On the Right Track: Railroads, Mobility and InnovationDuring Two Centuries’

David Andersson∗ Thor Berger† Erik Prawitz‡

PRELIMINARY DRAFT NOT FOR CIRCULATIONAugust 20, 2018

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Abstract

We exploit the historical rollout of the Swedish railroad network to identify howreductions in communication and transportation costs affect local innovative activity.To identify causal effects, we exploit the fact that state planners aimed to connect majorcities along the cheapest routes. We find that network access led to large increases inlocal innovative activity, both along the extensive and intensive margins. After anetwork connection was established, new inventors entered into the innovation sectorfrom a diverse set of social backgrounds. Inventors also became more productive,producing higher-quality patents and in new types of industries. Meanwhile, we findthat patent transfers and collaborations increased. Lastly, we show that innovationclusters established due to the routing of the network persist until the present day.

∗Department of Management and Engineering, Linköping University and Uppsala Centre for BusinessHistory.†Department of Economic History, School of Economics and Management, Lund University.‡Research Institute of Industrial Economics.

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1 Introduction

Over the past decades, the world has become ever more interconnected. Airplanes, mobilephones, and the Internet have brought far-flung places in connection with one another. It iswidely believed that this extension of markets and the ensuing “death of distance” has ledto an accelerated pace of innovation (e.g., Cairncross, 1997; Catalini et al., 2018; Campanteand Yanagizawa-Drott, 2018). At the same time, spatial inequalities in innovative activityhave persisted and, rather than involving broader segments of the population in innovation,inventors are still primarily drawn from the top of the income distribution (Aghion et al.,2018; Chetty et al., 2017). Ultimately, whether integration serves to extend the incentive toinvent to new localities and a broader subset of the population remains an empirical question.

In this paper, we leverage the rollout of the Swedish railroad to identify the causal effectsof lowered transportation costs on local innovative activity. We digitize handwritten recordson the universe of patents granted between 1830 and 1910—in total 17,000 patents with7,500 listed inventors—that we pair with digital maps of the rollout of the 11,000 km longrailroad network.1 Using this data, we exploit the differential arrival of the railroad acrossthe 2,400 historical municipalities to identify its impact on local innovative activity.

Identifying causal links between integration and innovation is, however, empirically chal-lenging for several reasons. Most importantly, investments in major infrastructure networksare political in nature. Thus, investments may be allocated to places with a higher inno-vative potential, or to places with worse fundamentals due to “pork-barrel” spending. Anideal experiment would provide a setting where a random subset of previously isolated lo-cations dramatically improved their connectivity with other places, which would allow forclean identification of how an improved connectivity affects local innovative activity.

Our analysis exploits the fact that the major lines of the network were allocated by a singlestate planner—Colonel Ericson—with the explicit goal of connecting the capital Stockholmwith major cities in the west and south along the shortest possible routes. Consequently,these lines traversed many areas that were not explicitly targeted by state planners. Wefurther reduce concerns of selection on unobservables by using data on land cover and slopegradients combined with Dijkstra’s (1959) optimal route algorithm to identify bilateral least-cost paths between destinations selected by state planners.2 We show in a series of balance

1Throughout the paper, we simply define an “inventor” as a firm or individual holding a patent as in priorwork on innovation (e.g., Chetty et al., 2018). It is well-known that patents are not be a perfect proxy forinnovation(Griliches, 1990; Moser, 2005). Yet, patents have major advantages over other innovation proxies:they are often available over long time series, facilitating empirical analysis, and are a partial indicator formeasuring technological development (Andersen, 2001, ch. 1).

2See Faber (2014) for a similar approach to estimating the causal impact of Chinese highways on growthand urbanization.

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tests that proximity to these least-cost paths is not correlated with a rich set of proxies forlocal inventive potential and economic development in the pre-rail era, which underscoresthe plausible exogeneity of the instrument.

We leverage the variation arising from these least-cost routes in a difference-in-differencesframework to show that the rollout of the network led to sizeable increases in local innovativeactivity. After a network connection is established, we document a 17 percentage pointincrease in the probability that a municipality participates in innovation and a 36 percentincrease in patent output relative to non-connected locations. We find little evidence thatthese localized increases reflect a displacement of innovative activity from nearby areas, forexample, through the migration of firms and inventors. Instead, we find that the unfoldingnetwork spread innovation to previously isolated rural areas with no history of patenting,which contributed both to the sustained spatial diffusion and aggregate increase in inventiveactivity during the decades leading up to World War I.

A localized increase in inventive activity after a network connection is established canbe driven by two distinct margins: an entry of new inventors and/or increases in inventorproductivity. We therefore proceed to document that the rollout of the railroad network ledto a sizeable increase in the number of active inventors in a municipality. An increase in thestock of inventors was primarily driven by the entry of independent inventors with no priorhistory of patenting. A recent literature has emphasized the technical ability of individualsat the top of the skill distribution—e.g., chemists and engineers—in driving invention duringindustrialization, while assigning a non-existent role to “lower-tail” worker skills (e.g., Nelsonand Phelps, 1996; Mokyr, 2005; Mokyr and Voth, 2009).3

Consequently, we separately examine the entry of “elite” inventors—scientifically savvyengineers, trained at technical universities—and “non-elite” inventors with lower levels oftechnical expertise. We find that new inventors that entered the innovation sector after anetwork connection was established hailed from a diverse set of economic and social back-grounds based on the skill and status of their occupation and surnames. With the exceptionof farmers and farm workers, we find that the arrival of the railroad led to an inflow also ofmedium- and lower-skilled workers into the innovation sector. An elastic supply of inventorsalso from the lower rungs of the social ladder is consistent with the view of 19th-centurySweden as an “impoverished sophisticate” that provided a large pool of potential inventorsequipped with high levels of basic human capital (Sandberg, 1979).

A “democratization” of invention in areas penetrated by the railroad network contrastsrecent literature downplaying the role of the skill level of the modal worker in accounting for

3Indeed, a rich literature argues that basic human capital was of limited importance during the earlyphase of Britain’s industrialization (Mitch, 1993; Galor, 2005).

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the acceleration in invention during industrialization (Squicciarini and Voigtlander, 2016).Yet, these potentially more marginal inventors may have had limited contributions to innova-tion. We shed further light on the contribution of elite and non-elite inventors by analyzingthe evolution of inventor productivity. We find that elite inventors experienced a sustainedincrease in productivity (i.e., patent output) after a network connection was established.4

Although the effects are somewhat more imprecise, we also find that non-elite inventorsbecome more productive after they gained access to the growing network. We largely cor-roborate these results using proxies for patent quality. In particular, the establishment of anetwork connection induced elite inventors to develop more inventions in high-tech industrialsectors, while both elite and non-elite inventors produced patents of higher average valueand inventions in new fields leading to an increased scope of local innovation.

We explore two potential underlying mechanisms. First, we explore whether networkconnections reduced the frictions involved in knowledge diffusion by studying patent transfers(Jaffe et al., 1993). Arguably, lowering the cost of transferring patents increases the financialincentive invent, particularly for inventors in places where the extent of the local market(and therefore the value of a patent) is limited. We document an increase in the transferof patent rights after a municipality is connected to the network and that transfers aremost visible from rural areas. Second, we examine knowledge spillovers between inventorsthrough patent collaborations. If such collaborations require coordination and the exchangeof complex ideas, we would expect them to increase as the cost of face-to-face interactionsfall. We find that the coming of the railroad led to the establishment of cross-municipalitycollaborations between inventors, over increasingly longer distances.

We conclude by analyzing whether the innovation clusters that had been establishedaround the railroad by the early-20th century persisted over the next century. While thereexist a rich set of potential channels of persistence, we document two facts that suggestthat connectivity remains an important determinant of innovation also today. First, wedocument a striking persistence of spatial differences in inventive activity over the 20thcentury showing that patenting output in the early-21st century is substantially higher inplaces where network connections had been established a century before. Second, we examinechanges in local innovation in areas where network connections were opened and closed duringthis 100-year period: where connections disappeared we find a reduction in invention, whilewe find an increase in areas where connections were established. While these results shouldbe interpreted carefully, we take this as suggestive evidence that transportation costs still

4We find no evidence that the inventive output of firms increased consistent with the argument that theR&D of firms to a larger extent relies on knowledge produced within the boundaries of the individual firm(Agrawal, Galasso & Oettl, 2016).

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remain a key determinant of the local rate of technological progress today.Our work relates to the growing literature that examines the impact of transportation

technologies such as airplanes (Feyrer, 2009; Campante & Yanagizawa-Drott, 2017), high-ways (Baum-Snow, 2007; Duranton & Turner, 2012; Faber, 2014), railroads (Donaldson &Hornbeck, 2016; Donaldson, 2017; Yamasaki, 2017), as well as steamships (Pascali, 2017), onagricultural development, trade, or urbanization.5 In particular, Agrawal, Galasso & Oettl(2016) show that highways facilitated the diffusion of knowledge within metropolitan areasin the 1980s United States and Perlman (2016) finds that US counties located close to railtracks had more patents per capita in the 19th century. We also contribute to a recentliterature that has turned to studying the determinants of who becomes an inventor, high-lighting the significant underrepresentation of individuals from economically disadvantagedbackgrounds (Chetty et al., 2017; Aghion et al., 2017; and Depalo and Di Addario, 2015). Asubset of this literature has documented the persistent impacts of transportation networkson urban populations (Jedwab & Moradi, 2016; Berger & Enflo, 2017), while we provide thefirst evidence on the persistence of local innovation that relates to a recent literature aimingat identifying the persistence in, for example, entrepreneurship (Glaeser, Kerr & Kerr, 2015).

Due to its historical context, our paper also relates to a number of studies in economic his-tory concerning the Swedish late 19th century growth spurt (see e.g. O’Rourke &Williamson,1995; Ljungberg, 1996). Similarly to Andersson, Karadja & Prawitz (2017), we study innova-tive activity during this period, but while they study the relationship between mass migrationand innovation, we highlight a different important determinant of the increase in innovativeactivity. Studying the same time period, Tyrefors Hinnerich, Lindgren & Pettersson-Lidbom(2017) explores the relationship between the power of the landed elite and local economicdevelopment. They find that a power shift from the landed elite to industrialists leads tomore investments in railroads, a faster structural change, and higher firm productivity.

The remainder of this paper is organized as follows. Section 2 provides an overviewof the historical background. Section 3 introduces our data, while section 4 describes theempirical framework and our instrumental variables strategy. Section 5 and 6 present ourresults concerning the introduction of railroads and innovative activity during the period1830–1910. Section 7 documents the persistence of the effects up until the present day andsection 8 concludes the paper.

5In particular, our paper is related to Berger (2017) and Berger & Enflo (2017) who study the effectsof the Swedish railroad expansion during the 19th century on industrial development and urbanization,respectively. While our identification strategy share similarities with theirs, our paper focuses on a distinctdimension of economic development, namely innovation, rather than broader measures of structural changeand draws upon considerably more spatially disaggregated data.

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Figure 1:Patents and Inventors

Notes: This figure shows the number of granted patents and the number of elite, non-elite, and firm inventors.

2 Historical background

2.1 A “Technological Revolution"

At the same time as the railroad network was established, the Swedish industrial revolutiongained momentum (Jörberg, 1988), which is reflected in the founding of large industrial firmssuch as Sandvik (1862), Atlas Copco (1873) and Ericsson (1876) and Alfa Laval (1883) andASEA (1890). All had in common that they based their business on developing cutting-edgetechnology that was spread around the world under the protection of patents, from perfectingthe Bessemer method and the telephone to roller bearings, diesel engines, milk separatorsand the modern lighthouse. Indeed, the large number of ground-breaking novel technologiesmade Heckscher (1941) declare the period to be an era of “technological revolution”.

