Economic History and Cliometrics Lab

Working Paper # 12

Land Reform and Government Support: Voting Incentives

in the Countryside

FELIPE GONZÁLEZ

This Version: October, 2010

www.ehcliolab.cl

Economic History and Cliometrics Laboratory Working Paper Series The EH Clio Lab WP series disseminates research developed by lab researchers and students quickly in order to generate comments and suggestions for revision or improvement before publication. They may have been presented at conferences or workshops already, but will not yet have been published in journals. The EH Clio Lab is a research group that applies economic tools –theory as well as quantitative tools applied in economics- to the study of economic history. The current two main research topics: (i) “The Republic in Numbers” and (ii) papers on more specific historical issues and problems, using data both from the República and other sources. The latter consists in the collection and construction of a large number of statistical series about Chile`s development process during the past two centuries. The EH Clio Lab receives funding from the Millenium Nuclei Research in Social Sciences, Planning Ministry (MIDEPLAN), Republic of Chile, (icm@mideplan.cl).

Land Reform and Government Support: Voting Incentives in the Countryside Felipe González Economic History and Cliometrics Lab Working Paper #12 October, 2010 Abstract This paper studies the effects of land reform on political support for the incumbent party. Using agricultural and housing census data at the county level two major findings are presented. First, using different estimation techniques I found that incumbent support increases in 4-6% in counties with land reform. Second, agricultural workers seem to be the main group changing its voting patterns in these counties. I discuss several mechanisms that could be behind these results and empirically explores a few of them. Migration to counties with land reform is unlikely to be a mechanism, and an increase in public goods supply can partially explain the increase in government support. JEL Classification Number: D72, J43, N56, Q1

Land Reform and Government Support:

Voting Incentives in the Countryside∗

FELIPE GONZALEZ

Abstract. This paper studies the effects of land reform on political support for the

incumbent party. Using agricultural and housing census data at the county level two

major findings are presented. First, using different estimation techniques I found that

incumbent support increases in 4-6% in counties with land reform. Second, agricultural

workers seem to be the main group changing its voting patterns in these counties. I

discuss several mechanisms that could be behind these results and empirically explores

a few of them. Migration to counties with land reform is unlikely to be a mechanism,

and an increase in public goods supply can partially explain the increase in government

support.

I am here to fulfill my promises, to stand strong by my beliefs and to never weaken my position (...)

I am here because I wish too see the fall in the concentration of land, so that farmers can become

landowners in order to produce their own income and, thus, have a fair wage.

Eduardo Frei Montalva, first speech as President of Chile (November, 1964).

1 Introduction

Land reform was an important economic policy during the sixties in Latin America. In

1961, during the Punta del Este conference and under the general consensus of all Latin

American governments, the Alliance for Progress was born. A main objective of this Alliance

was to make a deep transformation of unfair agrarian structures (Huerta 1989, p.14). Chile

was not the exception: its high land concentration and limited ability to feed the growing

∗October, 2010. Pontificia Universidad Catolica de Chile, Department of Economics. I would like to thank

Francisco Gallego, Gert Wagner, Tomas Rau, Jose Dıaz, Matıas Tapia, Rolf Luders, Raimundo Soto, Claudio

Ferraz, Guillermo Marshall, Carlos Alvarado, and Ignacio Cuesta for useful comments and suggestions. I

also thank seminar participants at PUC-Chile and the Annual Meeting of the Chilean Economic Society

(SECHI). Any comment to the author’s email address fagonza4@gmail.com

population with its agricultural production lead to the general agreement that an agrarian

reform was needed (Tello, 1965). Thus, in 1962 an agrarian reform began under the right

wing government of Jorge Alessandri (1958 – 1964) and then continued under the centre

government of Eduardo Frei (1964 – 1970).

This happened after the introduction of the secret ballot (1958), which prevented landown-

ers to buy the votes of agricultural workers (Robinson and Baland, 2008). In the sixties,

therefore, a new class of voter became important: agricultural workers. This is relevant

because despite the general view of Chile as a copper producer, agriculture is also an im-

portant economic activity and rural laborers represented a large share of the population

(more than 65% of the labor force in counties like Freire and Calbuco, 1970). If land reform

affects agricultural workers, or some variable that they take into consideration when they

evaluate different political alternatives, they might change their voting patterns in response.

This paper precisely analyzes this and examines whether land reform during the sixties af-

fected support for the incumbent party at the 1970 presidential election. It also examines

different mechanisms to explain why this could have happened. My hypothesis is that land

reform increased incumbent support and agricultural workers are the voters who explain

this. To test this I use disaggregated data at the county (municipality) level, the smallest

administrative unit.

The study of how voters react to government policies is vast, and several channels

through which a government policy might affect political preferences of people have been

proposed. The two most common examples of how voters could react to policies are, first,

to consider voter’s reactions to macroeconomic conditions like the rate of unemployment

and income growth (Stigler 1973, Kramer 1971, Fair 1978, see Hibbs 2006 for a review and

Cerda and Vergara (2007) for the Chilean case); and second, to consider voter’s reactions to

government expenditures, transfers, or redistributive policies in general (Levitt and Snyder

1997, Manacorda et al. 2010, Schady 2000). The first research agenda typically argues that

macroeconomic conditions affect political preferences of some groups, mainly because these

groups evaluate different political alternatives according to certain measures like income

growth or the unemployment rate. The latter research agenda argues that voters change

their beliefs about future government behavior in response to different policies, i.e. that

different policies show the level of competitiveness of the incumbent party and the voter

interprets these as efficient (or inefficient) future behavior.

There are several benefits and differences from working with land reform in Chile that

make this paper a contribution to the literature. First, data from the Agrarian Reform

Corporation (CORA) files is available and, therefore, we know the exact amount of land

that entered into the process at each county from 1962 to 1970. The main advantage

of using this information is that there is a lot of heterogeneity among Chilean counties

2

level of land reform, which enable us to make good comparisons between counties affected

with land reform and those not affected. Second, all relevant counties are considered, and

several county characteristics can be used as covariates. Some relevant controls I use are:

income related variables (assets), supply of public goods, level of rurality, average years

of education, electoral registration, distance to trade points and region’s capital, and the

percentage of different kinds of workers (e.g. agricultural workers). Third —and this is the

main difference with several other agrarian reform process analyzed in the literature (see

Bardhan and Mookherjee 2010, for example)— the institution in charge of the agrarian

reform process (CORA) depended directly from the central, not local governments. This

puts limits to the use of land reform by local governments for political reasons, and enable us

to focus only on the central government incentives. Fourth, there was a general agreement

across political coalitions that an agrarian reform process was needed. The first political

party that developed an agrarian reform law to be presented at the Congress was the

Socialist Party (left wing, 1933), but the law enacted in 1962 was written by the Radical

Party (centre–right wing), and the process actually started under a right wing government.

My empirical strategy is to take voting data at the county level before the agrarian

reform process started (and after the introduction of the secret ballot, i.e. at the 1958

presidential elections) and use this information to control for fixed county characteristics

affecting votes for the incumbent party (e.g. ideology). Then, I estimate first-difference

OLS regressions between presidential elections in 1970 and 1958 to control for time and

county fixed effects, and control for several variables affecting government support that

vary across county and time.

Results suggest that counties with land reform increased government support in at least

6%. This result is robust to the inclusion of a large set of relevant covariates. Due to

potential econometric issues I also use geographical instruments and estimate two stage

least squares. This exercise confirms first-difference OLS results and suggest that the effect

could be even larger. Finally, I use the agrarian reform done by the Church during 1962

and 1963 as robustness check and falsification exercise. This provides further support to

my main result.

Different channels and mechanisms are empirically evaluated and I cannot reject the

hypothesis that agricultural workers were the swing voters, i.e. those who changed their

voting patterns in counties with land reform: in counties where 70% of the labor force is

an agricultural worker, political support for the government increases in 17%, while when

this group is only 30% of the labor force, government support rises in only 6%. Although

it is possible that they evaluated the incumbent according to land reform implementation

directly, other mechanisms are also examined. Particularly interesting is the fact that

land reform is strongly correlated with an increase in public goods provision. Agricultural

3

workers might take this into consideration when they decide to vote for the incumbent.

However I cannot rule out other potential mechanisms.

The rest of the paper is organized as follows. Section 2 presents the relevant historical

background in order to understand the context of this research. Section 3 presents the

theoretical mechanisms which I argue are relevant to understand the political effects of

land reform. Section 4 presents my main results under different estimation methods and a

robustness and falsification exercise using a different agrarian reform. Section 5 examines

mechanisms and provides empirical support for the claim that agricultural workers were the

swing voters. Finally, section 6 concludes.

2 Chilean Rural Society and the Agrarian Reform

The influence of agriculture on Chilean society is unmeasurable, and in many ways is much

more important than mining activities such as cooper and nitrate, the other historically

important economic activities in Chile. Rural society has many special features that makes

it interesting as a subject of study in itself. As McBride (1970) puts it:

Chile’s social structure was built on land bases, and the entire life of the nation had to

be shaped in relation to land (...) The condition of each person was determined by the

ownership or not ownership of an hacienda.

