Does centralization foster human capital accumulation? Quasi-experimental evidence from Italy’s Liberal Age

Gabriele Cappelli, Universitat Autònoma de Barcelonagabriele.cappelli@uab.cat

Michelangelo Vasta, University of Sienavasta@unisi.it

Abstract

The impact of educational reforms on inputs into schooling and educational outcomes remains under researched in economics and economic history. This gap is filled by the present paper, which offers a framework to explore the impact of a move towards centralized education that was introduced in Italy in 1911. Our analysis is based on a stratified sample of 1074 Italian municipalities observed between 1911 and 1931, and on a clear identification strategy based on the peculiar features of the norms aimed to centralize Italy’s school system. The evidence that we present shows that centralization fostered the growth of primary education at the country level by accelerating the pace of human capital convergence in the most disadvantaged areas of the country – especially in southern regions.

February 2016

*** Preliminary draft – Please do not cite without the authors’ permission ***

Acknowledgments: we wish to thank Sara Pecchioli for outstanding research assistance, as well as Lucio Barabesi and Lorenzo Fattorini for their invaluable suggestions concerning sampling methods. We also thank the participants of the Barcelona FRESH Meeting hosted by the University of Barcelona in December 2015, as well as those who took part in the Economic History Workshop hosted by the University of Florence in February 2016. All errors and omissions remain ours.

1. Introduction

Does decentralized education make sense everywhere? This question has prompted

new research on the role that education systems play in the accumulation of human capital

across countries (Hanushek and Woessmann, 2013). Several recent contributions aim to

shed light on the issue, given the importance of local school management and administrative

structures within education systems. Decentralized education – or school autonomy – can be

defined as the delegation of school management to lower levels of the public administration.

Despite the worldwide implementation of reforms aimed to strengthen decentralized school

systems (see e.g. Fiske, 1996), a theoretical framework on the trade-offs between

decentralized and centralized education has long been neglected by economists. Galiani and

Perez-Truglia (2013) have recently highlighted several pros and cons of school

decentralization, thanks to a simple mathematical model. They show that, on the one hand,

decentralized systems might reduce information asymmetries and respond more adequately

to the specific demand of each different community within the system, and improve

accountability and participation through empowerment. On the other hand, local

preferences towards education might be very different between the central government and

local ruling elites, so that the latter may capture local governments and hamper the diffusion

of mass education – an idea that dates back to the seminal work of Engerman and Sokoloff

(2002) on the impact of land inequality on the development of education via factor

endowments.

The conventional wisdom supports the view that school autonomy can be beneficial

for educational outcomes, but recent research has shown that this might not always be the

case. For example, Hanushek and Woessmann (2013) convincingly argue that only countries

characterized by a mid or high level of development have actually benefitted from a shift to

school autonomy in recent years, while other systems might have been dragged back by

decentralization. By the same token, the results offered by Galiani, Gertler and Schargrodsky

(2008) on Argentina push us to rethink the role played by school autonomy. They rely on a

quasi-natural experiment to exploit exogenous variability in school decentralization. They

find that decentralization – introduced between 1992 and 1994 – brought about a clear

(albeit small) improvement in the performance of Argentinian students between 1994 and

1999. However, the gains were exclusively located in non-poor municipalities, while

decentralization did not improve the average test scores of the poorest. By adopting an

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historical perspective, Peter Lindert has put forward that increased centralized education

may work better than local school autonomy in countries characterized by large social and

economic regional disparities and slow internal convergence – especially if large areas are

characterized by substantial land and income inequality, poor institutions and limited

electoral franchise that can drag back the development of mass schooling (Lindert, 2004, pp.

104 – 105).

Indeed, a variety of studies in the field of economic history has confirmed the

existence of a link between the empowerment of the masses and the development of

education – whether measured by electoral franchise or land inequality (see Mitch, 2013 for

an extensive overview): in the case of nineteenth-century Prussia (Cinnirella and Hornung,

2013) and Spain (Beltrán-Tapia and Martínez-Galarraga, 2015), Victorian England (Goñi,

2013), mid-nineteenth-century US (Go and Lindert, 2010), colonial Korea (Go and Park, 2012)

and twentieth-century China (Gao, 2015). However, some communities may simply lack

economic resources or basic human capabilities to implement compulsory education, and

will thus be bound to lag behind more prosperous areas. The case of early-twentieth-century

BRICs (Chaudhary, Musacchio, Nafziger et al., 2012) confirms that the extent of socio-

economic regional disparities and the regional distribution of fiscal capacity can have a large

impact on the development of mass education – possibly even independent from, and

stronger than, that of political voice. Indeed, this is what Cappelli (2015a) finds for

nineteenth-century Italy: local institutional factors did play some role and worsened regional

disparities in schooling (see A'Hearn, Auria and Vecchi, 2011, Felice, 2014) but relative

municipal fiscal capacity across Italy’s regions and provinces – which was found to be weakly

correlated with electoral franchise on the eve of the twentieth century – remained the most

important determinant of inputs into schooling.

Despite this growing body of research on the impact of decentralized school systems

on inputs into schooling and educational outcomes in the late-nineteenth century, research

on the effect of school centralization in the early-twentieth century is very scant. In

particular, robust identification strategies based on quasi-experiments and a time frame long

enough to capture changes brought about by educational reforms is still lacking.

The aim of this paper is to fill this gap, and shed new light on the impact of shifts in

education policy (towards more centralization vis-à-vis decentralization) on education

systems by relying on a quasi-natural experiment drawing on the history of Italy’s primary

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schooling at the beginning of the twentieth century. Why is Italy a very interesting case

study in a comparative perspective? At the time of the country’s political unification (1861),

during the Liberal age, a decentralized school system based on local autonomy was put in

place. This system was established by the Casati Law (1859), introduced in the Kingdom of

Sardinia (which, despite the name, consisted mainly of Piedmont and Liguria) during the

process of unification and later extended to all the regions that became part of the new

Kingdom of Italy after 1861. The decentralized education system allegedly failed to bring

about sustained human capital accumulation, especially in the Southern regions. Regional

disparities after unification were really impressive: in 1871, the Northwestern areas of the

country had, on average, an adult (13+) literacy rate of more than 55 percent, while in

Southern regions (islands included) the literacy rate was about 17 percent, with lows of

about 10 percent in some Sicilian provinces (Felice and Vasta, 2012, Table A4 and Cappelli,

2015a, Figure 1). By way of comparison, adult (15+) literacy rates in Northern Africa are

estimated to have been equal to about 6 percent in 1870 (see Prados de la Escosura, 2011,

Table D2). However, despite this remarkable scope for convergence, catching-up was

remarkably slow throughout the late-nineteenth century. Although average adult literacy

rate grew steadily during 1861 – 1901 at a pace of 1.7 percent per year, literacy in the south

grew only slightly faster (c. 2.0 percent annually) and accelerated remarkably (up to 3.9

percent per annum) only after school centralization was introduced – after an intense and

long debate on the limited results due to the adoption of widespread school autonomy

(Cappelli, 2015a).

