The Diamond OLG Model

Convergence of OLG Economy to Steady State¶

# Some initial setup
from HARK.ConsumptionSaving.ConsIndShockModel import (
    PerfForesightConsumerType,
    init_perfect_foresight,
)
import seaborn as sns
from matplotlib import rc, rcParams
import ipywidgets as widgets
from ipywidgets import interact, fixed
from matplotlib import pyplot as plt
import numpy as np
import scipy as sp

plt.style.use("seaborn-v0_8-darkgrid")
palette = plt.get_cmap("Dark2")

# For plots

rc("font", weight="bold")
rcParams["text.latex.preamble"] = r"\usepackage{sfmath} \boldmath"

sns.set_style("white", {"axes.edgecolor": "black"})


years_per_gen = 30

# Define a function that plots something given some inputs
def plot1(Epsilon, DiscFac, PopGrowth, YearsPerGeneration, Initialk):
    """Inputs:
    Epsilon: Elasticity of output with respect to capital/labour ratio
    DiscFac: One period discount factor
    YearPerGeneration: No. of years per generation
    PopGrowth: Gross growth rate of population in one period"""

    #
    kMax = 0.1
    # Define some parameters
    Beta = DiscFac**YearsPerGeneration
    xi = PopGrowth**YearsPerGeneration
    Q = (1 - Epsilon) * (Beta / (1 + Beta)) / xi
    kBar = Q ** (1 / (1 - Epsilon))

    # Create an agent that will solve the consumption problem
    PFagent = PerfForesightConsumerType(**init_perfect_foresight)
    PFagent.cycles = 1  # let the agent live the cycle of periods just once
    PFagent.T_cycle = 1  # Number of non-terminal periods in the cycle
    # Income only in the first period
    PFagent.assign_parameters(PermGroFac=[0.000001])
    PFagent.LivPrb = [1.0]

    PFagent.DiscFac = Beta

    # Hark seems to have trouble with log-utility
    # so pass rho = 1 + something small.
    PFagent.CRRA = 1.001

    PFagent.solve()

    # Plot the OLG capital accumulation curve and 45 deg line
    plt.figure(figsize=(9, 6))
    kt_range = np.linspace(0, kMax, 300)

    # Analitical solution plot
    ktp1 = Q * kt_range**Epsilon
    plt.plot(kt_range, ktp1, "b-", label="Capital accumulation curve")
    plt.plot(kt_range, kt_range, "k-", label="45 Degree line")

    # Plot the path
    kt_ar = Initialk
    ktp1_ar = 0.0
    for i in range(3):
        # Compute Next Period's capital using HARK
        wage = (1 - Epsilon) * kt_ar**Epsilon
        c = PFagent.solution[0].cFunc(wage)
        a = wage - c
        k1 = a / xi

        plt.arrow(
            kt_ar,
            ktp1_ar,
            0.0,
            k1 - ktp1_ar,
            length_includes_head=True,
            lw=0.01,
            width=0.0005,
            color="black",
            edgecolor=None,
        )
        plt.arrow(
            kt_ar,
            k1,
            k1 - kt_ar,
            0.0,
            length_includes_head=True,
            lw=0.01,
            width=0.0005,
            color="black",
            edgecolor=None,
        )

        # Update arrow
        kt_ar = k1
        ktp1_ar = kt_ar

    # Plot kbar and initial k
    plt.plot(kBar, kBar, "ro", label=r"$\bar{k}$")
    plt.plot(Initialk, 0.0005, "co", label="$k_0$")

    plt.title("Convergence of OLG Economy to Steady State", fontsize=20, y=1.05)
    plt.legend()
    plt.xlim(0, kMax)
    plt.ylim(0, kMax)
    plt.axvline(0, color="black")
    plt.axhline(0, color="black")
    plt.xlabel("$k_t$", fontsize=12)
    plt.ylabel("$k_{t+1}$", fontsize=12)
    plt.show()

    return None

# Define some widgets to control the plot

# Define a slider for Epsilon
Epsilon_widget1 = widgets.FloatSlider(
    min=0.2,
    max=0.4,
    step=0.01,
    value=0.33,
    continuous_update=False,
    readout_format=".3f",
    description=r"Capital Share $\epsilon$",
    style={"description_width": "initial"},
)

# Define a slider for the discount factor
DiscFac_widget1 = widgets.FloatSlider(
    min=0.94,
    max=0.99,
    step=0.001,
    value=0.96,
    continuous_update=False,
    readout_format=".3f",
    description=r"Discount Factor $\beta$",
    style={"description_width": "initial"},
)

