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THE POLITICAL ECONOMY OF PLACE-BASED POLICIES: EVIDENCE FROM LAND INVASIONS AND LAND REFORM IN

BASILICATA, ITALY

Marco Percoco Department of Institutional Analysis and Public Management

Università Bocconi

February 2017

Abstract

The aim of this paper is to analyse the political economy that characterized the design and implementation of a particular place-based policy aimed at addressing wealth inequality, i.e., land reform. It focuses on Basilicata in the Italian South, where massive land invasions took place between December 1949 and March 1950 in reaction to long lasting and extreme inequality. The invading peasants were solicited and even coordinated by local Communist Party leaders. Empirical analysis shows that land invasions were driven not only by the concentration of land ownership, but also by the strength of the Communist Party. Furthermore, it is found that towns that had experienced land invasions were more likely to be affected by the agrarian reform, and that this policy was more intense in those towns. These results are interpreted as evidence that land ownership was the object of a clear consolidation strategy by the Communist Party and of issue ownership competition with the subsequent implementation of land reform.

(*) The author benefited from the comments of Tommaso Nannicini, Francesco Passarelli and participants in seminars at the Universities of Strathclyde and Florence. Financial support from the Italian Ministry of Education and Research in the form of the FIRB grant “Social and spatial interactions in the accumulation of civic and human capital” is gratefully acknowledged. Please address correspondence to: Marco Percoco, Department of Policy Analysis and Public Management, Università Bocconi, via Rontgen 1, 20121 Milano, Italy. Email to: marco.percoco@unibocconi.it.

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1. Introduction

Why are place-based policies implemented in some places and not in others? We argue in this paper

that, after controlling for local socio-economic characteristics, politics in the form of electoral

competition is a central determinant in explaining the geography of policy implementation. More

specifically, we focus on land reform in Italy as a particular place-based policy aiming to reduce local

wealth inequality, as well as how the spatial distribution of interventions was driven by the political

economy of local conflicts.

Land plays a crucial role in the economy of less developed countries where agricultural

production is the primary source of subsistence for large fractions of the population. The distribution

of land property rights, therefore, is one of the main determinants of income distribution and even of

possibilities (e.g., access to education and health services). Individuals in general, and peasants in

particular, are caught in a sort of poverty trap in societies characterized by large levels of land

inequality and credit market imperfections. This situation is widely considered to be crucial in

determining riots, as in the cases of Brazil (Hidalgo et al., 2010), South Africa (Simmons, 2001),

Mexico (Dell, 2012) and Uganda (Deininger and Castagnini, 2006).

This paper contributes to the literature on the relationship between wealth inequality and local

conflicts, by proposing evidence on the relevance of political motives for land invasions in a

backward region of Italy (Basilicata) during the years 1949-1950.

The invasion of plots of land, in most cases carried out by landless peasants, is a particular

type of civil unrest driven by economic and social factors. In highly unequal societies, severe income

shocks may result in riots and land invasions. On this point, Miguel et al. (2004) analysed the

relationship between income and the outbreak of a civil conflict, using the change in rainfall

precipitation as an exogenous instrument to account for endogeneity. They found that in African

villages where the land is very unequally distributed, there are twice the number of land invasions by

the rural poor as there are in villages with a lower level of inequality. Similarly, Dell (2012) uses

variation in drought levels as an instrument for changes in income to analyse the impact of riots during

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the Mexican Revolution of the early 1900s on subsequent policies implemented by the government

and their long-term impact on development.

Some recent studies have identified two main channels through which income variables can

become the cause of a conflict: (1) a reduction in the opportunity cost of conflict and (2) an increase

in the benefits of conflict. Dube and Vargas (2006), in their study of the Colombian civil conflict,

identify two opposing effects of an income shock. The first is that of a price change in areas where

labour-intensive industries are prevalent. In this case, an increase in the price of coffee, by increasing

the wages of workers, reduces the likelihood of riots. In contrast, a change in the price of goods in

capital-intensive sectors, such as oil, triggers the rent-seeking mechanism, or the incentive mechanism

for institutions and companies to extract profits from natural resources in the shortest possible time,

deteriorating the prospects for long-term development.

While the effect of income changes on the probability of conflict can be rather simple and

direct, the relationship between inequality and conflict is ambiguous. A large literature argues that

the relationship between inequality and conflicts is non-monotonic (Acemoglu and Robinson, 2001,

2006; Grossman and Kim, 1995; Esteban and Ray, 1992, 1994, 2002). Acemoglu and Robinson

(2001) in particular argue that in situations characterized by highly unequal asset distribution, the rich

may be more likely to spend resources to suppress riots. In such cases, a more unequal community

paradoxically shows a lower propensity to riot. Acemoglu and Robinson (2006) propose a model in

which the distribution of income is endogenous and divided between landlords, capitalists and

workers. The elite faces the cost of repression and/or democracy, which are functions of: a) repression

and the use of force, which is more costly for capitalists than for landowners; b) democracies will

rationally tax land and income from land at higher rates than capital and income from capital.

