Market vs. Endowment: Explaining Early

Industrial Location in Italy (1871-1911)

Anna Missiaia∗

London School of Economics and Political Science

Abstract

This paper seeks to explain the location of industries in Italy inthe period 1871-1911. The analytical framework takes into accountof both the Heckscher-Ohlin (H-O) theory on factor endowment andthe New Economic Geography (NEG) theory on access to markets.The methodology used here is based on Midelfart et al. (2000) and itshistorical applications such as Crafts and Mulatu (2006), Wolf (2007),Martinez-Galarraga (2012) and Klein and Crafts (2012). The locationof industries is explained through interactions between characteristicsof the regions and characteristics of the industrial sectors, of bothH-O type and NEG-type. The main finding of this paper is thatendowments, and in particular human capital, were the driving forcebehind the first Italian industrialization. Market access at this pointof industrialization shows mixed results.

JEL Classification Numbers: N63, N93, R3, R12.Keywords: Economic Geography; Economic History of Italy; MarketPotential; Industrial Location; Regional Inequality.

∗London School of Economics and Political Science, Economic History Department,Houghton Street, London, WC2A 2AE, United Kingdom, e-mail:a.missiaia@lse.ac.uk.EHES conference - London, 6-7 September 2013 - Preliminary version. Please do not citewithout permission. The author is grateful to Brian A’Hearn, Carlo Ciccarelli, EmanueleFelice, Giovanni Federico, Stefano Fenoaltea, Alex Klein, Joan Roses, Max Schulze, VeraZamagni for advice and suggestions, and participants of the seminars at the LondonSchool of Economics, Bocconi University, Carlos III Summer Schools and EuropeanBusiness History Association Summer School, FRESH Winter School and CliometricsWorld Conference for helpful comments.

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1 Introduction

This paper investigates the evolution of Italian regional industrializationduring the period between Unification and First World War. The mainresearch question of the paper is what accounts for the industrial locationin the early Italian industrialization. The aim is to explain the patterns ofthe industrialization of regions within an analytical framework that takesaccount of both the Heckscher-Ohlin (H-O) view on factor endowmentand the New Economic Geography (NEG) view on market access. Themethodology used is theoretically grounded on the seminal work byMidelfart et al. (2000) on the location of European industry from the 1970sto 2000. This methodology has also been applied to historical cases by Craftsand Mulatu (2006), Wolf (2007), Martinez-Galarraga (2012) and Klein andCrafts (2012).The choice of this period, often referred to as the Liberal period, has aprecise reason.1 These years correspond to the first industrialization of thecountry. At the time of Unification, Italy was predominantly agricultural,with some early manufacturing in the Northwest, especially in the textilesector (Cafagna, 1989). In this period, all the Italian regions experienceindustrial growth at least to some extent. By 1911, all the modern sectorshad been at some level established (Zamagni, 2003). This process was far inmagnitude from the big industrial boom of the 1960s and 1970s, when Italybecame one of the most industrialized countries in the world. However, thisfirst wave of industrialization is worth analysing for several reasons. First ofall, this is the period immediately after the Unification, when Italy becamepolitically unified after centuries. All internal borders and tariffs wereremoved and the administrative and legal framework became the same forall regions. Secondly, in the Liberal period some sectors are established forthe first time and some regions experience their very first industrialization:choosing this period gives a chance to partially avoid path dependency biasthat would be more serious later in time. This period also has limitedstate intervention, at least in relative terms compared to the Fascist and thepost-Second World War era.2 Finally, when evaluating the role of economicgeography in economic history, we want to isolate geographic factors fromothers, such as political and institutional factors. Ending the analysis onthe eve of the First World War is quite convenient in an economic geographyperspective in order to leave out the troubled period of the First World Warand the subsequent twenty years of Fascist regime and the Second WorldWar.The methodology used here follows Klein and Crafts (2012) and it is quitestraightforward: the dependent variable is the share of employment by

1The definition of this period as ”Liberal” is used in contrast to the subsequent Fascistperiod and goes back to Toniolo (1990), one of the classics of Italian historiography.

2For an overview of the history of State intervention see Felice (2007).

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region and by sector over the total national employment of the sector.This is explained with interactions between industry characteristics andregion characteristics of both the H-O- and the NEG- type plus region,sector and year controls. Regional characteristics considered include marketaccess, energy endowment, agricultural labour and skilled labour availability.Industry characteristics include measures of energy-, labour- and skillintensity, intermediate input use, mean plant size and sales to industry.The contributions of this work are several. Firstly, it provides a furtherstudy on the formation of the North-South gap by describing how industrieslocated in the post-Unification period. Historical cases have shownthat endowments or market conditions can have different impacts onindustrialization patterns. Understanding these differences is interesting inorder to cast light on how regions first industrialized. Moreover, the Italiancase is particularly fruitful for the application of economic geography inhistorical perspective due to its large and persistent regional imbalances.The development of the industrial sector had a big role in the regionaldivergence in Italy of this period and studying how industries located isinformative on the overall disparities among regions. This paper provides anew methodology for the study of regional divergence in Italy. It proposesquantitative testing for several hypothesis regarding market and endowmentforces. Some of these have been previously brought forward to explain thegap but were never formally tested.The paper is organized as follows. Section 2 briefly summarizes the patternsof regional industrialisation and regional divergence in this period. Section3 explains the methodology for the studying the location of industries aswell as the methodology used to calculate market potentials. Section 4 givesan overview of the sources. Section 5 shows the empirical results on regionalmarket potentials and on the determinants of industrial location in Italy.Section 6 concludes.

