Software Open Access

Data and code for the publication "Day-to-day temperature variability reduces economic growth"

Maximilian Kotz

Data collector(s)
Matthias Kalkuhl
Project member(s)
Leonie Wenz; Annika Stechemesser; Anders Levermann

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 (https://gadm.org/data.html) 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 (??? denotes three letter country code).

World bank GDP and population data: WB_GDP.csv

Economic and regional climate data: T_econ.dta. Economic data is provided by Matthias Kalkuhl and is documented at https://doi.org/10.1016/j.jeem.2020.102360.

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 precipitation data, 1979-2018, interpolated to 0.5x0.5 grid by ISIMIP. Available from ISIMIP (https://www.isimip.org/) 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 to partition data based on regional and national income and to plot Fig. S4 using T_econ.dta and gadm36_levels.gpkg.

 

7. partition_regressions.R to estimate marginal effects of partitioned data, using the output of partitions.py.

 

8. partitions_plot.py, plot Figs. 4, S1 and S5 using the output of partition_regressions.R and T_econ.dta.

Files (4.0 GB)
Name Size
corrientes_ARG_T_days.npy
md5:2e035a8fab9662fe785cc65714fd0b8c
572.8 kB Download
gadm36_levels.gpkg
md5:89d9bda3e93a9dbbc5a39b83d06ae6ee
3.8 GB Download
ocampo_MEX_T_days.npy
md5:c1f8d94ca47c37a480e0d41bea25defd
374.1 kB Download
P5_measures.zip
md5:f504491c91e74b5e42adddeb97e16a61
10.4 MB Download
P_scatter.py
md5:8bbaa6d58d8f9683c6b854913b84620f
6.3 kB Download
partition_regressions.R
md5:c1f7016d0c556f1d7b19df8348373360
4.2 kB Download
partitions.py
md5:b69b165b227738ba21cb6d4981e89357
6.5 kB Download
partitions_plot.py
md5:8720e1cca0ce93bd361a4554cfe8d4fb
8.5 kB Download
plot_Fig_1.py
md5:e850a6990961615f82035009ea1e35f9
5.1 kB Download
plot_Fig_2.py
md5:d1026c53f02c48c13decea0b99ab5926
2.9 kB Download
plot_Fig_3.py
md5:dfc833ac6235f4cd298d751efaaf2493
3.3 kB Download
READ_ME.txt
md5:c287a74f3d0d8b0fc6819c7a7988b997
2.6 kB Download
Regression_Table_1.do
md5:9ebef92bfe6b5ff2985c86a6851278c5
1.7 kB Download
T5_measures.zip
md5:66a39a464c85c7e55dec6e0ee70c098f
30.5 MB Download
T_econ.dta
md5:ca05960abbe60e3f629ee08db89f50bf
37.1 MB Download
T_econ_5_lags.dta
md5:ce0b8d8691e24e403072062659e48e4c
16.7 MB Download
T_econ_seas_g10.dta
md5:4bf36dc17e2e98841b9bd5aac92a5c0b
17.9 MB Download
T_econ_seas_g15.dta
md5:068595926f12e4a4c73b71af4d1e2f0e
16.0 MB Download
T_econ_seas_g20.dta
md5:107e0d87cd0681029bb101a692ff1fa7
13.6 MB Download
T_econ_seas_g5.dta
md5:5d2d754e61db7e20dbc9a7a0dffc9acb
21.3 MB Download
T_scatter.py
md5:598e38e7f5f36c27437b148dc28c9ee7
7.3 kB Download
Table_S1.do
md5:c9eacd492cdd92de60bab7b9fb87ab06
1.4 kB Download
Table_S10.do
md5:608ee8356b94949fc92fd4d3129fd0a3
1.9 kB Download
Table_S11.do
md5:54ff0dec103fc466e6ed3d85ab5ee156
275 Bytes Download
Table_S2.do
md5:9e35ab24b1405b3ca91509120bf5af3b
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Table_S3.do
md5:af3fed45bdd48f2bc9cf384bbf3651c2
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Table_S4.do
md5:b872ae167d5b7cc6fdc6bd476977534f
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Table_S5.do
md5:fec745b0c9695428d65c35e80731bb75
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Table_S6.do
md5:4aab3ed8761343b35a630fcd3da26ae2
1.5 kB Download
Table_S7.do
md5:4b610de513b49ee0248c861324c3e737
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Table_S8.do
md5:f09de3377947798fc96b48b55bb18ec5
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Table_S9.do
md5:a04695f850d49a3bab4f6603d80c0245
1.3 kB Download
WB_GDP.csv
md5:ec7d3c19c6810dd296c8c45b28cd632b
12.9 kB Download
  • Kalkuhl, M., Wenz, L. The impact of climate conditions on economic production. Evidence from a global panel of regions. Journal of Environmental Economics and Management (2020). https://doi.org/10.1016/j.jeem.2020.102360

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