were the majority. Municipal Boroughs are cities indicated with symbols.

Counties are outlined with white, light grey, or dark grey backgrounds. The darkest boroughs or counties are where most MPs were with the Whig majority. Summarizing this information across all parliaments, Bogart (2013) provides an index of Whig strength and Tory strength for all 269 constituencies in England and Wales from 1690 to 1740. The top 51 percent of constituencies in terms of strength for each party are labeled as strongholds.

Two other constituency-level variables are an indicator for whether the constituency had a contested election in the most recent parliamentary election and the number of incumbents. A contest involved two or more candidates for the same seat in the Commons. It provides an indicator of local political competition (Cruickshanks, Handley, and Hayton 2002, Sedgwick

1970). Incumbents are defined as MPs that served in the same constituency in the previous parliament as documented in the History of Parliament.

The final step is the creation of spatial variables for cities based on nearby constituencies.

The key variables are the number of upstream and downstream majority party MPs within 10,

20, or 25 miles of a city where a river navigation project could be initiated. As an example, the black-filled cities in figure 3 are the municipal boroughs within the 25 mile buffer around

Northampton. The central points of Northamptonshire, Buckinghamshire, and Bedfordshire counties are within the 25 mile buffer and their MPs party characteristics are also included in the upstream or downstream areas.

Summary statistics for the variables in the ‘river bill data’ are shown in the table 2. Each cell pertains to one of 782 cities in the Blome list matched to one of the 67 river navigation bills.

‘Supporting Petition’ and ‘Opposing Petition’ identify whether at least one group in a city supported the navigation bill or opposed it. The second panel shows dummy variables for city characteristics drawn from Blome. Most are self-explanatory except for ‘Local Government

Officials,’ which indicates that a city was governed by a mayor, city council, bailiff, or some other official. In 75 percent of the cities, Blome omits any description of local officials suggesting a rudimentary local government. The third panel shows spatial variables for each city- bill pair. ‘Distance to Project’ is the distance to the city that is farthest away from the coast or navigation head and intersects with the navigation project. ‘Downstream’ identifies whether the city is downstream from the project. The last three measure political characteristics of the city.

For example, the variable, ‘Majority party MPs, 20 miles’ counts MPs classified with the majority party within 20 miles.

Summary statistics for the variables in the ‘river navigation adoption data’ are shown in table

3. Here each cell pertains to one of 419 potential adopting cities in Blome matched to each of the

14 parliaments spanning 1690 to 1740 and totaling 5813 city-parliament observations. The main outcome variable is an indicator for whether the city had a navigation act in that parliament. Note that if the adopting city had a river act in a previous parliament then they are dropped from all subsequent parliaments because each city has only one candidate river project.12 There are several parliament-time varying variables which are fixed across cities. The key variable here is

12 Note also there are three cities that Blome records as having a harbor or having river navigation in 1670, but are included in the potential adopter list because by 1690 because they report in the Journals of the Commons that their navigation had deteriorated due to maintenance or changes in environmental conditions.

21 the indicator for Whig majorities. The remaining variables are meant to control for macroeconomic conditions and political events like war (see Bogart 2011).

The city level variables include the characteristics from Blome and the two market potential variables. Note that the variable, ‘Elevation change,’ varies across time for some cities as the river navigation head moved upstream with the completion of earlier projects. The same applies to the variable, ‘Distance to the navigation head,’ which also evolved. The two variables thus capture the effects of network changes. The last set of variables measure economic and political characteristics among all cities and political constituencies in the upstream and downstream areas near the city. To give one example, ‘Manufacturing, Upstream & within 25 miles’ counts the number of cities with manufacturing activity that are upstream and within 25 miles. In principle, any distance could have been chosen for these variables. The next section justifies the use of 25 miles as the benchmark. Later the robustness is checked using other distances like 10 miles upstream and downstream.

VI. Results

Earlier it was argued that opposition groups were more likely to be downstream from a navigation project and supporters were more likely to be upstream. In this section this hypothesis is tested. The results also identify the distances at which support and opposition were most likely to occur for projects and whether the characteristics of cities matter.

The location of opponents and supporters is studied using the river bill data summarized in table 2. A difference in means test shows that a city is 56 percent more likely to have a group oppose a river bill if it is downstream from the project and it is 168 percent more likely to have a group support a river bill if it is upstream from the project. The differences are both statistically

22 significant. The data also show that supporters and opponents were relatively close to the project.

The average distance between a city and any given project is 125 miles. By comparison the average distance of a city with an opposing group and the project is 19.7 miles and the average distance of a city with a supporting group and the project is 22.5 miles.

The same patterns emerge in a regression analysis. Column 1 in table 4 reports odds ratios from a logit regression of city opposition on the dummy variable for downstream and distance to the project. Downstream cities have an odds ratio significantly above 1, implying they have a higher probability of opposition. Column 3 reports a similar logit regression for city support.

Here downstream cities have an odds ratio significantly below 1, implying they have a lower probability of support. Columns 2 and 4 report results from a model that includes indicators for several distance-bins including zero to five miles, five to ten miles, up to forty-five to fifty miles.

The omitted group includes cities that are greater than 50 miles distant. The estimates show decreasing odds of having a supporting or opposing group as distance from the project increases.

The odds of support are much higher if a city is less than 5 miles. Opposition is also high if a city is less than 5 miles but it remains sizeable up to 25 miles. Overall it appears that being within a

25 mile radius and being downstream is a good predictor of opposition. Being upstream is a good predictor of support as is being very close to the project.

The characteristics of cities also influenced the probability of opposition and support. Table 5 shows the odds ratios for different city-level variables. The models include downstream and distance bins as in the previous table with similar results but the coefficients are not reported to save space. Several characteristics are associated with support and opposition. For example, cities on major highways or that are on navigable rivers are more likely to oppose and support

23 bills. The same is true of cities with local government officials and greater local market potential.

Whig majorities are also associated with higher odds of opposition and support.

Some characteristics are more associated with support or opposition. Manufacturing cities are more likely to have groups supporting river navigation. Presumably they gained more than other types of cities from navigation improvements. Also manufacturing cities might have thought that their influence was greater in the Commons so they were more willing to organize and lobby the

Commons. There is another result that cities with more majority party MPs within 20 miles had higher odds of opposition. The interpretation given by the earlier model is that opponents of river navigation projects expected to influence legislative decisions more when their area was well connected to the majority party. For supporters the pattern is less clear. The odds of them supporting is only marginally higher with more majority party MPs within 20 miles.