Industrial firms were often advancing the technological frontier, yet the period stretchingfrom the mid-19th century to the outbreak of World War I was “the era of independentinventors” (Hughes, 1988). Figure 1 depicts the number of inventors and patent holders, aswell as the total number of patents granted to firms and individuals residing in Sweden be-

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tween 1830 and 1910. Notably, firms received a small share of patents over the entire period.In Figure 1, we also present the number of active inventors per year where we distinguishbetween “elite” (chemists, engineers, and inventors with doctoral degrees) and “non-elite”(e.g., farmers and industrial workers) inventors over the period 1830–1910. Almost half of allinventors were highly-educated engineers, which made them by far the most important groupof inventors prior to World War I. At the same time, a significant share of inventors alsocame from humbler origins. Artisans and industrial workers both contributed to the rise ofpatented inventions. Almost 20 percent of inventors were medium- or lower-skilled workers,reporting occupations such as blacksmith, carpenter, or mechanic. Although elite inventorswere relatively more numerous in the beginning of the period, there was a sustained growthin the number of both elite and non–elite inventors from the beginning of the railroad agein the 1860s until the outbreak of World War I.

An aggregate increase in the number of inventors is also mirrored in a sharply decreas-ing spatial concentration of inventors over time. While inventors are much more spatiallyconcentrated than the general population, the concentration of inventors decreased consid-erably over time as depicted in Figure 2A. 6 Notably, these trends are the opposite of thespatial concentration of the population, which saw a secular increase over the same period.As shown in Figure 2B, these changes are driven by two opposite trends: while the ruralpopulation was drawn to urban locations in the late-19th century, the share of inventors(and patents) located in the countryside increased over the half century before World WarI.

A growing number of individuals becoming involved in innovative activity was facilitatedby important institutional changes that served to incentivize inventive activity. In particular,monopolies and rent-generating privileges from earlier eras were increasingly replaced bymore modern patent laws (Mokyr, 2009). In Sweden, this development went from fightingmonopolies to promoting industries during the 19th century as it evolved from a registrationsystem to the establishment of hard previous technical examinations similar to the Americanand German systems.7 A low cost of obtaining a patent and the reduced uncertainty of apatents’ value after it having passed rigorous examinations arguably served to extend the

6Although one concern is that changes in the index is driven by a volume effect, we have constructed analternative Herfindahl index by taking 1,000 random draws of 100 patents in each decade and calculated theindex from these draws, which yields a very similar trend as the baseline index (not reported).

7The application process was such that: “He who wants to obtain a patent, shall send to the PatentOffice a written application and attach two copys of a description of the invention along with the drawingsneeded to clarify the description, also in two copies, and when needed also models, samples or other materialneeded.../.../...The description shall be as clear and exhaustive so that an expert should, with its help, beable to practice the invention” (§4, SFS 1884:25, Kongl. Maj:ts nådiga förordning angående patent). Whenthe application was filed at the Patent Office, an examinator (patent engineer) was assigned to the patentto investigate whether the invention was patentable, new and sufficiently useful and important.

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Figure 2:Spatial Concentration of Innovative Activity and Population

Notes: Figure A displays a Herfindahl–Hirschman Index for the number of inventors, patents, and popula-tion across municipalities, while Figure B graphs the share of inventors and population residing in urbanmunicipalities.

incentive to develop patentable inventions to broader subsets of the population that lackedcapital to draw on (Khan and Sokoloff, 2004).

2.2 Expansion of the Railroad Network

Around the mid-19th century, the railroad era began in Sweden. In the Riksdag of 1853/54, itwas decided that the main parts of the network were to be constructed, funded, and operatedby the state. A central role for the state was motivated by the belief that state control wasrequired to align construction with the “public good”, while the underdevelopment of thedomestic capital market and the widely dispersed population made it impossible to rely onmarket forces to bring about an extensive national network (Westlund, 1998).

Appointed by the king, Colonel Nils Ericson was designated to be chief planner andwas endowed “dictatorial powers” to design the railroad network (Rydfors, 1906). ColonelEricson presented his proposal in 1856 (see Figure 4 below). It connected the capital Stock-holm with the other main trading ports in the West and the South and was to follow theshortest routes between these destinations while avoiding steep terrain, the coastlines due tostrategic military concerns, and pre-existing transportation networks to reduce intermodalcompetition. Among influential observers, the appointment of a man who stubbornly arguedin favor of a sparse network connecting a few destinations, rather than to ensure that net-work links were established with the historically important economic centers, led to harsh

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(a) 1870 (b) 1880 (c) 1890 (d) 1900

Figure 3:Inventors and the Rollout of the Railroad Network 1870–1900

Notes: This figure displays the railroad network for the years 1870, 1880, 1890 and 1900 depicted in blackand the location of each inventor. Each dot corresponds to one unique inventor patenting at least once inthe subsequent decade. Also shown shaded in green is the population density in 1865 divided into decileswhere darker shades correspond to higher density.

criticism (Heckscher, 1954).Construction of the network began in the 1850s and in the 1860s the first parts of the net-

work were opened for traffic.8 Although the backbone of the network had been constructedby the early 1870s, many historically important economic centers were left without a con-nection (Berger & Enflo, 2017).9 Against the backdrop of an international railroad boom inthe 1870s, the construction of railroads became increasingly driven by private companies es-tablishing additional connections to the network so that most economically important areashad been linked up to the network by the early 20th century.10

8Although Ericson’s proposal was later rejected in parliament, the government viewed his original proposalas a program that should be constructed in a stepwise manner (Rydfors, 1906, p.99), which resulted in theemerging network shown in Figure 4 closely resembling Ericsson’s original proposal (Heckscher, 1954, p.241).

9In particular, the first wave of railroad construction avoided the mining regions in central Sweden(Bergslagen) and historically important naval cities in the Southeast.

10Although private companies increasingly drove the expansion of the network after 1870, the state retainedstrong control over its evolution due to a strict centralization of the concession process and the setting of

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As the railroad network spread throughout the country, it fundamentally altered the po-tential to move goods overland. After a network connection was established, freight costswere reduced by about three-quarters compared to the pre-rail era (Sjöberg, 1956). 11 Al-though the impact of the railroads is visible in sharp increases in the flows of goods, it alsoled to considerable increases in the movement of people: travel costs decreased by at leasthalf and speed increased manifold (Sjöberg, 1956).12 Although harder to quantify, this in-creased mobility is likely to have raised the diffusion of ideas and knowledge by enablingtravel at lower cost and by lowering the distribution costs of books, technical journals, andnewspapers. Indeed, contemporary observers emphasized the importance of the railroad inthese respects, arguing it could compete with the invention of the printed book as a meansof spreading new ideas Rydfors (1906, p.30).

3 Data

Our main data set is built up by the full universe of all granted Swedish patents during the19th century up until World War I. The patent data was compiled and digitized from thearchives of the Swedish Patent and Registration Office (PRV) and include detailed infor-mation, such as patent duration, application and grant year, and patent class. Moreover,they include name, address and occupation of the patent holders and inventors behind eachpatent.13 A total of 18,250 patents were granted by the PRV to individuals residing inSweden over the period. Approximately 90 percent of granted patents contain non-missinginformation on the place of residence for the inventor(s) or the patent holder(s) that en-ables us to geolocate each individual/patent by using the longitude and latitude of the placedenoted on the patent. After cross-validating geolocations manually, we obtain more than17,000 geolocated patents and the associated inventors.

We further manually code the occupation of each individual and patent holder usingthe Historical International Standard Classification of Occupations (HISCO) system. To

fares along joint private-public lines (Nicander, 1980). As these later connections had to be approved by thegovernment, we digitize historical documents that report several of these later line proposals which we useas the basis for a set of placebo tests.

11Transportation costs were often prohibitively high prior to the railroad. Notably, while waterways offeredcheaper transport than by road, many routes became impossible to travel during winter time. Moreover,the road network was poorly developed, which led Heckscher (1954, p.240) to argue that “[t]he lack of adeveloped highway system was acutely embarrassing in a country as extensive and sparsely populated asSweden". Crucially, the lack of an integrated road network further raised transport costs as it necessitatedusing a patchwork of alternative modes to move goods—cargo was commonly required to be reloaded up toa dozen times along a single freight route (Heckscher, 1954, p.240).

12see Appendix Figure C.1.13See Andersson & Tell (2016) and Andersson (2016) for a detailed overview of the individual level patent

data.

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identify technical elites or inventors with “upper-tail” knowledge, we follow Mokyr (2005)and separate inventors into “elite” and “non-elite” based on their reported occupation at thetime of being granted their first patent. To identify technical elites, we focus on engineersand occupations with similar levels of technical expertise required, which yields 2,200 uniqueinventors in our data. Approximately half of this elite group report engineer (ingenjor) astheir main occupation.14 Non-elite inventors is thus a residual category comprising 3,700individuals with occupations requiring middling or relatively low skills, such as blacksmithor carpenter. Naturally, albeit closely following Mokyr’s (2005) emphasis on the criticalrole of “the level of education and sophistication of a small and pivotal elite of engineers,mechanics, and chemists” in accounting for the development of new types of technologies,this classification is based on subjective criteria. We therefore provide extensive evidenceusing alternative ways to classify inventors by relying on conventional measures of the skilland status content of occupations, as well as surnames, to show that none of our main resultsare driven by our classification approach.

In our main analysis, we collapse our data in ten-year periods and organize them at themunicipal level based on historical administrative boundaries based on maps obtained fromthe Swedish National Archives (Riksarkivet). To get consistent borders over time, urban mu-nicipalities are collapsed with their adjacent rural municipality (or municipalities) as theseborders sometimes changed due to urban expansion. For patents linked to multiple individ-uals or firms located in different municipalities, we let each municipality get one patent each.As a consequence, we may interpret our patent variable as the local involvement in inno-vative activity. We end up with a municipality-level panel with nearly 2,400 municipalitiesand follow the development for these for each decade during the period 1830–1910.

We next digitize the rollout of the 11,000 km long railroad network for each decade duringthe period 1860–1910. We obtain historical maps of the evolution of the railroad networkfrom Statistics Sweden, and create digital versions of these maps using GIS software. Werestrict our analysis to railroad lines that connects a municipality to the network.

We make use of a rich data set of geographic controls and economic baseline controls.Elevation and land cover are based on data drawn from the GLC2000 and the CGIAR-SRTM obtained through the DIVA-GIS dataservice (http://www.diva-gis.org/). Populationdata from the 1860s are from Palm (2000) and Riksarkivet. Census data for 1880, 1890,1900 and 1910, which we both use to compute population and occupational compositionat the municipal level, as well as to link inventors to individuals in the censuses, are fromRiksarkivet and the North Atlantic Population Project (NAPP).

14We also classify factor owners and individuals with extensive technical training as elite inventors. Seethe Appendix for a full list of the mapping of occupational strings.

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4 Empirical framework

4.1 Railroads and Innovative Activity

To measure the effect of railroad access on innovative activity, we use two alternative esti-mation strategies in our main analysis. First, we exploit variation in the final network toidentify relative differences in inventive activity during each decade 1830-1900, which allowsus to evaluate the existence of pre-trends. Second, we use a standard difference-in-differencesapproach that allows us to obtain point estimates with meaningful absolute magnitudes.

Our first specification takes the following form:

Yirt = γi + βtRailroad1900i + X′iδt + φrt + εirt, (1)

where Yirt is an outcome such as the number of patents in a municipality i, in region r,in time period t.15 The key variable of interest on the right-hand-side of equation (1) isRailroad1900

i , capturing the access to the completed railroad network in 1900. We includemunicipality fixed effects, γi, to capture any time-invariant effect within a municipality as wellas region-by-period fixed effects, φrt, to capture any regional economic shocks. Furthermore,we include a set of time-invariant control variables, Xi, interacted with time period fixedeffects, δt, to flexibly capture potential predicted differential changes across municipalities.This set of controls includes variables capturing local geographic conditions as well as baselineeconomic conditions, which together may affect both the demand and supply side of railroadconstruction. We discuss these controls in detail below.

Our second strategy is a conventional difference-in-differences regression with staggeredtreatment, which constitutes our main estimating equation throughout most of the analysis:

Yirt = γi + βRailroadit + X′iδt + φrt + εirt. (2)

Here Railroadit is an indicator variable switching to 1 in the decade that a network con-nection is established in a municipality.16 In both equation (1) and (2), the identifying

15We use the natural logarithm to reduce the skew in our outcome variables and to scale outcomes tobaseline per capita levels, as municipalities vary in population size, by additionally including the naturallogarithm of the baseline population on the right-hand-side of equation (1). As there is a considerableamount of municipalities without any patent (inventor), we add one to the number of patents (inventors)before taking the natural logarithm. In the robustness section, we show that our outcomes are robust tousing the inverse hyperbolic sine function, instead of the natural logarithm, as well as to explicitly definingpatents in baseline per capita terms (see Appendix Table A.6.)

16Our results are robust to relaxing this assumption and adopt a continuous specification where we simplyproxy for railroad access by the natural logarithm of the distance to the nearest railroad (see AppendixTable A.3). We prefer the indicator specification as the cut-off around 5 kilometers is found to be the drivingvariation behind our results, as envisaged in Appendix Figure A.10, where we study a flexible specification

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variation stems entirely from the variation within municipalities over time, after controllingfor municipality fixed effects, γi, to capture any time-invariant effect within a municipality aswell as region-by-period fixed effects, φrt, to capture any regional economic shocks. Again,we include a set of time-invariant control variables, Xi, interacted with time period fixedeffects, δt, to flexibly capture potential predicted differential changes across municipalities.In our baseline estimations, we cluster standard errors at the municipal level to adjust forheteroskedasticity and within-municipality correlation over time.

Although the railroads traversed many locations not explicitly targeted by the state plan-ners, the placement of the actual network may still potentially be endogenous. In particular,if the timing of railroad placement is a function of local economic conditions, OLS estimatesof the above equations may be biased. The direction of this potential bias is a priori ambigu-ous, however: it should be negative if declining economic areas were targeted and positive ifareas with a high growth potential were targeted. To address these potential concerns, wetherefore complement the analysis with an instrumental variables strategies.

4.2 Instrumenting for the Railroad Network

In designing our instrumental variables strategy, we exploit unique features of the rollout ofthe network, described in section 2. In particular, we use methods from transport engineeringto calculate cost-minimizing routes between the destinations targeted by state planners toidentify municipalities that “accidentally” were traversed by the network. We next describethe construction of these least-cost routes.

Guided by historical documents, we start by singling out four nodal cities (Gothenburg,Malmö and Östersund in Sweden, and Kongsvinger in Norway) that were deemed to beparticularly important to connect with the capital Stockholm. In a next step, we calculateleast-cost paths between Stockholm and each of these destinations. More precisely, we usedata on land cover and slope gradients to calculate the construction costs associated witheach cell between Stockholm and our destinations. When creating the cost function, wereclassify the cost to increase monotonously with increasing slope values, while assigningthe highest cost to cells that are covered by water to reflect the prohibitively high cost oftraversing major water bodies. Then, we run Dijkstra’s (1959) algorithm to identify thebilateral cost-minimizing routes between Stockholm and the target destinations using thecost layers derived from the land cover and slope data.

Figure 4 depicts the least-cost paths in red. As seen in the figure, our predicted networkmirrors the early phase of railroad expansion in the period 1860 to 1880 as depicted in Figure

for different cut-offs.

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!

!

!

!

Figure 4:The railroad network, the least–cost paths and the Ericson plan

Notes: This figure displays the railroad network (in 1900) in black, the least-cost path in red and ColonelEricson’s proposal in blue.

1. Importantly, it is further evident that several of the lines in Colonel Ericson’s proposal(that were subsequently constructed) are located approximately along the least-cost paths,lending support to the anecdotal evidence concerning Ericson’s approach when designing thenetwork.

While the the least-cost paths capture a static network, we are ultimately interested instudying a dynamic relationship as given by equation (2). We therefore proceed by inter-acting the least-cost paths with a time period indicator for each decade after construction.This results in four instruments: the predicted railroad network interacted with an indicatorfor the decades starting with 1870, 1880, 1890 and 1900. As such, the instruments can bethought of as capturing the predicted development of the network during each decade.

Importantly, since slope and land cover are crucial for calculating the least-cost paths,the existence of a predicted railroad in a municipality is likely to be correlated with local

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geographic features. As such, we condition for the local geography of each municipality bycontrolling for the following variables: the mean slope, the mean elevation as well as thestandard deviation of the elevation, the area, the latitude and longitude of the municipalitycentroid, the distance to the nearest town as well as the mean cost of construction based onthe land cover and slope data.17

Moreover, as the distance to the least-cost paths may be mechanically correlated withthe distance to the targeted destinations, we explicitly control for the distance between eachmunicipality centroid and the nearest targeted destination and, additionally, exclude directlytargeted destinations in all our main regressions.

Formally, the first-stage relationship for our main difference–in–differences equation (2)takes the following form:

Railroadirt = γi +∑

d

λdln(Dist. to Least-Cost Path)i + G′iψt + φrt + εirt, (3)

where λd is an indicator variable taking the value of one if the time period is equal tod = 1870, 1880, 1890, 1900 and zero otherwise. As such we have four instruments, one foreach decade after the introduction of the railroad.18

4.2.1 Identifying Assumptions and Balance Tests

The λd’s in equation (3) capture the effect of the distance to the least-cost path for eachdecade after railroad introduction. Thus, the identifying assumption is that the distance tothese predicted least-cost paths is quasi-random only when conditioning on local geographicfeatures and the distance to the nearest targeted endpoint of the network (both includedin G), as well as municipality and region-by-year fixed effects. To explicitly test for quasi-randomness, we perform a balance test by regressing potentially related variables on thecross-sectional variation of our instruments, conditional on the local geographic conditions.To be precise, we run the following type of cross-sectional regressions:

Bir = α + ρln(Distance to Predicted Railroad)i + G′iψ + ξr + uir, (4)

for a municipality i and region r. The outcome variables Bir, are either levels or changes inbaseline economic conditions before railroad construction.

17We take the natural logarithm of all these variables. In practice, since the controls we use are time-invariant, we interact the controls with a full set of time period fixed effects.

18When instrumenting the fixed railroad in 1900 interacted with time period fixed effects in equation (1),we let d = 1840, 1850, 1860, 1870, 1880, 1890, 1900 to have as many excluded instruments as instrumentedvariables.

15

Table 1: Balance test of instrument

(1) (2)Urban indicator -0.001 (0.004)ln(Population 1865) 0.009 (0.012)Any patent before 1860 -0.002 (0.003)Any elite patent before 1860 0.000 (0.000)Any non-elite patent before 1860 0.002 (0.001)Technical university -0.014 (0.010)Share agricultural population 0.002 (0.001)Mortality 1850s -0.000 (0.000)Change in Population 1810-65 0.003 (0.008)Change in Mortality 1850-60 -0.000 (0.000)

Notes: OLS regressions. Each row is a separate regression with the indicated dependent variable regressedon the distance to the nearest least-cost path, in logs. Standard errors are given in parentheses and areclustered at the region level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

We present the results in Table 1.19 The balance test suggests that there are no observeddifferential levels or trends in economic conditions before the construction of railroads be-tween municipalities receiving a network connection and municipalities that do not. Never-theless, we control for a set of baseline level controls in our main specifications by adding :population at the baseline (1865), an indicator for whether a municipality had town privi-leges or not, as well as an indicator for whether a municipality had any granted patent priorto 1860 or not.20

Although our instrument arguably passes the test of quasi-randomness presented above,obtaining railroad access, even by chance, could itself imply further complementary invest-ments at later stages. If those have direct effects on innovative activity, we could erroneouslyattribute the effect on innovative activity to railroads. To indirectly test for if railroads af-fected further state involvement in various dimensions, we obtain a total measure of statetransfers to each municipality. While Appendix Table A.2 show that network connection ispositively correlated with public investment in 1900, the distance to the least-cost path isnot.

19While the balance test exploits cross-sectional variation, we additionally present pre-trends in innovativeactivity using decade-to-decade variation in our main panel regressions below.

20As some of the outcome variables used in the balance tests have a considerable number of missingobservations, we do not use them as controls in our main specifications.

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Figure 5:First-stage relationship – Railroads and distance to the least-cost paths

Notes: The figures display the non-parametric relationship between network access (i.e., an indicator captur-ing whether a municipality is located within 5 km of a railroad) and our instrument by decade, conditionalon region fixed effects as well as our set of local geography and baseline economic controls. Observations aresorted into 100 groups of equal size and the dots indicate the mean value in each group. A linear regressionline based on the underlying (ungrouped) data is also shown.

4.2.2 First Stage Results

We start by visualizing the first stage by plotting the non-parametric relationship betweennetwork access and our instrument, conditional on region fixed effects as well as our setof local geography and baseline economic controls, for each separate decade 1870-1900. Asseen from Figure 5, the negative relationship is evident in all four post-railroad constructiondecades, thereby suggesting that our instruments are highly relevant.

Proceeding to the difference-in-differences specification, Table 2 documents the results.The first column displays the effect when controlling for our set of local geography controlsinteracted with time period fixed effects as well as controlling for municipality fixed effects.The second column adds our set of local pre-railroad economic controls interacted with timeperiod fixed effects, while the third column additionally adds region-by-year fixed effects. Asseen from the table, the coefficients remain stable when adding controls in column 2 and 3,thereby suggesting that our instrument, conditional on local geography, have no or limited

17

correlation with potential baseline economic determinants of railroad access. In other words,it suggests that the local geography controls, including the distance to the nearest nodalpoint, do a good job in ensuring the quasi-randomness of the instrument.

Table 2: DiD Reduced-Form Estimates - Distance to Least-Cost PathDependent variable: Network Connection (=1) Any patent ln(Patents)

(1) (2) (3) (4) (5) (6) (7) (8) (9)ln(Dist. Least-Cost Path)×1870 -0.065∗∗∗ -0.065∗∗∗ -0.066∗∗∗ 0.002 0.001 0.000 0.003 0.002 0.002

(0.008) (0.008) (0.008) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)ln(Dist. Least-Cost Path)×1880 -0.067∗∗∗ -0.067∗∗∗ -0.065∗∗∗ -0.007 -0.009∗∗ -0.011∗∗ -0.010 -0.012∗∗ -0.015∗∗

(0.009) (0.009) (0.009) (0.005) (0.005) (0.005) (0.007) (0.006) (0.006)ln(Dist. Least-Cost Path)×1890 -0.054∗∗∗ -0.054∗∗∗ -0.057∗∗∗ -0.012∗ -0.015∗∗ -0.016∗∗ -0.018∗ -0.023∗∗∗ -0.026∗∗∗

(0.009) (0.009) (0.009) (0.007) (0.007) (0.007) (0.010) (0.009) (0.009)ln(Dist. Least-Cost Path)×1900 -0.065∗∗∗ -0.065∗∗∗ -0.069∗∗∗ -0.013∗ -0.016∗∗ -0.018∗∗ -0.027∗ -0.035∗∗∗ -0.040∗∗∗

(0.009) (0.009) (0.009) (0.008) (0.008) (0.008) (0.015) (0.012) (0.012)Local Geography×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes Yes No Yes Yes No Yes YesRegion FE×Year FE No No Yes No No Yes No No YesObservations 19056 19056 19056 19056 19056 19056 19056 19056 19056Mean dep. var. 0.17 0.17 0.17 0.07 0.07 0.07 0.09 0.09 0.09

Notes: OLS regressions. The table displays the effect of the distance to the nearest least-cost path (in logs)interacted with four separate indicator variables for the decades starting in 1870, 1880, 1890, 1900 on anindicator for railroad access (within 5 km). All regressions include municipality fixed effects and year fixedeffects. Standard errors are given in parentheses and are clustered at the municipality level. ∗∗∗ - p < 0.01,∗∗ - p < 0.05, ∗ - p < 0.1.

