{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", " \n", " \"QuantEcon\"\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A Lake Model of Employment and Unemployment\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents\n", "\n", "- [A Lake Model of Employment and Unemployment](#A-Lake-Model-of-Employment-and-Unemployment) \n", " - [Overview](#Overview) \n", " - [The Model](#The-Model) \n", " - [Implementation](#Implementation) \n", " - [Dynamics of an Individual Worker](#Dynamics-of-an-Individual-Worker) \n", " - [Endogenous Job Finding Rate](#Endogenous-Job-Finding-Rate) \n", " - [Exercises](#Exercises) \n", " - [Solutions](#Solutions) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "This lecture describes what has come to be called a *lake model*.\n", "\n", "The lake model is a basic tool for modeling unemployment.\n", "\n", "It allows us to analyze\n", "\n", "- flows between unemployment and employment \n", "- how these flows influence steady state employment and unemployment rates \n", "\n", "\n", "It is a good model for interpreting monthly labor department reports on gross and net jobs created and jobs destroyed.\n", "\n", "The “lakes” in the model are the pools of employed and unemployed.\n", "\n", "The “flows” between the lakes are caused by\n", "\n", "- firing and hiring \n", "- entry and exit from the labor force \n", "\n", "\n", "For the first part of this lecture, the parameters governing transitions into\n", "and out of unemployment and employment are exogenous.\n", "\n", "Later, we’ll determine some of these transition rates endogenously using the [McCall search model](../dynamic_programming/mccall_model.html).\n", "\n", "We’ll also use some nifty concepts like ergodicity, which provides a fundamental link between *cross-sectional* and *long run time series* distributions.\n", "\n", "These concepts will help us build an equilibrium model of ex ante homogeneous workers whose different luck generates variations in their ex post experiences." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prerequisites\n", "\n", "Before working through what follows, we recommend you read the [lecture\n", "on finite Markov chains](../tools_and_techniques/finite_markov.html).\n", "\n", "You will also need some basic [linear algebra](../tools_and_techniques/linear_algebra.html) and probability." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Model\n", "\n", "The economy is inhabited by a very large number of ex ante identical workers.\n", "\n", "The workers live forever, spending their lives moving between unemployment and employment.\n", "\n", "Their rates of transition between employment and unemployment are governed by the following parameters:\n", "\n", "- $ \\lambda $, the job finding rate for currently unemployed workers \n", "- $ \\alpha $, the dismissal rate for currently employed workers \n", "- $ b $, the entry rate into the labor force \n", "- $ d $, the exit rate from the labor force \n", "\n", "\n", "The growth rate of the labor force evidently equals $ g=b-d $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregate Variables\n", "\n", "We want to derive the dynamics of the following aggregates\n", "\n", "- $ E_t $, the total number of employed workers at date $ t $ \n", "- $ U_t $, the total number of unemployed workers at $ t $ \n", "- $ N_t $, the number of workers in the labor force at $ t $ \n", "\n", "\n", "We also want to know the values of the following objects\n", "\n", "- The employment rate $ e_t := E_t/N_t $. \n", "- The unemployment rate $ u_t := U_t/N_t $. \n", "\n", "\n", "(Here and below, capital letters represent stocks and lowercase letters represent flows)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Laws of Motion for Stock Variables\n", "\n", "We begin by constructing laws of motion for the aggregate variables $ E_t,U_t, N_t $.\n", "\n", "Of the mass of workers $ E_t $ who are employed at date $ t $,\n", "\n", "- $ (1-d)E_t $ will remain in the labor force \n", "- of these, $ (1-\\alpha)(1-d)E_t $ will remain employed \n", "\n", "\n", "Of the mass of workers $ U_t $ workers who are currently unemployed,\n", "\n", "- $ (1-d)U_t $ will remain in the labor force \n", "- of these, $ (1-d) \\lambda U_t $ will become employed \n", "\n", "\n", "Therefore, the number of workers who will be employed at date $ t+1 $ will be\n", "\n", "$$\n", "E_{t+1} = (1-d)(1-\\alpha)E_t + (1-d)\\lambda U_t\n", "$$\n", "\n", "A similar analysis implies\n", "\n", "$$\n", "U_{t+1} = (1-d)\\alpha E_t + (1-d)(1-\\lambda)U_t + b (E_t+U_t)\n", "$$\n", "\n", "The value $ b(E_t+U_t) $ is the mass of new workers entering the labor force unemployed.\n", "\n", "The total stock of workers $ N_t=E_t+U_t $ evolves as\n", "\n", "$$\n", "N_{t+1} = (1+b-d)N_t = (1+g)N_t\n", "$$\n", "\n", "Letting $ X_t := \\left(\\begin{matrix}U_t\\\\E_t\\end{matrix}\\right) $, the law of motion for $ X $ is\n", "\n", "$$\n", "X_{t+1} = A X_t\n", "\\quad \\text{where} \\quad\n", "A :=\n", "\\begin{pmatrix}\n", " (1-d)(1-\\lambda) + b & (1-d)\\alpha + b \\\\\n", " (1-d)\\lambda & (1-d)(1-\\alpha)\n", "\\end{pmatrix}\n", "$$\n", "\n", "This law tells us how total employment and unemployment evolve over time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Laws of Motion for Rates\n", "\n", "Now let’s derive the law of motion for rates.\n", "\n", "To get these we can divide both sides of $ X_{t+1} = A X_t $ by $ N_{t+1} $ to get\n", "\n", "$$\n", "\\begin{pmatrix}\n", " U_{t+1}/N_{t+1} \\\\\n", " E_{t+1}/N_{t+1}\n", "\\end{pmatrix}\n", "=\n", "\\frac1{1+g} A\n", "\\begin{pmatrix}\n", " U_{t}/N_{t}\n", " \\\\\n", " E_{t}/N_{t}\n", "\\end{pmatrix}\n", "$$\n", "\n", "Letting\n", "\n", "$$\n", "x_t :=\n", "\\left(\\begin{matrix}\n", " u_t\\\\ e_t\n", "\\end{matrix}\\right)\n", "= \\left(\\begin{matrix}\n", " U_t/N_t\\\\ E_t/N_t\n", "\\end{matrix}\\right)\n", "$$\n", "\n", "we can also write this as\n", "\n", "$$\n", "x_{t+1} = \\hat A x_t\n", "\\quad \\text{where} \\quad\n", "\\hat A := \\frac{1}{1 + g} A\n", "$$\n", "\n", "You can check that $ e_t + u_t = 1 $ implies that $ e_{t+1}+u_{t+1} = 1 $.\n", "\n", "This follows from the fact that the columns of $ \\hat A $ sum to 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation\n", "\n", "Let’s code up these equations.\n", "\n", "Here’s the code:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide-output": true }, "outputs": [], "source": [ "using InstantiateFromURL\n", "# optionally add arguments to force installation: instantiate = true, precompile = true\n", "github_project(\"QuantEcon/quantecon-notebooks-julia\", version = \"0.8.0\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide-output": true }, "outputs": [], "source": [ "using LinearAlgebra, Statistics\n", "using Distributions, Expectations, NLsolve, Parameters, Plots\n", "using QuantEcon, Roots, Random" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hide-output": false }, "outputs": [], "source": [ "gr(fmt = :png);" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "simulate_rate_path (generic function with 1 method)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LakeModel = @with_kw (λ = 0.283, α = 0.013, b = 0.0124, d = 0.00822)\n", "\n", "function transition_matrices(lm)\n", " @unpack λ, α, b, d = lm\n", " g = b - d\n", " A = [(1 - λ) * (1 - d) + b (1 - d) * α + b\n", " (1 - d) * λ (1 - d) * (1 - α)]\n", "  = A ./ (1 + g)\n", " return (A = A,  = Â)\n", "end\n", "\n", "function rate_steady_state(lm)\n", " @unpack  = transition_matrices(lm)\n", " sol = fixedpoint(x ->  * x, fill(0.5, 2))\n", " converged(sol) || error(\"Failed to converge in $(result.iterations) iterations\")\n", " return sol.zero\n", "end\n", "\n", "function simulate_stock_path(lm, X0, T)\n", " @unpack A = transition_matrices(lm)\n", " X_path = zeros(eltype(X0), 2, T)\n", " X = copy(X0)\n", " for t in 1:T\n", " X_path[:, t] = X\n", " X = A * X\n", " end\n", " return X_path\n", "end\n", "\n", "function simulate_rate_path(lm, x0, T)\n", " @unpack  = transition_matrices(lm)\n", " x_path = zeros(eltype(x0), 2, T)\n", " x = copy(x0)\n", " for t in 1:T\n", " x_path[:, t] = x\n", " x =  * x\n", " end\n", " return x_path\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s observe these matrices for the baseline model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×2 Array{Float64,2}:\n", " 0.723506 0.0252931\n", " 0.280674 0.978887" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel()\n", "A,  = transition_matrices(lm)\n", "A" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×2 Array{Float64,2}:\n", " 0.720495 0.0251879\n", " 0.279505 0.974812" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Â" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And a revised model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×2 Array{Float64,2}:\n", " 0.723506 1.99596\n", " 0.280674 -0.99178" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel(α = 2.0)\n", "A,  = transition_matrices(lm)\n", "A" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×2 Array{Float64,2}:\n", " 0.720495 1.98765\n", " 0.279505 -0.987652" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Â" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregate Dynamics\n", "\n", "Let’s run a simulation under the default parameters (see above) starting from $ X_0 = (12, 138) $" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel()\n", "N_0 = 150 # population\n", "e_0 = 0.92 # initial employment rate\n", "u_0 = 1 - e_0 # initial unemployment rate\n", "T = 50 # simulation length\n", "\n", "U_0 = u_0 * N_0\n", "E_0 = e_0 * N_0\n", "X_0 = [U_0; E_0]\n", "\n", "X_path = simulate_stock_path(lm, X_0, T)\n", "\n", "x1 = X_path[1, :]\n", "x2 = X_path[2, :]\n", "x3 = dropdims(sum(X_path, dims = 1), dims = 1)\n", "\n", "plt_unemp = plot(title = \"Unemployment\", 1:T, x1, color = :blue, lw = 2, grid = true, label = \"\")\n", "plt_emp = plot(title = \"Employment\", 1:T, x2, color = :blue, lw = 2, grid = true, label = \"\")\n", "plt_labor = plot(title = \"Labor force\", 1:T, x3, color = :blue, lw = 2, grid = true, label = \"\")\n", "\n", "plot(plt_unemp, plt_emp, plt_labor, layout = (3, 1), size = (800, 600))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The aggregates $ E_t $ and $ U_t $ don’t converge because their sum $ E_t + U_t $ grows at rate $ g $.