The Diffusion of River Navigation Acts

The equation for the probability that a city would get a river navigation act draws on the likely locations for the supporters and opposition groups. The baseline equation is a logit model:

where the variable if city i has a river act in its jurisdiction in parliament t and has not had a river act in any previous parliament, is a vector of characteristics for city , is a vector for the characteristics of all cities and political constituencies upstream and within 25 miles, is the same for all cities and constituencies downstream and within 25 miles, and includes parliament-varying variables. A city random effect is also included to address unobservable characteristics.

The number of upstream and downstream majority party MPs are two of the main variables of interest in , and . The assumption is that they are exogenous conditional on the inclusion of city characteristics and random effects. One could argue instead that interest groups campaigned to get majority party MPs elected in order to get a river navigation act near their constituency, or perhaps even to encourage its rejection. The problem with this argument is that actors could not easily anticipate which party would have a majority in the next election and so they could not easily manipulate their political connections by electing a majority party MP.

Even if local interest groups could anticipate the majority party and elect an MP to implement their navigation plan, their ability to do so was largely dependent on a fixed set of characteristics, like having high human capital. Later I address this issue using a city fixed effects specification, which exploits variation within cities across parliaments. In the fixed effects model, majority party representation can be correlated with the error and still give unbiased estimates.

Table 6 reports the odds ratios and standard errors for all variables in the baseline random effects logit model with parliament fixed effects. The first group of odds applies to city-level characteristics. As expected, greater elevation is associated with lower odds. The coefficient suggests that elevation was a major determinant of where rivers were made navigable. A one- foot drop in elevation from the city to the nearest navigation head lowered its odds by 2.8 percent. Manufacturing cities are estimated to have 150 percent higher odds of getting river navigation acts than non-manufacturing cities with otherwise similar characteristics. The result is indicative of the greater benefits of navigation improvements to manufacturing cities. Cities with a harbor are also more likely to get navigation acts. In part this reflects the small number of cities with harbors in the potential adopter list and the obvious importance of navigation. Another finding is that cities with local government officials, like a mayor or city council, have 224

25 percent higher odds. This finding suggests a key role for local organizational capacity in getting river navigation acts. The market potential variables have opposing effects. Greater local market potential is associated with higher odds implying that more populous cities nearby that could use the navigation increased the likelihood of an act. The distant market potential based on the distance to major cities via the waterway network is associated with lower odds. My conjecture is that cities with poor potential waterway connections had more to gain because they did not get exposed to competition from larger cities. A similar explanation could apply to the variable for the distance to a navigation head, which is associated with higher odds.

The second group of variables in table 6 applies to city and political characteristics upstream and within 25 miles. The results imply that adding one more manufacturing city upstream increases the odds of a river navigation act by 19 percent. As manufacturing cities were supporters of navigation this result is not surprising. Having one more upstream city on a major highway increases the odds by 13 percent. This suggests that having a greater road capacity to bring goods to the new upstream navigation head increased support for the project. Similarly the higher odds on greater market potential upstream and the lower odds on having more cities with navigable rivers upstream suggests that demand to bring goods to a new upstream navigation head affected support. In terms of political connections, the results show that adding one more upstream majority party MP increased the odds by 13 percent, but the effect is not statistically significant. The imprecision is partly due to the differences between Whig and Tory majorities as will be shown shortly.

The third group of variables applies to city and political characteristics downstream and within 25 miles. One notable finding is that adding one more downstream majority party MP decreased the odds by 24 percent. This statistically significant result can be interpreted as a

26 core-supporter effect, in which the majority party targeted rejections to opposition groups where they were strong. It is also consistent with opponents being more aggressive in fighting bills when the majority party was strong in their area. This last interpretation is bolstered by the earlier finding that downstream cities with more majority party MPs were more likely to formally oppose bills.

In other downstream results, more contested elections are associated with lower odds, but the coefficient is not precisely estimated. This finding is notable as it shows there is little evidence for swing-constituency effects. The results also show that adding one more manufacturing city downstream increased the odds by 22 percent. It appears that manufacturing cities were generally supporters of navigation regardless of being downstream. Perhaps they did not fear greater competition from upstream manufacturing cities when navigation was extended. The specialization of British manufacturing cities could imply trade complementarities rather than rivalries.

The relative magnitudes implied by the party strength variables helps to further illuminate the results. A one standard deviation increase in downstream majority party MPs of 2.91 lowers the odds of a river navigation act by 55 percent.13 A one standard deviation increase in upstream majority party MPs of 3.6 increases the odds by 55 percent. By comparison, a one standard deviation increase in local market potential of 9.09 increases the odds by 40 percent, while a one standard deviation increase in the elevation from the city to the navigation head lowered the odds by 98 percent. It appears that the party strength effects are sizeable and are in the mid-range of

‘fundamental’ characteristics, like market potential and elevation.

13 The increased odd are equal to exp(2.91*-.2709), where -.2709 is the coefficient from the random effects model.

Majority Party Effects: Whigs and Tories

One of the main findings thus far is that more downstream majority party MPs near a city significantly lowered its probability of having a river act, whereas more upstream majority party

MPs had a less conclusive effect. These results are based on the assumption that greater party strength upstream and downstream had the same effect when the Whigs and Tories were in the majority. The party strength effect need not be the same however. Possibly the two parties responded differently to their core supporters. The Whigs are thought to have been a more cohesive party than the Tories (Holmes 1987). Cohesion could matter if political parties target policies in order to mobilize voters as suggested by Cox (2009). Presumably with less cohesion a party would rely more on targeting because mobilization is more problematic.

There is another argument which suggests that the Whigs were more pro-navigation in general. One view is that the Whigs were supported by financial interests who valued river navigation improvements more than landowners who were stronger supporters of the Tories. The

Whig leaders and politicians are also thought to be more pro-manufacturing (see Pincus 2009,

Pincus and Robinson 2012, Dudley 2013).

The differences between the Whigs and Tories are investigated by including an indicator variable for Whig majorities and interactions between the number of MPs, majority party MPs, and contests. The model also includes parliament-varying macroeconomic variables which might be correlated with Whig majorities. The odds ratios are shown in table 7. The model is the same as before, a random effects logit specification. The coefficients for city-level characteristics, upstream characteristics, and downstream characteristics are not reported to save space.

Parliaments with Whig majorities are associated with higher odds, but the coefficient is not

28 statistically significant. Thus there is weak evidence that the Whigs were generally pro- navigation while the Tories were generally anti-navigation. If that were the case then the indicator for Whig majorities should have an odds ratio significantly greater than one. One reason for a ‘weak’ Whig effect is that they had a majority in years when macro-economic conditions made it more profitable to undertake river navigation projects. The Whig effect increases in magnitude and is close to statistical significance when variables for harvest failures, inflation, and the number of days in a parliamentary session are dropped from the random effects logit model.14

Although there is not strong evidence that the Whigs were pro-navigation in general, there is some evidence that the bias from party strength was weaker under the Whigs. The effect of having more upstream majority party MPs when the Tories are in the majority is shown by the odds ratio for ‘Upstream Majority MPs,’ which is above one and statistically significant. The differential effect when the Whigs were in the majority is shown by the ‘Whigs*Upstream

Majority MPs,’ which is significantly below one. Summing the coefficients for the variables gives the effect of having more upstream majority party MPs when the Whigs are in the majority. The hypothesis test reported at the bottom shows that the sum is not statistically different from zero. Thus the model estimates imply that upstream party strength mattered when the Tories were in the majority, but not when the Whigs were in the majority.

The downstream party interaction effects are similar. Having more downstream majority party MPs when the Tories are in the majority is shown by the odds ratio for ‘Downstream

Majority MPs,’ which is less than one and statistically significant. The differential downstream

14 The Whig majority odds ratio becomes 5.236 and has a p-value of 0.11.The results are not shown to save space but are available upon request from the author.

29 effect when the Whigs were in the majority is above one but is not significant. The combined

Whig effect associated with the sum of the two coefficients implies an odds ratio closer to one than under Tory majorities but still statistically different from one. Thus the Whigs and Tories both appear to have targeted rejections to navigation opponents if they were strongly represented in their area, but under the Tories it was more pronounced.15

Robustness

In this section, the robustness of the earlier results is examined using alternative functional forms and different specifications. The use of random effects could bias the estimates if unobservable city characteristics are correlated with variables of interest like the number of majority party MPs. The conditional fixed effects logit model allows for correlation between unobserved factors and variables of interest. Another advantage is that it drops any city in

Blome’s potential adopter list which did not get a navigation act and therefore the ‘rare-events’ problem, in which few cities got navigation acts, is less relevant.16 The results are reported in columns 1 and 2 of table 8. The conclusions are similar to the baseline model. Parliaments with

Whig majorities are associated with higher odds in general. It is also the case that under Whig majorities more upstream majority party MPs turns out to lower the odds, rather than having a neutral effect.

The results for a fixed effects linear probability model are reported in columns 3 and 4 of table 8. The conclusions are similar and more strongly favor a difference between Whig and

15 There is one additional pattern which is notable. Under Whig majorities having more contested elections in downstream areas is associated with lower odds. If contested elections are taken as a measure of political competition, then one interpretation is that the Whigs were more likely to target rejections in areas where opponents of river navigation might switch parties. 16 One drawback is that convergence cannot be achieved with the year ending parliament variable or parliament-time fixed effects so these were dropped (see Allison and Christakis 2006 for a discussion). Also all parliament-invariant variables are dropped as they are subsumed in the city-fixed effect.

Tory majorities. Under Tory majorities there was a bias favoring navigation acts when upstream areas had more majority party MPs. Under the Whigs the bias was less and not statistically different from zero (see the hypothesis test at the bottom). Moreover, only under a Tory majority was there a significant bias against acts when downstream areas had more majority party MPs.

The baseline model assumes a spatial structure in which opposition groups and supporters are within 25 miles. The distance of 25 miles was chosen because the probability of opposition dropped significantly for cities beyond 25 miles from a project. That being said, one may wonder whether more narrow spatial structures yield different conclusions. Table 9 shows results when upstream and downstream political variables at a radius of 10 miles are added. The estimates show that having more upstream majority party MPs within 10 miles does not significantly increase the odds of getting a river act, although it is close to being significant when the Whigs are in the majority. Earlier it was found that the probability of support was significantly higher within 10 miles of the project, but the probability of opposition was also significantly higher at this distance. My conjecture is that the effects of supporter and opposition political connections within 10 miles cancelled one another leading to a neutral effect. Note also in table 9 that the results for political variables at 25 miles are the same in sign and significance. The main takeaway is that political characteristics of constituencies within 25 miles contain the key information on the influence of opposition and support.

A number of other specifications were run but are not reported to save space.17 The probit model provides an alternative functional form assumption to the logit. When a random effects probit model was run the results were nearly identical to the logit. The baseline model allows for differential effects from increasing MPs and increasing majority party MPs. An alternative

17 The results are available from the author upon request.

31 specification examines the ‘relative density’ of majority party MPs using the fraction of majority party MPs in total MPs. The conclusions are the same if the relative density of MPs is used instead of the absolute number. A variable was created for the size of the majority party in each parliament and it was interacted with the upstream and downstream majority party MPs. The interaction variables are not statistically significant and their addition does not change the main results. The variables for upstream and downstream majority party MPs are designed to capture the political connections of MPs in the current parliament. An alternative measure is the number of MPs from Whig and Tory ‘strongholds,’ defined by the top 51 percent of constituencies from

1690 to 1740 in terms of strength for each party. The addition of MPs from strongholds has no significant effect. By contrast more upstream majority party MPs when the Tories are in the majority still increases the odds significantly. Lastly, incumbency of MPs is another measure of political connections. To consider their role the number of upstream and downstream incumbents within 25 miles were added to the logit models. Incumbents have no significant effect in any of the specifications.

Why Did Political Connections Differ under the Whigs and Tories?

It has been shown that upstream party strength mattered when the Tories were in the majority, but less when the Whigs were in the majority. The downstream party effects were more uniform, but still stronger under the Tories. Why was this so? One explanation is related to differences in the electoral consequences of river bills between the two parties. Suppose that

Tory voters who supported river navigation were more inclined to reward a Tory majority at the next election when they passed a river bill and punish a Tory majority if they rejected a river bill.

Tory voters who opposed river navigation might have been similarly inclined to reward or punish a Tory majority if they did not act upon their policy preferences.