Although the reform represented a major change concerning the administration of

primary schools, its peculiar features and norms – which we present below – allow one to

study its impact on the system as a quasi-natural experiment. This is possible thanks to a

new detailed dataset at municipal level based on a stratified random sample, which we have

constructed by relying on both untapped and well-known primary sources. On the one hand,

we aim to offer new evidence on the role that centralization can play in reducing regional

inequalities in education and, possibly, foster the national educational performance by

studying the impact of reforms connected to school autonomy in the span of 20 years. This

is, to our knowledge, the first work to analyse the impact of a shift to centralization through

quasi-experimental evidence. On the other hand, we contribute to the understanding of

Italy’s economic history, since the education system and its impact on human-capital

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accumulation are thought to have played a major role in the development of the country’s

regional disparities in the long run.

We move from the hypothesis that, given the existence of very large disparities in

human capital across the country’s regions on the eve of unification in 1861 – which

persisted until well into the twentieth century – centralization brought about an

improvement in the average pace of literacy growth and a major leap forward for the least

developed areas of Italy – and that the link between centralization and the faster growth of

literacy observed is causal. First, we test our hypothesis by relying on standard OLS

regression analysis. However, given the peculiar traits of the reform that centralized Italy’s

education system in 1911, we rely on econometric strategies aimed to ensure that the

treatment can be taken as exogenous, like Regression Discontinuity Design (RDD) and

Propensity Score Matching (PSM). Preliminary evidence suggests that Italy’s peculiar

centralization did bring about a leap forward in the diffusion of literacy before WWII broke

down.

2. The Italian primary education system (1859-1923)

Italy’s first national primary education system was established during the process of

political unification, in 1859, through the Casati Law – and later extended to all the other

regions that became part of the new Kingdom of Italy. Although the norms and curricula

were supervised centrally by the government via the Ministry of Education, the

administrative system was very decentralized – as primary schools were both funded and

managed by the comuni, Italy’s municipalities. The Law put in place a system of compulsory

yet free-of-charge primary education, organized in a compulsory two-year period for all

students and two additional years for the children who lived in municipalities with more

than 4,000 inhabitants or those that had existent secondary schools. Under this system,

education had to be offered free of charge proportionally to the municipalities’ spending

capacity and according to their people’s need. The governance of the school system was

centralized: the Ministry of Education set forth the curricula and established national norms

regulating schooling and the teachers’ wages. More local bodies, like the Consiglio Scolastico

Provinciale (provincial schooling board, from now on CSP) played a merely advisory role –

and were tightly controlled by the prefetto (prefect) on behalf of the central authority. By

contrast, the funding and management of primary education was decentralized: each

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municipality was obliged to build schools, hire teachers and enforce compulsory attendance.

Furthermore, the decentralized system was lacking any sort of redistributive mechanism

aimed at reducing the remarkable regional inequalities in local fiscal capacity. It is also worth

noting that the law established very weak enforcement mechanisms. For example, it put

forward the existence of sanctions for parents and schools that did not make an effort to

respect compulsory attendance that, according to the Casati Law, had to be found in the

penal code; yet, no rules concerning the attendance of primary schools were actually

included in the penal code before 1877, so that this legislative vacuum greatly hampered

attendance in the early development of the country’s education system. Except from the

norms contained in the Coppino Law of 1877 (which homogenized the system across regions

and introduced light sanctions for nonattendance), the Casati Law remained basically

unchanged until the early twentieth century.

The public debate on the opportunity to centralize the primary education system

gained momentum in the early days of the twentieth century, when adult (15+) literacy was

still as low as 29 percent in the South and Islands. Although right-wing parties and Catholic

movements had long opposed a more decided intervention by the central government in

local matters, only after 1900 a convergence between moderate forces on both sides of the

parliament and growing demands by state-school teachers to improve their living standards

brought about an intense debate. Some preliminary interventions were carried out in 1903

(Nasi Law), 1904 (Orlando Law) and 1906 (Special Law of the Southern regions of Italy). A

decisive push to the debate about the opportunity for the state to step in more decidedly in

matters concerning primary education was provoked by the Corradini Inquiry, whose results

on limited regional convergence and poor national performance in schooling resonated in

political circles. Under renewed pressure, a clear shift was introduced by the Daneo-Credaro

Reform, named after the two Ministries of Education who conceived the new norms on the

soon-to-be centralized education system.1

The Daneo–Credaro Bill was passed on 4 June 1911. The type of centralization

introduced by the new norms might be labelled as “mixed”. On the one hand, it affected the

function and importance of the provincial school boards – which had previously played a

mere advisory role: the CSP was re-designed to manage resources allocated to primary

education and to hire teachers. It allowed a great deal of administrative decentralization

1 Edoardo Daneo and Luigi Credaro were ministers of education in 1909-10 and 1910-14, respectively.6

that largely limited the power of the city councils – reducing the administrative distance

between the central government and the comuni to a substantial extent. On the other hand,

the system was centralized financially far more than it had ever been, as the state was fully

committed to paying the teachers’ salary. Although the municipalities still largely managed

the building of schools, they could now apply for interest-free state loans in order to do this.

Finally, according to the bill, the municipalities that were included in the CSP system had to

transfer an amount of money equal to their previous year’s budget to the Treasury (art. 17).

The government would then redistribute these funds to the provincial schooling boards (art.

19), which would in turn finance education (art. 20).

Beyond the mixed nature of the administrative structure introduced by the Reform,

there existed another peculiar feature of the system: provincial and district (circondari)

capitals were excluded ex-ante from the state-regulated administrative structure based on

the CSP. As we will explain more in detail below, this allow us to compare the performance

of comuni that retained autonomy with those that shifted to centralization.

