{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", " \n", " \"QuantEcon\"\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Consumption and Tax Smoothing with Complete and Incomplete Markets\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents\n", "\n", "- [Consumption and Tax Smoothing with Complete and Incomplete Markets](#Consumption-and-Tax-Smoothing-with-Complete-and-Incomplete-Markets) \n", " - [Overview](#Overview) \n", " - [Background](#Background) \n", " - [Model 1 (Complete Markets)](#Model-1-%28Complete-Markets%29) \n", " - [Model 2 (One-Period Risk Free Debt Only)](#Model-2-%28One-Period-Risk-Free-Debt-Only%29) \n", " - [Example: Tax Smoothing with Complete Markets](#Example:-Tax-Smoothing-with-Complete-Markets) \n", " - [Linear State Space Version of Complete Markets Model](#Linear-State-Space-Version-of-Complete-Markets-Model) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "This lecture describes two types of consumption-smoothing and tax-smoothing models\n", "\n", "- one is in the **complete markets** tradition of Lucas and Stokey [[LS83]](https://lectures.quantecon.org/zreferences.html#lucasstokey1983) \n", "- the other is in the **incomplete markets** tradition of Hall [[Hal78]](https://lectures.quantecon.org/zreferences.html#hall1978) and Barro [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979) \n", "\n", "\n", "*Complete markets* allow a consumer or government to buy or sell claims contingent on all possible states of the world\n", "\n", "*Incomplete markets* allow a consumer or government to buy or sell only a limited set of securities, often only a single risk-free security\n", "\n", "Hall [[Hal78]](https://lectures.quantecon.org/zreferences.html#hall1978) and Barro [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979) both assumed that the only asset that can be traded is a risk-free one period bond\n", "\n", "Hall assumed an exogenous stochastic process of nonfinancial income and\n", "an exogenous gross interest rate on one period risk-free debt that equals\n", "$ \\beta^{-1} $, where $ \\beta \\in (0,1) $ is also a consumer’s\n", "intertemporal discount factor\n", "\n", "Barro [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979) made an analogous assumption about the risk-free interest\n", "rate in a tax-smoothing model that we regard as isomorphic to Hall’s\n", "consumption-smoothing model\n", "\n", "We maintain Hall and Barro’s assumption about the interest rate when we describe an\n", "incomplete markets version of our model\n", "\n", "In addition, we extend their assumption about the interest rate to an appropriate counterpart that we use in a “complete markets” model in the style of\n", "Lucas and Stokey [[LS83]](https://lectures.quantecon.org/zreferences.html#lucasstokey1983)\n", "\n", "While we are equally interested in consumption-smoothing and tax-smoothing\n", "models, for the most part we focus explicitly on consumption-smoothing\n", "versions of these models\n", "\n", "But for each version of the consumption-smoothing model there is a natural tax-smoothing counterpart obtained simply by\n", "\n", "- relabeling consumption as tax collections and nonfinancial income as government expenditures \n", "- relabeling the consumer’s debt as the government’s *assets* \n", "\n", "\n", "For elaborations on this theme, please see [Optimal Savings II: LQ Techniques](https://lectures.quantecon.org/perm_income_cons.html) and later parts of this lecture\n", "\n", "We’ll consider two closely related alternative assumptions about the consumer’s\n", "exogenous nonfinancial income process (or in the tax-smoothing\n", "interpretation, the government’s exogenous expenditure process):\n", "\n", "- that it obeys a finite $ N $ state Markov chain (setting $ N=2 $ most of the time) \n", "- that it is described by a linear state space model with a continuous\n", " state vector in $ {\\mathbb R}^n $ driven by a Gaussian vector iid shock\n", " process \n", "\n", "\n", "We’ll spend most of this lecture studying the finite-state Markov specification, but will briefly treat the linear state space specification before concluding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relationship to Other Lectures\n", "\n", "This lecture can be viewed as a followup to [Optimal Savings II: LQ Techniques](https://lectures.quantecon.org/perm_income_cons.html) and a warm up for a model of tax smoothing described in [opt_tax_recur](https://lectures.quantecon.org/dynamic_programming_squared/opt_tax_recur.html)\n", "\n", "Linear-quadratic versions of the Lucas-Stokey tax-smoothing model are described in [lqramsey](https://lectures.quantecon.org/dynamic_programming_squared/lqramsey.html)\n", "\n", "The key differences between those lectures and this one are\n", "\n", "- Here the decision maker takes all prices as exogenous, meaning that his decisions do not affect them \n", "- In [lqramsey](https://lectures.quantecon.org/dynamic_programming_squared/lqramsey.html) and [opt_tax_recur](https://lectures.quantecon.org/dynamic_programming_squared/opt_tax_recur.html), the decision maker – the government in the case of these lectures – recognizes that his decisions affect prices \n", "\n", "\n", "So these later lectures are partly about how the government should manipulate prices of government debt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "Outcomes in consumption-smoothing (or tax-smoothing) models emerge from two\n", "sources:\n", "\n", "- a decision maker – a consumer in the consumption-smoothing model or\n", " a government in the tax-smoothing model – who wants to maximize an\n", " intertemporal objective function that expresses its preference for\n", " paths of consumption (or tax collections) that are *smooth* in the\n", " sense of not varying across time and Markov states \n", "- a set of trading opportunities that allow the optimizer to transform\n", " a possibly erratic nonfinancial income (or government expenditure)\n", " process into a smoother consumption (or tax collections) process by\n", " purchasing or selling financial securities \n", "\n", "\n", "In the complete markets version of the model, each period the consumer\n", "can buy or sell one-period ahead state-contingent securities whose\n", "payoffs depend on next period’s realization of the Markov state\n", "\n", "In the two-state Markov chain case, there are two such securities each period\n", "\n", "In an $ N $ state Markov state version of the model, $ N $ such securities are traded each period\n", "\n", "These state-contingent