I investigate this mechanism by creating two new variables ‘Upstream majority party electoral success’ and ‘Downstream majority party electoral success.’ The former measures the number of upstream MPs within 25 miles that remained with the majority party in the next parliament if the majority party was unchanged, or the number of upstream MPs within 25 miles that were lost by the majority party in the next parliament if the majority party changed.18 With these variables, I test how the majority party did in the next parliament depending on river bill outcomes in the current parliament. To this end, a dummy variable, , is constructed equal to one if there was a failed river bill in city i in parliament t and equal to zero if the river bill succeeded and became an act. The sample is restricted to cities where there was a river bill introduced as otherwise a failed river bill would be irrelevant.

The first columns in table 10 show that upstream majority party electoral success was lower if the river bill failed, although the coefficient is not significant. A term for an interaction with

Whig majorities suggests the effect of a failed river bill on upstream majority party electoral success was more neutral under the Whigs. The results in the third and fourth columns show that downstream majority party electoral success was significantly higher if the river bill failed.

Again the interaction effect with Whig majorities suggests the increase was more neutral.

18 Upstream majority party electoral success in the next election is equal to:

where is the number of upstream majority party MPs within 25 miles of city i in parliament t and is the indicator for parliaments when the majority party just changed in the Commons. Notice that upstream majority party success is positive if increases from parliament t to t+1 when there is no change in the majority party in the Commons. When there is a change in the majority party, upstream majority party success is positive if decreases from parliament t to t+1. 33

The coefficients are used to predict gains/losses for each majority party when river bills succeed or fail. The results are shown at the bottom along with a 95% prediction interval. When the Whigs were the majority party their downstream success in the next election was fairly similar if a river bill failed. On the other hand, when the Tories were the majority party their downstream success in the next election was much better if the river bill failed. In downstream areas the predicted swing in Tory party MPs when a bill was approved or rejected under its majority was 1.65. For the Whigs the same predicted swing was just 0.45. There is a similar difference in the predicted swing for Tory majorities in upstream areas.

VII. Implications for Britain’s Navigation Development

In this last section, the estimates are used to quantitatively examine how Britain’s navigation development would have been different had the identity of the majority party been different. The magnitude of the party bias is quantified by a counter-factual of ‘Whig Dominance.’ In the counter-factual it is assumed that the Whigs always had the majority by controlling their stronghold constituencies. Outside the strongholds the Whigs are assumed to have no MPs. It depicts one plausible scenario of Whig-party rule in England and Wales. The coefficients from the baseline random effects logit model are used to predict the probability of a river navigation act in each city and parliament assuming Whig party dominance. A calculation is then made for the average predicted probability of getting a river act at any point between 1690 and 1740.19 A related statistic of interest is the correlation between the predicted probability of a city getting an act between 1690 and 1740 in the counter-factual and in the actual data.

19 I incorporate the probability that there is no river act in a city previously. In other words, if there is a high probability of an act in parliament t then the estimated probability of an act in the next session should be lower all else equal.

The results are shown in columns 1 and 2 of table 11. The average predicted probability of a city getting a river act is 31 percent higher under Whig Dominance than in the actual data (0.131 vs. 0.1194). The estimates also show that the correlation between the predicted probability under the counter-factual of Whig dominance and the actual data is fairly high (ρ =0.941). It implies little significant long-term differences across cities if the Whigs were dominant by controlling their strongholds compared to what happened which was a shuffling of parties.

The last counter-factual considers Tory dominance defined in the same way where the Tories always have the majority and are assumed to have Tory MPs in their strongholds and none elsewhere. The results in table 11 show that the average predicted probability of getting a river act at any point between 1690 and 1740 is 58 percent lower (0.0546 vs. 0.1194). Also the correlation between the counter-factual and the actual data is lower. Thus a plausible scenario of

Tory dominance could have led to fewer navigation projects and a different distribution of projects across space. To illustrate this last point, I identified the cities with the largest decrease in the predicted probability of an act under Tory dominance compared to the actual data. A number of economically important cities like Bristol, Manchester, Leeds, and Chester which got river navigation acts, would have been 40 to 80 percent less likely to get one under Tory dominance.

VIII. Conclusion

There were remarkable changes in Britain’s political system after the Glorious Revolution. One of the most important was the emergence of a competitive two party system. The Whigs and

Tories traded places as the majority party in the House of Commons seven times between 1690 and 1741. At the same time Britain embarked on many new policies, including the establishment

35 of river navigation companies which extended the waterway network. In this paper, I study whether the identity of the majority party and geographic distribution of party strength influenced the diffusion of river navigation acts. The empirical analysis shows the strength of majority party representation in upstream versus downstream areas differentially affected the probability of a river act, with the effects of party strength generally being stronger under Tory majorities than Whig majorities. A contest model provides an interpretation of these results. It proposes that opposition groups will fight harder against river navigation bills and Parliament will be less likely to pass bills when the majority party is strongly represented in opposition areas. The model also allows for differences between the two major parties in how they react to the lobbying efforts of opponents and supporters. The results confirm that party strength considerations mattered more under Tory majorities and suggest it was linked to electoral politics. Lastly it was shown that the identity of the majority party likely mattered for Britain’s navigation development. A plausible scenario of Tory dominance is predicted to have fewer navigation projects and a different distribution of projects across space.

The findings have several implications. First, the evidence supports the view that governance differed under Whig and Tory majorities as has been argued by Stasavage (2003,

2005), Pincus (2009), and Pincus and Robinson (2011). Second, the evidence suggests there were political barriers to entry in Britain’s transportation market, especially under the Tories. If the results are mapped into the framework of North, Wallis, and Weingast (2009), they imply that Britain was still transitioning to open access even after the Glorious Revolution. Third, there is evidence that opposition to transportation improvements plays a significant role in determining where and when they get implemented. The British case suggests opponents lobby against transport improvements much like supporters, and the vigor with which they do so depends on

36 their economic and political characteristics. In closing readers should note the broader applications. The theoretical model outlined above could be extended to any adversarial context where there is a bias to some group because of their political characteristics. Its application to other settings can provide new insights on the political economy of development.