Every city or town could also formally apply to be included in (or excluded from) its

relative CSP within three years of the approval of the reform. This clause was seldom used by

municipalities to regain autonomy after the Reform, but about a third of the district and

provincial capitals actually opted to shift to the CSP system before 1915. Furthermore, it is

worth noting that the shift to the state-administrative system happened in the span of years

– although the majority of Italy’s municipalities moved towards centralization between 1914

and 1915 – with just a handful changing their status after 1916. A number of criteria had to

be met by the comuni who wanted to re-gain their autonomy: first, they had to have

respected the rules and norms concerning primary schooling for five years consecutively – a

condition that had to be tested by the provincial schooling board. Secondly, the illiteracy

rate (age 6+) had to be lower than 25 percent based on the 1911 census. These norms, too,

can be used to design quasi-natural experiments to test the impact of the Daneo-Credaro

Reform.

The last educational reform prior to World War II was conceived by the philosopher

Giovanni Gentile, minister of public education, and approved when Fascism was on the rise

(1923). It adopted a more elitist view of education. However, the changes concerned mainly

secondary education, while primary schooling was left largely intact, especially as far as the

funding of schools was concerned. In fact, state aid was improved and the system further

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centralized. Therefore, the effect of the Daneo–Credaro Reform endured until World War II

broke out – which allow an exploration of the impact of the Daneo-Credaro Reform on two

sub-periods, namely 1911 – 21 and 1911 – 31.

3. The Italian educational system and the move towards centralization: a review of the literature

The debate on the state of Italy’s primary education and its influence of human-capital

regional disparities on the national economic performance dates back to the many inquiries

on the state of primary schooling published by the Ministry of Education between 1865 and

1923. These all pointed out the need to strengthen compulsory attendance, the presence of

very limited fiscal capacity to provide primary education of some areas of the country, as

well as limited demand for schooling by households and the small effort to improve the state

of primary education by the city councils of the poorest areas of the country. In particular,

they called for a more substantial intervention by the state because of the very limited

amount of resources available to municipalities to fund education under a decentralized

system.

A decisive role in pushing the central government to step in and take charge of primary

schooling was played by the Corradini Inquiry,2 published between 1910 and 1912 (Ministero

della Pubblica Istruzione, 1910-1912). This monumental piece of work played an important

role in the move towards centralization, but a subsequent assessment of the results

achieved by the new policy was hampered by the rise of Fascism and the lack of detailed

inquiries during the Interwar period.3

Contemporary research on primary schooling in Italy in a historical perspective was

pioneered by Vigo, who confirmed the presence of remarkable regional inequalities in

education before and after the unification of the country, c. 1830 – 1900 (Vigo, 1971). The

same pattern has been found by Zamagni (1993b) and Vasta (1996) who provided the first

quantitative approach at the issue. Later on, Conte, Della Torre and Vasta (2007) and Felice

(2007), who have estimated levels of human capital across Italy’s regions (1871 – 2001) by

2 Camillo Corradini, born in Avezzano in 1867, had previously worked as Secretary of the Ministry of War, and was appointed head of the cabinet at the Ministry of Education in 1903. He gained extensive experience by cooperating with the Minister of Education on the draft of the Orlando Law approved in 1904, and later became General Manager of Primary and Mass Education (Istruzione Primaria e Popolare) in 1908.3 After 1911, complete statistics on the matter were compiled again starting in 1923, but (except from the first inquiry) they contained only data at the regional level and fewer details compared to the Liberal Age. See Cappelli (2015b).

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using adult literacy and gross enrolment ratios provided further insights. Similar results have

been offered by Hippe and Baten (2012) and by A'Hearn, Delfino and Nuvolari (2015), who

analyse numeracy at provincial level during the late-nineteenth century. Finally, regional

mean years of schooling have been reconstructed by Felice and Vasta (2015), who

reconstruct the series of Human Development Index at regional level.

All these studies provide evidence that Italy’s human-capital accumulation was slowed

down by large regional disparities, which persisted until the post-war period. Why? Scholars

have put forward that the country’s decentralized education system was a failure, given the

extent of regional differences in the municipal fiscal capacity to fund primary schools

(Zamagni, 1993a, Vasta, 1999). Felice and Vasta (2015) have recently confirmed that

“convergence in education was significant in the Liberal Age mostly as a consequence of the

huge differentials in 1871”, but that “much more could be done”. By using recent

comparative data provided by Morrisson and Murtin (2009), Nuvolari and Vasta (2015a, fig.

1) emphasized the existence of a significant long lasting gap between Italy and the other

major industrialised countries.

Although research on political debates surrounding education policy is abundant

(Bertoni Jovine, 1954, De Fort, 1979, Cives, 1990), for a long time no attempt has been made

to tackle this specific issue. Recent research has reconstructed the regional pattern of the

diffusion of primary schooling by adopting a long-term perspective, 1863–1936 (Cappelli,

2015b). This shows that decentralized primary schooling contributed to the persistence of

Italy’s regional inequalities in education, and offers preliminary evidence that the Daneo-

Credaro Reform passed in 1911 improved the diffusion of schooling by centralizing the

funding of primary education, empowering provincial schooling boards and establishing a

preliminary redistributive mechanism. However, following a different hypothesis, A'Hearn et

al. (2011) contend that economic backwardness did not itself delay the rise of mass

schooling. They put forward that institutional mechanisms played the largest role, following

the literature on the importance of local political voice: a wider voting franchise was likely to

supply more resources for the development of schooling at the local level – by pushing ruling

elites to act in the interest of the majority of people (Lindert, 2004, Go and Lindert, 2010,

Sokoloff and Zolt, 2007). Cappelli (2015b) has tested the relative importance of political

voice and fiscal capacity – as a proxy for the relative prosperity of Italy’s comuni – across the

country’s provinces in the period 1871 – 1911. By using both descriptive and econometric

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evidence he argues that, contrary to expectations, fiscal capacity evolved rather

independently from electoral franchise at the end of the nineteenth century, and that it

affected the persistence of regional disparities in education under decentralization to a

larger extent than local institutional features.

In this paper, starting from the intuition by Vasta (1999), we follow the recent attempt

by Cappelli (2015b) to assess the impact of the Daneo Credaro 1911 reform on Italy’s

regional disparities in schooling and overall growth of mass education, carried out by using

new sources and a panel-data econometric model with heterogeneous effects. We expand

data availability by reconstructing a cross-sectional dataset at municipal level between 1911

and 1931, in order to study the impact of the Reform thanks to the presence of a treatment

and a control group and to the use of econometric techniques aimed to transform a

potentially endogenous treatment in a quasi-natural experiment.

