From 2e69aa8861ed57df0d1b503a3d815f9f9c345482 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 31 May 2023 14:30:28 +0000 Subject: [PATCH] build based on 356d1b0 --- dev/index.html | 2 +- dev/lib/internals/index.html | 4 ++-- dev/lib/public/index.html | 8 ++++---- dev/model/index.html | 2 +- dev/search/index.html | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dev/index.html b/dev/index.html index 51f8855..701f2eb 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · RauhValladaresEsteban2023.jl
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RauhValladaresEsteban2023.jl

This package solves the on-the-job human capital accumulation model with heterogeneity in the initial human capital endowment and the lifelong ability to accumulate human capital, and endogenous labor supply at the extensive and intensive margins in On the Black-White Gaps in Labor Supply and Earnings over the Lifecycle in the US by Christopher Rauh and Arnau Valladares-Esteban.

In Model, we provide the equations that describe the model that this package solves. All the functions and types used in the package are documented in the Library: public and internal functionality.

+Home · RauhValladaresEsteban2023.jl
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RauhValladaresEsteban2023.jl

This package solves the on-the-job human capital accumulation model with heterogeneity in the initial human capital endowment and the lifelong ability to accumulate human capital, and endogenous labor supply at the extensive and intensive margins in On the Black-White Gaps in Labor Supply and Earnings over the Lifecycle in the US by Christopher Rauh and Arnau Valladares-Esteban.

In Model, we provide the equations that describe the model that this package solves. All the functions and types used in the package are documented in the Library: public and internal functionality.

diff --git a/dev/lib/internals/index.html b/dev/lib/internals/index.html index edee3bf..336f956 100644 --- a/dev/lib/internals/index.html +++ b/dev/lib/internals/index.html @@ -1,3 +1,3 @@ -Internal · RauhValladaresEsteban2023.jl
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Internals Documentation

Index

Internals Interface

RauhValladaresEsteban2023.aggregatemeans — Method
aggregatemeans(v::Vector{MicroData}, w::Vector{Float64})

Computes aggregate means from the vector of MicroData instances v using weights w. Returns an AggregateData instance.

Arguments

  • v::Vector{MicroData}: A vector of MicroData instances containing agent/individual-level data.
  • w::Vector{Float64}: A vector of weights. The length of w must be equal to the length of v.
source
RauhValladaresEsteban2023.fh′ — Method
fh′(h::Float64, a::Float64, n::Float64, p::ModelParameters)::Float64

Returns the value of human capital in the next period.

Arguments

  • h::Float64: Human capital in the current period.
  • a::Float64: Ability to acumulate human capital.
  • n::Float64: Hours worked.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ϕ and δ.
source
RauhValladaresEsteban2023.findn — Method
findn(h::Float64, a::Float64, uᴴ::Float64, fV′, p::ModelParameters)

Returns the optimal hours worked and the utility value associated to the optimal hours.

Arguments

  • h::Float64: Human capital.
  • a::Float64: Ability to accumulate human capital.
  • uᴴ::Float64: Utility of staying at home.
  • fV′: Function computing the continuation value of the next period with respect to hours worked in the current period.
  • p::ModelParameters: Instance of ModelParameters containing parameter values β.
source
RauhValladaresEsteban2023.fuᴴ — Method
fuᴴ(h::Float64, κ::Float64, j::Int64, p::ModelParameters)::Float64

Returns the utility value of staying at come.

Arguments

  • h::Float64: Human capital.
  • κ::Float64: Realisation of the stay-at-home value shock.
  • j::Int64: Age.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ψ, γ, η, κ₀, κ₁, and κ₂.
source
RauhValladaresEsteban2023.fuᵂ — Method
fuᵂ(h::Float64, n::Float64, p::ModelParameters)::Float64

Returns the utility value of working.

Arguments

  • h::Float64: Human capital.
  • n::Float64: Hours worked.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ω, γ, and ψ.
source
RauhValladaresEsteban2023.gss — Function
gss(f::Function, a::Float64, b::Float64, tol::Float64 = 1e-6)

Returns argument that maximises value of function f over the interval [a,b] and value of function f at the maximising point using the Golden search method.

Arguments:

  • f: Univariate function.
  • a: Lower bound.
  • b: Upper bound.
  • tol: Tolerance of the stopping.
source
RauhValladaresEsteban2023.loadCSVinput — Method
loadCSVinput(inputname::String, group::String)

Loads data from a CSV file located in the model_inputs folder and returns a DataFrame containing data specific to the provided group.

Arguments

  • inputname::String: The relative path to the CSV file, including the file name. For example, model_inputs/parameters.csv.
  • group::String: The name of the column in the CSV file that contains the group information. For example: Black.

Notes

  • The function assumes that the CSV file has a header row containing column names.
  • The group column in the loaded DataFrame is converted to lowercase for consistent matching.
source
RauhValladaresEsteban2023.loaddeciledata — Function
loaddeciledata(filename::String, group::String = "")

Loads decile data from a predefined CSV file and returns it as a MicroData structure.

Arguments

  • filename::String: The path to the CSV file containing the decile data. Example: nlsydata/afqt_decile_1.csv.
  • group::String: (optional) The group to which the data should be restricted. Valid values are "Black", "White", or an empty string (default).

Returns

A MicroData instance containing the loaded decile data.

Notes

  • The CSV file is expected to have the following columns: "wage", "hours", "employed", and "weights" (or columns that contain these strings as substrings).
  • If group is specified, the data will be restricted to the specified group ("Black" or "White") based on the values in the "black" column.

Examples

data = loaddeciledata("nlsydata/afqt_decile_1.csv")  # Load all decile data
-data = loaddeciledata("nlsydata/afqt_decile_1.csv", "black")  # Load decile data for the black group only
source
RauhValladaresEsteban2023.micro2aggregate — Method
micro2aggregate(f, d::MicroData)

Computes aggregate statistics from the MicroData instance d using the specified statistical function f. The function computes aggregate statistics for wage, hours, employment, total income (all individuals), and income (employed individuals).

Arguments

  • f: A function that computes the desired aggregate statistic.
  • d::MicroData: A MicroData instance containing agent/individual-level data.