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Table 1: Bills for new Navigation Projects initiated in the House of Commons and the Majority Party, 1690-1719

(1) (2) (3) (4) (5)

Number Number Number of Matched Number of Matched Majority Parliament of Bills of Acts Supporting Petitions Opposing petitions Party 2 0 0 0 Tory 1690-1695 1695-1698 8 2 33 29 Whig 1698-1700 10 5 27 16 Whig Jan. 1701 1 0 1 0 Tory Nov. 1701 2 1 2 0 Whig 1702-1705 5 1 5 5 Tory 1705-1708 2 1 2 0 Tory 1708-1710 1 0 14 3 Whig 1710-1713 5 1 5 4 Tory 1713-1715 1 1 3 0 Tory 1715-1722 12 9 35 21 Whig 1722-1727 4 4 10 2 Whig 1727-1734 5 3 5 1 Whig 1734-1741 9 3 10 4 Whig

All 67 31 152 85 Sources: For bills, acts, and petitions see Journals of the House of Commons various years. For the Majority Party see Cruickshanks, Handley, and Hayton (2002) and Sedgwick's (1970).

Table 2: Summary Statistics, River Bill Data

Variable obs. mean st. dev. min max

Supporting petition 52394 0.0029 0.0543 0 1 Opposing petition 52394 0.0016 0.0402 0 1 Year Parliament Ended 52394 1715.23 15.253 1695 1741

Manufacturing 52394 0.171 0.376 0 1 Mining 52394 0.039 0.195 0 1 Harbor 52394 0.0677 0.251 0 1 Major Highway 52394 0.631 0.482 0 1 Coast or Navigable River 52394 0.161 0.367 0 1 Free School 52394 0.067 0.251 0 1 Alms House 52394 0.02 0.141 0 1 Local Government Officials 52394 0.241 0.428 0 1

Market Potential Local (000s) 52394 18.796 31.863 4.656 779.56 Distance to Project 52394 125.89 64.15 0 374.14 Downstream 52394 0.429 0.495 0 1 MPs, 20 miles 52394 11.052 6.628 0 34 Majority MPs, 20 miles 52394 5.186 3.908 0 25.388 Contests, 20 Miles 52394 2.752 2.153 0 13 Sources: see text.

Table 3: Summary Statistics, River Navigation Adoption data

Outcomes obs. mean st. dev. min max River Bill 5813 0.0184 0.134 0 1 River Act 5813 0.0087 0.0933 0 1

Parliament-Varying obs. mean st. dev. min max Year Parliament Ended 5813 1711.76 13.402 1695 1741 Whig 5813 0.565 0.495 1 1 Interest Rate 5813 5.19 0.215 4.605 5.429 Foreign War 5813 0.447 0.488 0 1 Bad Harvest 5813 0.332 0.385 0 1 Inflation Rate 5813 -0.616 3.515 -7.015 4.389 Coastal Trade Growth 5813 0.918 1.071 -0.806 2.673 Yearly Days in Session 5813 123.5 14.642 101 152

City-level obs. mean st. dev. min max Manufacturing 5813 0.211 0.408 0 1 Mining 5813 0.037 0.19 0 1 Harbor 5813 0.008 0.09 0 1 Major Highway 5813 0.632 0.482 0 1 Coast or Navigable River 5813 0.002 0.049 0 1 Free School 5813 0.079 0.27 0 1 Alms House 5813 0.02 0.141 0 1 Local Government Officials 5813 0.211 0.408 0 1 Market Potential Local (000s) 5813 16.801 9.086 5.891 115.24 Market Potential distant (000s) 5813 8.436 14.747 0.227 105.078 Elevation Change (feet) 5813 146.53 132.51 0 924 Distance to navigation head (miles) 5813 28.062 19.381 0.79 133.99

Upstream & within 25 miles obs. mean st. dev. min max Manufacturing 5813 2.377 2.268 0 11 Mining 5813 0.391 0.782 0 4 Harbor 5813 0.642 1.005 0 5 Major Highway 5813 8.148 6.061 0 34 Coast or Navigable River 5813 1.809 3.229 0 20 Free School 5813 1.029 1.243 0 6 Alms House 5813 0.327 0.687 0 4 Local Government Officials 5813 2.907 1.626 0 8 Market Potential Local (00,000s) 5813 2.82 3.844 0.0852 23.095

MPs 5813 9.701 6.058 0 30 Majority MPs 5813 4.774 3.632 0 21.77 Contests 5813 2.218 1.885 0 11 Incumbent MPs 5813 4.692 3.65 0 21 Whig Stronghold MPs 5813 4.749 3.82 0 22 Tory Stronghold MPs 5813 4.692 3.65 0 20

Downstream & within 25 miles obs. mean st. dev. min max Manufacturing 5813 2.341 2.17 0 11 Mining 5813 0.386 0.697 0 3 Harbor 5813 0.408 0.751 0 5 Major Highway 5813 8.308 5.856 0 31 Coast or Navigable River 5813 1.583 2.644 0 19 Free School 5813 0.871 1.154 0 6 Alms House 5813 0.308 0.623 0 4 Local Government Officials 5813 2.507 1.518 0 8 Market Potential Local (00,000s) 5813 2.604 3.126 0.0679 22.908 MPs 5813 7.29 4.916 0 26 Majority MPs 5813 3.569 2.94 0 17.724 Contests 5813 1.782 1.625 0 9 Incumbent MPs 5813 3.549 2.98 0 18 Whig Stronghold MPs 5813 3.663 3.07 0 14 Tory Stronghold MPs 5813 3.732 3.32 0 16 Sources: see text.

