the location of the italian manufacturing industry, 1871

The location of the Italian manufacturing industry,

1871-1911: a sectoral analysis

Roberto Basile∗ Carlo Ciccarelli†

May 2015

Abstract

Using a new dataset on value added at 1911 prices at province level for 12industries, we analyze the spatial location patterns of manufacturing activity inItaly during the period 1871-1911. We test the e�ect of domestic market potentialand factor endowment, focusing on water supply. The results show that, as trans-portation costs decreased and barriers to domestic trade were eliminated, Italianprovinces became more and more specialized, and manufacturing activity increas-ingly concentrated in a few provinces, mostly belonging to the North-West partof the country. The estimation results corroborate the hypothesis that both com-parative advantages (water endowment e�ect) and market potential (home-markete�ect) have been responsible of this process of spatial concentration. The locationof some traditional industries characterized by a low capital-labor ratio (such asclothing and wood) was mainly driven by water endowment, while the location offast growing new sectors characterized by a medium/high capital labor ratio (suchas engineering, metal-making, chemicals, textile and paper) was mainly driven bythe domestic market potential.

Keywords: Market potential, Factor endowment, Concentration, Italy.Jel codes: R12, R15, N13

PAPER PREPARED FOR THE EHES 2015 CONF. - PISA 4-5 SEPT. 2015.PRELIMINARY AND INCOMPLETE VERSION - PLEASE DO NOT QUOTE

WITHOUT AUTHORS PERMISSION.

∗Department of Economics, Second University of Naples, Corso Gran Priorato di Malta, 1 - 81043,Capua (CE), Italy. Email : [email protected]†Department of Economics and Finance, University of Rome �Tor Vergata�, Via Columbia 2, 00133

Rome, Italy. [email protected]

1 Introduction

Economic theory suggests that the spatial distribution of natural advantages and marketaccess are the key determinants of industrial location. Regional di�erences in natural en-dowments (such as water supply, climate, and mineral deposits) contribute to determineregional comparative advantages and, therefore, regional specialization. An uneven spa-tial distribution of market access encourages �rms to concentrate in regions with highermarket potential, to bene�t from increasing returns, and to export goods and servicesto other regions. These agglomeration (or centripetal) forces tend to contrast marketcompetition (or centrifugal) forces arising from the concentration of �rms and inducingto both lower local market prices and higher local factor prices. The equilibrium be-tween these o�setting forces mainly depends on the degree of market integration andtransportation costs. Under autarky, each region produces essentially the goods thatconsumes, the location of industries is stable, and the level of industrial concentration islow. When trade costs decrease and product markets tend to integrate, the neoclassicaltrade model (Heckscher-Ohlin) predicts that regional specialization will arise as regionsproduce and export products that are relatively intensive in their abundant resources(comparative advantages). Moreover, when transportation costs decrease, the New Eco-nomic Geography (NEG) literature predicts that manufacturing activities characterizedby increasing returns to scale tend to concentrate in the regions with higher demand(home-market e�ect), while the remaining regions su�er de-industrialization. Therefore,a regional division of labor spontaneously arises through a process of uneven development.

The relative e�ect of comparative advantages (factor endowment) and increasing re-turns (market access) on industrial location can be hardly quanti�ed using empiricaldata referring to modern economies, as the two factors tend to coexist and interact in avery complex way along with the e�ect of (endogenous) policy interventions. Midelfart-Knarvik and Overman (2002) show for instance that the European industrial policy in�u-enced strongly the industrial location patterns across the EU regions; Basile, Castellani,and Zanfei (2008) also provide evidence that EU Structural funds a�ected signi�cantlythe location choice of multinational �rms in Europe. But if industrial policies are endoge-nously driven by the actual spatial distribution of economic activity, then it can be quitehard to quantify the genuine (net of industrial policies) e�ect of comparative advantagesand/or market potential on industrial location. However, the use of historical data cov-ering the years following the political uni�cation of a country (that is when the domesticmarket integration was full of obstacles, and no systematic national industrial policy tookplace), may provide an opportunity to better contrast the di�erent explanations for thespatial concentration of industry and, in particular, to appreciate the increasing role ofthe home-market e�ect. Examples of studies in this direction are Wolf (2007), for thecase of Poland, Rosés (2003), for the case of Spain, and A'Hearn and Venables (2013), forthe case of Italy. In particular, the latter explores the interactions between external tradeand regional disparities in the Italian economy since its uni�cation (1861). The authorsargue that the economic superiority of Northern regions over the rest of the country wasinitially based on natural advantages (in particular the endowment of water), while fromthe late 1880s onwards domestic market access became a key determinant of industriallocation, inducing fast growing new sectors (such as engineering) to locate in regions witha large domestic market, that is, essentially, in the North. From 1945 onwards, with the

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gradual process of European integration, foreign market access represented instead thedecisive factor; and the North, again, had the advantage of proximity to these markets.

Following these recent contributions, in the present paper we analyze the spatiallocation patterns of industrial activity in Italy during 1871-1911. During this periodthe country experimented a strong process of domestic market integration, with thedismantling of institutional barriers to domestic trade, the adoption of a common currency(with the national central bank, the Bank f Italy, instituted in 1893), the harmonizationof local institutions and the creation of a national railway network. Going along the lineof reasoning put forwards by A'Hearn and Venables (2013), we test the hypothesis thatthe process of internal economic integration has ampli�ed the regional asymmetries indomestic market accessibility, thus a�ecting the industrial location, especially for the fastgrowing modern sectors, characterized by increasing returns technologies. On the otherhand, the lack of a modern electricity transmission system during the sample periodimposed the need to exploit local sources of power. In the Italian case, as widely pointedout (see, for instance, Cafagna, 1989; Bardini, 1998; Fenoaltea, 2011), abundance of waterrepresented an essential source of power in a country without coal.1 The second hypothesisis therefore that the location of manufacturing activity was strongly correlated to watersupply.

We test the relative importance of factors' endowment (water supply) and market po-tential in the early phases of Italian industrialization using recently developed value addeddata at 1911 prices at the provincial NUTS-3 level (69 provinces) for the following 12manufacturing sectors: foodstu�s, textile, tobacco, clothing, leather, wood, non-metallicmineral products, engineering, metalmaking, chemicals, paper, and sundry manufactur-ing (Ciccarelli and Fenoaltea, 2013, 2014). In other words, we try to assess the argumentsdeveloped by A'Hearn and Venables (2013) using the most detailed information (bothfrom the territorial and the sectoral point of view) on the spatial distribution of economicactivity available for the post-uni�cation period.

Our results clearly show that as transportation costs decreased and barriers to do-mestic trade were eliminated, Italian provinces became more and more specialized, andmanufacturing activity became increasingly concentrated in a few provinces, mostly be-longing to the North-West part of the country. The estimation results corroborate thehypothesis that both comparative advantages (water endowment e�ect) and market po-tential (home-market e�ect) have been responsible of this process of spatial concentration.The location of some traditional industries (such as clothing) was mainly driven by waterendowment, while the location of fast growing new sectors (such as engineering, metal-making, chemicals, textile, and paper) was increasingly driven by the domestic marketpotential.

The rest of the paper is organized as follows. Section 2 describes the basic historicaland economic features of the manufacturing industry. Section 3 illustrates its spatialdistribution and di�usion. Section 4 presents the estimation results of a Geoadditivemodel of industrial location considering factor endowment and market potential. Section5 concludes.

1Fernihough and O'Rourke (2014) consider the importance of geographical proximity to coal as afactor underpinning comparative European economic development during the Industrial revolution.

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2 Setting the scene

Figure 1 illustrates the division of the Italian territory that roughly prevailed duringthe 1815-1860 period. Before the country's uni�cation in 1861 the Italian territory wasdivided in seven States (Kingdom of Sardinia, Kingdom of Lombardy-Venetia, Duchyof Parma and Piacenza, Duchy of Modena and Reggio, Gran Duchy of Tuscany, PapalStates, and the Kingdom of Two Sicilies) characterized by extremely di�erent institutionsand economic policies The coin was di�erent among pre-unitarian states. Primary schoolwas mandatory in certain states but not in other and trade-policy di�ered considerably.2

Figure 1

Italian provinces at 1911 borders grouped into pre-unitarian States (1850 ca.)

Italy was uni�ed in 1861, although Venetia and Latium were annexed to the countryonly in 1866 and 1870, respectively. Between 1861 and 1870 the national capital wasmoved from Turin to Florence, and �nally to Rome. Soon after the political uni�cation,policy makers realized that there was an urgent need of statistical evidence. Populationcensuses every ten years were established, and dozens of annual reports to the ItalianParliament, and other o�cial publications concerning the main economic sectors (publicbudget and taxation, international trade, railroads, public school system) were regularlyproduced. The new o�cial historical statistics divided the Italian territory into 16 regions(compartimenti, roughly NUTS-2 units), and 69 provinces (province, roughly NUTS-3units). The borders of these administrative units did not change during 1871-1911.

As we will document along the rest of the paper, economists and economic histo-rians largely agree in considering factor endowment and market potential as the mainfactors that in�uenced industrial location in nineteenth century Italy. The present study

2The analysis of the impact of these economic and institutional di�erences among pre-unitarian stateson the industrial and more generally socioeconomic evolution of post-uni�cation Italy is clearly beyondthe scope of this paper. Felice (2014) provides an account of the long-term role played by institutionson regional growth and development in Italy. The author essentially follows Acemoglu, Johnson, andRobinson (2004) and argues that extractive institutions historically prevailed in southern regions, whileinclusive institutions characterized essentially the North of Italy.

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acknowledges the point, and provides a �rst attempt to quantify the matter within aproper statistical model estimated at the province level.

2.1 Factor endowment: the role of water supply

Factor endowment matters economically depending on the prevailing technology. In 1862,the Francesco Rossi wool mill was built in Schio (province of Vicenza, in the North-Eastof Italy). The factory was 80 meters in length, 13.9 in width, with 6 �oors above theground, 125 cast-iron columns, 300 windows, with all the convenience of water and steamfor normal use and lost steam heating. In the �rst �oor there were 13 mule-jennies,alternatively opening and closing in three rows, with 3,600 spindles. The cutting tookplace on the second �oor. On the third �oor there were 60 Jacquard looms. Under the roofthere was a hall in which a hundred and twenty women did the darning.3 The Rossi woolfactory in Schio represented an example of �vertical-factory�, that is a particularly tallbuildings characterizing the industrial architecture of the time. Far from being an isolatedcase, these vertical-factories became increasingly di�used in the North (the Cantoni cottonfactory in Lombardy provides another famous example).4 The main raison d'être of thevertical factories was the need to have machines as close as possible to the drive shaft ofthe driving force, to avoid energy loss, and to obtain a regular movement of machinery.So a single tree moved by the force of the water (or of the steam) could serve manyoverlapping levels in each of which the machines could not be too far away from thepower source. As e�caciously summarized by Negri and Negri (1981), pp. 138-139 �Theplacement of the �rst industrial complexes in the territory is strongly determined bythe availability of water power. It is a waterfall in fact that provides the energy thatguarantees - through a system of wheels and shafts - that the machines are in constantmotion�.5

The central role of natural endowment (water, above all) for industrial location in19th century Italy was recently reiterated by Fenoaltea (2011), providing perhaps themost careful and sharp account of Italian industrialization over the 1871-1911 period.According to Fenoaltea, �the roots of the success of the Northern regions seem [...] en-vironmental rather than historical� (p. 231): factor endowment thus, more than socioe-conomic variables such as human and social capital. Modern (or factory-based) systemof production replaced gradually artisanal production, and �gave a strong advantage tothe locations with a year-round supply of water (for power, and also, in the speci�c caseof textiles, for the repeated washing of the material); and in Italy such locations aboundonly on the northern edge of the Po valley, where the Alpine run-o� o�sets the lack ofsummer rain�. And, when analyzing the evolution of the location of the textile industryduring 1871-1911, Fenoaltea (2011, p. 232) mentions among the natural advantages ofnorthern regions �the water that �owed in the rivers, the water suspended in the air [...]�and the presence of �mountain glaciers�.

3The description of the Rossi wool factory summarized in the text was made by FrancescoRossi himself in a letter of 22nd April 1863 to his friend Fedele Lampertico and available athttp://www.schioindustrialheritage.com/uk, last accessed June 2015.

4The Biellese, Vicentino, Novarese, Varesotto, Brianza, Cremasco, Val Trompia, and Val Brembanaindustrial districts in Northern Italy are examples of �little Manchester� considered in Cafagna (1999).A detailed account on the matter can be also found in Romano (1990), pp. 209 �.

5On this point also see Cafagna (1989, p. 196).

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To measure the relative water endowment at provincial level during 1871-1911, thepresent study considers ultimately two variables.6 The �rst one refers to the economic"relevance" of Italian rivers. The historical source Annuario Statistico Italiano (1886, pp.22-27) provides a list of 100 rivers �owing through the 69 Italian provinces. These riversare here ranked, with a value ranging from 1 to 5, according to the weight assigned tothem by the experts of the Italian Military Geographic Institute (IGM) in a publicationthat culminates a research project started in the 1960s (IGM, 2007).7 The importance (orweight) of each river is established on the base of �the length of each river and its socio-economic relevance� (IGM, 2007, preface). In the IGM ranking, the value of 1 means"high relevance", while the value of 5 means "low relevance". Thus, our �rst measure ofwater endowment in each province i is:

RIV ERi =∑

k∈i(6− IGMrankk),

where k refers to each river �owing through province i.8

The second variable refers to presence of waterfalls in the various provinces (source:Pavolini, 1985).Interestingly, as we will document in our econometric analysis, the in-teraction between the continuous variable RIV ERi and the waterfall dummy variableproved to add signi�cant help in understanding of industrial location of certain manufac-turing sectors, and supports nicely the need for industrial use of a year-round supply ofwater considered by Fenoaltea (2011). The provincial distribution of these water-relatedvariables is illustrated in Figure 2. The map on the left panel (based on the IGM data)shows a rather homogeneous distribution of rivers among Italian provinces. The onlyexception are the provinces in the deep South of continental Italy. The map in the rightpanel (based on Pavolini, 1985) shows instead that the main Italian waterfalls whereconcentrated in northwestern provinces.

To summarize, the �rst factories, above all wool and cotton mills, were located wherethe rivers were steeper. They moved gradually towards the plain with the adoption ofsteam �rst, and of the electricity then. Over time, the original river�forest�vertical-milltriad was gradually replaced by large horizontal -factories located in plain, and oftenbordering the main urban settlements. The point is clearly stated in Ciccarelli andFenoaltea (2013), pp. 81-82 noticing that �Already in the nineteenth century the spreadand increasing e�ciency of the steam engine had meant that energy could be moved bymoving fuel, that power could be generated anywhere; but only electricity brought thee�ective separation of generation and use, and by the early twentieth century power couldbe economically transmitted over previously inconceivable distances. Industrial locationpulls were revolutionized: the most energy intensive industries alone remained tied tothe waterfalls, most manufacturing could pro�tably move closer to the sources of the rawmaterials, closer to the market, saving on the transportation of goods at a now a�ordablecost in the transmission of energy. In practice, at the margin industry abandoned theremote sources of power for the major nodes of communication, the centres of trade, inshort for the very locations that had already nurtured the largest urban concentrations.�

6See the Appendix for details on sources and methods.7The Military Geographic Institute (IGM), created in 1861, is the national mapping agency for Italy.8The IGM (2007) list includes, of course, the rivers Strona (in Piedmont), Olona (in Lombardy), and

Astice (in Venetia) �entered in the Italian economic history for the role they played in the early steps ofItalian industrialization� Cafagna (1989), p. 196.

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(a) Rivers (b) Waterfalls

Figure 2

Water endowment: panel (a) shows the geographical distribution of the RIV ERi variable, a measureof the "relevance" of the rivers �owing through province i; panel (b) shows the geographical distributionof the number of waterfalls.

The previous arguments lead us naturally to consider the role played by domestic marketpotential.

2.2 Domestic market integration during the post-uni�cation pe-

riod

In order to analyze the importance of domestic market access as a key driver of the spatialdistribution of economic activity (as suggested by the NEG theory; Fujita, Krugman, andVenables, 1999; Combes, Mayer, and Thisse, 2008), a sound measure of accessibility todemand is required. In line with Crafts (2005), we constructs market potential estimatesfor each Italian province i between 1871 and 1911 using Harris (1954)'s formula, that isas a weighted average of total value added of all provinces j:

MKTPOTit =N∑j=1

V Ajtdij,

j 6= i (1)

with dij the great circle distance in km between the centroids of provinces i and j. Inpractice, this indicator equates the potential demand for goods and services produced in alocation with that location's proximity to consumer markets. Thus, it can be interpretedas the volume of economic activity to which a region has access after having subtractedthe necessary transport costs to cover the distance to reach all the other provinces.9 Animportant point is that historical GDP estimates at the provincial level for the case ofItaly are not available. We thus proxy for provincial GDP by allocating total regionalGDP (NUTS-2 units) estimates for 1871, 1881, 1901, and 1911 to provinces (NUTS-3

9The trade cost to access the market internal to the province is assumed to be proportional to onethird of the radius of a circle with an area equal to that of the province, i.e. dii = 2/3

√Areai/π.

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units) using the provincial shares of regional population obtained by population census.10

As shown in Figure 3, the values of Harris market potential appear to be more and moreconcentrated in the northwest. It is important to stress that within northern regions thereis a substantial amount of heterogeneity.11 In Lombardy, the undisputed economic leaderamong Italian regions, only the provinces of Milan (MI) and Como (CO) neighboring theregion of Piedmont (in the very northwest of Italy and bordering France) score particularlywell.12 Similarly in Liguria only the province of Genoa registers high market potentialwhile Porto Maurizio (PM) does not. But there is more than that. Estimated marketpotential is also high in Florence, Rome, and Naples, that is the provinces with the pre-unitarian city capitals of the Gran Duchy of Tuscany, the Papal States, and the Kingdomof Two Sicilies. This last evidence matches perfectly well with the intuition by Fenoaltea(2003) that in his (NUTS-2) study on Italian regional industrialization noticed that inthe early 1870s �The industrial, manufacturing regions are those with the former capitals,of the preceding decades and centuries� and that �In such a context the appropriate unitof analysis is not in fact the region, but (in Italy) the much smaller province�.

The evolution of trade-policy and the improvement of transport and communicationtechnologies have in�uenced the dynamics of transport and trade costs and, thus, thedynamics of the domestic market potential. The historical literature generally agree onthe following periodization of the post-uni�cation trade policy: i) the free-trade period:1861-1878; ii) the start of protectionism: 1878-1887; iii) the full embracement of pro-tectionism: 1887-1913. As any periodization, the one proposed has of course a certaindegree of arbitrariness. During the free trade period (1861-1878) the mild tari� of Pied-mont (the political core of the Kingdom of Sardinia) was extended to the rest of thecountry. Southern Italy, where protectionism was the rule under the Kingdom of TwoSicilies, was particularly a�ected. Table 1 summarizes this for selected products. Thee�ects on the economy of Southern regions of the mild tari� in force in Italy in the after-math of the uni�cation has been evaluated in di�erent ways. Certain scholars argue thatit was particularly detrimental for Southern industry (see, for instance Milone, 1953),other scholars points rather to its positive e�ects (see, for instance, Ciccarelli and Fenoal-tea, 2013). In 1878 and 1887 import duties were generally increased, and the historicalliterature focused in particular on that on wheat, iron, textile, and sugar (see Federico,Natoli, Tattara, and Vasta, 2011; Fenoaltea, 2011, for an exhaustive account).13 Ciccarelliand Proietti (2013) discuss the possible e�ect of protectionism on industrial specializa-tion of Italian provinces during 1871-1911 by means of a graphical tool named �dynamic

10GDP estimates at historical NUTS-2 borders have been kindly provided by Emanuele Felice. Theseestimates di�er from the ones published by same author (Felice, 2013), which are at current borders.

11This is, we believe, a neat example of the advantage of using provincial (NUTS-3) over regional(NUTS-2) �gures.

12Up to the late 1880s, when Italy started a nonsense trade-war with France, nearly a half of Italy'stotal exports, albeit rather limited in size, were directed to the France market (Federico, Natoli, Tattara,and Vasta, 2011, pp. 42-43).

13The Italian import duties of the time were essentially tied to the physical weight of the importedgoods, and not to their value. As a consequence their e�ectiveness depended, other things being equal,on the price level. The price level was lowered in the last decades of the 19th century, reach a minimumin 1895 ca, and start growing rapidly between 1900 and 1913. As a result the degree of protectionismon Italian goods was, for a given structure of import duties, less e�ective during the belle époque of thepre-WWI years.

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(a) Year: 1871 (b) Year: 1881

(c) Year: 1901 (d) Year: 1911

Figure 3

Harris market potential

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Table 1

Import duties on selected products before and after the Uni�cation (lire per 100 kg)

(1) (2) (3)PRE-1861 POST-1861

Southa Northb Italyc

TEXTILES:cotton:

yarn 28 20 10cloth 90 75 40

wool:�ber 21 0 0yarn 193 60 40cloth 0 300 140

OTHER PRODUCTS:cereals 8 2.48 0raw sugar 40 17.85 18co�ee 47 34.96 30bar iron 18 0 0

a �South� denotes the tari� of the Kingdom of Two Sicilies; b �North� denotes the tari� of the Kingdomof Sardinia; c �Italy� denotes the tari� in force in Italy after the 1863 Franco-Italian trade-agreement.Source: C. Correnti, P. Maestri, Annuario Statistico Italiano. Anno II - 1864 (Turin, Tipogra�a Letter-aria, 1864), p. 513.

specialization Biplot�.14 The study suggests that the adoption of protectionism duringthe late nineteenth century did change, at least for industrial sectors like the textile one,the specialization trajectory of selected northern provinces.

Protectionism generally reduce the international �ows of commodities. Despite risingimport duties, especially after the 1887 tari�, the share of export over GDP increasedfrom about 6.5 percent in 1862-1887 to about 9.3% in 1887-1914, while the share ofimport over GDP increased from about 8% in 1862-1887 to about 12% during the sameperiod.15 In 1862, more then 90% of Italian imports were from European countries, whilein 1911 only 69%. Imports from USA increased from about 5% in 1862 to about 20%in 1911 (Federico, Natoli, Tattara, and Vasta, 2011, p. 32). The temporal evolutionof the structure of the Italian international trade is also particularly informative on thetransformation of the industrial system. In the aftermath of the country's uni�cationthe most signi�cant imports were represented by textile goods (of cotton, wool and silk),while imports of industrial raw material (coal, raw cotton) were of relatively reducedimportance. In the pre-WWI years, raw cotton and coal represented instead the mostsigni�cant items among Italian imports. Over time thus, following a standard processof import-substitution, the imports of �nal goods was replaced by that of raw materials,

14The �dynamic specialization Biplot� graphical tool proposed in Ciccarelli and Proietti (2013) islargely based on the BiplotGUI package for R elaborated by la Grange, le Roux, and Gardner-Lubbe(2009).

15The degree of openness (absolute value of import plus export over GDP) increased accordingly,passing from about 14.5 to 21.2.

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1861 1886 1909

Figure 4

The evolution of the Italian railway network, 1861-1909

whereas �nal goods were increasingly produced domestically. The structure of exportalso changed considerably: while olive oil, silk, and citrus fruit reduced their importance,the exports of manufactures (including cotton textile and machinery) started growing(Fenoaltea, 2011; Federico, Natoli, Tattara, and Vasta, 2011).

As for the contribution of the improvement of transport and communication tech-nologies to the dynamics of market potential, it is worth noticing that while both roads-and sea-transports were relevant to the Italian peninsula, the extension of the railroadnetwork represented the main novelty brought out by the political uni�cation of 1861.To illustrate the reduction in transport costs over 1871-1911, Figure 4 summarizes therapid progress of this network.

Railroads were already active in 1861 in the North-West and Tuscany while, withthe exception of the area surrounding Naples, they were completely absent in the South.The ports of Genoa, Venice, and Ancona were connected to Milan. The port of Genoawas connected by railways to Turin in 1853. Turin and Milan were connected by railwaysbetween 1855 and 1859. These connections contribute substantially to the early industrialdevelopment of the North-West area.16 High quality coal, an input of growing importancewith the di�usion of factory-based industry, was largely imported from the UK, especiallyfrom Cardi� and Newcastle. It was delivered to the main Italian ports of Genoa andNaples, and distributed across the country by railways. The coast of imported coal wasabout 50 to 80 lire per ton depending on the distance from the nearest port (against acost of about 7 lire per ton in England, Sapori, 1961, p. 8). Coal was also imported byrailways. According to Mario Abrate, among the leading expert of industrialization inPiedmont, in the early 1870s the coal delivered to Turin from St. Etienne (central easternFrance) by railways had a price of 35 lire per tons (of which about 15 lire for the coal

16Venetia was part of the Habsburg Empire up to 1866. The Austrian policy aimed at strengtheningthe commercial relation between Wien and Trieste (in the region of Venetia), and its port on the upperAdriatic sea, and, at the same time, to downsize Genoa's commercial ambitions, tied naturally to thedevelopment of its port in the upper Mediterranean sea. The opening of the railway line connecting thecapital city of Turin with the Port of Genoa in 1853 represented thus a clear attempt of the Italians, aboveall Cavour, to hinder the development of north-eastern trade routes dear to Austrian policy makers, andto foster the economic development of the Italian North-West.

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itself, and 20 lire as transport cost, while the the coal shipped from Cardi� to the port ofGenoa, and then to Turin by railways had a total cost of about 55 lire per ton (Abrate,1970, pp. 28-29).17 The subsequent development of the Italian railways was surprisinglyfast. While Northern provinces bene�ted from the early availability of railroads, the mainnetwork was extended quickly to the rest of the country and already in 1886 (see, again,Figure 4) it was almost completed. Over the next couple of decades the national networkwas then �lled in completely.18

2.3 The structure of economic activity

During the second half of the nineteenth century the sectoral composition of total grossvalue added (GVA) changed considerably. Industry and services increased at the expenseof agriculture (see Table 2). From 1871 to 1911 the share of GVA covered by agriculturedecreased from 49 to 38 percent, while that of industry increased from about 16 to about24 percent.19

Table 2

The changing composition of Italy's GDP: sectoral shares (percentages), 1871, 1881, 1891, 1901, 1911

1871 1881 1891 1901 1911AGR 49.11 47.27 45.72 44.11 38.41

IND 15.73 16.87 18.20 19.29 24.46manufacturing 12.37 13.08 14.09 15.73 18.98

SER 35.16 35.86 36.08 36.6 37.13TOT 100.00 100.00 100.00 100.00 100.00

Source: authors' elaboration on Fenoaltea (2005), providing value added estimates at 1911 prices; indus-try includes four major aggregates: extractive, manufacturing, utilities, and constructions.

Using a new dataset developed by Ciccarelli and Fenoaltea (2013, 2014), Table 3focuses on the manufacturing sector and reports value added percentage shares at bench-mark years (1871, 1881, 1901, and 1911) when population censuses were taken. With afew exceptions, sectors tied to the production of consumption goods (such as foodstu�sand leather) show a constant reduction in their share of value added. Sector tied to theproduction of durable goods (such as metalmaking and paper) show an opposite long-term trend, with a rapid acceleration in the 1901-1911 decade. The last column of Table 3summarizes 1871-1911 trends, with numbers below one prevailing in the upper rows ofthe table, while those above one in the lower ones.

17The �rst French railway line was completed in the late 1820s, and connected the coal mines nearSaint-Étienne and the banks of the Loire River in Andrézieux, near Lyone. The trains, which consistedof three mine cars, were powered by gravity on downhill trips. Horses pulled them back up until mid1840s.

18Scholars also stressed that the Italian rail fares of the time were excessively expensive, and thattraditional costal shipping played and important role within the Italian transport system even after theintroduction of railways (Fenoaltea, 2011; Schram, 1997).

19The sectoral composition of Italian GVA was similar to that of other Southern European countries,while Northern countries, above all England, in 1911 ca had only about one �fth of the labor forceemployed in the agricultural sector (Broadberry, Federico, and Klein, 2010).

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The above level of disaggregation, albeit rather �ne, is not fully satisfactory. Withinthe vast and variegated engineering sector, to exemplify, the manufacturing of naval shipand rolling stock was certainly more sophisticated from a technological point of view, thanthe maintenance of agricultural tools (such as spades, hoes, and ploughs) representingpossibly a substantial part of blacksmiths' traditional activity. Within textile, to givea second example, traditional �bers (hemp, linen, jute, silk, wool) were processed verydi�erently from cotton and arti�cial silk. The non-metallic mineral product sectors, toprovide a �nal example, includes the famous white marble of Carrara (with its highvalue per-ton making it a relatively mobile product) and bricks and tiles, tied to theconstruction sector, with low value per-ton and, as consequence, a production activityof local nature, with kilns di�used virtually everywhere. One can further note thatin 1911 the foodstu�s, textile, and engineering sectors represented alone more the 50percent of total value added in manufacturing. According to Ciccarelli and Proietti(2013), describing industrial specialization trajectories of Italian provinces during 1871-1911, these three sectors act as �su�cient statistics� for the whole manufacturing sectorin that are able to capture much of the total variability of provincial specialization withinmanufacturing.20

Table 3

Manufacturing sectors: value added shares

Sectors 1871 1881 1901 1911 1911/1871

Food 33.5 30.4 25.4 21.5 0.6Tobacco 1.6 1.3 0.9 0.7 0.4Textile 10.3 10.3 12.8 11.1 1.1Clothing 6.9 7.4 6.8 6.3 0.9Wood 10.0 9.3 9.7 10.0 1.0Leather 10.5 11.5 11.4 7.8 0.7

Metal 0.6 1.0 1.7 3.1 5.2Engin 17.5 17.8 18.6 21.5 1.2NonMet 3.6 4.2 4.2 6.6 1.8Chem 2.1 2.4 3.0 4.3 2.0Paper 2.7 3.5 4.8 6.3 2.3Sundry 0.7 0.7 0.6 0.7 1.0

Source: our elaborations on data produced by Ciccarelli and Fenoaltea (2013, 2014).

The next sections discuss the geographical distribution of manufacturing activity andits possible determinants. The remaining of this section illustrates the procedure thatwe followed to obtained the new industrial value added estimates for Italy's provinces.The �rst ever estimates of provincial value added at 1911 prices for Italian industry andfor the years 1871, 1881, 1901, and 1911, were presented by Ciccarelli and Fenoaltea(2010) and further discussed in Ciccarelli and Fenoaltea (2013). The authors allocatedexisting regional estimates to provinces, by using the provincial labor force shares of

20Much of this section's content is inspired by the �The structure of industrial production� section inCiccarelli and Proietti (2013). However, we use here updated provincial �gures of valued added for theengineering sector, obtained by allocating to provinces trough sectoral labor force shares of the regionaltotal, the new regional estimates by Ciccarelli and Fenoaltea (2014).

13

regional totals, separately for each of the 15 industrial sectors (extractive, manufacturing,constructions, and utilities; with manufacturing further disaggregated into 12 sectors asillustrated in Table 3). While annual 1861-1913 regional estimates (NUTS-2 units) arebased on a rich set of detailed historical sources, provincial estimates (NUTS-3 units)rest essentially on the information on labor force as reported in the population censusesand are thus only available for the years 1871, 1881, 1901, and 1911.21 In this paperwe update the provincial estimates by Ciccarelli and Fenoaltea (2013) by incorporatingat the provincial level the new regional estimates on the engineering industry providedby Ciccarelli and Fenoaltea (2014). While Ciccarelli (2015) summarizes in detail thestate of play of regional historical statistics for Italy's industry, the important point hereis that the new provincial estimates (NUTS-3) on industrial value added used in thispaper are strictly related to estimates at the regional level (NUTS-2 units). Labor force�gures obtained from population censuses allow one to map value added estimates oneach industrial sector from 16 regions to 69 provinces. The implicit assumption is thatwithin a given region, within a given industrial sector, and within a given census year,the productivity of labor is homogeneous across provinces. In other words, di�erences inlabor productivity are here only accounted for by the regional dimension of the data notthe provincial one. This is an unfortunate, yet hardly avoidable, assumption. Detailedhistorical sources that allow one to avoid this assumption are only available at the regional(NUTS-2 units).22

3 The spatial di�usion of manufacturing activity

In this section, we use the value added data at 1911 prices described above to answer sev-eral questions about the spatial di�usion of industrial activity in 19th century Italy. Howsimilar was the industrial structure across di�erent provinces in Italy? Did it changeduring the 1871-1911 period? How concentrated was the manufacturing activity as awhole and how concentrated was a given industry? Which industries tend to agglomer-ate, which industries were rather dispersed? And do we �nd an increase or decrease inconcentration over time?

In order to answer these questions, we clarify some measurement issues. First, wecompute the share of a certain manufacturing industry k in the total manufacturingactivity of province i

(ski (t)

), de�ned as

ski (t) =xki (t)∑k x

ki (t)

(2)

21Population censuses only reports information on individual professions, and not on industrial sectors.Data on the hundreds of individuals professions have to be grouped to industrial sectors, separately byprovince, and the procedure is in part arbitrary. To exemplify: a nineteenth century chemist producedand sold the drugs at the same time. But only the former activity (production of drugs) belongs to themanufacturing sector. The latter (sale of drugs) belongs instead to the service sector. Ciccarelli andMissiaia (2013) reports and analyzes the main pros and cons of the provincial labor force data used byCiccarelli and Fenoaltea (2010, 2013) to obtain their provincial value added estimates.

22The long-term project on regional industrial production carried on by Ciccarelliand Fenoaltea (2009, 2014), and sponsored by the Bank of Italy is illustrated athttps://www.bancaditalia.it/pubblicazioni/altre-pubblicazioni-storiche/produzione-industriale-1861-1913/.

14

where xki (t) measures the level of economic activity k at location i and time t.Second, we compute the share of a certain location i in the total manufacturing

activity of industry k as

lki (t) =xki (t)∑i x

ki (t)

(3)

Third, we de�ne the location quotient as

LQki (t) =

lki (t)∑k x

ki (t)/

∑i

∑k x

ki (t)

=ski (t)∑

i xki (t)/

∑i

∑k x

ki (t)

(4)

In section 4, we will use the location quotient as our dependent variable in an econo-metric analysis of the determinants of industrial location.

3.1 Regional specialization and geographical concentration of eco-

nomic activities

Using the shares ski (t) we can address the question of how specialized were Italianprovinces by using Krugman's specialization index Ki(t), de�ned as:

Ki(t) =∑k

|(ski (t)− s−ki (t))| (5)

where s−ki (t) is the share of industry k in the total production of all provinces exceptprovince i. Thus, the Krugman index summarizes a province's di�erence in specializationwith respect to the rest of Italy over all industries. It takes the value of zero if a province'sindustrial structure is identical to the rest of Italy, and the value of two if the region has noindustries in common with the rest of Italy. Table 4 shows means and standard deviationsof Krugman index for the four sample periods, while �gure 5 maps its spatial distribution.The results clearly show that on average the degree of industrial specialization of Italianprovinces monotonically increased over the sample period (from 0.26 to 0.38), while itsdispersion slightly raised. The increment in the value of Krugman index was particularlypronounced for selected provinces of the North-West (including Turin, Milan, Genoa,Novara, Como, Cremona, Bergamo), and Tuscany (Massa Carrara, Lucca, Pisa, andLeghorn).

Table 4

Industry specialization of Italian provinces. Krugman index (value added data)

Year 1871 1881 1901 1911

Mean 0.26 0.29 0.35 0.38St.dev 0.10 0.11 0.14 0.13CV 0.38 0.36 0.39 0.35


15

(a) Year: 1871 (b) Year: 1881

(c) Year: 1901 (d) Year: 1911

Figure 5

Specialization: Krugman index

16

Next, we try to assess whether this increase in specialization corresponded to a higherspatial concentration of industries, by using a relative Theil index of industrial concen-tration:

Ck(t) =∑i

viVLQk

i (t) ln(LQk

i (t))

(6)

where vi is the value added in province i and V is the total value added in Italy. Thehigher the value of Ck(t), the higher the concentration of industry k.

Table 5 reports the value of the Theil index for both the whole manufacturing sec-tor (the location quotient is in this case computed by normalizing with respect to totaleconomic activity) and for the 12 individual industries (the location quotient is in thiscase computed by normalizing with respect to total manufacturing activity) at bench-mark years during 1871-1911. We observe that, along with the progressive integrationof commodities and factor markets (falling transport costs), the manufacturing activitybecame slightly more concentrated throughout the entire period (the value of the Theilindex for the manufacturing industry as a whole was 0.03 in 1871 and became 0.08 in1911).

The aggregate coe�cient hides the notable tendency towards the concentration of sev-eral industries (namely, foodstu�s, textile, wood, leather, metalmaking, and engineering).Instead, the relative level of concentration remained stable in two sectors (clothing andchemicals) and only three industries became relatively more dispersed (tobacco, paperand non-metallic mineral products). Nevertheless, tobacco remained the most concen-trated sector, followed by metalmaking and textile.23 The most dispersed sectors wereinsteadWood, Food, and Engineering. However, the ranking of the Theil indices remainedvery stable over time (the Spearman rank correlation between the Theil indices in 1871and in 1911 is 0.95 with a p-value of 0.000). Interestingly, the stability in the level ofconcentration across industries observed for 19th century Italy is a pattern also found instudies concerning more recent periods, both for Italy (Arbia, Dedominicis, and De Groot,2011) and other countries (Alonso-Villar, Chamorro-Rivas, and González-Cerdeira, 2004;Devereux, Gri�th, and Simpson, 2004; Dumais, Ellison, and Glaeser, 2002).

3.2 Global spatial dependence

The relative Theil index Ck(t) provides useful information about the extent to whichindustries in post-uni�cation Italy were concentrated in a limited number of areas, butdoes not take into consideration whether those areas were close together or far apart. Inother words, it does not take into account the spatial structure of the data. Every regionis treated as an island, and its position in space relative to other regions is not taken intoaccount. Thus, the relative Theil index Ck(t) is an a-spatial measure of concentration: thesame degree of concentration can be compatible with very di�erent localization schemes.For example, two industries may appear equally geographically concentrated, while oneis located in two neighboring regions, and the other splits between the northern and thesouthern part of the country.

23The tobacco sector was managed by the State in a regime a public monopoly. Manufacturingwas concentrated in a few State-owned factories. In 1911, tobacco products were manufactured in Bari,Bologna, Cagliari, Catania, Chiaravalle, Florence, Lecce, Lucca, Milan, Modena, Naples, Palermo, Rome,Sestri, Turin, and Venice.

17

Table 5

Industry concentration: Theil index (value added data)

Sectors 1871 1881 1901 1911

Food 0.02 0.02 0.04 0.06Tobacco 1.23 1.05 1.04 0.81Textile 0.26 0.3 0.45 0.45Clothing 0.11 0.13 0.1 0.11Wood 0.02 0.02 0.03 0.05Leather 0.05 0.06 0.11 0.16Metal 0.38 0.57 0.86 0.74Engin 0.05 0.03 0.06 0.07NonMet 0.17 0.17 0.19 0.09Chem 0.18 0.16 0.23 0.17Paper 0.21 0.21 0.17 0.16Sundry 0.33 0.89 0.62 0.46

Manufacturing 0.03 0.04 0.07 0.08


As pointed out by Arbia, Basile, and Mantuano (2005); Basile and Mantuano (2008);Arbia, Dedominicis, and De Groot (2011), a more accurate analysis of the spatial dis-tribution of economic activities requires the combination of traditional measures of geo-graphical concentration and methodologies that account for spatial dependence, in thatthey provide di�erent and complementary information about the concentration of thevarious sectors. Spatial autocorrelation is present when the values of one variable ob-served at nearby locations are more similar than those observed in locations that are farapart. More precisely, positive spatial autocorrelation occurs when high or low valuesof a variable tend to cluster together in space and negative spatial autocorrelation whenhigh values are surrounded by low values and vice-versa. Among the spatial dependencemeasures, the most widely used is the Moran's I index (Moran, 1950):

I =

(N∑

i

∑j wij

)(∑i

∑j wij

(LQi − LQ

) (LQj − LQ

)∑i

(LQi − LQ

)2)

(7)

where N is the total number of provinces, LQi and LQj are the observed values of thelocation quotient for the locations i and j (with mean LQ), and the �rst term is a scalingconstant. This statistic compares the value of a continuous variable at any location withthe value of the same variable at surrounding locations. The spatial structure of the datais formally expressed in a spatial weight matrix W (Anselin, 2013) with generic elementswij (with i 6= j). In the rest of the paper we will employ row-standardized spatial weightsmatrix (W ), whose elements wij on the main diagonal are set to zero whereas wij = 1 ifdij < d and wij = 0 if dij > d, with dij the great circle distance between the centroids ofregion i and region j and d a cut-o� distance.

Table 6 reports the values of the Moran's I statistics for the whole manufacturingsector and for the 12 sectors, as well the respective p-values. First of all, it is worthnoticing that the degree of spatial autocorrelation of LQ for the manufacturing as wholeincreased over time passing from a null to a statistically signi�cant and positive value.

18

Thus, both a-spatial and spatial concentration of manufacturing activity increased overtime.

An increase of spatial autocorrelation is also observed for traditional sectors (leather,wood, clothing, textile, and foodstu�s). In 1911, Textile and Leather were the sectorswith the highest levels of spatial autocorrelation.24 For three sectors (non-metallic mineralproducts, metalmaking and engineering) we found a decrease of spatial autocorrelation,albeit the Moran's I statistic remained positive and statistically signi�cant except forengineering. Only for three sectors (paper, chemicals and tobacco) we found no evidenceof signi�cant spatial dependence over the whole sample period. Also the rank correlationbetween the Moran's I indices in 1871 and in 1911 is positive and quite high (0.67) witha p-value of 0.015.

Table 6

Spatial autocorrelation: global Moran's I index (value added data)

Sectors 1871 1881 1901 1911

Food 0.15 0.31 0.25 0.290.02 0.00 0.00 0.00

Tobacco -0.04 0.00 0.02 0.010.63 0.41 0.32 0.38

Textile 0.33 0.38 0.47 0.490.00 0.00 0.00 0.00

Clothing 0.29 0.30 0.40 0.350.00 0.00 0.00 0.00

Wood 0.11 0.10 0.22 0.370.05 0.06 0.00 0.00

Leather 0.60 0.62 0.69 0.730.00 0.00 0.00 0.00

Metal 0.28 0.26 0.17 0.170.00 0.00 0.00 0.00

Engin 0.17 0.19 0.12 0.020.01 0.00 0.03 0.34

NonMet 0.20 0.08 0.12 0.110.00 0.03 0.00 0.02

Chem -0.04 0.15 0.00 0.050.61 0.02 0.40 0.19

Paper 0.11 0.07 0.06 0.070.05 0.12 0.17 0.13

Sundry 0.05 -0.00 0.01 0.150.19 0.37 0.36 0.01

Manufacturing 0.05 -0.00 0.01 0.150.47 0.41 0.09 0.02


24Interestingly, an upsurge of spatial dependence in Italian traditional sectors characterized by the useof basic technologies also emerged for more recent periods (Arbia, Dedominicis, and De Groot, 2011).These are sectors in which operate �rms of small and medium size localized in well-de�ned industrialclusters in the Northern and Central part of the country (Emilia, Tuscany and Marches).

19

As a further step of our analysis, we can jointly consider the results produced by theTheil measure of geographical concentration and the Moran's I measure of spatial depen-dence described above. Similarly to Guillain and Le Gallo (2010), we can identify fourdi�erent patterns in the distribution of economic activities where either or both geograph-ical concentration and spatial dependence occur: (i) high concentration and low spatialdependence, (ii) high concentration and high spatial dependence, (iii) low concentrationand high spatial dependence, (iv) low concentration and low spatial dependence. To thispurpose, we combine the a-spatial Theil index and the Moran's I index in a scatterplot(Figure 6) for each period, excluding tobacco and sundry. The four patterns in the dis-tribution of economic activities are identi�ed by including a vertical and an horizontaldashed line at the median values of the each indicator.

During 1871-1911, the composition of the four patterns of spatial distribution changedconsiderably. Three sectors (chemicals, paper and metalmaking) registered a high levelof concentration and a low degree of spatial dependence in 1911 (pattern i)). Thismeans that these three industries concentrated their activity in a small number of areasthat were not close to each other. In these sectors, indeed, economies of scale may bereached only by increasing the plant size and concentrating the production in a smallnumber of locations. However, in 1871, metalmaking belonged to the pattern ii) (highconcentration and high degree of spatial dependence), along with textile and non-metallicmineral products. Textile was still in this second group in 1911, along with leather.Thus, textile was the mostly "spatially" clustered sector over the whole sample period,being highly concentrated and at the same time strongly agglomerated in all censusperiods. This feature still characterizes the textile sector in the modern period (see Arbia,Dedominicis, and De Groot, 2011). Clothing was instead the sector that persistentlybelonged to the third spatial pattern (low concentration and high spatial dependence) overthe while sample period. In 1871, leather was in the low-concentration/high-dependencecluster, but then moved to the high-high group in 1911. In 1911, instead, foodstu�s andwood were in the low-concentration/high-dependence group, but they belonged to thelow-low club in 1871. Finally, we �nd that engineering, foodstu�s and wood were themostly "spatially" spread industries (pattern iv)) in 1871. Engineering was still in thisgroup in 1911, along with non-metallic mineral products that was in pattern ii) (highTheil and high Moran) in 1871.

3.3 Local spatial patterns in the distribution of economic activ-

ities

The Moran's I statistic of spatial autocorrelation considered in the previous section cap-tures the overall spatial clustering present in the data. A positive and signi�cant measureof Moran's I captures the existence of both high-value clustering and low-value clustering,while a negative autocorrelation captures the presence of high-values next to low-values.In other words, only one dominant type of autocorrelation can be detected. If two struc-tures, such as high-value clustering and low-value clustering, coexist, the global statisticof spatial autocorrelation cannot distinguish them (Zhang and Lin, 2007). In contrast,the local version of the Moran's I statistic is speci�cally designed to �nd evidence oflocal spatial patterns in the empirical data, that is to detect local spatial associations('hot spots '), such as high-value clusters, low-value clusters, and negative autocorrelation

20

(a) Year: 1871 (b) Year: 1881

(c) Year: 1901 (d) Year: 1911

Figure 6

Spatial concentration: scatterplot between the a-spatial Theil index of concentration and the globalI Moran index of spatial autocorrelation. Value added data

21

(Anselin, 1995). The local Moran statistic for an observation i is de�ned as:

Ii = LQi

∑j 6=i

wijLQj (8)

The results of the local Moran's I tests for the whole manufacturing are shown in Figure7, along with the chroroplet maps of the spatial distribution of the location quotients.25

Signi�cant values of the local Moran statistic are classi�ed as: HH for locations with highlevels of the LQ for a speci�c sector surrounded by regions with high levels of the LQ; LLfor locations with low levels surrounded by locations where the LQ are also low; HL forlocations with high values surrounded by locations with low values, and LH for locationswith low values surrounded by locations with high values. While the �rst two typologies(namely HH and LL) suggest clustering of similar values, the last two situations (HLand LH) capture the presence of regional outliers in the spatial distribution of economicactivities.

In 1871, the manufacturing activity as a whole mainly clustered in a few Northernprovinces (Figure 7). In particular, Milan, Novara, Bergamo, Como, Brescia, Veronaand Vicenza formed the HH group. In the same period, however, some other provinces(such Genoa, Florence, Venice, Ancona, Naples and Palermo) appeared as spatial outliers,showing a high value of LQ surrounded by low values (HL). Actually, although in 1871the South as a whole was less industrialized than the North, Naples registered the secondhighest LQ value in manufacturing (1.41) after Milan (1.57) and higher than Turin (1.35),while Palermo had a LQ value in manufacturing (1.32) higher than Genoa (1.20). In 1911,however, the main HH cluster moved towards the North-West side of the country, thusextending to Turin and excluding Verona and Vicenza. At the same time, only Genoa,Ancona and Naples remained in the HL category, while Pisa and Leghorn formed a newcluster of HH values. On the other hand, the big cluster of low values (LL) moved moreand more to the South.

As for the single industrial branches, Figures 8-17 show several heterogeneous spatialdistributions of the location quotients and of the resulting local Moran's I signi�cancemaps. Let's start with the case of textile. Already one of the country's most importantsectors, the textile industries' development would be particularly dynamic over the comingdecades, and would remain particularly concentrated in the Northwest. Although in 1871there was an outpost of textile production in Naples (this province appears asHL outlier),the main cluster was already in the North-western provinces (Lombardy, Piedmont andLiguria); in 1911 Naples was not an outlier anymore, while production concentrated evenmore in the North.

As for the other sectors, Clothing tended to concentrate in Central-Adriatic provinces;Non-metal mineral products clustered in Tuscany and in a few outliers. The number ofprovinces relatively more specialized in Metal-making and in Engineering reduced overtime, thus con�rming the tendency towards spatial concentration discussed above. Inparticular, in 1911 the manufacturing of metal-making products was concentrated in Pisa,Leghorn, Porto Maurizo, Perugia and Arezzo, while Engineering was clustered in Porto

25It is interesting to consider the two maps jointly in order to fully appreciate the value of using localindicators. In fact, a pure inspection of the spatial patterns of the location quotients is not able tocapture the existence of production clusters. Once we inspect the local Moran map we can easily identifysigni�cant industrial local clusters.

22

(a) 1871 (b) 1911

(c) Local I Moran: 1871 (d) Local I Moran: 1911

Figure 7

MAN: Choroplet maps and local Moran's I of LQ values. Value added data

23

Maurizio, Alessandria, Genoa, Milan, Brescia, and Venice, while older positive clustersdisappeared. Also the spatial clustering of Chemical activity changed over time, in favorof some provinces in the Center and other in the North. Leather and Foodstu� clusteredin the South, while the spatial distribution of the Wood industry showed relevant changesover time, mostly moving from the North to the South.

The empirical spatial data analysis consider so far provided a clear evidence of an up-surge of the interregional division of labor and of the spatial concentration of industrialactivity across the Italian provinces during the post-uni�cation period (1871-1911). Theabove �ndings corroborate, with updated value added data and proper exploratory spatialdata analysis, the main evidence suggested by Ciccarelli and Proietti (2013). However,this evidence does not point to any particular set of explanations. An increased inter-regional division of labor might be seen as evidence in favor of HO-type mechanisms ofindustrial location. It might equally be seen as the other side of a process of concentrationin some industries, due to NEG-type mechanisms. The balance of these o�setting forcesis a priori uncertain and is left to the econometric analysis carried out in the next section.

24

(a) 1871 (b) 1911


Figure 8

FOOD: Choroplet maps and local Moran's I of LQ values. Value added data

25

(a) 1871 (b) 1911


Figure 9

TEXTILE: Choroplet maps and local Moran's I of LQ values. Value added data

26

(a) 1871 (b) 1911


Figure 10

CLOTHING: Choroplet maps and local Moran's I of LQ values. Value added data

27

(a) 1871 (b) 1911


Figure 11

WOOD: Choroplet maps and local Moran's I of LQ values. Value added data

28

(a) 1871 (b) 1911


Figure 12

LEATHER: Choroplet maps and local Moran's I of LQ values. Value added data

29

(a) 1871 (b) 1911


Figure 13

NONMET: Choroplet maps and local Moran's I of LQ values. Value added data

30

(a) 1871 (b) 1911


Figure 14

METAL: Choroplet maps and local Moran's I of LQ values. Value added data

31

(a) 1871 (b) 1911


Figure 15

ENGIN: Choroplet maps and local Moran's I of LQ values. Value added data

32

(a) 1871 (b) 1911


Figure 16

CHEM: Choroplet maps and local Moran's I of LQ values. Value added data

33

(a) 1871 (b) 1911


Figure 17

PAPER: Choroplet maps and local Moran's I of LQ values. Value added data

4 Testing the e�ect of water endowments and market

potential on industry location

4.1 Econometric speci�cation

The previous section described the spatial distribution of manufacturing industries dur-ing 1871-1911. As already pointed out in the introduction, the location of industrialsectors over the sample period is mainly driven by natural advantages (HO hypothesis)and/or (domestic) market access (NEG hypothesis). A'Hearn and Venables (2013) andMidelfart-Knarvik and Overman (2002) provided theoretical frameworks to show howthese economic geography forces operate. Generally speaking, the spatial distributionof perfectly competitive sectors - where �rms produce with constant returns to scale- is mainly driven by the comparative advantages of the di�erent regions; while, thelocation of monopolistically competitive sectors characterized by increasing-return tech-nologies is mainly driven by the market potential, so as �rms tend to concentrate in oneor few markets and to export to the other markets in order to take advantage of scaleeconomies. The industrial location patterns can be thought as the equilibrium outcomebetween these agglomeration forces, that encourage �rms to concentrate over space, and

34

dispersion/competitive forces, that encourage economic activities to spread out.This section examines the relevance of factor endowment and market potential in

shaping the location of industries across Italian provinces during 1871-1911. Industriallocation is measured in relative terms, that is using the log of the location quotientsLQk

i,t. As for factor endowment, we focus on water abundance, measured with the logof the variable RIV ER described in section 2.1 and its interaction with the dummyvariable WATERFALLS, indicating the existence of waterfalls in the province. Marketpotential is measured by the log of the Harris (1954)'s formula (MKTPOT ) (see section2.2). Obviously, WATERFALLS and RIV ER are time invariant, while MKTPOT istime varying.

To avoid mis-speci�cation biases due to a wrong functional form, we adopt a semipara-metric additive model where all variables enter additively as smooth functions, withoutimposing any speci�c functional relationship (linear or nonlinear) with the response vari-able. Thus, for each sector k, the model is speci�ed as:

[M1] ln(LQk

i,t

)= α +βWATERFALLSi

+f1 [ln (RIV ERi|WATERFALLSi = 0)]


+f3 [ln (MKTPOTi,t)]

+h (noi, ei) + γt + εi,t

εi,t ∼ iidN(0, σ2

ε

)i = 1, ...,N t = 1, ...,T

where f[p]s represent unknown smooth functions of the univariate terms. Namely, f1(.)and f2(.) capture the smooth e�ect of water endowment; while f3(.) captures the smoothe�ect of market potential.26

Time �xed e�ects (γt) are introduced in the model to control for time-related factorsbiases. Moreover, a geoadditive term, h (noi, ei), that is a smooth interaction betweenthe spatial coordinates of the provinces (or smooth spatial trend), is introduced in orderto avoid mis-speci�cation biases due to unobserved spatial heterogeneity. Controlling forunobserved heterogeneity is indeed a fundamental task in our empirical analysis, as othernatural advantages (apart from water endowment) may in�uence industry location. Thus,failing to control for unobserved heterogeneity can introduce omitted-variable biases andpreclude causal inference. Unobserved heterogeneity may also be spatially correlated,thus introducing spatial autocorrelation in the residuals. As McMillen (2012) shows,in this case a simple semiparametric model, with a smooth spatial trend (the so-calledGeoadditive Model), can remove both unobserved spatial heterogeneity and spatial de-pendence (see also Basile, Durban, Minguez, Montero, and Mur, 2014).

In the spatial econometrics literature, spatial dependence due to (spatially correlated)unobserved heterogeneity is classi�ed as `nuisance' spatial dependence, in contrast to

26Apart from the semiparametric form, this speci�cation is di�erent from the one used in the relatedliterature, for example byWolf (2007); Ellison and Glaeser (1999); Midelfart-Knarvik, Overman, Redding,and Venables (2000); Midelfart-Knarvik, Overman, and Venables (2001). These authors pool the databy regions, sectors and time, and regress the location quotient on a set of interactions between thevectors of location characteristics (factors endowment and market potential) and a vector of industrycharacteristics (measuring industries' factor intensities and the share of intermediate inputs in GDP).Our dataset is reach enough to avoid considering the above interaction and pooling of the data, andallow us to estimate separate models for �homogeneous� groups of industrial activity.

35

`substantive' spatial dependence due to spillovers, pure contagion and imitation e�ects.While nuisance spatial dependence is well captured by the spatial trend surface, sub-stantive spatial dependence should be modeled through traditional spatial econometricmodels. Given these considerations, we will also consider an alternative speci�cation ofthe location regression equation, a semiparametric Spatial Autoregressive model (SAR)model, that is a semiparametric model without spatial trend but with the spatial lag ofthe dependent variable:

[M2] ln(LQk

i,t

)= α +

∑j

wij ln(LQk

j,t

)+ βWATERFALLSi



+f3 [ln (MKTPOTi,t)]

+γt + εi,t


ε

)i = 1, ...,N t = 1, ...,T

Each univariate smooth term in equations [M1 ] and [M2 ] can be approximated by alinear combination of qk known basis functions bqk(xk):

fk (xk) =∑qk

βqkbqk(xk)

with βqk unknown parameters to be estimated. To reduce mis-speci�cation bias, q′ksshould be large enough, which results in a danger of over-�tting. The smoothness of thefunctions can be controlled by penalizing 'wiggly' functions when �tting the model. Ameasure of 'wiggliness', Jk ≡ β

′

kSkβk, is associated with each k smooth function, with Sk

a positive semi-de�nite matrix. Equivalently, each univariate smooth spatial lag term canbe approximated by mk (Wxk) =

∑qkδqkbqk(Wxk). The penalized spline base�learners

can be extended to the two dimensions to handle interactions by using tensor products.In this case, smooth bases are built up from products of 'marginal' bases functions. Forexample,

h(no, e) =∑qno

∑qe

βqno,qebqno(xno)bqe(xe)

As is well known, any semiparametric model can be expressed as a mixed model and,thus, it is possible to estimate all the parameters (including the smoothing parameters)using restricted maximum likelihood methods (REML) (Ruppert, Wand, and Carroll,2003).27

4.2 Endogeneity

A complication with the estimation of models [M1 ] and [M2 ] is given by the presence ofendogenous variables (lnMKTPOTi,t and

∑j wij ln

(LQk

j,t

)) on the right hand side. The

27To estimate this model, we have used the method described by Wood (2006) which allows for auto-matic and integrated smoothing parameters selection through the minimization of the Restricted Maxi-mum Likelihood (REML). Wood has implemented this approach in the R package mgcv.

36

term∑

j wij ln(LQk

j,t

)is the spatial lag of the dependent variable and, thus, is endogenous

by construction. As for lnMKTPOTi,t, New Economic Geography models describe aprocess characterized by reverse causality in which market potential, by attracting �rmsand workers, increases production in a particular location, and this, in turn, raises itsmarket potential.

To address these problems, we extend the REML methodology to estimate the pa-rameters of models [M1 ] and [M2 ] in a 2-step �control function� (CF) approach (Blundelland Powell, 2003), that is an alternative to standard instrumental variable (IV) meth-ods. In the �rst step, each endogenous variable is regressed on a set of conformableinstrumental variables, using a semiparametric model. The residuals from the �rst stepsare then included in the original model ([M1 ] or [M2 ]) to control for the endogeneity oflnMKTPOTi,t and/or

∑j wij ln

(LQk

j,t

). Since the second step contains generated re-

gressors (i.e. the �rst-step residuals), a bootstrap procedure is recommended to computep-values.

This procedure requires �nding good instruments, that is, variables that are correlatedwith the endogenous explanatory variables but not with the residuals of the regression.Among the instruments used in the empirical literature for market potential, the mostcommon include geographical distances from the main economic centres (Redding andVenables, 2004; Klein and Crafts, 2012) or the use of lagged variables for the marketpotential (Wolf, 2007; Martinez-Galarraga, 2012). We use the distance from Milan asinstrumental variable. As for the endogeneity of the spatial lag of the dependent variable,we follow the standard literature in using the spatial lags of the exogenous variables (i.e.water endowment) as good instruments (Kelejian and Prucha, 1997).

As the �rst-step equations of models [M1 ] and [M2 ] are the same for all sectorsanalyzed, we report here the results of their estimation (see Table 7). These results clearlyshow that the distance from Milan is a highly correlated with the market potential andthe spatial lags of water endowment variables are highly correlated with the spatial lagof the dependent variable.