\n", "\n", "On the other hand, the vector of employment and unemployment rates $ x_t $ can be in a steady state $ \\bar x $ if\n", "there exists an $ \\bar x $ such that\n", "\n", "- $ \\bar x = \\hat A \\bar x $ \n", "- the components satisfy $ \\bar e + \\bar u = 1 $ \n", "\n", "\n", "This equation tells us that a steady state level $ \\bar x $ is an eigenvector of $ \\hat A $ associated with a unit eigenvalue.\n", "\n", "We also have $ x_t \\to \\bar x $ as $ t \\to \\infty $ provided that the remaining eigenvalue of $ \\hat A $ has modulus less that 1.\n", "\n", "This is the case for our default parameters:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "(0.6953067378358462, 1.0)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel()\n", "A,  = transition_matrices(lm)\n", "e, f = eigvals(Â)\n", "abs(e), abs(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s look at the convergence of the unemployment and employment rate to steady state levels (dashed red line)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel()\n", "e_0 = 0.92 # initial employment rate\n", "u_0 = 1 - e_0 # initial unemployment rate\n", "T = 50 # simulation length\n", "\n", "xbar = rate_steady_state(lm)\n", "x_0 = [u_0; e_0]\n", "x_path = simulate_rate_path(lm, x_0, T)\n", "\n", "plt_unemp = plot(title =\"Unemployment rate\", 1:T, x_path[1, :],color = :blue, lw = 2,\n", " alpha = 0.5, grid = true, label = \"\")\n", "plot!(plt_unemp, [xbar[1]], color=:red, linetype = :hline, linestyle = :dash, lw = 2, label = \"\")\n", "plt_emp = plot(title = \"Employment rate\", 1:T, x_path[2, :],color = :blue, lw = 2, alpha = 0.5,\n", " grid = true, label = \"\")\n", "plot!(plt_emp, [xbar[2]], color=:red, linetype = :hline, linestyle = :dash, lw = 2, label = \"\")\n", "plot(plt_unemp, plt_emp, layout = (2, 1), size=(700,500))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamics of an Individual Worker\n", "\n", "An individual worker’s employment dynamics are governed by a [finite state Markov process](../tools_and_techniques/finite_markov.html).\n", "\n", "The worker can be in one of two states:\n", "\n", "- $ s_t=0 $ means unemployed \n", "- $ s_t=1 $ means employed \n", "\n", "\n", "Let’s start off under the assumption that $ b = d = 0 $.\n", "\n", "The associated transition matrix is then\n", "\n", "$$\n", "P = \\left(\n", " \\begin{matrix}\n", " 1 - \\lambda & \\lambda \\\\\n", " \\alpha & 1 - \\alpha\n", " \\end{matrix}\n", " \\right)\n", "$$\n", "\n", "Let $ \\psi_t $ denote the [marginal distribution](../tools_and_techniques/finite_markov.html#mc-md) over employment / unemployment states for the worker at time $ t $.\n", "\n", "As usual, we regard it as a row vector.\n", "\n", "We know [from an earlier discussion](../tools_and_techniques/finite_markov.html#mc-md) that $ \\psi_t $ follows the law of motion\n", "\n", "$$\n", "\\psi_{t+1} = \\psi_t P\n", "$$\n", "\n", "We also know from the [lecture on finite Markov chains](../tools_and_techniques/finite_markov.html)\n", "that if $ \\alpha \\in (0, 1) $ and $ \\lambda \\in (0, 1) $, then\n", "$ P $ has a unique stationary distribution, denoted here by $ \\psi^* $.\n", "\n", "The unique stationary distribution satisfies\n", "\n", "$$\n", "\\psi^*[0] = \\frac{\\alpha}{\\alpha + \\lambda}\n", "$$\n", "\n", "Not surprisingly, probability mass on the unemployment state increases with\n", "the dismissal rate and falls with the job finding rate rate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ergodicity\n", "\n", "Let’s look at a typical lifetime of employment-unemployment spells.\n", "\n", "We want to compute the average amounts of time an infinitely lived worker would spend employed and unemployed.\n", "\n", "Let\n", "\n", "$$\n", "\\bar s_{u,T} := \\frac1{T} \\sum_{t=1}^T \\mathbb 1\\{s_t = 0\\}\n", "$$\n", "\n", "and\n", "\n", "$$\n", "\\bar s_{e,T} := \\frac1{T} \\sum_{t=1}^T \\mathbb 1\\{s_t = 1\\}\n", "$$\n", "\n", "(As usual, $ \\mathbb 1\\{Q\\} = 1 $ if statement $ Q $ is true and 0 otherwise)\n", "\n", "These are the fraction of time a worker spends unemployed and employed, respectively, up until period $ T $.\n", "\n", "If $ \\alpha \\in (0, 1) $ and $ \\lambda \\in (0, 1) $, then $ P $ is [ergodic](../tools_and_techniques/finite_markov.html#ergodicity), and hence we have\n", "\n", "$$\n", "\\lim_{T \\to \\infty} \\bar s_{u, T} = \\psi^*[0]\n", "\\quad \\text{and} \\quad\n", "\\lim_{T \\to \\infty} \\bar s_{e, T} = \\psi^*[1]\n", "$$\n", "\n", "with probability one.\n", "\n", "Inspection tells us that $ P $ is exactly the transpose of $ \\hat A $ under the assumption $ b=d=0 $.\n", "\n", "Thus, the percentages of time that an infinitely lived worker spends employed and unemployed equal the fractions of workers employed and unemployed in the steady state distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convergence rate\n", "\n", "How long does it take for time series sample averages to converge to cross sectional averages?\n", "\n", "We can use [QuantEcon.jl’s](http://quantecon.org/quantecon-jl)\n", "MarkovChain type to investigate this.\n", "\n", "Let’s plot the path of the sample averages over 5,000 periods" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hide-output": false }, "outputs": [], "source": [ "using QuantEcon, Roots, Random" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×2 Array{Float64,2}:\n", " 0.717 0.283\n", " 0.013 0.987" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel(d = 0, b = 0)\n", "T = 5000 # Simulation length\n", "\n", "@unpack α, λ = lm\n", "P = [(1 - λ) λ\n", " α (1 - α)]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Random.seed!(42)\n", "mc = MarkovChain(P, [0; 1]) # 0=unemployed, 1=employed\n", "xbar = rate_steady_state(lm)\n", "\n", "s_path = simulate(mc, T; init=2)\n", "s̄_e = cumsum(s_path) ./ (1:T)\n", "s̄_u = 1 .- s̄_e\n", "s_bars = [s̄_u s̄_e]\n", "\n", "plt_unemp = plot(title = \"Percent of time unemployed\", 1:T, s_bars[:,1],color = :blue, lw = 2,\n", " alpha = 0.5, label = \"\", grid = true)\n", "plot!(plt_unemp, [xbar[1]], linetype = :hline, linestyle = :dash, color=:red, lw = 2, label = \"\")\n", "plt_emp = plot(title = \"Percent of time employed\", 1:T, s_bars[:,2],color = :blue, lw = 2,\n", " alpha = 0.5, label = \"\", grid = true)\n", "plot!(plt_emp, [xbar[2]], linetype = :hline, linestyle = :dash, color=:red, lw = 2, label = \"\")\n", "plot(plt_unemp, plt_emp, layout = (2, 1), size=(700,500))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The stationary probabilities are given by the dashed red line.\n", "\n", "In this case it takes much of the sample for these two objects to converge.\n", "\n", "This is largely due to the high persistence in the Markov chain." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Endogenous Job Finding Rate\n", "\n", "We now make the hiring rate endogenous.\n", "\n", "The transition rate from unemployment to employment will be determined by the McCall search model [[McC70]](../zreferences.html#mccall1970).\n", "\n", "All details relevant to the following discussion can be found in [our treatment](../dynamic_programming/mccall_model.html) of that model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reservation Wage\n", "\n", "The most important thing to remember about the model is that optimal decisions\n", "are characterized by a reservation wage $ \\bar w $.\n", "\n", "- If the wage offer $ w $ in hand is greater than or equal to $ \\bar w $, then the worker accepts. \n", "- Otherwise, the worker rejects. \n", "\n", "\n", "As we saw in [our discussion of the model](../dynamic_programming/mccall_model.html), the reservation wage depends on the wage offer distribution and the parameters\n", "\n", "- $ \\alpha $, the separation rate \n", "- $ \\beta $, the discount factor \n", "- $ \\gamma $, the offer arrival rate \n", "- $ c $, unemployment compensation " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linking the McCall Search Model to the Lake Model\n", "\n", "Suppose that all workers inside a lake model behave according to the McCall search model.\n", "\n", "The exogenous probability of leaving employment remains $ \\alpha $.\n", "\n", "But their optimal decision rules determine the probability $ \\lambda $ of leaving unemployment.\n", "\n", "This is now\n", "\n", "\n", "\n", "$$\n", "\\lambda\n", "= \\gamma \\mathbb P \\{ w_t \\geq \\bar w\\}\n", "= \\gamma \\sum_{w' \\geq \\bar w} p(w') \\tag{1}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fiscal Policy\n", "\n", "We can use the McCall search version of the Lake Model to find an optimal level of unemployment insurance.\n", "\n", "We assume that the government sets unemployment compensation $ c $.\n", "\n", "The government imposes a lump sum tax $ \\tau $ sufficient to finance total unemployment payments.\n", "\n", "To attain a balanced budget at a steady state, taxes, the steady state unemployment rate $ u $, and the unemployment compensation rate must satisfy\n", "\n", "$$\n", "\\tau = u c\n", "$$\n", "\n", "The lump sum tax applies to everyone, including unemployed workers.