Table 4: Logit Estimates for Location of Opposition and Support Opposition Support

(1) (2) (3) (4) Odds Ratio Odds Ratio Odds Ratio Odds Ratio Variables Rob. Stan. error Rob. Stan. error Rob. Stan. error Rob. Stand. error

Downstream 1.591 1.619 0.334 0.397 0.348** 0.377** 0.065*** 0.08***

Distance to Project 0.914 0. 925 0.007*** 0.006***

0 ≤ Distance <5 mi. 1698 2211 1039*** 797***

5 mi. ≤ Distance <10 mi. 483 265 300*** 110***

10 mi. ≤ Distance <15 mi. 513 225 286*** 82***

15 mi. ≤ Distance <20 mi. 474 138 260*** 53***

20 mi. ≤ Distance <25 mi. 196 87 116*** 35***

25 mi. ≤ Distance <30 mi. 16 77 18** 31***

30 mi. ≤ Distance <35 mi. 80 49 51*** 21***

35 mi. ≤ Distance <40 mi. 38 60 29*** 24***

40 mi. ≤ Distance <45 mi. 44 16 31*** 9.7***

45 mi. ≤ Distance <50 mi. 21 14

18*** 8***

N 52394 52394 52394 52394 Pseudo R- Square 0.348 0.346 0.354 0.376 Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 5: Logit Estimates for Characteristics of Opposition and Support Opposition Support

(1) (2) Odds Ratio Odds Ratio Variables Rob. Stand. error Rob. Stand. error

Manufacturing 1.032 1. 965 0.307 0.415***

Mining 1.008 1.548 0.552 0.702

Harbo r 0.614 0.36 0.279 0.165**

Major Highway 1.608 2.307 0.431* 0.556***

Coast or Navigable River 1.783 1.793 0.445** 0.388***

Free School 0.611 0.971 0.275 0.326

Alms House 1.309 0.461 0.798 0.374

Local Government Officials 2.937 2.522 0.72*** 0.504***

Market Potential Local (000s) 1.004 1.005

0.0017*** 0.0017***

Year 0.965 0.978 0.0071*** 0.005***

Whig 3.841 4.222 1.439*** 1.208***

MPs, 20 miles 0.929 1.002 0.038* 0.032

Majority MPs, 20 miles 1.169 1.066 0.069*** 0.047

Contests, 20 Miles 0.879 0.946 0.075 0.068

Downstream and Distance Controls Yes Yes N 52394 52394 Pseudo R-Square 0.415 0.446 Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 6: The Diffusion of River Navigation Acts: Baseline Random Effects Logit Model

City Level Variables Odds Ratio Standard error 2.538 1.03** Manufacturing

2.194 2.08 Mining

10.885 12.07** Harbor

0.639 0.482 Major Highway

1.181 0.701 Free School

0.666 0.732 Alms House

3.244 1.284*** Local Government Officials

Market Potential Local 1.038 0.015** (000s)

Market Potential distant 0.957 0.014*** (000s)

0.972 0.005*** Elevation Change (feet)

Distance to navigation head 1.09 0.019*** (miles) Upstream & within 25 mi. Downst ream & within 25 mi.

Standard Standard Odds Ratio Odds Ratio error error 1.187 0.118* 1.215 0.14* Manufacturing 0.726 0.215 1.516 0.438 Mining 0.869 0.252 0.821 0.29 Harbor 1.128 0.065** 0.927 0.073 Major Highway 0.72 0.093** 1.346 0.147*** Coast or Navigable River

0.722 0.15 0.965 0.223 Free School 1.546 0.615 1.006 0.494 Alms House 0.819 0.161 0.729 0.146 Local Government Officials

Market Potential Local 1.276 0.137** 1.012 0.137 (00,000s)

MPs 0.887 0.017 1.281 0.105*** 1.128 0.116 0.762 0.092** Majority MPs 0.949 0.114 0.769 0.132 Contests

City Random effects Yes Parliament Fixed Effects Yes N 5813 LR chi2() 112.11 Prob > chi2 0 Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 7: The Diffusion of River Navigation Acts: Whig and Tory Effects

Odds Standard Parliament Variables Ratio error

Whig 3.554 4.37

4.578 13.03 Interest Rate

2.878 5.28 Foreign War

0.056 0.067** Bad Harvest

1.306 0.159** Inflation Rate

1.907 1.09 Coastal Trade Growth

1.048 0.017*** Yearly Days in Session

Year Parliament Ended 1.058 0.034*

Upstream & Downstream

Upstream MPs 0.687 0.126**

1.694 0.368** Upstream Majority MPs

1.293 0.421 Upstream Contests

Whig * Upstream MPs 1.42 2.83 *

0.535 0.135** Whig * Upstream Majority MPs

0.657 0.241 Whig * Upstream Contests

Downstream MPs 1.231 0.177

0.527 0.131*** Downstream Majority MPs

1.52 0.571 Downstream Contests

Whig * Downstream MPs 1.113 0.176

1.439 0.397 Whig * Downstream Majority MPs

0.457 0.19* Whig * Downstream Contests

City Level, Upstream, Downstream controls Yes City Random effects Yes Parliament Fixed Effects No N 5813 Wald 117.75 Prob > chi2 0

Null Hypotheses Wald Prob > chi2

Upstream Majority MPs + Whig * Upstream Majority MPs=0 0.61 0.43 Downstream Majority MPs + Whig * Downstream Majority MPs=0 3.94 0.047 Downstream Contests +Whig * Downstream Contest=0 3.85 0.049

Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 8: The Diffusion of River Navigation Acts: Fixed Effects Models Conditional Fixed Effects Linear probability Logit Model Fixed Effects

Variables (1) (2) (3) (4) Odds Ratio Stand. error Coeff. Stand. error

Whig 10.666 14.2 *

Upstream Majority MPs 2.274 0.561*** 0.002 0.001**

0.816 0.287 -0.001 0.001 Upstream Contests

Whig * Upstream MPs 1.74 0.409 ** 0.0005 0.001

0.209 0.071*** -0.003 0.001** Whig * Upstream Majority MPs

1.868 0.786 0.001 0.002 Whig * Upstream Contests

Downstream Majority MPs 0.61 0.141 ** - 0.0023 0.00 11**

1.373 0.455 -0.0004 0.002 Downstream Contests

Whig * Downstream MPs 1.005 0.167 0.0004 0.001

1.16 0.349 0.0014 0.002 Whig * Downstream Majority MPs

0.705 0.287 -0.0034 0.002 Whig * Downstream Contests

City Level, Upstream, Downstream controls No No City Fixed effects Yes Yes Parliament Fixed Effects No Yes N 437 5813 LR chi2/F stat 59.64 5.5 Prob > chi2 0 0

Null Hypotheses Wald P -value Wald P -value

Upstream Majority MPs + Whig*Upstream Majority MPs=0 15.04 0.001 0.77 0.38 Downstream Majority MPs + Whig* Downstream Majority MPs=0 3.46 0.062 0.55 0.45

Table 9: The Diffusion of River Navigation Acts: Political Variables at 10 and 25 mile radius Conditional Fixed Effects Logit