Note

  • The function handles missing values by replacing them with zeros for the total income variable.
source
RauhValladaresEsteban2023.policyfunction — Method
policyfunction(ia::Int64, p::ModelParameters)

Returns the policy function for optimal labor supply decisions.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.raw2micro — Method
raw2micro(p::ModelParameters; hours::Matrix{Float64}, hk::Matrix{Float64}, empsample::Bool = true)

Converts simulation results of hours worked and human capital to MicroData format optionally applying constraints that allow comparison to NLSY data.

Arguments

  • p::ModelParameters: Instance of ModelParameters containing relevant parameter values.
  • hours::Matrix{Float64}: Matrix of simulated hours worked.
  • hk::Matrix{Float64}: Matrix of simulated human capital.
  • empsample::Bool = true: (Optional) Flag indicating whether to use treshold to allocate employment indicator.
source
RauhValladaresEsteban2023.simulate — Function
simulate(ia::Int64, iκs::Matrix{Int64}, n::Vector{Matrix{Float64}}, p::ModelParameters, empsample::Bool = true)

Returns MicroData instance after simulating economy based on the realisations of the staying-at-home shock (iκs) and the policy function (n) for one level of ability to accumulate human capital.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • iκs::Matrix{Int64}: Matrix of the indices of realisations of the staying-at-home shock.
  • n::Vector{Matrix{Float64}}: Policy function for hours worked.
  • p::ModelParameters: Instance of ModelParameters containing relevant parameter values.
  • empsample::Bool = true: Flag indicating whether to use treshold to allocate employment indicator.
source
RauhValladaresEsteban2023.simulateindices — Method
simulateindices(MC::MarkovChain, nagents::Int64, nperiods::Int64; t₀::Int64 = 100)::Matrix{Int64}

Simulates indices for a given number of agents and periods using a Markov chain.

Arguments

  • MC::MarkovChain: MarkovChain instance.
  • nagents::Int64: Number of agents.
  • nperiods::Int64: Number of periods to simulate.
  • t₀::Int64 (optional): Amount of periods ignored when returning simulation (default: 100).

Note

  • The final matrix of simulated indices is returned, starting from period t₀ + 1.
source
RauhValladaresEsteban2023.simulateraw — Method
simulateraw(ia::Int64, iκs::Matrix{Int64}, n::Vector{Matrix{Float64}}, p::ModelParameters)

Simulates hours worked and human capital based on realisations of the staying-at-home shock (iκs) and the policy function (n) for one level of ability to accumulate human capital.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • iκs::Matrix{Int64}: Matrix of the indices of realisations of the staying-at-home shock.
  • n::Vector{Matrix{Float64}}: Policy function for hours worked.
  • p::ModelParameters: Instance of ModelParameters containing model relevant parameter values.
source
RauhValladaresEsteban2023.statsf — Method
statsf(f, y::Matrix, w::Matrix)

Applies the statistical function f to a matrix y weighted by corresponding elements in the matrix w, after skipping missing values.

Arguments

  • f: A statistical function to apply to the matrix y weighted by w.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
  • w::Matrix: A matrix containing the weights to be applied to each element of y. The size of w should match the size of y.
source
RauhValladaresEsteban2023.statsf — Method
statsf(f, y::Matrix)

Applies the statistical function f to the matrix y, skipping missing values.

Arguments

  • f: A statistical function to apply to the matrix y.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
source
RauhValladaresEsteban2023.AgeData — Type

AgeData

A composite type that represents a summary statistic by age for hourly wages, hours worked, and employment status. For example, average wages by age.

Fields

  • wage::Vector{Float64}: Hourly wage.
  • hours::Vector{Float64}: Hours worked.
  • employed::Vector{Float64}: Employment status.
source
RauhValladaresEsteban2023.MicroData — Type

MicroData

A composite type that represents individual-level data on wages, hours worked, employment status, and individual population weights. Each row corresponds to an agent/individual, and each column represents a period.

Fields

  • wage::Matrix{Union{Missing, Float64}}: Hourly wage.
  • hours::Matrix{Union{Missing, Float64}}: Hours worked.
  • employed::Matrix{Union{Missing, Bool}}: Employment indicator.
  • weights::Matrix{Union{Missing, Int64}}: Individual frequency weights.
source
RauhValladaresEsteban2023.ModelParameters — Type
ModelParameters

Composite type that contains all the parameters that describe a model economy.

Fields

Prices

  • r::Float64: Interest rate.
  • ω::Float64: Wage rate.

Human capital function parameters

  • ϕ::Float64: Curvature with respect to hours worked.
  • δ::Float64: Depreciation.

Preferences

  • β::Float64: Discount rate.
  • ψ::Float64: Weight of leisure in utility function.
  • γ::Float64: Curvature of leisure in utility function.
  • κ₀::Float64: Intercept value staying at home.
  • κ₁::Float64: Slope value staying at home.
  • κ₂::Float64: Curvature value staying at home.
  • η::Float64: Curvature of human capital in value staying at home.
  • ρ_κ::Float64: Persistance value staying at home.
  • σ_κ::Float64: Standard deviation value staying at home.

Model size

  • gp_h::Int64: Grid points human capital policy funtion.
  • gp_κ::Int64. Grid points value staying at home.
  • J::Int64: Number of periods.
  • nagents::Int64: Number of agents in Monte Carlo per ability level.
  • nlevels::Int64: umber of ability levels.

Grids & Markov Chains

  • grid_a::Vector{Float64}: Grid ability.
  • grid_h₁::Vector{Float64}: Grid initial human capital.
  • grids_h::Array{Vector{Float64}, 2}: Array of grids of human capital.
  • grid_κ::Vector{Float64}: Grid stochastic value staying at home.
  • MCκ::MarkovChain: Markov Chain utility staying at home.
  • P_κ::Array{Float64,2}: Transition probabilities utility staying at home.

Conversion/comparison to real data

  • popshare::Vector{Float64}: Population shares; weight of each AFQT decile.
  • lbhours::Float64: Lower bound hours worked for being employed.
  • initialage::Int64: Real age at model/data entry.
  • agerange::UnitRange{Int64}: Range of ages in model/data.
source
+Internal · RauhValladaresEsteban2023.jl
Edit on GitHub

Internals Documentation

Index

Internals Interface

RauhValladaresEsteban2023.aggregatemeans — Method
aggregatemeans(v::Vector{MicroData}, w::Vector{Float64})

Computes aggregate means from the vector of MicroData instances v using weights w. Returns an AggregateData instance.