Democracies in unequal societies (often young democracies) redistribute land before redistributing

other assets. The rationale for this choice is that capital is more mobile, and hence its taxation may

cause larger distortions in terms of capital flight. Consequently, land reforms are a viable policy tool

for the reduction of inequality. This preference is the main reason why landowners are less prone to

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being in favour of democratic transitions and are more willing to use force to preserve non-

democracy. In non-democracies and oligarchic societies, elites may even set wages under conditions

of extreme monopsony, which are less likely to hold under democracy. This leads to a situation in

which political institutions are path-dependent and the structure of the economy remains stable (i.e.,

no industrial structural change is observed).1

Recently, Hidalgo et al. (2010) find that income shocks are the main determinant to explain

land invasions in Brazil, and that this effect is even stronger in municipalities characterized by a

bimodal distribution. De Luca and Sekeris (2012) show that the intensity of conflict is non-monotonic

in conditions of land inequality and that most severe conflicts occur where there are intermediate

levels of land inequality. They focus on inequality within groups of landlords and find that if

inequality is extremely polarized, then small landlords may be willing to transfer land property to

large landlords, who sufficiently internalize the public good nature of the land to redistribute on their

own.

The literature on social conflicts postulates that riots are more likely to occur if the opportunity

cost of engaging in conflicts decreases, or if the returns of violence increase. Land invasions are

redistributive conflicts, and this narrows the analysis with respect to civil wars with a great variety of

possible causes.

The relationship between land invasions and land inequality is not obvious for several reasons:

a) on the one hand, as with other types of inequality, unequal land distribution may

provide the land-poor with incentives to riot;

b) as argued by Acemoglu and Robinson (2001, 2006), extreme inequality could imply a

control of public resources by an elite willing to repress the masses;

1 Interestingly, by observing land riots during the 1960s and 1970s, another strand of literature has argued that inequality does not cause riots and civil wars per se. Rather, frustration and discontent or the destabilization of the traditional social system are necessary conditions for peasant insurgencies (Gurr, 1971; Paige, 1975; Peterson, 2001; Scott, 1976). According to this view, the concept of land inequality is substituted by the concept of “land maldistribution” which postulates that it is the discrepancy between the actual distribution of land and what peasants think to be a “fair” distribution that causes discontent, and hence, riots (Midlarsky, 1982; Midlarsky and Roberts, 1985; Muller, 1985; Muller and Seligson, 1987; Russett, 1964).

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c) high levels of land inequality may be associated with clientelism (Baland and

Robinson, 2003), which could decrease the probability of rioting because potential

rioters may fear to losing employment or credit.

Hidalgo et al. (2010) provide some arguments regarding the land tenure system, since fixed

rent contracts may mitigate the effects of transitory shocks. However, in the case of Basilicata,

agricultural contracts were homogeneous across the whole regional area. In Brazil, land invaders were

often granted land and credit once the agrarian reform had been passed; in addition, land invasions

do not seem to be a mass movement as in the Italian South, where the Communist Party organized

most of the protests.

In this paper we analyse the determinants of the geography of land invasions in Basilicata, a

region in the Italian South. We contribute to the literature by showing that the political motives behind

this form of local conflict, as well as the political consequences, were particularly important. By

considering land invasions that occurred in Basilicata between December 1949 and March 1950, we

have found that inequality in land ownership was a major driver, along with the strength of the

Communist Party. Furthermore, we have found that subsequent land reform was more likely to be

more strongly implemented in towns where land invasions took place. As land reform was passed

and managed by the Christian Democratic Party, we interpret our results as evidence of political

competition over the issue of land ownership inequality. Furthermore, the analysis is suggestive of

the fact that place-based policies, such as land reform, may be subject to socio-political pressure at

both the design and implementation stages.

The reminder of the paper is organized as follows. In section 2, we present the geographical

context within which land invasions took place and propose our hypothesis on the political economy

behind land reforms; in section 3, we present our methodology and the data. Section 4 contains

econometric evidence and section 5 concludes.

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2. Land invasions in the Italian South

The North-South socio-economic divide in Italy is a well-known and established fact. Italian

historical literature has identified one of the main determinants of the persisting spatial imbalance,

among other things, in the structure of agricultural markets (Galasso, 1974). Land inequality was

particularly high in Southern Italy, and the South had also been witnessing peasant riots since the

Unification of the country occurred in 1861. However, it was only in 1943-1944 that forms of

organized riot took place in most of the Southern regions, coordinated by workers’ unions and parties

(Tarrow, 1981).

In the aftermath of World War II, claims for the redistribution of land were made by landless

peasants in the South. Because of the increase in the prices of wheat and flour, several episodes of

unrest took place in Lazio, Puglia, Calabria and Sicilia in the years 1945-1946 (Calice, 1986). During

this period, landowners put pressure on the Government to repress the peasants’ strikes and claims

(Lisanti, 1999).