This, together with Chile’s high land concentration are one of the most important character-

istics of rural areas. Indeed, Conning and Robinson (2007) calculate that land gini in Chile

was about 0.94 in 1965.1 Many historians hypothesized that this high land concentration

has its origins in colonial times (e.g. Bauer 1975 and Baraona 1960), but the lack of data is

the main reason why a more rigorous study does not exist on this subject. The persistent

high land concentration undoubtedly contributed to the formation of Chilean rural society.

These features were part of some kind of rural equilibrium in which rural laborers worked

for a landlord and had no opportunity to become landowners. This equilibrium was abruptly

disturb by the agrarian reform in the sixties. However, before the sixties there was also

a concern about this high concentration of property, which translated into the creation of

a government institution called Caja de Colonizacion Agrıcola in 1928 (CCA from now

on, Huerta 1989, p.42-43).2 But this policy was not very effective, and only 430 thousand

physical hectares were acquired by the CCA in 30 years (1929–1958). This is small in

1Other land gini coefficients presented in Conning and Robinson (2007) are: Argentina 0.79, Brazil 0.84,

Bolivia 0.94, Bangladesh 0.42, India 0.62, France 0.54, and United States 0.73.2The main objectives of this institution were to colonize State lands, make the division of this land,

intensify and industrialize agricultural production, provide credits to the beneficiaries, and afforest land

unsuitable for agricultural activities, among others.

4

comparison with the more than 2 millions physical hectares that entered into the agrarian

reform process between 1964–1970 it seems very small (CIDA, 1966). This situation made

it clear that a real agrarian reform could not be carried out by the CCA. However, why was

it made in the sixties and not before?

2.1 The Beginning of an Agrarian Reform

Between the creation of the CCA and the sixties, many things happened that made a real

agrarian reform possible. First, several political parties started to create their own agrarian

reform projects and presented them to the Congress. The first one in writing and agrarian

reform law was the socialist Marmaduque Grove in 1933, although neither this or other

projects were accepted by the Congress before the sixties (Huerta 1989, p.66). Second,

population was growing faster than agricultural production. From 1945 to 1960 the average

annual rate of growth of agricultural production was 1.8%, while the average annual rate

of population growth was about 2.2% (Tello, 1965). Chile went from being a net exporter

of agricultural products in the thirties, to have a growing trade deficit at the beginning

of the sixties. Indeed, during years 1936–1938 there was a trade surplus in agricultural

products of 1.1 millions US$, while in 1963 the annual deficit was around 124 millions US$

(Chonchol, 1976). Third, politics was ruled by a group of people with too much political

power, who also were the majority of landowners. However, this situation changed in the

fifties with the introduction of the secret ballot and the female vote. Huerta (1989) offers a

good description of this:

There is a total resistance to an structural Agrarian Reform before the fifties. The

reason is clear, it implies transmission of power, social modifications, more political

participation. Even though the agrarian problem start as an economic issue, it soon

transformed into a political problem (...) Agricultural workers have been absent as

participants of the national problems, they do not have means of expression.

Fourth, the Church’s position and the general agreement at the National Agricultural So-

ciety was that an agrarian reform was of prime necessity. Indeed, Huerta (1989) argues

that the Church’s agrarian reform before 1962 had an important effect on the national de-

bate. And fifth, the Cuban Revolution had a social impact that made redistributive policies

necessary to satisfy the social demand for it (Eckstein, 1986).

2.2 Agrarian Reform Laws

Under this scenario the agrarian reform process legally started in 1962. This process is

characterized by its two main laws that allowed the government to expropriate plots for

future redistribution.

5

The first law enacted was the Agrarian Reform Law #15.020 in 1962 under the right

wing government of Jorge Alessandri. This law created the Agrarian Reform Corporation

(CORA, replacing the old CCA). The CORA was a central government dependent institu-

tion in charge of the expropriation of plots. The main objectives of this law were, first, to

give access to land to those who work on it, second, to improve the living standards of the

rural population, and third, to increase agricultural production and soil productivity (Law

15.020 art. 3, Diario Oficial N.25, November 27, 1962).3

The second law (Law #16.640) was enacted in July 1967 under the centre government

of Eduardo Frei Montalva. The general agreement about the need for a more intense

land reform was reflected in the 94% of approval of this law at the Congress (Barraclough,

1971). This second law augmented the causals for expropriation of a plot and, consequently,

accelerated the agrarian reform process. Among the new causals the most important was

the one which dictated that a plot could be expropriated if it was bigger than 80 basic

irrigated hectares (BIH). This is important because after 1967 a well exploited plot could

also be expropriated. Also important was the fact that the definition of abandonment

and poor exploitation provided the CORA some discretion for expropriating a plot. The

result is that before 1967 less than 300 hundred thousand physical hectares (PH) entered

into the process, while before the 1970 presidential election more than 2 million PH were

expropriated by the CORA.

2.3 Politics and the Agrarian Reform under Different Governments

During the sixties there were three political coalitions: the right, the centre, and the left

wing. The right wing was composed by the Liberal and Conservative parties between 1958

and 1965, and by the National Party between 1967 and 1970. The centre was represented

by the Christian Democratic Party (CDP) and the Radical Party (RP) in 1958, but only by

the former in 1970. The left wing consisted in the union of the Socialist and the Communist

Party, and after 1969 it was also composed by the RP. Therefore, when I refer to the votes

for the CDP in 1958 I implicitly mean votes either for the CDP or the RP in 1958, but only

to the votes for the CDP in 1970.

3Plots could be expropriated if: 1. the plot was abandoned and poorly exploited, 2. the CORA needed

to do irrigation works, 3. the owner of the plot had unpaid debts, 4. the owner had illegal leases, 5. the

CORA finds the plot useful, 6. the plot is mainly composed by marsh land, 7. the plot was to small and the

CORA wanted to group several small plots, 8. the plot has legally unclear ownership, 9. the plot is owned

by a corporation, and 10. if the plot is mainly composed by Araucarias (a type of tree). Basic requirements

to receive land were: 1. be Chilean, 2. be and agricultural worker, 3. be eighteen years old, 4. be skilled

in agricultural activities, 5. not to be a landowner (or own a very small plot), and 6. be married or a

householder.

6

Between 1958 and 1964 the right wing government was in office with President Jorge

Alessandri. Only a few plots entered into the agrarian reform process during these years.

The only plots reformed by the CORA were the ones owned by the State (Correa et al.,

2001).4 The agrarian reform really started under the government of the Christian Democrat

Eduardo Frei Montalva, who was President of Chile between 1964 and 1970.

3 Why Land Reform Matters: Theoretical Mechanisms

This section discusses the main channels through which land reform could have affected

government support. This is important because my empirical approach in section 4 is not

able to disentangle different mechanisms that explain my result. For a formal discussion

it is necessary to first introduce a voting scheme in which voters express their preferences

(this is motivated by the work of Fair 1978). I assume there are two different voters in rural

counties (landlords and agricultural workers) and three different political candidates.

3.1 Voting Scheme

Let there be three political parties: the incumbent party from the political centre A, the

opposition party from the right wing B, and the opposition party from the left wing C. I

assume landlords do not support the left wing party and rural laborers are more likely to

vote for the left wing party (although they can also vote for the centre or right wing). I

also assume that parties A and C would like to expropriate relatively more than party B.Under this setting landlords do not have economic incentives to vote for A or C. Therefore,I will assume they always vote for the right wing candidate which, nevertheless, seems an

accurate assumption for the Chilean case.

Let an agricultural worker decides for which party to vote under the following rule of

comparison among utilities:

Vote for Party k if Uk > Um ∀k �= m, with k,m ∈ {A,B, C}

And randomizes his vote if Uk = Um. Let a worker utility be formed according to the

following process:

Ukω,c = ξω + ζc +Xc + ηcω (1)

Where ξω and ζc are agricultural worker and county fixed effects not related to land reform,

Xc is variable directly affected by land reform, and ηcω is a random shock with zero mean.

However, more needs to be said about what variables Xc are affected by land reform and,

at the same time, affect voting behavior. I now turn to discuss this.

4In fact, several historians refer to this agrarian reform period as “Reforma de Macetero” (Pot Reform),

in direct reference to the small amount of reformed land.

7

3.2 Theoretical Mechanisms

If workers voted relatively more for the incumbent party in counties with land reform, why

did they do it? There are (at least) four different explanations.

1. Land reform affected some relevant variable before the election: If this happened and

workers evaluated different alternatives according to this variable they are more likely

to vote for the incumbent party in counties with land reform. This could be the case

if, for example, workers’ income increased relatively more in counties with land reform

(and this is caused by land reform).

2. Workers migrated to counties with land reform: If agricultural workers expect some

relevant variable to change in the future in a county with land reform, and this is

beneficial for them, they might choose to migrate to it from a county without land

reform if the benefits of doing so are bigger than the costs. This is a mechanism if

workers are more prone to vote for the incumbent (as Petras and Zeitlin 1970 suggests).

3. Workers expected some relevant variable to change in the future: This could happen

if, for example, workers assigned a higher probability to the event of becoming a

landowner under a future government of the incumbent in counties with land reform,

and they prefer being a landowner than being a landowner’s employee.

4. Workers evaluated political alternatives directly with land reform: This means that

neither present, past and/or future variables need to be affected and the incumbent

receives relatively more votes in counties with land reform. Why do workers evaluated

the incumbent according to land reform? It could be a sign of competitiveness or

signaling about concern for workers (reciprocity).