# Define a slider for pop. growth
PopGrowth_widget1 = widgets.FloatSlider(
    min=0.98,
    max=1.05,
    step=0.001,
    value=1.01,
    continuous_update=False,
    readout_format=".3f",
    description=r"Pop. Growth $\Xi$",
    style={"description_width": "initial"},
)

# Define a slider for initial k
Initialk_widget1 = widgets.FloatSlider(
    min=0.01,
    max=0.1,
    step=0.01,
    value=0.08,
    continuous_update=True,
    readout_format=".3f",
    description="Init. capital ratio",
    style={"description_width": "initial"},
)

# Make the widget
interact(
    plot1,
    Epsilon=Epsilon_widget1,
    DiscFac=DiscFac_widget1,
    PopGrowth=PopGrowth_widget1,
    YearsPerGeneration=fixed(years_per_gen),
    Initialk=Initialk_widget1,
);

Gross and Net Per Capita Output as a Function of k¶

# Define a function that plots something given some inputs
def plot2(Epsilon, PopGrowth, YearsPerGeneration):
    """Inputs:
    Epsilon: Elasticity of output with respect to capital/labour ratio
    DiscFac: One period discount factor
    YearPerGeneration: No. of years per generation
    PopGrowth: Gross growth rate of population in one period"""

    kMax = 5.5

    # Define some parameters
    xi = PopGrowth**YearsPerGeneration
    Xi = xi - 1
    Xi ** (1 / (Epsilon - 1))

    # Plot the production function and depreciation/dilution curves
    kt_range = np.linspace(0, kMax, 500)
    plt.figure(figsize=(18, 6))
    plt.suptitle("Gross and Net Per Capita Output as a Function of $k$", fontsize=20)
    plt.subplot(1, 2, 1)
    plt.plot(kt_range, kt_range**Epsilon, "b-", label="$f(k)$")
    plt.plot(kt_range, Xi * kt_range, "k-", label="$Xi * k$")
    plt.legend()
    plt.xlim(0, kMax)
    plt.ylim(0, 3)
    plt.xlabel("$k_t$")
    plt.axvline(0, color="black")
    plt.axhline(0, color="black")

    plt.subplot(1, 2, 2)
    plt.plot(kt_range, kt_range**Epsilon - Xi * kt_range, "k-", label="$f(k) - Xi * k$")
    plt.legend()
    plt.xlim(0, kMax)
    plt.ylim(0, 1)
    plt.xlabel("$k_t$", fontsize=12)
    plt.axvline(0, color="black")
    plt.axhline(0, color="black")

    plt.show()

    return None

# Define some widgets to control the plot

# Define a slider for Epsilon
Epsilon_widget2 = widgets.FloatSlider(
    min=0.2,
    max=0.4,
    step=0.01,
    value=0.33,
    continuous_update=False,
    readout_format=".3f",
    description=r"Capital Share $\epsilon$",
    style={"description_width": "initial"},
)

# Define a slider for pop. growth
PopGrowth_widget2 = widgets.FloatSlider(
    min=0.98,
    max=1.05,
    step=0.001,
    value=1.01,
    continuous_update=False,
    readout_format=".3f",
    description=r"Pop. Growth $\Xi$",
    style={"description_width": "initial"},
)

# Make the widget
interact(
    plot2,
    Epsilon=Epsilon_widget2,
    PopGrowth=PopGrowth_widget2,
    YearsPerGeneration=fixed(years_per_gen),
);

Suppose that initially this economy had no Social Security system and we are interested in the eﬀects of introducing a Pay-As-You-Go Social Security system that is expected to remain a constant size from generation to generation from now on: $z_{2, t + 1} = - z_{1, t + 1}$ while $z_{1, t + 1} = z_{1, t}$ , so that taxes are greater than transfers when young and transfers are greater than taxes when old.

# Define a function that plots something given some inputs
def plot3(Epsilon, DiscFac, z, Rfree, YearsPerGeneration):
    """Inputs:
    Epsilon: Elasticity of output with respect to capital/labour ratio
    DiscFac: One period discount factor
    z: Social Security Transfers
    Rfree: Interest rate
    YearPerGeneration: No. of years per generation
    """
    # unchanged parameters
    kMax = 0.1
    PopGrowth = 1

    # Define some parameters
    Beta = DiscFac**YearsPerGeneration
    xi = PopGrowth**YearsPerGeneration
    # Before introduction of PAYG System
    Q = (1 - Epsilon) * (Beta / (1 + Beta)) / xi
    kBar_old = Q ** (1 / (1 - Epsilon))

    # After introduction of PAYG System
    transfers = z * (1 + (Rfree * Beta)) / (Rfree * (1 + Beta))
    solution = sp.optimize.root(lambda k: k - Q * (k**Epsilon) + transfers, kBar_old)
    kBar_new = solution.x