securities are commonly called Arrow securities, after [Kenneth Arrow](https://en.wikipedia.org/wiki/Kenneth_Arrow) who first theorized about them\n", "\n", "In the incomplete markets version of the model, the consumer can buy and sell only one security each period, a risk-free bond with gross return $ \\beta^{-1} $" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finite State Markov Income Process\n", "\n", "In each version of the consumption-smoothing model, nonfinancial income is governed by a two-state Markov chain (it’s easy to generalize this to an $ N $ state Markov chain)\n", "\n", "In particular, the *state of the world* is given by $ s_t $ that follows\n", "a Markov chain with transition probability matrix\n", "\n", "$$\n", "P_{ij} = \\mathbb P \\{s_{t+1} = \\bar s_j \\,|\\, s_t = \\bar s_i \\}\n", "$$\n", "\n", "Nonfinancial income $ \\{y_t\\} $ obeys\n", "\n", "$$\n", "y_t =\n", "\\begin{cases}\n", " \\bar y_1 & \\quad \\text{if } s_t = \\bar s_1 \\\\\n", " \\bar y_2 & \\quad \\text{if } s_t = \\bar s_2\n", "\\end{cases}\n", "$$\n", "\n", "A consumer wishes to maximize\n", "\n", "\n", "\n", "$$\n", "\\mathbb E\n", "\\left[\n", " \\sum_{t=0}^\\infty \\beta^t u(c_t)\n", "\\right]\n", "\\quad\n", "\\text{where} \\quad\n", "u(c_t) = - (c_t -\\gamma)^2\n", "\\quad \\text{and} \\quad\n", " 0 < \\beta < 1 \\tag{1}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Remark About Isomorphism\n", "\n", "We can regard these as Barro [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979) tax-smoothing models if we set\n", "$ c_t = T_t $ and $ G_t = y_t $, where $ T_t $ is total tax\n", "collections and $ \\{G_t\\} $ is an exogenous government expenditures\n", "process" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Market Structure\n", "\n", "The two models differ in how effectively the market structure allows the\n", "consumer to transfer resources across time and Markov states, there\n", "being more transfer opportunities in the complete markets setting than\n", "in the incomplete markets setting\n", "\n", "Watch how these differences in opportunities affect\n", "\n", "- how smooth consumption is across time and Markov states \n", "- how the consumer chooses to make his levels of indebtedness behave\n", " over time and across Markov states " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 1 (Complete Markets)\n", "\n", "At each date $ t \\geq 0 $, the consumer trades **one-period ahead\n", "Arrow securities**\n", "\n", "We assume that prices of these securities are exogenous to the consumer\n", "(or in the tax-smoothing version of the model, to the government)\n", "\n", "*Exogenous* means that they are unaffected by the decision maker\n", "\n", "In Markov state $ s_t $ at time $ t $, one unit of consumption\n", "in state $ s_{t+1} $ at time $ t+1 $ costs $ q(s_{t+1} \\,|\\, s_t) $ units of the time $ t $ consumption good\n", "\n", "At time $ t=0 $, the consumer starts with an inherited level of debt\n", "due at time $ 0 $ of $ b_0 $ units of time $ 0 $ consumption\n", "goods\n", "\n", "The consumer’s budget constraint at $ t \\geq 0 $ in Markov\n", "state $ s_t $ is\n", "\n", "$$\n", "c_t + b_t\n", "\\leq y(s_t) +\n", "\\sum_j q(\\bar s_j \\,|\\, s_t ) \\, b_{t+1}(\\bar s_j \\,|\\, s_t)\n", "$$\n", "\n", "where $ b_t $ is the consumer’s one-period debt that falls due at time $ t $ and $ b_{t+1}(\\bar s_j\\,|\\, s_t) $ are the consumer’s time\n", "$ t $ sales of the time $ t+1 $ consumption good in Markov state $ \\bar s_j $, a source of time $ t $ revenues\n", "\n", "An analogue of Hall’s assumption that the one-period risk-free gross\n", "interest rate is $ \\beta^{-1} $ is\n", "\n", "\n", "\n", "$$\n", "q(\\bar s_j \\,|\\, \\bar s_i) = \\beta P_{ij} \\tag{2}\n", "$$\n", "\n", "To understand this, observe that in state $ \\bar s_i $ it costs $ \\sum_j q(\\bar s_j \\,|\\, \\bar s_i) $ to purchase one unit of consumption next period *for sure*, i.e., meaning no matter what state of the world occurs at $ t+1 $\n", "\n", "Hence the implied price of a risk-free claim on one unit of consumption next\n", "period is\n", "\n", "$$\n", "\\sum_j q(\\bar s_j \\,|\\, \\bar s_i) = \\sum_j \\beta P_{ij} = \\beta\n", "$$\n", "\n", "This confirms that [(2)](#equation-cs-2) is a natural analogue of Hall’s assumption about the\n", "risk-free one-period interest rate\n", "\n", "First-order necessary conditions for maximizing the consumer’s expected utility are\n", "\n", "$$\n", "\\beta \\frac{u'(c_{t+1})}{u'(c_t) } \\mathbb P\\{s_{t+1}\\,|\\, s_t \\}\n", " = q(s_{t+1} \\,|\\, s_t)\n", "$$\n", "\n", "or, under our assumption [(2)](#equation-cs-2) on Arrow security prices,\n", "\n", "\n", "\n", "$$\n", "c_{t+1} = c_t \\tag{3}\n", "$$\n", "\n", "Thus, our consumer sets $ c_t = \\bar c $ for all $ t \\geq 0 $ for some value $ \\bar c $ that it is our job now to determine\n", "\n", "**Guess:** We’ll make the plausible guess that\n", "\n", "\n", "\n", "$$\n", "b_{t+1}(\\bar s_j \\,|\\, s_t = \\bar s_i) = b(\\bar s_j) ,\n", " \\quad i=1,2; \\;\\; j= 1,2 \\tag{4}\n", "$$\n", "\n", "so that the amount borrowed today turns out to depend only on *tomorrow’s* Markov state. (Why is this is a plausible guess?)\n", "\n", "To determine $ \\bar c $, we shall pursue the implications of the consumer’s budget constraints in each Markov state today and our guess [(4)](#equation-eq-guess) about the consumer’s debt level choices\n", "\n", "For $ t \\geq 1 $, these imply\n", "\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", " \\bar c + b(\\bar s_1) & = y(\\bar s_1) + q(\\bar s_1\\,|\\, \\bar s_1) b(\\bar s_1) + q(\\bar s_2 \\,|\\, \\bar s_1) b(\\bar s_2) \\cr\n", " \\bar c + b(\\bar s_2) & = y(\\bar s_2) + q(\\bar s_1\\,|\\, \\bar s_2) b(\\bar s_1) + q(\\bar s_2 \\,|\\, \\bar s_2) b(\\bar s_2),\n", "\\end{aligned} \\tag{5}\n", "$$\n", "\n", "or\n", "\n", "$$\n", "\\begin{bmatrix}\n", " b(\\bar s_1) \\cr b(\\bar s_2)\n", "\\end{bmatrix} +\n", "\\begin{bmatrix}\n", "\\bar c \\cr \\bar c\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", " y(\\bar s_1) \\cr y(\\bar s_2)\n", "\\end{bmatrix}\n", "+ \\beta\n", "\\begin{bmatrix}\n", " P_{11} & P_{12} \\cr P_{21} & P_{22}\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", " b(\\bar s_1) \\cr b(\\bar s_2)\n", "\\end{bmatrix}\n", "$$\n", "\n", "These are $ 2 $ equations in the $ 3 $ unknowns\n", "$ \\bar c, b(\\bar s_1), b(\\bar s_2) $\n", "\n", "To get a third equation, we assume that at time $ t=0 $, $ b_0 $\n", "is the debt due; and we assume that at time $ t=0 $, the Markov\n", "state is $ \\bar s_1 $\n", "\n", "Then the budget constraint at time $ t=0 $ is\n", "\n", "\n", "\n", "$$\n", "\\bar c + b_0 = y(\\bar s_1) + q(\\bar s_1 \\,|\\, \\bar s_1) b(\\bar s_1) + q(\\bar s_2\\,|\\,\\bar s_1) b(\\bar s_2) \\tag{6}\n", "$$\n", "\n", "If we substitute [(6)](#equation-cs-5) into the first equation of [(5)](#equation-cs-4a) and rearrange, we\n", "discover that\n", "\n", "\n", "\n", "$$\n", "b(\\bar s_1) = b_0 \\tag{7}\n", "$$\n", "\n", "We can then use the second equation of [(5)](#equation-cs-4a) to deduce the restriction\n", "\n", "\n", "\n", "$$\n", "y(\\bar s_1) - y(\\bar s_2) + [q(\\bar s_1\\,|\\, \\bar s_1) - q(\\bar s_1\\,|\\, \\bar s_2) - 1 ] b_0 +\n", "[q(\\bar s_2\\,|\\,\\bar s_1) + 1 - q(\\bar s_2 \\,|\\, \\bar s_2) ] b(\\bar s_2) = 0 , \\tag{8}\n", "$$\n", "\n", "an equation in the unknown $ b(\\bar s_2) $\n", "\n", "Knowing $ b(\\bar s_1) $ and $ b(\\bar s_2) $, we can solve equation [(6)](#equation-cs-5) for the constant level of consumption $ \\bar c $" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Key outcomes\n", "\n", "The preceding calculations indicate that in the complete markets version\n", "of our model, we obtain the following striking results:\n", "\n", "- The consumer chooses to make consumption perfectly constant across\n", " time and Markov states \n", "\n", "\n", "We computed the constant level of consumption $ \\bar c $ and indicated how that level depends on the underlying specifications of preferences, Arrow securities prices, the stochastic process of exogenous nonfinancial income, and the initial debt level $ b_0 $\n", "\n", "- The consumer’s debt neither accumulates, nor decumulates, nor drifts.\n", " Instead the debt level each period is an exact function of the Markov\n", " state, so in the two-state Markov case, it switches between two\n", " values \n", "- We have verified guess [(4)](#equation-eq-guess) \n", "\n", "\n", "We computed how one of those debt levels depends entirely on initial debt – it equals it – and how the other value depends on virtually all remaining parameters of the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code\n", "\n", "Here’s some code that, among other things, contains a function called consumption_complete()\n", "\n", "This function computes $ b(\\bar s_1), b(\\bar s_2), \\bar c $ as outcomes given a set of parameters, under the assumption of complete markets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide-output": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32m\u001b[1mActivated\u001b[0m /home/qebuild/repos/lecture-source-jl/_build/website/jupyter/Project.toml\u001b[39m\n", "\u001b[36m\u001b[1mInfo\u001b[0m quantecon-notebooks-julia 0.1.0 activated, 0.2.0 requested\u001b[39m\n" ] } ], "source": [ "using InstantiateFromURL\n", "github_project(\"QuantEcon/quantecon-notebooks-julia\", version = \"0.2.0\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide-output": false }, "outputs": [], "source": [ "using LinearAlgebra, Statistics\n", "using Parameters, Plots, QuantEcon, Random\n", "gr(fmt = :png);" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "consumption_incomplete (generic function with 1 method)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ConsumptionProblem = @with_kw (β = 0.96,\n", " y = [2.0, 1.5],\n", " b0 = 3.0,\n", " P = [0.8 0.2\n", " 0.4 0.6])\n", "\n", "function consumption_complete(cp)\n", "\n", " @unpack β, P, y, b0 = cp # Unpack\n", "\n", " y1, y2 = y # extract income levels\n", " b1 = b0 # b1 is known to be equal to b0\n", " Q = β * P # assumed price system\n", "\n", " # Using equation (7) calculate b2\n", " b2 = (y2 - y1 - (Q[1, 1] - Q[2, 1] - 1) * b1) / (Q[1, 2] + 1 - Q[2, 2])\n", "\n", " # Using equation (5) calculae c̄\n", " c̄ = y1 - b0 + ([b1 b2] * Q[1, :])[1]\n", "\n", " return c̄, b1, b2\n", "end\n", "\n", "function consumption_incomplete(cp; N_simul = 150)\n", "\n", " @unpack β, P, y, b0 = cp # unpack\n", "\n", " # for the simulation use the MarkovChain type\n", " mc = MarkovChain(P)\n", "\n", " # useful variables\n", " y = y\n", " v = inv(I - β * P) * y\n", "\n", " # simulate state path\n", " s_path = simulate(mc, N_simul, init=1)\n", "\n", " # store consumption and debt path\n", " b_path, c_path = ones(N_simul + 1), ones(N_simul)\n", " b_path[1] = b0\n", "\n", " # optimal decisions from (12) and (13)\n", " db = ((1 - β) * v - y) / β\n", "\n", " for (i, s) in enumerate(s_path)\n", " c_path[i] = (1 - β) * (v[s, 1] - b_path[i])\n", " b_path[i + 1] = b_path[i] + db[s, 1]\n", " end\n", "\n", " return c_path, b_path[1:end - 1], y[s_path], s_path\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s test by checking that $ \\bar c $ and $ b_2 $ satisfy the budget constraint" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cp = ConsumptionProblem()\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "isapprox((c̄ + b2 - cp.y[2] - debt_complete' * (cp.β * cp.P)[2, :])[1], 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we’ll take the outcomes produced by this code – in particular the implied\n", "consumption and debt paths – and compare them with outcomes\n", "from an incomplete markets model in the spirit of Hall [[Hal78]](https://lectures.quantecon.org/zreferences.html#hall1978) and Barro [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979) (and also, for those who love history, Gallatin (1807) [[Gal37]](https://lectures.quantecon.org/zreferences.html#gallatin))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 2 (One-Period Risk Free Debt Only)\n", "\n", "This is a version of the original models of Hall (1978) and Barro (1979)\n", "in which the decision maker’s ability to substitute intertemporally is\n", "constrained by his ability to buy or sell only one security, a risk-free\n", "one-period bond bearing a constant gross interest rate that equals\n", "$ \\beta^{-1} $\n", "\n", "Given an initial debt $ b_0 $ at time $ 0 $, the\n", "consumer faces a sequence of budget constraints\n", "\n", "$$\n", "c_t + b_t = y_t + \\beta b_{t+1}, \\quad t \\geq 0\n", "$$\n", "\n", "where $ \\beta $ is the price at time $ t $ of a risk-free claim\n", "on one unit of time consumption at time $ t+1 $\n", "\n", "First-order conditions for the consumer’s problem are\n", "\n", "$$\n", "\\sum_{j} u'(c_{t+1,j}) P_{ij} = u'(c_{t, i})\n", "$$\n", "\n", "For our assumed quadratic utility function this implies\n", "\n", "\n", "\n", "$$\n", "\\sum_j c_{t+1,j} P_{ij} = c_{t,i} , \\tag{9}\n", "$$\n", "\n", "which is Hall’s (1978) conclusion that consumption follows a random walk\n", "\n", "As we