DOI: 10.5281/zenodo.10152116
title: Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania

description: Reference
Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. https://doi.org/10.1073/pnas.2207398120 
Abstract
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.
Replication Data
We include anonymized household survey data from our analysis to support open and reproducible science. 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. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the hh_data folder and includes:

hh_imputed10_2018: anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times
hh_compensation_2018: anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs
hh_migration_2018: anonymized household extract for 2018 representing household migration behavior following LSLAs
hh_rsdata_2018: extracted remote sensing data at the household geo-location for 2018
hh_land_2019:  anonymized household extract for 2019 of land variables
Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in lsms_data folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). 
Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the gis_data folder.
Finally, our data replication includes several model outputs (found in mod_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 main_analysis.Rmd script. 
Replication Code
We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:

main_analysis.Rmd: main analysis supporting the finding, graphs, and tables reported in our main manuscript
compensation.R: analysis of benefits and compensation received directly by households from LSLAs
landvalue.R: analysis of household land values as a function of distance from LSLAs
migration.R: analysis of migration behavior following LSLAs
selection_bias.R: analysis of LSLA selection bias between control and treatment enumeration areas