Land Redistribution, Crop Choice, and Development: Evidence from Reform and Counter-Reform in Chile∗

Nicol´asA. Lillo Bustos†

September 2018

Abstract

This paper uses unique historical data on the Chilean land reform of the 1960s and 1970s to estimate the impact that redistribution had on land inequality, crop choice, and development. In a panel fixed effects estimation with a continuous treatment, I show that land redistribution had a persistent negative effect on land inequality, and that areas treated with more redistribution increased their share of land cultivated with vineyards, and lowered the share of land destined to forest plantations. I also find evidence supporting the beneficial welfare effects of land reform through its effect on increasing human capital, public infrastructure, and private dwelling quality. The fact that a military coup interrupted the reform process allows for the comparison of the effects of reform and counter-reform, which sheds light on the mechanisms through which redistribution operated. I find that the switch to vineyards and away from forestry is driven by the size constraint and not the owner’s type.

∗I am grateful to Sascha Becker, Fabian Waldinger, Luigi Pascali, James Fenske, Erik Hornung, Sharun Mukand, and seminar participants at Warwick and Oxford for their helpful comments and advice. I also wish to thank Daron Acemoglu, Francisco Gallego, James Robinson, Felipe Gonzalez, and Jos´eIgnacio Cuesta for providing crucial data on the land reform. †Department of Economics, Pontificia Universidad Javeriana E-mail: nicolas [email protected]

1 1 Introduction

Throughout history, land reforms have been used by states as one-off redistributive policies to address extreme inequality and poverty. During the second half of the 20th century, Cold War realpolitik incentivised the United States to push for land reform in Latin America to avoid the spread of Communism in the western hemisphere (Welch, 1985). The influential Engerman and Sokoloff (ES) hypothesis states that land suitability for crops with economies of scale lead to the concentration of land in some parts of the New World and that lead to divergence in institutions and development (Engerman and Sokoloff, 1997; Sokoloff and Engerman, 2000). In this paper I show that redistribution can have a persistent effect on land inequality and crop choice, and improve development outcomes. In particular, I focus on the effects of the Chilean land reform of the 1960s and 1970s, which affected approximately 68 percent of the country’s agricultural area.1 The results show that land redistribution significantly lowered land inequality, increased the share of vineyards, and lowered the share of forestry plantations. Moreover, I show that the land reform had a positive impact in educational attainment, provision of public infrastructure (utilities), and increased dwelling quality. These are important results because they provide evidence to support the claim that policies aimed at the redistribution of assets can affect paths of development. In particular, had there not been land redistribution, the share of land used for forestry plantations would be significantly higher and the share of land used for vineyards would be significantly lower than they currently are. Given that the cultivation of crops such as vineyards is more labour intensive than forestry plantations, it is important to un- derstand why certain areas of Chile concentrated in forestry as opposed to other crops. Moreover, forestry plantations have been linked to poverty and conflict in the Chilean context, and thus it is important to understand where and why certain areas specialise in these crops (Andersson et al., 2016; Toledo et al., 2017). A particular feature of the Chilean land reform was that, due to a military coup in 1973, there was also a counter-reform process, and I show that its effects roughly mirror those of the reform process. Additionally, the existence of these two treatments allows me to explore certain mechanisms. The land reform legislation allowed the orig- inal landowner to keep hold of a reserve of land no larger than the cut-off size that triggered expropriation. I show that the amount of land in a municipality kept as a reserve had a similar impact on the outcomes as the land that was redistributed to new

1Approximately 9.6 million hectares were expropriated throughout the process, and on the eve of the reform, in 1965, Chile’s agricultural area reached 140,690 km2.

2 owners. In contrast, the land that was kept in the hands of the original landowners that were benefited by the counter-reform had a positive effect on the land used as forestry plantations and no effect on land inequality. This suggests that the mechanism through which land reform affected land inequality and crop choice was not through the identity of the landowner but through the effect on size. The ES hypothesis has been used in the literature to identify the effect of land inequality on a series of outcomes. Easterly(2001) explicitly tests the ES in a cross- country setting and looks at the effects of land inequality on developmental outcomes. The mechanism is explored by Galor and Moav(2006), who develop a model to show that landed elites have the incentive to block investment in human capital promoting institutions such as public education. In Galor et al.(2009), this model is expanded and tested with state level data for the United States in the early twentieth century. Their findings are in line with their predictions and the source of variation comes from the interaction of relative prices and the suitability of wheat versus cotton. Thus, the vari- ation in land inequality is an outcome of crop choice based on geographic and climatic characteristics. Fiszbein(2017) looks at how crop diversity can lead to different paths of development, and again the source of variation is land suitability for certain crops. Ramcharan(2010) uses geographic variation and suitability of crops to identify the im- pact of land inequality on the demand for redistribution, in a cross-sectional framework. In the Latin American context, Bruhn and Gallego(2012) explore the ES hypothesis in depth and show that indeed economies of scale and factor endowments have a long term effect on growth and development. The literature on the effects of other land reforms is vast. The case of the Indian land reforms is studied in Besley and Burgess(2000) and Besley et al.(2016). The former ar- gues that legislated land reform led to lower poverty levels, and the latter uses variations coming from the linguistic differences of India’s states to estimate heterogeneous effects of land reform by caste. Cinnirella and Hornung(2016) employ the closest identifying strategy to this paper since they use panel fixed effects estimation paired with a political reform in the shape of the emancipation of serfs in Prussia to estimate the impact of land concentration on the expansion of mass education. Our data comes mainly from two sources. Agricultural outcomes and land distribu- tion are obtained from the digitalisation of historical agricultural censuses. Data on the land reform come from the digitalised expropriation records of the government agency in charge of the reform process (Cuesta et al., 2017). The agricultural censuses con- tain detailed information on crop choice and land distribution at the municipality level. The expropriation records provide information on the nearly 5,700 expropriated plots

3 and whether they were subject to the counter-reform process or not. I aggregate the expropriation data to the municipality level for our analysis. I estimate the effects of land redistribution on land inequality and crop choice using panel fixed effects estimation in a difference-in-differences setting with a continuous treatment variable (share of municipality area that was expropriated). Throughout, I take into account the ES hypothesis by controlling by soil type and a battery of geographic, climatic, and weather controls. However, one contribution of this paper is that it does not exploit the ES hypothesis for identification, but instead it relies on a quasi-natural experiment that allows identification from the change in trends caused by the land reform. The rest of this article is organised as follows. Section2 describes and discusses the history and process of land reform in Chile. Section3 presents the data and section4 discusses the empirical strategy. Section5 presents and analyses the results and finally section6 concludes.

2 Historical Background of The Chilean Land Reform

In this section, I discuss the historical period surrounding the implementation of the land reform and I describe its operative details. An important aspect that I discuss below is how the different policy changes led to distinct reform outcomes for a given plot of land. This is crucial for the different results that I will present in section5. The Cuban Revolution of 1959 was a shock to US foreign policy in Latin Amer- ica.2 The Kennedy administration pushed for a treaty that aimed at modernising the backward and repressive societies in Latin America. The working theory was that, like the Marshall Plan in Europe a decade and a half earlier, foreign aid tied to political and economic reforms would block Marxism from taking root in Latin America (Welch, 1985). In 1961 the Charter of the Punta del Este Conference was signed by twenty Latin American countries, creating the ‘Alliance for Progress’. The charter called for higher growth and better income distribution in a number of well specified goals, including per capita growth targets (2.5 percent per year), increases in adult literacy, increases in the legal requirements of education attainment, increases in life expectancy at birth, price stability, and democratisation across the region. In exchange, the United States would provide aid to the countries that undertook these reforms, although no explicit timing is mentioned in the charter. 2For a detailed history of the Chilean Land Reform see Guerrero Y. and Vald´es(1988).

