SyntheticFirms

The SyntheticFirms class is an abstract base class that serves as a container for preprocessed firm-level data. It manages firm data including industry assignments, employment, wages, production, financial positions, and various economic indicators.

Core Functionality

The class stores firm data in a pandas DataFrame with the following key columns:

• Industry: The industry classification of the firm
• Number of Employees: Employee count
• Total Wages: Total wage bill
• Production: Production output (in local currency)
• Price: Product price (in local currency)
• Labour Inputs: Labor input quantities
• Inventory: Current inventory levels
• Demand: Current demand
• Deposits: Bank deposits
• Debt: Outstanding debt
• Equity: Firm equity
• Employees ID: List of employee IDs
• Corresponding Bank ID: Associated bank ID
• Taxes paid on Production: Production taxes
• Interest paid: Total interest payments
• Profits: Current profits
• Unit Costs: Production costs per unit
• Corporate Taxes Paid: Corporate tax payments
• Debt Installments: Scheduled debt payments

Key Attributes

• country_name: Country identifier for data collection
• scale: Scaling factor for synthetic data
• year: Reference year for preprocessing
• industries: List of industry classifications
• number_of_firms_by_industry: Array of firm counts by industry
• firm_data: Main DataFrame containing firm information
• total_firm_deposits: Aggregate firm deposits
• total_firm_debt: Aggregate firm debt
• capital_inputs_productivity_matrix: Capital productivity parameters
• intermediate_inputs_productivity_matrix: Input productivity parameters
• capital_inputs_depreciation_matrix: Capital depreciation rates
• labour_productivity_by_industry: Labor productivity by industry

Main Methods

• reset_function_parameters: Updates firm operational parameters
• set_additional_initial_conditions: Sets up initial conditions for firm operations
• total_emissions: Calculates total emissions from inputs and capital
• number_of_firms: Property returning total number of firms

Implementation

Module for preprocessing synthetic firm data.

This module provides a framework for preprocessing and organizing firm-level data that will be used to initialize behavioral models. Key preprocessing includes:

1. Firm Data Organization:
2. Industry classification
3. Employee allocation
4. Financial data processing

5. Production parameters

6. Initial State Processing:

7. Balance sheet data
8. Production capacity
9. Input requirements

10. Financial positions

11. Parameter Processing:

12. Productivity metrics
13. Cost structures
14. Tax parameters
15. Interest rates

Note

This module is NOT used for simulating firm behavior. It only handles the preprocessing and organization of firm data that will later be used to initialize behavioral models in the simulation package.

SyntheticFirms

Container for preprocessed firm-level data.

The firm data is stored in a pandas DataFrame with the following columns

• Industry: The industry of the firm.
• Number of Employees: The number of employees of the firm.
• Total Wages: The total wages of the firm.
• Total Wages Paid: The total wages paid by the firm.
• Production: The production of the firm (in LCU).
• Price in USD: The price of the firm (in USD).
• Price: The price of the firm (in LCU).
• Labour Inputs: The labour inputs of the firm (in LCU).
• Inventory: The inventory of the firm (in LCU).
• Demand: The demand of the firm (in LCU).
• Deposits: The deposits of the firm (in LCU).
• Debt: The debt of the firm (in LCU).
• Equity: The equity of the firm (in LCU).
• Employees ID: The IDs of the employees of the firm.
• Corresponding Bank ID: The ID of the corresponding bank of the firm.
• Taxes paid on Production: The taxes paid on production of the firm (in LCU).
• Interest paid on deposits: The interest paid on deposits of the firm (in LCU).
• Interest paid on loans: The interest paid on loans of the firm (in LCU).
• Interest paid: The interest paid of the firm (in LCU).
• Profits: The profits of the firm (in LCU).
• Unit Costs: The unit costs of the firm (in LCU).
• Corporate Taxes Paid: The corporate taxes paid of the firm (in LCU).
• Debt Installments: The debt installments of the firm (in LCU).