From a political perspective, the years between 1945 and 1948 are of particular interest for at

least two reasons. First, after the fall of Fascism, a government composed of the Christian-Democratic

Party (DC), the Communist Party (PCI) and the Socialist Party (PSI) was in office until the first

political elections in 1948, when the DC won and the PCI and PSI went to the opposition. Second,

the PCI initially neglected peasants as potential supporters, as they were considered as sort of agrarian

proto-capitalists. Only during the Congress of Cosenza in October 1948 did the PCI decide to lead

the protest and support claims for land redistribution (Cerchia, 1976; Tarrow, 1981).

On 30 October 1949, during the invasion of a large land estate in Melissa (Calabria), three

peasants were shot to death by the police. A general strike was announced by the main leftist union

(CGIL) on 31 October (Bevilacqua, 1996). Unrest took place in Basilicata in November, when

invasions of large estates were organized in 19 towns. On 2 December, 3000 farmers from Bernalda

invaded the Policoro woods; on 7 December, the Lacava estate in Montescaglioso was occupied.

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From these towns in the province of Matera, unrest spread to Melfi and to Atella, Maschito, Muro

Lucano, Rionera, Rotonda and Venosa (Angelini, 1999; Manupelli, 1999).

These riots led the government to pass emergency measures in the form of the so-called ‘Gullo

decrees’, which imposed constraints on land ownership and limitations to extensions of latifundia, as

well as promoting land remediation and the transfer of some state-owned land to peasants

(Bevilacqua, 1996). The ‘Gullo decrees’, however, were the beginning of a more general land reform.

In December 1949, the government submitted a bill to implement of the reform in Calabria for the

approval of the Senate, which was passed in May 1950. This law, also known as the ‘Sila Law’,

indicated that private estates larger than 300 acres were to be classed as land subject to expropriation.

Land recipients were required to pay the price of the land over 30 years with a 3.5% nominal interest

rate (Prinzi, 1956). This feature of the reform is therefore interesting in the light of eventual credit

constraints faced by peasants, and can be thought of as an exogenous variation in access to credit.

Land reform was extended to other areas in Italy via the ‘Stralcio law’ of October 1950, which

extended the rules foreseen by the ‘Sila law’ to the Po Delta in the North, Tuscan-Lazio Maremma in

the Central North, Fucino in Abruzzo, Campania (Piana del Sele and Piana del Volturno - Garigliano),

Puglia-Basilicata–Molise and Sardinia. Interestingly, the effects of the reform were defined at city

level, so that within a given affected region or province, only some cities were actually affected by

expropriations.

By the end of 1951, the ‘Ente di riforma’ in Puglia-Basilicata-Molise had issued all of its

expropriation decrees and 200,000 hectares were acquired from nearly 1,500 landowners. A total of

94% of the territory expropriated was low-yielding wheat land, pasture, uncultivated land or

woodland, and 96% was owned by absentee landlords either renting to tenants on insecure contracts

or run by agents employing wage labour (King, 1973; Prinzi, 1956). The total area subject to reform

was about 8.6 million hectares, with mildly positive effects in terms of education and no significant

effects in terms of the industrialization of the area (Percoco, 2016).

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It worth noting, in this context, that the thesis of Tarrow (1981), for the use of the PCI in the

agrarian question, represents an important piece of empirical evidence that will be presented in the

next section. The thesis, which is subject to verification, is in fact concerned with the positive

correlation between the movement of land for employment in the period 1949-1950 and the

percentage of votes obtained by the PCI in the municipal elections of 1948.

From this description of the historical context in which the land invasions and land reform

took place, it seems that peasants forced the government to pass land reform. This admittedly simple

structure of events may actually hide some more interesting hypotheses that could potentially be

tested using data.

Land reform is a clear case of place-based policy, perhaps extreme in its mechanism, in which

the distribution of the ownership of a typical non-tradable good, land, is manipulated through

expropriation and re-allocation. In the case of Italy, the government agreed to pass land reform only

after massive invasions of large estates took place. From a more general perspective, these facts can

be rationalized by proposing some hypotheses on the geography of political competition. In the

following sections, we will test whether the local strength of the PCI is a predictor of the geography

of land invasions. In other words, we will verify whether the probability the occurrence and extent of

land invasion in the towns of Basilicata is correlated with the percentage of votes won by the PCI in

the previous election. This research hypothesis might seem trivial, as we provided anecdotal evidence

on the crucial role played by the PCI in coordinating and even promoting land invasions. Despite this

historical fact, the sign of the correlation between the strength of the PCI and the geography of land

invasions at town level remains undetermined a priori. A negative correlation, i.e., the stronger the

PCI in a given town, the lower the probability that they organized land invasions, will imply that the

strategy of the Communist Party was to use land invasions to spread their influence in the region in

towns where its strength was lower. However, given the cost of coordinating riots, the PCI may have

preferred to focus on towns where political consent was already large. Therefore, the sign of the

correlation is informative of the spatial strategy of PCI.

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Furthermore, we have highlighted how the government was forced to pass land reform as a

consequence of the invasions. This leads us to propose the hypothesis that land reform was stronger

where, controlling for land inequality, land invasions took place. If this correlation is found to hold

in the data, therefore, this will be evidence of the issue of ownership competition between the DC and

the PCI over a clear place-based policy.