4 Relevant for this paper is the explicit call for ‘comprehensive agrarian reform’ that would lead to the ‘[...] effective transformation, where required, of unjust structures and systems of land tenure and use, with a view to replacing latifundia and dwarf holdings by an equitable system of land tenure[...]’. The United States would provide funding and aid for the technical assistance projects for the purpose of ‘[...] field investigations and studies, including those relating to development problems, the organization of national planning agencies and the preparation of development programs, agrarian reform and rural development, health, cooperatives, housing, education and professional training, and taxation and tax administration; [...]’ (The Avalon Project, 1961). In Chile the conservative President Jorge Alessandri (1958-1964) pushed law 15,020 through Congress, which entered into force in late November, 1962. This law set the legal stage for future land reform, by introducing the legal instruments for expropriation and establishing the legal entity that would carry out the land reform from expropriation to redistribution: the Corporation for Land Reform (CORA). It defined an ‘economic unit’, which was the size of a farm that, depending on soil quality and other characteristics, would have a size that would constitute the upper bound for farms. Anything above that size would be considered a latifundio. However, Alessandri’s administration would not aggressively pursue land reform, and used the law mainly to reallocate land in Chile’s extreme regions as a means to colonise and reassert sovereignty over under-populated areas. In November 1964, a reformist Christian Democrat, President Eduardo Frei Mon- talva (1964 - 1970) took office. Frei had campaigned on accelerating the land reform process and immediately started using law 15,020 towards that end, but found that the expropriation clauses were too vague, which made it more difficult. Thus in 1965 a new law was sent to congress, law 16,640, which was passed and entered into effect in late July 1967. This law was the definitive framework by which the land reform process would be carried out during the ensuing years. It established the exclusive legal justifications for expropriation, and determined the procedure by which expropriated land would transi- tion from the former owners to the new. First, the property would remain whole and administered by the former peasants organised in an entity that functioned much like a cooperative, called an asentamiento. After a period of three years during which the asentamiento would receive technical and financial help from CORA and other govern- ment agencies, the peasants had the choice of becoming a cooperative or subdividing the land into private plots. The law also allowed the landowner to keep a share of the land, as long as that share did not exceed a certain threshold established in the expropriation articles (see below). The land that the original landowner kept was termed the reserva.

5 Figure 1: Simplified diagram of possible Land Reform outcomes.

Redistributed to peasants

Auctioned to highest bidder

Reformed Transfered to another public entity

Original Landowner's share Revoked Expropriated

Not Expropriated Original Landowner recovers entire plot.

Original Landowner keeps the land. Note: This diagram illustrates the different possible outcomes of the land reform process. Given that a plot was expropriated, it could be reformed or the expropriation could have been revoked by Pinochet after the military coup of September, 1973. If it was not revoked, the original landowner could keep a share that did not exceed 80 BIH, and the rest of the land could be redistributed to peasants, auctioned off to the highest bidder, or transferred to another public entity.

Law 16,640 clearly stated the reasons by which a plot could be expropriated in rela- tively unambiguous terms. Title I, Chapter I contained the articles which were explicit about the conditions for expropriation and left very little room for manoeuvre to both the landowners and the government. In particular, articles 3 and 6 stated clearly that plots which exceeded 80 Basic Irrigated Hectares (BIH)3 were subject to expropriation whether they were owned by a natural person (article 3) or by a juridical person such as a firm (article 6). There was some ambiguity still left in article 4, which allowed CORA to expropriate abandoned or inefficient plots, without defining what inefficiency means. However, article 4 could not be used for expropriation until after three years from the date of entry into force of the law. Another space for manoeuvre was created by article 10, which allowed landowners to offer their land to CORA voluntarily. The rest of the articles in Title I, Chapter I deal with subdivisions, swamplands, and extreme zones of Chile. More importantly, article 15 explicitly prohibits the expropriation of plots owned by a single natural person if the sum of the surface of the plots is less than 80 BIH. By the end of Frei’s administration, the leftist coalition led by Salvador Allende was critical of the slow pace of the reform. Allende had come close to winning the election of 1958, in which the right wing candidate, Jorge Alessandri, won due to the electoral rule that said that when no candidate reached 50 percent of the popular vote it was Congress

3A BIH is an irrigation weighted average of physical hectares. The conversion coefficients from physical hectare to BIH was also defined in law 16,640, article 172, and varied across regions.

6 that chose from the two candidates with most votes. Baland and Robinson(2008) show that the introduction of the secret ballot for the 1958 elections had a negative effect on the conservative parties because it allowed tenant peasants to vote freely without worrying about retribution from their landowners. However, this effect was not strong enough to prevent Alessandri’s election. In Baland and Robinson(2012), they show that land prices reacted negatively to the introduction of the secret ballot. Allende had been a force in Chilean politics for thirty years, first as a member of the Chamber of Deputies and later as a senator. In the 1970 election the pace of land reform was a major issue, and Allende narrowly won the popular vote obtaining 36.6 percent of votes, against 35.3 percent obtained by Alessandri and 28.1 percent obtained by Radomiro Tomic, the Christian Democrat candidate (Frei was barred from participating because consecutive re-elections were not allowed). Gonz´alez(2013) explores how the amount of land expropriated during Frei’s administration affected the election results. He finds a positive effect of expropriations on the vote for the Christian Democrat candidate, but it was not enough to avoid a victory by Allende. The Christian Democrats supported Allende when congress had to decide between Alessandri and him and thus the first democratically elected Marxist became president of a country in the Western Hemisphere. US foreign policy was not slow to react, and in the context of the Cold War, a Marxist government in Latin America was unacceptable to Washington. In September 1973, General Augusto Pinochet led a military coup backed by the CIA that toppled Allende and a seventeen year dictatorship ensued. Pinochet could not reverse land reform because it had been sanctioned by the judiciary which implicitly had supported the overthrow of Allende. Thus, the dictatorship would simply revoke the expropriations of the plots that had not been expropriated correctly or that were not far along the reform process. However, this counter-reform of sorts did not completely roll back all expropriations. In fact, the land reform could be considered as continuing during the dictatorship. Even though expropriations were stopped under Pinochet, the distribution of land continued. The asentamientos were subdivided into small 10 BIH plots called parcelas and granted to peasants that met a series of conditions (male, married, and not a member of a leftist group or agricultural trade union). The aim of revocations was not only to counter-reform but also to get land back into the hands of farmers as quickly as possible since political instability had brought agricultural production to a halt. Thus, given that a hectare of land was expropriated, there were five possible out- comes: redistribution, auction, transfer to another public entity, reserve of the original owner, or the expropriation could be revoked. Figure1 shows the paths to the different

7 reform outcomes. A more detailed descriptive analysis of the different reform outcomes is provided in section 3.1. Section 4.2 explains in more detail how I exploit the differ- ent paths of reform to look into the mechanisms through which the land reform and counter-reform had an impact. It could be said that given that Pinochet revoked some expropriations, this negated the effects of the land reform. Moreover, the land reform process has been criticised because there is some anecdotal evidence that the beneficiaries of the reform sold their land after the sale embargoes were lifted. Bowles(2012) argues that the reform was not ex-post optimal and thus the intended effects were not persistent, which meant that the land reform only managed to transfer wealth to the landless peasants at a great cost. On the other hand, one argument in favour of the land reform is that it led to a process of modernisation of the Chilean agricultural sector. Bellisario(2006, 2007b,a, 2013) argues that the expropriations accelerated the transition towards a more capitalist agricultural sector. Bowles’s argument, taken to the extreme, would imply that areas where there was relatively more reform should not experience a long term reduction in land inequality. However, given the lumpiness of land markets, the splitting of the large plots into smaller fractions should lead to a more liquid land market. Even if all beneficiaries sold their land, unless the buyers were the previous large land owners one would expect that land inequality would fall. Bellisario’s claim that land reform led to a modernisation of the agricultural sector through a change in the entrepreneurial characteristics of land owners is not inconsistent with the land market liquidity mechanism. However, in section 5.3,I show that there was no significant difference in crop choice between the original owners who kept a reserve of land and the new owners that were benefited with the reform.

3 Data

In this section, I describe the data, its sources, and how I construct my final dataset that I will use to carry out the empirical analysis. In section 3.1, I describe the expropriations data, and how the land reform was implemented. Section 3.2 presents the agricultural censuses and how I construct the dependent variables in the analysis, which are land inequality and the share of a municipality’s planted area corresponding to each type of crop. Finally, section 3.3 describes the geographic control variables included in the analysis.

8 Figure 2: Hectares Expropriated by Month

Frei Law 16,640 Allende Coup 600,000

400,000 Hectares

200,000

0 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

Expropriation Date

Note: This graph shows the number of hectares expropriated by month, as well as markers for important events during the land reform process, including data from the entire country. Source: Author’s calculations based on CORA data, via Cuesta et al.(2017)

3.1 Land Reform Expropriation Cards

In this paper I use a unique dataset on expropriations carried out during the land reform. This dataset was obtained from the digitisation of the ‘Summary Expropriation Cards’ that CORA holds in microfilm (Cuesta et al., 2017). The ‘Summary Expropriation Card’ of each plot contains identifying information such as plot name, plot owner, the tax number of each plot, and the municipality and province where the plot was located. Moreover, each card contains information on the expropriation process itself: the size of the plot according to the land registry, how much was expropriated by CORA in physical hectares and in BIH, the reason for expropriation, the date of expropriation and the number of the council agreement that determined the expropriation. In addition, the card also has details on whether the land owner kept a reserve, the size of the reserve, the council agreement determining this and the corresponding date.