saw in our first lecture on the [permanent income model](https://lectures.quantecon.org/perm_income.html), this leads to\n", "\n", "\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} - (1 -\\beta)^{-1} c_t \\tag{10}\n", "$$\n", "\n", "and\n", "\n", "\n", "\n", "$$\n", "c_t = (1-\\beta)\n", " \\left[\n", " \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} - b_t\n", " \\right] . \\tag{11}\n", "$$\n", "\n", "Equation [(11)](#equation-cs-10) expresses $ c_t $ as a net interest rate factor $ 1 - \\beta $ times the sum\n", "of the expected present value of nonfinancial income $ \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} $ and financial wealth $ -b_t $\n", "\n", "Substituting [(11)](#equation-cs-10) into the one-period budget constraint and rearranging leads to\n", "\n", "\n", "\n", "$$\n", "b_{t+1} - b_t\n", "= \\beta^{-1} \\left[ (1-\\beta)\n", "\\mathbb E_t \\sum_{j=0}^\\infty\\beta^j y_{t+j} - y_t \\right] \\tag{12}\n", "$$\n", "\n", "Now let’s do a useful calculation that will yield a convenient expression for the key term $ \\mathbb E_t \\sum_{j=0}^\\infty\\beta^j y_{t+j} $ in our finite Markov chain setting\n", "\n", "Define\n", "\n", "$$\n", "v_t := \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j}\n", "$$\n", "\n", "In our finite Markov chain setting, $ v_t = v(1) $ when $ s_t= \\bar s_1 $ and $ v_t = v(2) $ when $ s_t=\\bar s_2 $\n", "\n", "Therefore, we can write\n", "\n", "$$\n", "\\begin{aligned}\n", " v(1) & = y(1) + \\beta P_{11} v(1) + \\beta P_{12} v(2)\n", " \\\\\n", " v(2) & = y(2) + \\beta P_{21} v(1) + \\beta P_{22} v(2)\n", "\\end{aligned}\n", "$$\n", "\n", "or\n", "\n", "$$\n", "\\vec v = \\vec y + \\beta P \\vec v\n", "$$\n", "\n", "where $ \\vec v = \\begin{bmatrix} v(1) \\cr v(2) \\end{bmatrix} $ and $ \\vec y = \\begin{bmatrix} y(1) \\cr y(2) \\end{bmatrix} $\n", "\n", "We can also write the last expression as\n", "\n", "$$\n", "\\vec v = (I - \\beta P)^{-1} \\vec y\n", "$$\n", "\n", "In our finite Markov chain setting, from expression [(11)](#equation-cs-10), consumption at date $ t $ when debt is $ b_t $ and the Markov state today is $ s_t = i $ is evidently\n", "\n", "\n", "\n", "$$\n", "c(b_t, i) = (1 - \\beta) \\left( [(I - \\beta P)^{-1} \\vec y]_i - b_t \\right) \\tag{13}\n", "$$\n", "\n", "and the increment in debt is\n", "\n", "\n", "\n", "$$\n", "b_{t+1} - b_t = \\beta^{-1} [ (1- \\beta) v(i) - y(i) ] \\tag{14}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary of Outcomes\n", "\n", "In contrast to outcomes in the complete markets model, in the incomplete\n", "markets model\n", "\n", "- consumption drifts over time as a random walk; the level of\n", " consumption at time $ t $ depends on the level of debt that the\n", " consumer brings into the period as well as the expected discounted\n", " present value of nonfinancial income at $ t $ \n", "- the consumer’s debt drifts upward over time in response to low\n", " realizations of nonfinancial income and drifts downward over time in\n", " response to high realizations of nonfinancial income \n", "- the drift over time in the consumer’s debt and the dependence of\n", " current consumption on today’s debt level account for the drift over\n", " time in consumption " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Incomplete