3. Methodology

3.1 Baseline model and data

The main hypothesis of this paper is that political motives induced land invasions. In other

words, we aim to verify whether peasant riots in Basilicata were also driven by the strength of the

PCI. Our baseline regression assumes the following form:

(1) 𝑦𝑦𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖 + 𝛾𝛾𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 + 𝛿𝛿𝑐𝑐𝑐𝑐𝑙𝑙𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑐𝑐𝑖𝑖 + 𝜀𝜀𝑖𝑖

where the dependent variable is a measure of land invasions occurring in city i, regressed on

a measure of the concentration of land ownership (land), the percentage of votes won by the PCI in

the 1948 elections and a series of controls.2

As for the dependent variable, we shall make use of several indicators: a dummy variable

indicating whether the city has experienced some form of land invasion between 1949 and 1950, the

total amount of land invaded per capita and the percentage of the land invaded. The data are from

Angelini (1999) and Manupelli (1999), who assembled data on all land invasions occurring in

Basilicata from the official criminal records of Prefetture, i.e., local police offices.

2 In our analysis, we will refer to the PCI, although in the 1948 election, the PCI was allied with the Socialist Party in the Fronte Popolare, so the percentage of votes refers to the coalition. We prefer to use the notation “PCI”, since it was by far the stronger party and because of the internal debate over the issue of land inequality.

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The concentration of land ownership is measured either through a Land Gini coefficient or

through a polarization index. The distribution of land in 1946 was recovered from the Census

conducted by INEA (1956). For each city, information on the size and number of properties was

organized into 11 size groups (in acres): from 0 to 0.5, 0.5 to 2, 2 to 5, 5 to 10, 10 to 20, 25 to 50, 50

to 100, 100 to 500, 500 to 1000 and larger than 1000. The formula for calculating the Gini coefficient

is

∑∑ +−

−+=

jj

jj

an

ajn

nLandGini

)1(211

where n is the number of farms, aj is mean farm size and i denotes the rank, according to which farms

are ranked in ascending order of aj.

The polarization index is defined as (Esteban and Ray, 1994):

𝑃𝑃𝑐𝑐𝑙𝑙𝑙𝑙𝑐𝑐𝑃𝑃𝑃𝑃𝑙𝑙𝑐𝑐𝑃𝑃𝑐𝑐𝑙𝑙 = ��𝜋𝜋𝑘𝑘(1+𝛼𝛼)

𝑗𝑗

�𝜇𝜇𝑘𝑘 − 𝜇𝜇𝑗𝑗�𝑘𝑘

where πk is the percentage of owners in group k and µk is the percentage of land owned by the owners

in group k. In our case, we have considered three groups and the parameter α was set to 1.

Among the controls, we consider: a Herfindahl index of political competition calculated

according to the percentage of votes won by parties in the 1948 election, literacy, the percentage of

houses with a drinkable water supply, the percentage of the population working in agriculture and the

percentage of the population inactive in the labour market. All controls have been considered at the

1938 level and are from the Census, with the sole exception of electoral outcomes, the source for

which is the Atlante storico elettorale (Corbetta and Piretti, 2009).

Table 1 reports the descriptive statistics for the variables in our dataset for the whole sample

and for the two sub-samples of cities with invaded land plots and cities with no riots. Interestingly

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enough, the two groups of cities have similar development indicators, while sizeable differences

emerge in terms of the political and land inequality variables. In particular, land invasions took place

in 37.4% of towns with 0.02 ha invaded per capita, a figure which rises to 0.05 ha if we consider only

towns where invasions occurred. The table reports the descriptive statistics with consideration given

to towns with and without invasions, so that a number of interesting patterns emerge, in line with the

political economy arguments we have made in previous sections.

In particular, the PCI won a disproportionate percentage of votes in the elections of 1948 in

towns where land invasions took place when compared to towns without invasions. A symmetric,

although smaller, difference emerges in the case of the DC. Interestingly, when socio-economic

characteristics are considered, only differences in the concentration of land ownership (measured

either through Land Gini or a polarization index) are significant.

As expected, towns where invasions took place demonstrate a larger probability of being

affected by the subsequent land reform, although this point needs to be discussed by keeping land

inequality constant. Figure 1 depicts the spatial distribution of land invasions.

It should be noted that Hidalgo et al. (2010) make use of panel data for their analysis. In our

case, we use a pure cross-section of the towns in Basilicata. This choice was made because of the

limited time-span over which land invasions took place (only four months, between December 1949

and March 1950) and, consequently, due to the lack of high frequency data. The estimation of (1)

implies an analysis of the structural determinants of land invasions and not how they reacted to

contingent factors such as income shocks.

The main issue with estimating equation (1) is endogeneity, which affects our two main

variables: land and PCI. To properly deal with this problem, we shall estimate model (1) with the

help of two instruments: the amount of church estates that had been expropriated by the end of the

19th century and the number of workers’ cooperatives by the end of the 19th century.