Table 7

Estimation results �rst step of the control function approach

Variable First step M1 First step a M2 First step b M2F test edf F test edf F test edf

f1(ln(River)|Waterfalls=0) 16.781 1.993 1.129 1.770 4.544 3.347(0.000) (0.324) (0.002)

f2(ln(River)|Waterfalls=1) 11.900 1.000 7.961 3.040 4.040 2.594(0.001) (0.000) (0.008)

f3(no,e) 14.349 18.531(0.000)

f4(DistMi) 25.073 1.640 29.460 1.983 40.931 1.930(0.000) (0.000) (0.000)

f5(ln(River)) 5.819 1.000 7.431 3.638(0.017) (0.000)

f6(Waterfalls) 5.588 3.176 7.645 1.506(0.001) (0.001)

37

4.3 Hypotheses

The aim of this section is to provide a possible guidance in the interpretation of theestimation results resulting form the second step of our CF approach (see section 4.5).To this end, we group manufacturing sectors into three categories, depending on themagnitude of their capital intensity, that is their capital to labor ratio (K/L). By doingso, we can make speci�c hypotheses on the relative e�ect of market potential on thelocation of the industry. Indeed, just as the e�ect of water endowment o industriallocation may depend on the prevailing technology, the e�ect of market potential maydepend on the degree of scale economies prevailing in the various sectors. Economicactivities with increasing returns to scale tend to establish themselves in regions thatenjoy good market access, while economic activities with constant returns technologies areessentially in�uenced by factor endowment. We argue thus that the K/L ratio representsa good measure of scale economies. We expect in particular that the location of "highK/L" sectors is relatively more in�uenced by the market potential, while the location of"low K/L" sectors is relatively more in�uenced by the water endowment. In the yearsfollowing the country's uni�cation many manufacturing sectors were still organized onan artisanal basis. In the absence of economies of scale, we might expect low-mediumK/L industries to locate close to their customers (the e�ect of market potential is null ornegligible), a point made in Fenoaltea (2003, 2011). Di�erently, in the case of industrialactivities with "medium" and "high" K/L, one may expect an increasing role of marketpotential during the second part of our sample period, as these kinds of activities becamemore and more organized on a factory basis.

Grouping manufacturing sectors on the base of capital intensity is not new in the lit-erature. Zamagni (1993, pp. 86-87) groups industrial sectors into advanced, intermediate,and traditional industries depending on their capital intensity and number of workers perplant. Federico (2006) groups industrial sectors into light industries and heavy industries.The former are those that �are featured by a (relatively) low capital intensity and by theprevailing orientation towards the �nal consumer�, the latter �are more capital-intensiveand produce mainly inputs for other sectors�. However, from a practical point of view,grouping manufacturing sectors on the base of capital intensity is not an immediate task.First of all, the �rst Italian historical source with information on capital stock K is theindustrial census of 1911 (IC 1911), while no systematic information is available for ear-lier years. IC 1911 reports in particular data on horse-power per worker, a variable thatis frequently used to proxy capital intensity both in comparative studies (Rostas, 1948;Broadberry and Crafts, 1990; Broadberry and Fremdling, 1990; de Jong, 2003), and stud-ies on the Italian case (Zamagni, 1978, 1993; Bardini, 1998; Federico, 2006). Second, IC(1911) omits shops with only one worker or those who work at home, and more gener-ally understates actual industrial employment (Fenoaltea, 2014). Third, sectoral data onK/L (actually on HP/L) represent averages and, as such, are not per se informative ofwithin-sector heterogeneity.28

With these important caveats in mind, it is reassuring that Italy's economic histori-

28An example referring to the vast and variegated engineering sector should help clarifying the point.Fenoaltea (2014) disaggregates the engineering sector in 11 elementary components (ranging from black-smiths, to shipyards, to precious-metals products). The K/L ratio for the engineering sector in 1911 isthere estimated to be equal to 0.33. However, this last �gure is an average that includes an estimate ofK/L = 0.05 for precious-metals products, of about 0.20 for blacksmith, and of about 0.60 for shipyards.

38

ans substantially agree on classifying clothing, leather, wood, and non-metallic mineralproducts as "low K/L" sectors; metal-making, chemicals, foodstu�s, and paper as "highK/L"; and the textile and engineering sector as "medium K/L".29 Table 8, mainly basedon Zamagni (1978, 1993) summarizes our grouping of manufacturing sectors in threecategories depending on their capital intensity. It is �nally comforting that our "K/L"thresholds (roughly 0 to 0.4, 0.4 to 0.7, and above 0.7) used to group 1911 industrial sec-tors depending on their capital intensity dovetail nicely with those (0.4 and 0.6) reportedin Federico (2006), Table 2.6 and used by the author to group 1927 industrial sectors intolight and heavy industries.

Table 8

Horse-power per worker (HP/L) in 1911

High HP/L

Metalmaking 2.62Chemicals 1.30Foodstu�s (excl. sugar) 0.94

sugar 2.18Paper 0.73

Medium HP/L

Textile 0.52Engineering 0.51

Low HP/L

Non-metallic mineral products 0.36Wood 0.23Leather 0.09Clothing 0.07

4.4 Model selection and diagnostics

Using the CF approach we have estimated models M1 (i.e. the geoadditive model) andM2 (i.e. the semiparametric SAR model), as well as three restricted speci�cations of M1and M2, that is: i) a semiparametric model without spatial trend [M3 ]; ii) a geoadditivemodel with linear terms for the main explanatory variables (water endowment and marketpotential) [M4 ]; and iii) a linear SAR model [M5 ]. We compare the performance of these�ve competing models in terms of Bayesian Information Criterion (BIC ), residual spatialautocorrelation (through a Moran I test of the residuals of the second step) and thepresence of a spatial trend in the second step residuals30 (Table 9).

In the case of whole manufacturing, the geoadditive model [M1 ] outperforms all theothers: it reaches the lowest BIC value and there is no evidence of residual trend and spa-tial autocorrelation in the residuals. It is worth noticing the the geoadditive model withlinear terms (model [M4]) has the second best performance, while the SAR model (both

29As noticed in Zamagni (1993, p. 85), �the relatively high level of horsepower per worker in thefoodstu�s industry should not be misinterpreted, since it is largely due to the �our-milling industry, 61%of whose power capacity still consisted in hydraulic energy.

30To check for the presence of a residual spatial trend, we regress the residuals of second step on thesmooth interaction between latitude and longitude by using a P-spline method.

39

semiparametric [M2 ] and linear [M5 ]) and the semiparametric model without spatialtrend [M3 ] are not able to capture the spatial pattern in the data. Similar comments ap-ply for each group of sectors, high K/L sectors (i.e., paper, chemicals and metal-making),medium K/L sectors and low K/L sectors (i.e. clothing, leather, wood and non-metallicminerals). The above evidence points clearly to the superiority of the geoadditive modelmodel in capturing the spatial pattern present in the data. This suggests that, over thesample period, spatial spillovers (that is substantive spatial dependence) were not reallyimportant, while most of the spatial autocorrelation in the relative location values mightbe attributed to the presence of spatially autocorrelated unobserved heterogeneity. Thus,in the next section we focus on the results of the geoadditive model M1.

Table 9

Model comparison

M1 M2 M3 M4 M5Manufacturing

BIC 2.494 2.767 2.862 2.702 2.927Moran test stat. 1.024 2.895 3.713 1.330 3.008p-value (0.153) (0.002) (0.000) (0.092) (0.001)Trend No trend Trend Trend No trend Trend

High K/L

BIC 4.184 4.254 4.443 4.180 4.304Moran test stat. 0.749 1.671 6.591 0.736 1.500p-value (0.227) (0.047) (0.000) (0.231) (0.067)Trend No trend Trend Trend No trend Trend

Medium K/L

BIC 2.477 2.621 2.980 2.664 2.686Moran test stat. 0.371 -2.266 8.381 1.508 -2.526p-value (0.355) (0.988) (0.000) (0.066) (0.994)Trend No trend Trend Trend No trend No trend

Low K/L

BIC 2.022 2.116 2.307 2.134 2.212Moran test stat. -0.789 -1.032 4.783 0.908 -0.448p-value (0.785) (0.849) (0.000) (0.182) (0.673)Trend No trend No trend Trend No trend Trend

4.5 Empirical results

Table 10 gives the estimation results of the second step of the geoadditive model [M4 ],obtained by pooling over the 69 provinces and the 4 years (1871, 1881, 1901, and 1911) inour sample and including the �rst-step residuals in the original semiparametric regressionto correct the inconsistency due to the endogeneity of MKTPOT . For the parametricterms (the intercept and the dummy variableWATERFALLS), we report the estimatedcoe�cients and the corresponding bootstrapped p-values. All the estimated models in-clude time dummies, but the results for these controls are not reported. For the thenonparametric smooth terms, the table also shows the estimated degree of freedom (edf ),

40

a broad measure of nonlinearity (an edf equal to 1 indicates linearity, while a value higherthan 1 indicates nonlinearity).

Table 10

Estimation results second step of the control function approach - M1

MAN High K/L Medium K/L Low K/L

Parametric terms

Intercept Coe�. -0.023 -0.217 -0.067 0.027p-value (0.407) (0.000) (0.012) (0.276)

Dummy Waterfalls Coe�. -0.265 0.083 -0.199 0.015p-value (0.000) (0.334) (0.000) (0.711)

Nonparametric terms

f1(ln(River)|Waterfalls=0) edf 2.091 3.073 3.839 1.000f2(ln(River)|Waterfalls=1) edf 3.032 1.000 2.988 2.285f3(ln(Mktpot)) edf 2.624 1.000 2.388 2.155f4(no,e) edf 18.747 9.970 13.127 23.024f5(ln(Res. F.S.)) edf 1.000 3.392 1.762 1.000

R2-adj. 0.715 0.525 0.734 0.709Dev.expl. 0.747 0.564 0.762 0.745REML 43.156 -166.306 57.551 124.715

The estimation results for the whole manufacturing activity clearly suggest thatthe three univariate smooth terms (f1(RIV ER), f2(RIV ER ∗ WATERFALLS) andf3(MKTPOT )) enter nonlinearly the model: the edf is always higher than 1. The lin-earity assumption is also con�rmed by a more formal F test, comparing the geoadditivemodel M1 and the geoadditive model M4 (the results are available upon request). Fig-ure 18 portrays the smooth partial e�ect of RIV ER, RIV ER ∗WATERFALLS andMKTPOT , along with the point-wise 95% con�dence bands (obtained using a boot-strap procedure). Strong nonlinearities in the partial e�ect of RIV ER and RIV ER ∗WATERFALLS are clearly detected. Speci�cally, it emerges a U -shaped relationshipbetween water endowment and LQ, whenWATERFALLS = 0. However, the large con-�dence bands indicate a poor precision of the estimate and suggest to be very cautious inthe interpretation of the plot. Instead, the e�ect of water endowment is magni�ed whenWATERFALLS = 1, that is when there is a presence of waterfalls in the province.In this case the precision of the estimate is much higher and the results indicate a pos-itive e�ect of water endowment up to a certain threshold. Net of the e�ect of waterendowment and after correcting for the endogeneity bias, it also emerges a positive andmonotonic relationship between industrial location and market potential, con�rming thateven at an early stage of Italy's industrialization �rms tended to settle in regions withthe highest market potential to minimize costs. Speci�cally, an increase in MKTPOT isassociated with an increase in LQ, but only after a threshold in the level of MKTPOT .And, as discussed in Section 2, the North West was the area with the highest marketpotential (and with the highest endowment of waterfalls as well) at the beginning ofthe sample period. The country's uni�cation improved its relative position within thedomestic market. The graphical representation of the estimated smooth trend surface,f4(no, e), is not reported for reason of space, but this term is also highly signi�cant,suggesting the presence of unexplained spatial heterogeneity. Finally, the smooth term

41

f5(Res.F.S.), capturing the e�ect of the �rst-step residuals is also signi�cant, con�rmingthe endogeneity of MKTPOT .

