Land Reform and Productivity: A Quantitative Analysis with Micro Data

Adamopoulos, Tasso; Restuccia, Diego

doi:10.3386/w25780

Cited by 35 publications

(45 citation statements)

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“…Therefore, treating the United States as a frictionless benchmark in cross‐country studies is a reasonable, and perhaps useful, approximation. Two other papers that study misallocation across farms are Adamopoulos and Restuccia () and Restuccia and Santaeulalia‐Llopis (). Like Adamopoulos and Restuccia (), these papers focus on developing countries.…”

Section: Introductionmentioning

confidence: 99%

Technology, Policy Distortions, and the Rise of Large Farms

Cai

2018

Int Economic Review

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Between 1900 and 2002, mean farm size in the United States tripled; productive resources were increasingly concentrated in large farms. These observations are difficult to explain as results of profit maximization by farmers in a frictionless equilibrium model, given exogenous factor endowment and technology. I show that notable shifts in the farm size distribution coincided with important changes in farm legislation and that farm programs provided larger farms more subsidy per dollar of output produced. These farm-level distortions are crucial for the increasing dominance of large farms but have little impact on employment and productivity in agriculture. * Manuscript . 2 A more detailed account of these facts is provided in Section 2. 3 Large farms are those with a plot of land above 200 hectares-approximately the size of 380 standard American football fields. Small farms are those with land below 20 hectares.

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Section: Introductionmentioning

confidence: 99%

Technology, Policy Distortions, and the Rise of Large Farms

Cai

2018

Int Economic Review

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“…Reallocation of resources from less to more efficient plants contributed substantially to aggregate productivity growth during the period. Adamopoulos and Restuccia () study a comprehensive land reform in Philippines using panel data of farmers before and after the reform. The reform entailed a cap in maximum land holdings in addition to government intervention in the land market by directing excess land to landless/smallholders and restricting reallocation.…”

Section: The Role Of Misallocationmentioning

confidence: 99%

Misallocation and aggregate productivity across time and space

Restuccia

2019

Canadian J of Economics

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Productivity is at the core of the large differences in income per capita across countries. What accounts for international productivity differences? I discuss cross‐country differences in the allocation of inputs across heterogeneous production units—misallocation—as a potential factor in accounting for aggregate productivity. Policies and institutions generating misallocation are prevalent in poor and developing countries and may also be responsible for differences in the selection of operating producers and technology used, contributing substantially to aggregate productivity differences across countries.

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“…The model is able to explain almost all the observed differences between the U.S. and poor countries with respect to capital intensity and 72% of the agricultural productivity differ-2 See, for example, Adamopoulos and Restuccia (2014), Adamopoulos and Restuccia (2015), and Chen (2016), among others. ences with untitled land and aggregate factors.…”

Section: Introductionmentioning

confidence: 98%

“…If farmers cannot buy or rent additional land to expand their farm size, then they have little incentive to adopt the modern technology. Other forms of land misallocation, such as imposing ceilings on farm size, as described in Adamopoulos and Restuccia (2015), have similar impact on technology adoption and agricultural productivity.…”

Section: Introductionmentioning

confidence: 99%

“…5 Two closely related papers are Gollin et al (2007) and Yang and Zhu (2013). They both model the choice between a 3 See, for example, Gollin et al (2002), Gollin et al (2004), Gollin et al (2007), , Adamopoulos (2011), Lagakos andWaugh (2013), Gollin and Rogerson (2014), Donovan (2014), Gollin et al (2014a), Adamopoulos and Restuccia (2014), Tombe (2015), Adamopoulos and Restuccia (2015), Gottlieb and Grobovšek (2015), and Chen (2016), among others.…”

Section: Introductionmentioning

confidence: 99%

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Technology adoption, capital deepening, and international productivity differences

Chen

2020

Journal of Development Economics

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Cross-country differences in capital intensity are larger in the agricultural sector than in the non-agricultural sector, indicating that rich and poor countries differ in agricultural technology adoption. I build a two-sector general equilibrium model featuring technology adoption in agriculture. As the economy develops, farmers gradually adopt a modern capital-intensive technology to replace the traditional labour-intensive technology, as is observed in the U.S. historical data. Using this model, I find that the technology adoption channel is key to accounting for low agricultural capital intensity and labour productivity in poor countries. In the model, measured aggregate factors -land endowment, economy-wide productivity, and barriers to investment -can explain 1.56-fold more in rich-poor agricultural productivity differences compared to a model without technology adoption. I further show that land market frictions in agriculture impede technology adoption and magnify productivity differences.

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Land Reform and Productivity: A Quantitative Analysis with Micro Data

Cited by 35 publications

References 39 publications

Technology, Policy Distortions, and the Rise of Large Farms

Technology, Policy Distortions, and the Rise of Large Farms

Misallocation and aggregate productivity across time and space

Technology adoption, capital deepening, and international productivity differences

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