Although section 5 intends to show light on some of these hypothetical mechanisms, in

general it is hard to disentangle which is relatively more important because there is not

enough data at the county level (for variables such as income) before and after land reform.

It is useful to emphasize that under this framework agricultural workers can also vote

for the left wing. In fact, they might prefer to do it if, for example, they believe their

income will be higher under a left wing government. However, I argue they do not vote in a

different way for the left wing between counties with and without land reform because they

do not associate it with the left wing. The main theoretical argument of this section is that

agricultural workers voted relatively more for the incumbent party in counties with land

reform. This could have happened if any of the above mentioned mechanisms are present.

8

4 Land Reform and Government Support

This section empirically explores the effects of land reform on government support. First,

I present descriptive statistics of the main variables. Then, estimates are presented un-

der three different estimation methods: differences-in-differences, first-difference OLS, and

instrumental variables. Finally, I use a different agrarian reform as robustness check and

falsification exercise.

4.1 Descriptive Statistics and Land Reform Variables

Table 1 presents summary statistics for the main variables in rural counties between regions

IV and X, the main agricultural area of Chile (see Appendix A for details). Government

support is measured as the percentage of votes the CDP obtained at the 1970 presidential

elections. The mean of this variable in 1970 is 30.7%, which is somehow smaller than the

34.5% in 1958. This reflects the typically documented shift from the centre to the left and

right wing during the second half of the sixties (e.g. Collier and Sater 2004).

The first land reform variable I use is a Dummy. I classify 61 of the 210 counties (29%)

as having land reform. The Dummy equals 1 if more than 7% of the county surface (in

physical hectares) entered into the agrarian reform process until August 1970 (one month

before presidential election, robust to different definitions). Also, 149 counties (71%) are

classified as having no land reform (Dummy equals 0). Among these, 79 out of the 149

(53%) have at least 1 neighbor county with land reform. This leaves us with 70 “isolated”

counties that are not affected with land reform and do not have a border in common with

a county with land reform.

The second land reform measure I use is the amount of land that entered into the

agrarian reform process until August 1970 over county surface (also in physical hectares).

This variable has a mean of 0.12 (median equals 0.036) with a standard deviation of 0.205

(69 counties with zero land reform).

Table 1 also shows that the percentage of agricultural workers increased substantially

between 1958 and 1970 (from 21% to 50%), which could be reflecting an increase in the

importance of agricultural activities in rural areas. This increase has the same pattern in

counties with land reform (HEC from now on, for high expropriation counties) and with-

out land reform (LEC from now on, for low expropriation counties), although agricultural

workers were a smaller percentage of the labor force in HEC in 1958 (17% versus 23%). It is

important to control for this variable because if agricultural workers have a certain political

preference and they are affected by land reform, land reform may have had no effect on

government support, and what I am capturing is the effect of a change in labor composition.

It is not important to include any other type of worker as covariate if we believe that these

9

Table 1: Summary Statistics before and after Land Reform

Before Land Reform (1958) After Land Reform (1970)

Sample: All All LEC HEC All All LEC HEC

Mean St. Dev. Mean Mean Difference Mean St. Dev. Mean Mean Difference

Main Variables

CDP votes 0.345 (0.116) 0.361 0.303 0.058*** 0.307 (0.065) 0.307 0.306 0.001

Agricultural Workers 0.211 (0.139) 0.229 0.167 0.062*** 0.507 (0.159) 0.508 0.505 0.003

Rurality 0.695 (0.179) 0.685 0.720 -0.035 0.600 (0.188) 0.593 0.616 -0.023

Electoral Registration 0.229 (0.227) 0.230 0.228 0.002 0.231 (0.270) 0.236 0.219 0.017

Distance to Region’s Capital 0.683 (0.396) 0.677 0.696 -0.019 0.683 (0.396) 0.677 0.696 -0.019

Distance to Closest Port 1.041 (0.601) 1.089 0.925 0.163* 1.041 (0.601) 1.089 0.925 0.163*

Conditions and Public Goods

Education 2.653 (0.652) 2.654 2.651 0.003 3.502 (0.648) 3.507 3.490 0.017

Electricity 0.373 (0.186) 0.359 0.408 -0.489* 0.482 (0.188) 0.462 0.531 -0.068**

Hot Water 0.049 (0.043) 0.051 0.043 0.008 0.084 (0.065) 0.085 0.079 0.006

Literacy 0.672 (0.066) 0.673 0.668 0.005 0.734 (0.052) 0.735 0.733 0.002

Water Supply 0.244 (0.157) 0.247 0.238 0.008 0.521 (0.155) 0.515 0.537 -0.022

Income Related

Cars — — — — — 0.055 (0.024) 0.052 0.061 -0.009

Television — — — — — 0.046 (0.054) 0.042 0.054 -0.011

Radio 0.296 (0.158) 0.285 0.323 -0.039 0.638 (0.119) 0.620 0.683 -0.064***

Notes: Significance level for column labeled “Difference”: *** p<0.01, ** p<0.05, * p<0.1. Summary Statistics for 210 non-urban

counties between regions IV and X (All). HEC: High expropriation counties, where more than 7% of the county surface entered into

the agrarian reform process before August 1970. LEC: Low expropriation counties, where less than 7% of the county surface entered

into the agrarian reform process before August 1970. See Appendix A for sources and definition of variables.

are not correlated with land reform.5

Another potentially important variable which I can control for is electoral registration.

In 1958 voted 1.23 millions of voters, while in 1970 the number more than doubled to 2.92

millions (Hellinger 1978, p.255). Table 1 shows that a county represented on average 0.23%

of the electorate (county votes over national votes). This is important because if more

people registered in HEC, and this is not caused by land reform, I would obtain biased

estimates of the effect of land reform on government support.

Conditions and Public Goods, and Income Related variables are included as covariates

to control for two possible effects. First, to isolate the effect of land reform it is important to

control for any other government action that might be changing people’s attitude towards

5Indeed, results are robust to the inclusion of a wide variety of variables that reflect changes in the

percentages of different types of workers (see Table Appendix B.2, last column).

10

the government. If a county is receiving transfers from the central government between

1958 and 1970 —taxes, subsidies, public goods, or others— this could increase government

support, regardless the level of land reform in that county. Second, wage increases in

one county could be associated by its residents as good economic policy by the central

government, and might change government support.

Finally, I can also control for the Church’s Agrarian Reform between 1962 and 1963

(distribution of plots to farmers over county surface, in physical hectares). I will turn to

this point later on because this was a different (but related) land reform. For now let me say

that it is important to control for this because this could have changed incumbent support

due to the fact that the Church is associated with the CDP.

Table 1 also shows an improvement in living standards between 1958 and 1970, measured

by increases in average education years (from 2.6 to 3.5) and literacy rate (from 67% to

73%), and increases in the percentage of houses with electricity (from 37% to 48%), hot

water (from 5% to 8%), and water supply (from 24% to 52%). It also shows an increase in

asset property measured by the percentage of houses with at least one car, television, and

radio.

4.2 Differences-in-Differences: Benchmark Estimates

Let me consider the simplest framework. If land reform was randomly assigned through

counties, we can estimate the effect of land reform on government support with differences-

in-differences with no need to control for any other variable. The identification assumption

of this method is that CDP votes are a linear function in the following way:

Vct = γc + λt + εct (2)

Vct� = γc + λt� + δ · Land Reformc + εct� (3)

Where Land Reformc is a land reform measure, γc is a county time-invariant fixed effect,

λt is a time fixed effect affecting all counties, and εct is a random shock with zero mean.

Subscripts t and t� are time periods before and after land reform respectively. Under these

set of assumptions we can estimate the effect of land reform on government support by

taking the difference between HEC and LEC after land reform assignment (equation 3), and

subtracting the result from the same difference before land reform assignment (equation 2).

The key identification assumption of this strategy is that the change in government support

at HEC and LEC is the same in the absence of land reform treatment, but only because

some counties are affected with land reform they differ differently after the treatment.

Estimates in Table 2 support a positive effect of land reform on government support.

The second column shows that HEC were less prone to support the CDP in 1958, but

11

Table 2: Differences-in-Differences Estimates

Presidential Election 1958 Presidential Election 1970

Control Treated Diff Control Treated Diff Diff-in-Diff

(% votes) (% votes) (λt1 − λt0) (% votes) (% votes) (λt1 − λt0 + δ) δ

Left Wing 28.9 31.5 2.7 33.6 32.9 -0.7 -3.4

(0.99) (1.54) (1.83) (0.99) (1.54) (1.83) (2.58)

Center 36.3 30.3 -5.9 30.7 30.6 -0.1 5.8***

(0.76) (1.18) (1.40) (0.76) (1.18) (1.40) (1.98)

Right Wing 34.9 38.2 3.3 35.7 36.5 0.8 -2.5

(0.89) (1.39) (1.65) (0.89) (1.39) (1.65) (2.33)

Left + Center 65.1 61.9 -3.3 64.3 63.5 -0.8 2.5

(0.89) (1.39) (1.65) (0.89) (1.39) (1.65) (2.3)

Notes: Significance level: *** p<0.01, ** p<0.05, * p<0.1.Greek letters from the following equations: Vct = γc+λt+δXct+εct

and Vct� = γc + λt� + δXct� + εct� , where t = 1958 and t� = 1970.

after land reform their support for the incumbent party is the same than in LEC. If we

interpret this directly it does not exactly mean that HEC increased its government support

in absolute terms (i.e. relative to before the assignment) but rather than as a national

phenomenon counties are decreasing their political support for the CDP, but this did not

happen in HEC. To see this lets take a look at votes in non-treated counties (LEC). These

counties are voting around 6% less for the CDP, and this translates into 5% more votes for

the left wing, and 1% more votes for the right wing party.