4. Sources and methodology

Given the very large number of Italian municipalities on the eve of the twentieth

century (8000+) we sought to obtain a representative sample to explore the impact of the

Daneo-Credaro Reform across the country’s regions. We were able to draw a stratified

random sample of 1,074 Italian municipalities, which has been obtained in the following

way: first, we include all the municipalities that – according to the Daneo-Credaro Law (1911,

art. 14) – were granted autonomy in education policy, i.e. the provincial and district capitals

(272 observations). We include all of them given their small number compared to the total

number of other Italian municipalities (8,000+). Secondly, for the latter, we obtain a 10

percent sample (802 observations). In order to do so, we calculate, for each province, the

number of municipalities and their cumulative population excluding district and provincial

capitals. Then we calculate the share of these provincial figures on the national total

(excluding capitals). We use this figure on provincial population as a share of national total

to understand how many observations (municipalities) we need to draw from each province.

For example, Ancona’s population, excluding the capitals, is 1 percent of Italy’s population

(capitals excluded): therefore, we need to extract 1 percent of our sample from this

province. However, discrepancies between the provincial weight in terms of population and

that in terms of number of municipalities may introduce misrepresentation in our sample

(e.g. many provinces in the North had a lot of municipalities, but of very small size, therefore

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their actual bearing in the population would undetected by our sampling scheme). For this

reason, we calculate the median municipal population of each province (after having

ordered municipal populations) and draw randomly half of the observations from above the

median, and half of them from below it. It is worth noting that, for the provinces that

contain less than 0.5 percent of Italy’s population (e.g. Livorno and Ravenna) we still draw

four municipalities, in order to be able to collect data from both above and below the

median. If the number of municipalities to be drawn is uneven, we take the one in excess

from above the median. In order to draw the sample, we generate random numbers without

replacement.

How representative is our random 10-percent sample? We compare 1911 literacy

rates (6+) across the country’s provinces calculated through our sample (provincial mean

from municipal data) with figures on adult literacy (15+) obtained from census data on Italy’s

whole population: the reaches 0.99, which is confirmed by the pattern shown in Figure 1

below.

Figure 1 – Literacy rates (age 6+) in 1911 from our sample data compared to adult literacy rates (15+) obtained from census data. Note: ρ=0.99

Sample (10 Percent) Data Census (Population) Data

ALR 15+25.00 to 37.5037.50 to 50.0050.00 to 62.5062.50 to 75.0075.00 to 87.5087.50 to 100.00

In order to capture the impact of the Daneo-Credaro Reform (our main regressor of

interest), we had to track down if – and when – each of the municipalities in our sample

shifted to centralized primary education. To be sure, the Law was implemented through

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dozens of Royal Decrees, each of which normally concerned very few municipalities under

the same district or province. We used the information contained in the Gazzetta Ufficiale

(Official Gazette of the Italian Republic) to reconstruct the pattern of diffusion and

implementation of the Reform. Figure 2 shows that no municipality in our dataset actually

shifted to centralization between 1911 and 1912 – and only four comuni moved to

centralized education in 1913. Instead, between the beginning of 1914 and the end of 1915,

c. 85 percent of Italy’s municipalities shifted to the centralized school system, which basically

included all the comuni that were bound to change by the new norms plus more than 100

district capitals – which were instead supposed to retain their autonomy. In the following

years, only minor changes took place, with only about ten municipalities in total changing

status between 1916 and 1922.

Figure 2 – Share of municipalities in our dataset that shifted to the state-administered school system between 1911 and 1922 (by type of municipality)

1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 19220.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

All other municipalities District and provincial capitals

Note: only c. 75 percent of the municipalities in our sample were supposed to move towards centralization. However, c. 100 district capitals out of 272 applied to be included in the CSP system – giving up on school autonomy.

Based on the pattern observed, we code the Reform dummy variable in two ways

according to the outcome variable used. When we focus on the growth of literacy in 1911 –

21, we code a dummy taking value 1 if the shift happened before 1916. On the one hand,

this choice takes into account the majority of changes introduced by the Daneo-Credaro

Reform in 1911; on the other hand, it excludes those few municipalities that introduced

centralization towards the end of the decade – on which, most probably, the Reform could

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not exert any decisive effect given the limited time span concerned. Instead, when we focus

on the growth of literacy between 1911 and 1931, we take into account all the changes

brought about by the Reform until 1922. Finally, we deal with the comuni who re-gained

autonomy in a rather simple way: assuming that the effects of centralization could be felt

only if the system was implemented at length, we simply re-assign a value equal to one to

the municipalities that chose to shift to school autonomy after the Reform had been passed.

It is worth noting that this concerns only (literally) a handful of cases in our dataset.

Our outcome variable is the average annual growth of literacy (age 6+) measured

between 1911 and 1921 as well as in the period 1911 – 1931; in turn, this was calculated

from literacy rates based on 1911, 1921 and 1931 census data. We include the initial level of

literacy among the regressors because we expect municipalities with lower education to

experience faster accumulation of human capital over time.

Finally, we draw on the Corradini Inquiry to collect detailed data on inputs into

schooling across Italy’s municipalities in 1907-08.4 The inquiry provided several reports by

inspectors who witnessed the state of schools across Italy’s provinces, and a huge base of

data on municipal expenditure, teachers, school housing, didactic material, performance,

state subsidies etc. The data were presented in the first three volumes (published in 1910),

while the fourth one, circulated after that the Daneo-Credaro Reform (1912) had already

been passed, explored the impact of centralization on the municipalities’ balance sheets and

assessed the implementation of the reform (Ministero della Pubblica Istruzione, 1910-1912).

Unfortunately, this wealth of information was published at the provincial level, but only a

limited amount of information was delivered at the municipal level. We could use the Inquiry

to draw municipal data (year 1907) on urban and rural schools per capita, both compulsory

and elective, municipal expenditure on education per capita and state expenditure as a

share of municipal expenditure on education.

Other important variables that can affect the development of primary schooling are

taken into account, in an attempt to capture supply and demand-side determinants of

4 This monumental exploration of the state of Italy’s primary schooling at the beginning of the twentieth century was organized in four distinct volumes. The first contained a long concluding report, together with a summary on the evolution of the legislation on primary schooling since 1890. The report also addressed the contemporary norms regulation schools, the administrative organization of the education system and the state of schooling across the regions of the country. The main topic was the link between the large regional disparities in literacy and enrolments and the existence of a decentralized system of funding, according to which the municipalities had to invest in new schools and pay the teachers, as well as any other expenditure connected to primary education.