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3.2 Instruments

Expropriation of Church estates

Our first instrument, thought to be a long-term exogenous variation for the concentration of

land ownership, is related to the decision made by the Italian government to expropriate a substantial

part of Church-owned estates and to then auction them.

In 1866, the new Kingdom of Italy (the unification took place in 1861) engaged in a war

against Austria, the so-called Third War of Independence. To respond to these serious financial

constraints, the Government issued two laws in 1866 and 1867, respectively. The first of those laws,

the Regio Decreto n. 3036 of July 1866, denied the legal status of religious orders and congregations

(with the exclusion of the assets of parishes and seminaries) so that they were no longer allowed to

own estates. As a consequence, all assets falling under these categories were expropriated and

auctioned. The second law was Law n. 3848 of 15 August 1867. It established the expropriation of

all religious institutes not directly linked to the cult (e.g., hostels for pilgrims, rural firms producing

goods, etc.). The assets of those institutes were again auctioned (Lerra, 1996).

The two aforementioned laws had an impact on land ownership, especially in the South where

about three million hectares were auctioned, about 2.5 million of which were in Southern regions.

The expropriation aimed to both improve the central government’s financial conditions and to

redistribute wealth from the Church to the “new” Italian citizens.

However, peasants and agricultural workers were only earning subsistence-level incomes and

therefore did not have the opportunity to participate in the auctions or buy land plots. Given these

constraints, the redistribution of the Church’s estates benefited wealthy individuals, typically the

owners of large landed estates. Furthermore, both laws stated that particularly large estates had to be

given to State creditors to buy out public debt.

Consequently, the redistribution of the Church’s assets had an adverse impact on the

concentration of land ownership, and this is the main reason for which we believe that this event can

be considered as a good instrument for the concentration of land ownership in 1948.

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In our analysis, we consider the expropriation and re-allocation of Church estates as an

exogenous variation for the concentration of wealth. The reason for this assumption lies in the auction

mechanism for the reallocation of expropriated estates, which favoured affluent members of local

elites. This implies that a substantial portion of the properties were delivered into the hands of already

rich individuals, meaning that wealth inequality may have worsened. Given the substantial stability

of wealth inequality over the time, we hypothesize that towns in which more estates were auctioned

will show higher levels of land inequality in 1948. In particular, we make use of the (log) number of

rural estates per capita (where the population is defined according to 1938 levels), as reported in Lerra

(1996).

The exclusionary restriction is that our instrument is uncorrelated to other explanatory

variables and invasions in Basilicata. As discussed above, we think that the two main factors

explaining unrest are related to inequality and the strength of the PCI; the latter will also be

instrumented, so that it will functionally depend on the expropriation of Church estates, although we

do not have evidence or historical anecdotes correlating such an event to public support for the

Communist Party. Furthermore, our instrument is thought to capture the persistence of wealth

inequality, so that it will rule out the eventual strategic behaviour of landowners who might have sold

estates before the expropriations began and during land invasions.

Workers’ cooperatives in 1901 and towns of confinement

As stated above, our empirical model contains two endogenous regressors, land concentration

and the strength of the PCI, as measured by the percentage of votes won in the 1948 elections. To

instrument the PCI, we propose a dummy variable indicating whether there was a workers’

cooperative in place in town i in 1901.

The rationale for using this instrument relies on the fact that the PCI, established in 1919,

relied heavily on the network of local cooperatives to organize the party at local level during its first

years (Spriano, 1967). The link between the PCI and workers’ cooperatives was even stricter in

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Basilicata, where blue collar workers were mainly non-existent because of the substantial absence of

the manufacturing sector (Calice, 1986).

The story of the cooperative movement in the South of Italy is peculiar, as the establishment

of cooperatives was only legalized after the country’s Unification in 1861, whereas in the North it

was already possible from the beginning of the 19th century. At the end of the 19th century, the socialist

movement started to organize workers into producer and consumer cooperatives. Interestingly, while

in the North cooperatives were mainly composed of blue collar workers, in the South, and in

Basilicata in particular, agricultural workers and even peasants were in the majority.

Lisanti (1990) counts 37 cooperatives in Basilicata in 1915 and 128 in 1922. Such a surge in

this number is apparently depends on two factors (Lisanti, 1990):

a) 51,195 soldiers were called up in Basilicata during WWI. Of those, 33,553 were peasants

and agricultural workers, meaning that the contraction in labour supply called for local

collective action to maintain production at subsistence level;

b) During the period after WWI, the Socialist movement began a strategy of development

and expansion in the countryside and in the South, using cooperatives as the basis upon

which to build a network of organizations diffused throughout the territories.

These considerations lead us to consider the presence of cooperatives in 1922 as an instrument

for the percentage of votes won by the PCI and PSI. In particular, our variable is coded as a dummy

variable indicating the eventual presence of a cooperative in the town. We think that our variable

meets the exclusionary restriction, as between 1923 and 1924, cooperatives suspended operations and

almost disappeared as a consequence of stricter Fascist laws and, perhaps even more importantly, as

a consequence of several episodes of violence perpetrated by Fascist squads.