5 Main Results

In this section, we present our main results showing that the establishment of networkconnections leads to significant increases in local innovation. We then separately examinechanges along the extensive and intensive margin showing that the railroads encouraged theentry of new inventors from a diverse set of social backgrounds and led to overall increasesin inventor productivity. We then conclude by examining the long-run impacts of networkconnections on spatial patterns of innovation up until the present day.

5.1 Network Connections and Local Innovative Activity

We start our empirical analysis by assessing whether the expansion of the railroad networkincreased the probability that municipalities exhibit any involvement in innovative activity.That is, we ask whether (potential) inventors in a municipality are more likely to produce atleast one patent after a network connection is established. In Figure 6, panel A, we present

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Figure 6:Rollout of the railroad network and the spread of innovative activity

Notes: OLS and 2SLS regressions. The figure displays two separate regression models based on equation (1)displaying the effect by decade of access to the railroad network on a binary variable indicating whether amunicipality has at least on patent in a given decade (A) and the natural log number of patents (B). OLSestimates are denoted with blue diamonds and 2SLS estimates are denoted with red circles. Bars indicate95 percent confidence intervals. Standard errors are clustered at the municipality level. A grey solid verticalline denotes the year when the first parts of the network were in operation.

OLS and 2SLS estimates based on equation (1) where the outcome is a binary indicator forwhether a municipality has at least one patent in a given decade.21 Notably, there is noevidence of pre-existing differences between connected and non-connected municipalities inthe likelihood to be involved in innovative activity prior to when the first major railroadlines were built in the 1860s. As seen in the figure, it is only after the 1870s that we observerelative increases in innovative activity in connected areas, a timing that resonates well withwhen the railroad expansion took off as displayed above in Figure C.1.22

Figure 6A shows a sustained increase in the probability that a municipality exhibitedany innovative activity with the OLS estimates (denoted by blue diamonds) showing that aconnected municipality was approximately 13 percentage points more likely to have at leastone patent in the early 20th century relative to baseline. We also present 2SLS estimates(denoted by red circles) where we use as excluded instruments the natural log of the distanceto the predicted network interacted with a full set of time period fixed effects. As with the

21Appendix Tables B.2 and B.3 document the full output in table format.22In Appendix Figure B.4, we display the corresponding reduced form estimates with different measures

of innovative activity regressed on the full set of time period interactions with the natural log of the distanceto the predicted railroad networks. The non-parametric reduced form relationship is additionally depicted inFigure B.3 for each separate decade. It is clearly seen that the negative relationship between the instrumentsand innovative activity increases over time.

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Table 3: The Effect of Network Connections on Local Innovative ActivityDependent variable: Any patent ln(Patents)

Main sample Rural s. Zero pat. s. Main sample Rural s. Zero pat. s.

Panel A: OLS (1) (2) (3) (4) (5) (6) (7) (8)Network Connection (=1) 0.102∗∗∗ 0.084∗∗∗ 0.083∗∗∗ 0.085∗∗∗ 0.163∗∗∗ 0.121∗∗∗ 0.114∗∗∗ 0.121∗∗∗

(0.010) (0.009) (0.009) (0.009) (0.016) (0.014) (0.014) (0.014)

Panel B: 2SLS (1) (2) (3) (4) (5) (6) (7) (8)Network Connection (=1) 0.142∗∗ 0.169∗∗∗ 0.160∗∗∗ 0.162∗∗∗ 0.262∗∗ 0.310∗∗∗ 0.284∗∗∗ 0.283∗∗∗

(0.060) (0.055) (0.055) (0.055) (0.116) (0.091) (0.086) (0.089)Municipality FE Yes Yes Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes Yes Yes No Yes Yes YesFirst-Stage F-stat 27.22 26.70 25.35 26.14 27.22 26.70 25.35 26.14Observations 19056 19056 18152 18920 19056 19056 18152 18920Mean dep. var. 0.07 0.07 0.06 0.07 0.09 0.09 0.07 0.09

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for network access (within 5km) on whether a municipality has any patent (columns 1–4) and the natural log of the number of patents(columns 5–8). Results are shown for the main sample (columns 1, 2, 5, and 6), the rural sample (columns3, and 7) and the peripheral sample (columns 4 and 8). Standard errors are given in parentheses and areclustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

OLS estimates, we note that the both 2SLS specifications display parallel trends beforethe introduction of railroads in the 1860s. Studying the magnitudes of the 2SLS estimatesin Figure 6, we find larger effects as compared to the OLS estimates: the 2SLS estimatessuggest a relative increase in the probability that a municipality has at least one patent by27 percentage points by the end of our period.

Figure 6B shows that these patterns are virtually identical when instead focusing onthe natural log of the number of patents as the outcome. OLS estimates show that thenumber of patents in connected municipalities increased by about 22 percent (0.201 logpoints) relative to baseline by the early 20th century. 2SLS estimates are again larger inmagnitude, suggesting a 90 percent (0.640 log points) increase. Together, these estimatesprovide a clear pattern of how the evolving railroad network gradually affected innovativeactivity in the latter part of the 19th century, while the lack of pre-trends suggests that wecan use a standard difference-in-differences approach to recover the causal impact of networkconnections on local innovative activity.

Table 3 presents difference-in-differences estimates based on equation (2) for the same twooutcomes. In columns 1 and 5, we condition on municipality fixed effects, region-by-periodfixed effects, and local geography controls interacted with period fixed effects. In columns 2

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and 6, we include the full set of covariates reflecting pre-rail economic conditions interactedwith time period fixed effects. Panel A displays OLS estimates, while panel B provides their2SLS counterparts.23

Thus, column 1–2 in panel A displays estimates of the effect of railroads on the prob-ability of having at least one patent for our main sample, which suggests it increased by8-10 percentage points after a network connection is established. Similarly, columns 5–6suggest increases in innovative activity also when measured by the number of patents, whichincreased by approximately 12 percent (or 0.121 log points) after a connection was estab-lished as suggested by column 6. Similarly, the same columns in panel B present our 2SLSestimates that reveal a positive and precisely estimated impact of network connections onlocal innovative activity: in terms of the number of patents, for example, our preferred 2SLSspecification in column 6 suggests a 35 percent (about 0.300 log points) increase on averagein the decades following access, which again is substantially larger than the correspondingOLS estimate in column 6.24

We next show that increases in local innovative activity are also evident among munic-ipalities that were presumably more isolated or where we observe no innovative activity inthe pre-rail era. Column 3 and 7 provide the effect of network connections in a samplerestricted to rural municipalities and demonstrates that the average effects are similar forthis subsample. Additionally, column 4 and 8 show that the results are virtually unaffectedif studying only localities with no granted patent prior to the establishment of the railroadnetwork.

A sharp increase in the relative rate of innovation in areas traversed by the railroadnetwork raises the important question whether these gains reflect truly “new” innovation ac-tivity or whether they simply are driven by a spatial reallocation of inventive activity fromother municipalities. Understanding whether such general equilibrium effects are empiri-cally relevant is important because it would suggest a smaller contribution of the railroadsto aggregate innovation. We provide evidence on potential reallocation using two distinctapproaches in the Appendix. First, Appendix Figure A.10 graphs difference–in–differencesestimates, where we allow the treatment effect to vary flexibly across 5 km bins of distanceto the railroad network, which suggests that nearby non-connected areas do not exhibitrelative reductions in patenting activity suggestive of limited reallocation. Second, we ex-clude nearby untreated areas (i.e., without a network connection) from the control groupand estimate our baseline difference-in-differences model.25 Appendix Figure A.10 displays

23Reduced form effects of the instruments can be envisaged in Table 2.24We present analogous estimates of equation (2) separately for 14 different major industry classes in the

Appendix (see Appendix Figure C.1) showing that patenting activity increased within most industries.25As pointed out by Redding & Turner (2014), the treatment effect corresponds to the compound effect

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2SLS estimates from seven individual regressions where we sequentially exclude plausiblyuntreated areas. Although estimated magnitudes decline when omitting areas in proximityto the network (i.e., 5-20 km), they remain very stable in magnitude and statistical precisionwhen omitting more distant areas. While these estimates are suggestive of a reallocation ofinnovative activity from nearby non-connected areas, it suggests that reallocation is limitedin magnitude and thus unlikely affects the interpretation of our main results.

Consistently throughout our analysis, we find that our 2SLS estimates are larger in mag-nitude than the corresponding OLS estimates. One explanation for the discrepancy betweenOLS and 2SLS estimates is that network connections are allocated to less favourable regions,which induces a downward bias in the OLS estimates. Yet, an alternative interpretation maybe that the difference in magnitudes could stem from the fact that the 2SLS estimates maycapture a local average treatment effect (LATE), as opposed to the average treatment effect(ATE) in the OLS. This could, for example, be the case if our IV strategy captures the maintrunk lines rather than the average rail line.26 However, the small increase in the coefficient,when adding the pre-rail economic controls, is consistent with the notion that economicallyless developed locations are in general more likely to receive public transport infrastructure(Baum-Snow, 2007; Duranton & Turner, 2012; Redding & Turner, 2014). In our view, themost plausible explanation therefore involves network connections being allocated to areaswith lower unobserved propensities for innovation.

5.1.1 Additional robustness checks

We perform a number of additional robustness checks to establish that our main results arerobust.

One immediate concern is that the arrival of the railroad induced inventors to developtechnologies directly related to the railroad in high-tech sectors such as machinery. Weprovide estimates in the Appendix where we restrict our outcomes to (non-)railroad-relatedpatents, which reveal a very similar increase in local innovation not directly linked to therailroad (see Appendix Table A.10).27 While these estimates show that the arrival of the

of growth and spatial reallocation corresponding to β = 2d + a, where d is the amount of activity that isreallocated between the treated and untreated area and a is the pure growth effect. Stable estimates whenlimiting the control group to residual areas suggest that d ≈ 0 and that the estimated impacts mainly reflectgrowth rather than spatial reallocation.

26Moreover, it may be the case that the effect for always takers, in the jargon of a potential outcome model,is smaller than for the compliers of the instrument. In our setting, this could be the case if the marginaleffect of railroads for municipalities, such as larger towns, that would obtain a railroad at least at some pointin time, disregarding the cost of passing the network through those municipalities, is smaller than the effecton municipalities that heavily rely on accidentally being located in the proximity of the predicted network.

27We define railroad-related patents as patents belonging to specific DPK subclasses directly related to therailroad sector, or where a patent contains one of a set of rail-related keywords (e.g., “rail*”, “loco*”, etc.).

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railroad led to a local increase in the output of inventions related to the sector, the estimatesremain stable in magnitude and statistical precision also when we restrict our attention topatents not related to the arrival of this new technology.