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2 Regional Divergence in Italy, 1871-1911

Italy’s political Unification dates back to 1861. It was the result of twoindependence wars against the Hapsburg Empire and the Expedition of theThousand through which Southern regions were annexed to Italy. Figure 1shows the administrative boundaries of the regions. In 1866, after the thirdindependence war, Venetia became part of Italy. Finally in 1870 Latiumwas annexed, too. 3

Figure 1: Italian Regions, 1871-1911

The economic conditions on the eve of Unification were quite diverseacross the Italian regions. Northern regions had a different agriculturalsystem both in terms of the type of crops produced and in terms ofthe organization. The North had an intensive agriculture based on theproduction of cotton and silk, while the South had large parts of theirterritories employed in wheat production (Cafagna, 1989). The processof divergence across regions in terms of GDP per capita and level ofindustrialization accelerated after the political Unification. Figure 2 showsthe relative per capita income disparities based on Felice (2009a) andBrunetti et al. (2011).

3As boundaries changed several times during the decade 1861-1871, we decided to startthe analysis from 1871. Therefore these variations do not affect our period of analysis.

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Figure 2: Regional Income Disparities in Italy, 1871-1911, (per capita valueadded, Italy=100)

Source: Felice (2009a) for 1881 and 1901 and Brunetti et al. (2011) for 1871 and 1911

The GDP figures show a tendency of the Northwestern regions toforge ahead in the period 1871-1911 compared to other regions, especiallycompared to the South. It is in this part of Italy, between Piedmont,Lombardy and Liguria, later named the ”Industrial Triangle”, that modernindustrial sectors started flourishing. To give a sense of the different presenceof industries in the regions, figure 3 shows the percentage of industrialemployment over the total labour force of the region.

This measure suggests that the distribution of industrial activity duringthe first Italian industrialization was quite unbalanced at the regionallevel. This paper aims at explaining the patterns of industrialization, inparticular looking at location decisions, exploiting geographical factors ofboth endowment and market ”nature”. The methodology used is explainedin the next section.

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Figure 3: Regional Industrial Employment as Share Population, (1871-1911)

Source: Population Censuses, (1871-1911)

3 Explaining Industrial Location Across Regions

3.1 The Model

The methodology used in this paper goes back to Midelfart et al. (2000),of which a reduced form has been applied to historical cases by Craftsand Mulatu (2006), Wolf (2007), Martinez-Galarraga (2012) and Klein andCrafts (2012). We rely on the form used in the latter of these works. Themodel is described by equation (1)

ln(si,k)t =∑w

βw [j] +∑i

γi Region

+∑k

ρk Industry +∑t

αt Year(1)

where (si,k)t is the employment of industry k in region i as share of the totalemployment of k. [j] is a vector of interactions between region characteristicsand industry characteristics.

∑w βw are the coefficients of the interactions

that we are interested in. A set of dummies for each region and for eachindustrial sector is included to control for size differences among regions andsectors and for any other peculiarities of either one. Finally, time dummiesare included. The model has a time dimension t as it will be estimated bothas repeated cross section and as pooled OLS regression. The intuition behindthis model is the following. The coefficients of the interactions indicatewhether industries with a high level of a given characteristic tend to be over

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represented in regions that are more abundant in the corresponding regionalcharacteristic. For example, if energy endowment is important, we expectthe interaction between power production in the regions and horsepower usein the sectors to be significant, meaning that industrial sectors with moreuse of energy tend to locate in regions with higher production of power. Theestimating equation is the following:

ln(si,k)t = β1 (Agr. Empolyment × Agr. Production)

+ β2(Literacy Rate × Whitecollars)

+ β3 (Deposits Per Capita × Horsepower)

+ β4 (Coal Price × Horsepower)

+ β5 (Waterpower × Horsepower)

+ β6 (Hydroelectricpower × Horsepower)

+ β7 (Market Potential × Forward Linkages)

+ β8 (Market Potential × Backward Linkages)

+ β9 (Market Potential × Mean Plant Size)

+∑i

γi Region +∑k

ρk Industry +∑t

αt Year

(2)

The analysis considers all 16 Italian regions and 15 industrial sectorsaccording to the population and industrial censuses of the period. Thepopulation censuses have been carried out in 1871, 1881, 1901 and 1911.4

Therefore, this analysis is going to be based on 10-year benchmarks,excluding 1891. The interactions are the ones presented in tables 1 and 2.

Table 1 shows the H-O type interactions. The first one is an agriculturalinteraction that links the percentage of labour force in agriculture to thepercentage of inputs from agriculture. The second interaction measuresthe availability of human capital in the regions, through literacy rates.The intensity of human capital in an industrial sector is measured as thepercentage of white collar workers over total number of workers in eachsector. The fourth interaction captures the availability and intensity ofcapital, measured through credit per capita in the regions and the capitalintensity proxied again by horsepower per unit of production. The last one isan energy endowment interaction. For this interaction it was decided to keepseparate the three main sources of energy used in Italy in the period: waterpower, hydroelectric power and coal. Coal production is not included asItalian regions had a very low production of coal, relying mostly on imports.Instead of production, we use a proxy for prices.5 The intensity in the useof energy is measured by horsepower per unit of production. The NEG-type

4The census of 1891 has not been carried on because of budget cuts5Direct prices for all regions are not available, therefore we construct a proxy. The

procedure is explained in the next section.

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Table 1: H-O Interactions.

RegionCharacteristic

Description IndustryCharacteristic

Description

Energy endowment Hydroelectricpower production

Energy intensity Horsepower perunit of production

Energy endowment Water powerproduction

Energy intensity Horsepower perunit of production

Energy endowment Coal price Energy intensity Horsepower perunit of production

Human capitalendowment

Literacy rate Human capitalintensity

Share of whitecollars in workforce

Agriculturalemployment

Agriculturalemployment aspercentage of totalemployment

Agricultural input Use of primarygoods as share oftotal value ofproduction

Capital availability Deposits per capitain the region

Capital intensity Horsepower perworker

Table 2: NEG Interactions.