The fact that workers’ cooperatives were suppressed is of great importance for our analysis,

as their spatial distribution on the eve of the advent of fascism can be assumed to be an exogenous

determinant of public support for the PCI during the first election after WWII, in 1948, a period in

which cooperatives had not yet been re-organized. In other words, we assume the presence of

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cooperatives to be a measure of structural support for the PCI by local populations. In so doing, we

exclude effects related to unrest that occurred before 1949-1950, eventually coordinated by

Communist leaders. With our instrument, we isolate the effect of the PCI on land invasions as

predicted by the historical propensity of the local population to engage in cooperatives supported by

the Communist Party. In other words, our instrument rules out the hypothesis that the outcome of the

1948 elections was driven by foresights of land invasions and land reform.

4. Results

We start our analysis firstly by analysing the determinants of land invasions. Table 2 displays

the results of probit estimates of our model, where the dependent variable is dichotomous and

indicates whether or not land invasions occurred in the town. Models differ for the variable proxying

land inequality, being either Land Gini or a polarization index. Interestingly, all indicators show the

same, positive sign with similar significance. The strength of the PCI is also always positive and

significant. Table 2 also reports OLS estimates, where the dependent variables are the quantity of

land invaded per capita and the percentage of land invaded out of the total arable land in the town. In

this case too, the PCI and land inequality variables are positively associated with riots.

Particularly in model (1), the coefficient associated with the PCI is positive and statistically

significant, indicating that an increase in votes for the PCI increased the probability of rioting. In

addition, the estimated coefficient for Land Gini is positive and significant at the 5% level, indicating

that two separate forces were at work in determining land invasions: a political coordinator, as proxied

by the PCI, and wealth inequality. As for the control variables, it should be noted that only the

percentage of inactive population in the labour market has a significant coefficient.

Model 2 considers the polarization index as an indicator of inequality and reports results very

similar to model 1. Model 3 reports OLS estimates, in which the dependent variable is the logarithm

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of land invaded per capita augmented by 0.01 to also account for zeroes.3 In such cases, the coefficient

for the PCI is estimated at 0.607, indicating that a 10% relative increase in the percentage of votes

won by the Communist Party increases the quantity of land invaded by 6.07%. Moreover, the

estimated coefficient for Land Gini is positive and statistically significant. It is interesting to note that

in this model, the Herfindahl index of political competition has a positive and significant coefficient,

indicating that the polarization of votes was a major driver for land invasions. Model 4 confirms the

results of model 3 in the case in which the polarization index is used as an indicator of inequality.

Models 5 and 6 are OLS estimates, where the dependent variable is the logarithms of the

percentage of land invaded out of the city’s total surface area, augmented by 0.01. In this case too,

the major results are qualitatively confirmed.

Overall, the estimates of table 2 seem to confirm the view that land invasions in Basilicata

were driven by both inequality and politics. However, these results need to be corroborated by IV

estimates to account for the endogeneity of our explanatory variables of interest.

Table 3 reports first stage regressions to exogenously explain the cross-sectional variation in

the percentage of votes won by the PCI and Land Gini (or polarization index). According to the

estimates in the table, all instruments are statistically significant in the regression, in which the

dependent variable is the outcome they are supposed to explain exogenously. In particular, the

presence of workers’ cooperatives has an elasticity of 1.291, indicating that the presence of a

cooperative in a town in 1922 generated a 12.91% relative increase in the percentage of votes won

by the PCI in 1948. Furthermore, Church estates present an elasticity equal to 5.91% with respect to

the Land Gini coefficient. It should be noted that the values of F-statistics are always above standard

thresholds for weak instruments.

3 All covariates are also in logarithms.

17

Table 4 reports estimates of instrumental variable models, all confirming the results of the

OLS regressions in table 2, i.e., a strong and significant support for the consolidation strategy

envisaged by PCI.

Taken together, our results suggest that, after controlling for land inequality and its

endogeneity, land invasions were more likely to occur and to a larger extent in towns where the PCI

was stronger. This result suggests that land inequality was used as a political issue to consolidate the

consensus, rather than to spread it out across the region. The costs of coordinating such invasions

might have been high, meaning that only towns where the Communist movement was already strong

experienced land invasions.

As discussed in section 2, as a reaction to the invasions, the DC-led government passed and

implemented land reform. In what follows, we will hence test whether the geography of land reform

was influenced by land invasions, so that the political use of land inequality initially taken advantage

of by the PCI was then passed to the DC by means of land reform. In order to test for this hypothesis,

we will estimate regressions in the form of equation (1), although we will substitute the PCI with an

indicator for land invasions. In other words, we will estimate an equation in the form:

(2) 𝑐𝑐𝑟𝑟𝑟𝑟𝑐𝑐𝑐𝑐𝑟𝑟𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖 + 𝛾𝛾𝑃𝑃𝑙𝑙𝑖𝑖𝑙𝑙𝑐𝑐𝑃𝑃𝑐𝑐𝑙𝑙𝑐𝑐𝑖𝑖 + 𝛿𝛿𝑐𝑐𝑐𝑐𝑙𝑙𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑐𝑐𝑖𝑖 + 𝜀𝜀𝑖𝑖

where the dependent variable is a measure of the intensity of land reform, i.e., a dummy variable

indicating whether the town was affected by land reform or not, the amount of land expropriated per

capita and the amount of land reassigned per capita. The source of these variables is Prinzi (1956).