(1) (2) (3) (4) Variables Odds Ratio Stand. error Odds Ratio Stand. error

Whig 3.821 1.41*** 42.823 50.08***

Upstream Majority MPs 10 miles 1.452 0.399 1.325 0.523

0.846 0.097 1.153 0.174 Upstream Majority MPs 25 miles

Whig * Upstream Majority MPs 10 miles 1. 243 0.532

0.55 0.101*** Whig * Upstream Majority MPs 25 miles

Downstream Majority MPs 10 1.024 0.357 1.414 0.773 miles

Downstream Majority MPs 25 0.713 0.099** 0.644 0.144** miles

Whig * Downstream Majority MPs 10 miles 0.761 0.448

1.076 0.254 Whig * Downstream Majority MPs 25 miles

City Level, Upstream, Downstream controls No No City Fixed effects Yes Yes Parliament Fixed Effects No No N 437 437 LR chi2/F stat 29.17 44.44 Prob > chi2 0 0

Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 10: Majority Party Electoral Success and Failed Bills

Upstream Downstream

(1) (2) (3) (4) Variables Robust Stand. Robust Stand. Coefficient error Coefficient error

River bill Fail - 1.116 0.813 1.635 0.711**

River bill Fail*Whig 1.339 0.966 - 1.185 0.868

Whig - 0.307 0.593 0.88 0.514*

Constant 0.35 0.638 - 0.978 0.614

N 92 92 R-Square 0.04 0.06

Downstream Upstream

-0.027 0.089 Mean Majority Party Gain/Loss Standard Deviation MP Gain/Loss 2.02 1.96

Whigs are majority party

Predicted MP gain/loss if river bill succeeds 0.043 - 0.097

95% Prediction Interval (-4.02,4.10) (-4.00,3.81)

0.266 0.351 Predicted MP gain/loss if river bill fails 95% Prediction Interval (-3.82,4.35) (-3.58,4.28)

Tories are majority party

Predicted MP gain/loss if river bill succeeds 0.35 - 0.978

95% Prediction Interval (-3.85,4.55) (-5.02,3.07)

-0.765 0.657 Predicted MP gain/loss if river bill fails 95% Prediction Interval (-4.88.3.34) (-3.30.4.61) Notes: ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels respectively.

Table 11: Counter-factual River Development

counter-factual (1) (2) (3) Whig Party Tory Party Actual Data Dominance Dominance Average Probability of Getting a River Act at any point, 1690-1740 0.1194 0.131 0.0546

Ratio to Data's Average Probability of Getting Act 1690-1740 1.3166 0.4168

Correlation with Data's Average Probability of Getting Act 0.941 0.827 1690-1740 Notes: see text for details on calculations.

Figure 1: Acts and Navigable Rivers, 1690-1715

Sources: see text.

Figure 2: Diffusion of Access to Waterway Transport 0.7

0.6

0.5

0.4

0.3 Share of Major Cities Major of Share 0.2

0.1

Share of Major Cities on Navigable Rivers or Coast Share of Major Cities on Navigable Rivers or Coast or with Authorization to Improve Navigation

Sources: see text.

Figure 3: A close-up of Northampton and the River Nene

Sources: see text.

Figure 4: Geography of Whig Majority Party Representation in 1708

Notes: A constituency is considering to be well represented by the majority party if the fraction of MPs in the majority is above 0.8. A constituency is not well represented if the fraction of MPs in the majority party is below 0.2. The consistency has mixed representation if the fraction of MPs in the majority party is in-between 0.2 and 0.8. Sources: see text.

For Online Publication

Theoretical Appendix

The following stage game gives the probability of a promoter introducing a bill, the probability of opposition, and the probability of a bill being approved. The stages are as follows.

In stage 1 the promoter decides whether to introduce a bill. In stage 2 the opposition decides whether to formally oppose the bill. If there is no opposition then the bill is approved, otherwise in stage 3 each side exerts efforts in producing evidence. In stage 4, the contest is concluded and the regulator makes an approval decision leading to final payoffs.

Working backwards, the equilibrium probability that a proposal is approved in stage 4 is

given by where and are equilibrium evidence provided by

the promoter and opposition in stage 3. Turning to stage 3, I adopt the simplifying assumption that the promoter’s cost of producing units of evidence is . In other words the marginal cost of producing one extra unit of evidence is always one, irrespective of the project’s characteristics. 20 With this assumption, the objective function for the promoters is

, where the first term is the expected gain or the probability the bill is approved multiplied by the promoters potential profits . The second term is the total cost of producing evidence. Note that the promoter earns only if the bill is approved and otherwise their payoff is normalized to 0. The objective function for the opposition is similar,

, where the first term is their expected loss if the project is approved and the second is the total cost of producing evidence. Note that the bill fails with probability in which case the opposition’s payoff is normalized to 0.

20 I could also model differences in the costs of effort between entrant and incumbent. One approach assumes the costs differ according to political connections. This assumption gives qualitatively the same results as changes in and so I do not model it here. 59

The decisions to produce evidence are made strategically and the Nash equilibrium is derived from best response functions. For expositional ease the evidence choices are described as effort

levels. The equilibrium efforts and satisfy the following relationship:

. Notice that the ratio of the equilibrium efforts levels is equal to the ratio of losses [ ] to benefits .Thus more effort is expended by the opposition relative to the promoter when there

is greater expected losses for the opposition relative to the gains to the promoter. After simplification, one can show that the equilibrium bill success function has the form

. Notice that increases in and decreases in . Notice also that

decreases in . The main results of the model depend on these comparative statics.

In stage 2, the opposition decides whether to petition against the bill, giving them a chance to block the project. If so, the opposition must incur a fixed cost . Anticipating their efforts and

those of the promoter, the opposition’s expected payoff from formal opposition is

, where and are defined above. If there is no formal opposition the bill will be approved by the regulator with probability one and their payoff is . Thus the opposition will formally

oppose if , which simplifies to . If the fixed cost is assumed to be a random variable with a C.D.F. then the probability of opposition is

The final step is stage 1 where the promoter decides whether to introduce a bill. The promoter must pay a fixed cost and in return they get the chance of approval. The promoter is assumed to anticipate opposition efforts and their own efforts at a later stage. If the promoter anticipates no opposition effort they will get approved for sure and will introduce if the gains

60 exceed the fixed cost . The more interesting case is where the promoter anticipates some

opposition. The promoter’s expected payoff if they introduce a bill simplifies to .