\n", "\n", "Thus, the post-tax income of an employed worker with wage $ w $ is $ w - \\tau $.\n", "\n", "The post-tax income of an unemployed worker is $ c - \\tau $.\n", "\n", "For each specification $ (c, \\tau) $ of government policy, we can solve for the worker’s optimal reservation wage.\n", "\n", "This determines $ \\lambda $ via [(1)](#equation-lake-lamda) evaluated at post tax wages, which in turn determines a steady state unemployment rate $ u(c, \\tau) $.\n", "\n", "For a given level of unemployment benefit $ c $, we can solve for a tax that balances the budget in the steady state\n", "\n", "$$\n", "\\tau = u(c, \\tau) c\n", "$$\n", "\n", "To evaluate alternative government tax-unemployment compensation pairs, we require a welfare criterion.\n", "\n", "We use a steady state welfare criterion\n", "\n", "$$\n", "W := e \\, {\\mathbb E} [V \\, | \\, \\text{employed}] + u \\, U\n", "$$\n", "\n", "where the notation $ V $ and $ U $ is as defined in the [McCall search model lecture](../dynamic_programming/mccall_model.html).\n", "\n", "The wage offer distribution will be a discretized version of the lognormal distribution $ LN(\\log(20),1) $, as shown in the next figure\n", "\n", "\n", "\n", " \n", "We take a period to be a month.\n", "\n", "We set $ b $ and $ d $ to match monthly [birth](http://www.cdc.gov/nchs/fastats/births.htm) and [death rates](http://www.cdc.gov/nchs/fastats/deaths.htm), respectively, in the U.S. population\n", "\n", "- $ b = 0.0124 $ \n", "- $ d = 0.00822 $ \n", "\n", "\n", "Following [[DFH06]](../zreferences.html#davis2006flow), we set $ \\alpha $, the hazard rate of leaving employment, to\n", "\n", "- $ \\alpha = 0.013 $ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fiscal Policy Code\n", "\n", "We will make use of (with some tweaks) the code we wrote in the [McCall model lecture](../dynamic_programming/mccall_model.html), embedded below for convenience." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "solve_mccall_model (generic function with 1 method)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function solve_mccall_model(mcm; U_iv = 1.0, V_iv = ones(length(mcm.w)), tol = 1e-5,\n", " iter = 2_000)\n", " @unpack α, β, σ, c, γ, w, E, u = mcm\n", "\n", " # necessary objects\n", " u_w = u.(w, σ)\n", " u_c = u(c, σ)\n", "\n", " # Bellman operator T. Fixed point is x* s.t. T(x*) = x*\n", " function T(x)\n", " V = x[1:end-1]\n", " U = x[end]\n", " [u_w + β * ((1 - α) * V .+ α * U); u_c + β * (1 - γ) * U + β * γ * E * max.(U, V)]\n", " end\n", "\n", " # value function iteration\n", " x_iv = [V_iv; U_iv] # initial x val\n", " xstar = fixedpoint(T, x_iv, iterations = iter, xtol = tol, m = 0).zero\n", " V = xstar[1:end-1]\n", " U = xstar[end]\n", "\n", " # compute the reservation wage\n", " w_barindex = searchsortedfirst(V .- U, 0.0)\n", " if w_barindex >= length(w) # if this is true, you never want to accept\n", " w̄ = Inf\n", " else\n", " w̄ = w[w_barindex] # otherwise, return the number\n", " end\n", "\n", " # return a NamedTuple, so we can select values by name\n", " return (V = V, U = U, w̄ = w̄)\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the McCall object" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "##NamedTuple_kw#267 (generic function with 2 methods)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a default utility function\n", "u(c, σ) = c > 0 ? (c^(1 - σ) - 1) / (1 - σ) : -10e-6\n", "\n", "# model constructor\n", "McCallModel = @with_kw (α = 0.2,\n", " β = 0.98, # discount rate\n", " γ = 0.7,\n", " c = 6.0, # unemployment compensation\n", " σ = 2.0,\n", " u = u, # utility function\n", " w = range(10, 20, length = 60), # wage values\n", " E = Expectation(BetaBinomial(59, 600, 400))) # distribution over wage values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let’s compute and plot welfare, employment, unemployment, and tax revenue as a\n", "function of the unemployment compensation rate" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# some global variables that will stay constant\n", "α = 0.013\n", "α_q = (1 - (1 - α)^3)\n", "b_param = 0.0124\n", "d_param = 0.00822\n", "β = 0.98\n", "γ = 1.0\n", "σ = 2.0\n", "\n", "# the default wage distribution: a discretized log normal\n", "log_wage_mean, wage_grid_size, max_wage = 20, 200, 170\n", "w_vec = range(1e-3, max_wage, length = wage_grid_size + 1)\n", "\n", "logw_dist = Normal(log(log_wage_mean), 1)\n", "cdf_logw = cdf.(logw_dist, log.(w_vec))\n", "pdf_logw = cdf_logw[2:end] - cdf_logw[1:end-1]\n", "\n", "p_vec = pdf_logw ./ sum(pdf_logw)\n", "w_vec = (w_vec[1:end-1] + w_vec[2:end]) / 2\n", "\n", "E = expectation(Categorical(p_vec)) # expectation object\n", "\n", "function compute_optimal_quantities(c, τ)\n", " mcm = McCallModel(α = α_q,\n", " β = β,\n", " γ = γ,\n", " c = c - τ, # post-tax compensation\n", " σ = σ,\n", " w = w_vec .- τ, # post-tax wages\n", " E = E) # expectation operator\n", "\n", " @unpack V, U, w̄ = solve_mccall_model(mcm)\n", " indicator = wage -> wage > w̄\n", " λ = γ * E * indicator.(w_vec .- τ)\n", "\n", " return w̄, λ, V, U\n", "end\n", "\n", "function compute_steady_state_quantities(c, τ)\n", " w̄, λ_param, V, U = compute_optimal_quantities(c, τ)\n", "\n", " # compute steady state employment and unemployment rates\n", " lm = LakeModel(λ = λ_param, α = α_q, b = b_param, d = d_param)\n", " x = rate_steady_state(lm)\n", " u_rate, e_rate = x\n", "\n", " # compute steady state welfare\n", " indicator(wage) = wage > w̄\n", " indicator(wage) = wage > w̄\n", " decisions = indicator.(w_vec .- τ)\n", " w = (E * (V .* decisions)) / (E * decisions)\n", " welfare = e_rate .* w + u_rate .* U\n", "\n", " return u_rate, e_rate, welfare\n", "end\n", "\n", "function find_balanced_budget_tax(c)\n", " function steady_state_budget(t)\n", " u_rate, e_rate, w = compute_steady_state_quantities(c, t)\n", " return t - u_rate * c\n", " end\n", "\n", " τ = find_zero(steady_state_budget, (0.0, 0.9c))\n", " return τ\n", "end\n", "\n", "# levels of unemployment insurance we wish to study\n", "Nc = 60\n", "c_vec = range(5, 140, length = Nc)\n", "\n", "tax_vec = zeros(Nc)\n", "unempl_vec = similar(tax_vec)\n", "empl_vec = similar(tax_vec)\n", "welfare_vec = similar(tax_vec)\n", "\n", "for i in 1:Nc\n", " t = find_balanced_budget_tax(c_vec[i])\n", " u_rate, e_rate, welfare = compute_steady_state_quantities(c_vec[i], t)\n", " tax_vec[i] = t\n", " unempl_vec[i] = u_rate\n", " empl_vec[i] = e_rate\n", " welfare_vec[i] = welfare\n", "end\n", "\n", "plt_unemp = plot(title = \"Unemployment\", c_vec, unempl_vec, color = :blue, lw = 2, alpha=0.7,\n", " label = \"\",grid = true)\n", "plt_tax = plot(title = \"Tax\", c_vec, tax_vec, color = :blue, lw = 2, alpha=0.7, label = \"\",\n", " grid = true)\n", "plt_emp = plot(title = \"Employment\", c_vec, empl_vec, color = :blue, lw = 2, alpha=0.7, label = \"\",\n", " grid = true)\n", "plt_welf = plot(title = \"Welfare\", c_vec, welfare_vec, color = :blue, lw = 2, alpha=0.7, label = \"\",\n", " grid = true)\n", "\n", "plot(plt_unemp, plt_emp, plt_tax, plt_welf, layout = (2,2), size = (800, 700))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welfare first increases and then decreases as unemployment benefits rise.\n", "\n", "The level that maximizes steady state welfare is approximately 62." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1\n", "\n", "Consider an economy with initial stock of workers $ N_0 = 100 $ at the\n", "steady state level of employment in the baseline parameterization\n", "\n", "- $ \\alpha = 0.013 $ \n", "- $ \\lambda = 0.283 $ \n", "- $ b = 0.0124 $ \n", "- $ d = 0.00822 $ \n", "\n", "\n", "(The values for $ \\alpha $ and $ \\lambda $ follow [[DFH06]](../zreferences.html#davis2006flow))\n", "\n", "Suppose that in response to new legislation the hiring rate reduces to $ \\lambda = 0.2 $.\n", "\n", "Plot the transition dynamics of the unemployment and employment stocks for 50 periods.\n", "\n", "Plot the transition dynamics for the rates.\n", "\n", "How long does the economy take to converge to its new steady state?\n", "\n", "What is the new steady state level of employment?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2\n", "\n", "Consider an economy with initial stock of workers $ N_0 = 100 $ at the\n", "steady state level of employment in the baseline parameterization.\n", "\n", "Suppose that for 20 periods the birth rate was temporarily high ($ b = 0.0025 $) and then returned to its original level.\n", "\n", "Plot the transition dynamics of the unemployment and employment stocks for 50 periods.\n", "\n", "Plot the transition dynamics for the rates.\n", "\n", "How long does the economy take to return to its original steady state?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1\n", "\n", "We begin by constructing an object containing the default parameters and assigning the\n", "steady state values to x0" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hide-output": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial Steady State: [0.08266626766923271, 0.9173337323307673]\n" ] } ], "source": [ "lm = LakeModel()\n", "x0 = rate_steady_state(lm)\n", "println(\"Initial Steady State: $x0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize the simulation values" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N0 = 100\n", "T = 50" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New legislation changes $ \\lambda $ to $ 0.2 $" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "(λ = 0.2, α = 0.013, b = 0.0124, d = 0.00822)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel(λ = 0.2)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hide-output": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New Steady State: [0.11309294549489424, 0.8869070545051054]\n" ] } ], "source": [ "xbar = rate_steady_state(lm) # new steady state\n", "X_path = simulate_stock_path(lm, x0 * N0, T)\n", "x_path = simulate_rate_path(lm, x0, T)\n", "println(\"New Steady State: $xbar\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now plot stocks" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1 = X_path[1, :]\n", "x2 = X_path[2, :]\n", "x3 = dropdims(sum(X_path, dims = 1), dims = 1)\n", "\n", "plt_unemp = plot(title = \"Unemployment\", 1:T, x1, color = :blue, grid = true, label = \"\",\n", " bg_inside = :lightgrey)\n", "plt_emp = plot(title = \"Employment\", 1:T, x2, color = :blue, grid = true, label = \"\",\n", " bg_inside = :lightgrey)\n", "plt_labor = plot(title = \"Labor force\", 1:T, x3, color = :blue, grid = true, label = \"\",\n", " bg_inside = :lightgrey)\n", "\n", "plot(plt_unemp, plt_emp, plt_labor, layout = (3, 1), size = (800, 600))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And how the rates evolve" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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E++ybRgipqqrKysoyNTXVfv5RLpczCajOFXWOMjMz05mIxigtLY2Li3vy5ImNjU3fvn2tra3z8/PLy8udnJy037qcnBy5XM7n893d3bWPvXTpUnp6ulgsdnJy6tSpk3brAEC/kGABAAs0CZZ+T6tUKn18fPLz89PS0p5d1gEAoNFgDhYANAWZmZlyuXzNmjUZGRkfffQRsisAYBdGsACABXofwWrXrl1CQgIhxM/P78qVK6+41hcAQAPBCBYAsGD58uXMGp76Mnfu3OLiYg8Pj8GDB+ssyw4A0PgwggUAAACgZ1hoFAAAAEDPkGABAAAA6BkSLAAAAAA9Q4IFAAAAoGdIsAAAAAD0DAkWAAAAgJ4hwQIAAADQMyRYAAAAAHpmuCu5q1SqS5cu+fv7axfSNE3TNJeLvNDQURTF4/HYjgJqhp4yCvjqMxb4QBmLevaUiYlJjZ9Hw13JvaCgwM/PLyoqSruQoiiVSiUSidiKCl6RTCYzNTVlOwqoGXrKKKhUKoqi8NVn+PCBMhb17ClfX1+xWPzyOvj3EAAAAICeIcECAAAA0DMkWAAAAAB6hgQLAAAAQM8M9ylCQogJTfPKyv5TRFG0SsUTiQghlLm5Tn3dylpogUBtYqJdwlEquZWVL6qvNjWl//t8AVcm41DU80/O5arNzP5zcrWaW1HxwpOLxbRQ+J+TKxSc6uoX1TfGlvLlch5FNYeW/n1yo20pT6Eg/53p2VRbatR9Sj0zvb2ptlS7TymKI5NxefKXtZQy1WkpxZPJXnhysVgt0G0pV/nClqqkui3ll7+wpWq+QG1iIpfzKerv+DlKJU/x4j6V6P7fa1wt/U8wRthStZrzdzB1+7/3BfH/p7IhP0W4381t1n8/9tpSLlygBQLtklZvvMFRKp9buXTQoKeLF2uXWJ486bRs2YtOnrZtW2XbttolnjNnmiQkPLdyla9vamiodono4UPvyZNfdPKc+fOLR43SLnFcs8bqyJEX1TfGltI0zeFwmkNLGcbbUrmX15Nff9Uu0XtLFWpRVdXf32UyGbf3uCCOspoQQtMc7Zo0TR51H3ZpwneEkMpKrlLJIYS0unGs7+/fMHuftW3kwXSHjpozq1Rk/pmxXgW3tM+pkWHe6ps3wpntigqeWk08KlLWXHtb55yaQza5Lz1u/5/34ZP0r4fmhb4omF5+aUrOf76vou578tXP79NjknFfW67XLhkuP7C0ZO6zYTNGSE7d4HXRLjkqH9CZuv7ckydz27xtelG7pLU66VRF7+dWJoR8Tlb9QqZpl6win08jv7yoviPJqSb/aWkOcRSS5/9J20cmfEI2aZdMIPs2kU9edPK3yJlrpCuzzePRpqbq2rb0tOyFLf2feEWoYKp2yfeKLycrd76ofgtplk5LH5S7vKilvwnGLxBvYL76mJIxyv2rFZ++6OT171N2W6pdYowt/fzzx8OGKUhdv3tVZ8+KunR57lEaBp1gYZkG44VnlQ1WVRWnqoorl3NVKk55OVcuVyiVZoQQpqS6mqNQcCmKyGQ8Qkh5OVetJgoFt7qawwwnEEJUKo5cziWEKJWcykouIYQ5ihBSXc1VKDjadQghIhEtEqmZbYlEzef//Z0jlVL//CUiYrFaKPz3u8jERC0Q/PtS+yiGubmakP+UmJqq+c+MyJuZUc9dqsbUlHrRCjgCAW1ion7+PkIIIRwOkUpr/scrIYTDoaXSl53qRYRCtVj8n9bVf5kGU1M1j2eg3/ZNCb76jEUjLNNg0LcIAUAbk7VUVPDkco5Cwa2o4MlkXIWCW1nJLSvjVVZyKiu5MhlXJuMpFJzKSi6TGMlkPIoiZWU8JkMSCmmxWM1kMFKpmqYpCwuaw/k7p2H2crnEzIwihJiZqXk8Ym+vEoloZjiBEMLn0xKJmmjlIsxR2huaOqAXKhVFUZRI9EqJHQAYAiRYAKyhKE5xMa+sjFdezisv55aX88rLeRUVvNLSv7e1y8vLeSoVMTVVm5mpTUzUYrFaKqUkErVYrJZI1ObmarFYbWur8vRUSySUWExLJGqRSC0S0czQhbk5xeXSZma6SQ/+wQ0A0BCQYAE0CJmMW1zMLyrilZTwi4t5JSX8oiJecTGfeVlczCsu5ldUcK2sKHNzSipVS6WU5j9zc7WLi/Kfl2pNoeZGGwAAGDgkWAB1pFBwc3MFBQX8nBxBYSE/J0dQUMDPzRXk5/Pz8vhcLrGyomxsVJaWKktLytJSZWVFeXjIrawoCwuVldXfJWw3AgAAGgQSLICXoShOdrYgM1OYmSnMyhLk5Qlyc/kFBYLcXL5KxXFwUNnYKB0dlba2KkfH6rZt5Y6OKltbpYODCqNNAADNGRIsgL+VlPCYRCozU5iVJczIEGRmCvPzBba2SldXpatrtYtLdffuFfb2Kjs7pb296hUfJQMAgGYICRY0RyoVJy1N9PCh6OFDcWqqKCNDmJkp5HJpV9dqJpdq06YyOLjU1bXa2Vmps0AAAABAjZBgQdOnVnMyMoT374tSU8UPHogePRJnZAhcXJS+vgpf36q33y51da12da02N8eIFAAA6AcSLGiC8vL4SUkmjx6JHzwQpaaKU1OF9vYqJp16882yGTPyvbyqtNexBAAA0C8kWNAUVFVxkpNN7twxuXNHcueORKHgvvaa3M+vKiCg4r33Cr29q5jVLwEAABoHEiwwVsww1c2bkps3Te/dEzs5Kdu0qXz9ddmHH+b5+FRxODWfAQAAoIEgwQKjoVBw79wxuX1bcveu5PZtE4GAbt++skMH+dy5Oa+9psCyCAAAYDiQYIGhe/RIFBsrjYkxu3NH0rKlol07+ZAhxV9//dTe/vm/fw4AAMA6JFhgiCorubdvSy5ckJ4/b87h0AEBFaNHF69Zk8H8AjEAAICBQ4IFBuThQ1FUlHlcnGlCgknbtpUBAbK1a9PbtKlkOy4AAIDaQYIFLCst5cXGmsXGSmNjzczNqaCgivffL+jSRY45VQAAYLzqkmBRFBUeHp6bmxsUFNS6dWvtXTRNP3jwID09vUOHDnZ2dkxhSUnJ3bt3TUxMunTpoqkZGRmZmprasWPHbt261acBYKSqqzkXL0r//NPy2jXTrl1lQUEVH3+c5+xczXZcAAAAesCtwzFDhw5dunTp3bt3e/Xq9fvvv2vv8vb2DgoKGjJkSGxsLFOyYcMGBweHsWPHfvbZZ5pqn3766SeffJKUlDRq1Ki1a9fWpwFgdJKSTFascAoObhkWZtuzZ8Vff91bvz59zJgiZFcAANBk1HoEKyoq6ubNmw8ePDA1Ne3Ro8fXX389YsQIzj+LDsXGxjo7O2sPa02cOHHmzJmhoaF79+5lStLT03fs2PHo0SNnZ+eJEye+9dZbM2fOlEgkemkPGKy0NNHJkxbh4ZYCAd2/f+m+fakuLsioAACgaap1gnXq1Kn+/fubmpoSQoYNGzZx4sS0tDQvLy9mr7Ozs059GxsbnZIzZ8506dKFqdm1a1dTU9O4uLg333yzLuGDwSst5UVEWBw/bpmVJQwOLv3hh8zOneVsBwUAANCwap1gZWVlubm5MdsmJiZWVlZZWVmaBOsVz+Di4qJ56ezsnJWV9dyaKpVq586d2iVqtVqtVvP5/LfeesvR0bG2wUOjkcvVly9LwsOtL12S9uhRPnlyTlBQOY9HE0JUKraDAy0qlUqFLjF4KpWKoigej8d2IFADfKCMRT17iqZr/jXbWidYNE1ztH6FhMvlqtW1e9rr1c9A03RhYaFOiVqt5vF4SqWytteFxlFczD9wwO7wYds2bSoHDChasiTNxOTvnkKPGSDmM8V2FFAD9T/YDgRqgA+UsWiEnqp1guXk5JSbm8tsV1dXFxcXP3tbsMYzREdHa17m5OS86AwCgeDzzz/XLqEoSqVSiUSiWkYNjeHpU+GePTYnTlj27l2+bVtSmzbMv7axFIhBUyqVQqGQ7SigBlwul6Io9JThwwfKWNSzpziv8Hu3tX6KsG/fvpGRkUqlkhASERHh5ubm7e1dUVGRn5//6me4cuVKcXExISQlJaWgoKB79+61DQMMyoMH4sWLXceO9SGEHD368McfMz08FGwHBQAAwJpajy7079/fyclp6NChPXv23Lx58w8//MDlcrdv3/7bb7/FxcX99NNP9+/fz8nJ2bp166lTp7744ovy8vItW7YkJyc/fPhw5syZ/v7+M2bMGD58+IABA0aMGLF79+65c+daWFg0RNugEdy4IQkJsUtMNBkzpujUqftSKX7KBgAAoPYJFpfLPXv27N69e7Oysg4ePBgUFEQIGThw4GuvvUYI8ff3d3d379evH1PZwsJCLBb369dPU+Lq6koI2bNnz6FDh+7fv79q1aohQ4borTXQWNRqEhUl/eUXu6Ii/oQJhT/9lC4S1TzjDwAAoJngvMpMeFYUFBT4+flFRUVpF2IOFuuUSs6pUxYhIXZCIT1pUsHgwaVc7nP+F5LJZMxaHmDg0FNGgXmKEF99hg8fKGNRz57y9fUVi8Uvr4MJyPCqqqs5Bw7YhIba+PlVffXV065dZWxHBAAAYKCQYMEriY01W77cydu7evPmJ61aYQI7AADAyyDBghrk5QnWrXO4dUvy5ZfZvXqVsx0OAACAEajLjz1DM6FScX791Wb0aB9X1+qjRx8guwIAAHhFGMGC57tyxXT5cmdX1+r9+/GrzAAAALWDBAt04Z4gAABAPeEWIfyLuSc4ZgzuCQIAANQLRrDgb5p7gvv24Z4gAABAvSDBgr/vCd6+LfniC9wTBAAA0APcImzujh+3HD3a18OjCvcEAQAA9AUjWM1XdTVnzRrH2FizkJDHvr5YOxQAAEBvkGA1Uzk5gs8+c7e1Ve7fn2pmRrEdDgAAQJOCW4TNUXS0dNw4nzffLFu/Ph3ZFQAAgN5hBKt5oWmya5fdgQPW69end+woZzscAACApgkJVjNSXMxbtMitupqzb98jW1sV2+EAAAA0WbhF2FwkJpqMH+/TsmXlL7+kIbsCAABoUBjBahYOH7besMF+yZKnb75ZxnYsAAAATR8SrCZOLud++61LWppo375UV1eszw4AANAYcIuwKUtLE02c6M3n02FhyK4AAAAaD0awmqyzZ81/+MF59uzcUaOK2Y4FAACgeUGC1TRt3273xx9W27en+flhiXYAAIDGhgSrCQoJsTtxwnLXrlR7ezwtCAAAwAIkWE1NSIjdsWOWISGP7eyQXQEAALADCVaTsmWL/ZkzFjt3IrsCAABgExKspmPzZvu//rLYufMx1hEFAABgFxKsJmLTJvsLF8x37061ssKPNwMAALCsLutgnT17NigoqFWrVnPnzlUo/vOQ2rZt26ZPnx4cHHz79m2mhKbpFStWtGvXzt/fPzQ0lCmcNWtW8D9mz55dzzbApk0OFy+a79jxGNkVAACAIaj1CFZOTs6IESN27NjRtWvXadOmLVmyZOXKlZq90dHRHTt2PHLkSFFREVMSFhb2yy+/HD16tLS0dPjw4b6+voGBgVeuXBk3blznzp0JIRYWFvpqTPO0caNDVJR0x47HlpbIrgAAAAxCrROs0NDQnj17jhs3jhCybNmywYMHL126VCAQMHt//fVXQsiaNWs09bdu3frFF1+0a9eOEDJt2rRt27YFBgYSQjp37tyvXz+9tKE527DBISYG2RUAAIBhqfUtwsTERH9/f2a7c+fORUVF2dnZL6/PjFQRQvz9/RMSEpjtr776qmfPnrNnz87KyqptDMBYv94hJka6fTuyKwAAAMNS6xGs/Pz8Dh06MNsikUgikeTl5bm7uz+3skKhKC8v19wEtLS0zM/PJ4R8/PHH3t7eHA5n+/btPXv2vH37tlQqffbwysrK3r176xTSNM3hcFauXMmMijVPNE3Wr/e4eVOycWOiQKCSydgO6BlyuZztEOCVoKeMgkqlUqvVKhUeEDZ0+EAZi3r2FEXVPK5R6wTL0tKyoqKC2VapVJWVlVZWVi+qLBaLTUxMZP/8/S8vL2cqT7DOJo8AACAASURBVJkyhSkJCgry9vY+d+7csGHDnnv4kSNHtEsoiqIoSigUWlhYaO5LNjc0TVatcrpzR7JzZ5qFhYgQEdsRPZ+pqSnbIcArQU8ZPpVKRVGUSGSgH3bQhg+UsahPT/F4vBrr1DrB8vb2vnfvHrN9//59kUjk7Oz8kvpeXl737t1jBr3u3bvn7e2tE6KVlZUmY9PB4XBsbW21SyiKUqlUzflbhqbJypVON29KduxIMzfHnUEAAABDVOs5WJMmTQoPD09JSaFp+qeffhozZoyJicnp06e3b99OCCkrKysuLlar1RUVFcXFxRRFvffeexs3bqyqqioqKgoJCXn33XcLCwuvXbtGCKFpeteuXQ8ePOjZs6f+W9YU0TRZvtwpIcFk587HyK4AAAAMVq1HsFq3br106dIePXpwOJy2bdsePHiQEHL37t34+PgZM2YMGTLk7t27hJDJkycTQuLi4ubMmXPjxg1HR0eapt9///2RI0emp6ePGzcuOzuby+V6enr+9ttvL5rCBTo2bHBITjb5+ec0MzM127EAAADAC3Fomq7DYRRFKRSKV79/qVAouFyuUCjULiGEiMXiFx1SUFDg5+cXFRWlc91me4vw/HnzFSucDhx4aBSricpkMkxEMAroKaOAOVjGAh8oY1HPnvL19X1JAsOo40/l8Hi8WkX2bBw1RgbanjwRffut8+bNT4wiuwIAAGjm6vJTOdDI5HLu3Lnuc+bktm1byXYsAAAAUDMkWEbgf/9z6dhRPnJkMduBAAAAwCup4y1CaDShobZPnoh+/TWV7UAAAADgVSHBMmi3bkl27bLduzdVJMJjgwAAAEYDtwgNV0EBf8ECt2XLMl1cqtmOBQAAAGoBCZaBUqk4n33mPn58UWDg85e5BwAAAIOFBMtArVzpZGFBTZmSz3YgAAAAUGuYg2WIwsMt4+JMDxxI5XDYDgUAAABqDwmWwbl/X7xqlePOnY/NzLCmKAAAgFHCLULDUlbGmzvXfdGibF/fKrZjAQAAgDpCgmVA1Gry5ZeuffqUvf12KduxAAAAQN0hwTIgP/9sX17Omzs3l+1AAAAAoF4wB8tQxMWZHTlitX//I4GAZjsWAAAAqBckWAbh6VPB4sWuP/2UYW+vYjsWAAAAqC/cImRfVRVn7lz3GTPyOneWsR0LAAAA6AESLPatWuXk7V01fnwR24EAAACAfuAWIcsSEkwuXDA/duwB24EAAACA3mAEi01qNef7710++yxbKsWaogAAAE0HEiw27d9vLZFQAwZg1SsAAIAmBbcIWVNYyN++3W7Xrsf4wUEAAIAmBiNYrFm1ynHUqGJvb/wkDgAAQFODBIsd8fGmt26ZTp+ez3YgAAAAoH9IsFigVHK+/975yy+fmpio2Y4FAAAA9A8JFgtCQ22dnav79ClnOxAAAABoEJjk3tiyswV79tjs35/KdiAAAADQUOoygqVQKCIiIiIjI6uqnjNBOysrKz4+XrskJyfnjz/+uHnzpnZhcnLyH3/8kZaWVocAjNry5U7vvlvo4lLNdiAAAADQUGqdYBUUFHTs2PHHH39cunRp586di4r+/YGXO3fuODk5+fj4dOnSRa3+e3bRX3/91bZt27CwsNGjR3/44YdM4dKlS9988819+/Z169YtLCxMLy0xCjExZqmp4smTC9gOBAAAABpQrROsjRs3tmrV6vz58xcuXPD29t68ebNml7u7+7lz53RGqj7//PP/+7//O3z48JUrVw4ePHj37t28vLwff/zx4sWLBw8e3L9//+eff15d3SyGc6qquMuWOX/zzVOhkGY7FgAAAGhAtU6wjh49On78eGZ7/Pjxx44d0+yytLRs3bo1l/vvOVNTUxMTE8eOHUsIsbW1DQ4OPnr0aHh4ePv27Vu0aEEI6du3LyEkLi6uns0wCtu327VvX9m9ewXbgQAAAEDDqvUk98zMTDc3N2bb3d09MzPz5ZWtra1NTU019bOysjgcjru7O1PC4XBcXV1fdBKKok6cOKFdolar1Wo1n88PCAiwtraubfAsSk8X/fab1aFDDzQ3T5s2pqfYjgJqhp4yCup/sB0I1ADdZCzq2VM0XfOdqFonWNXV1QKBgNkWCATPneeuXZnP//cSAoFAoVDoFAqFwhedhKKoqKgo7RKaptVqNY/H8/X1NTMzq23wLPrxR48pU7ItLWXN43bof/4/AUOGnjIKKpWKoigOflfL4OEDZSzq2VMNkmA5OTnl5/+9/nh+fr6Tk9PLKxcVFanVaua+YX5+vrOzs5OT05UrVzR18vLyXnQSoVD4