Arguments

  • v::Vector{MicroData}: A vector of MicroData instances containing agent/individual-level data.
  • w::Vector{Float64}: A vector of weights. The length of w must be equal to the length of v.
source
RauhValladaresEsteban2023.fh′ — Method
fh′(h::Float64, a::Float64, n::Float64, p::ModelParameters)::Float64

Returns the value of human capital in the next period.

Arguments

  • h::Float64: Human capital in the current period.
  • a::Float64: Ability to acumulate human capital.
  • n::Float64: Hours worked.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ϕ and δ.
source
RauhValladaresEsteban2023.findn — Method
findn(h::Float64, a::Float64, uᴴ::Float64, fV′, p::ModelParameters)

Returns the optimal hours worked and the utility value associated to the optimal hours.

Arguments

  • h::Float64: Human capital.
  • a::Float64: Ability to accumulate human capital.
  • uᴴ::Float64: Utility of staying at home.
  • fV′: Function computing the continuation value of the next period with respect to hours worked in the current period.
  • p::ModelParameters: Instance of ModelParameters containing parameter values β.
source
RauhValladaresEsteban2023.fuᴴ — Method
fuᴴ(h::Float64, κ::Float64, j::Int64, p::ModelParameters)::Float64

Returns the utility value of staying at come.

Arguments

  • h::Float64: Human capital.
  • κ::Float64: Realisation of the stay-at-home value shock.
  • j::Int64: Age.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ψ, γ, η, κ₀, κ₁, and κ₂.
source
RauhValladaresEsteban2023.fuᵂ — Method
fuᵂ(h::Float64, n::Float64, p::ModelParameters)::Float64

Returns the utility value of working.

Arguments

  • h::Float64: Human capital.
  • n::Float64: Hours worked.
  • p::ModelParameters: Instance of ModelParameters containing parameter values ω, γ, and ψ.
source
RauhValladaresEsteban2023.gss — Function
gss(f::Function, a::Float64, b::Float64, tol::Float64 = 1e-6)

Returns argument that maximises value of function f over the interval [a,b] and value of function f at the maximising point using the Golden search method.

Arguments:

  • f: Univariate function.
  • a: Lower bound.
  • b: Upper bound.
  • tol: Tolerance of the stopping.
source
RauhValladaresEsteban2023.loadCSVinput — Method
loadCSVinput(inputname::String, group::String)

Loads data from a CSV file located in the model_inputs folder and returns a DataFrame containing data specific to the provided group.

Arguments

  • inputname::String: The relative path to the CSV file, including the file name. For example, model_inputs/parameters.csv.
  • group::String: The name of the column in the CSV file that contains the group information. For example: Black.

Notes

  • The function assumes that the CSV file has a header row containing column names.
  • The group column in the loaded DataFrame is converted to lowercase for consistent matching.
source
RauhValladaresEsteban2023.loaddeciledata — Function
loaddeciledata(filename::String, group::String = "")

Loads decile data from a predefined CSV file and returns it as a MicroData structure.

Arguments

  • filename::String: The path to the CSV file containing the decile data. Example: nlsydata/afqt_decile_1.csv.
  • group::String: (optional) The group to which the data should be restricted. Valid values are "Black", "White", or an empty string (default).

Returns

A MicroData instance containing the loaded decile data.

Notes

  • The CSV file is expected to have the following columns: "wage", "hours", "employed", and "weights" (or columns that contain these strings as substrings).
  • If group is specified, the data will be restricted to the specified group ("Black" or "White") based on the values in the "black" column.

Examples

data = loaddeciledata("nlsydata/afqt_decile_1.csv")  # Load all decile data
+data = loaddeciledata("nlsydata/afqt_decile_1.csv", "black")  # Load decile data for the black group only
source
RauhValladaresEsteban2023.micro2aggregate — Method
micro2aggregate(f, d::MicroData)

Computes aggregate statistics from the MicroData instance d using the specified statistical function f. The function computes aggregate statistics for wage, hours, employment, total income (all individuals), and income (employed individuals).

Arguments

  • f: A function that computes the desired aggregate statistic.
  • d::MicroData: A MicroData instance containing agent/individual-level data.

Note

  • The function handles missing values by replacing them with zeros for the total income variable.
source
RauhValladaresEsteban2023.policyfunction — Method
policyfunction(ia::Int64, p::ModelParameters)

Returns the policy function for optimal labor supply decisions.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.raw2micro — Method
raw2micro(p::ModelParameters; hours::Matrix{Float64}, hk::Matrix{Float64}, empsample::Bool = true)

Converts simulation results of hours worked and human capital to MicroData format optionally applying constraints that allow comparison to NLSY data.

Arguments

  • p::ModelParameters: Instance of ModelParameters containing relevant parameter values.
  • hours::Matrix{Float64}: Matrix of simulated hours worked.
  • hk::Matrix{Float64}: Matrix of simulated human capital.
  • empsample::Bool = true: (Optional) Flag indicating whether to use treshold to allocate employment indicator.
source
RauhValladaresEsteban2023.simulate — Function
simulate(ia::Int64, iκs::Matrix{Int64}, n::Vector{Matrix{Float64}}, p::ModelParameters, empsample::Bool = true)

Returns MicroData instance after simulating economy based on the realisations of the staying-at-home shock (iκs) and the policy function (n) for one level of ability to accumulate human capital.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • iκs::Matrix{Int64}: Matrix of the indices of realisations of the staying-at-home shock.
  • n::Vector{Matrix{Float64}}: Policy function for hours worked.
  • p::ModelParameters: Instance of ModelParameters containing relevant parameter values.
  • empsample::Bool = true: Flag indicating whether to use treshold to allocate employment indicator.
source
RauhValladaresEsteban2023.simulateindices — Method
simulateindices(MC::MarkovChain, nagents::Int64, nperiods::Int64; t₀::Int64 = 100)::Matrix{Int64}

Simulates indices for a given number of agents and periods using a Markov chain.

Arguments

  • MC::MarkovChain: MarkovChain instance.
  • nagents::Int64: Number of agents.
  • nperiods::Int64: Number of periods to simulate.
  • t₀::Int64 (optional): Amount of periods ignored when returning simulation (default: 100).

Note

  • The final matrix of simulated indices is returned, starting from period t₀ + 1.
source
RauhValladaresEsteban2023.simulateraw — Method
simulateraw(ia::Int64, iκs::Matrix{Int64}, n::Vector{Matrix{Float64}}, p::ModelParameters)

Simulates hours worked and human capital based on realisations of the staying-at-home shock (iκs) and the policy function (n) for one level of ability to accumulate human capital.

Arguments

  • ia::Int64: Index of the ability to accumulate human capital.
  • iκs::Matrix{Int64}: Matrix of the indices of realisations of the staying-at-home shock.
  • n::Vector{Matrix{Float64}}: Policy function for hours worked.
  • p::ModelParameters: Instance of ModelParameters containing model relevant parameter values.
source
RauhValladaresEsteban2023.statsf — Method
statsf(f, y::Matrix, w::Matrix)

Applies the statistical function f to a matrix y weighted by corresponding elements in the matrix w, after skipping missing values.

Arguments

  • f: A statistical function to apply to the matrix y weighted by w.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
  • w::Matrix: A matrix containing the weights to be applied to each element of y. The size of w should match the size of y.
source
RauhValladaresEsteban2023.statsf — Method
statsf(f, y::Matrix)

Applies the statistical function f to the matrix y, skipping missing values.

Arguments

  • f: A statistical function to apply to the matrix y.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
source
RauhValladaresEsteban2023.AgeData — Type

AgeData

A composite type that represents a summary statistic by age for hourly wages, hours worked, and employment status. For example, average wages by age.

Fields

  • wage::Vector{Float64}: Hourly wage.
  • hours::Vector{Float64}: Hours worked.
  • employed::Vector{Float64}: Employment status.
source
RauhValladaresEsteban2023.MicroData — Type

MicroData

A composite type that represents individual-level data on wages, hours worked, employment status, and individual population weights. Each row corresponds to an agent/individual, and each column represents a period.

Fields

  • wage::Matrix{Union{Missing, Float64}}: Hourly wage.
  • hours::Matrix{Union{Missing, Float64}}: Hours worked.
  • employed::Matrix{Union{Missing, Bool}}: Employment indicator.
  • weights::Matrix{Union{Missing, Int64}}: Individual frequency weights.
source
RauhValladaresEsteban2023.ModelParameters — Type
ModelParameters

Composite type that contains all the parameters that describe a model economy.

Fields

Prices

  • r::Float64: Interest rate.
  • ω::Float64: Wage rate.

Human capital function parameters

  • ϕ::Float64: Curvature with respect to hours worked.
  • δ::Float64: Depreciation.

Preferences

  • β::Float64: Discount rate.
  • ψ::Float64: Weight of leisure in utility function.
  • γ::Float64: Curvature of leisure in utility function.
  • κ₀::Float64: Intercept value staying at home.
  • κ₁::Float64: Slope value staying at home.
  • κ₂::Float64: Curvature value staying at home.
  • η::Float64: Curvature of human capital in value staying at home.
  • ρ_κ::Float64: Persistance value staying at home.
  • σ_κ::Float64: Standard deviation value staying at home.

Model size

  • gp_h::Int64: Grid points human capital policy funtion.
  • gp_κ::Int64. Grid points value staying at home.
  • J::Int64: Number of periods.
  • nagents::Int64: Number of agents in Monte Carlo per ability level.
  • nlevels::Int64: umber of ability levels.

Grids & Markov Chains

  • grid_a::Vector{Float64}: Grid ability.
  • grid_h₁::Vector{Float64}: Grid initial human capital.
  • grids_h::Array{Vector{Float64}, 2}: Array of grids of human capital.
  • grid_κ::Vector{Float64}: Grid stochastic value staying at home.
  • MCκ::MarkovChain: Markov Chain utility staying at home.
  • P_κ::Array{Float64,2}: Transition probabilities utility staying at home.

Conversion/comparison to real data

  • popshare::Vector{Float64}: Population shares; weight of each AFQT decile.
  • lbhours::Float64: Lower bound hours worked for being employed.
  • initialage::Int64: Real age at model/data entry.
  • agerange::UnitRange{Int64}: Range of ages in model/data.
source
diff --git a/dev/lib/public/index.html b/dev/lib/public/index.html index 76596a9..a3b0e6a 100644 --- a/dev/lib/public/index.html +++ b/dev/lib/public/index.html @@ -1,6 +1,6 @@ -Public · RauhValladaresEsteban2023.jl
Edit on GitHub

Public Documentation

Index

Public Interface

RauhValladaresEsteban2023.catmicrodata — Method
catmicrodata(grps::Vector, v::Vector{MicroData})

Concatenates multiple MicroData instances based on groupings provided in grps.

Arguments

  • grps::Vector: Vector of groupings, where each element is a single index (an Int64) or a range of indices (UnitRange{Int64}). The indices correspond to the positions of MicroData instances in the input vector v.
  • v::Vector{MicroData}: Vector of MicroData instances to be concatenated.

Notes

  • The length of the grps vector must be less than or equal to the length of the v vector.
  • Each element in grps specifies a grouping of MicroData instances to be concatenated together.
  • The output vector z has the same length as the grps vector.
source
RauhValladaresEsteban2023.catmicrodata — Method
catmicrodata(v::Vector{MicroData})

Concatenates multiple instances of MicroData structures stored in vector (v) and returns a new MicroData instance with the concatenated data.

Arguments

  • v::Vector{MicroData}: A vector of MicroData instances to be concatenated.

Notes

  • The function assumes that all MicroData instances in the input vector have the same structure.
  • The concatenation is performed by vertically stacking the corresponding fields of each MicroData instance.
  • The resulting MicroData instance will have the same field names as the input instances.
source
RauhValladaresEsteban2023.gap — Method
gap(b::AggregateData, w::AggregateData)

Calculates the gap between two AggregateData instances b and w for each corresponding field.

Arguments

  • b::AggregateData: The numerator AggregateData instance.
  • w::AggregateData: The denominator AggregateData instance.
source
RauhValladaresEsteban2023.gap — Method
gap(b::Float64, w::Float64)

Calculates the gap between the two values b and w as $1 - \frac{b}{w}$.

Arguments

  • b::Float64: The numerator value.
  • w::Float64: The denominator value.
source
RauhValladaresEsteban2023.generateparameters — Method
generateparameters(group::String)

Generates an instance of ModelParameters by loading and processing input data specific to the provided group.

