gcubed.exogenous_adjustments.labor_productivity

This module provides the Productivity class which loads baseline productivity growth projections.

varname: str = 'LATC'

label_productivity_growth: str = 'productivity growth'

label_us_sector_convergence_rates: str = 'convergence rate across US sectors'

label_regional_sector_ratio_to_us: str = 'sector ratio to the USA leader'

label_regional_sector_catchup_to_us_rates: str = 'catchup rate'

class LaborProductivity(gcubed.base.Base):

Overview

Provides convenience methods for all classes.

All G-Cubed classes inherit from this base class.

LaborProductivity(sym_data: gcubed.sym_data.SymData)

Constructor

This class loads labor productivity parameters and converts them into effective labor productivity projections for all sectors in all regions.

### Arguments

sym_data: The information about the SYM model definition.

The model configuration is accessible via this argument, thus enabling discovery of the location of the information determining productivity growth rates.

That data is available in 3 files:

#### technology_advancement_rates_file.csv

The CSV file containing the information about the advancement rates for technology in each sector, through all projection years. Values are expressed as a percentage so a value of 2.0 means that the technology will advance by 2% in the associated year. The data is stored with sectors for rows and projection years for columns. The row labels are the SYM sector codes. The columns are the projection years in YYYY format out to the last projection year.

For example, for the 2R model:

sector	2018	...	2150
a01	1.4	...	1.4
a02	1.4	...	1.4

#### technology_gaps_file.csv

The CSV file containing the information about the technology gaps in each region for each sector, expressed as a percentage. Thus, a value of 50 means the region has a sector that is 50% as efficient as is possible in the first projection year. The data is stored with sector rows and region columns. The row labels are the SYM sector codes. The column labels are the SYM region codes.

For example, for the 2R model:

sector	USA	ROW
a01	90	100
a02	100	90

Note that the maximum value is 100 and the minimum value must be positive.

It is not mandatory, but it is typical that at least one region is on the technology frontier, with a sector value of 100.

#### technology_gaps_file.csv

The CSV file containing the information about the catchup rates for technology in each sector, through all projection years. Values are expressed as a percentage so a value of 2.0 means that the technology gap will close by 2% in the associated year. The data is stored with region in the first column and sector in the second column. The columns are the projection years in YYYY format out to the last projection year.

For example, for the 2R model:

region	sector	2018	...	2150
USA	a01	2	...	2
USA	a02	2	...	2
ROW	a01	2	...	2
ROW	a02	2	...	2

Note that the maximum value is 100 and the minimum value must be great than -100.

configuration

The model configuration

sym_data

The SYM model details

technology_advancement_rates: pandas.core.frame.DataFrame

The dataframe of technology advancement rates for all sectors in all projection years.

technology_gaps: pandas.core.frame.DataFrame

The dataframe of technology gaps for all regions and all sectors.

technology_catchup_rates: pandas.core.frame.DataFrame

The dataframe of technology catchup rates for all regions and all sectors.

labor_productivity_growth_rates: pandas.core.frame.DataFrame

The dataframe of labor productivity growth rates for all sectors in all regions. The columns are projection years.

The rows are a multi-index with the level 0 being the region code and level 1 being the sector code.

region_productivity_growth_rates: dict[str, pandas.core.frame.DataFrame]

The dictionary of dataframes used to store sectoral productivity growth rate projections for each region. It is indexed by region code. The dataframes are indexed by sector code. The columns of the dataframes are the projection years.

def region_growth_rates(self, region: str) -> pandas.core.frame.DataFrame:

Overview

Accessor for productivity growth projections for all sectors in the specified region.

Arguments

region: The region for which the productivity growth rates are required.

Returns

The dataframe of productivity growth rate projections for all sectors in the region with rows indexed by sector code and columns indexed by projection year.

Exceptions

Raises an exception if the region is not valid.

Inherited Members

gcubed.base.Base: zeros; load_data; get_year_labels; log_calling_stack