f//3f9olFEWpVCqRSFTbsNl18qRFQYHo3XfLeDwx27E0EoqixOLm0lijhp4yCkyCZXRffc0QPlDGop49pT0b6oV1anvSwMDA8+fPM9vnzp0LDAx8SeUWLVqYm5tfunSJEKJWqy9cuBAYGBgYGHj58uXKykpCSHp6emZmZteuXWsbhhGRybhr1jguWfKUx8PcdgAAgGah1iNYc+fO7dWrl6OjI03TO3fujImJIYT07t17wIABc+bM+e6775iFGxYtWiSVSr/++uu5c+dOmzZt0aJFkZGRpqamAwYM4PF4PXr0GD169DvvvLNly5YpU6bY2trqv2UGY/Nmh549Kzp0kLMdCAAAADSSWidYHTt2vHDhArN41cWLF9u1a0cI+fDDD729vblcrpWVlZWV1YoVKwghEomEEPLll196eHhER0f7+fmtX7+ex+MRQn7//fctW7bExMR88MEHU6ZM0XObDMmDB+KTJy2OHn3AdiAAAADQeDivMlGLFQUFBX5+fjqT3I1rDhZNk/fe8x4xonjkyGK2Y2lsMplM8/QoGDL0lFHAHCxjgQ+UsahnT/n6+tY4hQs/9tyAfv/diqI4w4c3u+wKAACgmcOPPTeU0lLepk0OW7emvcKjBgAAANCk4I9/Q1m3zuHtt0tbtVKwHQgAAAA0NoxgNYinTwWRkRYnT95nOxAAAABgAUawGsTu3bYjRxZJpRTbgQAAAAALMIKlf0VF/PBwy2PHsDQDAABAM4URLP0LC7MZOLDUzk7FdiAAAADADiRYelZRwT182Pq99wrYDgQAAABYgwRLzw4etAkKKndzq2Y7EAAAAGANEix9qqri7NtnM3Uqhq8AAACaNSRY+nT0qNVrr1X6+mLtKwAAgGYNCZbeqNWcPXtsp0zJZzsQAAAAYBkSLL0JD7dwclJ27ChnOxAAAABgGRIs/aBpEhJiO20ahq8AAAAACZaeXLhgzueT11+vYDsQAAAAYB8SLP0ICbGdMSOPw2E7DgAAADAASLD04OpV05IS3ptvlrEdCAAAABgEJFh68MsvdlOnFnDxXgIAAAAhBAlW/SUnix8/Fg0aVMJ2IAAAAGAokGDV1/bt9u+/XyAQ0GwHAgAAAIYCCVa9pKWJ4uMlI0YUsx0IAAAAGBAkWPXyyy+2EycWSiRqtgMBAAAAA8JnOwAjlpsruHjRPDz8PtuBAAAAgGHBCFbd7dplO2JEsbk5xXYgAAAAYFgwglVHJSW88HDLI0cesB0IAAAAGByMYNVRWJht//6l9vYqtgMBAAAAg1OXEaxDhw6FhIRwudwZM2YMHz5ce1d8fPyKFSsKCgoGDRo0b948Ho+nUqk2bNhw5swZiUQye/bsvn37EkKWLVuWnp7OHOLl5fXll1/WvyWNSSbj/vab9a+/PmI7EAAAADBEtU6wIiMjZ82aFRoaqlKpPvjgA3t7+x49ejC7CgoKgoODv/32286dO3/88cdqtfrzzz///PPP4+Li1q1bl5GRMW7cuPPnz7dt2/b3338fNGhQu3btCCHW1tZ6blPDO3jQOiCg3N29mu1AAAAAwBDVOsHauHHj/PnzBw0aRAj5+OOPN23apEmwdu/e3a1bt08//ZQQsnr16ilTpixYsGDXrl3Hjx/v3r179+7do6OjN2/evHXrVkJIr169+vXrp9e2NJLqas7evbabNqWxHQgAAAAYqFrPwbp58+brr7/ObAcEBNy4cUOz69atW5pdr7/+emZmZk5Ojlwut7CwYAotLS3j4+OZ7TVr1kycOHH58uXl5eX1akGjO3bMqnXrytatFWwHAgAAAAaq1iNYubm5VlZWzLa1tXVOTo72rq5duzLbUqlUKBQWFBQEBgaGhISsXbu2tLT08OHDTDo1duxYV1dXLpe7c+fOAwcOXL16VSQSPXsthUIxevRo7RKapmma5nK5ixcvbtWqVW2Drz+1mrNrl/X//pcql8sb/+pGpLKyksPhsB0F1Aw9ZRRUKhVFURSFRWEMHT5QxqKePaVW17zAeK0TLDMzs8rKSmZbJpNpRqd0dimVSqVSaW5uvm3btkmTJrm6ukokkl69ejEjXl988QVTbeTIkV5eXpGRkcw9Rx1CofCHH37QLmG+YoRCoYeHh0QiqW3w9RcebunoSAUEqAlh4epGhKZpVjoIags9ZRSYBOu5/xAFg4IPlLGoZ09xuTXfAKx1guXh4fHo0SNm3tWjR4/c3d21d6WmpjLbjx49EggEjo6OYrH4ypUrlZWVJiYmixYtatOmjfbZRCKRk5NTUVHRixrQunVr7RKKolQqFYvfMiEhtvPm5dRcDwAAAJqxWs/BGj9+/M6dO1UqlVKp3LVr1zvvvEMIOXDgwLVr18aPH3/s2DHmpuG2bdtGjBghFouLi4tpmjYxMbl9+/aOHTs++uijsrKyzMxM5mynT59OSkrq3r27flvVQBISTBQKbmBgBduBAAAAgEGr9QjWrFmzIiIifH191Wp1hw4dpk6dSgjZvHnz0KFDFy5c+P7777dr187BwYGiqJMnTxJCdu/evWLFCktLy/z8/NWrVwcFBT18+NDf39/GxobD4ZSWlv78889+fn76b1kDOHHCcujQEtxeBwAAgJfj0DRdh8PS09M5HI6bmxvzkqIoLpfLzBcrLCwsLCz09fXV3KHMy8srKiry9PQUi8VMiUqlYs7g7u7O4/Gee4mCggI/P7+oqCjtQhZvEapUnDffbLl3b6qrK5a/qplMJjM1NWU7CqgZesooYA6WscAHyljUs6d8fX01Kc2L1PG3CLWnXhFCtJMkGxsbGxsb7b329vb29vb/uSqf7+3tXbdLsyUqSurtXYXsCgAAAGqE3yJ8Vcz9QbajAAAAACOABOuVlJbyrlwxDQ4uYzsQAAAAMAJIsF7JqVMWPXuWm5lhlT8AAACoGRKsV3LihNWQIbg/CAAAAK8ECVbNnjwRZmUJAgJkbAcCAAAAxgEJVs2OH7caMqSEx6vLehYAAADQDCHBqgFNk/BwC9wfBAAAgFeHBKsG166Zmpur/fwUbAcCAAAARgMJVg2OH7ccPLiY7SgAAADAmCDBehmFgnv+vPnAgaVsBwIAAADGBAnWy0RGmnfsKLe1VbEdCAAAABgTJFgvg5/HAQAAgDpAgvVCeXn8xESTN94oZzsQAAAAMDJIsF7ozz8tg4NLRSI124EAAACAkUGC9UJ//mmJ5a8AAACgDpBgPV9Skolczu3USc52IAAAAGB8kGA934kTlsOGlXA4bMcBAAAARggJ1nNQFOf0aYvBg3F/EAAAAOoCCdZzREebubtXublVsx0IAAAAGCUkWM9x4oQVprcDAABAnSHB0lVWxouLM33rrTK2AwEAAABjhQRL1+nTFkFBFVIpxXYgAAAAYKyQYOk6cQLLXwEAAEC9IMH6j/R0YUaGsEePCrYDAQAAACOGBOs/jh+3HDSohMej2Q4EAAAAjFhdEqyqqqqbN29mZGQ8u4um6cTExOTkZO3C8vLy27dv5+bmahfm5+fHx8eXlRnQXHKaJuHhlkOH4v4gAAAA1EutE6w7d+74+PjMmjWrS5cuCxcu1N5VVlYWEBAwZsyY4cOH9+3bVy6XE0L27t3r6+s7b968Dh06rFq1iqm5Y8eOVq1azZkzx8fH5+TJk3ppSf3Fx5uKxeqWLRVsBwIAAADGrdYJ1sKFC2fMmBEXF3fr1q2QkJDbt29rdm3cuNHc3DwhISExMbG6unrnzp2FhYUzZ848duzYuXPnbty4sXz58sePH5eUlMybN+/MmTMxMTE///zzrFmzKMogHtk7ccJy+HAMXwEAAEB91S7BKioqioyMnDZtGiHEyclp0KBBBw8e1Ow9dOjQlClTuFwun8+fPHnyoUOHEhISTExMAgICCCHOzs6dOnU6ePDgn3/+6efn16VLF0LIiBEjKioqrly5otdG1UVVFffcOfOBA5FgAQAAQH3xa1U7MzNTIBA4OzszL728vFJTUzV7nzx54uXlxWx7e3unp6fb2tqWlZWVl5dLpVKaprOystLT09Vqtbe3N1ONy+V6eHikp6f36NHj2cup1eqrV6/qlFAUJRAIWrduLZVKaxX8y509a962rdzOTqXHcwIAAEDzVLsESyaTiUQizUsTE5OKin9XNJDL5WKxmNkWi8UVFRVt2rQJDAx85513pk6deubMmcLCQrlc/vKTaFMqlZs3b9YppGmaw+HMnz+/RYsWtQr+5Y4dcxs0KI+ZNwb1V1lZyeFw2I4CaoaeMgoqlYqiKAOZTQEvgQ+UsahnT6nV6hrr1C7BcnR0LC8vVyqVAoGAEFJQUODo6Ki9t6ioiNkuLCx0dHTkcDjh4eHbt2+PjIwMDAyUy+Wenp42NjY3btzQHFVYWOjk5PTcy4lEotDQUO0SiqJUKpV2fqYXhYX8pCSzDRuyxGKJfs/cbNE0LZHgzTQC6CmjwCRYev/qA73DB8pY1LOnuNyaZ1jVbg6Wm5ubvb39pUuXmJexsbHMVCqGv79/TEyMzi4TE5M5c+Zs2bJl1KhRZ86c6d27d5cuXa5evapUKgkheXl5qampHTt2rFUYenfihGW/fmVicc0JKQAAAECNajeCxefzP/nkk08//XTVqlWXLl168uTJO++8o1arXVxcTp8+PWfOnJEjR/r5+SmVym3btkVGRhJCtm/fbmdnp1Qq169f36dPnz59+hBCWrRoMX369HfffXfNmjWjRo1ycXFpkMa9shMnLBcvzmY3BgAAAGgyapdgEUIWLVpkYWGxceNGZ2fnqKgoU1NTmqbHjh1rZWXVoUOHsLCw3bt383i8I0eOMCNYEolk7969KpVqwoQJM2fOZE7y559//vjjj2vXru3SpcsXX3yh5zbVUmqqqLyc17mzjN0wAAAAoMng0LSB/ixMQUGBn59fVFSUdmFDzMEKDbVNTxd+881TPZ4TZDKZqakp21FAzdBTRgFzsIwFPlDGop495evrq3mq70XwW4QkOtosKKic7SgAAACg6WjuCVZlJTcxUdK9O+4PAgAAgN409wTr8mXTtm3lEgmeHwQAAAC9ae4JVkyMtGfP5y9zCgAAAFA3SLAwAQsAAAD0rFknWI8eiWia4+1dxXYgAAAA0KQ06wQrJkbaqxeGrwAAAEDPmnWChQUaAAAAoCE03wSLWaChWzcs0AAAAAB61nwTrMuXzbBAAwAAADSE5ptgRUebYYEGAAAAaAjNN8GKjZX27IkJWAAAAKB/zTTBevRIRAjt5YUFGgAAAED/mmmChQXcAQAAoOE00wQLCzQAAABAw2mOCRYWaAAAAIAG1RwTrMuXzdq1wwINAAAA0FCaY4KF+4MAAADQoJpjghUbixnuAAAA0ICaXYKFBRoAAACgoTW7BCs6WtqrF4avAAAAoAE1uwQrJkaKCVgAAADQoJpXglVZyU1MNOnaFQs0AAAAQANqXgkWFmgAAACARtC8Eiws0AAAAACNoHklWFigAQAAABoBvw7HnD9/PiwsjM/nT5s2rVu3btq7Hj58uHHjxqKioiFDhowdO5YQolar9+7de/78eR6PN2DAgJEjRxJCNm7cmJWVxRzi5ub28ccf17shNXv4UMThYIEGAAAAaHC1HsGKiYkZPnx4jx492rVrFxwcnJiYqNlVXFwcGBhoZmY2aNCgBQsW7Nq1ixCyfPnypUuXDhw48I033pg9e3ZISAghZPfu3YWFhVZWVlZWVubm5npsz0sjx/AVAAAANIZaj2CtWbPms88+mzZtGiHk/v37mzZt2rp1K7MrNDS0ffv2y5YtI4TweLxvv/32gw8+OHPmzLx580aPHk0IefTo0enTp6dMmUIIGTduXL9+/fTZlJrExEjffbegMa8IAAAAzVOtR7CuXLnSu3dvZrt3796XL1/W7Lp8+XKvXr00u5KSksrKygICAs6fP69UKuVy+cWLF19//XWmQlhY2Pz58/fs2aNUKuvdipphgQYAAABoNLUbwVKr1bm5uTY2NsxLOzu77Oxszd6cnBxNgmVjY8PhcLKzs7///vv+/fubm5vTND1gwIC5c+cSQvr06ePo6MjhcNasWRMSEnL27Fkej/fs5aqqqj766CPtEpqmaZrmcrmzZs3y8fF59cgvXrRq06ackAq5vFYthjqqrKzkcDhsRwE1Q08ZBZVKRVEURVFsBwI1wAfKWNSzp9Tqmtd7ql2CxeVyxWJxdXU181KhUEgkEs1eExMTza7q6mqapiUSycyZM+3t7dPS0qqrqz/44INFixatXLly9erVTLXp06d7e3ufO3cuODj4OcHx+cxMee0mURQlEAhcXFzEYvGrR371qk2vXrJaHQL1QVEU3m2jgJ4yCkyCJRKJ2A4EaoAPlLGoZ09xuTXfAKz1HCwXF5f09PTOnTsTQtLT011dXTW7XF1d09PTme309HQ+n+/g4HDo0KHz5887ODgQQubMmTNnzpyVK1dqDjE3N/fy8nr69Olzr8Xj8fr06aNdQlGUSqWqw7fMpUvSyZMLX+UdAb3gcrl4t40CesoocLlcZvCe7UCgBvhAGYtG6Klan3306NF79uwh/6y/MGrUKELIX3/9de/evVGjRh09erS8vJwQsmfPnqFDhwqFQhcXl1u3bjHH3rx508XFRS6XV1T8/TTfjRs3EhISmHSt4TALNHh6YoEGAAAAaAy1HsGaO3dunz59Xn/99erqapFINHXqVELIN998M2HChNmzZ3fr1q1Tp06enp7JyckRERGEkLVr177//vtHjx5VKpWJiYlHjhxJT0/v1q1bhw4dOBzOrVu3vvvuu3bt2um/ZVqwQAMAAAA0plonWHZ2drdu3bp27Rqfz/f392dG2I4fPy6RSLhc7sGDBxMSEoqKirp06cJMzxo8ePDjx48TEhJ4PF7btm2ZwkePHt27d4/L5bZq1cra2lrvrdKBBRoAAACgMdVlJXc+nx8QEKBdwkyxYrRt21anvlQq1alvZ2dnZ2dXh0vXgVyOBRoAAACgUTX9uXiXL5u1by+XSGp+ohIAAABAL5p+ghUdbRYUVM52FAAAANCMNP0E69IlzHAHAACARtXEEyws0AAAAACNr4knWNHR0l69MHwFAAAAjaqJJ1gxMVJMwAIAAIBG1pQTLLmcm5Rk0qULFmgAAACARtWUEyws0AAAAACsaMoJFhZoAAAAAFY05QQLCzQAAAAAK5psgpWXx7e2VmGBBgAAAGh8TTbBsrdX7d//iO0oAAAAoDlqsgkWAAAAAFuQYAEAAADomZElWNnZ2Y8e4cafEbh7925paSnbUUANqqqqrl27xnYUULPMzMzHjx+zHQXU7Pbt2xUVeLjK0CkUivj4+Ia+ipElWFFRUYcPH2Y7CqjZpk2bkAobvtLS0u+++47tKKBm58+fP3bsGNtRQM3WrVuXlpbGdhRQg4KCgmXLljX0VYwswQIAAAAwfByaptmO4fnkcnmfPn1u3bqlXUjTNE3TXC7yQkOnVqs5HA6Hw2E7EKgBRVE8Ho/tKKAG+OozFhRFcblcfPUZvnp+9V2+fLlTp04vr2O4CRYhRKVSqVQqtqMAAAAA+JdIJKoxjTboBAsAAADAGGHAGQAAAEDPkGABAAAA6BkSLAAAAAA9M6YEa9euXR07dmzTps3SpUsxdcygFBUVrVy5cuzYscHBwZrnEsrKyqZNm9aiRYuePXvGxsayGyEwTp48OXz48FatWnXt2nXNmjXM5yg9PX3EiBE+Pj6DBw9++PAh2zECIYR8+umnnTp18vX1DQ4OPnv2LFN48eLFoKCgFi1azJw5E6tZGpSDBw8GBwffuXOHeRkREREQEODn5/fJJ59UVlayGxsQQo4cORKsJTMzkxCSmZk5atQoHx+fAQMG3Lt3T+8XNZoEKzY2dsGCBZs3bz5y5EhYWNiuXbvYjgj+lZ+fn5qa2r59+8jISLVazRTOnz8/Pz//3Llz06ZNGzx4cElJCbtBAiHk9u3bY8aM+fPPP1evXr127dqtW7cSQsaOHevl5RUdHd2pU6fhw4fjXy+GoHv37qGhoefOnRszZsyQIUMyMjIKCwuHDRv24Ycfnjt3LjMzc+HChWzHCH97+vTpsmXLrl69WlxcTAjJzs4eNWrU/PnzIyIiUlJSvv76a7YDBJKens7hcL74h7W1NSFkwoQJzs7OUVFRAQEBw4YN0/zx0hvaSLz77rsLFy5ktkNCQrp168ZuPPAsZun2qqoqmqbLyspMTEySkpKYXUFBQVu3bmU1OtD11VdfjR079tatW6ampnK5nKZplUpla2sbHR3NdmjwH87Ozn/99deGDRveeOMNpuTOnTtmZmYymYzdwIAxYsSIY8eO2dvbX7hwgabpFStWDBw4kNl1+fJlKyur6upqVgMEes2aNZMnT9YuSUxMNDExqaiooGmaoihHR8ezZ8/q96JGM4KVmJjo7+/PbHfu3DkhIYHdeODlmN9Na926NfPS398/MTGR1YhAV1xcXNu2bRMTE9u0aWNiYkII4fF4HTp0wIfLQKSmpsbFxf3www9WVlaBgYHa34Ht2rWrrq7GT7IYgn379gmFwmHDhmlKdP5alZSUZGVlsRQd/OvcuXPdu3cfOXLk6dOnCSFJSUktW7Y0NTUlhHC53E6dOun9q4+v39M1nIKCAnNzc2bb0tJSLpfLPAb1nAAAIABJREFU5XKJRMJuVPAi2v1FCLG0tExOTmYxHtCxbt26J0+ezJs3LyQkRKen8vPzWQwMNH799ddTp07du3dvyZIlEomkoKDA2dlZs9fCwgI9xbqCgoJvv/32woULOoXt27dntgUCgUQiyc/P9/T0bPzwQKNbt24bN250dna+du3a6NGjDx069OwfKb1/oIwmwbK0tNRM6iwvLxeLxcy/ucEwWVpaymQyzcuysjIbGxsW4wFtoaGhP/3004ULF8zMzJ7tKWZ2ArDuf//73//+97+cnBx/f39vb2+dniovL0dPsW7evHkTJkxQKBSpqakURWVnZ5eUlGj/tVKpVHK5HD3FusDAQGaja9euGRkZISEho0ePbuivPqO5Rejj45OSksJsp6SkeHt748eeDJmHh0dlZSXzpAb5p8vYDQkY+/fvX7x4cUREhI+PDyHE29v7wYMHFEUxe+/du4eeMiiOjo7MHXYfHx/NMHBaWhpFUe7u7uzGBsXFxWFhYcyDaSUlJZ999tm+ffu0/1rdv39fJBJpDz0C66ytrWUymbe398OHD5VKJVOYkpLCfCXqk36ndDWcY8eOeXl55eXlVVZWBgUFrVixgu2I4F9qtbqoqOjGjRuEkJycnOLiYpqmhw4d+umnn6rV6mvXrpmammZmZrIdJtC//fabjY1NdHR0UVFRUVFRWVkZRVG+vr4///wzTdP79u1zdnbGhFzWFRQUXLp0iaIomqYvXrxobm4eHR2dlpZmamp648YNiqJmzZo1atQotsOE/9BMck9JSZFKpQkJCRRFffDBB++++y7boQF99uxZ5qGQxMRET0/PdevWqdXq1q1bb9iwgabpw4cPOzg4ME9o6ZHRJFhqtXrBggXm5uYWFhbjx4+vrKxkOyL4l1wu99bSqVMnmqafPHkSEBBgZWVla2u7Z88etmMEmqbp9957T7unxo0bR9P0lStXWrRoYW1t7enpefHiRbZjBDo9Pb19+/YikUgikXh7e//yyy9MeUhIiI2NjaWlZVBQUEZGBrtBgo6uXbteuXKF2d6yZYu1tbWlpWWfPn1ycnLYDQxomp40aZJQKJRKpXZ2dosXL1apVDRNX79+vWXLltbW1u7u7np/hJCmaSP7sWeVSqVWq4VCIduBwKvCswjGAj1laCiKUqlUIpFIpxw9ZRRomlYoFJgrbFBkMhnz2KC2hvtAGVmCBQAAAGD4jGaSOwAAAICxQIIFAAAAoGdIsAAAAAD0DAkWAAAAgJ4hwQIAAADQMyRYAAAAAHqGBAsAAABAz5BgAQAAAOgZEiwAAAAAPUOCBQAAAKBnSLAAAAAA9AwJFgAAAICeIcECAAAA0DMkWAAAAAB6hgQLAAAAQM+QYAEAAADoGRIsAAAAAD1DggUAje3GjRtTp049fPgw24EAADQUJFgAzdHSpUuFL1ZSUtKgV3/8+HFISMjVq1cb9CqN5tChQ9u3b6coquEukZWVtX379osXLzbcJQBAv/hsBwAALKAoSqlUurm5+fj4PLuXz8c3Qy0sWbIkJSXlvffe4/F4DXSJlJSUmTNnTp06tXfv3g10CQDQL3yNAjRfY8aM+emnn9iOAgCgCUKCBQDPl5CQUFVV5e/vr1AoTp06lZaW5uTkNHjwYDMzM6bC9evXr169qlar+/bt26ZNG6YwLy8vIyPD1dXVwcHh5s2bcXFxKpWqe/fu3bt3f8m15HJ5RETE48eP+Xx++/btg4KCuFwuIaS8vPz+/fvm5uYtWrTQOYTZZWFh4evrW1hYyITn7OyckpJy8eJFpVLZo0ePzp07M5XLyspOnjyZlZXl6ek5cOBAExMTnVOdPXs2LS2Nx+N17ty5R48eHA5Hc+CDBw9sbW09PDyysrLOnDlTXFzcokWLt99+WygUymSylJQUhUJBCLl586ZQKCSEiESitm3bPreZjx8/LioqatWqlUQiiY2NvXXrFk3Ts2fPZvZmZmZev349PT2dpmlfX9++ffsycaalpT148IAQUlBQEB8fz1Rm3mFmW6VSxcTEJCQkKJVKHx+f4OBgnQYCAAtoAGh+vv32W0LI/PnzX1LH29ubEHLz5k0PDw/NN4aTk1NCQkJFRcXw4cM1hVwud82aNcxRGzZsIISsXLly/Pjx2l81w4cPl8vlTB1mevvChQuZl3/88Ye9vb125fbt2ycnJ9M0XV5ebm5ubmlpKZPJdMJbsmQJIWTFihU0TYeGhhJCvvnmm3nz5mmfZ/bs2TRNh4eHW1paagpbtWqVnZ2tOc+2bdu09xJCunXrxmQ5NE3/9ddfhJDp06evX79eIBBo6rRu3TozM/PKlSvPfql6e3u/6C195513CCHHjx8PDAxkKgsEAmaXpkTD3t7+9OnTNE1Pnjz52ausXr2aOTA2NlYn+3RycoqMjHyF/wsAoAEhwQJojl49wfLw8Jg4cWJkZGRcXBzzl75r167vvfde69atDx48eOPGjc2bN0skEj6ff+/ePfqfBMvZ2dnNze33339PT0+Pjo4OCAgghLz33nvMmbUTrJiYGD6fLxKJVq5cmZyczDxgyGQJ+fn5NE1/9NFHhJA9e/Zox6ZSqdzd3QUCwdOnT+l/EiwPDw8HB4edO3fGx8fv37+fGeBZtWqVqanpggULoqOjL1y4EBwcTAiZNGkSc56ff/6ZEOLq6rpz5847d+5cuXJl1qxZHA7ntddeq6yspP9JsDw9PaVS6apVq65cuRIREcFMhBo1alRpaWlERISbmxshJDw8PCIiIiIiIiYm5kVvKZNgubu7d+nSJTQ09NKlS5p2+fv7L1u27OzZs/fu3bt69eqSJUvEYrGpqWla2v+3d99xTdz/H8A/SUhCBnsPQSGAtYoiggtXXV9tVbRIbR3FamtVfjir1fZb7bBV66jV2jpprbVq67a2jjpAsGqdX8GFyJYRZggBksv9/rg2TRliIHAJvJ5/9HH53CeX93lNfHn3uc+l3blzZ/Xq1YSQ//znP6f/lpaWRtP0zZs3RSKRSCRavnz55cuXb9++vX79eolEIhaLk5OTG/k/BwAYAwIWQFvEBCw3N7detcyaNYvpwwSsyZMn695FURRzssTZ2bmkpETXvnTpUkLIqlWr6L8DFofDuXbtmq5DWVmZk5MTIeTOnTv0vwNWnz59CCEbNmzQLy8iIoIQsmjRIpqmk5KSCCFhYWH6HY4cOUIIiYyMZF4yAYvP5+unit27dzNndFasWKFrLC4ulkqlYrFYq9UWFhZKpVJbW9v09HT9jTORbuvWrfTfAYsQ8vvvv+s6lJSUWFtb8/n86upqmqY7duxICGEC2dMxAcvT07O8vLzBzps2bSKE/Pe//6Vp+syZM4SQadOm1ejDnPc6dOiQfuP+/fsJIRMmTGjwIwCg+WCaBoC2q7CwMKmWtLQ0/T7z58/XLXO53H79+hFCpk6damNjo2tnzuikpqbqWgYOHKgb/0QIsbKyeuuttwghhw4d0t94Xl5eYmKivb09s1bn3XffJYQcPHiQENKpU6e+fftevHiRSVqMbdu2EUJqvGvEiBHPPfec7mX//v2ZmufMmaNrtLW17dq1a0VFRW5u7qFDh8rLy6dMmeLl5aW/HSZgnThxQtfSo0ePF154QffSxsamZ8+earU6MzOTGC46OloikTTYbcyYMYSQp0xm8fjx44SEhM6dO+tfriWEjB8/3snJibm82IjyAMAoMMgdoO2Kjo5u8C7CGuN7mBNR/v7+tRvz8vJ0Ld26dauxHaZFPyQRQpKTkwkhnTp1srS01G/v2rWrhYXFo0ePqqqqhELhzJkzExIStm/fvn79ekJIVlbWr7/+6uvrO2jQIP131VmVu7t7jTTDtOfn59+4cYMQ8uDBAybP6VRXVxNC0tPTdS0BAQE1doe5/piXl8ec5zPI888/X7sxNzd31apVZ8+ezc7OLiws1LXL5fL6tnP9+nVCCEVRNeonhPB4vIKCgpKSEjs7O0PLAwCjQMACgKcRi8X6L5mb+2rcpMY06p8vYUKMPmYYu0Kh0G8sLy/XrdJnYWHh4OCQl5enUCiEQuH48ePnz5//7bffrlixQiwWb9++naKoGTNmMJ/79FJrNOratVptcXExIeTixYu1x6rb2dnpTwb2lI0Qwzk6OtZoyc7ODg0NffLkSUhISFRUFPPpVVVVy5Yte8r8pcx8sI8fP966dWvttXZ2dsztjQDACgQsADC+/Pz8Gi3M+S1ra2v9Risrqzo7q9VquVzO4XCY/gKBYOrUqatWrTp48OCrr74aGxsrEAjqvLfOIMynr127tsalxpa3fv36nJycZcuWMWPjGA8fPmTulKwPU/+YMWP27t3b3BUCgKEwBgsAjI+5elW7pcYEUc8//zyHw0lOTlapVDU6UxTl7+/PzCxFCJk5cyaPx9u6deuJEycyMjLGjRtX+7yXoYKCggghiYmJTdwOM32DRqNp9BZu3bpFCGGGwOvo/xnW+RFM/ZcuXWrciTQAaFYIWABgfHFxcfqjs0tLS7dt28bhcMaNG6ffzcnJacCAAUVFRVu2bNFvX7lyJSGEuZeQ4e3tPXz48Pj4+Pfff5/UGt7eOBEREVZWVvv27bt582bttczly2fh7u5OCMnKymp0JcxFw4yMDF1LdXX1J598onvp4eFR+yP8/PzCwsIyMjKYySZqePb6AaA54BIhQNt19uzZ6Ojo2u1z586VyWRN2bKXl1d4ePjnn38eEhLy+PHj9957r7CwcPr06cyMBvpWr17dt2/fxYsXl5WVjRkzRqlUbt68+fDhw+3atVu4cKF+z5kzZ544ceL27dsBAQEDBw5sSnkMBweHTZs2RUVFDRgwYMGCBX369HFxccnMzPzf//63a9eu2bNnz5o161m2ExIScvLkyddffz0iIsLGxsba2rrGuagGDRo0aO/evTNmzFizZk2XLl1SU1M//fTTiooKXQcvLy9nZ+fz58/Pnj37ueeeEwgEvXr1CgwM3LJlS+/evaOjo69fvz5q1CgfHx+5XP7w4cOffvrJzs6Oma8BAFiBgAXQdt28ebPOkzfjx49vYsCKjo6+e/fupEmTdC2vvfYaM7FTDSEhIcePH58+ffqyZct0Q4569uy5Z8+eGhOsjxgxwtvbOz09/c0339Q9yqaJpkyZIpVK58+fX2O0k5+fX+07B+uzaNGi+/fvHzlyhDlp5+PjY2jAmjZtWkJCwq5du3Qn7bp27Xrw4EHdzZh8Pn/Pnj2zZ8/evHkz07JmzZrAwMBOnTolJia+9dZbO3bs2LFjh26Djo6OtW8tBICWxMFEKQBtUFFRkf5cADV4enqKRKLMzEy1Wl1jGoKioqKSkhJnZ2fdEwkJIdXV1VlZWWKx2NXVdePGjTExMWvXrp0/f35ycvLVq1c1Gk1oaGiXLl10/ZVKZU5Ojr29vYODA9NSVVWVkJCQkpLC5/MDAwO7d+9eO0JpNBpvb+/CwsKsrKwaN+KVl5fn5+fb2dnpz0pA0/Tjx4/5fD4z07pOXl6eUqn08PAQCoW6LV+5cuX+/ftqtdrNzc3Pz093pk2lUj158sTKyqrGfZH5+fnl5eXu7u66CSZoms7NzVWpVLU/sca79D9aX3Jy8q1btyoqKgICApj5V9PS0gQCgaenp65PZWVlbm6uVqt1cHDQn4rs3r17165dUygUjo6OXl5ewcHBPB6vzhoAoGUgYAGAMekHLONuee/eva+++mpUVFRsbKxxtwwAYHS4RAgAJo05h5Sdnb1o0SIul7to0SK2KwIAaBgCFgCYtISEBOYJzYSQTz75RP9hOAAAJguXCAHAmK5du3bmzJkXXnghJCTEKBtMS0vbt2+fVCrt2bNnjx49jLJNAIDmhoAFAAAAYGSYaBQAAADAyBCwAAAAAIwMAQsAAADAyBCwAAAAAIwMAQsAAADAyBCwAAAAAIwMAQsAAADAyBCwAAAAAIzMdB+Vo9FoEhMTg4OD9RtpmqZpmstFLjR1FEXxeDy2q4CG4UiZBfz0mQt8ocxFE4+USCRq8PtoujO5y+Vyf3//uLg4/UaKojQajVAoZKsqeEZKpVIikbBdBTQMR8osaDQaiqLw02f68IUyF008UjKZzNLS8ul98O8hAAAAACNDwAIAAAAwMgQsAAAAACNDwAIAAAAwMgQsAAAAACNrtQGruNhi0yZntqsAAACAtqjVBiwrK+rHHx2Kikx3oi8AAABorVptwLKwoENClJcuSdkuBAAAANqcVhuwCCFhYYr4eAQsAAAAaGmtOWD161eemCjVatmuAwAAANqY1hywXFzUDg6apCQR24UAAABA29KaAxYhpF+/8osXrdiuAgAAANqWVh6wwsIUCFgAAADQwlp5wOrevSItTYDJGgAAAKAltfKAhckaAAAAoOW18oBFMFkDAAAAtLjWH7AwWQMAAAC0sNYfsFxc1I6Omjt3MFkDAAAAtJDGBKwjR474+fnZ2NiEh4fL5XL9VceOHQsMDLS3t+/fv/+DBw8IIU+ePJk3b97AgQN79Oih/fs80qRJk3r8LSoqqsl70QBM1gAAAAAtyeCAlZubO2nSpK+++io3N9fKymrevHm6VQ8ePHj11Vc3bNggl8vDw8NfeeUVQohSqbS2to6IiLh27Zqu5927d+fMmbN///79+/evWLHCKHvyFJisAQAAAFqSwQHrhx9+6Nu377Bhw0Qi0X//+9+ffvqprKyMWXXmzJnQ0NBBgwZxudy5c+c+fPjw8uXLMpnsww8/HDZsWI3tuLm5+fj4+Pj4eHh4GGE/niooqCI9HZM1AAAAQAsxOGDdv38/MDCQWfb39+dwOOnp6cxLHo+nVquZZa1WS1HU/fv369vO22+/HRAQMG7cuKSkpKd8nLYeBtWMyRoAAACgJRl8Uqe4uNjT01P30traurCwkFkeNmzY/Pnz9+7dO3jw4PXr16vVat2qGpYvXx4QEMDlcjdv3jx48OCkpCQHB4fa3SoqKrp161bnFjZv3ty9e/dnLzs0tPD8eetBg3Ke/S3QFCqVisPhsF0FNAxHyixoNBqKoiiKYrsQaAC+UOaiiUfqWU70GByw7O3tFQqF7mVpaamjoyOz3KFDh+PHj69YseKzzz4bM2ZM165d67v8N2rUKGZh7dq1x48fP3fuXERERO1uYrH4zz//1G+hKEqj0QiFQkPLHjy4+ptvbC0tJVwubeh7oRFomhaLxWxXAQ3DkTILTMBqxE8ftDB8ocxFE48Ul9vwBUCDA1ZAQMDJkyeZZeY+QW9vb93aQYMGDRo0iBBSUFCwZs2a+s4/6XA4HD6f3wL/LHNxUTs6qu/csQwMVDX3ZwEAAEAbZ/AYrIkTJyYmJp48eVKlUn300UeRkZFWVlbHjh1btmwZIeTChQtKpfLRo0dRUVGjR4/29/enKOratWvMQKtr167dunUrLy/v6NGjhYWFxcXFn3zySU5OzsCBA42+Y7VhsgYAAABoGQYHLBcXlz179sTExLi7u1dUVKxfv54QUlRUlJGRQQhZvXq1h4dHWFiYTCaLjY0lhKhUqhkzZqxYsSI4OHjmzJkLFy7UaDSrV68OCAjw8/M7f/78b7/95uLiYvQdqw2TNQAAAEDL4NC0iY5Jksvl/v7+cXFx+o2NHoNFCNFoOAMGdDx27KG9vcZINUK9lEqlRCJhuwpoGI6UWcAYLHOBL5S5aOKRkslklpaWT+/T+h+Vo2NhQYeGKhMTMVkDAAAANK82FLAIIWFhivh4BCwAAABoXm0tYJUnJlpptZikBAAAAJpR2wpYLi5qJyf1nTsNXDcFAAAAaIq2FbAIJmsAAACA5tfmAlZYmCI+HgELAAAAmlGbC1jdu1dkZAiKigyewh4AAADgGbW5gMXjYbIGAAAAaF5tLmARTNYAAAAAzawtBqx+/RSYrAEAAACaT1sMWM7OGmdn9Z07IrYLAQAAgNapLQYsgquEAAAA0JzaaMDCbFgAAADQfNpowAoKwmQNAAAA0FzaaMDCZA0AAADQfNpowCIYhgUAAADNpu0GrP79MVkDAAAANIu2G7CcnDBZAwAAADSLthuwCK4SAgAAQPNo0wELkzUAAABAc2jTAYuZrKGwEJM1AAAAgDG16YDF49E9e2KyBgAAADCyNh2wCCFhYQpcJQQAAADjausBq18/RWKiFJM1AAAAgBG19YCFyRoAAADA6Np6wCKYrAEAAACMrTEBq6Sk5Pvvv//uu+8KCgpqrFIoFAcOHNixY0d6erqusbS0ND4+/s8//9TveebMma1bt165cqURBRhX//7l8fEYhgUAAABGY3DAys/P79q169GjR3///fcuXbo8fvxYt+rJkyeBgYH79u27detWjx494uLiCCFffvmls7NzZGTkggULdD1jYmKio6OTk5NffvnlL774wih70mjdulXk5fEzMgTslgEAAACtBoemaYPesHz58tu3bx88eJAQMmPGDIFAsHHjRmbV/Pnzs7Oz9+3bRwiJjY2NjY2Ni4srLCyUSqXffffdDz/8cOHCBUJIRkZGQEDAo0eP3N3dr169Onz48KysLLFYXOOD5HK5v78/k9J0KIrSaDRCobDRO1yn1avdpFJq1qx84262LVMqlRKJhO0qoGE4UmZBo9FQFGX0nz4wOnyhzEUTj5RMJrO0tHx6H4PPYJ04cSI8PJxZDg8PP3HihG5VSkpKUFAQs9y9e/f4+PiSkhIHB4caPwonT57s0aOHu7s7ISQkJEQikVy6dMnQMoxr1KjiI0dsDYyaAAAAAHUzeBLz7OxsJhsRQjw8PLKzs2ma5nA4hJAOHTrcunWLWXXjxg1CSE5Ojq2tbe0teHh46F66u7tnZ2fX+VkajWbHjh36LVqtVqvVWlhYDB8+3NXV1dDi6+PnVy6RUFevCrt3Vxprm22cRqPRaDRsVwENw5EyC8wZLB6Px3Yh0AB8ocxFE4/Us1z9Mzhg6eIUIYTD+dcVxgULFvTq1WvChAlubm4JCQlcLlfXs74tEEK4XK5Wq63vswoLC2u0aLVaHo+nVqvre1fjjBxZdOyYXbduCiNusy1jjhTbVUDDcKTMgvZvbBcCDcAXyly0wJEyOGC5ubnl5/81VikvL8/NzU2Xlry8vJKSkk6fPs3lcqdOnRoUFNS+ffs6txAfH697mZubqzslVgOfz1+0aJF+SzONwSKEjB6tGDvW9b338i0t8d0wArVaLRDgvgEzgCNlFrhcLkVROFKmD18oc9HEI1Xn+aMaDB6DNXjw4F9//ZVZ/vXXXwcPHkwIKSwsVCgUhBA7O7vIyMiIiIgff/xx2LBhIlEdE3i+8MILly9fLi4uJoTcu3dPLpf37NnT0DKMztlZ06WL6tw5zNcAAAAATWXwGazo6Ojg4OCYmBixWLxz586LFy8SQqZMmRIUFDRp0qRPPvkkICDg9u3biYmJzD2Dt27d2rx58927d1NSUmbMmBEcHPzWW2+Fh4ePGDFi7Nix33777dy5c21sbIy/Z4YbPbrk2DHbESNK2S4EAAAAzJvB0zQQQrKysvbu3avRaCIjI318fAgh586ds7e39/f3379//6NHjzw8PMaPH29vb08IycnJSUhI0L3X09Ozd+/eFEXt27fvwYMHwcHBo0aNqvNTWnKaBkZVFXfIkIBDhx46OmKIYlPhXmVzgSNlFjBNg7nAF8pctMA0DY0JWC2j5QMWIeSDDzxksqopU+TNtP22A78y5gJHyiwgYJkLfKHMhSnOg9W6jRpVcuhQzXklAAAAAAyCgPUvPXooVSru/fsNxFIAAACAp0DA+hcOh7z0UumxYziJBQAAAI2HgFXT6NHFx4/bUlTDU1wAAAAA1AkBqyYvr2pPz+pLl6RsFwIAAADmCgGrDqNGlRw9iquEAAAA0EgIWHUYMaI0Pl6qUOC5qgAAANAYCFh1sLamevdWnjplzXYhAAAAYJYQsOo2alQx7iUEAACAxkHAqlv//uXp6cKsLDwUHQAAAAyGgFU3Ho8ePrz0+HGcxAIAAACDIWDVi7mX0FQf1QgAAACmCwGrXs8/r7K01N68KWa7EAAAADAzCFhPg8fmAAAAQCMgYD3NSy8VnzplU1WFPyUAAAAwAKLD0zg7azp1Ul24YMV2IQAAAGBOELAagMfmAAAAgKEQsBowZEjZjRtiudyC7UIAAADAbCBgNUAk0g4apPj1Vxu2CwEAAACzgYDVMDw2BwAAAAyCgNWw0FBlaSnv4UNLtgsBAAAA84CA1TAOh7z4IibEAgAAgGeFgPVMxowp+eUXW62Ww3YhAAAAYAYQsJ6Jt3eVi4v60iUJ24UAAACAGUDAelYY6g4AAADPCAHrWY0cWRoXZ1VezmO7EAAAADB1jQlYGzZsCAwM7Nat2zfffKPfTtP0119/3bNnz+eff3758uUURTGNK1eu7NKlS3Bw8Hfffcf0nDVr1tC//d///V/Td6MF2NhQoaHKM2es2S4EAAAATJ3BE5QfOnRozZo1R44c0Wg0Y8aM6dChw/Dhw5lVu3btWr169cGDB62tradMmWJlZbVgwYLvv/9++/bthw4dKi0tDQ8Pl8lkffv2vXz58iuvvNK9e3dCiI2N2czhOWpUyQ8/OISHF7NdCAAAAJg0g89gffPNN/PmzevevXtoaGh0dLT+Saz9+/fPnDkzKCjI19d36dKlmzZtYvovXry4S5cuYWFh06dP37JlC9O5e/fuQ4YMGTJkSEhIiLF2prn176949EiYliZkuxAAAAAwaQYHrDt37jBnngghwcHBd+7c0a1Sq9UCgYBZFggEaWlpCoWivv7vv/9+//79Y2JicnJymrQHLYjPp199tWjnTke2CwEAAACTZvAlQrlcrruoZ2trW1BQoFs1fPjwnTt3vvbaaxKJZMOGDYSQ7OxshUJRu//s2bN9fHwIIVu3bg0LC7t165aVlVXtz1KpVAMGDKjRSNM0h8NZtWpVly5dDC2+6caMyYyI6Pp049KkAAAgAElEQVT662pX1+qW/3QzUlFRwXYJ8ExwpMyCRqPRarUajYbtQqAB+EKZiyYeKWaU+dMZHLBsbGyUSiWzrFAo7OzsdKtiYmKys7ODg4PFYvGUKVNOnDjh6ekpEolq93/jjTeYln79+vn4+Jw9e3bMmDG1P8vS0vLAgQM1domiKIFAYGNjw+fzDS2+6SQSMnZsyYEDXosWPWn5TzcvEgmmDTMPOFKmT6PRUBQlFGJ8ghnAF8pcNOVI8XgNTylg8CVCHx+f+/fvM8v3799nTkQx+Hz+unXrMjMz79+/36lTpw4dOkil0g4dOtTXnynRzs6uvLy8zs/icDiOtTg4ODg6OrKSrhhRUfJjx2yLiw3OpgAAANBGGBywpkyZsnnzZqVSqVAotmzZMnnyZELIpk2bzp8/X1RUxFwBTElJWbp06YIFC5j+GzdurKqqKioq2rlz5+TJk4uKiv78809CCE3TsbGxDx8+DAsLM/Z+NSNHR82QIaU//GDPdiEAAABgogw+DfPmm29euXLFw8ODEPLKK68wAevUqVNcLtfOzu6FF16gKIrD4SxatGjWrFmEkDlz5ly/ft3V1ZWm6aioqHHjxmVkZERGRj558oTL5bZv3/6nn37y9vY2+o41q2nT5K+95jN1qlwi0bJdCwAAAJgcDk3TjXhbZWUlh8Opc0BASUmJjY0Nh8Op0Z/L5eruMWRaCCGWlpb1fYRcLvf394+Li9NvpChKo9GYwkCERYvadeqkioqSs12IiVIqlRiIYBZwpMwCxmCZC3yhzEUTj5RMJntKgGE08lE5lpaW9X3VbW1ta6Qrpr9+umJaGizOlE2fXrBrl2NVVc09BQAAAMCzCBvJ37+yY0fVsWN2DXcFAACANgYBq/Hefrtgxw5HisJJLAAAAPgXBKzGCwyscHZWnzyJxz8DAADAvyBgNcmbb8p37HBq1H0CAAAA0GohYDVJWJiCx6Pj4+t4zg8AAAC0WQhYTfXGG/ItW5zYrgIAAABMCAJWUw0bVlZaanH9OiY+AQAAgL8gYDUVl0tHRRVs2+bIdiEAAABgKhCwjCA8vOTRI8vkZBHbhQAAAIBJQMAyAgsLeuLEwp07cRILAAAACEHAMpbIyKI//5Skp+NJYQAAAICAZSQikTYysig2FiexAAAAAAHLeCZOLDxzxvrJEz7bhQAAAADLELCMxsaGGj26ePduB7YLAQAAAJYhYBlTVFTh4cN2xcU8tgsBAAAANiFgGZOzs3ro0LIff8RJLAAAgDYNAcvIpk0r2LvXXqnEHywAAEDbhRxgZO3aVYeGKg8csGe7EAAAAGANApbxvflmwbffOlZVcdguBAAAANiBgGV8AQGVAQGVx4/bsl0IAAAAsAMBq1lMn16wY4eTVouTWAAAAG0RAlazCA5WOjmpDx/GSSwAAIC2CAGrubz//pMvv3TBnFgAAABtEAJWc/Hzq/zPf0q//NKF7UIAAACgpSFgNaPo6Lz4eKtbt8RsFwIAAAAtqjEBq6Cg4OjRo3/++SdN0zVWFRUVnTp1Kj4+nqIoXWNubu6RI0du3Lih3/Pu3btHjhxJS0trRAHmQirVzp+f9+GH7hSF0e4AAABtiMEB6+LFi506dfruu+8mTpw4efJk/VVxcXGdOnXasWPH4sWLBwwYoFKpCCGnTp3q3Lnz999/HxERMXPmTKbnihUrBg8evGfPntDQ0O+//94oe2KaRo4ssbfX7NuHeUcBAADaEE7ts1BP169fv4iIiDlz5pSWlnbs2PHAgQN9+vRhVnXp0mX27Nlvv/02TdNDhw6NiIh4++23u3XrFhMT88Ybb8jlcj8/v7i4OBcXl/bt29+8edPf3//333+fNGlSenq6QCCo8UFyudzf3z8uLk6/kaIojUYjFAqbss8tLD1dMHmyz88/pzg7a9iupeUolUqJRMJ2FdAwHCmzoNFoKIoyr5++tglfKHPRxCMlk8ksLS2f3sewM1gFBQUXL16cOHEiIcTGxmbkyJGHDh1iVlEUdffu3f79+xNCOBxO//79Dxw4kJqampSU9MorrxBCHB0dhw4deujQoV9++SUwMNDf358Q8sILLxBCLl26ZPjemQ1v7+qIiOJ161zZLgQAAABaiIVBvbOysiwtLR0dHZmXXl5e9+/fZ5Z5PJ6Li8vDhw87depECHnw4EFmZmZWVpa9vb0uJHp5eWVnZ3M4HC8vL6aFw+F4enpmZWXV+XEURR07dky/RavVarVaCwuL3r1729ubzXW3N9/MGzfO/48/xKGh5WzX0kKYI8V2FdAwHCmzoP0b24VAA3CYzEUTj9SzXP0zLGBVV1fz+XzdSz6fX1lZqXu5ZMmSWbNmpaSkZGVlJSQkcLnc6upqCwuLGv1rNAoEgqqqqjo/jqKoGpcIaZrWarU8Hk8mk0mlUoOKZxGHQ955J/3jj9v9+GOSQGDYNVkzVeN/FTBZOFJmgblEyOHgdhlThy+UuWjikTJ+wHJ1dS0vL6+srGQuPRYUFLi7u+vWRkdHd+3aNS4uLjg42MfH5+jRo66urkVFRVqtlsvlMv3d3Nzc3NwuX76se1d+fr6bm1udHycQCD7//HP9FnMcg8V44YWqw4fV+/d7Tp9ewHYtLYGiqAavT4MpwJEyCxiDZS7whTIXTTxSTKppoI9BW/T09PTy8jp37hwhhKbpc+fO9e3bl+hFuX79+r333nsTJ07ct2/fsGHD/P39ra2tExMTCSFarfb8+fNhYWF9+vT5448/mHsMMzIysrKyevToYei+maMlS3J27XLIzq45nB8AAABaGcPOYPF4vIULF86aNWvZsmUJCQlKpXLcuHEURVlYWFy5cuXx48ePHz+2tbU9dOhQVVVVdHS0QCCYO3fu9OnTlyxZcubMGbFYPGLECB6P16dPn4iIiAkTJmzevHnq1KlOTk7NtHsmxc1NPXly4apVrl9+mcF2LQAAANCMDJ4HKzo6es2aNVeuXHF3d09ISBAKhVwud+XKle3atevUqVNxcfH169dHjx4dFxcnEokIIe++++4HH3zwxx9/+Pv7nz9/nsfjEUIOHjw4aNCgixcvTp069csvvzT+bpmqqCh5errw/HkrtgsBAACAZmTwPFgtptXMg1XDn39K3nvP8/DhhyJRa77TBJPBmAscKbOAMVjmAl8oc2Fy82BB0/XooezWrWL79jZxVRQAAKBtQsBiwTvvPPn5Z7vUVPxjFAAAoHVCwGKBo6PmzTcLPv7Y3VQvzwIAAECTIGCx47XXipRK3m+/2bBdCAAAABgfAhY7uFz6gw+y16xxKy/nsV0LAAAAGBkCFms6d1b176/YvNmZ7UIAAADAyBCw2DR3bu6vv9rcvYvnKgAAALQqCFhssrGh/u//8j76yAMPXwcAAGhNELBYNnZssYUFvXevA9uFAAAAgNEgYLGMwyGrVmVt2+Z0/bqY7VoAAADAOBCw2OfuXr18efY773jl5xv27G0AAAAwTQhYJmHAAMW4cUULFnhpNBy2awEAAICmQsAyFTNn5ltZUV984cJ2IQAAANBUCFimgsslK1dmnT1rffIkpncHAAAwbwhYJsTamlq/PuPTT91SUvAcaAAAADOGgGVaAgIqFy3KnTvXC4/QAQAAMF8IWCbnxRdLevVSLl3qQdNslwIAAACNgoBlit5990lpqUVsrBPbhQAAAEBjIGCZIgsLes2ajB9+sE9IkLJdCwAAABgMActEOTlpPvssa+lSz5wcAdu1AAAAgGEQsExXaKgyKkq+cGG76mrMPgoAAGBOELBMWlSU3N29euVKN7YLAQAAAAMgYJk0Dod89FH29euSgwft2K4FAAAAnhUClqkTi7UbNmRs2OCSlCRiuxYAAAB4JghYZsDbu2r58pz5872KizH7KAAAgBlAwDIPgwaVDR9e+s477bRaDHgHAAAwdY0JWIcOHRo5cuTIkSMPHjxYY9XRo0fHjh07ZMiQhQsXyuVyQohGo1m/fv2IESPGjRt37tw5ptuKFStm/G3lypVN3Ic2Ys6cPJrmrF3rwnYhAAAA0AALQ98QFxc3bdq02NhYLpcbFRVlb28/cOBAZtWFCxcmT54cGxvboUOHTz/99NVXXz19+vSiRYsSExO/+OKLzMzM8ePHnz9/vnPnzgcPHnzxxRe7dOlCCLG3tzfuLrVWPB69bl3Gm292WLfOdf78XLbLAQAAgHoZHLA2btwYExMzZswYQsjcuXM3bdqkC1iXLl0aOHDguHHjCCFLliwJCwsjhMTGxh49erRXr169evW6ePHiV1999fXXXxNC+vfvP2TIECPuSVtgY0Pt2PF4xoz2n3/u+s47yFgAAAAmyuBLhDdu3OjVqxez3KtXr+vXr+tWvfDCCzdu3Lh9+3Z5efmuXbtGjhxJUVRFRYWNjQ3TwdbW9tq1a8zyunXrJk6c+NlnnykUiibvRRtiZUVt2ZJ244Zk9WpXtmsBAACAuhl8BisvL8/O7q85mRwcHPLy8nSrQkNDo6KiunfvLhQKXV1dT58+zePx+vbtu3PnzvXr15eVlf38889MnIqMjPTw8ODxeDt27Ni7d++VK1eEQmHtz6qsrIyIiNBvoWmapmkul7t06dKOHTsaWnzrwOORL75IjokJWLbMedGiNI5JjnpXqVQc06wM/g1HyixoNBqKoiiKYrsQaAC+UOaiiUdKq9U22MfggCWVSlUqFbOsVCqtra11q7Zu3XrkyJGcnBxnZ+evv/566NChycnJW7ZsmTRpUrt27SwtLQcMGMCc8Vq8eDHzlnHjxnXo0OHMmTMvvvhi7c8SCAQff/yxfgvzEyMQCLy9vcVisaHFtxpiMdm+PXPGDO+1a2X//W+OCX6daZpuywfIjOBImQUmYNX5D1EwKfhCmYsmHikut+ELgAZfIvT29k5NTWWWU1NTvby8dKtOnToVHh7u7OxMCHnrrbfS09NTUlL8/PwuX7784MGDlJQUZ2fnTp066W9NKBS6ubkVFRXVtwPP1dKxY8fnnnsO/wdLpdSWLen371t+9JE7TbNdDQAAAOgxOGBNmDBhx44darVao9Hs2LFjwoQJhJD9+/dfvnzZ19c3Pj5erVYTQs6dO2dhYdGuXbvi4mImJ966dWvbtm0zZ84sKyvLyspitnby5Mnk5OSePXsad6/aCKmU2r49LS1N+NFHHs9wthIAAABaiMEBa8aMGdbW1jKZTCaTicXiGTNmEEK++eabM2fOLF68mKIoHx+fnj17TpgwYcuWLdbW1t9++62rq2vHjh0HDRq0evXqsLCw/Pz8559/3tfXVyaTTZw48ZtvvvH392+GXWsTRCLt5s3p6ekCZCwAAADTwaEbdXkpMzOTpmnd9UGtVsvhcJjxYgUFBaWlpV5eXgKBgFmbn59fVFTUvn17S0tLpkWj0aSnp3O5XC8vLx6v7se/yOVyf3//uLg4/UaKojQaDQYi1KBScWfP9m7XrnrZsuxnuC7cEpRKpUQiYbsKaBiOlFnAGCxzgS+UuWjikZLJZLpIU59G/m3crl07/dFXXC5XNxrfyclJJpPp0hUhxNnZuWPHjvqlWFhY+Pr6dujQob50BQYRibRffZWelSVYvhznsQAAANhnGqc7oMmYjJWTg4wFAADAPgSs1sPSUrtpU3pODn/JEjwTGgAAgE0IWK2KpaV206aMoiLeu+96UhQyFgAAADsQsFobS0vtxo0ZRUW8JUs8q6pwfAEAAFiAv4BbIUtL7VdfZfD59MSJPhkZgobfAAAAAEaFgNU6CYXaFSuypkyRT5nic+qUdcNvAAAAAOMx+FmEYEZGjy7x86tasKDd9euShQtzLSzwSB0AAICWgDNYrdxzz6n273+Um8ufNq19QQHyNAAAQEtAwGr9pFJq/fqMAQPKIyNlly9L2S4HAACg9UPAahM4HPLGGwWrV2cuXeqxebMzZiIFAABoVghYbUhIiHLPntRLl6Rz5ngrFHhIEQAAQHNBwGpbXFzU33772Men8tVXfR88aOBBlQAAANA4CFhtDo9Hz5uXN29e7ltvtT9wwI7tcgAAAFohBKw2avDgsm+/fbx7t8PSpZjwHQAAwMjwN2vb1b591Q8/pKrVnMmTfZKSRGyXAwAA0HogYLVpYrH2888zJ02S/9//eX/4oXtJCUa+AwAAGAECFpDRo0uOHn0oFNKjR/vt3u2g1XLYrggAAMC8IWABIYRIpdS77z7Zvj3t9GmbCRN8bt0Ss10RAACAGUPAgn/4+1d++23qpEmF8+Z5LV3qWVyMR+sAAAA0BgIW/AuH89cVQ2traswYGa4YAgAANAICFtQBVwwBAACaAgEL6sVcMZw+Xf7OO+1wxRAAAODZIWDB03A4ZNiw0oMHU2xsqPBw2U8/2Ws0uGIIAADQAAQsaJhUSi1e/GTbtrRTp6xffNH/hx8cVCr8nwMAAFAv/DUJz8rfv3LbtrQNG9KTkkQjRvhv3uyMiUkBAADq1JiAVV1dffPmzczMzNqrNBpNcnJycnKyRqPRNZaXl9+6dSs/P1+/p1wuv3btmkKhaEQBwKKOHSs//TRr9+7UsjLe6NH+K1e6PXnCZ7soAAAA02JwwEpOTpbJZDNmzAgJCZk7d67+qqSkJD8/v6ioqNdff93Pzy8pKYkQsmfPHl9f3zlz5gQGBq5evZrpuW3bNn9//5iYGB8fnxMnThhlT6AleXpWv/vuk8OHH1pbU5GRsuho7+RkPM0QAADgLxyapg16w+jRo7t06bJixYr8/PzOnTv/8ssvISEhzKoJEyY4ODh89dVXhJDZs2cXFRVt2rTJ29v71KlTffr0ycnJef75569fv25nZ9euXbtz58716NHjwIEDCxYsePToEY9X82KTXC739/ePi4vTb6QoSqPRCIXCJuwyGJlSyT10yC421tHDQ/3GGwUDByoIIUqlUiKRsF0aNAxHyixoNBqKovDTZ/rwhTIXTTxSMpnM0tLy6X0MO4OlUChOnDjx5ptvEkKcnZ1HjRq1b98+3VqKolxdXZllV1dXrVZ7584dkUjUp08fQoi7u3tQUNC+ffuOHz/u5+fXo0cPQkh4eHh5efnly5cNKgNMikSinTSp8LffHkREFK1b5/rKK75Hj9pielIAAGjLDJvZKDs7mxDi5eXFvPTx8bl9+7Zu7Ycffjht2rTS0lJCSEJCQmxsLEVRZWVlCoXCysqKpuns7OyMjAytVuvj48O8hcfjeXt7Z2RkMCGsBq1We/Xq1RotFEXx+fyOHTtaWVkZVDw0Kz6fHj265KWXSs6csdmxw3HHDofIyJIRI0rt7TUNvxkAAKB1MSxgKZVKPp/P5f513kskEpWXl+vW5ufn6watKxSK3NzcAQMG9O3bd8KECdOmTTt58mRhYWFFRYVSqdQ/0V1jI/rUavXGjRtrNNI0zeFw5s2b5+/vb1Dx0DL69lX27ZuTmCg8c8bzq69kgYHl//mPvH//YqFQy3ZpUIeKigq2S4CGaTQarVarf/MQmCZ8ocxFE48URVEN9jEsYLm6ulZWVqpUKpFIRAiRy+W6a4KEkFmzZi1ZsmTy5MmEkMDAwOjo6Dt37vzyyy9bt249c+ZMnz59Kioq2rdv7+DgcP36dd27CgsL3dzc6vw4oVC4a9euGruEMVhmoU8f5dChuVVVeRcuWB896rJqlU+fPopRo0r69Svn8Qwb9gfNDUNGTB/GYJkRfKHMRVOOVO2B47UZNgbL1dXVw8MjISGBeZmYmMgMpWKUlJQ4OTkxy87OziUlJYQQkUg0Z86czZs3R0REnDx5csCAAT169Lhy5YparSaE5Ofnp6amduvWzaAywFwIhfSwYaWbNqX/9tv93r2VO3c6DRvmv3Kl2927uOUQAABaM8POYPF4vJiYmLlz565du/bq1at3796dOHEiIcTT0/PQoUPjxo1bsmQJcwFxyZIl48aNI4Rs3brV2dlZrVZv2LBh4MCBgwYNIoT4+fm99dZbkydPXrt27csvv+zh4dEMuwYmxMaGiogoiogoSk0V/vabzfz57ZjsFR5e7O6uZrs6AAAAIzP48b0LFy6USCQbN250dXW9cOGCtbU1ISQiIsLR0XHdunXffPPNtm3bCCFvvPHGjBkzCCFisXj37t1qtXrChAlMCyHk2LFjn3322dq1a4ODg999912j7hGYNB+fqlmz8t9+O//aNcmxY7aRkbLOnVVDhpT27Vvu5oakBQAArYTB82C1GMyDZdaecYqRqirOhQvW585ZJSZK7eyovn0VYWHl3bsrhUIT/d+y9cG0PWYBY7DMBb5Q5qIF5sEy+AwWgBExFwqHDSslhKSkCOPirHfudJw/v13nzqrevZW9epV36qRiu0YAAACDIWCBqZDJqmSygjfeKFCpuJcvSy5csNq714vLpXv3Lu/dW9mnj0IqxUQPAABgHhCwwOSIRNqBAxXMI3dSUiwvXpTu32/3wQcenTtX9Oyp7Nq1onNnlViMsAUAAKYLAQtMmkxWKZNVRkXJVSrulSuSq1clX33lcu+epadndWBgRdeuqi5dKnx8qjh4MA8AAJgSBCwwDyKRdsAAxYABCkIIRXEePxYkJ4tu3BB/+61jXh7fz68yKEgZFFTRtavKzg6zXQMAAMsQsMD88Hi0TFYlk1WNHl1CCCkutrh9W3T7tnj3boclS8SOjurAQFXnzhUyWZWfX6WtbcMPNAAAADAuBCwwe3Z2Gt3JLa2WpKZa3rolSkoS/fabbUqKkM+nZbJKJpAxC1IpIhcAADQvBCxoVbjcv4ZtvfxyMdOSn89/9Ej48KHl//4nOnjQLjVVaGVF+fpW+flV+vpWyWSVPj5VGDIPAADGhYAFrZyzs9rZWd27d7muJSdH8OiR8OFD4dWrkj177B8/FkokWk/Pak/Pak9PNbPg4VHt4qLmGvasTgAAgL8gYEGb4+5e7e5e3a+fQteSn2+RnS3IyhJkZQmuXpUcOmSblSUoKbFwc6tu107N5C0meDk7a+ztMYgeAAAagIAFQJydNc7OmqCgCv3GqipOTs5fqSszk3/9ujg7W1BQYKFU8hwdNc7OamdnjZOT7r9qJyeNi4taIsHVRgAAQMACqIdQSHfoUNWhQ1WN9upqjlzOz8uzKCiwyM/nFxTwU1KEeXl8udwiN5dP08TFRe3oqHFw0NjbU7a2GltbytZW4+BA2dlpmJcCAZ60CADQyiFgARhGIKCZi4x1rlWpuLm5/MJCi4ICi5ISi+JiXlqasLhYXFRkUVzMY1qEQtreXmNnp7Gzo2xtKRsbjbW1ViqlrKwoKytKt8wstPDeAQCAUSBgARiTSKSt87yXvvJyXmHhX2GrpMSitJSnUPAyMgQKBU+h4JWVccvLecxyeTnX2vqvsGVlRUmllKWlVizWWltrLS21IpFWKtWKxZRIpBWJaGtrSiTSWlpqJRKtVEphhD4AAIsQsABamlRKSaWUt3fd58D0abWkvJxXVvZX8FIqeSoVV6XilpXxVCqOXG6Rnq5r5CgUvIoKbmUlt6KCq1DwOBwilVJ8Ps2kLoGAlkq1XC5tbU1ZWBCxmBIKaaFQy+dXicV8gUBraUlzuTTzRG0mnzHvsrCgxWIth0OsrChCCPOy2f+MAADMHAIWgOnicom1NWVt3ZgLhVotp7ycW13NqazkVlZyq6s55eVciuIoFDyNhlNRwa2s5FRXc0tKOGo1t7raorKSw7yFEKJQ8GiaqFRctZqjVnNUKi5NE4WCRwhh3ksIYaKb/oJAQFtaagkhTHRjypBItDzeX2POdDvCJDzdsv6dAXy+ViT6Z4waE/L01v71Wfpq//nUeJeOUKgVCuseANe4P2QAgPqYdMCaWFXVbsEC/Raapmma5nK5hJCs1atpHk9/bbvFi4mm7lvolSEhRRMm6LdIrl6137u3vo/Oj4mp8vbWb3HZuFGQllZnZ7W7e+6/6+Tn5LiuXVvfxovHji0PC9NvsTt0SHrxYn39zXFPKYri8XhtYU8ZJrinzMkqpuUpe1rh5FT47rv6Lf/sqXUd/XV7ygQvQojDkYM2ly4SQrRaDkURZkH7964cf3GjmuYzy2VlPELIywdmcygNRf31gG6tluiW77n2PSWbplL98+xuv+zE/6Tu0L2k6X86E0K2tPsgw1LGbJYRk/9h++qHFMXR1DrLlm3h9b5wdXX1P29vp03/pGrxX2vVNR8YvpO88Rv5D7PMnNKbrI4dojlZxx8KIYSQaaLdIiuO/nPHNxVN4dF1H9NLwv67pG/rJ9EQZdyrxVvr2/gGpw/SBM/pb53ZU91LCwtat/KJwGuT9yf6b3etyvi/jPfr2/gxp8l/2A7Vb3kp//vepafr6/+BbCfF+dffHZ+kTuVz6t7T2/Zhx9pN128JLLo4KnN7fRv/zu/9LLFMvyUq5RMPZUqdnfNF7bb5f6zf4qzKfPPBf+vb+EmPSX86DtFvGZ69u4f8TH39VwZur7Gn796eXt8xZfaU+eljWlr3nuq3mOOejh2bHRamJY397eV8+SUJCKjvUxgmHbD+Z2FRNny4fotWq6Uois/nE0JoTs0fxNIhQzh03f88rXZ3r9Gi9vCosXF9GhubGi3loaEW9fxpUlJpjRattfVTNl7t5VWjRdWxo1Yiqa+/Oe5pZWWlpaVlW9jTv4ox2z1VWtT8EXjGPeXzaT6fIoTwevirXUT6fbiE6AaADRpcTv49HMzapn99e2rn7t6t8xP9FkGOQHSnX33FLA8tp2zT9Fskl/0sSp3r7NxZKu3b555+C6+8XJrYu76Nd+2orfa6wyyXl/O0WiJ5YG+Z1be+/scGpiiUfP09c7nYt749fcnFo59/WlUVp6rqrz8cSYGl9aNB9W18+nNVFaJ0gUCga3G+01lY7ql7yZyAZJbtRVbTnpfrv12gouikf/0lpK+3l/3zzv/q75DhRefX239qcBHN+dcxrf5jsEpdd2d7e8/h7cr0W6yL7Msy6/0fLFjGf3fPk84AAAiqSURBVF7yr/583x5lSt86O3MsrYYH/KuzsJJb1qHejft6ODs4/qu/c7ZvmZxXX/+hgeU19rTcrd7vKbOnzE8f09K691S/xRz31M2tmolAjfvtFdb6ma2NQ9ezXdbJ5XJ/f/+4uDj9RoqiNBqNUChkqyp4RkqlUlJ/vADTgSNlFjQaDUVR+OkzffhCmYsmHimZTKZL0vXBjUYAAAAARmZmAevJkyePHj1iuwpo2P/+97/S0lK2q4AGVFVVXb16le0qoGFZWVmPHz9muwpo2K1bt8rLyxvuB6yqrKy8du1ac3+KmQWsuLi4n3/+me0qoGGbNm1CFDZ9paWlH374IdtVQMPOnTt3+PBhtquAhn3xxRdp9dxQAqZDLpevWLGiuT/FzAIWAAAAgOkz3UHuFRUVgwYNunnzpn6j/jQNYMq0Wi2Hw+HUulcOTI3+XeVgsvDTZy4oiuJyufjpM31N/On7448/goKCnt7HdAMWIUSj0WjqmZ0CAAAAgBVCobDBGG3SAQsAAADAHOGEMwAAAICRIWABAAAAGBkCFgAAAICRmVPAio2N7datW6dOnT755BMMHTMpRUVFq1atioyMHDp0qO6+hLKysunTp/v5+fXr1y8hIYHdCoFx4sSJ8PDwjh07hoSErFu3jvkeZWRkjB071tfX96WXXkpJqfsJrNDCYmJigoKCZDLZ0KFDf//9d6bxwoULYWFhfn5+M2bMwGyWJmXfvn1Dhw69ffs28/L06dO9e/f29/ePjo5WqVTs1gaEkAMHDgzVk5WVRQjJysp6+eWXfX19R4wYcf/+faN/qNkErISEhIULF3711VcHDhz4/vvvY2Nj2a4I/lFQUJCamhoYGHjmzBmtVss0zp8/v6Cg4OzZs9OnT3/ppZdKSkrYLRIIIbdu3Ro/fvzx48fXrFmzfv36r7/+mhASGRnZoUOH+Pj4oKCg8PBw/OvFFPTs2fO77747e/bs+PHjR40alZmZWVhYOGbMmLfffvvs2bNZWVnvvPMO2zXCX3JyclasWHHlypXi4mJCyJMnT15++eX58+efPn363r1777//PtsFAsnIyOBwOIv/Zm9vTwh57bXX3N3d4+LievfuPWbMGN1fXkZDm4nJkye/8847zPLOnTtDQ0PZrQdqY6Zur6qqomm6rKxMJBIlJyczq8LCwr7++mtWq4Oa3nvvvcjIyJs3b0okkoqKCpqmNRqNo6NjfHw826XBv7i7u586derLL78cOHAg03L79m2pVKpUKtktDBhjx449fPiws7Pz+fPnaZpeuXLlyJEjmVV//PGHnZ1ddXU1qwUCvW7dutdff12/JSkpSSQSlZeX0zRNUZSrq+vvv/9u3A81mzNYSUlJwcHBzHL37t3v3LnDbj3wdMxz05577jnmZXBwcFJSEqsVQU2XLl3q3LlzUlJSp06dRCIRIYTH43Xt2hVfLhORmpp66dKljz/+2M7Orm/fvvq/gV26dKmursYjWUzBnj17BALBmDFjdC01/rYqKSnJzs5mqTr4x9mzZ3v27Dlu3LjffvuNEJKcnBwQECCRSAghXC43KCjI6D99FsbdXPORy+XW1tbMsq2tbUVFRUVFhVgsZrcqqI/+8SKE2Nra3r17l8V6oIYvvvgiPT193rx5O3furHGkCgoKWCwMdHbv3v3rr7/ev39/2bJlYrFYLpe7u7vr1trY2OBIsU4uly9fvvz8+fM1GgMDA5llPp8vFosLCgrat2/f8uWBTmho6MaNG93d3a9evRoREbF///7af0kZ/QtlNgHL1tZWN6hToVBYWloy/+YG02Rra6tUKnUvy8rKHBwcWKwH9H333Xdr1649f/68VCqtfaSY0QnAug8++OCDDz7Izc0NDg728fGpcaQUCgWOFOvmzZv32muvVVZWpqamUhT15MmTkpIS/b+tNBpNRUUFjhTr+vbtyyyEhIRkZmbu3LkzIiKiuX/6zOYSoa+v771795jle/fu+fj44GFPpszb21ulUjF3apC/Dxm7JQHjxx9/XLp06enTp319fQkhPj4+Dx8+pCiKWXv//n0cKZPi6urKXGH39fXVnQZOS0ujKMrLy4vd2qC4uPj7779nbkwrKSlZsGDBnj179P+2evDggVAo1D/1CKyzt7dXKpU+Pj4pKSlqtZppvHfvHvOTaEzGHdLVfA4fPtyhQ4f8/HyVShUWFrZy5Uq2K4J/aLXaoqKi69evE0Jyc3OLi4tpmh49enRMTIxWq7169apEIsnKymK7TKB/+uknBweH+Pj4oqKioqKisrIyiqJkMtk333xD0/SePXvc3d0xIJd1crk8MTGRoiiapi9cuGBtbR0fH5+WliaRSK5fv05R1KxZs15++WW2y4R/0Q1yv3fvnpWV1Z07dyiKmjp16uTJk9kuDejff/+duSkkKSmpffv2X3zxhVarfe6557788kuapn/++WcXFxfmDi0jMpuApdVqFy5caG1tbWNj8+qrr6pUKrYrgn9UVFT46AkKCqJpOj09vXfv3nZ2do6Ojrt27WK7RqBpmp4yZYr+kXrllVdomr58+bKfn5+9vX379u0vXLjAdo1AZ2RkBAYGCoVCsVjs4+Ozfft2pn3nzp0ODg62trZhYWGZmZnsFgk1hISEXL58mVnevHmzvb29ra3toEGDcnNz2S0MaJqeNGmSQCCwsrJycnJaunSpRqOhafrPP/8MCAiwt7f38vIy+i2ENE2b2cOeNRqNVqsVCARsFwLPCvcimAscKVNDUZRGoxEKhTXacaTMAk3TlZWVGCtsUpRKJXPboL7m+0KZWcACAAAAMH1mM8gdAAAAwFwgYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJEhYAEAAAAYGQIWAAAAgJH9P7nHAaLkC7JYAAAAAElFTkSuQmCC" }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt_unemp = plot(title = \"Unemployment rate\", 1:T, x_path[1,:], color = :blue, grid = true,\n", " label = \"\", bg_inside = :lightgrey)\n", "plot!(plt_unemp, [xbar[1]], linetype = :hline, linestyle = :dash, color =:red, label = \"\")\n", "\n", "plt_emp = plot(title = \"Employment rate\", 1:T, x_path[2,:], color = :blue, grid = true,\n", " label = \"\", bg_inside = :lightgrey)\n", "plot!(plt_emp, [xbar[2]], linetype = :hline, linestyle = :dash, color =:red, label = \"\")\n", "\n", "plot(plt_unemp, plt_emp, layout = (2, 1), size = (800, 600))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that it takes 20 periods for the economy to converge to it’s new\n", "steady state levels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2\n", "\n", "This next exercise has the economy experiencing a boom in entrances to\n", "the labor market and then later returning to the original levels.\n", "\n", "For 20 periods the economy has a new entry rate into the labor market.\n", "\n", "Let’s start off at the baseline parameterization and record the steady\n", "state" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Float64,1}:\n", " 0.08266626766923271\n", " 0.9173337323307673" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel()\n", "x0 = rate_steady_state(lm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the other parameters:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b̂ = 0.003\n", "T̂ = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s increase $ b $ to the new value and simulate for 20 periods" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×20 Array{Float64,2}:\n", " 0.0826663 0.0739981 0.0679141 … 0.0536612 0.0536401 0.0536253\n", " 0.917334 0.926002 0.932086 0.946339 0.94636 0.946375" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel(b=b̂)\n", "X_path1 = simulate_stock_path(lm, x0 * N0, T̂) # simulate stocks\n", "x_path1 = simulate_rate_path(lm, x0, T̂) # simulate rates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we reset $ b $ to the original value and then, using the state\n", "after 20 periods for the new initial conditions, we simulate for the\n", "additional 30 periods" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×31 Array{Float64,2}:\n", " 0.0536401 0.0624842 0.0686335 … 0.0826652 0.0826655 0.0826657\n", " 0.94636 0.937516 0.931366 0.917335 0.917335 0.917334" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = LakeModel(b = 0.0124)\n", "X_path2 = simulate_stock_path(lm, X_path1[:, end-1], T-T̂+1) # simulate stocks\n", "x_path2 = simulate_rate_path(lm, x_path1[:, end-1], T-T̂+1) # simulate rates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we combine these two paths and plot" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "2×50 Array{Float64,2}:\n", " 8.26663 7.36118 6.72069 6.26524 … 8.45538 8.49076 8.52628\n", " 91.7334 92.1168 92.238 92.1769 93.8293 94.2215 94.6153" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_path = hcat(x_path1, x_path2[:, 2:end]) # note [2:] to avoid doubling period 20\n", "X_path = hcat(X_path1, X_path2[:, 2:end])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1 = X_path[1,:]\n", "x2 = X_path[2,:]\n", "x3 = dropdims(sum(X_path, dims = 1), dims = 1)\n", "\n", "plt_unemp = plot(title = \"Unemployment\", 1:T, x1, color = :blue, lw = 2, alpha = 0.7,\n", " grid = true, label = \"\", bg_inside = :lightgrey)\n", "plot!(plt_unemp, ylims = extrema(x1) .+ (-1, 1))\n", "\n", "plt_emp = plot(title = \"Employment\", 1:T, x2, color = :blue, lw = 2, alpha = 0.7, grid = true,\n", " label = \"\", bg_inside = :lightgrey)\n", "plot!(plt_emp, ylims = extrema(x2) .+ (-1, 1))\n", "\n", "plt_labor = plot(title = \"Labor force\", 1:T, x3, color = :blue, alpha = 0.7, grid = true,\n", " label = \"\", bg_inside = :lightgrey)\n", "plot!(plt_labor, ylims = extrema(x3) .+ (-1, 1))\n", "plot(plt_unemp, plt_emp, plt_labor, layout = (3, 1), size = (800, 600))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the rates" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt_unemp = plot(title = \"Unemployment Rate\", 1:T, x_path[1,:], color = :blue, grid = true,\n", " label = \"\", bg_inside = :lightgrey, lw = 2)\n", "plot!(plt_unemp, [x0[1]], linetype = :hline, linestyle = :dash, color =:red, label = \"\", lw = 2)\n", "\n", "plt_emp = plot(title = \"Employment Rate\", 1:T, x_path[2,:], color = :blue, grid = true,\n", " label = \"\", bg_inside = :lightgrey, lw = 2)\n", "plot!(plt_emp, [x0[2]], linetype = :hline, linestyle = :dash, color =:red, label = \"\", lw = 2)\n", "\n", "plot(plt_unemp, plt_emp, layout = (2, 1), size = (800, 600))" ] } ], "metadata": { "date": 1591310625.2871375, "download_nb": 1, "download_nb_path": "https://julia.quantecon.org/", "filename": "lake_model.rst", "filename_with_path": "multi_agent_models/lake_model", "kernelspec": { "display_name": "Julia 1.4.2", "language": "julia", "name": "julia-1.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.4.2" }, "title": "A Lake Model of Employment and Unemployment" }, "nbformat": 4, "nbformat_minor": 2 }