RegionCharacteristic

Description IndustryCharacteristic

Description

Market potential GDPs weighted bytransport costs

Forward linkages Percentage of salesto domesticindustry asintermediates andcapital goods

Market potential GDP weighted bytransport costs

Backward linkages Use ofintermediates aspercentage ofproduction

Market potential GDP weighted bytransport costs

Economies of scale Mean plant size interms of workersper plant

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of interactions of table 2 are based on the calculation of market potentialas in Harris (1954). The next section explains how market potentials arecalculated. Forward and backward linkages are the value of inputs andoutputs taken from other sectors with respect to the total value added ofthe sector. The third interaction relates the average number of workersper plant with market potential and captures the tendency of firms witheconomies of scale to locate in central regions.

3.2 Measuring Market Access

The notion of market potential used in this work is the one proposedby Harris (1954), which has seen several developments and extensionsthereafter. In its original formulation, the market potential of region Ais defined as the sum of the GDP of all the adjacent regions, each weightedby their distance from region A, plus the own GDP of region A adjustedby some measure of its size. The calculation of market potential followingHarris (1954) is shown in equation (3):

MPc =∑w

GDPw ×Dγc,w (3)

with Dγc,w the distance between region c and w. The parameter γ is set as

-1 and is defined as in equation(4):

Dc,c = 0.333 ×√

Areacπ

(4)

The idea behind market potential is quite straightforward. For a givenregion A, the larger the GDP of the other regions, the better the accessto markets of the region; the larger the distance between region A and theother regions, the lower the weight of the GDP of each of these regions in themarket access of the region considered. Finally, equation (4) shows how todeal with the GDP of the own region, which represent the contribution of thehome GDP to the overall market access: Harris (1954) proposes a formulafor own distance that takes into account the size of the region, making theGDP of the region weight less in the calculation the larger the region is.The rational here is that for a given level of GDP, the larger the region themore spread out, and therefore harder to access, its own GDP is. This typeof formulation of market potential has been used in several works. Crafts(2005) applies it to the regions of Britain (1870-1910), Schulze (2007) onthe Habsburg regions (1870-1910) and Martinez-Galarraga (2012) on Spain(1859-1929).

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4 Description of Sources

In this section we describe the sources for the dependent variable, the regioncharacteristics and the industry characteristics.

4.1 Employment Figures

The dependent variable of the model is the logarithm of the percentageof employment per region, per sector with respect to the national sectoremployment. For instance, the chemical sector in Piedmont in 1871 had 1424workers while the Italian chemical sectors had 10736 workers. Therefore,one data point will be equal to 1424 divided by 10736, with correspondsto 13.45%. The employment figures are taken from Ciccarelli and Missiaia(2013), which presents labour force estimates from the population censusesat provincial and regional level.6 Ciccarelli and Missiaia (2013) discuss inlength the shortcomings related to the misreported textile figures for womenin the Southern regions.7 In order to use the data, we decided to correctfemale textile employment by capping the number of women at no morethan four times the men, as in Fenoaltea (2003). The employment data areproposed by sector and by region in figures 4 and 5.

Figure 4: Total industrial employment by industrial sector, (1871-1911)

Source: Population Censuses, (1871-1911)

6The cited work uses provincial figures to then aggregate regional figures. This causesslight differences with the numbers proposed by Fenoaltea (2003) at regional level. Weconsidered the figures aggregated from provinces to be safer from mistakes in calculationmade at the time of the census.

7The over representation is due to a very high number of women reported in textilesin Southern regions compared to the rest of the country. The problem is so severe that itis impossible to use the numbers without an ad hoc correction.

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Figure 5: Total industrial employment by industrial region, (1871-1911)

Source: Population Censuses, (1871-1911)

4.2 Regional Characteristics

This section describes the sources for each of the regional characteristics.Table 3 lists all characteristics except market potentials. Table 6 lists thethree different formulations of market potential used in the paper.

4.2.1 Energy Endowment

To describe the role of energy, we use three different endowment variables.Coal production is not included as Italian regions had a very low productionof coal, relying mostly on imports. Instead, coal prices are used. Direct coalprices at regional level are not available for this period. The solution isto take the coal price per tonne in Genoa, which was the main port forthe import of coal at the time and augment it for the transport cost fromGenoa.8 The prices for Genoa are from Cianci (1933).9

Water power data is constructed from two ministerial sources: Bollettino diNotizie Agrarie (1884), which is a province level census on the production ofwater power in 1877 and Prospetti Riassuntivi delle Concessioni Assentitenegli anni dal 1870 al 1932 (1935) on the new concessions for water powerproduction between 1870 and 1932. The stock for 1877 is aggregated inregions and then the data on the new concessions is used to expand the

8See Bardini (1998) for a full discussion on energy production in Italy in the Liberalperiod

9Bardini (1994) does not provide a comprehensive dataset of coal prices across Italianoregions, which proved to be hard to come by even using primary sources. Bardini (1994),p.147 reports prices for Cardiff coal in 1899 for Genoa, Livorno and Catania. The price inGenoa was 33.10 lire per tonne, in Livorno between 28.7 and 33.8 and in Catania between35.1 and 49.6. This figures seem to support the idea that coal was getting into Italythrough Genoa and then to the other ports. Further research will be needed in order tosupport this assumption.

