{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DiscreteDP Example: Job Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Daisuke Oyama**\n", "\n", "*Faculty of Economics, University of Tokyo*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We study an optimal stopping problem, in the context of job search as discussed in\n", "[http://quant-econ.net/py/lake_model.html](http://quant-econ.net/py/lake_model.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using QuantEcon\n", "import QuantEcon: solve\n", "using Distributions\n", "using PyPlot\n", "using Roots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimal solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We skip the description of the model, just writing down the Bellman equation:\n", "\n", "\\begin{aligned}\n", "U &= u(c) + \\beta \\left[(1 - \\gamma) U + \\gamma E[V_s]\\right], \\\\\n", "V_s &= \\max\\left\\{U,\n", " u(w_s) + \\beta \\left[(1 - \\alpha) V_s + \\alpha U\\right]\n", " \\right\\}.\n", "\\end{aligned}\n", "\n", "For this class of problem, we can characterize the solution analytically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimal policy $\\sigma^*$ is monotone;\n", "it is characterized by a threshold $s^*$, for which\n", "$\\sigma^*(s) = 1$ if and only if $s \\geq s^*$,\n", "where actions $0$ and $1$ represent \"reject\" and \"accept\", respectively.\n", "The threshold is defined as follows:\n", "Let\n", "\n", "\\begin{aligned}\n", "g(s) &= u(w_s) - u(c), \\\\\n", "h(s) &= \\frac{\\beta \\gamma}{1 - \\beta (1 - \\alpha)}\n", " \\sum_{s' \\geq s} p_s u(w_s).\n", "\\end{aligned}\n", "\n", "It is easy to see that $g$ is increasing and $h$ is decreasing.\n", "Then the threshold $s^*$ is such that $s \\geq s^*$ if and only if $g(s) > h(s)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given $s^*$, the optimal values can be computed as follows:\n", "\n", "\\begin{aligned}\n", "U &=\n", "\\frac{\\{1 - (1 - \\alpha) \\beta\\} u(c) + \\beta \\gamma \\sum_{s \\geq s^*} p_s u(w_s)}\n", " {(1 - \\beta) \\left[\\{1 - (1 - \\alpha) \\beta\\} +\n", " \\beta \\gamma \\sum_{s \\geq s^*} p_s\\right]}, \\\\\n", "V_s &=\n", "\\begin{cases}\n", "U & \\text{if s < s^*} \\\\\n", "\\dfrac{u(w_s) + \\alpha \\beta U}{1 - (1 - \\alpha) \\beta} & \\text{if s \\geq s^*}.\n", "\\end{cases}\n", "\\end{aligned}\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimal policy defines a Markov chain over $\\{\\text{unemployed}, \\text{employed}\\}$.\n", "Its stationary distribution is\n", "$\\pi = \\left(\\frac{\\alpha}{\\alpha + \\lambda}, \\frac{\\lambda}{\\alpha + \\lambda}\\right)$,\n", "where\n", "$\\lambda = \\gamma \\sum_{s \\geq s^*} p(w_s)$;\n", "note that the flow from unemployed to employed is $\\lambda$,\n", "while the flow from employed to unemployed is $\\alpha$.\n", "\n", "The expected value at the stationary distribution is\n", "$$\n", "\\pi_0 U + \\pi_1 \\frac{\\sum_{s \\geq s^*} p_s V_s}{\\sum_{s \\geq s^*} p_s}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above analytical solution aside,\n", "we solve the job search problem using the DiscreteDP type:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type JobSearchModel\n", " # Parameters\n", " w::Vector{Float64}\n", " w_pdf::Vector{Float64}\n", " beta::Float64\n", " alpha::Float64\n", " gamma::Float64\n", " rho::Float64\n", " # Internal variables\n", " u::Function\n", " ddp::DiscreteDP\n", " num_states::Integer\n", " num_actions::Integer\n", " rej::Integer\n", " acc::Integer\n", " \n", " function JobSearchModel(w::Vector, w_pdf::Vector, beta::Float64;\n", " alpha::Float64=0., gamma::Float64=1., rho::Float64=0.)\n", " # Utility function\n", " function u(y::Vector{Float64})\n", " small_number = -9999999\n", " nonpositive = (y .<= 0)\n", " if rho == 1\n", " util = log(y)\n", " else\n", " util = (y.^(1 - rho) - 1)/(1 - rho)\n", " end\n", " util[nonpositive] = small_number\n", " return util\n", " end\n", " u_w = u(w)\n", " \n", " num_states = length(w) + 1\n", " num_actions = 2\n", " rej, acc = 1, 2\n", " \n", " # Reward array\n", " R0 = zeros(num_states, num_actions)\n", " R0[1:end-1, acc] = u_w\n", " \n", " # Transition probability array\n", " Q = zeros(num_states, num_actions, num_states)\n", " # Reject\n", " for s in 1:num_states\n", " Q[s, rej, 1:end-1] = w_pdf * gamma\n", " Q[s, rej, end] = 1 - gamma\n", " end\n", " # Accept\n", " for s in 1:num_states-1\n", " Q[s, acc, s] = 1 - alpha\n", " Q[s, acc, end] = alpha\n", " end\n", " Q[end, acc, 1:end-1] = w_pdf\n", " \n", " ddp = DiscreteDP(R0, Q, beta)\n", " \n", " js = new(w, w_pdf, beta, alpha, gamma, rho, u, ddp,\n", " num_states, num_actions, rej, acc)\n", " return js\n", " end\n", "end;" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function solve(js::JobSearchModel, c::Float64)\n", " n, m = js.num_states, js.num_actions\n", " rej = js.rej\n", " js.ddp.R[:, rej] = js.u([c])[1]\n", " js.ddp.R[end, :] = js.u([c])[1]\n", " \n", " res = solve(js.ddp, PFI)\n", " V::Vector{Float64} = res.v[1:end-1] # Values of jobs\n", " U::Float64 = res.v[end] # Value of unemployed\n", " C::Vector{Int64} = res.sigma[1:end-1] - 1 # Optimal policy\n", "\n", " lamb::Float64 = dot(js.w_pdf, C) * js.gamma\n", " pi::Vector{Float64} = [js.alpha, lamb]\n", " pi /= sum(pi) # Stationary distribution\n", " \n", " return V, U, C, pi\n", "end;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following paramter values are from [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "w = linspace(0, 175, 201) # wage grid\n", "\n", "# compute probability of each wage level \n", "logw_dist = Normal(log(20), 1)\n", "logw_dist_cdf = cdf(logw_dist, log(w))\n", "logw_dist_pdf = logw_dist_cdf[2:end] - logw_dist_cdf[1:end-1]\n", "logw_dist_pdf /= sum(logw_dist_pdf)\n", "w = Array((w[2:end] + w[1:end-1])/2);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gamma = 1.\n", "alpha = 0.013 # Monthly\n", "alpha_q = (1-(1-alpha)^3) # Quarterly\n", "beta = 0.99\n", "rho = 2. # risk-aversion;" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "js = JobSearchModel(w, logw_dist_pdf, beta, alpha=alpha_q, gamma=gamma, rho=rho);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the optimal solution for $c = 40$ for example:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = 40.\n", "V, U, C, pi = solve(js, c);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal policy: Accept if and only if w >= 65.1875\n" ] } ], "source": [ "s_star = length(w) - sum(C) + 1\n", "println(\"Optimal policy: Accept if and only if w >= $(w[s_star])\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(figsize=(6, 4))\n", "ax[:plot](w, V, label=L\"$V$\")\n", "ax[:plot]((w[1], w[end]), (U, U), \"r--\", label=L\"$U$\")\n", "ax[:set_title](\"Optimal value function\")\n", "ax[:set_xlabel](\"Wage\")\n", "ax[:set_ylabel](\"Value\")\n", "legend(loc=2)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimal unemployment insurance policy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We compute the optimal level of unemployment insurance\n", "as in the [lecture](http://quant-econ.net/py/lake_model.html#fiscal-policy),\n", "mimicking [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type UnemploymentInsurancePolicy\n", " w::Vector{Float64}\n", " w_pdf::Vector{Float64}\n", " beta::Float64\n", " alpha::Float64\n", " gamma::Float64\n", " rho::Float64\n", " \n", " function UnemploymentInsurancePolicy(w::Vector, w_pdf::Vector, beta::Float64;\n", " alpha::Float64=0., gamma::Float64=1., rho::Float64=0.)\n", " uip = new(w, w_pdf, beta, alpha_q, gamma, rho)\n", " return uip\n", " end\n", "end;" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function solve_job_search_model(uip::UnemploymentInsurancePolicy,\n", " c::Float64, T::Float64)\n", " js = JobSearchModel(uip.w-T, uip.w_pdf, uip.beta,\n", " alpha=uip.alpha, gamma=uip.gamma, rho=uip.rho)\n", " V, U, C, pi = solve(js, c-T)\n", " return V, U, C, pi\n", "end;" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function budget_balance(uip::UnemploymentInsurancePolicy, c::Float64, T::Float64)\n", " V, U, C, pi = solve_job_search_model(uip, c, T)\n", " return T - pi[1]*c\n", "end;" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function implement(uip::UnemploymentInsurancePolicy, c::Float64)\n", " # Budget balancing tax given c\n", " T = fzero(T -> budget_balance(uip, c, T), 0., c, xtolrel=1e-3)\n", " \n", " V, U, C, pi = solve_job_search_model(uip, c, T)\n", " \n", " EV = dot(C .* V, uip.w_pdf) / (dot(C, uip.w_pdf))\n", " W = pi[1] * U + pi[2] * EV\n", " \n", " return T, W, pi\n", "end;" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "uip = UnemploymentInsurancePolicy(w, logw_dist_pdf, beta,\n", " alpha=alpha_q, gamma=gamma, rho=rho);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal unemployment benefit: 67.4\n" ] } ], "source": [ "grid_size = 26\n", "cvec = linspace(5, 135, grid_size)\n", "Ts, Ws = Array(Float64, grid_size), Array(Float64, grid_size)\n", "pis = Array(Float64, 2, grid_size)\n", "\n", "for (i, c) in enumerate(cvec)\n", " T, W, pi = implement(uip, c)\n", " Ts[i], Ws[i], pis[:, i] = T, W, pi\n", "end\n", "i_max = indmax(Ws)\n", "println(\"Optimal unemployment benefit:$(cvec[i_max])\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "function plot(ax, y_vec, title)\n", " ax[:plot](cvec, y_vec)\n", " ax[:set_xlabel](L\"$c$\")\n", " ax[:vlines](cvec[i_max], ax[:get_ylim]()[1], y_vec[i_max], \"k\", \"-.\")\n", " ax[:set_title](title)\n", "end\n", "\n", "fig, axes = subplots(2, 2)\n", "plot(axes[1, 1], Ws, \"Welfare\")\n", "plot(axes[1, 2], Ts, \"Taxes\")\n", "plot(axes[2, 1], vec(pis[2, :]), \"Employment Rate\")\n", "plot(axes[2, 2], vec(pis[1, :]), \"Unemployment Rate\")\n", "\n", "tight_layout()\n", "show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(figsize=(6, 4))\n", "plot(ax, cvec-Ts, \"Net Compensation\")\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }