SyntheticCentralGovernment

The SyntheticCentralGovernment class is an abstract base class that serves as a container for preprocessed central government data. It manages government data including revenue streams, social benefits, tax collections, and financial positions.

Core Functionality

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

• Total Unemployment Benefits: Initial unemployment benefit levels
• Other Social Benefits: Initial levels of other social transfers
• Debt: Initial central government debt level
• Bank Equity Injection: Initial bank support levels
• Total Social Housing Rent: Initial social housing revenue
• Taxes on Production: Initial production tax revenue
• VAT: Initial value-added tax revenue
• Capital Formation Taxes: Initial capital tax revenue
• Export Taxes: Initial export tax revenue
• Corporate Taxes: Initial corporate tax revenue
• Employer SI Tax: Initial employer social insurance revenue
• Employee SI Tax: Initial employee social insurance revenue
• Income Taxes: Initial income tax revenue
• Rental Income Taxes: Initial rental tax revenue
• Revenue: Total initial revenue
• Taxes on Products: Initial product tax revenue

Key Attributes

• country_name: Country identifier for data collection
• year: Reference year for preprocessing
• central_gov_data: Main DataFrame containing government information
• other_benefits_model: Model for estimating other benefits
• unemployment_benefits_model: Model for estimating unemployment benefits

Main Methods

• update_fields: Updates preprocessed government data based on other economic agents
• set_revenue: Sets initial revenue values in the preprocessed government data

Implementation

Module for preprocessing synthetic central government data.

This module provides an abstract base class for preprocessing and organizing central government data that will be used to initialize behavioral models. Key preprocessing includes:

1. Data Collection and Organization:
2. Government revenue data preparation
3. Social benefits data processing

4. Tax revenue calculations

5. Financial Data Processing:

6. Tax revenue estimation
7. Benefits model parameter estimation

8. Revenue stream organization

9. Relationship Data:

10. Government-household transfers
11. Government-firm tax relationships
12. Social housing management

Note

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

SyntheticCentralGovernment

Abstract base class for preprocessing and organizing central government data.

This class provides a framework for collecting and organizing government data that will be used to initialize behavioral models. It is NOT used for simulating government behavior - it only handles data preprocessing.

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

• Total Unemployment Benefits: Initial unemployment benefit levels
• Other Social Benefits: Initial levels of other social transfers
• Debt: Initial central government debt level
• Bank Equity Injection: Initial bank support levels
• Total Social Housing Rent: Initial social housing revenue
• Taxes on Production: Initial production tax revenue
• VAT: Initial value-added tax revenue
• Capital Formation Taxes: Initial capital tax revenue
• Export Taxes: Initial export tax revenue
• Corporate Taxes: Initial corporate tax revenue
• Employer SI Tax: Initial employer social insurance revenue
• Employee SI Tax: Initial employee social insurance revenue
• Income Taxes: Initial income tax revenue
• Rental Income Taxes: Initial rental tax revenue
• Revenue: Total initial revenue
• Taxes on Products: Initial product tax revenue

Note

This is a data container class. The actual government behavior (spending decisions, tax adjustments, etc.) is implemented in the simulation package, which uses this preprocessed data for initialization.

Attributes:

Name	Type	Description
country_name	str	Country identifier for data collection
year	int	Reference year for preprocessing
central_gov_data	DataFrame	Preprocessed government data
other_benefits_model	Optional[LinearRegression]	Model for estimating other benefits
unemployment_benefits_model	Optional[LinearRegression]	Model for estimating unemployment benefits

country_name = country_name instance-attribute

year = year instance-attribute

central_gov_data = central_gov_data instance-attribute

unemployment_benefits_model = unemployment_benefits_model instance-attribute

other_benefits_model = other_benefits_model instance-attribute

__init__(country_name: str, year: int, central_gov_data: pd.DataFrame, other_benefits_model: Optional[LinearRegression], unemployment_benefits_model: Optional[LinearRegression]) abstractmethod

Initialize the central government data container.