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Table 3: Regional Characteristics

1871 1881 1901 1911 1871 1881 1901 1911

Water power Coal price

Piedmont 2.94 3.20 6.93 16.07 47.40 38.77 42.57 41.96

Liguria 2.23 2.65 3.03 5.83 38.96 30.25 35.65 35.65

Lombardy 2.94 3.08 6.07 16.96 46.80 38.17 42.08 41.51

Venetia 1.93 2.12 2.57 3.95 44.69 33.84 37.77 38.04

Emilia 2.35 2.37 2.43 2.78 54.38 43.82 45.94 45.45

Tuscany 1.04 1.12 1.30 2.07 44.40 35.59 39.93 39.58

Marches 2.26 2.29 2.48 6.94 44.20 33.53 37.58 37.84

Umbria 1.21 1.44 6.38 15.72 45.57 36.53 40.61 40.25

Latium 1.01 1.03 1.61 3.40 43.97 34.92 39.30 39.06

Abruzzi 1.32 1.37 4.29 6.98 69.17 55.40 51.85 51.63

Campania 2.67 2.77 3.61 4.94 51.54 40.95 43.60 43.32

Apulia 0.08 0.08 0.09 0.09 40.74 31.37 36.31 36.39

Lucania 0.76 0.77 0.79 0.84 50.02 40.05 43.11 42.73

Calabria 0.94 0.94 0.98 1.21 41.45 31.81 36.57 36.69

Sicily 0.49 0.50 0.51 0.64 41.12 31.61 36.45 36.55

Sardinia 0.06 0.06 0.06 0.06 40.79 31.40 36.33 36.41

Hydroelectric power Literacy rate

Piedmont 0.00 0.00 1.00 4.12 58% 66% 80% 88%

Liguria 0.00 0.00 0.78 2.21 44% 54% 71% 82%

Lombardy 0.00 0.00 1.60 5.76 56% 62% 76% 86%

Venetia 0.00 0.00 0.33 1.41 36% 45% 62% 73%

Emilia 0.00 0.00 0.16 0.66 29% 35% 51% 64%

Tuscany 0.00 0.00 0.07 0.45 34% 40% 52% 62%

Marches 0.00 0.00 0.40 1.52 22% 26% 36% 46%

Umbria 0.00 0.00 2.10 6.93 21% 27% 38% 49%

Latium 0.00 0.00 0.71 2.51 35% 43% 55% 66%

Abruzzi 0.00 0.00 0.33 1.63 16% 19% 29% 39%

Campania 0.00 0.00 0.18 0.50 21% 25% 34% 45%

Apulia 0.00 0.00 0.00 0.00 16% 20% 30% 39%

Lucania 0.00 0.00 0.01 0.04 13% 15% 23% 32%

Calabria 0.00 0.00 0.01 0.10 14% 16% 21% 30%

Sicily 0.00 0.00 0.01 0.14 15% 19% 28% 40%

Sardinia 0.00 0.00 0.00 0.00 18% 21% 31% 40%

Agricultural employment Deposits per capita

Piedmont 76% 71% 63% 58% 4.66 17.49 39.22 99.75

Liguria 58% 53% 45% 39% 6.87 28.46 35.17 45.52

Lombardy 64% 59% 53% 46% 59.93 88.46 166.79 166.83

Venetia 83% 83% 75% 60% 3.63 16.06 47.82 66.23

Emilia 67% 65% 64% 63% 21.05 48.04 84.74 108.83

Tuscany 64% 62% 57% 54% 26.70 38.25 78.65 125.73

Marches 72% 71% 70% 69% 9.07 32.79 49.21 70.89

Umbria 78% 76% 73% 71% 5.64 15.91 28.18 50.39

Latium 67% 62% 55% 50% 29.16 58.22 94.08 100.12

Abruzzi 76% 76% 77% 77% 0.30 2.40 5.59 13.14

Campania 61% 61% 58% 57% 0.08 0.61 2.06 31.19

Apulia 64% 64% 63% 62% 0.17 1.24 3.42 17.65

Lucania 72% 75% 75% 75% 0.24 1.88 1.41 4.01

Calabria 61% 60% 62% 65% 0.02 0.16 4.75 20.75

Sicily 55% 55% 55% 53% 0.43 5.64 4.61 27.23

Sardinia 73% 71% 66% 64% 0.74 10.37 9.47 4.46

Source: See text.

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series backward and forward in time. Hydroelectric power production istaken from Mortara (1934). Hydroelectric power production was presentonly in the last two benchmark years of the sample as the production before1901 was negligible. The intensity in the use of energy is measured byhorsepower unit of production in million lire. The horsepower figures comefrom the 1911 industrial census and the value added figures from (Fenoaltea,2003). Table 4 shows the contribution of different energy sources in theperiod examined.

Table 4: Contribution of different energy sources in kcal, 1871-1911

Coal Natural Gas Wood Water power Hydroelectric power

1871 13% 1% 77% 9% 0%

1881 26% 1% 63% 10% 0%

1891 40% 1% 49% 10% 0%

1901 46% 1% 43% 8% 2%

1911 58% 1% 28% 5% 8%

Source: Bardini (1994).

4.2.2 Human Capital, Agriculture and Credit

Literacy rates are used as measure of human capital endowment of theregions. A’Hearn et al. (2011) provide the latest estimates for literacy ratesat regional level for the whole population over the age of 15. This thresholdis quite convenient for this work as 15 and over is a reasonable range todescribe workers. The agricultural region characteristic is the percentageof labour force in agriculture. SVIMEZ (2011) provides estimates of theactive population in agriculture based on the population censuses. The lastinteraction captures the availability of capital measured through depositsper capita in the regions. Deposits per capita are provided for various typesof banks from the Annuario Statistico Italiano (1912) referring to 1911.Unfortunately not all years have information on all types of banks. Wedecided to use the information on Casse di Risparmio ordinarie, which areavailable for all years, as a proxy for all types of banks.