9 Figure 3: Hectares Reformed and Counter-Reformed by Year of Expropriation

Frei Law 16.640 Allende Coup 3000 2000 Counter−Reform Reform Thousand Hectares 1000 0

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 Year of Expropriation

Note: This graph shows the number of hectares expropriated and distributed, by year, during the land reform process. Relevant events are marked with vertical dashed lines. Redistributed land is a subset of expropriated land. Source: Author’s calculations based on CORA data, via Cuesta et al.(2017)

In some cases the top right corner of the card is crossed out which means that the expropriation was revoked. In such cases the card clearly indicates that the land was returned to the original owner and also provides the council agreement and corresponding date of the revocation. The cards have other information which has not been digitised at the moment. In particular, the front of the card also contains information on the date and the number of the council agreement that determined the compensation that the owner obtained for the expropriation. Also, there is information on the asentamiento that received the land, if the reform process reached that stage for this particular plot. The card provides the name of the asentamiento and the date it was constituted. In the back side of the card there is information on the allocation of land to the asentamiento, any direct transfers to any other public entity such as municipalities, ministries, the armed forces, etc., if

10 the land was auctioned off, and details on the winners of the auction. The expropriation data contains information on 5,702 expropriations of which 5,655 have non-missing information on the key variables. The total area expropriated equalled up to approximately 9.6 million hectares, while the total surface area of Chile is around 74.5 million hectares, which means that approximately 13 percent of Chile’s surface area entered the reform process. Figures2,3, and4 tell the broad story of the land reform process. Figure2 shows how the pace of reform accelerated during the Allende years, specially in 1971 and 1972. The area expropriated by Frei totalled 3.5 million hectares in six years. The Allende government managed to almost double that in just three years, with total hectares expropriated reaching 6 million hectares by September 1973. However, the size of the plots that were expropriated diminished during the Allende years. The average plot size during the Frei period was approximately 2,800 hectares, while during the Allende administration that number fell to approximately 1,400 hectares. Figure3 also shows the acceleration of reform by the Unidad Popular (UP, Allende’s coalition) government, and it also shows how the ratio of reform to counter-reform drops significantly for expropriations initiated after Allende became president. The legal basis for expropriation also varied between the two governments. The Frei government favoured voluntary expropriations (Article 10) which represented 41.9 percent of the total number of expropriations under law 16,640, while expropriations from natural persons due to excess size (Article 3) represented 36.4 percent and from juridical persons due to excess size (Article 6) represented 7.4 percent. Allende’s administration reversed this relationship, reducing the voluntary expropriations to 22.3 percent and increasing expropriations from natural persons due to excess size to 46.4 percent and from juridical persons to 7 percent. Allende’s government also started expropriating plots based on the inefficiency causal (Article 4), which ended up representing 22.1 percent of expropriations during the Allende period. In terms of hectares, the share of voluntary expropriations under the Frei government was 40.5 percent, which declined to 11 percent during Allende. For the expropriations from natural persons due to excess size, that share increased from 29.3 percent during Frei to 43.5 percent during Allende. The share of expropriated hectares due to ineffi- ciency increased from 0.5 percent during Frei to 11 percent during Allende. A significant percent of hectares were expropriated using the excess size owned by juridical entities causal (Article 6). While as a percent of the number of expropriations these only repre- sented 7.3 and 6.9 percent during the Frei and Allende periods respectively, in terms of hectares these figures increase significantly to 28.7 and 31.8 percent.

11 Figure 4: Hectares Distributed, Auctioned, Transferred, Reserved and Revoked by Reason of Expropriation and Expropriating Government

Distributed 2,500 Auctioned Transfered 2,000 Owner’s Reserve Revoked 1,500

1,000 Thousands of Hectares 500

0 Frei Allende Frei Allende Frei Allende Frei Allende Frei Allende Frei Allende Frei Allende

Natural Inefficiency Juridical Offered Law Other Unknown Person, (Art. 4) Person, by owner 15,020 Articles causal Excess Excess (Art. 10) Law Size Size 16,640 (Art. 3) (Art. 6)

Note: The graph shows the number of hectares by outcome of the reform process, article of expropriation, and who was president when the plot was expropriated. Frei was president between 1964 and 1970, and Allende was president between 1970 and 1973. Source: Author’s calculations based on CORA data, via Cuesta et al.(2017)

The different legal bases used to expropriate also led to different outcomes. The raw data can be grouped into four major outcomes of the reform process: Redistribution, auction, transfer to another public entity, or revocation. Of the 8.1 million hectares expropriated under law 16,640, 28.7 percent were returned to their original owner when Pinochet revoked the original expropriation. Of the main reasons for expropriation, the most affected by revocations were expropriations due to inefficiency, which saw 58.4 per- cent of expropriated hectares returned to the original owner. Voluntary expropriations were less affected by revocations as only 16.6 percent of expropriated hectares were re- voked. For expropriations from natural persons due to excess size, revocations returned 37.6 percent of expropriated hectares to their original landowners, and for expropriations from juridical persons due to excess size that number is just 14.9 percent.

12 Figure 5: Spatial Distribution of Expropriated Area

Expropriated Area / Total Area 0.00% - 5.43%

5.56% - 14.09%

14.15% - 26.10%

26.26% - 38.38%

38.71% - 87.82%

. 0 120 240 480 Km

3.2 Agricultural Censuses

All agricultural data comes from the Agricultural Censuses published by the National Institute of Statistics of Chile (INE) (Servicio Nacional de Estad´ısticasy Censos (Chile), 1959; Direcci´onde Estadisticas y censos (Chile), 1969; INE (Chile), 1981, 1998, 2007). There have been seven agricultural censuses in Chile, but only the last five have followed the guidelines of the FAO. These five censuses correspond to the years 1955, 1965, 1976, 1997 and 2007 (censuses V to VII).4 The field work for these censuses was carried out

4There were no censuses during the 1940s or during the 1980s.

13 Figure 6: Share of Area Planted by (Grouped) Census Crop Category, 1965-2007 1 .8

Cereals and Legumes, Industrial Crops, and Vegetables .6 Forest Plantations Vineyards Fruit Trees

.4 Forage Plants .2 Cumulative Share of Area Planted/Sowed 0 1955 1965 1976 1997 2007 Agricultural Census Year

Note: The graph shows the cumulative share of the area planted for each crop category, after grouping cereals and legumes, industrial crops, and vegetables. Source: Author’s calculations based on digitised Agricultural Censuses. in April, coinciding with the autumn in the southern hemisphere and the end of the agricultural calendar. The censuses define an agricultural operation as any piece of land that is used wholly or partially for agricultural activities by a producer, regardless of the legal ownership, size or location. Thus, an agricultural operation does not correspond one to one with a plot defined in the legal sense. Moreover, a producer is any entity, human or legal, that has the technical and economic initiative to exploit the agricultural operation. INE only provides microdata at the agricultural operation level for the sixth and seventh censuses. The III, IV, and V censuses are available at the municipality level and report aggregates of each variable, not averages (although it is straightforward to construct them for some variables). The main variables of interest are the land size distribution, which for censuses III-V are reported in a table which has the number of plots for each size category, and for censuses VI and VII which can be obtained from the

14 microdata. The definition of the size categories varies for each of the censuses for which I do not have microdata, and as such an standardization is carried out.5 Land inequality is measured using the land size distribution tables from the agri- cultural censuses. The distribution tables are collapsed into eleven size categories to construct a Land Gini coefficient. Each land size distribution table contains the number of agricultural operations that fit within each size category. Unfortunately, the amount of land in each size category is not available at the municipality level for the III, IV and V censuses. Moreover, the top size category does not have an explicit upper bound. The first issue has been dealt by the previous literature by imputing the midpoint of each size category as the average size category. However, in this paper I benefit from the fact that this information is available at the province level, and thus I input for each municipality the province level average size for the corresponding size category. Thus the second issue of an open ended interval for the top size category is dealt in a similar way.6 The agricultural censuses have detailed information on surface planted with dozens of crops, and in the case of cereals there is also information on yields. Crops are grouped into seven broad categories: Cereals and Legumes, Industrial Crops, Vegetables and Flowers, Forage Plants, Fruit Trees, Vineyards, and Forest Plantations. Starting with the census of 1965, a summary table is published with the surface planted with crops in each crop category, and from that I can calculate the percentage of area planted with a certain crop category. The seven different crop categories are grouped into four major categories according to export potential and scale requirements. Figure6 shows the share of land planted with a certain crop group between 1965 and 2007, aggregating across the entire country. As can be seen in the figure the share of area planted with Forest Plantations has increased significantly and displayed other crops except the crop group formed of Fruits, Vegetables, and Vineyards. Crops in which Chile does not have a special comparative advantage such as cereals and forage plants have yielded their initial dominance to these two new categories. The main source for this change has been attributed to Chile’s increasing trade openness, which began with unilateral import tariff reduction during the 1980s and continued into the 21st century

5I collapse the different size categories into 11: 0 to less than 1 hectares, 1 to less than 5, 5 to less than 10, 10 to less than 20, 20 to less than 50, 50 to less than 100, 100 to less than 200, 200 to less than 500, 500 to less than 1000, 1000 to less than 2000, and 2000 an more hectares. 6To check the robustness of this approach, two exercises were carried out. First, all the results were replicated using the mid-point of a size category bin in line with literature, and the results were very similar. Second, the VI and VII censuses do contain the surface of farms per size category, and a comparison of the imputed values and the real values was made using standard t-tests. The null hypothesis that both measures were the same could not be rejected at the 1% level.

