Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania

<em><strong>Data &amp; Code will be made available by August 25th.</strong></em> <strong>Article Abstract</strong> Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100-million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (p = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities. <strong>Replication Data</strong> We include anonymized household survey data for replication of our analysis. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. This data can be found in the <em>hh_data</em> folder. Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <em>lsms_data</em> folder). Finally, our data replication includes several models outputs, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <em>main_analysis.Rmd</em> script. <strong>Replication Code</strong> We provide replication code in the form of an R Markdown (.Rmd) file. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article.