The main pitfall with this approach is that identification assumptions in equations (2)

and (3) could be too restrictive. There might be omitted variables correlated with land

reform and government support and, therefore, estimates in Table 2 could be biased.

4.3 Controlling for Observables: First-Difference OLS

To deal with the potential omitted variables my strategy is to estimate first-difference OLS

regressions and to control for everything I can control for at the county level. Thus, I

take equations (2) and (3), add a matrix of control variables Xct, and differentiate in the

following way:

Vct = γc + λt + δXct + εct

Vct� = γc + λt� + δXct� + β · Land Reformc + εct�

Vct� − Vct = (λt� − λt) + δ(Xct� −Xct) + β · Land Reformc + (εct� − εct)

∆Vc = φ+ γMc + β · Land Reformc + ηc (4)

12

I take equation (4) to the data. In this case, to first-differentiate allow me to control for any

county characteristics γc that are constant over time (e.g. county ideology). The constant

term φ captures the time changing preferences of the entire electorate, and Mc control for

variables that vary over county and time that might affect government support.6

Table 3 present OLS estimates of equation (4). Column 1 show us the correlation

between the land reform Dummy and government support in the same way than difference-

in-difference estimates: land reform avoids a political migration of 6% from the CDP to

the left and right wing. A negative estimate of the constant term (−0.056) shows that the

electorate is migrating from the center. If we take these two estimates together we obtained

our benchmark result: political migration did not happen at HEC (0.058− 0.056 ≈ 0).

To think about counties as independent units of analysis might not be entirely appropri-

ate because counties can sometimes be very small administrative units (in terms of square

kilometers) and be close to each other. For this reason it is useful to add as a control vari-

able a dummy that equals one if a county is classified as LEC but has a border in common

with a HEC. The rationale is that it seems naive to assume that land reform only affects

votes within the county boundaries, because sometimes these are more de jure than de

facto. Moreover, it seems intuitive to think that the effect of land reform should be smaller

or non-significant in these neighbor counties. Column 2 provides some evidence in favor

of this intuition: the effect is around half, and both effects are positive and statistically

significant.7 In this column I also control for the Church’s agrarian reform, something that

happened between 1958 and 1970 in a few counties that might have affected CDP support.

Column 3 checks if these results are driven by differences in growth of agricultural

workers. I include agricultural workers growth because they are the biggest group in rural

counties, the most likely to migrate to a HEC, and according to Petras and Zeitlin (1970) are

more likely to support the CDP. Estimates show that results are not driven by this variable.

This column also shows that results are robust to the inclusion of rurality as covariate —the

change in the percentage of people living in rural areas— and that results are not driven by

the fact that HEC are voting (or enrolling at the electoral service) relatively less than LEC.

To control for potential trade or transportation policies affecting counties close to ports or

trading points I add the distance to the region’s capital and to the closest port (in hundred

of kilometers). It should not be necessary to control for these if they affect in the same way

in 1958 and 1970. However, the sixties were a decade of growing commerce and decreasing

transport costs, therefore, this variable might have affected differently in 1970 and in 1958.

6In this case, the interpretation of the constant term is straightforward: a negative estimate tell us that

counties are voting relatively less for the CDP, this is λt > λt� .7Robust standard errors corrected for spatial correlation are also used following Conley (1999) and the

same result arises.

13

Table 3: OLS Results — Robustness to Control Variables

Dependent variable: CDP votes in 1970 minus CDP votes in 1958

Land Reform Variable: Dummy Continuos

(1) (2) (3) (4) (5) (6) (7) (8)

Land Reform 0.058*** 0.081*** 0.067*** 0.069*** 0.049** 0.159*** 0.157*** 0.121***

(0.019) (0.021) (0.021) (0.021) (0.021) (0.038) (0.039) (0.037)

[0.019] [0.021] [0.023] [0.022] [0.021] [0.038] [0.038] [0.034]

Neighbor 0.048** 0.032 0.032 0.031 0.016 0.015 0.019

(0.020) (0.020) (0.020) (0.020) (0.016) (0.016) (0.016)

Church’s Reform 0.218* 0.084 0.075 0.068 0.146* 0.138 0.115

(0.111) (0.096) (0.106) (0.084) (0.082) (0.088) (0.070)

Agricultural Workers 0.211*** 0.217*** 0.198*** 0.247*** 0.250*** 0.223***

(0.054) (0.063) (0.062) (0.049) (0.059) (0.056)

Rurality -0.488*** -0.511*** -0.423** -0.401*** -0.391** -0.337*

(0.150) (0.162) (0.169) (0.149) (0.164) (0.163)

Electoral Registration 0.044 0.045 -0.067 0.043 0.049 -0.061

(0.062) (0.057) (0.046) (0.066) (0.057) (0.042)

Constant -0.056*** -0.081*** -0.194*** -0.154*** -0.339*** -0.195*** -0.157*** -0.345***

(0.010) (0.013) (0.035) (0.049) (0.074) (0.035) (0.049) (0.070)

Distances No No Yes Yes Yes Yes Yes Yes

Conditions and Public Goods No No No Yes Yes No Yes Yes

Income Related No No No No Yes No No Yes

Counties 210 210 210 210 210 210 210 210

R2 0.044 0.076 0.242 0.255 0.345 0.275 0.285 0.369

Notes: Robust standard errors in parenthesis. Conley standard errors (corrected for spatial correlation) in brackets. Significance

level: *** p<0.01, ** p<0.05, * p<0.1.

Result is also robust to the inclusion of these control variables. Column 6 is the equivalent to

column 3 but using the continuos land reform variable which is interpreted in the following

way: in counties where 35% of the land entered into the agrarian reform process government

support increased in 6%, and if 12% of the county was reformed incumbent support increased

in 2%.

Columns 4 and 5 control for Conditions and Public Goods and Income Related variables.

Controlling for these variables show us that people in counties with better conditions, more

public goods, and higher income are voting relatively more for the incumbent party (more

from this in section 5). This could mean two different things. First, that land reform

caused higher income, better conditions, and more public goods in the short term, and

these are channels through which it affects government support. Second, that land reform

is correlated with these variables, and estimates in columns 1-5 is not the effect of interest,

but rather the effect of this plus the effect of omitted variables. However, even if land reform

14

did not caused higher income, better conditions, and/or more public goods, in counties

with land reform government support increases in about 5%: this estimate is robust and

statistically significant (this effect is bigger in counties where more people live in rural areas,

see Appendix B).

4.4 Econometric Issues: Instrumental variables

So far first-difference OLS results suggest that government support increases in about 5%

in counties with land reform (Dummy) and 2% on the average county (continuos variable).

However, there might econometric problems with this estimate. In this case, the use of an

instrumental variables approach is useful for three different reasons. First, if land reform

causes changes in some covariates the effect of land reform might be different. If this is

the case land reform variables are only capturing effects not related to these covariates, i.e.

columns 8 and 5 in Table 3 are over-controlling. Second, land reform could be measured

with error for three different reasons: i) maybe what matters is expropriation weighted

by land quality, not in physical hectares, ii) I take expropriation until August 1970, but

I dropped a few expropriations without date,8 and iii) there could be expropriations not

reported in the CORA files. Finally, there is always the possibly that a non-observable

variable correlated with land reform is driving results.

An instrumental variables approach provides further support to OLS estimates and solve

these problems if the instruments are valid, which depends on the need for the instrument

to be strongly correlated with land reform (identification) and that the instruments are not

correlated with covariates acting as channels, the measurement error, and non-observable

variables affecting government support differently in 1958 and 1970 (exclusion restriction).

Possible covariates acting as channels are three. First, change in agricultural workers.

This is a channel if they are more likely to support the incumbent (as Petras and Zeitlin 1970

argue) and their migration is caused by land reform. Second, what I call county conditions.

This could be the case if land reform increased literacy rate or average education years.

Third, public goods, under the same reasoning. And fourth, income related variables. If

land reform caused higher wages, and this translated into more assets, these could also be

a channel.

Several different instruments are used: the distance from a county to the west coast

(and its square), and a dummy for landlocked counties for land reform Dummy; average

annual rain (in millimeters), number of dry months, and land gini for land reform mea-

8Only 12 out of the 5,422 expropriations have missing date of expropriation. Among these, only 6 were

bigger than 100 physical hectares.

15

Table 4: Instrumental Variables

Dependent variable: CDP votes in 1970 minus CDP votes in 1958

Land Reform Variable Dummy Continuos

(1) (2) (3) (4) (5) (6) (7) (8)

Land Reform 0.157* 0.142* 0.235** 0.186** 0.514*** 0.378** 0.491*** 0.576***

(0.087) (0.084) (0.100) (0.082) (0.170) (0.150) (0.128) (0.142)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Conditions and Public Goods Yes Yes Yes Yes Yes Yes Yes Yes

Income Related Yes Yes No No Yes Yes Yes No

Counties 210 210 210 210 210 210 210 210

F-test excluded instruments 9.319 5.697 8.879 6.803 9.228 11.01 25.23 23.34

CLR (p-value for Land Reform) 0.105 0.139 0.039 0.054 0.023 0.035 0.002 0.000

Hausman test (p-value) 0.201 0.253 0.057 0.084 0.027 0.112 0.005 0.002

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Conditional Likelihood Ratio

(CLR) from Moreira (2003) using Stata module from Mikusheva and Poi (2006) to show that weak instruments is not a problem.

Controls include: Neighbor, Church’s Reform, Agricultural Workers, Rurality, Electoral Registration, and Distances. Instruments in

columns: (1) and (3) Landlocked Dummy, (2) and (4) Distance to Weast Coast and square, (5) Annual Rainfall, (6) Number of Dry

Months in a Year, (7) Land Gini, (8) Land Gini.

sured as expropriations over county surface.9 The rationale behind the condition that the

isntruments are strongly correlated with land reform variables is that the main agricultural

area is geographically located in the so called Central Valley (Collier and Sater, 2004), this

is, away from the west coast and the Andes Mountains. Therefore, land reform should be

relatively more intensive in counties located in this area (landlocked dummy and distance

to weast coast). Also, counties where it rains relatively more should be counties with more

agricultural activities and, therefore, more prone to be affected by land reform (average an-

nual rain, dry months). Finally, history books suggest that counties where land was more

concentrated were more likely to be affected by land reform (land gini, e.g. Collier and

Sater 2004 and Huerta 1989).

Table 4 present estimates using the instruments and the same result arises: there is a

positive and significant effect of land reform on the incumbent political support. Further-

more, IV estimates suggest the effect is bigger than OLS. The coefficient for the continuos

variable (column 7) is interpreted in the following way: for the average county where 10%

of the surface was expropriated, government support increased in 5% relative to a county

9Land Gini coefficient for county c (with M plots) is: Ginic = 1− 2��M

k=1φk,i

�1

2ωk,i +

�Mj=k+1

ωj,i

��,

where φk,i is the estimated share of the county surface that plot k holds, and ωk,i is stands for the fraction

of total plots that plot j holds.

16

Table 5: Falsification Exercise using information before Land Reform

Dependent variable: CDP votes in 1961 minus CDP 1953

Instrument is: Dummy Distance to Annual Number of Dry Land

Landlocked West Coast Rainfall Months a Year Gini

Instrument 1 0.015 0.059 0.002 -0.002 -0.002

(0.017) (0.044) (0.010) (0.002) (0.280)

Instrument 2 -0.027

(0.027)

Counties 207 207 207 207 207

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Dependent variable is CDP votes

at Parliamentary Elections. Three counties were not found in 1953 and, therefore, were dropped from regressions.

with no land reform.

However, the validity of the instruments relies on the assumption that land reform

is the only channel through which the instruments affected the increase in government

support. As evidence for the validity of the instruments consider the following reasoning:

if this assumption is correct we should not observe a significant correlation between the

instruments and the increase in CDP support before land reform. This is shown in Table

5, where I take as dependent variable the percentage of votes the CDP obtained in 1961

minus the same percentage at the 1953 Parliamentary Elections and add as right hand side

variable the instruments and distances as control variables. In fact, if we allow violations

to perfect exogeneity by letting the instruments to have a direct effect on the dependent

variable, we would need very different coefficients to have a non-different from zero impact

of land reform.10

Overall, I argue that the three different estimation methods in Tables 2, 3 and 4 (and

the falsification exercise in Table 5) provide evidence in favor of a positive effect of land

reform on government support of about 4-6% in the average county affected with this policy.

4.5 Robustness Check: the Church’s Agrarian Reform

What if land reform was not carried out by the incumbent party but rather by a different

(non-political) institution? Voters should not change their voting patterns if this does

10This can be easily viewed using the methodology proposed in Conley et al. (2008). Violations to perfect

exogeneity means that θ �= 0 in the equation ∆Vc = φ+ β · Land Reformc + θZc + γMc + ηct, where Zc are

the instruments.

17

not change relative utility among voting for different political candidates. However, this

may not be entirely right if we believe that the non-political institution is related to some

political party. This subsection analyzes the Church’s agrarian reform, which consisted in

the distribution of its own plots among agricultural workers during 1962 and 1963. This is

exactly the case of a non-political institution related to some political party.

In Chile, the Church is closely associated to the CDP (Grayson, 1969), and then, its

actions might be interpreted as information about CDP’s actions. In fact, Hudson (1994)

suggests that the Church’s social actions at the beginning of the sixties had an important

effect on political support for the CDP. Then, I argue this is a good falsification exercise

because political support for the right wing party (the incumbent at the time) should not

have increased in counties where the Church distributed its plots, and is a also a good

robustness check because we should also see an increase in political support for the CDP.

In exactly the same spirit than in previous sections I take the incumbent and the CDP

political support at the 1961 Parliamentary Elections (before the Church’s agrarian reform)

and at the 1965 Parliamentary Elections (after the Church’s agrarian reform) and estimate

equation (4). The only problem in trying to recreate previous regressions is that I cannot

control for everything I would like to control for because of data restrictions. Furthermore,

an additional problem arises in this exercise: the Church’s agrarian reform was carried out

only in regions VI, VII and Metropolitan (RM).11 To account for this potentially endogenous

regional selection, I only take counties from these regions as the counterfactuals (or non-

treated counties) and include dummies for regions VI and VII to control for potential

selection bias.

Table 6 present first-difference OLS regressions to explain the incumbent political sup-

port. Different columns use different agrarian reform measures. I take the amount of land

assigned to rural families (in physical hectares) and divide it for different variables in order

to be able to compare across counties. The main difference with the most complete spec-

ification in Table 3 is that now I can only control for electoral registration, the neighbor

counties, and distances. It is also important to control for the effects of the CORA agrarian

reform in order to differentiate the effects of the Church’s agrarian reform from this.

Overall, estimates in Table 6 Panel A do not show an increase in government support in

counties with agrarian reform. In fact, they suggest that counties where the Church made

its own agrarian reform voters decreased their support for the right wing. This could mean

that people are voting relatively more for another party. If voters directly associated the

11The estates owned by the Church and assigned to rural families, with their respective size (in physical

hectares, PH) and county location, were: Alto Melipilla in Melipilla (164 PH), Los Silos de Pirque in Pirque

(181 PH), Las Pataguas in Pichidegua (1,470 PH), San Dionosio in Colbun (3,374 PH), and Alto las Cruces

in Talca (340 PH).

18

Table 6: The Church’s Agrarian Reform

Expropriation Expropriation Expropriation Land Reform Expropriation

over County over Agricultural over total Dummy over total

Surface Surface Workers Votes

Panel A : Dependent variable: Right wing votes in 1965 minus Right wing votes in 1961

Church’s Reform -0.185 -0.157 -0.129*** -0.103*** -0.093***

(0.117) (0.097) (0.023) (0.031) (0.017)

Neighbor 0.078* 0.077 0.075 0.073 0.075

(0.046) (0.046) (0.047) (0.047) (0.047)

Land Reform (until 1965) 0.118 0.131 0.090 0.303 0.092

(0.274) (0.275) (0.258) (0.366) (0.258)

Panel B : Dependent variable: CDP votes in 1965 minus CDP votes in 1961

Church’s Reform 0.184* 0.153* 0.127*** 0.089*** 0.093***

(0.103) (0.086) (0.024) (0.022) (0.017)

Neighbor -0.019 -0.018 -0.016 -0.015 -0.016

(0.029) (0.029) (0.030) (0.030) (0.030)

Land Reform (until 1965) 0.620*** 0.610*** 0.649*** 0.469* 0.647***

(0.194) (0.193) (0.197) (0.243) (0.197)

Electoral Registration Yes Yes Yes Yes Yes

Distances Yes Yes Yes Yes Yes

Region Dummies Yes Yes Yes Yes Yes

Counties 74 74 74 74 74

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Dependent variable in Panel A

(B) is the percentage of votes the Conservative and Liberal parties (Christian Democratic Party) obtained at the 1965 Parliamentary

Elections minus the same percentage in the 1961 Parliamentary Elections.

Church and the CDP, then an increase in CDP votes at this counties would also support my

results. Panel B explores this possibility. Estimates show that CDP increases its support

in counties where the Church made its own agrarian reform, even when we control for

electoral registration, distances, the neighbors, and regional factors. I argue that results in

both Panels are consistent with my previous results: voters increase their support for the

government in counties with agrarian reform because they associate this with the incumbent

political actions. Why do they do it? Next section explores different answers.

19

5 Swing Voters and Mechanisms

This section provides a formal discussion about how voters chose among different candi-

dates —i.e. discusses mechanisms linking land reform and government support— and also

argues that agricultural workers were the swing voters —i.e. those who vote differently

in counties with and without land reform. Moreover, I discuss how agricultural workers

could have evaluated different political alternatives and what mechanisms are relatively

more important. Although it is hard to empirically answer this due to data restrictions,

some interesting correlations are provided in order to give insights about an answer.

5.1 Swing Voters

The group most positively affected by land reform could be agricultural workers because

they are a big political group in rural areas and were affected by this policy. Therefore,

I suspect these could be the swing voters. Nevertheless, for a better understanding I also

analyze a large variety of different groups.

For this purpose I estimate the most complete specification and add the percentage of

different types of workers in 1970 (over labor force) and an interaction term between this

variable and the Land Reform Dummy. The rationale behind this strategy is to test if

different types of workers were voting relatively more for the incumbent in 1970 in counties

with land reform. The estimating equation is as follows:

∆Vc = φ + γMc + α(Wc,1970 · Land Reformc)

+ β · Land Reformc + ρWc,1970 + ρ1(Wc,1970 −Wc,1958) + ηct (5)

Where Wc,t stands for the percentage of a specific type of worker in county c and year t and

Mc still is the difference in covariates that vary across county and time. A positive estimate

of α means that a certain type of workers W voted relatively more for the incumbent in a

county with land reform.

Table 7 present estimates of equation (5). We can see in column 1 that the land reform

Dummy is no longer statistically significant. This is in fact expected if agricultural workers

are the swing voters. Moreover, the interaction term between the Dummy and agricultural

workers is statistically significant at the 5% and has the expected sign. This estimate is

interpreted in the following way: in counties classified as HEC where 70% of the labor force

is an agricultural worker, government support is 17% larger. On the other hand, in counties

classified as HEC where 30% of the labor force is an agricultural worker, government support

is only 7% larger. The rest of the columns support this finding using several different types

of workers as defined by the 1970 Housing Census. There is no other group of workers

voting relatively more for the incumbent party in counties with land reform.

20

Table 7: Swing Voters

Dependent variable: CDP votes in 1970 minus CDP votes in 1958

Different Types of Workers Agricultural Clerks Crafts and Plant Professionals Service and

Workers Trades and Machine and Technician Salesman

Land Reform -0.068 0.078** 0.126*** 0.060 0.096** 0.115***

(0.054) (0.037) (0.046) (0.053) (0.038) (0.043)

Land Reform × Type of Workers 0.236** -1.566 -0.577** -0.329 -1.429* -0.936*

(0.105) (1.277) (0.287) (1.044) (0.837) (0.517)

R2 0.359 0.368 0.359 0.362 0.391 0.366

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Type of Workers over labor force.

See Appendix A for sources and definition of variables. Land Reform measured by a Dummy that equals 1 if more than 7% of the

county surface enterd into the agrarian reform process before Agust 1970.

5.2 How Voters Evaluated Different Alternatives? Mechanisms

Following the empirical approach of Nunn (2008) and Bruhn and Gallego (2010) I now

examine different voting mechanisms. These mechanisms were already presented in section

3.2. However, mechanisms number 3 and 4 are only examined as residuals —i.e. if there is

something not explained by mechanisms 1 and 2, then these should be relevant.

According to Petras and Zeitlin (1970) agricultural workers were more prone to vote for

the CDP. Then, if they migrated relatively more to counties classified as HEC, and this is

caused by land reform, the incumbent support could have increased and, therefore, this is

a mechanism. However, column 1 and 5 in Table 8 do not show a positive and statistically

significant correlation between land reform and the change in agricultural workers. Hence,

this is unlikely to be one of the mechanisms.

Land reform could have had an effect on some variable before the 1970 presidential

election, and through this variable could have affected voting patterns. Although many

variables could have been affected by land reform, I argue that public goods are particu-

larly important because they could be interpreted as transfers (Manacorda et al., 2010),

government spending (Levitt and Snyder 1997 and Schady 2000), or inputs for agricultural

production (as in De Gorter and Zilberman 1990). There is empirical evidence that the first

two can increase government support and an increase in productivity could have (at least

in theory) increased it too. Therefore, I focus on the correlation between the percentage

of houses with water supply and electricity with land reform. I chose these variables as

proxies for public goods because of availability from the 1960 and 1970 Housing Census.

21

Table 8: Possible Mechanisms linking Land Reform and Government Support

Dependent variable: Difference between 1970 and 1960 in Percentage of

Agricultural Houses with Agricultural Houses with

Workers Water Electricity Radio Workers Water Electricity Radio

Land Reform -0.013 0.038*** 0.015** 0.028*** -0.062** 0.046* 0.004 0.035**

(0.013) (0.009) (0.008) (0.011) (0.025) (0.024) (0.013) (0.017)

Counties 210 210 210 210 210 210 210 210

Distances Yes Yes Yes Yes Yes Yes Yes Yes

Level Control Yes Yes Yes Yes Yes Yes Yes Yes

Land Reform variable Dummy Dummy Dummy Dummy Cont. Cont. Cont. Cont.

R2 0.784 0.241 0.120 0.537 0.789 0.207 0.104 0.526

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. See Appendix A for sources and

definition of variables. Level Control in columns: (1) and (5) Agricultural workers in 1960, (2) and (6) % houses with water supply

in 1960, (3) and (7) % houses with electricity, (4) and (8) % houses with radio, nad Rurality in 1960 in all columns.

Columns 2 & 3 and 5 & 6 in Table 8 show that counties with land reform increased rel-

atively more its electricity and water supply coverage (this could have been necessary in

order to complement land reform). This is evidence in favor of this mechanism because the

correlation is strong and has the expected sign.12 However, as columns 5 and 8 in Table

3 show, controlling for changes in public goods provision still leaves an unexplained part

of land reform that affects voting behavior. Therefore, I do not rule out that changes in

other variables, land reform in itself, and changes in beliefs about what is going to happen

in counties with land reform are also mechanisms used by agricultural workers to evaluate

the incumbent.

The main conclusion from this section is that the effect of land reform of government

support can be rationalized in the following way. When land reform was implemented in

a county public goods increased relatively more. Then, when agricultural workers decided

for which candidate to vote for they had a better evaluation of the incumbent (in relation

to the same worker in a county without land reform) for three different reasons. First,

they valued land reform (mechanism number 4 in section 3.2), they benefited from more

public goods (mechanism number 1), and they assigned a higher probability to the event of

becoming landowners (which is beneficial for them) or expected other variables to change

12Changes in wages are also a potential mechanism, but there is no data to be able to test this. However

column 4 & 8 in Table 8 shows that land reform is strongly correlated with the change in the percentage

of houses with radio (but not with the percentage of houses with television or cars in 1970). Changes in

literacy rate and years of education are not correlated with land reform Dummy (not shown).

22

in the future (mechanism number 3).

6 Concluding Remarks

The main purpose of this paper was to study if land reform can increase the incumbent

political support. To be able to put this premise into perspective, I use a framework

that emphasizes different mechanisms linking land reform and government support. The

empirical analysis shows that using three different estimation techniques counties with land

reform are more prone to vote for the incumbent party: the incumbent obtained 5% more

votes in these counties.

Also, agricultural workers seem to be the main group changing their voting patterns

between counties with and without land reform. I emphasize that several mechanisms could

be behind these results. Among these, particularly interesting is the fact that land reform

is strongly correlated with an increase in public goods provision, and is not correlated with

the change in the percentage of agricultural workers. Thus, I rule out the possibility that

a migration of agricultural workers to counties with land reform is a mechanism behind

my result. Although public goods seems to be a mechanism, there is a significant part of

the effect of land reform on government support that I cannot explain. I attribute this to

importance of land reform in itself as mechanism of evaluation —maybe because it shows

the level of competitiveness of the incumbent— and to possible changes in other relevant

variables (such as wages) before the 1970 presidential election.

References

Angrist, Joshua and Pischke, Jorn-Steffen: Mostly Harmless Econometrics. Prince-

ton University Press, 2009.

Baraona, Rafael: Valle de Putaendo. Santiago, 1960.

Bardhan, Pranab and Mookherjee, Dilip: �Determinants of Redistributive Politics:

An Empirical Analysis of Land Reforms in West Bengal, India�. American Economic

Review (forthcoming), 2010.

Barraclough, Solon: �Reforma Agraria: Historia y Perspectivas�. Cuadernos de

Realidad Nacional , 1971, CEREN(7).

Bauer, Arnold J.: La Sociedad Rural Chilena. Cambridge University Press, 1975.

Bruhn, Miriam and Gallego, Francisco: �Good, Bad, and Ugly Colonial Activities:

Do They Matter for Economic Development�. Economic History and Cliometrics Lab

Working Paper N.6 , 2010.

23

Cerda, Rodrigo and Vergara, Rodrigo: �Business Cycle and Political Election Out-

comes: Evidence from the Chilean Democracy�. Public Choice, 2007, 132(1 and 2),

pp. 125–136.

Chonchol, Jacques: �La Reforma Agraria en Chile (1964-1973)�. Trimestre Economico,

1976, XLIII(171), p. 600.

CIDA: Chile: Tenencia de la Tierra y Desarrollo Socioeconomico del Sector Agrıcola.

Inter-American Committee for Agricultural Development (CIDA), 1966.

Collier, Simon and Sater, William: A History of Chile, 1808-2002. Cambridge Uni-

versity Press, 2004.

Conley, Timothy: �GMM Estimation with Cross Sectional Dependence�. Journal of

Econometrics, 1999, pp. 1–45.

Conley, Timothy; Hansen, Christian and Rossi, Peter: �Plausibly Exogenous�.

Mimeo, University of Chicago, 2008.

Conning, Jonathan and Robinson, James: �Property Rights and the Political Organi-

zation of Agriculture�. Journal of Development Economics, 2007, 82, pp. 416–447.