15 with signing of a series of Free Trade Agreements with the United States, the European Union, and a series of APEC countries.

3.3 Geographic and Weather Controls

Even though all estimations are carried out controlling for region specific time trends, there is still a great deal of within region geographical and weather variation. Most geographical and climate characteristics of municipalities in Chile are obtained by geo- processing raster data from the FAO GAEZ database (IIASA/FAO, 2012). Weather data are obtained from the Climate Research Unit at the University of East Anglia, which provides monthly averages of temperature, precipitation, and cloud cover since the early twentieth century (Harris et al., 2014). A measure of ruggedness of the terrain is obtained from Nunn and Puga(2012). The geographic variables included can be categorised into two types: distances to relevant locations and raster data on soil and climate characteristics. The distances cat- egory includes distance to major population centres (Santiago and provincial capitals), distance to the main highway that crosses Chile (Pan-American route 5), distances to ports, and distance to the coast. Soil characteristics obtained from the FAO GAEZ database categorise soils into 36 different groups. In this paper, the predominant soil categories are individually identified and the remainder are collapsed into an ‘other’ cat- egory. The FAO GAEZ database also classifies locations according to Thermal Zones: Tropics, Subtropics, and Temperate with varying degrees of temperature subcategories (all in all 5 categories). The soil and thermal zone data are available in 30 arc-second grids, and thus the prevalent type is calculated for each municipality (i.e. the mode within the boundaries of the municipality).

3.4 Homogenisation of Municipality Boundaries

To conduct a careful analysis of the data, it is required that the unit of observation is comparable across time. Chile underwent a profound reorganisation of its subnational government structure in the late 1970s, called the ‘regionalisation’ process. It reduced the number of second-tier administrative units from 25 to 13, and substantial boundary changes between municipalities occurred in the extreme north and extreme south of the country. In this paper I combine the information in the legislative record with GIS data provided by the National Institute of Statistics (INE) for each population census to construct the boundary files for each agricultural census year. I then intersect the GIS layers of each year with the 1955 layer, compute the area in the intersections, and

16 then re-weight the data to match the 1955 boundaries using the area of the intersection between polygons as the weight. This is a procedure that is standard in the economic history literature (Hornbeck, 2010; Hornbeck and Naidu, 2012; Donaldson and Hornbeck, 2016).

4 Empirical Framework and Identification

This section describes the empirical strategy used to identify and estimate the effects of the land reform on land inequality and crop choice. I begin by describing the econometric model in section 4.1, and then in section 4.2 I discuss how using different measures of reform and counter-reform —which depend on whether the land came to be owned by new individuals or firms— allows the identification of a turnover effect.

4.1 Baseline model

The estimation strategy relies on panel fixed effects estimation of the effect of the reform. Thus the estimating equation is

K X (k) (k) 0 Yirt = αi + λrt + θt · Ei + δt · Xi + Zitδ + εirt (1) k=1 where Yirt is an outcome variable for municipality i, in region r at time t, Ei is the (k) percent of municipality i’s area that was expropriated, the Xi s are time invariant control variables k = 1,...,K for municipality i, Zit is a vector of time variant control variables for municipality i at time t, αi is a municipality-specific fixed effect, and λrt is a region-specific time fixed effect. The effect of expropriation on the outcome, θt, is allowed to vary across time, as well as the effect of the time invariant control variables, (k) δt . The error term εirt is allowed to be autocorrelated within each municipality so I estimate clustered standard errors (Bertrand et al., 2004).

The main variable of interest is Ei, the percent of municipality i’s area that was expropriated: Total Land Expropriated (hectares) E = . (2) i Municipality Area (hectares) Variations on the definition of treatment are discussed below in section 4.2. As was mentioned in sections2 and 3.1, not all expropriations ended up being redistributed to landless peasants. A significant share of expropriations were revoked and returned to the original landowner. Before continuing, it is important to note that the numerator in (2)

17 is simply the sum of hectares that were expropriated and redistributed and the hectares that were expropriated and then returned to the original landowner via revocation. The baseline specification in equation (1) allows the identification of the causal im- pact of reform in two ways. First, the panel structure of the data allows us to determine causality by removing the time invariant variation across municipalities that could con- found our estimates. Second, testing the significance of the βt’s in the pre-treatment period can help determine whether the post-treatment effect is causal or it is simply the product of a pre-existing trend between treatment and control municipalities.

However, it could be the case that treatment, Ei, is simply a proxy for some other characteristic that varies across treatment and control municipalities, which later in- teracts with post-treatment shocks to the outcome variables. That is why I include region-specific time fixed effects that capture in a very non-linear way differential trends across regions caused by policy changes and business cycles. Thus, the θt’s correspond to the treatment effect net of the region-specific cycle. One issue that must be dealt with is the correlation of reform with geographical characteristics. The identification strategy in this paper is based on panel estima- tion, and thus all time-invariant characteristics such as geography are captured by the municipality-specific fixed-effect αi’s. Nevertheless, given that the treatment variable is also time-invariant, the estimation of the effects of the reform amount to an event study that interacts treatment with time dummies. Thus, if reform is correlated with geographic characteristics, post reform shocks that interacted with geography might explain the perceived effect of the reform. This is a particularly sensitive issue since the post-reform period is marked by a series of free-market reforms that began during the dictatorship and continued when democ- racy was restored in March of 1990. The fact that Chile unilaterally reduced import tariffs in the late 1980s is of special consideration, which significantly reduced the costs of importing machinery and equipment, and could have had differential impact across municipalities (Cuesta et al., 2015). Moreover, in 2003-2004 Chile signed significant Free Trade Agreements with both the European Union and the United States. To control for the possible confounding effects of trade liberalisation, geographic characteristics are interacted with the time fixed effects. To the extent that trade liberalisation had an impact on crop choice, the main channel by which it operated was through comparative advantage and access to ports. Thus, interacting geographic characteristics with the time fixed effects captures this source of bias. Another threat to identification comes from political heterogeneity. Preference for reform can be the outcome of a local political equilibrium that is determined by unob-

18 served characteristics that can also be a determinant of crop choice and land inequality (Alesina and Rodrik, 1994; Bardhan and Mookherjee, 2010; de Janvry et al., 2014; Ram- charan, 2010). To the extent that these characteristics become activated only after the implementation of reform, particularly during the dictatorship, then a na¨ıve estimation of equation (1) will be biased. The direction of the bias is not clear a priori. For in- stance, reform could be higher in places where the agrarian labour movement was more organised, and this could have led to lower inequality regardless of expropriation Ba- land and Robinson(2008, 2012). On the other hand, the higher the political power of the landlords, the less likely that inequality would fall without the intervention of the central government (Galor and Moav, 2006; Galor et al., 2009). Thirdly, industrial interests could have shaped the implementation of the land reform. It is possible that potential entrants that wanted to purchase land from the large landholders and were being blocked lobbied the central government to reform certain places, although this is less likely due to the strained relationship between the Unidad Popular government and the entire private sector. Nevertheless, I indirectly address all these issues by controlling for the interaction of the 1961 parliamentary vote share of the right wing coalition and time fixed-effects. I choose the 1961 parliamentary data because it is the legislature that discussed and debated the first land reform law, and as such it serves as a proxy of the local political equilibrium.

4.2 Reform and Counter-Reform

As mentioned earlier, after the coup of 1973 the dictatorship began a process of counter- reform. Expropriated plots that had not been redistributed had the expropriation de- cree revoked, and thus the original land owner regained the entirety of the land. This is directly observable in the data. This ‘expropriation without redistribution’ can be considered as a separate treatment. To investigate how these two treatments impacted land inequality and crop-choice, I estimate the following equation:

K X (k) (k) 0 Yirt = αi + λrt + βt · Ri + γt · Ci + δt · Xi + Zitδ + εirt (3) k=1 The only difference between equations (1) and (3) is that the treatment has been split into Ri and Ci, where Ri corresponds to the percentage of municipality area that was expropriated and redistributed, and Ci is the percentage of municipality area that was expropriated and returned to the original landowner. As mentioned above, the sum of

Ri and Ci is equal to Ei defined in (2).

19 The ideal procedure to estimate the differential impacts of reform and counter-reform would be to estimate an equation similar to equation (3) but separately for reform and counter-reform municipalities. However, there is no clear separation between reform, counter-reform, and never expropriated locations. All outcomes are available at the municipality level, so the estimation of equation(3) amounts to a limited heterogeneity analysis.

