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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, ([email protected]).
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 [email protected]
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.
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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.