This study evaluates how environmental policy restricting land use influences incentives for landowning farmers and landless peasants, and affects illegal occupations and land conflicts in the Brazilian Amazon. I use a ten-year panel data for conflicts, agriculture, and deforestation in the Legal Amazon region of Brazil and employ various matching methods to estimate the causal effect of an anti-deforestation policy on land conflicts and occupations. My estimates show the policy increases the number of illegal occupations in the Priority List municipalities by 25.9% while the number of land conflicts decrease by 21.5%. Analyzing the heterogeneity in the impact with respect to land values suggests that landowners and squatters both made strategic choices about whether to engage in conflict depending on the value of the land being contested. Landowners directed their efforts in capital and labor investments in their lands when the value of engaging in conflict to protect the occupied parcel of their lands was not more than the cost. But, for higher valued lands, landowners chose violent conflict to defend their plots. There are no spatial spillovers of the policy.

USDA’s annual Agricultural Baseline Projections contribute significantly to agricultural policy in the US, and hence ensuring their accuracy is vital. The projections present a neutral policy scenario assuming a specific macroeconomic situation and allow the analyses of alternative policies and their micro and macroeconomic impact in the US. In this study, we investigate the heterogeneity in the incidence of herding in the USDA International Baseline Projection reports from 2002 to 2021. The evaluation of herding as it varies geographically, temporally, and by crop-variable allows us to relate it to trends in bias and make inferential judgments about the sources of this bias. We answer three main questions. First, we examine whether experts tend to herd together the projections for certain crops across different countries, producing similar projection trends. Second, we assess whether the bias is higher across crops or across countries with more substantial evidence for herding behavior. Third, we utilize the results from the first two questions and analyze whether the relationship between bias and herding changes as the projection horizon increases. In-depth familiarity with the baseline procedures that comes from studying the documentation and literature allows us to define case-based hypotheses about any potential bias in the projections.

The green revolution in India has been lauded for its positive impact on agricultural productivity and, in turn, the farmers’ lives. However, its effects on land redistribution and inequality, in the long run, are unclear. Large farmers may have benefited non-proportionately from the technologies that pervaded in the decades leading up to the turn of the 21st century. Richer farmers who own larger farms had early access to irrigation systems allowing them to profit more. Simultaneously, the smaller farmers who are unable to set up new irrigation technologies and thus cannot compete with the large farmers may be forced to sell their farms leading to systematic land redistribution. The poor farmers, in this process, may be worse off as a consequence of the green revolution in India. This raises the question of whether the policy implementation considered farmer welfare at all. Identifying the winners and losers in the green revolution is important not only to understand the current inequality in India but also to design appropriate agrarian and welfare policies. Moreover, this research has policy relevance specifically for countries in Africa that are more recent to new agricultural technologies and may inadvertently experience worsened inequality and welfare loss by non-optimal policies.