4.2.3 Market Potentials

Market potentials of Italian regions for 10 year benchmarks in the period1871-1911 are calculated considering the regional GDP plus the GDP ofthe main trading partners of Italy in this period. The unit of analysis hereare the 16 Italian regions that remained unchanged from 1871 to 1911. Theestimation of regional GDP disparities for Italy in 1871-1911 has been underdiscussion among scholars for a long time. Even if the work of reconstructionof the value added in the three main sectors has not been completed yet, themost up to date set of estimates of regional GDP per capita disparities comes

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from Felice (2009a) for 1881 and 1901 and Brunetti et al. (2011) for 1871,1891 and 1911. As for foreign trading partners GDP, we rely on Pradosde la Escosura (2000), Crafts (2005) and Klein (2009) for the US macroregions. To weight the sum of GDP, a distance measure is required. As inprevious works by Crafts (2005) and Schulze (2007), geographical distanceis substituted by transport costs. This is suitable because a straight linedistances per se do not take into account the differences in costs of alternativemeans of transportation nor the existence of railway lines and ports. Thefirst step is to choose a node for each region and trading partner.10 Thenext step is to compute the distance in terms of railway line or sea route(or a mix of the two) between each pair of nodes. For transport costs werely on Kaukiainen (2003) for shipping and Schulze (2007), Noyes (1905)and Bureau of Railway Economics (1915) for railway costs in Europe andthe US. For Italian railway rates I use a publication by Ferrovie dello Stato(1912), the Italian Railway State company. The last cost to be taken intoaccount is the existence of trade barriers between nodes. It is not the casefor Italian nodes, but whenever one of the two nodes is a foreign city, acorrection is needed. Following Crafts (2005) and Schulze (2007), tariffsare converted into distance equivalents. This is based on a gravity modelby Estevadeordal et al. (2002). The elasticities of the model are used toconvert ad valorem tariffs into a distance equivalent measure to add to theregular terminal component of the transport cost. The tariff between Italyand each trading partner is computed as ratio of the total custom revenues ofthe trading partner over its total imports. This gives an average tariff levelfor each country. The source used is Mitchell (2003) except for the UnitedKingdom where Mitchell (1988) is used, Capie (1994) for 1870 Germanyand Ferreres (2005) for Argentina. Table 5 shows the transport costs forItaly used to calculate market potentials.11 Table 6 shows the estimates ofregional market potentials for all four years under exam.

4.3 Industry Characteristics

This section describes the sources for the industry characteristics used inthe model. Table 7 reports the figures for all industry characteristics. Thetwo main sources for industry characteristics are the industrial census of1911 and the input-out table provided by Vitali (2003). The two sourcesare described in detail.

10The Italian nodes are: Genoa for Liguria, Venice for Veneto, Ancona for Marches,Naples for Campania, Bari for Apulia, Reggio Calabria for Calabria, Palermo for Sicily,Cagliari for Sardinia, Turin for Piedmont, Milan for Lombardy, Bologna for Emilia,Florence for Tuscany, Terni for Umbria, Rome for Latium, Pescara for Abruzzi, Potenzafor Basilicata. The foreign nodes are: London , Istanbul, Buenos Aires, Chicago, NewYork, New Orleans, San Francisco, Vienna, Budapest, Paris, Berlin and Zurich.

11For reasons of space we omit the freight rates of trading partners while the shippingrates used are the same for Italian and foreign nodes

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Table 5: Transport costs in Italy, 1871-1911

Shipping Freight Rate

Terminal Variable Terminal Variable

Component Component

1871 17.27 0.002697 1.69 0.05

1881 13.77 0.001690 1.71 0.05

1891 10.47 0.001087 1.42 0.04

1901 8.09 0.000995 1.39 0.04

1911 7.29 0.001125 1.26 0.04

Source: Bardini (1994).

Table 6: Market Potentials, 1871-1911

Total MP National MP International MP

1871 1881 1901 1911 1871 1881 1901 1911 1871 1881 1901 1911

Piedmont 108 93 85 86 Piedmont 120 118 113 109 Piedmont 113 97 92 92

Liguria 118 116 119 119 Liguria 121 119 126 127 Liguria 118 116 117 119

Lombardy 109 99 92 95 Lombardy 142 130 131 132 Lombardy 102 91 89 87

Venetia 106 108 110 109 Venetia 122 113 116 116 Venetia 102 107 109 108

Emilia 92 85 78 81 Emilia 123 112 104 109 Emilia 85 77 74 73

Tuscany 99 96 92 93 Tuscany 115 109 99 101 Tuscany 94 92 91 90

Marches 100 105 108 107 Marches 96 98 101 100 Marches 100 106 109 108

Umbria 82 80 77 78 Umbria 85 75 68 68 Umbria 80 78 78 76

Latium 94 96 94 95 Latium 88 92 87 88 Latium 94 95 95 95

Abruzzi 83 83 81 81 Abruzzi 80 77 70 70 Abruzzi 82 82 81 80

Campania 110 114 117 116 Campania 117 115 116 116 Campania 108 114 116 117

Apulia 100 107 111 109 Apulia 86 96 103 101 Apulia 103 109 111 111

Basilicata 80 83 81 82 Basilicata 53 68 64 64 Basilicata 84 84 83 82

Calabria 103 109 114 112 Calabria 80 88 96 96 Calabria 107 113 115 116

Sicily 113 118 123 122 Sicily 101 110 113 111 Sicily 117 121 122 125

Sardinia 104 111 117 116 Sardinia 72 81 92 91 Sardinia 112 117 119 121

Italy 100 100 100 100 Italy 100 100 100 100 Italy 100 100 100 100

Source: See text.

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Table 7: Industry Characteristics

Horsepowerper VA

Hydric powerper VA

Horsepowerper worker

Forwardlinkages

Mining 114 20 26.00 4.66

Foodstuffs 272 147 106.40 0.48

Tobacco 36 18 4.94 0.00

Textiles 408 233 34.34 1.88

Clothing 11 1 1.85 0.19

Leather 19 7 4.66 0.30

Wood 73 31 13.22 1.41

Metalmaking 931 222 229.12 4.67

Engineering 66 14 18.11 0.48

Non Met. Minerals 175 40 24.84 1.47

Chemicals 334 210 107.50 3.15

Paper 156 128 48.49 0.41

Sundry 42 19 7.32 3.15

Construction 2 1 1.27 0.53

Utilities 4161 2996 2436.40 0.06

BackwardLikages

Meanplant size

Shareof witecollar

Agriculturalinputs

Mining 0.25 17 3.58% 4.00

Foodstuffs 4.45 4 2.45% 3353.00

Tobacco 1.57 391 5.69% 33.00

Textiles 2.50 74 2.81% 671.00

Clothing 2.06 6 2.81% 4.00

Leather 0.89 4 1.28% 0.00

Wood 0.74 4 1.30% 56.00

Metalmaking 1.64 38 3.64% 0.00

Engineering 0.72 8 4.07% 0.00

Non Met. Minerals 0.47 15 2.58% 0.00

Chemicals 1.67 19 6.70% 24.00

Paper 0.14 17 4.71% 0.00

Sundry 2.59 15 3.57% 43.00

Construction 0.18 19 2.71% 26.00

Utilities 0.13 16 11.02% 3.00

Source: See text.