To further validate our results, we show in the Appendix that there are no similar in-creases in local innovation in areas that were assigned a network connection, but where noconnection was ultimately established. As described above, the original network proposedby Colonel Ericson was altered through subsequent parliamentary decisions, which resultedin parts of the intended network never being constructed.28 We estimate the “effects” forthese lines as well as lines that were part of another major proposal put forth in 1870, whichconsisted of additional lines proposed by a state committee and municipalities, respectively.Across all specifications, the estimates for these proposed lines are never statistically sig-nificant and they are typically small in magnitude (see Appendix Table A.4). A lack ofeffects for these placebo lines suggests that railroads were not allocated to areas that wouldhave exhibited a surge in innovation even in the absence of construction, or that an omittedfactor that is correlated both with the way network connections were allocated through theplanning process and local changes in innovation is driving our results.

We continue by showing that our results are similar when we use continuous variation inthe log distance to the network rather than a discrete connectivity measure (see AppendixTable A.3). Similarly, while we prefer a simple log transformation of our outcomes, the resultsare very similar when instead calculating the inverse hyperbolic sine of the number of patentsas well as when defining our outcomes in explicit per capita terms using population levelsin the pre-rail era (see Appendix Table A.6). Although our inference through is based onstandard errors clustered at the municipality level, using spatial correlation-robust standarderrors for our reduced form regressions yields similar magnitudes (see Appendix Table A.11).

Together, these results reveal a large and robust causal impact of the establishment ofnetwork connections on the spread of innovative activity, both in terms of increasing thelikelihood that a locality became involved in innovative activity and increasing the level ofpatenting output.

In particular, we search for the Swedish terms “*jernvä*” OR “*järnvä*” OR “*räls*” OR “*järnvagn*” OR“*jernvagn*” OR “*spår*” OR “*loko*” OR “*syll*”. We manually review each individual match to confirmthat the invention is related to the railroad sector.

28A notable deviation occurred due to the decision to shift the main line connecting Stockholm andMalmö eastward. While this alteration served to raise the construction costs, it was mainly motivated bythe fact that it shortened the distance between the two major cities and was thus presumably exogenous tocharacteristics of locations along this route (Rydfors 1906, p.86). Additional minor changes were the resultof more detailed surveys, which identified cheaper ways of routing segments of different lines.

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Figure 7:Rollout of the railroad network and the number of active inventors

Notes: OLS and 2SLS regressions. The figure displays two separate regression models based on equation (1)displaying the effect by decade of access to the railroad network on the natural log of the number of activeinventors. OLS estimates are denoted with blue diamonds and 2SLS estimates are denoted with red circles.Bars indicate 95 percent confidence intervals. Standard errors are clustered at the municipality level. A greysolid vertical line denotes the year when the first parts of the network were in operation.

6 Network connections and the extensive and intensivemargin of local innovation

A local increase in patenting may be driven by two distinct margins: an entry of new inventorsand an increase in the productivity of incumbent and new inventors. We next proceed todisentangle the impacts of the establishment of network connections on the extensive andintensive margin by studying the entry and output of inventors.

6.1 Extensive margin: Entry of inventors

We start by documenting an aggregate increase in the number of inventors in a municipalityafter a network connection is established. Figure 8 presents OLS and 2SLS estimates basedon equation (1) where the outcome is the number of active firm and independent for eachdecade and municipality. Reassuringly, there again is no evidence suggesting that areas thateventually would become connected to the network had a more inventors prior to the networkis constructed. After the construction of the major lines are finished in the 1860s, however,we see a divergence in the number of inventors that by the early 20th century has increased

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Table 4: Inventor Entry and the Extensive Margin of Local Innovation

Dependent variable: ln(Inventors)All New inventors

All Firm Independent Elite Non-Elite

Panel A: OLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.096∗∗∗ 0.075∗∗∗ 0.005∗∗ 0.073∗∗∗ 0.048∗∗∗ 0.059∗∗∗

(0.012) (0.010) (0.003) (0.010) (0.008) (0.009)

Panel B: 2SLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.289∗∗∗ 0.275∗∗∗ 0.044∗∗ 0.264∗∗∗ 0.188∗∗∗ 0.189∗∗∗

(0.076) (0.069) (0.019) (0.068) (0.053) (0.061)Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056 19056Mean dep. var. 0.08 0.06 0.01 0.06 0.04 0.05

Notes: OLS and 2SLS regressions. The table displays the effect of a network connection on the natural logof the number of inventors (column 1) and the natural log of the number of new inventors by inventor type(columns 2–6). Standard errors are given in parentheses and are clustered at the municipality level. ∗∗∗ -p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

by some 18 percent (0.166 log points) in a connected municipality. Again, 2SLS estimatesare substantially larger, suggesting a 71 percent (0.538 log points) increase.

We next more explicitly disentangle the entry of inventors. Focusing on the 2SLS esti-mates of equation (2) displayed in panel B of Table 4, column 1 shows that the establishmentof a network connection led to a 34 percent (0.289 log point) increase in the aggregate num-ber of active inventors in a municipality. To more explicitly examine the entry of inventors,we isolate “new” inventors that correspond to a firm or individual that patents for the firsttime in a given decade with no prior granted patent. Column 2 report 2SLS regressions forall new inventors revealing a large contribution of new inventors to the aggregate increase.We further decompose new inventors in columns 3–4 by separating between firm and inde-pendent inventors showing that the vast increase in the number of inventors was driven bynew independent inventors entering the innovation sector, rather than firms.

An accelerated pace of entry raises questions regarding independent inventors’ socialbackgrounds: did the improved interconnectedness mainly benefit elite groups, or did italso encourage entry into innovation among those from lower social ranks? We observe the

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Table 5: Entry of Inventors: Skill and Status

Dependent variable: ln(New Inventors)

HISCLASS HISCAM Surname Status New Surname

Panel A: OLS (1) (2) (3) (4) (5) (6) (7) (8)High Low High Low High Low High Low

Network Connection (=1) 0.067∗∗∗ 0.035∗∗∗ 0.039∗∗∗ 0.035∗∗∗ 0.053∗∗∗ 0.031∗∗∗ 0.046∗∗∗ 0.026∗∗∗(0.010) (0.007) (0.007) (0.007) (0.008) (0.006) (0.007) (0.005)

Panel B: 2SLS (1) (2) (3) (4) (5) (6) (7) (8)High Low High Low High Low High Low

Network Connection (=1) 0.246∗∗∗ 0.125∗∗∗ 0.192∗∗∗ 0.130∗∗∗ 0.232∗∗∗ 0.115∗∗∗ 0.203∗∗∗ 0.091∗∗∗(0.064) (0.046) (0.051) (0.046) (0.059) (0.044) (0.050) (0.035)

Municipality FE Yes Yes Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056 19056 19056 19056Mean dep. var. 0.05 0.03 0.03 0.03 0.05 0.03 0.03 0.02

Notes: 2SLS regressions. The table displays the effect of an indicator for network access on the natural logof the number of new high- and low-status inventors based on HISCLASS (columns 1–2), HISCAM (columns3–4), surname status (columns 5–6), and “new” surnames (columns 7–8). Standard errors are given inparentheses and are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

occupation at the time of patenting for most of the inventors in our data, which allowsus to proxy for the economic and social standing of individuals. As a starting point, wefollow Mokyr’s (2005) distinction between “upper-tail” and “lower-tail” skills and first classifyinventors into “elite” and “non-elite” based on their occupation at the time of patenting: wecategorize chemists, engineers, etc. as elite, while non-elite inventors consist of medium- orlower-skilled workers (e.g., blacksmiths, instrument makers, and mechanics). Columns 5 and6 reveal that the establishment of a network connection induced the entry of new elite andnon-elite inventors, with a larger relative impact on inventors presumably drawn from thelower-tail.

We corroborate these results by using a variety of alternative approaches to distinguishbetween elite and non-elite inventors. First, instead of relying on occupational titles weclassify occupations based on their skill level.29 Second, we code each inventor’s social status

29We code the reported occupation by each inventor at the time of their first patent to the HISCO system,which in turn allows us to infer their skill level using the HISCLASS scheme. We divide all inventors into two

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based on a continuous social status score specific to each occupation.30 Third, as a moreencompassing measure of an inventor’s social background, we use the status informationcontained in their surnames (Clark, 2014).31 In addition, we also calculate the number ofnew inventors that has a surnames that no other previous inventors in that same municipalityheld at the time of receiving a patent, which serves as a proxy for whether inventors arecoming from new families with no history of patenting.

Using these complementary ways to define the status of inventors, we present 2SLSestimates of equation (2) in Table 5 documenting an increased entry of inventors from bothhigh- and low-status backgrounds. In addition, we also present more granular evidence inthe Appendix that support this interpretation by breaking down the entry of new inventorsby major occupational and skill groups (see Appendix Tables A.7 and A.8). Together, theseestimates show that individuals that patented for the first time after a network connectionwas established hailed from a wide range of social backgrounds.

Similarly to above, we indirectly test whether the increase in inventors simply reflecta migration of inventors from nearby (non-connected areas). Appendix Figure A.10 graphsdifference–in–differences estimates, where we allow the treatment effect to vary flexibly across5 km bins of distance to the railroad network. As seen in the figures, nearby non-connectedareas do not exhibit relative reductions in inventor entry. This suggests that inventor mi-gration at most accounts for a small share of the overall increase in entry that we observeamong inventors in both high- and low-status groups.

6.2 Intensive margin: Inventor productivity

We next ask whether the establishment of a network connection increased inventor produc-tivity.32

Figure 8A displays OLS and 2SLS estimates from equation (1) showing that the averageproductivity of inventors increased in connected areas after the railroad network was rolled

groups corresponding to“high” and “low” skill status respectively corresponding to HISCLASS groups 1–6and 7–12 respectively. As discussed in the main text, we also present results using each individual HISCOand HISCLASS group in the Appendix.

30In particular, we use data on HISCAM scores and then simply define high (low) status as those with aHISCAM score above (below) the population-weighted mean of the status score across all males in the 1880census.

31We create a measure of the social status of each surname through the occupational distribution of malesin the 1880 census. In a first step, we calculate the average HISCAM score for each unique surname basedon the occupational distribution of employed males aged 25-55 (to reduce potential life cycle bias) and limitour analysis to surnames with at least 10 individuals in the 1880 census.

32Inventor productivity is measured as the average decadal number of patents per inventor in each munic-ipality. When constructing patents per inventor, we assume that all municipalities have a residual inventorby setting patents per inventor equal to zero for municipalities without patents in a specific decade.

27

0.2

.4.6

.81

ln(P

aten

ts p

er In

vent

or)

1840

1850

1860

1870

1880

1890

1900

Years

(a) Per Inventor

0.2

.4.6

.81

ln(P

aten

ts p

er In

vent

or)

1840

1850

1860

1870

1880

1890

1900

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(b) Per Elite Inventor

0.2

.4.6

.81

ln(P

aten

ts p

er In

vent

or)

1840

1850

1860

1870

1880

1890

1900

Years

(c) Per Non-Elite Inventor

Figure 8:Rollout of the railroad network and the productivity of inventors

Notes: OLS and 2SLS regressions. Each figure displays two separate regression models based on equation(1) displaying the effect by decade of access to the railroad network on the number of patents per inventor(A) and patents per elite- and non-elite inventor (B and C). OLS estimates are denoted with blue diamondsand 2SLS estimates are denoted with red circles. Bars indicate 95 percent confidence intervals. Standarderrors are clustered at the municipality level. A grey solid vertical line denotes the year when the first partsof the network were in operation.

out, while Figures 8B and 8C document similar increases among elite inventors and smallerincreases among non-elite inventors, respectively. Table 6 presents difference-in-differencesestimates showing that inventor productivity (i.e., patent output per decade) increased byabout 13 percent (0.121 log points) after a network connection was established (column 1).In the remaining columns of Table 6, we present similar productivity measures by inventortype. We find no impacts on the relative productivity of firms and a large average effecton independent inventors (columns 2 and 3). When decomposing independent inventors byelite status, we show that while the estimated magnitudes are larger for elite inventors, bothinventor types became more productive after a network connection was established. Weprovide additional estimates using alternative skill and status definitions in Appendix TableA.9 showing that productivity increases are evident among inventors from both elite andnon-elite backgrounds when defined by occupational status or surnames.