Arguments

  • group::String: The name of the predefined group. Valid options are 'Black' or 'White'.
source
RauhValladaresEsteban2023.groupingmean — Method
groupingmean(grps::Vector, v::Vector{AgeData}, p::ModelParameters)

Computes the weighted mean of each field in the vector of AgeData instances v for specific groupings defined by the indices in grps, using the popshare field from the ModelParameters object p as weights.

Arguments

  • grps::Vector: Vector of groupings, where each element is a single index (an Int64) or a range of indices (UnitRange{Int64}). The indices correspond to the positions of MicroData instances in the input vector v.
  • v::Vector{AgeData}: A vector of AgeData instances.
  • p::ModelParameters: A ModelParameters instance.

Notes

  • The length of the grps vector must be less than or equal to the length of the v vector.
  • Each element in grps specifies a grouping of MicroData instances to be concatenated together.
  • The output vector z has the same length as the grps vector.
source
RauhValladaresEsteban2023.groupingmean — Method
groupingmean(m::Vector{AgeData}, w::Vector{Float64})

Computes the weighted mean of each field in the vector of AgeData instnaces m using the corresponding weights in the vector w.

Arguments

  • m::Vector{AgeData}: A vector of AgeData instances.
  • w::Vector{Float64}: A vector of weights. The length of w must be equal to the length of m.
source
RauhValladaresEsteban2023.latexcf — Method
latexcf(gaps::Dict{String, AggregateData})

Generates a LaTeX table with counterfactual results based on the provided gaps dictionary.

Arguments

  • gaps::Dict{String, AggregateData}: Dictionary containing counterfactual results for different scenarios. The keys are scenario names, and the values are AggregateData instances representing the gaps.

Note

  • This function generates a LaTeX document and saves it as a file named "counterfactuals.tex" in the "tables" directory. The LaTeX document contains a table displaying the counterfactual results.
source
RauhValladaresEsteban2023.loadnlsydata — Function
loadnlsydata(group::String = "")

Loads NLSY (National Longitudinal Survey of Youth) data from the nlsy_data folder and returns it as an vector of MicroData.

Arguments

  • group::String: (optional) The group to which the data should be restricted. Valid values are "Black", "White", or an empty string (default).

Notes

  • The function reads all files in the nlsy_data folder and assumes that each file contains AFQT decile data in the expected format.
  • If group is specified, the data will be restricted to the specified group ("black" or "white") for each file.

Examples

data = loadnlsydata()  # Load NLSY data for all groups
-data = loadnlsydata("black")  # Load NLSY data for the black group only
source
RauhValladaresEsteban2023.meangap — Method
meangap(b::Vector{MicroData}, wb::Vector{Float64}, w::Vector{MicroData}, ww::Vector{Float64})

Calculates the mean gap between the two vectors of MicroData instances, b and w, weighted by the corresponding weight vectors wb and ww.

Arguments

  • b::Vector{MicroData}: The numerator vector of MicroData instances.
  • wb::Vector{Float64}: The weight vector corresponding to the numerator vector.
  • w::Vector{MicroData}: The denominator vector of MicroData instances.
  • ww::Vector{Float64}: The weight vector corresponding to the denominator vector.
source
RauhValladaresEsteban2023.modifyparameters — Method
modifyparameters(p::ModelParameters; np...)

Creates a new instance of ModelParameters by modifying the specified fields while copying the remaining fields from the input p.

Arguments:

  • p::ModelParameters: The original instance of ModelParameters.
  • np...: Keyword arguments representing the fields of ModelParameters to be modified along with their desired values. Example: ψ = 1.0, γ = 2.0.

Note

  • The function assumes that the specified keyword arguments correspond to the fields of ModelParameters.
source
RauhValladaresEsteban2023.modifyparameters — Method
modifyparameters(group::String; np...)

Creates a new instance of ModelParameters by modifying the fields specified in np... while copying the remaining fields from the generateparameters(group) function.

Arguments

  • group::String: The name of the predefined group. It can be either "Black" or "White".
  • np...: Keyword arguments representing the fields of ModelParameters with their desired values. Example: ψ = 1.0, γ = 2.0.

Note

  • The function assumes that the specified keyword arguments correspond to the fields of ModelParameters.
source
RauhValladaresEsteban2023.plotmvsd — Method
plotmvsd(
+Public · RauhValladaresEsteban2023.jl
Edit on GitHub
Public Documentation
Index
Public Interface
RauhValladaresEsteban2023.catmicrodata — Method
catmicrodata(grps::Vector, v::Vector{MicroData})
Concatenates multiple MicroData instances based on groupings provided in grps.
Arguments
  • grps::Vector: Vector of groupings, where each element is a single index (an Int64) or a range of indices (UnitRange{Int64}). The indices correspond to the positions of MicroData instances in the input vector v.
  • v::Vector{MicroData}: Vector of MicroData instances to be concatenated.
Notes
  • The length of the grps vector must be less than or equal to the length of the v vector.
  • Each element in grps specifies a grouping of MicroData instances to be concatenated together.
  • The output vector z has the same length as the grps vector.
source
RauhValladaresEsteban2023.catmicrodata — Method
catmicrodata(v::Vector{MicroData})
Concatenates multiple instances of MicroData structures stored in vector (v) and returns a new MicroData instance with the concatenated data.
Arguments
  • v::Vector{MicroData}: A vector of MicroData instances to be concatenated.
Notes
  • The function assumes that all MicroData instances in the input vector have the same structure.
  • The concatenation is performed by vertically stacking the corresponding fields of each MicroData instance.
  • The resulting MicroData instance will have the same field names as the input instances.
source
RauhValladaresEsteban2023.gap — Method
gap(b::AggregateData, w::AggregateData)
Calculates the gap between two AggregateData instances b and w for each corresponding field.
Arguments
  • b::AggregateData: The numerator AggregateData instance.
  • w::AggregateData: The denominator AggregateData instance.
source
RauhValladaresEsteban2023.gap — Method
gap(b::Float64, w::Float64)
Calculates the gap between the two values b and w as $1 - \frac{b}{w}$.
Arguments
  • b::Float64: The numerator value.
  • w::Float64: The denominator value.
source
RauhValladaresEsteban2023.generateparameters — Method
generateparameters(group::String)
Generates an instance of ModelParameters by loading and processing input data specific to the provided group.
Arguments
  • group::String: The name of the predefined group. Valid options are 'Black' or 'White'.
source
RauhValladaresEsteban2023.groupingmean — Method
groupingmean(grps::Vector, v::Vector{AgeData}, p::ModelParameters)
Computes the weighted mean of each field in the vector of AgeData instances v for specific groupings defined by the indices in grps, using the popshare field from the ModelParameters object p as weights.
Arguments
  • grps::Vector: Vector of groupings, where each element is a single index (an Int64) or a range of indices (UnitRange{Int64}). The indices correspond to the positions of MicroData instances in the input vector v.
  • v::Vector{AgeData}: A vector of AgeData instances.
  • p::ModelParameters: A ModelParameters instance.
Notes
  • The length of the grps vector must be less than or equal to the length of the v vector.
  • Each element in grps specifies a grouping of MicroData instances to be concatenated together.
  • The output vector z has the same length as the grps vector.
source
RauhValladaresEsteban2023.groupingmean — Method
groupingmean(m::Vector{AgeData}, w::Vector{Float64})
Computes the weighted mean of each field in the vector of AgeData instnaces m using the corresponding weights in the vector w.
Arguments
  • m::Vector{AgeData}: A vector of AgeData instances.
  • w::Vector{Float64}: A vector of weights. The length of w must be equal to the length of m.
source
RauhValladaresEsteban2023.latexcf — Method
latexcf(gaps::Dict{String, AggregateData})
Generates a LaTeX table with counterfactual results based on the provided gaps dictionary.
Arguments
  • gaps::Dict{String, AggregateData}: Dictionary containing counterfactual results for different scenarios. The keys are scenario names, and the values are AggregateData instances representing the gaps.
Note
  • This function generates a LaTeX document and saves it as a file named "counterfactuals.tex" in the "tables" directory. The LaTeX document contains a table displaying the counterfactual results.
source
RauhValladaresEsteban2023.loadnlsydata — Function
loadnlsydata(group::String = "")
Loads NLSY (National Longitudinal Survey of Youth) data from the nlsy_data folder and returns it as an vector of MicroData.
Arguments
  • group::String: (optional) The group to which the data should be restricted. Valid values are "Black", "White", or an empty string (default).
Notes
  • The function reads all files in the nlsy_data folder and assumes that each file contains AFQT decile data in the expected format.
  • If group is specified, the data will be restricted to the specified group ("black" or "white") for each file.
Examples
data = loadnlsydata()  # Load NLSY data for all groups
+data = loadnlsydata("black")  # Load NLSY data for the black group only
source
RauhValladaresEsteban2023.meangap — Method
meangap(b::Vector{MicroData}, wb::Vector{Float64}, w::Vector{MicroData}, ww::Vector{Float64})
Calculates the mean gap between the two vectors of MicroData instances, b and w, weighted by the corresponding weight vectors wb and ww.
Arguments
  • b::Vector{MicroData}: The numerator vector of MicroData instances.
  • wb::Vector{Float64}: The weight vector corresponding to the numerator vector.
  • w::Vector{MicroData}: The denominator vector of MicroData instances.
  • ww::Vector{Float64}: The weight vector corresponding to the denominator vector.
source
RauhValladaresEsteban2023.modifyparameters — Method
modifyparameters(p::ModelParameters; np...)
Creates a new instance of ModelParameters by modifying the specified fields while copying the remaining fields from the input p.
Arguments:
  • p::ModelParameters: The original instance of ModelParameters.
  • np...: Keyword arguments representing the fields of ModelParameters to be modified along with their desired values. Example: ψ = 1.0, γ = 2.0.
Note
  • The function assumes that the specified keyword arguments correspond to the fields of ModelParameters.
source
RauhValladaresEsteban2023.modifyparameters — Method
modifyparameters(group::String; np...)
Creates a new instance of ModelParameters by modifying the fields specified in np... while copying the remaining fields from the generateparameters(group) function.
Arguments
  • group::String: The name of the predefined group. It can be either "Black" or "White".
  • np...: Keyword arguments representing the fields of ModelParameters with their desired values. Example: ψ = 1.0, γ = 2.0.
Note
  • The function assumes that the specified keyword arguments correspond to the fields of ModelParameters.
source
RauhValladaresEsteban2023.plotmvsd — Method
plotmvsd(
     fn::Symbol,
     m::Vector{AgeData},
     d::Vector{AgeData};
@@ -17,7 +17,7 @@
     xlbl = "Age",
     flllph = 0.1,
     zα = 1.96
-    )
Plots multiple subplots the model simulated data and real data with error bars representing the standard error.
Arguments
  • fn::Symbol: Field name to plot from AgeData instances.
  • m::Vector{AgeData}: Vector of AgeData structures of model simulated data.
  • d::Vector{AgeData}: Vector of AgeData structures of real data.
  • de::Union{Vector{AgeData}, Nothing} = nothing: Optional vector of AgeData instances representing standard errors for the data. If not provided (default), error bars will not be shown.
  • plt_ttl::String = "": Optional title for the overall plot.
  • lyt::Tuple{Int64, Int64} = (1, 5): Layout of subplots in rows and columns.
  • sz::Tuple{Int64, Int64} = (2000, 500): Size of the overall plot.
  • mrgn::Tuple{Int64, Symbol} = (50, :px): Margin size and unit for the overall plot.
  • ylms::Tuple{Float64, Float64} = (0, NaN): Y-axis limits for the overall plot.
  • haxis: Horizontal axis values for each subplot. Defaults to 1:length(d).
  • spttls::Vector{String} = fill("", length(d)): Titles for each subplot. Defaults to an empty string for each subplot.
  • mlbl::String = "Model": Label for the model predictions in the legend.
  • dlbl::String = "Data": Label for the data in the legend.
  • xlbl::String = "Age": Label for the x-axis.
  • flllph::Float64 = 0.1: Fill alpha value for the error bars. Defaults to 0.1.
  • zα::Float64 = 1.96: Z-value corresponding to the desired level of confidence for the error bars. Defaults to 1.96, representing the 95% confidence level.
source
RauhValladaresEsteban2023.plotmvsd — Method
plotmvsd(
+    )
Plots multiple subplots the model simulated data and real data with error bars representing the standard error.
Arguments
  • fn::Symbol: Field name to plot from AgeData instances.
  • m::Vector{AgeData}: Vector of AgeData structures of model simulated data.
  • d::Vector{AgeData}: Vector of AgeData structures of real data.
  • de::Union{Vector{AgeData}, Nothing} = nothing: Optional vector of AgeData instances representing standard errors for the data. If not provided (default), error bars will not be shown.
  • plt_ttl::String = "": Optional title for the overall plot.
  • lyt::Tuple{Int64, Int64} = (1, 5): Layout of subplots in rows and columns.
  • sz::Tuple{Int64, Int64} = (2000, 500): Size of the overall plot.
  • mrgn::Tuple{Int64, Symbol} = (50, :px): Margin size and unit for the overall plot.
  • ylms::Tuple{Float64, Float64} = (0, NaN): Y-axis limits for the overall plot.
  • haxis: Horizontal axis values for each subplot. Defaults to 1:length(d).
  • spttls::Vector{String} = fill("", length(d)): Titles for each subplot. Defaults to an empty string for each subplot.
  • mlbl::String = "Model": Label for the model predictions in the legend.
  • dlbl::String = "Data": Label for the data in the legend.
  • xlbl::String = "Age": Label for the x-axis.
  • flllph::Float64 = 0.1: Fill alpha value for the error bars. Defaults to 0.1.
  • zα::Float64 = 1.96: Z-value corresponding to the desired level of confidence for the error bars. Defaults to 1.96, representing the 95% confidence level.
source
RauhValladaresEsteban2023.plotmvsd — Method
plotmvsd(
     m::Vector{Float64},
     d::Vector{Float64};
     de::Union{Vector{Float64}, Nothing} = nothing,
@@ -28,4 +28,4 @@
     xlbl = "Age",
     flllph = 0.1,
     zα = 1.96
-    )
Plots the model simulated data and real data with error bars representing the standard error.
Arguments
  • m::Vector{Float64}: Model simulated data.
  • d::Vector{Float64}: Real data.
  • de::Union{Vector{Float64}, Nothing} = nothing: Optional vector of standard errors for the data. If not provided (default), error bars will not be shown.
  • haxis: Horizontal axis values for the plot. Defaults to 1:length(d).
  • ttl::String = "": Optional title for the plot.
  • mlbl::String = "Model": Label for the model predictions in the legend.
  • dlbl::String = "Data": Label for the data in the legend.
  • xlbl::String = "Age": Label for the x-axis.
  • flllph::Float64 = 0.1: Fill alpha value for the error bars. Defaults to 0.1.
  • zα::Float64 = 1.96: Z-value corresponding to the desired level of confidence for the error bars. Defaults to 1.96, representing the 95% confidence level.
source
RauhValladaresEsteban2023.simulatewages — Method
simulatewages(hours::Matrix, a::Float64, h₁::Float64, p::ModelParameters)
Simulates wages based on hours worked, the ability to accumulate human capital, and the initial level of human capital.
Arguments
  • hours::Matrix: Matrix containing hours worked by agents (rows) for each period (columns).
  • a::Float64: Ability to accumulate human capital.
  • h₁::Float64: Initial level of human capital.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.simulatewages — Method
simulatewages(v::Vector{MicroData}, p::ModelParameters)
Simulates wages for each level of ability and initial human capital.
Arguments
  • v::Vector{MicroData}: Vector of MicroData containing amount of hours worked.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.solveconstantls — Method
solveconstantls(pb::ModelParameters, pw::ModelParameters)
Solves the model using the labor supply decisions of the economy defined by pb while using the human capital parameters of pw.
Arguments
  • pb::ModelParameters: Instance of ModelParameters to compute labor supply decisions.
  • pw::ModelParameters: Instance of ModelParameters to use human capital parameters.
Note
  • This function returns a vector of simulated data for each level of ability/h₁. Each element in the vector represents the simulated data for one level and is of type MicroData.
source
RauhValladaresEsteban2023.solvemodel — Function
solvemodel(p::ModelParameters, empsample::Bool = true)
Solves the model based on the parameters provided.
Arguments
  • p::ModelParameters: Instance of ModelParameters containing the model's parameter values.
  • empsample::Bool = true: (Optional) Flag indicating whether to use treshold to allocate employment indicator.
Note
  • This function returns a vector of simulated data for each level of ability/h₁. Each element in the vector represents the simulated data for one level and is of type MicroData.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, y::Matrix, w::Matrix)
Applies the statistical function f to each column of the matrix y weighted by corresponding elements in the matrix w, skipping missing values.
Arguments
  • f: A statistical function to apply to each column of the matrix y weighted by w.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
  • w::Matrix: A matrix containing the weights to be applied to each element of y. The size of w should match the size of y.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, y::Matrix)
Applies the statistical function f to each column of the matrix y, treating missing values as skipped.
Arguments
  • f: A statistical function to apply to each column of y.
  • y::Matrix: A matrix containing the data for which the statistics will be calculated.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, d::MicroData)
Applies the statistical function f to each column of the wage, hours, and employed fields in the MicroData instance d weighted by the corresponding elements in the weights field, after skipping missing values.
Arguments
  • f: A statistical function to apply to each column of the fields in d.
  • d::MicroData: A MicroData object containing wage, hours, employed, and weights fields.
source
RauhValladaresEsteban2023.sterror — Method
sterror(v::AbstractVector{<:Real}, w::AbstractWeights)
Calculates the standard error of a weighted sample vector v using the provided weights w.
Arguments
  • v::AbstractVector{<:Real}: A vector of real values representing the sample data.
  • w::AbstractWeights: An abstract type representing the weights associated with each data point in v.
Notes
  • The weights w must have the same length as the vector v.
  • The standard error is calculated as the standard deviation of v weighted by w, divided by the square root of the sample size (length(w)).
source
RauhValladaresEsteban2023.AggregateData — Type
AggregateData
Composite type that contains a summary statistic for hourly wages among the employed, hours worked among the employed, employment rate, income among all, and income among the employed. For example, average wages.
Fields
  • wage::Float64: Average wage among the employed.
  • hours::Float64: Hours worked among the employed.
  • employment::Float64: Employment rate.
  • incomeall::Float64: Average income among all.
  • incomeemp::Float64: Average income among the employed.
source

+    )

Plots the model simulated data and real data with error bars representing the standard error.

Arguments

  • m::Vector{Float64}: Model simulated data.
  • d::Vector{Float64}: Real data.
  • de::Union{Vector{Float64}, Nothing} = nothing: Optional vector of standard errors for the data. If not provided (default), error bars will not be shown.
  • haxis: Horizontal axis values for the plot. Defaults to 1:length(d).
  • ttl::String = "": Optional title for the plot.
  • mlbl::String = "Model": Label for the model predictions in the legend.
  • dlbl::String = "Data": Label for the data in the legend.
  • xlbl::String = "Age": Label for the x-axis.
  • flllph::Float64 = 0.1: Fill alpha value for the error bars. Defaults to 0.1.
  • zα::Float64 = 1.96: Z-value corresponding to the desired level of confidence for the error bars. Defaults to 1.96, representing the 95% confidence level.
source
RauhValladaresEsteban2023.simulatewages — Method
simulatewages(hours::Matrix, a::Float64, h₁::Float64, p::ModelParameters)

Simulates wages based on hours worked, the ability to accumulate human capital, and the initial level of human capital.

Arguments

  • hours::Matrix: Matrix containing hours worked by agents (rows) for each period (columns).
  • a::Float64: Ability to accumulate human capital.
  • h₁::Float64: Initial level of human capital.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.simulatewages — Method
simulatewages(v::Vector{MicroData}, p::ModelParameters)

Simulates wages for each level of ability and initial human capital.

Arguments

  • v::Vector{MicroData}: Vector of MicroData containing amount of hours worked.
  • p::ModelParameters: Instance of ModelParameters containing relevant model parameters.
source
RauhValladaresEsteban2023.solveconstantls — Method
solveconstantls(pb::ModelParameters, pw::ModelParameters)

Solves the model using the labor supply decisions of the economy defined by pb while using the human capital parameters of pw.

Arguments

  • pb::ModelParameters: Instance of ModelParameters to compute labor supply decisions.
  • pw::ModelParameters: Instance of ModelParameters to use human capital parameters.

Note

  • This function returns a vector of simulated data for each level of ability/h₁. Each element in the vector represents the simulated data for one level and is of type MicroData.
source
RauhValladaresEsteban2023.solvemodel — Function
solvemodel(p::ModelParameters, empsample::Bool = true)

Solves the model based on the parameters provided.

Arguments

  • p::ModelParameters: Instance of ModelParameters containing the model's parameter values.
  • empsample::Bool = true: (Optional) Flag indicating whether to use treshold to allocate employment indicator.

Note

  • This function returns a vector of simulated data for each level of ability/h₁. Each element in the vector represents the simulated data for one level and is of type MicroData.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, y::Matrix, w::Matrix)

Applies the statistical function f to each column of the matrix y weighted by corresponding elements in the matrix w, skipping missing values.

Arguments

  • f: A statistical function to apply to each column of the matrix y weighted by w.
  • y::Matrix: A matrix containing the data for which the statistic will be calculated.
  • w::Matrix: A matrix containing the weights to be applied to each element of y. The size of w should match the size of y.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, y::Matrix)

Applies the statistical function f to each column of the matrix y, treating missing values as skipped.

Arguments

  • f: A statistical function to apply to each column of y.
  • y::Matrix: A matrix containing the data for which the statistics will be calculated.
source
RauhValladaresEsteban2023.statsfbyage — Method
statsfbyage(f, d::MicroData)

Applies the statistical function f to each column of the wage, hours, and employed fields in the MicroData instance d weighted by the corresponding elements in the weights field, after skipping missing values.

Arguments

  • f: A statistical function to apply to each column of the fields in d.
  • d::MicroData: A MicroData object containing wage, hours, employed, and weights fields.
source
RauhValladaresEsteban2023.sterror — Method
sterror(v::AbstractVector{<:Real}, w::AbstractWeights)

Calculates the standard error of a weighted sample vector v using the provided weights w.

Arguments

  • v::AbstractVector{<:Real}: A vector of real values representing the sample data.
  • w::AbstractWeights: An abstract type representing the weights associated with each data point in v.

Notes

  • The weights w must have the same length as the vector v.
  • The standard error is calculated as the standard deviation of v weighted by w, divided by the square root of the sample size (length(w)).
source
RauhValladaresEsteban2023.AggregateData — Type
AggregateData

Composite type that contains a summary statistic for hourly wages among the employed, hours worked among the employed, employment rate, income among all, and income among the employed. For example, average wages.

Fields

  • wage::Float64: Average wage among the employed.
  • hours::Float64: Hours worked among the employed.
  • employment::Float64: Employment rate.
  • incomeall::Float64: Average income among all.
  • incomeemp::Float64: Average income among the employed.
source
diff --git a/dev/model/index.html b/dev/model/index.html index 2e2278a..9494dac 100644 --- a/dev/model/index.html +++ b/dev/model/index.html @@ -10,4 +10,4 @@ W_j(h,\kappa; a) = \max_{h,n} \quad & u^W(h,n) + \beta \mathbb{E}_{\kappa'} V_{j+1}(h',\kappa';a)\text{,} \\ \text{s.t.} \quad & h' = (1-\delta)h + a n^\phi \text{,} \\ \quad & 0 \leq n \leq 1\text{.} -\end{aligned}\]

The function $h'$ defines how human capital evolves over the lifecycle. The parameter $\phi$ defines the curvature of human capital next period with respect to time spent working. We assume that human capital depreciates at a constant rate $\delta$. We model human capital accumulation as a learning-by-doing technology. The labor supply decision trades off less leisure today versus more income today and higher human capital in the future.

+\end{aligned}\]

The function $h'$ defines how human capital evolves over the lifecycle. The parameter $\phi$ defines the curvature of human capital next period with respect to time spent working. We assume that human capital depreciates at a constant rate $\delta$. We model human capital accumulation as a learning-by-doing technology. The labor supply decision trades off less leisure today versus more income today and higher human capital in the future.

diff --git a/dev/search/index.html b/dev/search/index.html index de6c54e..248bef8 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · RauhValladaresEsteban2023.jl

Loading search...

    +Search · RauhValladaresEsteban2023.jl

    Loading search...