The variable invasions is a measure of land invasions, i.e., either the quantity of land invaded per

capita or the percentage of land invaded out of the total surface area of the town.

Table 5 reports the estimates of equation (2). All models have been estimated via IV

procedures, with all instruments used as exogenous variations for variables measuring land invasions

and Land Gini. Interestingly, after controlling for (exogenous) land inequality, it emerges that land

18

reform was more likely to be implemented by the DC-led national government in places where land

invasions occurred and were more extensive. Furthermore, land reform was more intense in terms of

land expropriated and reassigned to peasants where land invasions were more extensive, with

estimated local elasticities of 7.55-7.85% for land invaded per capita and 4.09% for the percentage

of land invaded.

5. Conclusion

Extreme wealth inequality is a major concern for policy makers since, under market

imperfections, it may hinder local development. Land inequality in particular is widely recognized as

one indicator of the secular backwardness of the Italian Mezzogiorno, where, especially in a period

of relatively low labour mobility, peasants were entrapped in a situation of low salaries and low social

mobility.

To address the issue, land reform was passed in 1949-1950 as one of the first attempts to deal

with spatial disparities in Italy by means of an extensive place-based policy where, in some areas,

large landed estates were expropriated and reassigned.

In this paper, we have analysed the political economy behind land reform in a specific region,

Basilicata, one of the poorest in the Italian South. We have found that the Communist Party used land

inequality to consolidate electoral outcomes in towns where its percentage of votes won was already

large, possibly because of the high costs of coordinating riots. Furthermore, we have found that land

reform was more intense where land invasions took place. We have interpreted these results as

evidence of political competition between the PCI and DC over the issue of land inequality.

There is a large literature on pork-barrel politics, where governing parties use policies and

public expenditure to gain or consolidate a consensus. In the case under scrutiny in this paper, we

have found that one of the most significant place-based policies in Italy was influenced by politics,

either to force the government to implement it or to seize back the ownership of the issue. Taken

19

together, our proposed propositions point to the major role played by politics in decisions over

policies aiming to foster local development.

In fact, as argued speculatively by Percoco (2016), land reform in the South, even though it

brought about mildly positive outcomes, especially in terms of education, occurred too late. The

reasons for such a late implementation may be found in politics, with the PCI initially against small

landowners and even peasants in the South and the DC against the redistribution of land. Only after

a political decision made by the PCI to strategically support peasant unrest, land reform was

implemented by the government.

On a more general ground, this implies that in some cases, reasons for delays, inefficiencies

or the ineffectiveness of place-based policies should be found in the political arena and not in the

policy itself. However, this is an open issue and some more research is needed to understand the

interplay between politics and the functioning of local development policies.

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Figure 1: Percentage of land invaded

23

Table 1: Descriptive statistics

(1) (2) (3) Whole sample Towns with

invasions Towns without

invasions Invasions (dummy) 0.374 (0.486) Land invaded per capita 0.0206 0.0551 (0.0613) (0.0909) PCI (%) 0.205 0.311 0.142 (0.176) (0.190) (0.132) DC (%) 0.535 0.482 0.566 (0.171) (0.146) (0.179) Herfindahl index of political competition

0.444 0.426 0.454

(0.127) (0.0983) (0.142) Land Gini 0.782 0.801 0.771 (0.0639) (0.0623) (0.0625) Polarization 0.460 0.498 0.438 (0.0950) (0.0855) (0.0936) % Workers in agriculture (1936) 0.361 0.342 0.373 (0.0787) (0.0715) (0.0811) % Inactive population (1936) 0.546 0.565 0.535 (0.0637) (0.0574) (0.0648) % Illiteracy 0.735 0.750 0.726 (0.0668) (0.0534) (0.0725) Percentage of houses with drinkable water

0.343 0.358 0.333

(0.209) (0.222) (0.202) Reform (dummy) 0.339 0.744 0.0972 (0.475) (0.441) (0.298) Land expropriated per capita 0.0564 0.135 0.00943 (0.154) (0.225) (0.0445) Land reassigned per capita 0.0417 0.0969 0.00871 (0.107) (0.151) (0.0431) Observations 115 43 72

24

Table 2: OLS estimates

(1) (2) (3) (4) (5) (6) Invasions

(Probit) Invasions (Probit)

Land invaded p.c. (OLS)

Land invaded p.c. (OLS)

Land invaded out of total surface area

(OLS)

Land invaded out of total surface area

(OLS)

PCI 0.470*** 0.421*** 0.607*** 0.568*** 1.439*** 1.343*** (0.128) (0.130) (0.126) (0.125) (0.328) (0.330)

Land Gini 3.002** 6.105*** 12.87** (1.508) (1.973) (5.464)