4.3 Disentangling size Effects

As described in section 3.1 and in figure1, conditional on the expropriation not being revoked, the land expropriated could be redistributed to landless peasants, auctioned off to the highest bidder, or transferred to another public entity. Even accounting for the amount of counter-reform, there is no reason to expect that the effect of reform would be the same if the final beneficiary is a landless peasant or the winner of a land auction. It is reasonable to expect that landless peasants did not have the financial resources to participate in auctions. However, I cannot a priori predict that a land auction will reinforce or subtract from the effects of expropriation and redistribution to landless peasants. I can, however, look into the role of new versus old owners. Non-revoked expro- priations (i.e., reform areas) can be split between land expropriated and given to new owners (landless peasant or auction winner) and the amount of land that remained in the hands of the previous landowner as a reserve. This is an interesting distinction to make since a new land distribution is composed of new owners and old owners with less land. Even though I do not have outcomes for each of these groups, I can look into the effects that different combinations of redistribution and reserve had on crop choice and land inequality. To explore these mechanisms I estimate an equation similar to (3), but where I have split Ri into two variables: Ni and Oi, denoting the percentage of land that switched hands to new owners and the land that remained in the hands of the old owners (ex- cluding counter-reform). This equation can be shown as

K N O X (k) (k) 0 Yirt = αi + λrt + βt · Ni + βt · Oi + γt · Ci + δt · Xi + Zitδ + εirt (4) k=1 where it is important to note that Ni + Oi = Ri from equation (3). To summarise, the methodological framework that I use estimates a panel fixed effects equation where the main focus is on estimating the effect that total land expropriated as a percentage of

20 Table 1: Summary of Main Specifications

Equation Variables of Interest Treatment Coefficients Relationships 0 (1) Ei θts

(3) Ri and Ci βt’s and γt’s Ri + Ci = Ei

N O Ni + Oi + Ci = Ei (4) Ni, Oi and Ci βt ’s, βt ’s and γt’s Ni + Oi = Ri municipality area had on land inequality and crop choice. I then split the treatment into subcategories depending on the outcome of the reform process. This is shown in table1.

5 Results

In this section, I present the results of employing the empirical strategies discussed in section4. I begin with section 5.1, which presents the results of estimating a reduced form model where the treatment variables are the share of land expropriated under different laws and/or articles. Then, in section 5.2, I describe the results of estimating the effects of reform and counter-reform separately. Finally, due to the fact that non-revoked expropriations allowed the original landowner to keep a reserve of land, in section 5.3 I estimate the effects of owner turnover by splitting the reform treatment into two: reformed with new owners and reformed with the original owners (reserve).

5.1 Overall Expropriations

This section takes a look at the effects of overall land expropriated on land inequality and crop choice. Table2 shows the result of estimating equation (1). This table contains panel fixed effect regression estimates for land inequality and crop choice. Column (1) shows the results for land inequality and columns (2) to (6) for crop choice. All regressions include the interaction of the year fixed effects with the region fixed effects, as well as a battery of geographical, climate, weather, demographic, and political control variables. The omitted year for this estimation is 1965, right before the land reform began in earnest. The results of table2 can be interpreted as the reduced form effect of expropriation. The coefficients for 1955 correspond to a test of the parallel trends assumption, in the case of a continuous treatment variable. It is shown that there was no statistical difference

21 Table 2: Overall Effect of Expropriation on Land Inequality and Crop Choice

(1) (2) (3) (4) (5) (6) Land Crop Choice Inequality Farm Forage Fruit Forest Other Vineyards Gini Plants Trees Plantations Crops Area Exprop. (share) × Year = 1955 -0.043** -0.091 0.027 0.027 -0.048 0.085 (0.018) (0.062) (0.017) (0.019) (0.040) (0.061) Year = 1976 -0.050** -0.127* 0.020 0.004 -0.082 0.185** (0.022) (0.076) (0.019) (0.014) (0.061) (0.078) Year = 1997 -0.094*** -0.010 -0.006 0.092** -0.199*** 0.123 (0.026) (0.069) (0.063) (0.036) (0.076) (0.085) Year = 2007 -0.099*** -0.109 0.030 0.150*** -0.173** 0.103 (0.028) (0.079) (0.072) (0.042) (0.085) (0.085)

1965 Mean dep. var 0.870 0.265 0.047 0.046 0.153 0.489 1965 Std. Dev. dep. var. 0.088 0.179 0.100 0.079 0.231 0.197 Observations 1040 1040 1040 1040 1040 1040 Municipalities 208 208 208 208 208 208 Within R2 0.444 0.523 0.746 0.399 0.745 0.760 Note: Standard errors clustered at the municipality level. Significance levels: * 10%, ** 5%, *** 1%. This table shows the estimated coefficients of estimating a panel fixed effects regression of the corresponding dependent variable on the share of the municipality area that was expropriated, interacted with year fixed effects. The base category for the year fixed effects is 1965. For land inequality, the dependent variable is the farm size Gini coefficient. For the crop choice variables the dependent variables are the share of area planted/sowed with the denoted crop category. All regressions are estimated independently, including region-specific year fixed effects. Control variables for soil characteristics, thermal zones, population density in 1960, market access in 1960, altitude, ruggedness index, and distances to ports, provincial capital, coast, and land border are included interacted with the year fixed effects. See text for variable definitions. in the pre-reform trends of the crop choice variables. However, for land inequality, it is shown that it was increasing between 1955 and 1965 in the treated municipalities, compared to the untreated municipalities. Nevertheless, the coefficients for 1955 are generally of the a relatively small magnitude compared to the 1976-2007 coefficients, and of the same sign. As can be observed in the table, total expropriations had a negative impact on the farm Gini, which stabilised by 1997. It also had a positive impact on the share of land planted with vineyards, and a negative impact on the share of land planted with forest plantations, although this last result is statistically weaker. The results suggest that expropriated areas became more equal in their land distribution, and more importantly, this effect persisted across time. The results also show that expropriated areas were more

22 likely to switch crops towards vineyards and less likely to switch to forest plantations. It is important to remember that both of these categories of crops were increasing their share over time, so a differential impact of reform should be interpreted as changes relative to the trend. However, given that the coefficients for vineyards are increasing in magnitude it can be interpreted that expropriated and non-expropriated areas are diverging. The coefficients for forage plants and the ‘other crops’ category are large but only the short run effect in 1976 is statistically significant, although only at the 10% significance level. The effect on fruit trees is small and statistically insignificant. The magnitude of the effects are also economically significant for land inequality, the share of area planted with vineyards, and the share of area planted with forest plantations. The standard deviation of the percent of land expropriated is approximately 0.184, which means that a one standard deviation increase in land expropriated reduces land inequality by 0.018, or approximately 21% of the standard deviation of the farm Gini in 1965. In the case of crop choice, the effect on vineyards stands out. By 1997, the effect of a one standard deviation increase in expropriations is an increase of 1.7 percentage points in the share of area planted with vineyards, which corresponds to 38% of the mean in 1965, or 22% of the standard deviation in that same year. These effects get stronger and statistically significant at the 1% significance level in 2007. In that year the effect of a one standard deviation increase in expropriations leads to a 2.8 percentage point increase in the share of area planted with vineyards, or 62% and 36% of the mean and standard deviation in 1965, respectively. Conversely, in the case of forest plantations, if we take the highest magnitude effect in 1997, a one standard deviation increase in expropriations reduced the share land planted with forest plantations by 2.9 percentage points, which represents approximately 19% of the mean and 12% of the standard deviation in 1965. The effect for 2007 is similar in magnitude but not statistically significant.7 Thus, the results of estimating the reduced form effects of expropriations on land in- equality and crop choice show that the reform was successful in lowering land inequality in the long run, and a secondary effect which was to shift crop choice towards vineyards and away from forest plantations, compared to areas where there were not expropri- ations. However, these reduced form effects hide interesting mechanisms produced by

7In the case of forage plants and other crops, although the coefficients are relatively large, the eco- nomic magnitudes are not significant. The largest effect of a one standard deviation increase in land expropriated is in 1976, and corresponds to a 2.5 percentage point decrease in area planted with forage plants, which is only 9% of the mean and 14% of the standard deviation in 1965. For other crops, the effect in 1976 is an increase of 3.1 percentage points which corresponds to 6% of the mean and 16% of the standard deviation in 1965.

23 Table 3: Effects of Reform and Counter-Reform on Land Inequality and Crop Choice

(1) (2) (3) (4) (5) (6) Land Crop Choice Inequality Farm Forage Fruit Forest Other Vineyards Gini Plants Trees Plantations Crops Area Reformed (%) × Year = 1955 -0.045** -0.011 0.025 0.021 -0.057 0.022 (0.018) (0.052) (0.016) (0.017) (0.039) (0.053) Year = 1976 -0.050** -0.070 0.014 0.003 -0.124** 0.177** (0.022) (0.072) (0.020) (0.014) (0.059) (0.074) Year = 1997 -0.096*** 0.049 -0.007 0.102*** -0.273*** 0.130 (0.026) (0.072) (0.064) (0.038) (0.084) (0.088) Year = 2007 -0.102*** -0.076 0.040 0.161*** -0.235** 0.111 (0.029) (0.085) (0.075) (0.045) (0.092) (0.088) Area Counter-Reformed (%) × Year = 1955 -0.032 -0.646*** 0.043 0.065 0.009 0.529*** (0.050) (0.197) (0.046) (0.041) (0.158) (0.176) Year = 1976 -0.048 -0.523*** 0.065 0.014 0.198 0.246 (0.039) (0.192) (0.056) (0.031) (0.167) (0.196) Year = 1997 -0.077 -0.409** 0.002 0.021 0.305 0.082 (0.050) (0.163) (0.143) (0.070) (0.185) (0.205) Year = 2007 -0.082 -0.337** -0.037 0.075 0.251 0.049 (0.053) (0.153) (0.135) (0.074) (0.198) (0.226) 1965 Mean dep. var 0.870 0.265 0.047 0.046 0.153 0.489 1965 Std. Dev. dep. var. 0.088 0.179 0.100 0.079 0.231 0.197 Observations 1040 1040 1040 1040 1040 1040 Municipalities 208 208 208 208 208 208 Within R2 0.444 0.536 0.746 0.405 0.750 0.764 Note: Standard errors clustered at the municipality level. Significance levels: * 10%, ** 5%, *** 1%. This table shows the estimated coefficients of estimating a panel fixed effects regression of the corre- sponding dependent variable on the share of the municipality area that was expropriated and reformed (Area Reformed (%)) and that was expropriated and returned to the original land-owner intact (Area Counter-Reformed (%)), interacted with year fixed effects. The base category for the year fixed effects is 1965. For land inequality, the dependent variable is the farm size Gini coefficient. For the crop choice variables the dependent variables are the share of area planted/sowed with the denoted crop category. All regressions are estimated independently, including region-specific year fixed effects. Control variables for soil characteristics, thermal zones, population density in 1960, market access in 1960, altitude, rugged- ness index, and distances to ports, provincial capital, coast, and land border are included interacted with the year fixed effects. See text for variable definitions. the interaction of reform and counter-reform. Next, in section 5.2, I show how bundling reform and counter-reform together hides the true effect of redistribution.

24 5.2 Reform and Counter-Reform

Since not all expropriated land was eventually distributed to peasants because of the dictatorship’s counter-reform, the coefficients estimates in table2 should be interpreted as an intent to treat. The richness of the expropriation data allows for the disaggregation of the expropriated hectares into two different treatments: reform land and counter- reform land. The former corresponds to land that was expropriated and redistributed (including perhaps a reserve for the original landowner) and the latter is defined as the land that was expropriated and then returned intact to the original landowner. Thus any given municipality is split into three categories of land: never expropriated, expropriated and revoked, and expropriated and not revoked. This split corresponds to the estimation of the specification in equation3, where reformed land has been denoted with the variable

Ri and the counter-reform land has been denoted with the variable Ci. The results of this estimation are presented in table3. The results are organised in the same way as in table2, but now there are two treatments: the share of area expro- priated and redistributed (Area Reformed (share)) and the share of area expropriated and returned to the original landowner intact (Area Counter-Reformed (share)). Land that remained in the hands of the original land owner, but previously belonged to a larger estate is included in the ‘reform’ areas. The estimates show that revocation had no statistically significant effect in the short and medium run on inequality, but there are signs that by the year 2007, areas where there was relatively more counter-reform begun to catch up in terms on land inequality to areas with relatively more effective reform. In terms of crop choice, there are some interesting dynamics that shed light on the mechanisms through which land reform had an effect. First, note that the effect of expropriations on vineyards is almost exclusively due to effective reform. The coefficients in column (4) are almost the same compared to the results in table2. The standard deviation of share of reformed land is approximately 0.174, qualitatively the same as that of area expropriated. Thus, an increase in area reformed of one standard deviation increased the area planted with vineyards in 2007 by 2.8 percentage points, or 54% of the mean and 31% of the standard deviation in 1965. In 1997, these figures were 30% and 17% respectively. In contrast, counter-reform had no effect statistically or economically on area planted with vineyards. Second, there is a striking differential effect of reform and counter-reform on forest plantations. The coefficients are relatively large and statistically different from each

25 other, suggesting a significant divergence.8 A one standard deviation increase in reform decreased the share of forest plantations by 4.8 percentage points, which corresponds to 31% of the mean and 20% of the standard deviation in 1965. Conversely, a one standard deviation increase in counter-reform (.064) yields an increase in the share of area planted with forest plantations in 1997 by 2.4 percentage points, or 16% of the mean and 11% of the standard deviation in 1965. Third, the effects on forage plants is dominated by the effect of counter-reform. A one standard deviation increase in counter-reform decreases the area planted relative to forage plants by 3.4 percentage points in 1976, the largest effect in the post-treatment period. This effect corresponds to 12% of the mean and 19% of the standard deviation in 1965. The coefficient for 1955, the pre-treatment period, is negative and statistically significant. This indicates that the share of land planted with forage plants was increasing in counter-reform areas relative to no expropriation at all, controlling for effective reform, but this increase was reversed after the land reform period. All in all, table3 clearly shows that effective reform lowered land inequality and shifted area planted away from forest plantations towards vineyards, compared to non- expropriated areas, while there was no significant or consistent effect on forage plants and fruit trees, and there was a medium run effect on other crops but that effect died out statistically over time. In contrast, counter-reform areas had non-significant effects on land inequality, and shifted crop choice towards forestry and away from forage plants, compared to non-expropriated areas. Putting these two effects together, effective reform leads to lower inequality, more vineyards, and less forage plants, relative to counter- reform areas. These results lend itself to an interesting counterfactual analysis. Had the dictator- ship not revoked the expropriations, but applied the same criteria to those lands as it did to the reformed sector, then we would observe that on average, municipalities that were expropriated would have had, in 2007, 3.5 percentage points more land allocated to vineyards (76% of the mean in 1965) and 5.8 percentage points less land allocated to forest plantations (38% of the mean in 1965).9 It is important to note that this is not a counterfactual of the existence of the dictatorship, but rather if Pinochet had decided not to revoke the expropriations and applied the same criteria for privatisation that was used for the non-revoked expropriations.

8The difference between coefficients of reform and counter-reform are 0.342*in 1976, 0.663***in1997, and 0.554**in2007. 9These effects correspond to the Average Treatment on the Treated, assuming the percent of counter- reform is zero, and the percent of reform is the mean of land expropriated for expropriations greater than zero.

26 5.3 New Owners, Old Owners with the same amount of land, and Old Owners with less land

In the previous section 5.2, I showed that effective reform lowered land inequality and shifted crop choice away from forest plantations towards vineyards, while counter-reform areas had no effect on land inequality and shifted crop choice away from forage plants towards forest plantations. What mechanisms drive these results? Is it because reform areas have smaller land holdings unsuitable for forest plantations (due to economies of size)? What role does owner turnover have on crop choice? To answer these questions I exploit the fact that I can observe the amount of land that remains in the hands of the original land-owners, but in a smaller land holding. As discussed earlier, the reform was such that the original landowners that were expropriated had the right to keep a reserve of land. If the identity of the landowner does not matter, and the main mechanism is size, then we should expect that land kept as a reserve should have the same impact on crop choice as land that was redistributed to new owners, because both types of owners are capped at 80 basic irrigated hectares in the immediate aftermath of the reform.10 Thus, expropriated land can be split into three categories:

• Land expropriated and redistributed to new owners.

• Land expropriated and then returned to the original landowner as a smaller reserve.

• Land expropriated and then returned to the original landowner intact (because the expropriation was revoked).

Table4 shows the results of estimating equation (4), where the total area expropri- ated has been split into the three categories described above. Although splitting the reformed land into two variables reduces statistical power, it can be seen that the sign of the coefficients is similar for land with new owners and reserve land. In the case of land inequality, is is clear that a significant part of the negative effect of land redistribution comes from simply reducing the size of the top land owners. For instance, in 2007, a one standard deviation increase in the share of land kept as a reserve (.0357) leads to a 1.1

10If the original landowners return to purchase the land from the peasants that were the beneficiaries of the land reform, then that could also homogenise the crop choice effects of the reform and reserve land. However, that would also imply that land inequality would rise back to the original 1965 levels, which is not what is shown in table3. Thus, even if the original landowners return, the evidence on land inequality implies that the owner mix in municipalities with relatively more redistributed land will necessarily be more diverse.

