This repository contains code and data for the reproduction of analysis and figures from: 

Kotz, Wenz, Stechemesser, Kalkuhl and Levermann (2020).

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Data included:

GADM dataset of regional shapefiles: gadm36_levels.gpkg
Calculated regional climate variables: T.5_???_measure.npy and P.5_???_measure.npy, contained in ZIPPED folders T5_measures.zip & P5_measures.zip
World bank GDP and population data: WB_GDP.csv
Economic and regional climate data: T_econ.dta
Economic and regoinal climate data with lagged variables: T_econ_5_lags.dta
Economic and regional climate data restricted to regions with certain seasonal temp. differences: T_econ_seas_g*.dta
Daily climate data used to plot Fig_1: corrientes_ARG_T_days.npy & ocampo_MEX_T_days.npy

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Data not included:
Raw ERA-5 daily temperature and preicpitation data, 1979-2018, interpolated to 0.5x0.5 grid by ISIMIP (https://www.isimip.org/). Available from ISIMIP or from authors upon request.

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Code included:

T_scatter.py - code to calculate and aggregate the main temperature variables (annual average and day-to-day variability) from ERA-5 grid to regional level.
P_scatter.py - code to calculate and aggregate precipitation variables (total annual) from ERA-5 grid to regional level.
Regression_Table_1 - Stata do file including code to run regressions for Table 1 of the manuscript.
Table_SX - Stata do files including code to run regressions for Tables S1-11 of the SI.
partitions.py - code to partition data based on national and regional income, to export sub-data sets for analysis in R, and for plotting Fig S4.
partition_regressions.R - code to calculate marginal effects used for plotting in Figs 4, S1 and S5.
partitions_plot.py - code to plot Figs 4, S1 and S5.
plot_Fig_1.py
plot_Fig_2.py
plot_Fig_3.py

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Order:

1. T_scatter.py and P_scatter.py to calculate regional climate variables using raw ERA-5 data and GADM regional shapefile data. (Note: set correct directory for raw ERA-5 data on l101 of T_scatter.py and l102 of P_scatter.py).

2. Regression_Table_1 and Table_SX to run main and supplementary regressions in Stata using the economic and climate data_set T_econ.dta (and T_econ_5_lags.dta for Table_S_10 and T_econ_seas_g*.dta for Table_S_2).

3. plot_Fig_1.py using corrientes_ARG_T_days.npy & ocampo_MEX_T_days.npy.

4. plot_Fig_2.py using T_econ.dta and gadm36_levels.gpkg.

5. plot_Fig_3.py using T_econ.dta, T.5_???_measure.npy and WB_GDP.csv.

6. partitions.py using T_econ.dta and gadm36_levels.gpkg.

7. partition_regressions.R using the output of partitions.py.

8. partitions_plot.py using the output of partition_regressions.R and T_econ.dta.