Correa, Sofia; Jocelyn-Holt, Alfredo; Rolle, Claudio; Vicuna, Manuel and

Figueroa, Consuelo: Historia del Siglo XX Chileno. Sudamericana, 2001.

De Gorter, Harry and Zilberman, David: �On the Political Economy of Public Goods

Inputs in Agriculture�. American Journal of Agricultural Economics, 1990, 72(1), pp.

131–137.

Eckstein, Susan: �The Impact of the Cuban Revolution: A Comparative Perspective�.

Comparative Studies in Society and History , 1986, 28(3), pp. 502–534.

Fair, Ray C.: �The Effect of Economic Events on Votes for President�. The Review of

Economic and Statistics , 1978, LX(2).

Grayson, George W.: �Chile’s Christian Democratic Party: Power, Factions, and

Ideology�. The Review of Politics, 1969, 31(2), pp. 147–171.

Hellinger, Daniel: �Electoral Change in the Chilean Countryside: The Presidential

Elections of 1958 and 1970�. The Western Political Quarterly , 1978, 31(2), pp. 253–

273.

Hibbs, Douglas A.: Voting and the Macroeconomy. Chapter 31, pp. 565–586. Oxford

University Press, 2006.

24

Hudson, Rex A.: Chile: A Country Study. Washington: GPO for the Library of Congress,

1994, 1994.

Huerta, Maria Antonieta: Otro Agro para Chile: la Historia de la Reforma Agraria en

el Proceso Social y Polıtico. CISEC-CESOC, 1989.

Kramer, Gerald H.: �Short-Term Fluctuations in U.S. Voting Behavior, 1896-1964�.

The American Political Science Review , 1971, 65(1), pp. 131–143.

Levitt, Steven D. and Snyder, James M.: �The Impact of Federal Spending on House

Election Outcomes�. Journal of Political Economy , 1997, 105(1), pp. 30–53.

Manacorda, Marco; Miguel, Edward and Vigorito, Andrea: �Government Trans-

fers and Political Support�, 2010.

McBride, Jorge M.: Chile: Land and Society. ICIRA, 1970.

Mikusheva, Anna and Poi, Brian: �Tests and Confidence Sets with Correct Size When

Instruments are Potentially Weak�. The Stata Journal , 2006, 6(3), pp. 335–347.

Moreira, Marcelo: �A Conditional Likelihood Ratio Test for Structural Models�.

Econometrica, 2003, 71(4), pp. 1027–1048.

Nunn, Nathan: �Slavery, Inequality, and Economic Development in the Americas: An

Examination of the Engerman-Sokoloff Hypothesis�. in E. Helpman (ed.), Institution

and Economic Performance, Harvard University Press , 2008.

Petras, James and Zeitlin, Maurice: �The Working-Class Vote in Chile: Christian

Democracy versus Marxism�. The British Journal of Sociology , 1970, 21(1), pp. 16–29.

Robinson, James and Baland, Jean-Marie: �Land and Power: Theory and Evidence

from Chile�. American Economic Review , 2008, 98(5), pp. 1737–1765.

Schady, Norbert R.: �The Political Economy of Expenditures by the Peruvian Social

Fund (FONCODES), 1991-1995�. American Political Science Review , 2000, 94(2).

Stigler, George J.: �General Economic Conditions and National Elections�. American

Economic Review , 1973, 63(2), pp. 160–167.

Tello, Carlos: �El Sector Agrıcola y el Desarrollo Economico de los Paıses

Latinoamericanos�. El Trimestre Economico, 1965.

25

A Data Construction

This appendix shows data construction from the CORA files, definitions and sources for

the main variables, and argues why only 210 counties are considered.

A.1 Agrarian Reform Index

There is information about the amount of expropriated land over surface in the county,

where both measures are in physical hectares (PH) for the 257 counties in the agrarian

reform database. Therefore, the de facto agrarian reform intensity index at county c (ARIc)

I consider in the empirical section has the following mathematical form:

ARIc,t =

�p∈c (Expropriated PH of plot p−Non Agrarian Transferences from plot p)t

(PH Surface of County c)t

Where p ∈ c recognized that there are many plots in a single county, and the numerator

captures the actual amount of land reform net of redistributed land with non agrarian

objectives.13 Because the agrarian reform process started in 1962 and finished in 1980 I

constructed an index until August of 1970, 1 month before the presidential election.

A.2 Counties between Regions IV and X

Land reform is intended to affect rural counties where agriculture is an important economic

activity. Therefore, my focus is only on 210 non-urban counties between regions IV and X,

the main agricultural area of Chile (see Figure B.1). As supporting evidence for this decision

lets consider arable hectares (suitable land for growing crops) across Chilean regions: in 1955

there were 5.5 million arable hectares between regions IV and X, and only 294 thousand

arables hectares in regions I, II, III, XI, and XII. (CIDA 1966, p.24). Thus, focus on rural

counties in the aforementioned regions seems natural to analyze the effects of land reform

on government support.

Excluded urban counties between regions IV and X are: La Serena, Vina del Mar, Quinta

Normal, Santiago, Maipu, San Miguel, Quilicura, Renca, Barrancas, Maestranza Conchalı,

Providencia, Nunoa, La Reina, La Cisterna, Puente Alto, Las Condes, La Florida, La

Granja, Rancagua, Lota, Talca, Concepcion, Penco, Coronel, and Temuco.

13Non-agrarian objectives are land transferences to non-agrarian state companies, sport clubs, munici-

palities, education ministry and other ministries. 6.6% of the expropriated land at the national level had

non-agrarian objectives.

Table Appendix A.1: Definition of Variables and Sources

Variable Definition and Source

Dummy High Expropriation Dummy equals 1 if more than 7% of the county surface was expropriated

before August 1970 (Agrarian Reform Corporation files).

High Expropriation Neighbor Identification of borders in common across counties

with Cartographica (GIS) using data from GIS Chile

(http://www.rulamahue.cl/mapoteca/catalogos/chile.html).

Agricultural Workers Percentage of “Skilled Agricultural” workers over labor force (1970 and

1960 Housing Census, IPUMS).

Rurality Percentage of people living in rural areas (1970 and 1960 Housing Cen-

sus, IPUMS).

Electoral Registration Number of voters in 1970 minus the number of voters in 1958 over voters

in 1958, Electoral Service (SERVEL)

CDP votes Percentage of votes for the Christian Democratic Party and the Radical

Party in 1958 and percentage of votes for the Christian Democratic Party

in 1970 (Electoral Service, SERVEL).

Right wing votes Percentage of votes for Jorge Alessandri in 1958 and 1970 (Electoral

Service, SERVEL).

Left wing votes Percentage of votes for Salvador Allende and Antonio Zamorano in 1958

and percentage of votes for Salvador Allende in 1970 (Electoral Service,

SERVEL).

Distance to Region’s Capital From a county’s centroid to the capital’s centroid using Google Maps

for latitude and longitude locations and Stata’s vincenty command for

calculations.

Distance to closest Port From a county’s centroid to the capital’s centroid using Google Maps

for latitude and longitude locations and Stata’s vincenty command for

calculations.

Dummy for Landlocked Dummy equals 1 if the county is landlocked. Iden-

tification using Cartographica with GIS Chile data

(http://www.rulamahue.cl/mapoteca/catalogos/chile.html)

Conditions and Public Goods Average years of education, percentage of people who know how to read

and write, and percentage of houses with electricity, water supply, and

hot water (1970 and 1960 Housing Census, IPUMS).

Income Related Percentage of houses with at least 1 car and 1 television (1970 Housing

Census, IPUMS) and with at least 1 radio (1960 and 1970 Housing

Census, IPUMS).

Church Agrarian Reform Counties where the Church distributed its own plots among agricultural

workers (Huerta, 1989).

Church Agrarian Reform Neighbor Identification of borders in common across counties with

Cartographica (GIS) using GIS data from GIS Chile

(http://www.rulamahue.cl/mapoteca/catalogos/chile.html).

B Robustness Exercises

B.1 Using Different Sub-samples

Table Appendix B.2 present two different exercises. First, the first eight columns show that

my main result is not driven by any particular region. Each column represents a different

OLS regression of equation (4) using different restricted samples. Second, the last column

control for changes in the percentage of different types of workers over the labor force (see

Table 7 for more details). Results are also robust to the inclusion of these covariates.

Finally, Table Appendix B.3 includes the percentage of the county surface expropriated

under each of the four most used expropriation causals.14 Results in this table show that

counties where most of the plots were expropriated under causals number 3 and 6 seem to be

changing its voting patterns relatively more. This is in fact intuitive because expropriation

causal number 4 is not widely used as the other three —and thus it is difficult to cause a

big effect on voting patterns— and expropriation causal number 10 is related to a plot that

is offered by the owner.

B.2 Interactions and Econometric Exercises

For a better understanding of results I also explore some interactions and perform some

econometric exercises. Column 1 in Table Appendix B.4 uses a HEC Dummy that equals

1 if the county is affected with land reform before 1965 as a proxy for the original HEC

Dummy. As I already mentioned, land reform before 1965 may not matter for several

reasons. First, as section 2 argues, the main expropriation causals used before 1965 were

completely different from those used after 1967. And second, we are still far away from

upcoming presidential elections. Estimates in column 1 shows that this variable does not

affect government support, a result in line with the one presented in columns 7 and 8 in

Table 3. Thus, it is possible that land reform had different effects in a dynamic setting,

where the effect is bigger the closer we are from upcoming elections.