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education. We coded the average altitude above sea level for each municipality, to capture

geographic features that might have hampered access to schools (ruggedness). The number

of citizens allowed to vote in local (municipal and provincial) elections is divided by total

population in order to capture the capability of the masses to influence local education

policy – following the growing importance attached to this factor by development

economists and economic historians (see e.g. Engerman and Sokoloff, 2002, Mansuri and

Rao, 2013, Pradhan, Suryadarma, Beatty et al., 2014). The number of local electors is drawn

from the electoral statistics published in 1897, which is the closest available publication to

1911 reporting municipal data (see Ministero di Agricoltura Industria Commercio 1897). We

capture the demand for skills by drawing data on industry from the industrial census

published in 1913, which refers to 1911 (Ministero di Agricoltura Industria Commercio 1913).

The census identified firms and factories with more than two employees (for more

information on Italy’s first industrial census, see Zamagni, 1978). We collect figures on Italy’s

industry in order to construct two indices, the number of industrial workers per 1,000

inhabitants and the number of available horse power (HP) per 1,000 inhabitants.

In a first step, we test the impact of the Daneo-Credaro Reform thanks to regression

analysis. Our first specification is a simple OLS model where the average annual growth of

literacy is the dependent variable, measured between 1911 and 1921 as well as 1911 – 1931.

In the latter case we restrict our sample to 5 percent, because some of the comuni changed

names and/or borders between 1921 and 1931, due to the Fascist reform, and – as a result –

it is impossible to calculate the growth rate of literacy in the period concerned. We regress

our dependent variable on the initial level of literacy to capture the fact that different

growth rates are expected given different initial levels of human capital. Our main focus is

the dummy aimed to capture whether a municipality shifted to the state-administrative

system after 1911, which was coded as explained above. In a different specification we

include a dummy for district and provincial capitals together with an interaction between

the latter and the Reform dummy – simply to check whether the Reform had a different

impact in district capitals compared to other comuni. Finally, we include a vector X of

control variables that might have influenced the development of literacy in early-twentieth-

century Italy (see Equation 1) – including urban and rural primary schools per 1,000 citizens,

elective schools per 1,000 inhabitants, the ratio between state and municipal expenditure on

primary education, municipal per capita expenditure on primary education (all measured in

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1907), altitude above sea level, the share of local electors on total population, the number of

industrial workers per 1,000 inhabitants, the number of HP per 1,000 inhabitants and

population density.

(1) litgri=β0+β1 liti+β2ref i+β3 capi+β4 (ref i x capi )+β5 X i+εi

5. Preliminary evidence: descriptive statistics and OLS results

The 10-percent sample confirms the evidence of a North-South gap in primary

education in early twentieth century Italy. We divide Italy into three macro-regions: North-

West (NW) including Piemonte, Lombardia and Liguria; the North-East and Centre (NEC),

including Veneto (and Venezia Giulia, i.e. the province of Udine), Emilia-Romagna, Toscana,

Umbria, Marche, Lazio; and the South and Islands (SI), which include Abruzzi, Puglia,

Campania, Basilicata, Calabria, Sicilia and Sardegna (see Figure 3 below).

Figure 3 – Italy’s 16 regions (first map) and three macro-regions with division by province (second map) between 1871 and 1919

PiemonteLombardia

LiguriaEmilia

Toscana

Umbria

Marche

Abruzzi e Molise

Campania Puglia

Basilicata

Calabria

Sicilia

Sardegna

Veneto

Lazio

Note: maps were drawn by following ISTAT (2000).

15

North-WestNorth-East-Centre

South and Islands

The macro-regional distribution of compulsory primary schools per capita in 1907,

according to the sample, shows the extent of Italy’s regional divide in education (Table 1). In

particular, on average, the North-West had about 50 percent more schools per inhabitant

than the South, a datum which is reflected by the aggregate provincial data reported by the

Corradini Inquiry – both in terms of schools per 1,000 inhabitants and expenditure per

school-age child. However, the dispersion around the mean within these two macro-regions

(measured by the Coefficient of Variation) is very similar – and surprisingly much higher than

in the regions of the North-East and Centre. This preliminary finding casts some doubt on an

interpretation of Italy’s regional disparities in education based only on the usual category of

“dualism”. Furthermore, it prompts more questions concerning the impact of the Reform

across regions, something that previous studies could not answer because of the lack of

municipal data: for example, did all rural and relatively poor municipalities benefit from the

Daneo-Credaro Reform in the same way across Italian regions? Can we identify different

success stories and failures across municipalities that shared similar features? Our

methodology aims to tackle these issues beyond a simple analysis of the impact of the

Reform between the north and the south.

Table 1 – Average number of compulsory primary schools per 1,000 inhabitants across Italy’s three macro-regions in 1907 and within-region dispersion around the mean (CV)

Macro-region

N. obs.

Mean of schools per 1,000 inhabitants VAR SD CV

NW 276 1.85 0.69 0.84 0.45NEC 389 1.58 0.20 0.44 0.28

SI 409 1.32 0.37 0.61 0.46Source: our own elaboration on Corradini Inquiry.

First of all, we explore whether the municipalities in the three different macro-regions

followed a pattern of regional convergence after the introduction of centralized schooling –

based on the 10 percent sample. Secondly, we divide municipalities into three classes

according to literacy rates in 1911: (1) ‘high’ if they had literacy rates above 80 percent; (2)

‘medium’ if they had between 50 and 80 percent; and (3) ‘low’ if they had literacy rates

lower than 50 percent. We also differentiate between district capitals, which were supposed

to retain autonomy (although many did not), and other comuni. Finally, we calculate the

growth of literacy between 1911 and 1921. The figures obtained are summed up by Table 2.

In Figure 4, we also plot the growth of literacy between 1911 and 1921 against literacy in

1911.

16

Table 2 – Annual average growth of literacy in 1911 – 1921 compared to literacy in 1911Macro-regions District Literacy Literacy level Literacy growth (year) N. obs.

NEC yes high 84.54 0.78 37NEC no med 65.66 1.73 188NEC yes med 68.62 1.69 77NEC no low 40.96 3.22 52NW no high 90.06 0.37 155NW yes high 88.98 0.48 52NW no med 74.56 1.28 59SI no med 57.14 1.52 37SI yes med 58.39 1.48 31SI no low 33.89 4.13 284SI yes low 40.28 2.87 57

Source: our own elaboration on Corradini Inquiry.Note: data are divided by macro-regions, literacy classes and according to whether they were district and provincial capitals or other municipalities (we kept only groups with a number of observations equal to or larger than 30).