Parameters:

Name	Type	Description	Default
country_name	str	Country identifier for data collection	required
year	int	Reference year for preprocessing	required
central_gov_data	DataFrame	Initial data to preprocess	required
other_benefits_model	Optional[LinearRegression]	Model for estimating other benefits	required
unemployment_benefits_model	Optional[LinearRegression]	Model for estimating unemployment benefits	required

update_fields(tax_data: TaxData, synthetic_population: SyntheticPopulation, synthetic_firms: SyntheticFirms, synthetic_banks: SyntheticBanks, industry_data: dict[str, pd.DataFrame]) -> None

Update preprocessed government data fields based on other economic agents.

This method processes and organizes data from various economic agents to prepare: 1. Social housing revenue data 2. Tax revenue calculations 3. Income and consumption data 4. Financial asset data

The preprocessing steps: 1. Calculate social housing metrics 2. Process firm and bank tax data 3. Calculate income-based revenues 4. Process consumption and investment taxes

Parameters:

Name	Type	Description	Default
tax_data	TaxData	Tax rates and parameters	required
synthetic_population	SyntheticPopulation	Population data container	required
synthetic_firms	SyntheticFirms	Firm data container	required
synthetic_banks	SyntheticBanks	Bank data container	required
industry_data	dict[str, DataFrame]	Industry-level economic data	required

set_revenue(total_social_housing_rent: float, firm_taxes_and_subsidies: float, firm_corporate_taxes: float, bank_corporate_taxes: float, firm_employer_si_tax: float, household_vat: float, export_tax: float, employee_si_tax: float, income_tax: float, rental_income_tax: float, household_gross_capital_inputs: float, cf_tax: float) -> None

Set initial revenue values in the preprocessed government data.

This method organizes and stores initial revenue values for: 1. Social housing revenue 2. Various tax revenues 3. Total government revenue 4. Product-specific taxes

The preprocessing steps: 1. Store individual revenue components 2. Calculate and store total revenue 3. Calculate and store product tax totals

Parameters:

Name	Type	Description	Default
total_social_housing_rent	float	Initial social housing revenue	required
firm_taxes_and_subsidies	float	Initial production tax revenue	required
firm_corporate_taxes	float	Initial firm tax revenue	required
bank_corporate_taxes	float	Initial bank tax revenue	required
firm_employer_si_tax	float	Initial employer SI revenue	required
household_vat	float	Initial VAT revenue	required
export_tax	float	Initial export tax revenue	required
employee_si_tax	float	Initial employee SI revenue	required
income_tax	float	Initial income tax revenue	required
rental_income_tax	float	Initial rental tax revenue	required
household_gross_capital_inputs	float	Initial capital inputs	required
cf_tax	float	Capital formation tax rate	required

DefaultSyntheticCentralGovernment

DefaultSyntheticCentralGovernment is a concrete implementation of SyntheticCentralGovernment that provides preprocessing of central government data using standard data sources. 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:

• Historical benefits data
• Tax revenue data
• Government debt data
• Bank support data

Initial State Processing

The class processes:

• Benefits data organization
• Tax revenue parameter estimation
• Initial state calculations
• Model parameter estimation

Parameter Estimation

Key parameters estimated include:

• Benefits models (unemployment and other benefits)
• Tax revenue parameters
• Initial debt levels
• Bank support levels

Factory Methods

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

1. Reading and processing data from various sources
2. Estimating benefits models using historical data
3. Setting up initial state data
4. Calculating initial parameters

Additional Methods

• build_unemployment_model: Estimates a model for preprocessing unemployment benefits data
• build_other_benefits_model: Estimates a model for preprocessing other benefits data

Implementation

Module for preprocessing synthetic central government data.

This module provides a concrete implementation for preprocessing central government data that will be used to initialize behavioral models. Key preprocessing includes:

1. Data Collection and Processing:
2. Historical benefits data preparation
3. Tax revenue data organization

4. Initial state calculations

5. Parameter Estimation:

6. Benefits model estimation
7. Tax revenue estimation

8. Data validation and consistency checks

9. Data Organization:

10. Standard data preprocessing
11. Exogenous data preprocessing
12. Historical data processing

Note

This module is NOT used for simulating government behavior. It preprocesses data that will be used to initialize behavioral models in the simulation package. The actual government decisions and operations are implemented elsewhere.