Although we find that inventors from all backgrounds produced more patents after anetwork connection was established, it remains an open question to what extent the creationof new ideas materialized in patents has an economic value. We next examine the value ofpatents using three approaches. First, we classify industries into high- and low-tech based onthe scheme provided in Nuvolari & Vasta (2015).33 Columns 1 and 2 of Table 7 presents OLSand 2SLS estimates where we the outcome is the number of high-tech patents per elite andnon-elite inventor respectively. After a network connection is established, the OLS estimates

33High-tech sectors include: chemicals, electricity, machinery and metals, steam engines, and weapons.

28

Table 6: Inventor Productivity and the Intensive Margin of Local Innovation

Dependent variable: ln(Patents/Inventor)All Firm Independent Elite Non-elite

Panel A: OLS (1) (2) (3) (4) (5)Network Connection (=1) 0.079∗∗∗ 0.013∗∗∗ 0.076∗∗∗ 0.046∗∗∗ 0.054∗∗∗

(0.009) (0.004) (0.009) (0.008) (0.007)

Panel B: 2SLS (1) (2) (3) (4) (5)Network Connection (=1) 0.122∗∗ 0.038 0.141∗∗∗ 0.184∗∗∗ 0.073∗

(0.053) (0.025) (0.051) (0.052) (0.039)Municipality FE Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056Mean dep. var. 0.06 0.01 0.06 0.03 0.04

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for railroad access (within5 km) on the natural log of the number of different types of inventors (columns 1–5) and the natural log ofthe number of patents per inventors by inventor group. Standard errors are given in parentheses and areclustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

29

Table 7: Patent QualityDependent variable: ln(Patents/Inventor)

High-tech patents Quality-weighted patents New industriesElite Non-Elite Elite Non-Elite Elite Non-Elite

Panel A: OLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.050∗∗∗ 0.033∗∗∗ 0.132∗∗∗ 0.147∗∗∗ 0.067∗∗∗ 0.074∗∗∗

(0.009) (0.006) (0.018) (0.016) (0.008) (0.009)

Panel B: 2SLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.120∗∗ 0.028 0.394∗∗∗ 0.178∗∗ 0.170∗∗∗ 0.102∗∗

(0.055) (0.033) (0.109) (0.086) (0.049) (0.047)Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056 19056Mean dep. var. 0.03 0.02 0.07 0.09 0.03 0.05

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for railroad access (within5 km) on the natural log of the number of different types of inventors (columns 1–5) and the natural log ofthe number of patents per inventors by inventor group. Standard errors are given in parentheses and areclustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

30

suggest a significant increase in the output of high-tech patents among both elite and non-elite inventors, while the 2SLS are larger in magnitude but not statistically significant.

Second, we infer the economic value of inventions by using information on the number ofyears patentees paid renewal fees for each technology class.34 We calculate the mean numberof years that renewal fees were paid for 89 distinct technology classes. Columns 3 and 4show that the output of patents in such high-value technology classes increased both amongelite and non-elite inventors after the establishment of a network connection.35

Third, columns 5 and 6 show that both elite and non-elite inventors were more likely topatent in a “new” industry after a municipality was penetrated by a railroad. New industriesare defined at the municipality level and correspond to those industries in which no inventorhas previously been granted a patent in.

Overall, these results reveal that the localized increase in inventive output after a munici-pality was connected to the network was driven by an increase in the average productivity ofinventors, as reflected in their patent output. While the entry of new groups of inventors fromlower-status backgrounds may be indicative of a growing share of marginal inventors, we findthat inventors irrespective of their economic and social standing became more productiveafter a network connection was established in their municipality of residence. After a net-work connection was established, both elite and non-elite inventors produced more patentsof higher-quality and contributed to a industrial renewal as they introduced new types oftechnologies to the local economy.36

6.3 Patent transfers and collaborations along the railroad network

Next, we turn to studying to what extent railroad access affected patent transfers and within-patent collaborations along the railroad line. Table 8 documents these results.37

The OLS estimates in Panel A suggest that railroad access increased both the proba-bility of a patent transfer and the probability of a “railroad collaboration” as envisaged incolumns 1-2 and 4-5, respectively. Turning to 2SLS estimates in Panel B, the positive effectremains, albeit the statistical significance is substantially lowered, mainly due to an increase

34The maximum number of years a patent could be in place was 15 years. Renewal fees are often arguedto be a good proxy for the economic value of patents as the patentee needs to decide upon renewing his orher patent based on the expected economic return from extending the patent right (see e.g. Schankerman &Pakes, 1986; Burhop, 2010).

35We define high- and low-value patents as technology classes with above/below the mean number of yearsrenewal fees were paid across all classes.

36Appendix Table A.5 documents that these findings hold true also in the rural sample as well as amongmunicipalities with no patent prior to the railroad era.

37We here define a patent transfer as the sell of a patent in the market for patents. At this stage we cannotobserve the location of the purchase of a patent.

31

Table 8: Patent Transfers and CollaborationsDependent variable: Any transfers Any rail-collab.

Main sample Rural s. Main sample Rural s.

Panel A: OLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.026∗∗∗ 0.018∗∗∗ 0.018∗∗∗ 0.016∗∗∗ 0.009∗∗ 0.008∗∗

(0.005) (0.004) (0.004) (0.004) (0.004) (0.003)

Panel B: 2SLS (1) (2) (3) (4) (5) (6)Network Connection (=1) 0.013 0.019 0.049∗∗ 0.036 0.042∗ 0.047∗∗

(0.026) (0.026) (0.022) (0.027) (0.024) (0.019)Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes Yes No Yes YesFirst-Stage F-stat 27.22 26.70 25.35 27.22 26.70 25.35Observations 19056 19056 18152 19056 19056 18152Mean dep. var. 0.01 0.01 0.01 0.01 0.01 0.01

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for network access (within5 km) on whether a municipality has any patent transfer (columns 1–3) and any collaboration betweeninventors with network access (columns 4–6). Results are shown for the main sample (columns 1, 2, 5, and6), the rural sample (columns 3, and 7) and the peripheral sample (columns 4 and 8). Standard errors aregiven in parentheses and are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

in standard errors.Interestingly, when we study the rural sample in columns 3 and 6, the magnitudes are

larger and more precisely estimated. Network connections increased the probability of apatent transfer by 4.9 percentage points, compared to the (non-statistically significant) 1.9percentage points in the main sample. A potential explanation is that rural localities hadcomparatively less scope for using the patent in production within the municipality.

Inventors in rural areas with network access also benefitted substantially by increasingcollaborations with other inventors along the railroad line. In terms of magnitude, networkaccess increased the probability of a railroad collaboration with 4.7 percentage points, similarin magnitude to the main sample.38

38Appendix Table B.4 provide additional results, suggesting that within-patent collaborations betweeninventors with no railroad access did not increase to a similar extent as collaborations along the railroadnetwork.

32

11.

21.

41.

61.

8ln

(Pat

ents

201

0)

-.5 0 .5 1 1.5Network Connection 1900 (=1)

Figure 9:Non-parametric relationship of innovative persistence

Notes: Non-parametric relationship between the natural log of the number of patents in 2005–2014 andrailroad access in 1900, while controlling for the baseline set of controls, region fixed effects, and the naturallog of patents in 1900. Observations are sorted into 30 groups of equal size and the dots indicate the meanvalue in each group. A linear regression line based on the underlying (ungrouped) data is also shown.

7 Epilogue: Long-run Persistence

As the railroad network expanded over the latter half of the 19th century it led to theformation of local innovation clusters by the early 20th century. We conclude our empiricalanalysis by asking whether these clusters that arose around the tracks persisted over the longrun. To examine the long-run impacts of network connections on local innovative activity, westart by examining persistence in spatial patterns by studying the effect of the 19th-centuryrailroads on the number of patents in the early 21st century (2005–2014) relative to thenumber of patents in the early 20th century (1900–1910).

Figure 9 displays this relationship non-parametrically, conditional on the familiar setof controls capturing local geographic conditions, pre-railroad economic controls, as well asregion fixed effects. We observe a striking persistence in the spatial distribution of innovativeactivity, which shows that the establishment of innovation clusters by the early 20th centurystill shapes the spatial distribution of innovative activity in Sweden today.

We further probe the robustness of this link by estimating long-difference regressionsthat relate localized changes in patenting activity over the past century to initial differences

33

in whether a municipality was connected to the railroad network or not. We present theseresults in the Appendix showing a large and statistically significant link between networkconnections established by 1900 and subsequent growth in local patenting activity over thenext century (see Table C.2). We also find correlations of similar negative and positivemagnitudes for municipalities that either lost or gained a network connection during the20th century. While these estimates naturally should be interpreted carefully given thehighly endogenous nature of the closing and opening of connections, it is reassuring that themagnitudes are of similar size.

The persistence and increase in the main effect on patents that we find is consistent withat least two stories. First, one explanation is that historical differences in innovation areamplified over time through local exposure effects to innovators through one’s family or placeof residence, which has significant causal effects on the propensity to become an inventor inadulthood (Bell et al., 2017). In other words, as network access promoted innovative activity,it may have led to an “innovative culture” that displays a strong persistence over the nextcentury that constitutes an intriguing hypotheses left to future work. Second, as indicatedby the estimates in Table C.2 there is suggestive evidence that links to major transportationnetworks remain a crucial determinant of spatial differences in innovation in the 21st century(Agrawal et al., 2016). Although these estimates should be interpreted carefully, they aresuggestive of an important role of communication and transportation costs in shaping localinnovation also today.

8 Conclusions

We have documented that the construction of a large transport infrastructure network,such as the 19th-century Swedish railroad network, may have substantial positive and long-lasting effects on innovative activity. To obtain an exogenous variation in railroad access,our analysis benefits from the particular historical features behind the relatively fast rolloutof the Swedish network.

Our estimates document that both the number of patents and inventors increased in areastraversed by the railroads. Patenting activity spread to rural locations as well as localitieswith no prior history of obtaining patents. New inventors entered into the innovation sectorfrom a diverse set of social backgrounds and were more productive in areas where networkconnections were established. We document that they contributed significantly to the spur ininnovative activity. A potential explanation may be the comparatively high level of “lower-tail” human capital in the Swedish population as famously suggested by Sandberg (1979).

Lastly, we document that the increase in innovative activity was highly persistent. Over

34

the 20th century, the differences are intensified rather than dissipated.