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4.3.1 Industrial Census of 1911

The Industrial Census of 1911 was the first industrial census of unifiedItaly. It provides information of the number of firms, the number of workersdivided by type of job and by industrial sector and the horsepower producedby firms. This census is the only one available for this period thereforethe industry characteristics extrapolated from it will be time invariant.The interactions that rely on this source are the human capital one withthe share of white collars per industrial sector and the market one thatinteracts market potential and economies of scale measured by mean plantsize. Also, all interactions that require a measure of energy intensity throughhorsepower rely on the industrial census.

4.3.2 Input-Output Tables

Market potential is interacted with three industry characteristics. The firstis mean plant size, measured as above. The other two rely on forward andbackward linkages that are estimated as value of output that are used asinput by other industrial sectors and input that come from other industrialsectors, all controlled for the value added of each sector. The source here isthe input-output table provided by Vitali (2003) for 1891 and 1911. Herewe use 1911 as reference year. The input-output tables by Vitali (2003)also provide the value of inputs from agriculture to industry used in theagricultural interaction.

5 Empirical Results

In this section we present two main bodies of evidence. The first is thecalculation of three different types of market potential; the second deals withthe econometric testing of the determinants of the location of industries.

5.1 Market Potentials of the Italian Regions

Here we provide the estimates for market potentials of all Italian regions forten year benchmarks, from 1871 to 1911, excluding 1891. These estimatesallow analysing the market access of different regions at early stages of theprocess of industrialization and throughout its evolution until 1911. Marketpotentials will later be used as explanatory variable to explain the locationof industries across regions. This section discusses three different versionsof market potential: the first proposed is national market potential thattakes into account the Italian regions only; the second one, the internationalmarket potential, takes into account the GDP of the main trading partnersof Italy only; the third one is total market potential that takes into accountthe GDP of both regions and trading partners.

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Figure 6 shows national market potential estimates of Italian regions settingthe national average equal to 100.

Figure 6: National Market Potential across regions, 1871-1911 (Italy=100)

Source: See text.

National market potential, as proposed here, shows two main results.The first is that especially at the beginning of the period the picture isquite in line with the classic North-South divide, with the North showing ahigher level of market access. The exceptions in the South are Campaniaand Sicily. The reason why these two regions have level comparable to theNorthern regions is that their total GDP at the beginning of the regionwere comparable to the ones of the regions in the North. Moreover, thesetwo regions have their economic centre on the sea. This makes them ableto exploit shipping, which is relatively cheaper than railways in this period.The second result is that this tendency over the period is a relative worseningof the market access of the North with respect to the South. This tendencycan be explained again looking at transport costs (see table 5). Shippingcosts dropped relatively faster in this period compared to railways, givingan advantage to regions that have their node on the coast. This is the caseof the main Southern regions. A similar result, connected to the access toshipping, is discussed in Schulze (2007) on the Habsburg Empire. In thatcase, the only two regions with access to the sea (Littoral and Dalmatia)have a persistent advantage over the others in terms of market potential(in spite of their lower GDP). This relationship between sea access andmarket potential is clear when trading partners of the Habsburg Empireare taken out of the sample. This is because Littoral and Dalmatia are the

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only regions in the sample connected to the trading partners directly by sea.Taking trading partners out, the two regions do not have such an advantageanymore. In Italy this advantage is present without trading partners as wellbecause shipping is an available option in internal trade as well.

Figure 7 shows the market potential of regions taking into account theGDP of foreign trading partners only. Here the South appears to be alwaysadvantaged as most trading partners were reached by the sea. Also, theGDP of some of the trading partners was quite large compared to the Italianone, making cheaper transport costs from ports even more influential in thecalculation.

Figure 7: International Market Potential across regions, 1871-1911(Italy=100)

Source: See text.

The alternative third version proposed here is total market potential thatincludes all the trading partners of Italy on top of all Italian regions. Asbefore, figure 8 shows the estimates with respect to the national average.Here the advantage of the South can still be seen very clearly.

This version of market potentials shows a quite different picture to thenational ones although less extreme compared to the international one. TheSouth here appears to perform increasingly better in the period and tostart from a higher level compared to the North, which experiences theopposite evolution. These estimates are very influenced by the GDP of thetrading partners, especially the US, which has a very high GDP comparedto Italy. Given that most trading partners are reached at least partially byshipping, the sea effect is even higher in this formulation of market potential.The next section shows the econometrics results using all these alternative

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Figure 8: Total Market Potential across regions, 1871-1911 (Italy=100)

Source: See text.

formulations of market potential.

5.2 Determinants of the Location of Industries

In this section we present the estimation results on the determinants ofthe location of industries. Table 8 shows the estimation of equation 2 asa repeated cross section and then as a pooled OLS. All coefficients arestandardized and for the last specification we cluster for each couple region-industry.12

Let us start with the cross sections. The main result that stands out isthat the interaction between literacy rate and share of white collar workersis positive and significant across years. The human capital interactionseems to be in fact the most consistent across specifications. It is correctlysigned, as we expect that regions with higher literacy rates will attractfirms from sectors that are more intensive in the use of skills (meaning thatthey will have more white collar workers). As for the market interactions,we use in this formulation total market potential. The results are mixed.The interaction with forward linkages is significant and correctly signed forthe first two years as well as the one for economies of scale. The othersshow no results or they show results that have a negative sign but not

12A two way clustering was attempted to capture region and industry effects separatelybut the number of clusters does not appear to be sufficient to perform the exercise.