Tables 10 looks within manufacturing and reports the results for the three groups ofindustries: high K/L, medium K/L and low K/L sectors. In line with our expectations,the location of "high K/L" sectors is more in�uenced by the market potential, while thelocation of "low K/L" sectors is more in�uenced by the water endowment. Indeed, inthe case of high K/L industries, only the market potential turns out to be a key driverof industrial location (a monotonic positive e�ect emerges), while the e�ect of water en-dowment is not signi�cant. On the other hand, the variable River enters signi�cantlyand positively in the regression equation for "low K/L" industries. These �ndings arealso consistent with the prediction of the Core-Periphery NEG model (Krugman, 1991),according to which only economic activities with increasing returns to scale tend to es-tablish themselves in regions that enjoy good market access. In the case of "mediumK/L" industries (textile and engineering), both water abundance and market potentialappear to play some role in the industrial location.

As discussed in section 4.3, the role of market potential might have changed duringthe sample period. In fact, we expect that with the ongoing domestic market integration,market access increased its role in driving industrial location of medium and high K/Lsectors. To assess this hypothesis, we also estimated an alternative model [M1a] thatincludes the interaction between market potential and time dummies:

[M1a] ln(LQk

i,t




+T∑δ=1

fδ [ln (MKTPOTi,t)]× t



ε

)i = 1, ...,N t = 1, ...,T

For the sake of completeness, we also estimated a model that imposes the linearity inthe e�ect of market potential (model [M1b]):

[M1b] ln(LQk

i,t




+T∑δ=1

πδ ln (MKTPOTi,t)× t



ε

)i = 1, ...,N t = 1, ...,T

Table 11 reports the estimation results of the second steps of models [M1a] and[M1b] (obviously a CF approach has been used to control for the endogeneity of marketpotential). Since the e�ect of water abundance is the same as the discussed above, weonly report the results for market potential. As the edf of the nonparametric terms

42

of model [M1a] are all equal to one, we can safely approximate them by linear terms.Thus, we focus on the estimation results of model [M1b]. In the case of medium K/Lthe coe�cients are signi�cant in all sub-periods and their amount is stable around 0.5,thus indicating the absence of time heterogeneity of the e�ect of market potential for thisgroup of industries.31 In the case of high K/L, instead, the coe�cients are signi�cantonly in the last two period and the elasticity increases over time passing from 0.04 to0.228, thus indicating a strong time heterogeneity of the e�ect of market potential for thisgroup of industries.32 These results strongly corroborate our expectation of an increasingrole of market potential for engineering and textile as these kinds of activities becamemore and more organized on a factory basis.

Table 11

Estimation results second step of the control function approach - M1a and M1b

High K/L Medium K/L

Nonparametric terms (Model M1a)

f3a(ln(Mktpot))|year=1871 edf 1.000 1.000f3b(ln(Mktpot))|year=1881 edf 1.000 1.000f3c(ln(Mktpot))|year=1901 edf 1.000 1.000f3d(ln(Mktpot))|year=1911 edf 1.000 1.000

Parametric terms (Model M1b)

ln(Mktpot)|year=1871 Coe�. 0.587 0.040p-value (0.000) (0.481)




31We re-estimated model [M1b] without time heterogeneity in the linear e�ect of market potential. Asimple likelihood ratio test reveals that this restricted model is not di�erent from [M1b]: the χ2 statisticwith 3 d.f. is 0.48 and the associated p-value is 0.923.

32In this case the likelihood ratio test suggests that the restricted model without time heterogeneityis signi�cantly di�erent from [M1b]: the χ2 statistic with 3 d.f. is 14.63 and the associated p-value is0.002.

43

(a) (b) (c)

Figure 18

Whole manufacturing. Smooth e�ects from semipar. M.1

(a) (b) (c)

Figure 19

High K/L. Smooth e�ects from semipar. M.1

44

(a) (b) (c)

Figure 20

Medium K/L. Smooth e�ects from semipar. M.1

(a) (b) (c)

Figure 21

Low K/L. Smooth e�ects from semipar. M.1

5 Conclusions

Using a new set of provincial (NUTS 3 units) data on industrial value added at 1911prices, this paper analyzed the spatial location patterns of manufacturing activity in Italyduring the period 1871-1911. Speci�cally, we have tested the relative e�ect of tangiblefactors, such as market size, transport costs and factor endowment (water abundance), asmain drivers of industrial location, ruling out the role of intangible factors such as knowl-edge spillovers. The results show that, as transportation costs decreased and barriersto domestic trade were eliminated, Italian provinces became more and more specialized,and manufacturing activity became increasingly concentrated in a few provinces, mostlybelonging to the North-West part of the country. The estimation results corroboratethe hypothesis that both comparative advantages (water endowment e�ect) and marketpotential (home-market e�ect) have been responsible of this process of spatial concen-tration. The location of some traditional industries characterized by a low capital-laborratio (such as clothing and wood) was mainly driven by water endowment, while thelocation of fast growing new sectors characterized by a medium/high capital labor ratio

45

(such as engineering, metalmaking, chemicals, textile and paper) was increasingly drivenby the domestic market potential. In the case of the last group of industries, indeed,increasing returns at the plant level created an incentive for geographical concentrationof the production and falling transport costs created an incentive to locate plants closeto large markets. This centripetal forces more than counterbalanced the centrifugal forceof dispersed natural resources.

Our results are quite in line with the Core-Periphery NEG model (Krugman, 1991)that predicts increasing polarization and regional specialization as a result of economicintegration. In this model, agglomeration economies derive from the interaction amongeconomies of scale, transportation costs and market size, while intangible external economies(such as information spillovers) do not play any role. Today NEG models are stronglycriticized on the base of the observation that they focus on forces and processes that wereimportant a century ago but are much less relevant today. The NEG approach seems lessand less applicable to the actual location patterns of advanced economies. Nevertheless,the current economic geography of fast-growing countries like Brazil, China, and Indiais highly reminiscent of the economic geography of Western World countries during thenineteenth century, and it �ts well into the NEG framework. Historical analyses likethe one presented in this paper might thus help understanding the current process ofeconomic modernization of developing countries.

Data Appendix

This appendix describes the sources and methods used to build the provincial datasetused in this paper.

Manufacturing value added

Industrial value added data are from Ciccarelli and Fenoaltea (2013), duly updated withthe new regional estimates by Ciccarelli and Fenoaltea (2014). The basic idea is to allocateto provinces the regional value added estimates at 1911 prices as follows. For each of the12 manufacturing sectors, provincial labor force shares of the regional total are computed.Sector-speci�c regional value added estimates are then allocated to provinces using theabove province-speci�c labor force shares. The calculation has been done separately foreach benchmark year (1871, 1881, 1901, and 1911). Using formulas:

vap,j,t = V Ar,j,t ∗ lfshp,j,t

were p = 1, 2, . . . , 69 denotes provinces; j = 1, 2, . . . , 12 denotes manufacturing sectors,t = 1871, 1881, 1901, and 1911 denotes the time index, va denote provincial value added,V A denotes regional value added, and lfsh denote the labor force share (of the regionaltotal).

Insert �gure ?? about here

Water endowment

The water endowment variables entering the regression models were obtained as follows.

46

Rivers:Data on Italian rivers are from the Italian Geographic Military Institute: Istituto Ge-

ogra�co Militare (2007), Classi�cazione dei corsi d'acqua d'Italia, Florence. The sourceinclude a detailed list of the most important 100 rivers classi�ed by province. To eachriver the experts of the IGM assign a score (ranging from 1 to 5) depending on �itslength in km, and its socio-economic relevance�. The data were inspected again histor-ical sources such as the Annuario statistico italiano, various years. Many other sourcesalso provided us useful information. The main consulted are: i) Baccarini A. (1877),Appunti di statistica idrogra�ca italiana: i �umi, Rome; iii) Ministero Agricoltura In-dustria e Commercio (1884), �Forza idraulica utilizzata nelle diverse provinci d'Italia�,Bollettini di notizie agrarie, luglio, pp. 893-896; iii) Nitti, F.S. (1905), La conquista dellaforza : l'elettricitÃ a buon mercato : la nazionalizzazione delle forze idrauliche, Rouxe Viarengo; iv) Ministero dei lavori pubblici (1926), Statistica delle grandi utilizzazioniidrauliche per forza motrice, Rome;

Waterfalls:Quantitative data (height and main jumps, in meters) on Italian waterfalls are from

the book: Pavolini, M. (1995), Cascate d'Italia, Udine. The data were compared, when-ever possible, against the world waterfalls database (WWD) available at www.worldwaterfalldatabase.com,last accessed March 2015.

Market potential

The market potential variable is measured by the logarithm of the Harris market potential.Historical GDP estimates at the provincial level for the case of Italy are not available. Wethus proxy for provincial GDP by allocating total regional GDP (NUTS-2 units) estimatesfor 1871, 1881, 1901, and 1911 to provinces (NUTS-3 units) using the provincial shares ofregional population obtained by population census. GDP estimates at historical NUTS-2borders have been kindly provided by Emanuele Felice. These estimates di�er from theones published by same author (Felice, 2013), which are at current borders.

47

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the location of the italian manufacturing industry, 1871

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