35

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Appendix A: Robustness and Placebo Tests

Table A.2: Test of exclusion restriction

Dependent variable: ln(State transfer 1900)(1) (2)

Network Connection (=1) 0.099∗∗∗(0.023)

ln(Dist. Least-Cost Path) -0.004(0.019)

Region FE Yes YesLocal Geography Controls Yes YesPre-Rail Controls×Year FE Yes YesObservations 2364 2364Mean dep. var. 7.37 7.37

Notes: OLS regressions. Standard errors are given in parentheses and are clustered at the region level. ∗∗∗- p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

40

Table A.3: DiD Estimates - Distance to Railroads

Dependent variable: Any patent ln(Patents) ln(Patents/Inventor)

Panel A: OLS (1) (2) (3)ln(Dist. rail) -0.027∗∗∗ -0.039∗∗∗ -0.026∗∗∗

(0.003) (0.005) (0.003)

Panel B: 2SLS (1) (2) (3)ln(Dist. rail) -0.024∗∗∗ -0.042∗∗∗ -0.017∗∗

(0.008) (0.013) (0.008)Municipality FE Yes Yes YesRegion FE×Year FE Yes Yes YesLocal Geography×Year FE Yes Yes YesPre-Rail Controls×Year FE Yes Yes YesFirst-Stage F-stat 70.24 70.24 70.24Observations 19056 19056 19056Mean dep. var. 0.07 0.09 0.06

Notes: OLS and 2SLS regressions. The table displays the effect of the log distance to a railroad on thenumber of patents, the number of inventors, and the number of patents per inventors, all in logs. SeeTable ?? for information on included Local Geography and Pre-Rail controls. Standard errors are given inparentheses and are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

41

Table A.4: Placebo - Never Realized Plans

Dependent variable: ln(Patents)(1) (2) (3) (4) (5)

Network Connection (=1) 0.121∗∗∗ 0.121∗∗∗ 0.122∗∗∗ 0.121∗∗∗ 0.121∗∗∗(0.014) (0.014) (0.014) (0.014) (0.014)

Placebo line (Ericson’s proposal) -0.017 -0.007(0.058) (0.053)

Placebo line (1870 committee proposal) 0.087 0.089(0.061) (0.061)

Placebo line (1870 municipality proposal) 0.072 0.072(0.047) (0.047)

Municipality FE Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes YesObservations 19056 19056 19056 19056 19056Mean dep. var. 0.09 0.09 0.09 0.09 0.09

Notes: OLS regressions. The dependent variable is the number of patents, in logs. See Table ?? forinformation on included Local Geography and Pre-Rail controls. Standard errors are given in parenthesesand are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

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Table A.5: Patent Quality Across Samples

Dependent variable: Any high-tech patent ln(Quality-weighted patents) Any new industry patentMain s. Rural s. Zero pat. s. Main s. Rural s. Zero pat. s. Main s. Rural s. Zero pat. s.

Panel A: OLS (1) (2) (3) (4) (5) (6) (7) (8) (9)Network Connection (=1) 0.045∗∗∗ 0.043∗∗∗ 0.045∗∗∗ 0.226∗∗∗ 0.215∗∗∗ 0.226∗∗∗ 0.081∗∗∗ 0.080∗∗∗ 0.083∗∗∗

(0.007) (0.007) (0.007) (0.025) (0.025) (0.025) (0.009) (0.009) (0.009)

Panel B: 2SLS (1) (2) (3) (4) (5) (6) (7) (8) (9)Network Connection (=1) 0.097∗∗ 0.095∗∗ 0.089∗∗ 0.528∗∗∗ 0.495∗∗∗ 0.486∗∗∗ 0.177∗∗∗ 0.165∗∗∗ 0.171∗∗∗

(0.044) (0.042) (0.044) (0.157) (0.153) (0.154) (0.053) (0.053) (0.052)Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 25.35 26.14 26.70 25.35 26.14 26.70 25.35 26.14Observations 19056 18152 18920 19056 18152 18920 19056 18152 18920Mean dep. var. 0.04 0.03 0.03 0.18 0.13 0.17 0.07 0.05 0.06

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for railroad access (within 5km) on whether a municipality has any high-technology patent (columns 1–3), the natural log of the numberof quality weighted patents (columns 4–6) and whether a municipality has any patent in a new industry(columns 7–9). Results are shown for the main sample (columns 1, 4, and 7), the rural sample (columns 2,5, and 8) and the peripheral sample (columns 3, 6, and 9). Standard errors are given in parentheses and areclustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

43

Table A.6: DiD Estimates - Functional Form of Number of Patents

Dependent variable: Patents

Panel A: OLS estimates (1) (2) (3) (4)ln ihs per cap. 1865 per cap. 1900

Network Connection (=1) 0.163∗∗∗ 0.206∗∗∗ 0.336∗∗∗ 0.168∗∗∗(0.016) (0.020) (0.061) (0.024)

Panel B: 2SLS estimates (1) (2) (3) (4)ln ihs per cap. 1865 per cap. 1900

Network Connection (=1) 0.310∗∗∗ 0.384∗∗∗ 0.685∗∗ 0.387∗∗∗(0.091) (0.114) (0.292) (0.148)

Municipality FE Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.69Observations 19056 19056 19056 18992Mean dep. var. 0.09 0.12 0.15 0.09

Notes: OLS and 2SLS regressions. The table displays different functional forms of the outcome variable.Column 1 displays the natural logarithm of the number of patents + 1. Column 2 displays the inversehyperbolic sine of the number of patents. Column 3 displays the number of patents per 1,000 inhabitants in1865. Standard errors are given in parentheses and are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗- p < 0.05, ∗ - p < 0.1.

44

Table A.7: Entry of Inventors by Major HISCO Groups

Dependent variable: ln(New Inventors by HISCO Group)(1) (2) (3) (4) (5) (6) (7)

Professionals Admin Clerical Sales Services Agriculture ProductionNetwork Connection (=1) 0.082∗∗ 0.140∗∗∗ 0.030∗ 0.048∗∗ 0.005 0.025 0.089∗∗

(0.035) (0.039) (0.018) (0.020) (0.015) (0.018) (0.037)Municipality FE Yes Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056 19056 19056

Notes: 2SLS regressions. The table displays the effect of an indicator for railroad access (within 5 km) onthe natural log of the number of different types of inventors (columns 1–5) and the natural log of the numberof patents per inventors by inventor group. Standard errors are given in parentheses and are clustered atthe municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

45

Table A.8: Entry of Inventors by Major HISCLASS GroupsDependent variable: ln(New Inventors by HISCLASS Group)

(1) (2) (3) (4)Higher managers Higher professionals Lower managers Lower professionals, clerical and sales personnel

Network Connection (=1) 0.031 0.093∗∗∗ 0.098∗∗∗ 0.055∗∗(0.027) (0.033) (0.034) (0.023)

(1) (2) (3) (4)Lower clerical and sales personnel Foremen Medium-skilled workers Farmers and fishermen

Network Connection (=1) 0.033 0.011 0.068∗∗ 0.012(0.022) (0.007) (0.031) (0.017)(1) (2) (3) (4)

Lower-skilled workers Lower-skilled farm workers Unskilled workers Unskilled farm workersNetwork Connection (=1) 0.037∗∗ 0.006 0.025∗∗ 0.002

(0.018) (0.005) (0.010) (0.004)Municipality FE Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056

Notes: 2SLS regressions. The table displays the effect of an indicator for railroad access (within 5 km) on the natural log of the number of differenttypes of inventors (columns 1–5) and the natural log of the number of patents per inventors by inventor group. Standard errors are given in parenthesesand are clustered at the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

46

Table A.9: Inventor Productivity by Different Inventor Group Measures

Dependent variable: ln(New Inventors)Skill Status Surname Status

(1) (2) (3) (4) (5) (6)High Low High Low High Low

Network Connection (=1) 0.165∗∗∗ 0.057∗ 0.142∗∗∗ 0.083∗∗ 0.154∗∗∗ 0.085∗∗(0.052) (0.033) (0.041) (0.036) (0.049) (0.036)

Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes Yes Yes YesFirst-Stage F-stat 26.70 26.70 26.70 26.70 26.70 26.70Observations 19056 19056 19056 19056 19056 19056

Notes: 2SLS regressions. The table displays the effect of an indicator for railroad access (within 5 km) onthe natural log of the number of different types of inventors (columns 1–5) and the natural log of the numberof patents per inventors by inventor group. Standard errors are given in parentheses and are clustered atthe municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

47

Table A.10: DiD estimates - Rail and Non-Rail Patents

Dependent variable: ln(Rail Patents) ln(Non-Rail Patents)

Panel A: OLS(1) (2) (3) (4)

Network Connection (=1) 0.013∗∗∗ 0.009∗∗∗ 0.158∗∗∗ 0.116∗∗∗(0.003) (0.003) (0.016) (0.014)

Panel B: 2SLS(1) (2) (3) (4)

Network Connection (=1) 0.022 0.019 0.252∗∗ 0.296∗∗∗(0.021) (0.023) (0.115) (0.090)

Municipality FE Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes YesPre-Rail Controls×Year FE No Yes No YesFirst-Stage F-stat 27 27 27 27Observations 19088.00 19088.00 19088.00 19088.00Mean dep. var. 0.01 0.01 0.10 0.10

Notes: OLS and 2SLS regressions.The dependent variable is the number of rail patents in columns 1–2 andthe number of non-rail patents in columns 3–4, both in logs. See Table ?? for information on includedLocal Geography and Pre-Rail controls. Standard errors are given in parentheses and are clustered at themunicipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

48

-.05

0.0

5.1

Any

pate

nts

0-5 5-10

10-15

15-20

20-25

25-30

30-35

Distance to Railroad Network (km)

(a) Any patent

-.05

0.0

5.1

.15

ln(P

aten

ts)

0-5 5-10

10-15

15-20

20-25

25-30

30-35

Distance to Railroad Network (km)

(b) Patents

-.05

0.0

5.1

.15

ln(P

aten

ts)

0-5 5-10

10-15

15-20

20-25

25-30

30-35

Distance to Railroad Network (km)

(c) New Inventors

-.04

-.02

0.0

2.0

4.0

6ln

(New

Hig

h-Sk

illed

Inve

ntor

s)

0-5 5-10

10-15

15-20

20-25

25-30

30-35

Distance to Railroad Network (km)

(d) New Inventors (Elite)

-.05

0.0

5.1

ln(N

ew L

ow-S

kille

d In

vent

ors)

0-5 5-10

10-15

15-20

20-25

25-30

30-35

Distance to Railroad Network (km)

(e) New Inventors (Non-elite)

Figure A.10:Spatial reallocation of innovation – Flexible distances

Notes: OLS regressions. The figures display estimates of a modified version of equation (2) where we include separate dummy variables for 5 km binsof distance to the railroad network in each decade on our outcomes: whether a municipality has any patent (A), the log number of patents (B), thelog number of new inventors (C), the log number of new elite inventors (D), and the log number of new non-elite inventors (D). Standard errors areclustered at the municipality level.

49

.05

.1.1

5ln

(Pat

ents

)

5-20k

m

20-35

km

35-50

km

50-65

km

65-80

km

80-95

km

95-11

0km

Excluded Areas (Distance to Network)

(a) OLS

0.1

.2.3

.4.5

ln(P

aten

ts)

5-20k

m

20-35

km

35-50

km

50-65

km

65-80

km

80-95

km

95-11

0km

Excluded Areas (Distance to Network)

(b) 2SLS (Least-Cost Path)

Figure A.10:Spatial reallocation – Dropping municipalities within distance windows

Notes: 2SLS regressions. The figures display coefficients for the effect of railroad access on the number ofpatents (ln). Each coefficient is a separate regression, where municipalities within a specified distance fromthe railroad network has been omitted. Standard errors are clustered at the municipality level.