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Table 8: The Determinants of the Location of Industries1871 1881 1901 1911 Pooled Pooled with clusters

Agr. Empolyment x Agr. Production-0.235 -0.082 -0.042 0.080 -0.071 -0.071(-0.68) (-0.45) (-0.15) (0.33) (-0.46) (-0.87)

Literacy Rate x White Collar0.414∗∗ 0.284∗∗∗ 0.526∗∗∗ 0.474∗∗ 0.540∗∗∗ 0.540∗∗∗

(3.20) (3.74) (3.48) (2.82) (7.29) (6.05)

Deposits Per Capita x Horsepower0.052 0.007 -0.034 -0.094 -0.067 -0.067(0.99) (0.20) (-0.46) (-1.14) (-1.80) (-1.49)

Coal Price x Horsepower0.779∗ 0.141 -0.236 -0.312 1.616∗∗∗ 1.616∗∗∗

(2.01) (0.69) (-0.39) (-0.55) (9.43) (6.68)

Waterpower x Horsepower-0.040 -0.034 0.114 0.305∗ -0.095 -0.091(-0.39) (-0.69) (0.90) (2.13) (-1.38) (-1.38)

Hydroelectricpower x Horsepower0.000 0.000 -0.020 -0.253 0.130∗ 0.130∗

(-0.21) (-1.96) (2.26) (1.59)

Total MP x Forward Linkages1.786∗∗∗ 0.700∗∗∗ 0.315 0.151 0.132∗∗ 0.132∗∗

(5.30) (4.05) (1.28) (0.59) (2.91) (2.95)

Total MP x Backward Linkages-0.539 -0.207 -0.325 -0.011 -0.169∗∗∗ -0.169∗∗∗

(-1.47) (-1.10) (-1.26) (-0.04) (-3.48) (-3.97)

Total MP x Mean Plant Size-0.172 -0.068 0.161 0.946∗∗∗ 0.111∗ 0.111∗

(-0.36) (-0.32) (0.58) (3.69) (2.55) (2.27)

Observations 229 234 235 238 936 936Region dummy yes yes yes yes yes yes

Industry dummy yes yes yes yes yes yesYear dummy yes yes yes yes yes yes

F-test 16.86∗∗∗ 60.85∗∗∗ 14.30∗∗∗ 15.39∗∗∗ 36.38∗∗∗ 36.43∗∗∗

Adjusted R2 0.720 0.905 0.683 0.698 0.608 0.608

Standardized beta coefficients; t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, standard errors clustered by region-industry in the last column

21

significant. Under the assumption that market has a positive effect onattracting industries that have more linkages, this sign would be positive. Asign opposite to that expected for one interaction or the lack of significanceof the other two suggests that market access did not have a clear positiveeffect on the industrialization of the Italian regions. This is not surprisingif we give a closer look at figure 8. Market potentials that include foreigntrading partners are not particularly lower in less industrialized regions arenot in the best relative position. The intuition behind these estimates isthat in this period having access to sea transportation through a port was agreat advantage in terms of transport costs. Shipping was cheaper comparedto railways (see table 5) and was becoming even cheaper in relative termsduring in the period 1871-1911. For the case of Italy this is very importantand gives rise to the somehow counter-intuitive result that Northern regions,which for the case of Lombardy and Piedmont were landlocked which arephysically closer to the centre of Europe, are disadvantaged in terms oftransport costs compared to the Southern regions that had a better accessto the sea.The number of observations for each cross section is relatively low (16regions multiplied by 15 sectors), therefore pooling the data can lead tomore precise estimates. The last two columns of table 8 show the resultsas a pooled OLS and then pooled OLS with clustering by region-sector.13

This type of clustering by region-industry takes into account the fact thatthe same region-industry pair will tend to have more similarities acrossyears compared to the other region-industry pair. The results for the twospecifications do not differ much, except for the presence of slightly lowert-statistics in the second case. Compared to the cross section, the results onhuman capital are confirmed. The results for the interaction between marketpotential and backward linkages is still negative but this time significant. Anew result stands out on the endowment side: the interaction between coalprices and horsepower per unit of production is significant but incorrectlysigned. The reason for this counter intuitive result lays in the way coal pricesare extrapolated. Augmenting the price in Genoa with variable transportcosts leads to obtaining a similar result to that of market potentials. Ifthe access to markets is influencing coal prices, it is not surprising that thecoefficient is negative.Table 9 shows the same estimation of equation 2 pooled with alternativeformulation of the market interactions. In particular, the first columnshows the results using national market potential; the second column usesinternational market potential, the third one adds national and internationalin the same regression; the fourth, fifth and sixth columns show total,

13We performed a Chow-test on the coefficients of the cross-section to test for poolability.The hypothesis of all coefficients being equal across years is not rejected, allowing us topool.

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national and international market potentials interacted with forward andbackward linkages referring to 1891 rather than 1911 from the input-outputtable by (Vitali, 2003). These last three formulation perform a robustnesscheck on the industry characteristic side.

The first three columns of table 9 shows that in spite of the differencesbetween the alternative formulations of market potentials, the results donot appear to change dramatically. We would expected National marketpotential to be more likely to influence industrial location given the similarpath to that of industrialization. However, even using national marketpotential estimates a clear result does not emerge. In all specifications themarket interactions give mixed results, showing both positive and negativecoefficients. When using national and international market potentialsjointly, the coefficients loose significance, suggesting that specificationproblems may arise. Human capital, on the other hand, appears in allcases to be consistently positive and significant. When looking at thedeterminants of industrial location, the size of the coefficients needs tobe discussed. Given that all explanatory variables are interactions, thereis no straightforward interpretation of their size. Klein and Crafts (2012)explain that when using standardized beta coefficients, the relative impactof the interactions can be inferred comparing the coefficients. In our case,the human capital interaction presents coefficients hat are about two tofive times larger than market interactions, when correctly signed.The coalinteraction has a particularly large coefficient, which admittedly will needfurther analysis to be explained.