Figure 4 – Regional conditional convergence in literacy rates, 1911 – 1921

As the scatter plot shows, literacy in the comuni of the south that shifted to the state-

school system and that were not district capitals grew more than what the fitting line

suggests. The result tallies with our expectation that the Reform benefitted the poorest

municipalities of Italy. Interestingly, both district capitals (one third of which shifted to

centralization) and non-capital municipalities in the South that were automatically included

17

in the CSP system are below the fitting line – which suggests that they somehow under-

performed in 1911 – 1921 given their initial level of literacy. However, understanding

whether the pattern identified can actually be ascribed to the impact of the Daneo-Credaro

Reform calls for multivariate regression analysis in order to control for confounding factors.

Indeed, given the multitude of determinants that might have affected education across

Italian regions, taking into account a variety of variables is crucial in order to shed light on

the issue. We turn to regression analysis to do so – thanks to the econometric model

presented in the previous section of the paper. Table 3 presents the results for the period

1911–21 (sample of non-district municipalities equal to 10 percent), while Table 4 concerns

the period 1911–31 (sample of non-district municipalities equal to 5 percent). With our

fullest models (model 4 in both Tables) we can explain between c. 50 and 75 percent of the

variation observed in the growth of literacy across Italy’s comuni. As the descriptive evidence

suggests, there is a tight link between initial literacy and its annual growth: a one-percent

change in the initial level of literacy is associated to a 0.08 percent decline in the annual

growth rate of literacy, a large effect.

18

Table 3 – OLS regression for the period 1911 – 1921

(1) (2) (3) (4)Y = Average annual growth of literacy 1911 – 1921

Literacy rate 1911-

0.0704***-

0.0699***-

0.0698***-

0.0796***(-0.6669) (-0.6617) (-0.6612) (-0.7533)

Early Reform 0.1974** 0.3794* 0.4358*(0.0313) (0.0602) (0.0692)

District (dummy) 0.1917 0.5941**(0.0353) (0.1096)

District (dummy) x Early Reform -0.2397 -0.4039(-0.0303) (-0.0511)

Comp. urb. schools per thousand 1911 -0.1569(-0.0457)

Comp. rur. schools per thousand 1911 0.2540(0.0950)

El. schools per thousand 1911 0.0659(0.0252)

Exp. state / exp. municipalities -0.0150(-0.0007)

Municipal edu. exp. per capita 0.0022(0.0024)

Altitude MSL -0.0004**(-0.0482)

Electors (loc.) / pop. 1.9938***(0.0561)

HP per 1,000 inh. 0.0001**(0.0089)

Ind. workers per 1,000 inh. 0.0018***(0.0558)

Pop. density 1911 0.0054**(0.0292)

Constant 6.4106*** 6.2137*** 6.0342*** 6.0947***(.) (.) (.) (.)

Observations 1,073 1,073 1,073 1,070Adjusted R-squared 0.444 0.445 0.444 0.457

Robust normalized beta coefficients in parentheses*** p<0.01, ** p<0.05, * p<0.1

Note: the dependent variable is the average annual growth of literacy rates between 1911 and 1921.

19

Table 4 – OLS regression for the period 1911 – 1931.

(1) (2) (3) (4)Y = Average annual growth of literacy 1911 – 1931

Literacy rate 1911 -0.0638*** -0.0628*** -0.0624*** -0.0719***(-0.8412) (-0.8274) (-0.8222) (-0.9467)

Reform 0.2492*** 0.1101 0.4246**(0.0633) (0.0280) (0.1080)

District (dummy) -0.2363* 0.3533(-0.0700) (0.1046)

District (dummy) x Reform -0.1340 -0.5679**(-0.0310) (-0.1313)

Comp. urb. schools per thousand 1911 0.0026(0.0011)

Comp. rur. schools per thousand 1911 0.3194*(0.1588)

El. schools per thousand 1911 0.0592(0.0240)

Exp. state / exp. municipalities -0.4645(-0.0288)

Municipal edu. exp. per capita 0.0064(0.0117)

Altitude MSL -0.0000(-0.0004)

Electors (loc.) / pop. 2.2964***(0.0718)

HP per 1,000 inh. 0.0003(0.0177)

Ind. workers per 1,000 inh. 0.0014**(0.0645)

Pop. density 1911 0.0023*(0.0215)

Constant 5.7497*** 5.4927*** 5.6932*** 5.3134***(.) (.) (.) (.)

Observations 677 677 677 675Adjusted R-squared 0.707 0.711 0.717 0.735

Robust normalized beta coefficients in parentheses*** p<0.01, ** p<0.05, * p<0.1

Note: the dependent variable is the average annual growth of literacy rates between 1911 and 1931.

The impact of the Reform is statistically significant, and has a large magnitude: the shift

to centralization is associated to a premium of c. 0.4 percent per annum in the fullest

specification (both in 1911-21 and 1911-31). Literacy in the district and provincial capitals

grew faster than elsewhere, as expected. Furthermore, the interaction-term strategy shows

that district capitals that shifted to centralization experienced no benefit (1911–21) or were

even hampered by the Reform (1911–31). Once the initial level of education is taken into

account, other variables proxying the intensity of inputs into schooling seem to be

redundant. The intensity of the effort of the central government to subsidize primary schools

20

before 1911 is not significant in any of the regressions performed. This may be due to the

limited impact of state subsidies prior to the twentieth century; yet, this result may also

depend on the fact that literacy rates measured in 1911, which we include in the model to

control for potential convergence, may already capture the impact of previous reforms. The

average altitude is statistically different from zero and has the right sign, but its impact is

negligible. All the other variables are highly significant (especially in 1911 – 21) and have the

expected sign in both models.

6. The making of comparable treatment and control groups: Propensity Score Matching

In this section of the paper we check the impact of the Daneo-Credaro Reform by

estimating the difference in the growth rate of literacy between a treatment and a control

group, which are drawn thanks to the use of Propensity Score Matching, henceforth PSM

(for an overview on PSM, see Rosenbaum and Rubin, 1983 and Rubin and Stuart, 2008; for

an application to job-market studies see Bryson, 2002; for a recent application to the

economic history of education Bavaria, see Semrad, 2015). In a nutshell, PSM can be used to

assess the impact of shifts in education policy that cannot be fully seen as a completely

exogenous treatment. In the case of early-twentieth century Italy, we know that the 1911

Daneo-Credaro Reform obliged all Italian municipalities to shift to centralization, except

from district and provincial capitals. Therefore, the Reform did generate a treatment and a

control group; yet, the assignment to treatment itself depended on the level of literacy and

economic development of the country’s municipalities: district and provincial capitals were

left with school autonomy because they fared better than others. For this reason, a simple

OLS regression will most likely overestimate the impact of the Reform on the growth of

literacy – because poorer municipalities are expected to catch up rather quickly with district

and provincial capitals.

