Overview over produced tables and figures
-------------------------------------------



01_Annual_Panel_Regression.do
-----------------------------

* Tab. 1-3 (stata output)
* Tab. 4 (tex/01_AnnualPanel/yp_main.tex)
* Fig. 1 (figs/Fig_1)

* Tab. S4 (tex/01_AnnualPanel/yp_rob.tex)
* Fig. S6 (figs/Fig_S6)



02_Long_Difference_Regression.do
--------------------------------

* Tab. 5 (tex/02_longDifference/LD_CRU_SQR_10_r2.tex)

* Tab. S6 (tex/02_longDifference/LD_CRU_10_r2.tex)
* Tab. S7 (tex/02_longDifference/LD_ERA_SQR_10_r2.tex)



03_Cross_Sectional_Regression.do
---------------------------------

* Tab. 6 (tex/03_CrossSectional/CS_CRU_10.tex)
* Fig. 2 (figs/Fig_2)

* Tab. S8 (tex/03_CrossSectional/CS_rob_10.tex)


04_Annual_Damage_Projections.do
-------------------------------

* Fig. 4 (figs/Fig_4)
* Fig. 5 (figs/Fig_5)
* Tab. 7 (stata output)


05_Damage_Map.do
----------------

* Fig. 3 (figs/Fig_3)


06_histcount.py
---------------

* Fig S1 (figs/Fig_S1)


07_histograms_withincountry.do
------------------------------

* Fig S2 (figs/Fig_S2)


08_lag_figs.py
--------------

* Fig S4 (figs/Fig_S4_S5/Fig_S4)
* Fig S5 (figs/Fig_S4_S5/Fig_S5)


09_lags_w_margeff.do
--------------------

* Table S2 (figs/Tables_S2_S3/Table_S2)
* Table S3 (figs/Tables_S2_S3/Table_S3)


Other scripts 
-----------------

10_disaggregate.py
-------------------------

* translates climate information on 0.5°-grid into information on 0.25°-grid by assuming that each 0.5°-grid cells consists of four 0.25°-grid cells and that value of climate variable is the same for all four cells
Input: Climate data on 0.5° grid, e.g. cru_ts3.23.1901.2014.tmp.dat.nc 
Output:  cru_ts3.23.1901.2014.tmp.dat_disaggregated.nc 
Projected climate data from Isimip is processed similarly (does not apply to ERA5 data which is already on 0.25° grid)


11_create_GADM_climate_script_disagg.R

* tranlates grid cell information into regional averages: 
Input:  Climate data on 0.25°-grid: cru_ts3.23.1901.2014.tmp.dat_disaggregated.nc 
• GADM region mask that assigns grid cells to administrative regions
Output: "GADM_tmp_0_disagg.csv" (or other climate variable instead of tmp): 
• Columns: time steps (here: monthly data from 1901-2014)
• Rows: regions
Location: ClimateDataProcessing/Data/Final/CRU


12_DICE2016R-091916ap.gms

* DICE model with adjusted damage functions ((un)comment lines 72-76 for different damage coefficients)
* DICE Output is colelcted and summarized in 12_DiceResults_shifted_damFct.xlsx (sheet "Comparison")