27 Table 4: Effects of Owner Turnover and Size Constraints on Land Inequality and Crop Choice

(1) (2) (3) (4) (5) (6) Land Crop Choice Inequality Farm Forage Fruit Forest Other Vineyards Gini Plants Trees Plantations Crops Area New Owner (%) × Year = 1955 -0.018 -0.067 0.045** 0.024 -0.086 0.084 (0.019) (0.062) (0.021) (0.019) (0.054) (0.064) Year = 1976 -0.022 -0.145* 0.031 0.006 -0.088 0.196** (0.020) (0.084) (0.027) (0.016) (0.082) (0.083) Year = 1997 -0.087*** 0.024 -0.014 0.130** -0.287*** 0.146 (0.024) (0.109) (0.074) (0.059) (0.105) (0.120) Year = 2007 -0.075*** -0.136 0.070 0.163** -0.228** 0.131 (0.027) (0.101) (0.084) (0.066) (0.112) (0.119) Area Reserve (%) × Year = 1955 -0.254*** 0.248 -0.122 -0.020 0.153 -0.260 (0.077) (0.269) (0.076) (0.071) (0.212) (0.294) Year = 1976 -0.293*** 0.429 -0.114 -0.042 -0.447 0.174 (0.107) (0.276) (0.097) (0.054) (0.319) (0.383) Year = 1997 -0.223* -0.022 0.199 -0.092 -0.397 0.312 (0.121) (0.412) (0.210) (0.165) (0.421) (0.480) Year = 2007 -0.366*** -0.021 -0.018 0.148 -0.233 0.123 (0.138) (0.423) (0.255) (0.237) (0.459) (0.498) Area Revoked (%) × Year = 1955 -0.037 -0.655*** 0.045 0.063 0.013 0.533*** (0.049) (0.194) (0.045) (0.041) (0.159) (0.174) Year = 1976 -0.054 -0.518*** 0.063 0.013 0.188 0.254 (0.039) (0.190) (0.056) (0.031) (0.169) (0.196) Year = 1997 -0.082 -0.423** 0.012 0.019 0.292 0.099 (0.051) (0.168) (0.133) (0.069) (0.182) (0.211) Year = 2007 -0.089* -0.354** -0.031 0.075 0.253 0.056 (0.053) (0.154) (0.131) (0.074) (0.199) (0.228) Transfers to State × Year Yes Yes Yes Yes Yes Yes FE 1965 Mean dep. var 0.870 0.265 0.047 0.046 0.153 0.489 1965 Std. Dev. dep. var. 0.088 0.179 0.100 0.079 0.231 0.197 Observations 1040 1040 1040 1040 1040 1040 Municipalities 208 208 208 208 208 208 Within R2 0.453 0.548 0.750 0.407 0.752 0.765 Note: Standard errors clustered at the municipality level. Significance levels: * 10%, ** 5%, *** 1%. This table shows the estimated coefficients of estimating a panel fixed effects regression of the corresponding dependent variable on the percent of the municipality area that was expropriated and ended up in the hands of new owners (Area New Owner (share)), that was expropriated but returned to the original land-owner as a reserve (Area Reserve (share)), and that was expropriated and ended up in the hands of the original land-owner intact (Area Revoked (share)), interacted with year fixed effects. The base category for the year fixed effects is 1965. For land inequality, the dependent variable is the farm size Gini coefficient. For the crop choice variables the dependent variables are the share of area planted/sowed with the denoted crop category. All regressions are estimated independently, including region-specific year fixed effects. Control variables for soil characteristics, thermal zones, population density in 1960, market access in 1960, altitude, ruggedness index, and distances to ports, provincial capital, coast, and land border are included interacted with the year fixed effects. See text for variable definitions.

Gini point decrease, while a one standard deviation increase in the land redistributed decreases the farm Gini by 1.3 percentage points.

28 Similar exercises can be carried out for the crop choice variables. For instance, in the case of vineyards, although reserve land has no statistically significant effect on the outcome, the coefficients follow a similar magnitude and pattern as the coefficients for land under new owners, and thus an opposite pattern compared to counter-reformed areas (Area Revoked (share)). This pattern is also visible in forest plantations. While area redistributed to new owners significantly reduces the share of area planted with forest plantations, the coefficients for reserve land are larger in magnitude (although imprecisely estimated). The point estimates suggest that owners that kept a reserve of their land were constrained by size when switching to forestry much like new owners. Thus, the results of this section show that there are no significant differences in be- haviour between reserve areas and redistributed areas, but there is a significant difference with counter-reform areas. This suggests that the effect of reform on land inequality and crop choice operated similarly through both types of owners. This highlights the role that size constraints had on the crop choice switch away from forest plantations and towards vineyards.

5.4 Development Outcomes

Up to this point I have examined how land redistribution leads to lower inequality and differential crop choices. In this section I examine how the land reform affected areas in terms of development outcomes. I obtain measures of educational attainment, dwelling quality, and access to utilities from the population censuses of 1960 to 2002, provided by IPUMS-International (Minnesota Population Center, 2017). These outcomes are important because they are proxies for human capital, private wealth, and public goods, respectively.11 One drawback of using the IPUMS-International data is that the lowest geographical unit available is different between the 1960-1970 and the 1982-2002 censuses. While for the latter years the municipality of residence is available (although sometimes grouped), for the former the lowest geographical division is the department, an administrative di- vision that was abolished with the administrative reforms of the late 1970s. Thus, I match the municipalities in the 1982-2002 census waves to the corresponding 1960-1970 departments and aggregate. This procedure greatly reduces the number of observations

11Unfortunately, there is no income data in the censuses, and comprehensive surveys with national coverage that do include income are only available from 1990 onwards, so they are not suitable for longitudinal analysis considering that the reform occurred in the 1960s and 1970s.

29 Table 5: Development Outcomes and their Proxies

Variable Proxies used Human Capital Share of adults (20+ years old), born in the same province of resi- dence, with at least primary education. Share of households with access to electricity. Public Goods Share of households with access to the sewage system. Share of households with access to running water. Share of households with finished floors (non-dirt floors). Private Wealth Share of households with a toilet. Share of households with a bath. to 87 departments, of which 68 are included in the estimation sample.12 Consequently, statistical power is lowered considerably. Nevertheless, there are still some very signifi- cant results that are robust to the battery of controls that I have included in the previous analysis at the municipality level. Land inequality has been shown to have a negative correlation with human capital accumulation. The empirical historical literature has shown a significant negative corre- lation between inequality and education. Goldin and Katz(1998, 1999) show evidence for the United States that, at the state level, the size of the middle class was positively correlated with the expansion of secondary education in the early 20th century. Galor et al.(2009), also in the US context, use panel data to estimate a negative effect of land concentration on education spending per capita between 1890 and 1930 at the state level.13 In terms of mechanisms, they argue through an expanded version of the model in Galor and Moav(2006) that complementarities between physical and human capital led capitalists to break ranks with the landed aristocracy and support the expansion of secondary education. This is a different explanation to that of Goldin and Katz(1999)

12The estimation sample is defined in the same way as for the municipality level results: geographical units located between the fourth province in the north of Chile and the twenty-second province according to the pre-1978 regionalisation reform. As in the sample used in the previous section, I have also excluded the department that contains Santiago’s urban area. 13In this paper, IV estimates rely on a first stage motivated by the Engerman and Sokoloff(1997); Sokoloff and Engerman(2000) hypothesis. Easterly(2001) follows a similar identification strategy in a cross-country setting to find that indeed land inequality negatively impacts schooling, and Vollrath (2013) uses the 1890 US census data to estimate a negative correlation between land inequality and school funding. However, in this latter study, the negative correlation is stronger for the northern states, and an exercise that would match land inequality in the southern states with that of the northern states yields that there is a significant gap that cannot be explained by differences in land inequality between north and south.