Polarization 1.900*** 2.710*** 5.539*** (0.646) (0.641) (1.707)

Herfindahl 0.574 0.540 1.470*** 1.482** 3.260** 3.330** (0.536) (0.571) (0.552) (0.577) (1.350) (1.421)

Illiteracy 1.108 2.104 -0.153 0.854 0.718 3.219 (1.615) (1.691) (1.524) (1.494) (4.229) (4.213)

Drinkable water -0.0112 -0.219 0.103 -0.107 0.308 -0.201 (0.208) (0.222) (0.236) (0.228) (0.590) (0.589)

Agriculture 2.225 2.578 1.687 2.008 6.317 6.756* (1.528) (1.577) (1.491) (1.446) (3.920) (3.935)

Inactive population

5.745** 6.563** 6.199** 6.402*** 17.75*** 18.00***

(2.879) (2.996) (2.561) (2.427) (6.759) (6.564) Observations 115 115 115 115 115 115

R-squared 0.327 0.344 0.275 0.283 All variables are in logarithms. Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

25

Table 3: First stage regressions

(1) (2) (3) VARIABLES PCI Land Gini Polarization index Workers’ coop. 1.291*** 0.00669 0.00348 (0.222) (0.00720) (0.0179) Church assets 0.0812 0.0591*** 0.161*** (0.0789) (0.0027) (0.0560) Herfindahl -1.830*** -0.0106 -0.0696 (0.384) (0.0256) (0.0708) Illiteracy 2.202* 0.0876 -0.117** (1.299) (0.108) (0.053) Drinkable water -0.0521 -0.0218*** 0.00786*** (0.187) (0.0062) (0.0056) Agriculture 0.736*** -0.147 -0.443** (0.184) (0.0891) (0.171) Inactive population 1.904 -0.347** -0.734** (2.436) (0.164) (0.326) Observations 115 115 115 R-squared 0.389 0.138 0.150 F-stat 18.93 22.27 23.45

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

26

Table 4: Determinants of land invasions (IV estimates)

(1) (2) (3) (4) (5) (6) VARIABLES Invasions

(IV Probit) Invasions

(IV Probit) Land invaded

p.c. (IV) Land invaded

p.c. (IV) Land invaded out of

total surface area (IV)

Land invaded out of total surface area

(IV) PCI 0.470*** 0.142** 0.784*** 0.516*** 1.081*** 0.950*** (0.132) (0.055) (0.064) (0.049) (0.376) (0.327) Land Gini 11.70*** 3.212** 2.857** (1.007) (2.001) (1.309) Polarization 4.858*** 1.622*** 1.961** (0.533) (0.493) (0.875) Herfindahl 0.0526 0.0284 1.026 0.561 3.917 2.027 (1.069) (1.002) (1.636) (2.219) (3.998) (5.623) Illiteracy 0.286 2.100 -0.860 4.737 -1.726 12.82 (1.468) (1.490) (3.505) (6.021) (7.972) (15.26) Drinkable water 0.265 -0.0597 0.703 -0.0969 1.738 -0.0234 (0.178) (0.188) (0.658) (0.649) (1.417) (1.579) Agriculture 2.426** 2.974** 5.607 8.783 14.80* 24.00 (1.194) (1.242) (3.619) (6.135) (8.371) (15.68) Inactive population

5.403** 5.270** 14.53* 16.88 33.89* 43.28

(2.238) (2.341) (7.568) (10.71) (17.57) (27.38) Observations 115 115 115 115 115 115 R-squared 0.349 0.401 0.368 0.433

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

27

Table 5: The consequences of land invasions

(1) (2) (3) (4) (5) (6) VARIABLES Reform

(IV Probit) Reform

(IV Probit) Expropriated

land (IV) Expropriated

land (IV) Reassigned land

(IV) Reassigned land

(IV) Land Gini 0.912*** 0.433*** 0.332** 0.401** 0.501*** 0.523*** (0.223) (0.131) (0.147) (0.198) (0.123) (0.117) Land invaded p.c. 0.817*** 0.785*** 0.755** (0.0726) (0.330) (0.380) Percentage of land invaded

0.311*** 0.409** 0.409***

(0.0264) (0.207) (0.129) Herfindahl -1.020** -0.956* -0.681 -0.835 -0.705 -0.885 (0.474) (0.494) (1.636) (2.201) (1.573) (2.039) Illiteracy 1.148 0.917 2.658 5.210 1.508 3.993 (1.966) (2.044) (4.108) (5.862) (3.896) (5.294) Drinkable water -0.115 -0.223 -0.889** -1.143* -0.424 -0.637 (0.204) (0.219) (0.419) (0.629) (0.441) (0.627) Agriculture 0.675 -0.184 2.211 3.031 -1.109 0.395 (1.175) (1.058) (3.987) (5.547) (3.137) (4.498) Inactive population 0.902 0.236 -2.587 -1.175 -6.897 -4.429 (2.342) (2.278) (7.229) (9.671) (5.987) (8.133) Observations 115 115 115 115 115 115 R-squared 0.404 0.550 0.507 0.587

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1