To overcome this methodological problem, PSM matches very similar observations

between treatment and control groups by using propensity scores, i.e. estimated

probabilities to be subject to treatment based on observable variables, which in turn should

be measured before the endogenous treatment is assigned. Two assumptions need not to be

violated in order to use PSM. One is unconfoundedness, the assumption that all variables

that might influence both the assignment to treatment and the outcome are taken into

account. This cannot be tested, and must be evaluated by using economic theory and

21

experience. The second assumption is common support: it means that we need enough units

with similar propensity scores (i.e. with a very similar chance to get treated) which are

actually sorted in two different groups – some are treated while others are not. If the two

conditions are met, PSM can be applied and the treatment can be seen as if it was

randomized.

Different steps must be followed in order to implement PSM. First, the propensity

scores need to be estimated via a logit or probit model. The optimal number of variables to

be included in this first model is still debated. Heinrich et al. suggest including the criteria

that determine the selection into treatment, if they are available and if they do not

undermine the common-support assumption. Furthermore, they argue that the researcher

should model selection into treatment by using a number of core variables that are deemed

to be important based on economic theory and experience. Finally, they recommend

expanding the number of factors included in further specifications by adding blocks of

variables and by testing whether the fitness of these models improve. This should lead to an

optimal model, which includes all important variables but excludes redundant factors (see

Heinrich, Maffioli and Vázquez, 2010, especially pp. 32-34).

Once the propensity scores are estimated, a visual inspection can show if there is

common support by plotting the frequency of observations over the range of scores for the

two groups separately (treated versus untreated observations). For the sake of matching,

the condition of common support can also be imposed via the matching algorithm. The third

step is the attempt to find the best match in the control group for each of the treated

observations. The most common algorithms are the one-to-one (with or without

replacement) and the nearest-neighbour matching: the latter allows each unit in the

treatment group to be matched with more than one unit in the control group. Furthermore,

a caliper (range) can be defined to force the search for the right match to move only within a

specific propensity-score distance from the treated observation: however, it must be noted

that using a strict caliper is likely to reduce the bias (difference) between the two groups, but

the reduced number of observations in the control group used to match treated units might

increase the variance and, hence, reduce the significance of the results. Since we have a

large treatment group and a relatively small control group (mostly made by district and

provincial capitals) we decided to use the nearest-neighbour algorithm without imposing any

caliper. This is particularly important to study the period 1911 – 31, based on the more

22

limited 5-percent sample. Table 5 below shows a first attempt to calculate the propensity

scores. Naturally, the fact that district and provincial capitals were excluded by policy makers

from the new state-driven school system comes to mind in calibrating the model. Indeed,

the result of a binary regression of the chance to get the treatment (ref21) on a dummy to

capture district capitals shows a high pseudo R squared (Table 5).

Table 5 – Probit model to estimate the propensity scores.(1)

VARIABLES ref21

District (dummy) -2.3614***(0.130)

Constant 2.0813***(0.105)

Observations 1,074Pseudo R-squared 0.474

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Note: the model is based on the choice to exclude district and provincial capitals from the new education system introduced by the Daneo-Credaro Reform.

Figure 5 – Propensity to be treated (propensity score) in the treatment and control groups based on the education reform

.4 .6 .8 1Propensity Score

Untreated Treated

Note: the graph below the horizontal line refers to the density of untreated observations, while the graph above the line refers to the treated observation.

23

However, as expected, this identification strategy predicts the assignment of the

treatment almost exactly, so that we lack a broad region of common support (Figure 5).

Including other observables together with the district dummy does not improve much the

issue of common support (estimates not reported). However, we use the relatively large

amount of information that we have on the municipal distribution of schooling to estimate

the propensity scores: the ratio behind this choice is that policy makers assumed that the

district capitals were the best performers in terms of schooling and they chose to exclude all

of them from the post-1911 education system; yet, other variables capturing inputs into

schooling and educational outcomes should also capture fairly well the chance to get the

treatment while, in addition, they should identify the latter less exactly – a property which is

required in the estimation of propensity scores.

We estimate the probability of ref 21 by using 10 variables. We include literacy rates in

1911, compulsory primary schools per 1,000 inhabitants in 1907, elective primary schools

per 1,000 inhabitants in 1907, municipal educational expenditure per capita in 1907,

municipal educational expenditure as a share of total expenditure in 1907, the ratio of

expenditure for education between the government and municipalities (1907), average

municipal altitude as a measure of ruggedness, (i.e. a proxy for issues with access to

schools), the share of local electors on total population, the number of industrial workers

per 1,000 inhabitants, the number of HP per 1,000 inhabitants and population density.

The results are shown in Table 6 (marginal effects are reported), while Figure 6 shows

the improved region of common support. All the variables have the expected signs and are

highly significant: the fact that the number of compulsory schools per capita is not a

significant predictor of being affected by the Daneo-Credaro Reform should not come as a

surprise, given the fact that we already take into account literacy rates. Furthermore, it is

worth noting that the positive sign of the share of the (municipal) educational expenditure

on total budget might be driven by a spurious relationship: poorer municipalities (with a

smaller budget) often had to make a substantial (relative) effort to comply with the national

norms that obliged city councils to provide free-of-charge and compulsory primary

schooling.

24

Table 6 – Probit model to estimate the propensity scores based on a vector of observables.(1)

1911 – 1921VARIABLES (mgfx) ref21

Literacy rate 1911 -0.0012**(0.001)

Comp. schools per thousand 1911 -0.0015(0.017)

El. schools per thousand 1911 0.1730***(0.039)

Exp. state / exp. municipalities 0.8430***(0.144)

Share educational on tot. exp. 0.6277***(0.107)

Municipal edu. exp. per capita -0.0147***(0.004)

Altitude MSL 0.0001*(0.000)

Electors (loc.) / pop. 0.2305(0.165)

HP per 1,000 inh. 0.0000(0.000)

Ind. workers per 1,000 inh. -0.0005***(0.000)

Pop. density 1911 -0.0002(0.001)

Observations 1,071Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Note: (Pseudo R Squared = 0.266).

Figure 6 shows that the common support region is far broader than before, i.e. the

second probit model identifies a large number of municipalities characterized by very similar

socio-economic features, which can then be matched to construct a sample of municipalities

that is “as if” randomized.

Figure 6 – Common support: propensity to be treated (propensity score) in the treatment and control groups based on a vector of observables.

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

Note: the graph below the horizontal line refers to the density of untreated observations, while the graph above the line refers to the treated observations.