50

Table A.11: Reduced Form Results with Standard Errors Robust to Spatial CorrelationDependent variable: Any patents ln(Patents) ln(Inventor) ln(Patents/inventor)Panel A: Spatial corr. 100 km

(1) (2) (3) (4)ln(Dist. Least-Cost Path)×1870 (0.003) (0.005) (0.004) (0.003)ln(Dist. Least-Cost Path)×1880 (0.004)∗∗∗ (0.005)∗∗∗ (0.004)∗∗∗ (0.004)∗∗ln(Dist. Least-Cost Path)×1890 (0.005)∗∗∗ (0.008)∗∗∗ (0.007)∗∗∗ (0.005)∗∗∗ln(Dist. Least-Cost Path)×1900 (0.006)∗∗∗ (0.012)∗∗∗ (0.010)∗∗∗ (0.006)∗∗

Panel B: Spatial corr. 200 km(1) (2) (3) (4)

ln(Dist. Least-Cost Path)×1870 (0.003) (0.005) (0.004) (0.003)ln(Dist. Least-Cost Path)×1880 (0.004)∗∗∗ (0.005)∗∗∗ (0.004)∗∗∗ (0.003)∗∗ln(Dist. Least-Cost Path)×1890 (0.005)∗∗∗ (0.009)∗∗∗ (0.008)∗∗∗ (0.005)∗∗ln(Dist. Least-Cost Path)×1900 (0.005)∗∗∗ (0.013)∗∗∗ (0.010)∗∗∗ (0.005)∗∗∗Municipality FE Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes YesPre-Rail Controls×Year FE Yes Yes Yes YesObservations 19056 19056 19056 19056Mean dep. var. 0.07 0.09 0.08 0.06

Notes: Standard errors are given in parenthesis and are robust to spatial correlation up till 100 km in panelA and up till 200 km in panel B. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

51

Appendix B: Instrumental variable estimation

B.1 Additional material

52

Table B.2: DiD Estimates (OLS) - Innovative Activity by DecadeDependent variable: ln(Patents) ln(Innovators) ln(Patents/Inventor)

(1) (2) (3) (4) (5) (6)Rail (5 km)×1840 -0.002 -0.001 -0.002 -0.001 -0.001 -0.000

(0.003) (0.002) (0.002) (0.001) (0.002) (0.002)Rail (5 km)×1850 0.002 -0.002 -0.000 -0.003∗ 0.002 -0.002

(0.004) (0.002) (0.003) (0.002) (0.004) (0.002)Rail (5 km)×1860 0.009∗ 0.000 0.008∗ 0.001 0.007∗ 0.001

(0.005) (0.005) (0.004) (0.004) (0.004) (0.004)Rail (5 km)×1870 0.026∗∗∗ 0.012 0.020∗∗∗ 0.008 0.021∗∗∗ 0.012∗

(0.008) (0.008) (0.006) (0.006) (0.007) (0.006)Rail (5 km)×1880 0.097∗∗∗ 0.061∗∗∗ 0.077∗∗∗ 0.047∗∗∗ 0.066∗∗∗ 0.048∗∗∗

(0.016) (0.014) (0.012) (0.011) (0.011) (0.010)Rail (5 km)×1890 0.185∗∗∗ 0.131∗∗∗ 0.154∗∗∗ 0.107∗∗∗ 0.105∗∗∗ 0.083∗∗∗

(0.023) (0.021) (0.019) (0.017) (0.015) (0.014)Rail (5 km)×1900 0.278∗∗∗ 0.201∗∗∗ 0.233∗∗∗ 0.166∗∗∗ 0.142∗∗∗ 0.116∗∗∗

(0.030) (0.027) (0.025) (0.022) (0.017) (0.017)Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes No Yes No YesObservations 19056 19056 19056 19056 19056 19056Mean dep. var. 0.09 0.09 0.08 0.08 0.06 0.06

Notes: OLS regressions. The dependent variable is either the number of patents, the number of inventors or the number of patents per inventor, allin logs. See Table ?? for information on included Local Geography and Pre-Rail controls. Standard errors are given in parentheses and are clusteredat the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

53

Table B.3: DiD Estimates (2SLS) - Innovative Activity by DecadeDependent variable: ln(Patents) ln(Inventors) ln(Patents/Inventor)

(1) (2) (3) (4) (5) (6)Rail (5 km)×1840 -0.008 -0.011 -0.003 -0.006 -0.007 -0.011

(0.014) (0.016) (0.011) (0.011) (0.014) (0.015)Rail (5 km)×1850 0.068 0.047 0.020 0.004 0.070 0.049

(0.059) (0.041) (0.024) (0.012) (0.059) (0.041)Rail (5 km)×1860 0.052 0.047 0.043 0.039 0.031 0.032

(0.049) (0.043) (0.041) (0.036) (0.037) (0.034)Rail (5 km)×1870 -0.011 -0.005 0.014 0.015 -0.026 -0.015

(0.054) (0.047) (0.049) (0.041) (0.039) (0.035)Rail (5 km)×1880 0.224∗ 0.237∗∗ 0.188∗ 0.203∗∗∗ 0.125∗ 0.137∗∗

(0.122) (0.095) (0.097) (0.075) (0.074) (0.066)Rail (5 km)×1890 0.335∗∗ 0.393∗∗∗ 0.293∗∗ 0.351∗∗∗ 0.167∗ 0.186∗∗

(0.161) (0.136) (0.137) (0.113) (0.087) (0.083)Rail (5 km)×1900 0.516∗∗ 0.603∗∗∗ 0.452∗∗ 0.538∗∗∗ 0.204∗ 0.221∗∗

(0.218) (0.180) (0.185) (0.150) (0.105) (0.101)Municipality FE Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes No Yes No YesFirst-Stage F-stat 9.09 8.77 9.09 8.77 9.09 8.77Observations 19056 19056 19056 19056 19056 19056Mean dep. var. 0.09 0.09 0.08 0.08 0.06 0.06

Notes: 2SLS regressions. The dependent variable is either the number of patents, the number of inventors or the number of patents per inventor, allin logs. See Table ?? for information on included Local Geography and Pre-Rail controls. Standard errors are given in parentheses and are clusteredat the municipality level. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

54

0.2

.4.6

.81

ln(P

aten

ts)

6 8 10 12Distance to least-cost path (ln)

(a) Least-Cost Path 1870

0.2

.4.6

.81

ln(P

aten

ts)

6 8 10 12Distance to least-cost path (ln)

(b) Least-Cost Path 1880

0.2

.4.6

.81

ln(P

aten

ts)

6 8 10 12Distance to least-cost path (ln)

(c) Least-Cost Path 1890

0.2

.4.6

.81

ln(P

aten

ts)

6 8 10 12Distance to least-cost path (ln)

(d) Least-Cost Path 1900

Figure B.3:Reduced From Relationship - Patents and Instruments

Notes: The figures display the non-parametric relationships between number of patents (ln) and our instrument by year, conditional on region fixedeffects as well as our set of local geography and baseline economic controls. Observations are sorted into 100 groups of equal size and the dots indicatethe mean value in each group. A linear regression line based on the underlying (ungrouped) data is also shown.

55

Table B.4Dependent variable: Any collaboration Any rail-collab. Any non rail-collab.

Main sample Rural s. Main sample Rural s. Main sample Rural s.

Panel A: OLS (1) (2) (3) (4) (5) (6) (7) (8) (9)Network Connection (=1) 0.029∗∗∗ 0.019∗∗∗ 0.017∗∗∗ 0.016∗∗∗ 0.009∗∗ 0.008∗∗ 0.005∗∗∗ 0.003 0.004∗∗

(0.005) (0.005) (0.005) (0.004) (0.004) (0.003) (0.002) (0.002) (0.002)

Panel B: 2SLS (1) (2) (3) (4) (5) (6) (7) (8) (9)Network Connection (=1) 0.023 0.037 0.037 0.036 0.042∗ 0.047∗∗ 0.024 0.024∗ 0.006

(0.036) (0.031) (0.028) (0.027) (0.024) (0.019) (0.015) (0.014) (0.009)Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes YesRegion FE×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesLocal Geography×Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesPre-Rail Controls×Year FE No Yes Yes No Yes Yes No Yes YesFirst-Stage F-stat 27.22 26.70 25.35 27.22 26.70 25.35 27.22 26.70 25.35Observations 19056 19056 18152 19056 19056 18152 19056 19056 18152Mean dep. var. 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00

Notes: OLS and 2SLS regressions. The table displays the effect of an indicator for network access (within5 km) on whether a municipality has any within-patent collaboration (columns 1–3), any within-patentcollaboration between inventors with network access (columns 4–6) and with no railroad access (columns7–9). Results are shown for the main sample (columns 1, 2, 4, 5, 7 and 8) and the rural sample (columns3, , 6 and 9). Standard errors are given in parentheses and are clustered at the municipality level. ∗∗∗ -p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

56

-.1-.0

8-.0

6-.0

4-.0

20

ln(P

aten

ts)

1840

1850

1860

1870

1880

1890

1900

Years

(a) Patents

-.1-.0

8-.0

6-.0

4-.0

20

ln(P

aten

ts)

1840

1850

1860

1870

1880

1890

1900

Years

(b) Inventors

-.1-.0

8-.0

6-.0

4-.0

20

ln(P

aten

ts p

er In

vent

or)

1840

1850

1860

1870

1880

1890

1900

Years

(c) Patents per Inventor

Figure B.4:Reduced Form Effects

Notes: OLS regressions. The dependent variable is either the number of patents (panel A), the number of inventors (panel B) or the number ofpatents per inventor (panel C), all in logs. Bars indicate 95 percent confidence intervals. Standard errors are clustered at the municipality level.

57

Appendix C: Additional material

(a) 1870 (b) 1880 (c) 1890 (d) 1900

Figure C.1:The railroad network 1870–1900

Notes: This figure displays the railroad network for the years 1870, 1880, 1890 and 1900 depicted in blackand deciles of patents per decade where darker shades correspond to a higher number of patents.

58

2040

6080

100

Trav

eled

km

per

inha

bita

nt

0.5

11.

52

2.5

Tick

ets

sold

per

inha

bita

nt

1870

1880

1890

1900

Year

Tickets Traveled km

Figure C.1:Tickets and traveled kilometers per inhabitant

Notes: The figure displays the aggregate number of tickets sold per inhabitant (in blue) and traveled kilo-meters per inhabitant (in red) in Sweden up till 1914 (in thousands).

AgricultureChemicals

ConstructionElectricity

Food and beveragesMachinery and metals

MiningOther manufacturers

Paper and printingScientific instruments

Steam enginesTextiles

TransportWeapons

0 .02 .04 .06ln(Patents)

(a) OLS

AgricultureChemicals

ConstructionElectricity

Food and beveragesMachinery and metals

MiningOther manufacturers

Paper and printingScientific instruments

Steam enginesTextiles

TransportWeapons

-.1 0 .1 .2 .3ln(Patents)

(b) 2SLS

Figure C.1:The effects of railroads on innovative activity across industries

Notes: OLS and 2SLS regressions. Each figure displays separate regression models from estimating equation(2) with the full set of controls. OLS estimates are displayed in A. 2SLS estimates using the least-cost andthe Ericson plan instruments are displayed in B. Standard errors are clustered at the municipality level andbars denote 95% CIs.

59

Table C.2: Cross-sectional 2SLS Estimates - Long-Differences in Innovation 1900-2014

Dependent variable: ln(Patents 2014) - ln(Patents 1900)(1) (2) (3) (4)

Network Connection (=1) 3.034∗∗∗ 3.255∗∗∗ 2.806∗∗∗ 2.982∗∗∗(0.993) (1.006) (0.717) (0.704)

Gained network connection (=1) 1.422∗∗∗ 1.176∗∗∗(0.434) (0.282)

Lost network connection (=1) -1.502∗∗∗ -1.547∗∗∗(0.384) (0.370)

Controls Yes Yes Yes YesObservations 2374 2374 2374 2374First-Stage F-stat 19.65 20.53 13.57 13.56Mean dep. var. 1.08 1.08 1.08 1.08

Notes: 2SLS regressions. Standard errors are given in parentheses and are clustered at the municipalitylevel. ∗∗∗ - p < 0.01, ∗∗ - p < 0.05, ∗ - p < 0.1.

60