Thus a rational, forward looking promoter will introduce a bill only if . Again allowing the fixed cost to be a random variable with C.D.F. yields the probability of a bill being introduced .

The stage game gives a probability that a promoter introduces a bill , the probability a bill is formally opposed if it is introduced, and the probability that a bill is approved if it is introduced . Multiplying the first and third terms gives the probability of a navigation act ( ) . All the comparative statics follow from the these functions and

Data Appendix

There is no universally accepted database for studying British cities in the eighteenth century, but there are a number of useful sources, like Richard Blome’s Britannia. Blome provides a list of cities in each county in England, Wales, and Scotland and describes their historical and contemporary features around the year 1670. Blome has been criticized for plagiarizing other’s work, but for the present purposes that is not relevant as Blome provides comprehensive information on cities. Blome’s description identifies whether the city actively had manufacturing or mining and of what type. A description of infrastructure is given, such as whether the city had river navigation, notable roads, or had a coastal harbor. Social services are also described including whether a free school was provided in the city and whether there was an alms house for the poor. Blome provides information on local government too such as whether the city was a

61 corporation and if it had a mayor, city council, or other types of public officials. Blome also provides a map of each county that includes cities, waterways, and coastal features. An example of Blome’s map for Suffolk county is provided in appendix Figure 1. Notice that the coastline is easily identified as are the main rivers and streams. City names can be identified by a zoom on the map.

Appendix Figure 1: Richard Blome’s Map of Suffok, 1673

Source: Blome (1673).

I use Blome in several ways. First, Blome’s list of 782 cities in English and Welsh counties is taken as the population of all cities in England and Wales. I call this the ‘Blome’ list.

Comparisons with other sources like Corfield (1982) show that all major cities with large populations are included in Blome’s list. Also an examination of the list gives no indication that it misses small cities of relevance. Second, Blome’s description of cities is used to classify their economic, infrastructural, and political characteristics. Indicator variables are created for manufacturing activity, mining activity, the existence of free schools, the existence of alms houses, whether the city had an active harbor, was on a major highway, and whether the city had river navigation. Most of these variables are dated around the year 1673 when Britannia was written. Thus they pre-date the extensions in river navigation beginning in 1690. The eighteen year gap between 1673 and 1690 might seem large, but the characteristics of cities did not evolve quickly in this period and so the characteristics in 1673 were very similar in 1690. Third,

Blome’s maps are used to identify which cities were inland and located near rivers or streams that could be made navigable.

Blome’s Britannia serves as a starting point for the analysis but it is necessary to supplement with other data. Not surprisingly Blome did not know the population of cities when he wrote in

1673. Official census-data on city populations was not available until 1801. Nevertheless the

Cambridge Population History group has estimated the pre-1801 population of all parishes in

England and Wales using census-like sources. The Cambridge Population History group has kindly provided their estimates of parish population in 1670 which I use to reconstruct the population of cities listed in Blome. The latter are matched with parishes in the Cambridge data and if necessary parishes are aggregated to form the boundaries of a city. Out of the 782 cities in the Blome list, 717 were successfully matched with the Cambridge data. The population of the

63 remaining 65 cities was estimated using a model that predicts population from Blome’s characteristics, most notably the amount of text he devotes to descriptions of each city.

The resulting city-level population data is to my knowledge the best that can be done with current information. The population distribution across cities suggests that the estimates are reasonable given the distribution (see Appendix figure 2). The distribution is skewed to the right as is often the case with modern data. Over half of the cities had a population under 1000 in

1670, while the mean population is 1584. A few large cities like London pull the average population higher.

Appendix Figure 2: The Distribution of City Population in England and Wales, 1670 120

100

Frequency 40

City Population

Blome’s maps also omit most highways. The information on which towns and cities were on major highways was augmented by the maps in Morden, The New Description and State of

England, which as published in 1701.

As discussed in the text, many of the key variables in this paper have a spatial component.

The first step in building these spatial variables is to geo-code the cities in the Blome list. I was able to successfully match all cities to the Ordinance Survey based on city name. From there, latitude and longitude coordinates are obtained. Locational data allows geographic features to be incorporated, like elevation changes and distance to the navigation head.

The market potential variables follow from the geo-coding of Blome cities. Market potential

is calculated in two ways. In the first, the market potential for city is ∑ , where

is the population of city and is the Euclidean distance between city and city . The sum is over all 782 cities in the Blome list. The distance between city and itself is taken to be

√ following the convention adopted by Keeble et al. (1982). The first market potential variable is meant to capture the size of nearby cities that could use the waterway network. It is referred to as ‘market potential local.’

In the second, market potential for city is ∑ , where is the population of major city and is the distance between city and major city if the river navigation project was completed and a shipper followed the shortest waterway transportation route. For this variable a GIS map of the waterway network in 1690 was created including inland waterways and a coastal route. Then a network analysis tool was used to calculate the distance from each city in the potential adopter list to major cities with a population above 2500 in 1700 that were already on the waterway network. The second market potential variable captures the

65 size of cities that could be more easily reached if the navigation project was completed. It is referred to as ‘market potential distant.’

The next step is to identify where river navigation bills were tried and where they were approved as acts. Here I rely on the daily records for the House of Commons have survived and are printed in the Journals of the House of Commons. The Journals identify all legislative bills introduced in the Commons including the period under study here. The resulting sample consists of 67 river navigation bills. Note that 23 of the 67 bills were for projects that were already introduced in an earlier parliament but had failed. The 67 river navigation bills are matched with the cities in Blome’s potential adopter list. It is required that the river navigation project cross a city’s boundary to be matched. In practice, matching is fairly straightforward because most references to bills in the Journals are specific in describing the cities near a project. For example, the River Avon bill discussed earlier clearly identifies the river as connecting to the city of

Bath.21 Once the bill is matched to a potential adopter city then a number of spatial variables are created, including the number of cities with various characteristics within a certain distance.

The supporters and opponents of river navigation bills are also matched with the cities in

Blome’s list using the petitions reported in the Journals. There were some petitions that could not be matched. For example, Landowners adjacent to the river under consideration were one of the most common opponents and were found in 33 percent of the bills. Landowners were not matched because they did not reside in a Blome city. Also some towns mentioned in the petition were not in Blome’s list. They were necessarily dropped in those cases.

21 Over the 67 bills there were only 2 bills that could not be matched with any town or city in the Blome list.