I also explore if there was an heterogeneous effect in counties with different rurality

levels, understood as the percentage of people living in rural areas. Even though I am

working with non-urban counties, rurality level varies within these. It seems intuitive to

think that the effect should be bigger in counties with more rural population, because the

percentage of the electorate affected by land reform is bigger. Column 2 in Table Appendix

14Indeed, 32% of the 2 millions of physical hectares expropriated between 1967 and 1970 were expropriated

using expropriation causal number 3 (Plot is bigger than 80 basic irrigated hectares), 1% using expropriation

causal number 4 (Plot is inefficient or abandoned), 29% using expropriation causal number 6 (Plot is owned

by a corporation), and 38% using expropriation causal number 10 (Plot is offered by the owner to the

CORA).

B.4 explores this possibility and suggest that the effect of land reform indeed varies with

the level of rurality. For example, in a HEC where 50% of the population live in rural areas,

government support increases in 9% (relative to LEC). On the other hand, in a HEC where

rural population is 90%, government support rises in 17%. Both interpretations consider

that land reform does not have an independent effect on the dependent variable, as its

statistical significance suggests.

As I already mentioned when I justified the inclusion of the dummy for a LEC that is

neighbor of a HEC as covariate, counties are small units of analysis, and land reform in one

county could have affected government support in a neighbor county. To further explore this

effect I estimate the most complete OLS specification, but using as dependent variable the

difference in CDP votes in the closest county. Distance to the closest county is measured in

kilometers from a county’s centroid to the neighbor minus the average distance between two

neighbor counties —the average distance between two counties is 17 kilometers. Column 3

shows that the effect for the average neighbor is an increase in government support of 6%

and that this effect is smaller the farther the neighbor county is and bigger the closest it is.

If a neighbor county’s distance to a HEC is 15 kilometers more than the average (the actual

case of the farthest county), the estimates suggest the effect of land reform on government

support is zero in the neighbor county. On the other hand, if a LEC is very close to a HEC

—say, 10 kilometers less than the average— government support seems to increase in about

10%. Column 4 includes the other two relevant distances as covariates and the significance

of the interaction is now significantly different from zero only at the 14%. Now, if land

reform is the only variable that has a spatial relevance —i.e. affects other counties besides

its own— then, under this setting the omitted variables problem is no longer relevant. The

rationale of this assertion relies on the fact that if this is true, then these omitted variables

are correlated with the HEC Dummy in its own county, not the neighbor’s. However, to

test if this is indeed the case we would need the omitted variables which, of course, are not

available.

Table

Appendix

B.2:Rob

ustnessexercise

excludingcountiesfrom

specificregion

s

Dep

endentvariab

le:CDP

votesin

1970

minusCDP

votesin

1958

Excluded

Region:

IVV

VI

VII

VIII

IXX

R.M

.Non

e

HEC

Dummy

0.053**

0.062***

0.047**

0.036*

0.044*

0.050**

0.044*

0.042**

0.046**

(0.023)

(0.022)

(0.024)

(0.022)

(0.024)

(0.023)

(0.023)

(0.021)

(0.022)

HighExp

ropriationNeigh

bor

0.026

0.037*

0.031

0.022

0.035*

0.032

0.035

0.028

0.028

(0.021)

(0.022)

(0.021)

(0.021)

(0.021)

(0.023)

(0.022)

(0.020)

(0.021)

AgriculturalWorkers

0.195***

0.158**

0.149**

0.173**

0.294***

0.246***

0.162**

0.208***

0.202***

(0.067)

(0.069)

(0.071)

(0.069)

(0.068)

(0.068)

(0.074)

(0.062)

(0.067)

Con

ditionsan

dPublicGoo

ds

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

IncomeRelated

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Distances

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

DifferentWorkers

No

No

No

No

No

No

No

No

Yes

Observations

197

182

181

184

173

188

179

193

210

R2

0.353

0.395

0.310

0.316

0.330

0.357

0.377

0.388

0.352

Notes:Rob

ust

stan

darderrors

inparenthesis.Significance

level:

***p<0.01

,**

p<0.05

,*p<0.1.

Table Appendix B.3: Expropriation under different Causals

Dependent variable: CDP votes in 1970 minus CDP votes in 1958

(1) (2) (3) (4) (5)

HEC Dummy 0.026 0.048** 0.043** 0.051** 0.030

(0.022) (0.022) (0.021) (0.022) (0.023)

Expropriation under Causal N.3 0.307*** 0.289***

(0.074) (0.078)

Expropriation under Causal N.4 0.106 -0.082

(0.219) (0.195)

Expropriation under Causal N.6 0.338*** 0.164*

(0.120) (0.091)

Expropriation under Causal N.10 -0.001 -0.055

(0.059) (0.061)

Conditions and Public Goods Yes Yes Yes Yes Yes

Income Related Yes Yes Yes Yes Yes

Other controls Yes Yes Yes Yes Yes

Observations 210 210 210 210 210

R2 0.378 0.344 0.359 0.343 0.383

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1.

Table Appendix B.4: Interactions and Falsification Exercises

Dependent variable is CDP votes in 1970 minus CDP votes in 1958 from:

Own county Closest County

(1) (2) (3) (4)

HEC Dummy 0.001 -0.064 0.060*** 0.074***

(0.025) (0.051) (0.022) (0.021)

HEC Dummy × Rurality in 1970 0.188**

(0.079)

HEC Dummy × Distance to closest County -0.004** -0.003

(0.002) (0.002)

Distance to Regions’ Capital -0.036 -0.037 -0.021

(0.023) (0.023) (0.021)

Distance to closest Port 0.060*** 0.064*** 0.086***

(0.014) (0.013) (0.015)

Controls Yes Yes Yes Yes

Conditions and Public Goods Yes Yes Yes Yes

Income Related Yes Yes Yes Yes

Counties 210 210 210 210

R2 0.323 0.354 0.247 0.350

Notes: Robust standard errors in parenthesis. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Ex-

propriation causals are: Plots larger than 80 BIH (causal N.3), Plots are inefficient or abandoned (Causal

N.4), Owners of the plot are juridical people (causal N.6), Plots were offered to the CORA by the owner

(causal N.10).

Figure B.1: Within the square are located regions IV to X (Collier and Sater, 2004)

La Serena

(a) Region IV

Valparaiso

(b) Region V

Rancagua

(c) Region R.M.

Rancagua

(d) Region VI

Talca

(e) Region VII (f) Region VIII

Temuco

(g) Region IX

Puerto Montt

(h) Region X

Figure B.2: Spatial Representation of High Expropriation

ECONOMIC HISTORY AND CLIOMETRICS LAB WORKING PAPER SERIES CERDA, RODRIGO: “The Impact of Government Spending on the Duration and the Intensity of Economic Crises: Latin America 1900-2000”. Economic History and Cliometrics Lab Working Paper #1, 2009. GALLEGO, FRANCISCO; WOODBERRY, ROBERT: “Christian Missionaries and Education in Former African Colonies: How Competition Mattered”. Economic History and Cliometrics Lab Working Paper #2, 2009. MATTA, JUAN JOSÉ: “El Efecto del Voto Obligatorio Sobre las Políticas Redistributivas: Teoría y Evidencia para un Corte Transversal de Países”. Economic History and Cliometrics Lab Working Paper #3, 2009 COX, LORETO: “Participación de la Mujer en el Trabajo en Chile: 1854-2000”. Economic History and Cliometrics Lab Working Paper #4, 2009 GALLEGO, FRANCISCO; WOODBERRY, ROBERt: “Christian Missionaries and Education in Former Colonies: How Institutions Mattered”. Economic History and Cliometrics Lab Working Paper #5, 2008. BRUHN, MIRIAM; GALLEGO, FRANCISCO: “Good, Bad and Ugly Colonial Activities: Do They Matter for Economic Development”. Economic History and Cliometrics Lab Working Paper #6, 2010. GALLEGO, FRANCISCO: “Historical Origins of Schooling: The Role of Democracy and Political Decentralization”. Economic History and Cliometrics Lab Working Paper #7, 2009. GALLEGO, FRANCISCO; RODRÍGUEZ, CARLOS; SAUMA, ENZO: “The Political Economy of School Size: Evidence from Chilean Rural Areas”. Economic History and Cliometrics Lab Working Paper #8, 2010. GALLEGO, FRANCISCO: “Skill Premium in Chile: Studying Skill Upgrading in the South”. Economic History and Cliometrics Lab Working Paper #9, 2010. GALLEGO, FRANCISCO; TESSADA, JOSÉ: “Sudden Stops, Financial Frictions, and Labor Market Flows: Evidence from Latin America”. Economic History and Cliometrics Lab Working Paper #10, 2010. BRUHN, MIRIAM; GALLEGO, FRANCISCO; ONORATO, MASSIMILIANO: “Legislative Malapportionment and Institutional Persistence”. Economic History and Cliometrics Lab Working Paper #11, 2010. GONZÁLEZ, FELIPE: “Land Reform and Government Support: Voting Incentives in the Countryside”. Economic History and Cliometrics Lab Working Paper #12, 2010.