    # Create an agent that will solve the consumption problemFac
    PFagent = PerfForesightConsumerType(**init_perfect_foresight)
    PFagent.cycles = 1  # let the agent live the cycle of periods just once
    PFagent.T_cycle = 1  # Number of non-terminal periods in the cycle
    # Income only in the first period
    PFagent.assign_parameters(PermGroFac=[0.000001], Rfree=[Rfree])
    PFagent.LivPrb = [1.0]

    PFagent.DiscFac = Beta

    # Hark seems to have trouble with log-utility
    # so pass rho = 1 + something small.
    PFagent.CRRA = 1.001

    PFagent.solve()

    # Plot the OLG capital accumulation curve and 45 deg line
    plt.figure(figsize=(9, 6))
    kt_range = np.linspace(0, kMax, 300)

    # Analitical solution plot
    ktp1 = Q * kt_range**Epsilon
    ktp1_PAYG = Q * kt_range**Epsilon - transfers
    plt.plot(kt_range, ktp1, "b-", label="Capital accumulation curve")
    plt.plot(kt_range, ktp1_PAYG, "g-", label="Capital accumulation curve with PAYG")
    plt.plot(kt_range, kt_range, "k-", label="45 Degree line")

    # Plot the path
    kt_PAYG = kBar_old
    ktp1_PAYG = kBar_old

    while np.abs(kBar_new - kt_PAYG) > 0.0015:  # Create as many arrows as necessary
        # Compute Next Period's capital using HARK
        wage = (1 - Epsilon) * kt_PAYG**Epsilon
        net_wage = (1 - z) * wage  # Agent has to pay transfers
        PFagent.solution[0].cFunc(net_wage)
        k1 = (wage * (Beta / (1 + Beta)) - transfers) / (
            xi
        )  # capital accumulation differs

        plt.arrow(
            kt_PAYG,
            k1,
            k1 - kt_PAYG,
            0.0,
            length_includes_head=True,
            lw=0.01,
            width=0.0005,
            color="black",
            edgecolor=None,
        )
        plt.arrow(
            kt_PAYG,
            ktp1_PAYG,
            0.0,
            k1 - ktp1_PAYG,
            length_includes_head=True,
            lw=0.01,
            width=0.0005,
            color="black",
            edgecolor=None,
        )
        # Update arrow
        kt_PAYG = k1
        ktp1_PAYG = kt_PAYG

    # Plot kbar and initial k
    plt.plot(kBar_old, kBar_old, "co", label=r"$\bar{k}$")
    plt.plot(kBar_new, kBar_new, "ro", label=r"$\bar{k}_{PAYG}$")

    plt.title(
        "Convergence of OLG Economy After Intro of PAYG Social Security System",
        fontsize=20,
        y=1.05,
    )
    plt.legend()
    plt.xlim(0, kMax)
    plt.ylim(0, kMax)
    plt.xlabel("$k_t$", fontsize=12)
    plt.ylabel("$k_{t+1}$", fontsize=12)
    plt.axvline(0, color="black")
    plt.axhline(0, color="black")
    plt.show()

    return None

# Define a slider for Epsilon
Epsilon_widget3 = widgets.FloatSlider(
    min=0.2,
    max=0.4,
    step=0.01,
    value=0.33,
    continuous_update=False,
    readout_format=".3f",
    description=r"Capital Share $\epsilon$",
    style={"description_width": "initial"},
)

# Define a slider for the discount factor
DiscFac_widget3 = widgets.FloatSlider(
    min=0.95,
    max=0.99,
    step=0.001,
    value=0.96,
    continuous_update=False,
    readout_format=".3f",
    description=r"Discount Factor $\beta$",
    style={"description_width": "initial"},
)

# Define a slider for the discount factor
Rfree_widget3 = widgets.FloatSlider(
    min=1.0,
    max=1.1,
    step=0.0001,
    value=1.03,
    continuous_update=True,
    readout_format=".3f",
    description="$Rfree$",
)

# Define a slider for z
z_widget3 = widgets.FloatSlider(
    min=0,
    max=0.02,
    step=0.001,
    value=0.015,
    continuous_update=True,
    readout_format=".3f",
    description="$z$",
)

# Make the widget
interact(
    plot3,
    Epsilon=Epsilon_widget3,
    DiscFac=DiscFac_widget3,
    Rfree=Rfree_widget3,
    z=z_widget3,
    YearsPerGeneration=fixed(years_per_gen),
);

The Diamond OLG Model

Convergence of OLG Economy to Steady State¶

Gross and Net Per Capita Output as a Function of k¶

Pay As You Go (PAYG) Social Security system¶