Attributes:

Name	Type	Description
country_name	str	Country identifier for data collection
scale	int	Scaling factor for synthetic data
year	int	Reference year for preprocessing
industries	list[str]	List of industry classifications
number_of_firms_by_industry	ndarray	Firm count by industry
firm_data	DataFrame	Main data container with firm information
intermediate_inputs_stock	ndarray	Initial intermediate input inventory
used_intermediate_inputs	ndarray	Initial input usage
capital_inputs_stock	ndarray	Initial capital stock
used_capital_inputs	ndarray	Initial capital usage
total_firm_deposits	float	Aggregate firm deposits
total_firm_debt	float	Aggregate firm debt
capital_inputs_productivity_matrix	ndarray	Capital productivity parameters
intermediate_inputs_productivity_matrix	ndarray	Input productivity parameters
capital_inputs_depreciation_matrix	ndarray	Capital depreciation rates
labour_productivity_by_industry	ndarray	Labor productivity by industry

country_name = country_name instance-attribute

scale = scale instance-attribute

year = year instance-attribute

industries = industries instance-attribute

number_of_firms_by_industry = number_of_firms_by_industry instance-attribute

firm_data = firm_data instance-attribute

intermediate_inputs_stock = intermediate_inputs_stock instance-attribute

used_intermediate_inputs = used_intermediate_inputs instance-attribute

capital_inputs_stock = capital_inputs_stock instance-attribute

used_capital_inputs = used_capital_inputs instance-attribute

total_firm_deposits = total_firm_deposits instance-attribute

total_firm_debt = total_firm_debt instance-attribute

capital_inputs_productivity_matrix = capital_inputs_productivity_matrix instance-attribute

intermediate_inputs_productivity_matrix = intermediate_inputs_productivity_matrix instance-attribute

capital_inputs_depreciation_matrix = capital_inputs_depreciation_matrix instance-attribute

labour_productivity_by_industry = labour_productivity_by_industry instance-attribute

number_of_firms: int property

Returns the total number of firms.

Returns:

Name	Type	Description
int	int	The total number of firms.

total_emissions property

__init__(country_name: str, scale: int, year: int, industries: list[str], number_of_firms_by_industry: np.ndarray, firm_data: pd.DataFrame, intermediate_inputs_stock: np.ndarray, used_intermediate_inputs: np.ndarray, capital_inputs_stock: np.ndarray, used_capital_inputs: np.ndarray, total_firm_deposits: float, total_firm_debt: float, capital_inputs_productivity_matrix: np.ndarray, intermediate_inputs_productivity_matrix: np.ndarray, capital_inputs_depreciation_matrix: np.ndarray, labour_productivity_by_industry: np.ndarray) abstractmethod

reset_function_parameters(capital_inputs_utilisation_rate: float, initial_inventory_to_input_fraction: float, intermediate_inputs_utilisation_rate: float, zero_initial_debt: bool, zero_initial_deposits: bool)

set_additional_initial_conditions(industry_data: dict[str, pd.DataFrame], synthetic_banks: SyntheticBanks, long_term_loans: LongtermLoans, short_term_loans: ShorttermLoans, tax_data: TaxData) -> None

DefaultSyntheticFirms

DefaultSyntheticFirms is a concrete implementation of SyntheticFirms that provides preprocessing of firm-level data using standard data sources (OECD, Eurostat, Compustat). This class handles the initialization and organization of data that will be used to initialize behavioral models in the simulation package.

Data Source Integration

The class integrates data from multiple sources:

• OECD economic indicators
• Eurostat business statistics
• Compustat firm-level data
• National accounts data

Initial State Processing

The class processes:

• Industry-level aggregates
• Firm size distributions
• Financial positions
• Production parameters

Parameter Estimation

Key parameters estimated include:

• Productivity metrics
• Input-output relationships
• Tax rates
• Interest rates

Factory Methods

The class provides a factory method from_readers that creates a DefaultSyntheticFirms instance by:

1. Reading and processing data from various sources
2. Initializing firm-level data based on industry statistics
3. Setting up financial relationships and parameters
4. Calculating initial conditions for firm operations

Additional Methods

• set_taxes_paid_on_production: Sets production taxes based on rates
• set_interest_paid: Calculates interest payments on deposits and loans
• set_firm_profits: Computes firm profits from sales and costs
• set_unit_costs: Calculates per-unit production costs
• set_corporate_taxes_paid: Computes corporate tax payments
• set_firm_debt_installments: Sets up debt payment schedules

Implementation

Module for preprocessing default synthetic firm data.

This module provides a concrete implementation for preprocessing firm-level data using standard data sources (OECD, Eurostat, Compustat). Key preprocessing includes:

1. Data Source Integration:
2. OECD economic indicators
3. Eurostat business statistics
4. Compustat firm-level data

5. National accounts data

6. Initial State Processing:

7. Industry-level aggregates
8. Firm size distributions
9. Financial positions

10. Production parameters

11. Parameter Estimation:

12. Productivity metrics
13. Input-output relationships
14. Tax rates
15. Interest rates

Note

This module is NOT used for simulating firm behavior. It only handles the preprocessing and organization of data from standard sources that will later be used to initialize behavioral models in the simulation package.

DefaultSyntheticFirms

Container for preprocessed firm data using standard data sources.

This class provides a concrete implementation for preprocessing firm-level data using standard data sources (OECD, Eurostat, Compustat). It processes and organizes data about firms' characteristics, financial positions, and production parameters. It does NOT implement any firm behavior - it only handles data preprocessing.

The preprocessing workflow includes: 1. Data Collection: - Reading from standard data sources - Handling missing data - Currency conversion - Scale adjustment

1. Firm Structure:
2. Industry classification
3. Size distribution estimation
4. Employee allocation

5. Bank relationship mapping

6. Financial Processing:

7. Balance sheet construction
8. Income statement elements
9. Tax calculations

10. Interest computations

11. Production Parameters:

12. Input requirements
13. Productivity metrics
14. Cost structures
15. Initial inventory levels

Note

This is a data container class. The actual firm behavior is implemented in the simulation package, which uses this preprocessed data for initialization.

__init__(country_name: str, scale: int, year: int, industries: list[str], number_of_firms_by_industry: np.ndarray, firm_data: pd.DataFrame, intermediate_inputs_stock: np.ndarray, used_intermediate_inputs: np.ndarray, capital_inputs_stock: np.ndarray, used_capital_inputs: np.ndarray, total_firm_deposits: float, total_firm_debt: float, capital_inputs_productivity_matrix: np.ndarray, intermediate_inputs_productivity_matrix: np.ndarray, capital_inputs_depreciation_matrix: np.ndarray, labour_productivity_by_industry: np.ndarray)

from_readers(readers: DataReaders, country_name: Country, year: int, industries: list[str], scale: int, industry_data: dict[str, pd.DataFrame], n_employees_per_industry: np.ndarray, firm_configuration: FirmsDataConfiguration, exchange_rate_from_eur: float = 1.0, proxy_country: Optional[Country] = None, emission_factors: Optional[EmissionsData] = None) -> DefaultSyntheticFirms classmethod

reset_function_parameters(capital_inputs_utilisation_rate: float, initial_inventory_to_input_fraction: float, intermediate_inputs_utilisation_rate: float, zero_initial_debt: bool, zero_initial_deposits: bool)

set_taxes_paid_on_production(taxes_less_subsidies_rates: np.ndarray) -> None

set_interest_paid(interest_rate_on_firm_deposits: np.ndarray, overdraft_rate_on_firm_deposits: np.ndarray, short_term_loan_interest: np.ndarray, long_term_loan_interest: np.ndarray) -> None

set_firm_profits(intermediate_inputs_productivity_matrix: np.ndarray, capital_inputs_depreciation_matrix: np.ndarray, tau_sif: float) -> None

set_unit_costs(intermediate_inputs_productivity_matrix: np.ndarray, capital_inputs_depreciation_matrix: np.ndarray, tau_sif: float) -> None

set_corporate_taxes_paid(tau_firm: float) -> None

set_firm_debt_installments(long_term_installments: np.ndarray, short_term_installments: np.ndarray) -> None

set_additional_initial_conditions(industry_data: dict[str, pd.DataFrame], synthetic_banks: SyntheticBanks, long_term_loans: LongtermLoans, short_term_loans: ShorttermLoans, tax_data: TaxData) -> None