vtra.mrio package¶
Submodules¶
vtra.mrio.functions module¶
MRIO utility functions
-
aggregate_table
(vnm_IO, vnm_IO_rowcol, in_million=True)[source]¶ Aggregate national Input-Output table to the amount of sectors used in the multiregional Input-Output table.
- Parameters
- vnm_IO - pandas dataframe of national Input-Output table
- vnm_IO_rowcol - pandas dataframe with all sectors in the national Input-Output table
- in_million - Specify whether we want to divide the table by 1000000, to have values in millions. The default value is set to True
- Outputs
- pandas Dataframe with aggregated national Input-Output table
-
create_indices
(data_path, provinces, write_to_csv=True)[source]¶ Create list of indices required to disaggregate the national table to the different regions.
- Parameters
- data_path - String name of data path
- provinces - pandas DataFrame with provincial/regional data
- write_to_csv - Boolean to specify whether you want to save output to .csv files. The default value is set to True
- Outputs
- set of .csv files with indices proxy data
-
create_level14_proxies
(data_path, od_table, own_production_ratio=0.8, write_to_csv=True)[source]¶ Function to create the level14 proxies, required to disaggregate the national table.
- Parameters
- data_path - String name of data path
- od_table - pandas DataFrame with the Origin-Destination matrix
- own_production_ratio - Specify how much supply and demand is locally supplied and used, and how much is imported/exported. The default is set to 0.8
- write_to_csv - Boolean to specify whether you want to save output to .csv files. The default value is set to True
- Outputs
- set of .csv files with level 14 proxy data
-
create_proxies
(data_path, notrade=False, own_production_ratio=0.9, min_rice=True)[source]¶ Create all proxies required in the disaggregation process.
- Parameters
- data_path - String name of data path
- notrade - Boolean to specify whether we should include trade in the disaggregation. This should be set to True in the first step of the disaggregation. The default is set to False
- min_rice - Boolean to determine whether you want to use the minimal rice value or the maximum rice value from the flow analysis. The default is set to True
- own_production_ratio - Specify how much supply and demand is locally supplied and used, and how much is imported/exported. The default is set to 0.8
- Outputs
- all proxy level .csv files.
-
create_regional_proxy
(data_path, regions, write_to_csv=True)[source]¶ Function to create the proxy to disaggregate the national table to the different regions.
- Parameters
- data_path - String name of data path
- regions - pandas DataFrame with provincial/regional data
- write_to_csv - Boolean to specify whether you want to save output to .csv files. The default value is set to True
- Outputs
- set of .csv files with regional proxy data
-
create_sector_proxies
(data_path, regions, write_to_csv=True)[source]¶ Create sector proxies required to disaggregate the national table to the different sectors in each region.
- Parameters
- data_path - String name of data path
- regions - pandas DataFrame with provincial/regional data
- write_to_csv - Boolean to specify whether you want to save output to .csv files. The default value is set to True
- Outputs
- set of .csv files with sector proxy data
-
create_zero_proxies
(data_path, od_table, notrade=False, write_to_csv=True)[source]¶ Function to create the trade proxies, required to disaggregate the national table.
- Parameters
- data_path - String name of data path
- od_table - pandas DataFrame with the Origin-Destination matrix
- notrade - Boolean to specify whether we should include trade in the disaggregation. This should be set to True in the first step of the disaggregation. The default is set to False
- write_to_csv - Boolean to specify whether you want to save output to .csv files. The default value is set to True
- Outputs
- set of .csv files with level 14 proxy data
-
estimate_gva
(regions, in_million=True)[source]¶ Functions to estimate the Gross Value Added for each sector in each province.
- Parameters
- regions - pandas DataFrame with provincial/regional data
- Outputs
- list with GVA values per sector in each province
-
get_final_sector_classification
()[source]¶ Return the list of sectors to be used in the new multiregional Input-Output table.
- Outputs:
- list of sectors
-
get_trade_value
(x, sum_use, sector, own_production_ratio=0.8)[source]¶ Function to get the trade value between a certain origin and destination.
- Parameters
- x - row in Origin-Destination dataframe
- sum_use - total use in a certain destination
- own_production_ratio - Specify how much supply and demand is locally supplied and used, and how much is imported/exported. The default is set to 0.8
- Outputs
- returns trade value
-
is_balanced
(io_table)[source]¶ Function to check if Input-Output table is balanced.
- Parameters
- io_table - Input-Output table.
- Outputs
- return print statement if table is balanced.
-
load_od
(data_path, min_rice=True)[source]¶ Load national Origin-Destination matrix as pandas DataFrame.
- Parameters
- data_path - String name of data path
- min_rice - Boolean to determine whether you want to use the minimal rice value or the maximum rice value from the flow analysis. The default is set to True
- Outputs
- pandas DataFrame with national Origin-Destination matrix
-
load_output
(data_path, provinces, notrade=True)[source]¶ Read output from disaggregation process and translate to usable pandas DataFrame
- Parameters
- data_path - String name of data path
- provinces - pandas DataFrame with provincial/regional data
- notrade - Boolean to specify whether we should include trade in the disaggregation. This should be set to True in the first step of the disaggregation. The default is set to False
- Outputs
- pandas DataFrame with disaggregated Input-Output table
-
load_provincial_stats
(data_path)[source]¶ Load shapefile with provincial-level data.
- Parameters
- data_path - String name of data path
- Outputs
- geopandas GeoDataFrame with provincial data.
-
load_sectors
(data_path)[source]¶ Load national Input-Output table and extracted all sectors
- Parameters
- data_path - String name of data path
- Outputs
- pandas Dataframe with all sectors in national Input-Output table
-
load_table
(data_path)[source]¶ Load national Input-Output table as pandas dataframe.
- Parameters
- file_path - String name of data path
- Outputs
- pandas Dataframe with Input-Output table that is going to be used
-
map_regions
()[source]¶ Create dictionary to map regions to consistent format.
- Outputs
- dictionary to map regions to consistent format
-
map_sect_vnm_to_eng
()[source]¶ Convert vietnamese sector names to simple sector classification.
- Outputs
- dictionary to map vietnamese sectors to simple sector names.
-
map_sectors
(vnm_IO_rowcol)[source]¶ Map the sectors of the loaded national Input-Output table to the sectors which are going to used in the multiregional Input-Output table.
- Parameters
- vnm_IO_rowcol - pandas dataframe with all sectors in the national Input-Output table.
- Outputs
- dictionary to map row sectors
- dictionary to map column sectors
vtra.mrio.ras_method module¶
RAS Method
Purpose¶
Estimate a new matrix X with exogenously given row and column totals that is a close as possible to a given original matrix X0 using the Generalized RAS (GRAS) approach
Usage¶
X = gras(X0, u, v) OR [X, r, s] = gras(X0, u, v) with or without eps included as the fourth argument, where
Input¶
- X0 = benchmark (base) matrix, not necessarily square
- u = column vector of (new) row totals
- v = column vector of (new) column totals
- eps = convergence tolerance level; if empty, the default threshold is 0.1e-5 (=0.000001)
Output¶
- X = estimated/adjusted/updated matrix
- r = substitution effects (row multipliers)
- s = fabrication effects (column multipliers)
References
- Junius T. and J. Oosterhaven (2003), The solution of updating or regionalizing a matrix with both positive and negative entries, Economic Systems Research, 15, pp. 87-96.
- Lenzen M., R. Wood and B. Gallego (2007), Some comments on the GRAS method, Economic Systems Research, 19, pp. 461-465.
- Temurshoev, U., R.E. Miller and M.C. Bouwmeester (2013), A note on the GRAS method, Economic Systems Research, 25, pp. 361-367.
vtra.mrio.run_mrio module¶
Run MRIO
-
mrio_to_excel
(Xin, min_rice=True)[source]¶ Save the newly created multiregional Input-Output table to Excel, in the format required for the MRIA calculation.
- Parameters
- Xin - pandas DataFrame of the new multiregional Input-Output table
- min_rice - Boolean to determine whether you want to use the minimal rice value or the maximum rice value from the flow analysis. The default is set to True
- Outputs
- .xlsx file with the multiregional Input-Output table
-
run_mrio_disaggregate
(notrade=False, min_rice=True, own_production_ratio=0.8)[source]¶ This function will disaggregate the (single-region) national Input-Output table to a provincial multiregional Input-Output table
- Parameters
- notrade - Boolean to specify whether we should include trade in the disaggregation. This should be set to True in the first step of the disaggregation. The default is set to False
- min_rice - Boolean to determine whether you want to use the minimal rice value or the maximum rice value from the flow analysis. The default is set to True
- own_production_ratio - Specify how much supply and demand is locally supplied and used, and how much is imported/exported. The default is set to 0.8
- Outputs
- .csv file containing the new multiregional Input-Output table.
- pandas DataFrame with a multiregional Input-Output table