We perform the matching of municipalities between the treatment and control groups

thanks to the nearest-neighbour algorithm with replacement. In a nutshell, each non-

treated individual can be matched to a number of treated individuals, which is an ideal

solution given the small size of our control group. Since some observations from the

treatment group do not match any municipalities in the control group, 55 municipalities are

left out, which is equal to roughly 5 percent of our observations in the 10-percent sample.

Table 7 below shows that the means of each variable in the matched (M) samples are

extremely balanced compared to the original unmatched sample (U), i.e. they are equal

between treatment and control group post-matching according to the distribution of our

observable variables (some limited bias remains only as far as altitude, state intervention

pre-1911 and industrial employment are concerned).

26

Table 7 – Output of the matching procedure based on PSM---------------------------------------------------------------------------------------- Unmatched | Mean %reduct | t-test | V(T)/Variable Matched | Treated Control %bias |bias| | t p>|t| | V(C)--------------------------+----------------------------------+---------------+----------lit1911 U | 58.857 68.724 -47.7 | -5.49 0.000 | 1.53* M | 58.571 59.729 -5.6 88.3 | -1.10 0.271 | 1.16* | | |scob1911 U | 1.6111 1.6339 -4.1 | -0.43 0.665 | 2.90* M | 1.606 1.5646 7.4 -81.6 | 1.55 0.122 | 2.21* | | |fac1911 U | .22344 .05973 23.2 | 2.23 0.026 | 47.03* M | .12691 .12875 -0.3 98.9 | -0.15 0.884 | 1.66* | | |state U | .20412 .11994 87.4 | 9.21 0.000 | 3.29* M | .18929 .1867 2.7 96.9 | 0.76 0.450 | 1.51* | | |share U | .21612 .17115 42.3 | 5.26 0.000 | 0.93 M | .21155 .2265 -14.1 66.7 | -2.63 0.009 | 0.53* | | |exp1911 U | 2.8045 4.4486 -54.2 | -7.92 0.000 | 0.38* M | 2.8239 2.7991 0.8 98.5 | 0.27 0.787 | 3.00* | | |alt U | 311.7 178.56 55.9 | 6.21 0.000 | 1.98* M | 295.27 262.96 13.6 75.7 | 2.85 0.004 | 1.70* | | |leletpop U | .09223 .08649 10.1 | 1.06 0.290 | 3.32* M | .0893 .08758 3.0 70.1 | 0.60 0.549 | 2.05* | | |hppop U | 41.016 47.822 -2.2 | -0.22 0.829 | 27.33* M | 28.228 24.324 1.3 42.6 | 1.03 0.304 | 3.77* | | |emplpop U | 41.671 87.953 -65.6 | -8.10 0.000 | 0.96 M | 42.828 50.938 -11.5 82.5 | -2.75 0.006 | 2.34* | | |dens1911 U | 2.4093 6.9088 -33.6 | -4.36 0.000 | 0.72* M | 2.4961 3.0263 -4.0 88.2 | -1.13 0.259 | 6.83* | | |----------------------------------------------------------------------------------------* if variance ratio outside [0.88; 1.14] for U and [0.87; 1.15] for M

----------------------------------------------------------------------------------- Sample | Ps R2 LR chi2 p>chi2 MeanBias MedBias B R %Var-----------+----------------------------------------------------------------------- Unmatched | 0.266 258.58 0.000 38.8 42.3 102.6* 2.84* 82 Matched | 0.013 31.15 0.001 5.8 4.0 27.4* 1.64 100-----------------------------------------------------------------------------------* if B>25%, R outside [0.5; 2]

Indeed, the Mean Bias is very low (equal to 5.8 percent) while both the B and R stats

lie within the desirable respective intervals. Although the unconfoundedness assumption

cannot be easily verified, economic theory and existent empirical evidence on the economic

history of schooling suggests that we are taking into account all the factors that were

important for the assignment of the treatment and that might be correlated with the

outcome at hand.

Table 8 shows the magnitude and significance of the Average Treatment Effect on the

Treated (ATT), calculated on the basis of the “as if” randomized sample obtained via PSM.

As expected, we see that without matching the effect of centralization would be

overestimated. However, the effect is still significant at 5 percent (t-stat = 2.13): according

27

to these estimates, the treated municipalities experienced an average annual growth of 2.29

percent in 1911-21, while the same value remained equal to 1.89 percent during the same

period, the difference being equal to a remarkable 0.39 percent per annum.

Table 8 – Average Treatment Effect on the Treated (ATT) after PSM, 1911 – 21---------------------------------------------------------------------------------------- Variable Sample | Treated Controls Difference S.E. T-stat----------------------------+----------------------------------------------------------- litgr21 Unmatched | 2.30060352 1.41311623 .887487297 .190751648 4.65 ATT | 2.28914668 1.89582025 .393326433 .184704747 2.13----------------------------------------------------------------------------------------

Similar results are achieved by applying the same procedure to our 5-percent sample,

in order to analyse the impact of centralization over the period 1911-31. In this case, using

the same observables for the PSM leads to a slightly larger imbalance between treatment

and control groups (Mean Bias equals 14.9 percent). The preliminary evidence concerning

the 1911-31 period is shown in Table 9 below. The significant difference remains.

Table 9 – Average Treatment Effect on the Treated (ATT) after PSM, 1911 – 31---------------------------------------------------------------------------------------- Variable Sample | Treated Controls Difference S.E. T-stat----------------------------+----------------------------------------------------------- litgr31 Unmatched | 2.01149982 1.04770878 .963791038 .146948109 6.56 ATT | 2.00112808 1.41308598 .5880421 .171363352 3.43----------------------------+-----------------------------------------------------------

7. Conclusions

This paper has offered fresh – albeit preliminary – evidence on the role that education

policy can play in the rise of literacy in developing countries. This result seems particularly

important considering that some new research have stressed the crucial role of basic

education in fostering the innovative activities and in promoting the process of

industrialization of Italy during the Liberal age (Nuvolari and Vasta, 2015b).

Italy’s peculiar shift to centralized primary schooling allowed us to explore the impact

of centralization on the development of literacy across Italian municipalities between 1911

and 1931. On one hand, as expected, we find a premium on literacy connected to the

implementation of the Daneo-Credaro Reform. On the other hand, the results also highlight

the presence of heterogeneous effects – within and across macro-regions. In a next step, we

aim to design a quasi-natural experiment to properly tackle these issues. First, we will use

PSM to obtain a treatment and a control group by matching municipalities that shared

similar features based on our observable variables in 1911. By doing so, we will come up

28

with a random sample where the treatment can be seen “as if” randomized, and provide

further robustness checks.

29

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