6 Concluding Remarks

This paper seeks to account for the factors that influenced the location ofItalian industries in the Liberal period (1871-1911). Market access is oneof the main explanatory variables of the model therefore the first step wasto estimate market potentials of Italian regions for 10-year benchmarks inthe period 1871-1911. The bottom line of these calculations is that nationalmarket potentials show a more traditional picture of Italy, with the Northbeing advantaged compared to the South, especially at the beginning of theperiod. What contradicts the North-South divide picture is that the gapbetween North and South in the period seems to narrow down during thesefour decades. This is due to the relative cost of shipping that decreasedcompared to railways in the period. This was an advantage for the mainSouthern regions such as Campania and Sicily, which have easy access to thesea, unlike Northern regions such as Piedmont and Lombardy. Introducingthe international markets in the picture, the results change. The North doesnot have a great advantage in terms of market access at the beginning ofthe period and, as it happens for the national one, it worsens its position

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Table 9: Robustness Checks on the NEG Interactions.National International Nat. and Int. Total National International

MP MP MP MP 1891 MP 1891 MP 1891

Agr. Empolyment x Agr. Production 1911-0.026 -0.016 0.041 -0.075 -0.026 -0.016(-0.30) (-0.21) (0.44) (-0.92) (-0.30) (-0.21)

Literacy Rate x White Collar0.531∗∗∗ 0.556∗∗∗ 0.531∗∗∗ 0.545∗∗∗ 0.531∗∗∗ 0.556∗∗∗

(6.15) (6.72) (6.03) (6.59) (6.15) (6.72)

Deposits Per Capita x Horsepower-0.062 -0.071 -0.054 -0.068 -0.062 -0.071(-1.34) (-1.57) (-1.23) (-1.51) (-1.34) (-1.57)

Coal Price x Horsepower1.609∗∗∗ 1.616∗∗∗ 1.636∗∗∗ 1.611∗∗∗ 1.609∗∗∗ 1.616∗∗∗

(6.62) (6.71) (6.85) (6.69) (6.62) (6.71)

Waterpower x Horsepower-0.104 -0.096 -0.099 -0.090 -0.104 -0.096(-0.99) (-0.92) (-0.97) (-0.86) (-0.99) (-0.92)

Hydroelectricpower x Horsepower0.140 0.131 0.132 0.126 0.140 0.131(1.68) (1.58) (1.62) (1.53) (1.68) (1.58)

National MP x Forward Linkages 19110.152∗∗ 0.209(3.23) (1.11)

National MP x Backward Linkages 1911-0.101∗ 0.145(-2.36) (1.09)

National MP x Mean Plant Size 19110.094∗ 0.070 0.085(1.97) (0.49) (1.79)

Interational MP x Forward Linkages 19110.106∗∗ -0.063(2.70) (-0.38)

Interational MP x Backward Linkages 1911-0.127∗∗∗ -0.239∗

(-3.50) (-2.06)

Interational MP x Mean Plant Size 19110.071 0.016 0.059(1.56) (0.12) (1.31)

Total MP x Forward Linkages 18910.112∗∗

(2.70)

Total MP x Backward Linkages 1891-0.176∗∗∗

(-4.65)

Total MP x Mean Plant Size 18910.097∗

(1.99)

National MP x Forward Linkages 18910.138∗∗

(3.18)

National MP x Forward Linkages 1891-0.140∗∗∗

(-3.70)

International MP x Forward Linkages 18910.080∗

(2.11)

International MP x Forward Linkages 1891-0.135∗∗∗

(-4.11)

Observations 936 936 936 936 936 936Region dummy yes yes yes yes yes yes

Industry dummy yes yes yes yes yes yesYear dummy yes yes yes yes yes yes

F-test 36.3∗∗∗ 36.16∗∗∗ 40.67∗∗∗ 35.94∗∗∗ 35.51∗∗∗ 39.67∗∗∗

Adjusted R2 0.605 0.605 0.606 0.607 0.605 0.604

Standardized beta coefficients; t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, standard errors clustered by region-industry24

compared to the South. Total market potential that includes both providesintermediate results, although it still suggests that the position of Southernregions of Italy on the markets was potentially quite good compared to therest of the country. This is especially true for the highly populated Southernregions that had a good access to the sea, namely Campania and Sicily. Thesecond step was to introduce a methodology that explains the percentage ofemployment per region per sector with respect to the national employmentof the sector using a set of interactions between industry characteristicsand region characteristics of both the the H-O and NEG type. We usedboth cross section and pooled OLS regression analysis with fixed effects forindustries, regions and years to estimate the model. The results suggest thatendowment, and in particular human capital endowment, was central in thelocation of the Italian industrial sector during the first industrialization ofthe country. The other important result is that market forces do not appearto be clearly correlated with industrialization providing mixed results. Thisis explained by the results on market potentials that show a reverse picturecompared to the relative industrialization of the regions. This can beexplained with the better access to cheap maritime transportation by theSouthern regions. The two main Northern industrialized regions, Piedmontand Lombardy had a worse access to cheap transportation. This resultholds using both domestic and international market potential. The focuson human capital as a determinant of the different development of Italianregions is not new (see Zamagni (1978) and more recently A’Hearn et al.(2011) and Felice and Vasta (2012)). Southern regions, as well as morebackwards regions of the North, especially Venetia, had a persistent gapin literacy rates over the whole period. The importance of human capitalhas also been underlined in other works on industrial location during earlystages of industrialization (see Crafts and Mulatu (2006) on Britain andWolf(2007). The case of Italy seems to follow a similar pattern.

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