DefaultSyntheticCGovernment

Default implementation for preprocessing central government data.

This class preprocesses and organizes central government data by collecting historical data and estimating parameters. These will be used to initialize behavioral models, but this class does NOT implement any behavioral logic.

The class provides two preprocessing paths: 1. Standard preprocessing using historical data 2. Exogenous data preprocessing when available

The preprocessing includes: - Benefits data organization - Tax revenue parameter estimation - Initial state calculations - Model parameter estimation

Note

This is a data container class. The actual government behavior (spending, tax policy, etc.) is implemented in the simulation package, which uses these preprocessed parameters.

Attributes:

Name	Type	Description
country_name	str	Country identifier for data collection
year	int	Reference year for preprocessing
central_gov_data	DataFrame	Preprocessed government data
other_benefits_model	Optional[LinearRegression]	Model for estimating other benefits
unemployment_benefits_model	Optional[LinearRegression]	Model for estimating unemployment benefits

__init__(country_name: str, year: int, central_gov_data: pd.DataFrame, other_benefits_model: Optional[LinearRegression], unemployment_benefits_model: Optional[LinearRegression])

Initialize the central government data container.

Parameters:

Name	Type	Description	Default
country_name	str	Country identifier for data collection	required
year	int	Reference year for preprocessing	required
central_gov_data	DataFrame	Initial data to preprocess	required
other_benefits_model	Optional[LinearRegression]	Model for estimating other benefits	required
unemployment_benefits_model	Optional[LinearRegression]	Model for estimating unemployment benefits	required

from_readers(readers: DataReaders, country_name: Country, year: int, year_range: int = 10, regression_window: int = 48, equity_injection: float = 0.0) -> SyntheticCentralGovernment classmethod

Create a preprocessed central government data container using standard data sources.

This method preprocesses data using historical sources to prepare: 1. Benefits data (unemployment and other benefits) 2. Initial debt levels 3. Parameter estimates for benefits models

The preprocessing steps: 1. Check for exogenous data availability 2. If available, estimate benefits models using historical data 3. If not available, use direct historical values 4. Prepare initial state data

Parameters:

Name	Type	Description	Default
readers	DataReaders	Data source readers	required
country_name	Country	Country to preprocess data for	required
year	int	Reference year for preprocessing	required
year_range	int	Years of historical data to use. Defaults to 10.	10
regression_window	int	Months of data for model estimation. Defaults to 48.	48
equity_injection	float	Initial bank support level. Defaults to 0.0.	0.0

Returns:

Name	Type	Description
SyntheticCentralGovernment	SyntheticCentralGovernment	Container with preprocessed government data

build_unemployment_model(benefits_inflation_data: pd.DataFrame, regression_window: int = 48)

Estimate a model for preprocessing unemployment benefits data.

This function estimates parameters for preprocessing unemployment benefits by: 1. Computing historical growth ratios 2. Fitting a model based on inflation and unemployment 3. Preparing parameters for initialization

Parameters:

Name	Type	Description	Default
benefits_inflation_data	DataFrame	Historical benefits and inflation data	required
regression_window	int	Months of data for estimation. Defaults to 48.	48

Returns:

Name	Type	Description
LinearRegression		Estimated model for preprocessing, or None if insufficient data

build_other_benefits_model(benefits_inflation_data: pd.DataFrame, regression_window: int = 48)

Estimate a model for preprocessing other benefits data.

This function estimates parameters for preprocessing non-unemployment benefits by: 1. Computing historical growth ratios 2. Fitting a model based on inflation and unemployment 3. Preparing parameters for initialization

Parameters:

Name	Type	Description	Default
benefits_inflation_data	DataFrame	Historical benefits and inflation data	required
regression_window	int	Months of data for estimation. Defaults to 48.	48

Returns:

Name	Type	Description
LinearRegression		Estimated model for preprocessing, or None if insufficient data