30 Table 6: Effects of Land Expropriation on Development Outcomes

(1) (2) (3) (4) (5) (6) (7) Human Public Infrastructure Dwelling Quality Capital Running Sewage Finished Education Electricity Toilet Bath Water System Floors Area Exprop. (share) × Year = 1970 -0.001 -0.059 0.056 0.120 -0.202* 0.080 0.124 (0.059) (0.074) (0.102) (0.080) (0.113) (0.079) (0.238) Year = 1982 0.147 -0.032 0.206 0.286* -0.097 0.222** 0.102 (0.106) (0.148) (0.154) (0.153) (0.129) (0.108) (0.156) Year = 1992 0.155 -0.076 0.210 0.290** -0.101 0.252** 0.135 (0.096) (0.135) (0.145) (0.139) (0.127) (0.105) (0.131) Year = 2002 0.178* -0.187 0.116 0.277** -0.134 0.294*** 0.111 (0.100) (0.141) (0.155) (0.123) (0.132) (0.089) (0.142)

1960 Mean dep. var 0.235 0.428 0.331 0.225 0.691 0.186 0.635 1960 Std. Dev. dep. var. 0.122 0.189 0.174 0.157 0.157 0.104 0.200 Observations 340 340 340 340 340 340 340 Departments 68 68 68 68 68 68 68 Within R2 0.983 0.963 0.966 0.966 0.945 0.977 0.975 Note: Standard errors clustered at the ‘department’ level. Significance levels: * 10%, ** 5%, *** 1%. This table shows the estimated coefficients of estimating a panel fixed effects regression of the corresponding dependent variable on the share of the department area that was expropriated, interacted with year fixed effects. The base category for the year fixed effects is 1960. Definitions for the dependent variables are provided in table5 All regressions are estimated independently, including region-specific year fixed effects. Control variables for soil characteristics, thermal zones, population density in 1960, market access in 1960, altitude, ruggedness index, and distances to ports, provincial capital, coast, and land border are included interacted with the year fixed effects. See text for variable definitions. which argues that the expansion of secondary education in the United States was driven by locations where there was greater social cohesion. The role of social cohesion is related to the issue of Tiebout sorting (Tiebout, 1956) and the provision of public goods. For the US, there is abundant evidence of sorting (Hoxby, 2000; Rhode and Strumpf, 2003; Alesina et al., 2004), but the institutional con- text is different because in Chile there is little local government autonomy to raise taxes. However, there is scope for local government action in lobbying the central government to provide the resources to finance public goods. If such is the case, then public goods such as education and public infrastructure will depend on the local political economy equilibrium. The role that elites play in this equilibrium can affect the level of services provided. That is why I use the spread of electricity, water, and sewage services as proxies for the provision of public goods. Acemoglu and Robinson(2000) argue that entrenched elites will block the spread of new technologies if they feel their power being threatened, because there is no credible way to promise ex post compensation. In the context of this paper, the provision of public utilities could be affected by the land reform

31 since more redistribution can weaken the bargaining power of landed elites. Acemoglu et al.(2014) use exogenous variation cause by the legacy of British rule in the degree of competition amongst elites in Sierra Leone’s chiefdoms to show that where there were more families eligible to provide a chief there are better development outcomes today. The outcomes and their proxies are detailed in table5. I estimate equation (1) for each of these outcomes, and the results are shown in table6. The results show that land expropriations had a positive effect on education, but imprecisely estimated. It only becomes marginally significant in 2002. However, the magnitude of the point estimates is considerable, with the effects in the post-treatment period ranging from 75 to 90% of the 1960 mean. In terms of public infrastructure, the effects are strong in terms of the sewage system, positive but insignificant for running water, and negative and insignificant for electricity. When comparing the point estimates to the mean in 1960 of the dependent variable, the negative effect on electricity is less impressive due to the high value of the base level. The effect on sewage access is strong statistically and in magnitude, since the results show that expropriated areas more than double access to the sewage system in the post-treatment period. Finally, the effects on dwelling quality exhibit a similar pattern. The negative point estimates are found for variables which had a high mean in 1960, such as possession of a toilet, while the positive and statistically significant effect is for bath, which had a low mean in 1960. The result on floor quality is also positive but not significant statistically or in magnitude. The results of this section suggest that land redistribution had a positive, albeit im- precisely estimated impact on human capital, public infrastructure, and private dwelling quality. These results are in line with the literature, but further research is required to untangle the precise mechanisms.

6 Conclusion

This paper analyses the effects of land redistribution on land inequality and crop choice as well as development outcomes using the Chilean land reform of 1967-1979 as the main source of exogenous variation. The effects of the reform are estimated for approximately 200 municipalities in the central part of Chile using data from historical agricultural cen- suses and recently digitalised expropriation data. The data on expropriations have been obtained from the original expropriation records that summarised key information on the reform process such as the amount of land expropriated, the reason for expropriation, as well as the outcomes of the reform.

32 A particular feature of the Chilean reform process was that due to the military coup of 1973 a counter-reform process revoked the expropriation of nearly half of the hectares expropriated by the previous democratic governments. This feature lends itself for a comparison of the effects of reform and counter-reform across municipalities. The results obtained show that effective reform was responsible for a sharp and persistent decrease in land inequality in the areas most affected by the reform. This persistent decrease in land inequality is also accompanied by an relative increase in the land cultivated with vineyards, as well as a sharp relative decrease in the land destined to forest plantations. These results are robust to regional trends and the inclusion of a variety of political, market, geographical, climatic, and weather controls. By exploiting the interaction of reform and counter-reform, I determine that the iden- tity of the landowner does not drive the results. In other words, former large landowners that end up with less land behave similarly to new land owners, who also have small holdings. Thus, the switch to vineyards and away from forestry plantations is driven by the size constrained imposed by the new smallholdings independent of the owner. Development outcomes are examined to look at the effects of the reform on human capital, public infrastructure, and private dwelling quality. Improvements were found in most of the indicators but statistical inference is challenging due to data limitations. In the last quarter of the twentieth century Chile experienced a sharp increase in living standards and growth, which has been often credited to an increase in trade open- ness and the subsequent specialisation in the crops in which Chile has a comparative advantage. The results of this paper suggest that exactly which (comparatively advanta- geous) crop is chosen is not entirely geographically determined but depends significantly on past redistributive policies. Thus, this paper shows that there is scope for policy in shaping the structure of the agricultural sector, and this shapes paths of development.

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38 Appendix A Robustness Checks

A.1 All possible outcomes of land reform

Figure1 shows that taking into consideration reform and counter-reform, there are six outcomes of the entire process. In table A.1 I show the results of estimating an equation similar to equation (4) but splitting the category ‘new owner’ into land redistributed to peasants and land auctioned to the highest bidder.

Table A.1: Effects of Reform Outcomes on Land Inequality and Crop Choice

(1) (2) (3) (4) (5) (6) Land Crop Choice Inequality Farm Forage Fruit Forest Other Vineyards Gini Plants Trees Plantations Crops Area Redistributed (%) × Year = 1955 -0.021 -0.055 0.041 0.018 -0.051 0.046 (0.024) (0.073) (0.032) (0.022) (0.061) (0.068) Year = 1976 -0.037 -0.193*** 0.028 -0.012 0.010 0.167* (0.026) (0.069) (0.036) (0.022) (0.068) (0.087) Year = 1997 -0.103*** -0.001 -0.047 0.089 -0.223* 0.181 (0.032) (0.107) (0.100) (0.057) (0.114) (0.143) Year = 2007 -0.086** -0.186* 0.058 0.130* -0.165 0.164 (0.037) (0.105) (0.106) (0.068) (0.119) (0.142) Area Auctioned (%) × Year = 1955 -0.066 -0.093 0.076 0.095 -0.384 0.306 (0.074) (0.411) (0.065) (0.282) (0.325) (0.435) Year = 1976 -0.011 0.037 0.026 -0.029 -0.698 0.664* (0.071) (0.607) (0.048) (0.116) (0.487) (0.398) Year = 1997 -0.069 0.160 0.367 -0.004 -0.617 0.095 (0.093) (0.345) (0.261) (0.207) (0.465) (0.401) Year = 2007 -0.090 0.087 0.236 0.055 -0.530 0.153 (0.110) (0.359) (0.356) (0.227) (0.517) (0.407) Area Reserve (%) × Year = 1955 -0.157* 0.304 -0.109 -0.023 -0.033 -0.139 (0.090) (0.307) (0.145) (0.079) (0.284) (0.364) Year = 1976 -0.220 0.570* -0.053 -0.011 -0.733** 0.227 (0.152) (0.316) (0.112) (0.074) (0.337) (0.352) Year = 1997 -0.185 0.065 0.209 0.054 -0.596 0.267 (0.155) (0.445) (0.287) (0.148) (0.451) (0.626) Year = 2007 -0.313 0.208 -0.009 0.242 -0.420 -0.021 (0.194) (0.528) (0.331) (0.261) (0.531) (0.641) Area Revoked (%) × Year = 1955 -0.013 -0.614*** 0.059 0.064 0.002 0.489*** (0.072) (0.205) (0.065) (0.056) (0.175) (0.174) Year = 1976 -0.044 -0.536** 0.088 -0.010 0.218 0.240 (0.044) (0.228) (0.054) (0.056) (0.203) (0.198) Year = 1997 -0.069 -0.451** 0.041 -0.009 0.387* 0.032 (0.055) (0.210) (0.149) (0.094) (0.215) (0.237) Year = 2007 -0.065 -0.367* 0.002 0.057 0.320 -0.012 (0.063) (0.220) (0.167) (0.082) (0.266) (0.257) Transfers to State × Year Yes Yes Yes Yes Yes Yes FE Observations 1040 1040 1040 1040 1040 1040 Municipalities 208 208 208 208 208 208 Within R2 0.450 0.563 0.761 0.409 0.752 0.771 Note: See notes for table4.

39