Markets Model\n", "\n", "The code above also contains a function called consumption_incomplete() that uses [(13)](#equation-cs-12) and [(14)](#equation-cs-13) to\n", "\n", "- simulate paths of $ y_t, c_t, b_{t+1} $ \n", "- plot these against values of of $ \\bar c, b(s_1), b(s_2) $ found in a corresponding complete markets economy \n", "\n", "\n", "Let’s try this, using the same parameters in both complete and incomplete markets economies" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAGQCAIAAADZR5NjAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOy9eZwbxZn//1TraB2jc07PeGY8vo/RmPuwgzEYYo4QcnFuSMIGCEvIuQnZff3yJfllSfIjYZdkA8sXkiXJEpIA4V4SzsQ4AYcbfOIDD77GMxp7TkkttVpdvz9KavWtltSSZnC9X375Nequ7qqubvXz6FNPPYUwxkChUCgUCoVCsQ+m0Q2gUCgUCoVC+aBBHSwKhUKhUCgUm6EOFoVCoVAoFIrNUAdrBrFx48Yrrriip6eHZdmOjo5Vq1bddtttqVSq0e2qOZlMJp1OSx+XLl2KEGpge6wgb/OsaDCFMltAGlpaWs4555zf/e53ZQUNW/liql4+NYK+Lo5NqIM1IxBF8ctf/vKZZ575+9//fnx8/LjjjvP5fK+88so3v/nN/v7+eDze6AbWlpUrV3q93ka3ojxmY5splNmC1+tdV2DVqlWCILzwwgtXXnnlJZdcwvO8jRXV54tMXxfHJtTBmhH86Ec/+tnPfjZnzpznn39+YmLilVde2bt37+Dg4CWXXDI4OHjZZZcdU5M933jjjenp6Ua3gkKhNIyenp7nC7z00kvj4+N/+9vfenp6Hn744W9/+9uNbh2FYgnqYDWeoaGh73znO6FQ6I033li3bp3D4SDbe3p6fvvb365cuXLDhg1btmxpbCPrid/vb2pqanQrKBTKTAEhtHr16ldffdXv999+++2HDx9udIsolNJQB6vx/PznP+d5/hvf+MacOXNUu5xO5ze+8Y1169Zt27aNbMEY/+pXvzr77LObm5vnzp17/vnnP/PMM1J5MroviuKPf/zj+fPnezyeJUuWfP/735eL6i+99NKnPvWpvr4+j8fT3d194YUX/uUvf5EfrmqDfCP5O5vNfu973+vr63O73b29vbfccksul3vzzTfPPffccDjc09Nz0003ZTIZ+SGZTOamm26aM2dOOBxet27dQw89JJ0fIbRz504oBF5om1H9JetekUmTAGDz5s2XX375ihUrfD5fa2vraaedds8990g6orbNAMBx3L/8y790d3d7PJ5FixZ997vflcd2mHQ7hUKxQnt7+/XXXy8Iwh/+8Adp41NPPXXhhRd2dXUFg8FTTz317rvvzuVy8qOmp6e/+MUvtrW1hUKhM88883/+53+kXbpfZBX0dUGpHExpNKtWrQKAwcFBK4U/85nPAEAgEFi/fv2ZZ57JsiwA3HLLLWTvkiVLAOCmm25qb2//7Gc/e9VVV5GB/6997WukwAMPPIAQcjqda9euvfzyy8844wyEEMMwTz/9tHS4qkb5RvL3Jz7xiXnz5l177bWXXnqp0+kEgCuuuCIYDJ533nnXXHMNEZ/+7d/+TX7IJz/5SZZl165du2rVKpfLBQDf/e53SYHbb7+9tbUVAG6//fbbb79d24wqL1lLySbdd9995D0Yi8Uuu+yyc889lxS47bbbdNtMTrh+/frOzs7Pf/7zn/vc5/x+PwB86UtfstLtFApFDgAsWbJEd9eLL74IADfeeCP5+M1vfhMAotHoBRdccOGFF0ajUQC4+OKLBUHAhW86eWmcddZZ0jf961//Ojlc+/LRQl8XlIqhDlbjaWlpcblc5I1gzp/+9CfyNT548CDZsnnz5o6ODpfLtXv3blx4F8RisXg8Tgq8+uqrANDW1kY+Ll++3OVybdu2TTrnk08+CQAf/ehHsWUH68Mf/nAymSRbHnzwQeKpSx7PH//4RwBYvXq1/JCOjo6tW7dKTWpubnY6nXv27NFWofpY/SVrKdmkxYsXA8D1118viiIp8Ne//hUATj31VJNuicViR44cIVvefvttAGhtbbXS7RQKRY6JgzU4OAgA559/Pi58Ky+44ILJyUmyd2pq6tJLLwWA//qv/8LG33SE0DvvvEO26L705NDXBaViqIPVeBwOR09Pj5WSF1xwAQD8+c9/lm+84447AOAb3/gGLnx1H3vsMXmB7u5u6bsdDAZDodD4+Li0N5fLbdq0ibxuLDpYmzdvlvYmk0kAYFk2k8mQLSSvhPR+JIeQ953EbbfdBgDf/OY3tVWoPlZ/yVpKNunee+/9+c9/vn//fmkvUe/lL31ttzz++OPyE/b09FjsdgqFIsfEweI4DgBWrlyJMb744osBYNeuXfICk5OTDofjjDPOwKbf9C9/+cvko0UHi74uKBVAY7AaTzAYHB4eVsUN6LJz506WZc8880z5xvXr1wPArl27pC2nn366vIDP55P+vvzyyycnJ/v6+q699toHH3xw3759DMOcdtppAwMD1hu8fPly1cm7u7vdbjfZojsb+bzzzpN/vOiii1RtNqL6SzbCpElXX331Nddc093dPT09vWnTpv/8z/8kfp45p512mvyjvB9s6XYKhTIyMgIAXV1dAEAiUz/ykY8slXHKKacAwPbt26VDKn75yKGvC0oFUAer8cyfP5/n+f379+vu3bNnz3nnnXfDDTcAwKFDhzo6OhhGcdfIu0Z+OBnv1+XOO++85557li9f/stf/vKyyy6bN2/esmXL7rjjDhP3DmsyREjzHE22qFDF78+dOxcA9u3bZ34U2HHJFTQplUp97WtfW7BgQTAYJFGxZBTAHHu7nUKhaCHf0Pnz5wPAwYMHAWDXrl07leRyOXmeF91v+vDwcFn10tcFpQKog9V4zjnnHAD45S9/qbv3D3/4wzPPPEO8nK6uruHhYVEU5QXIjGXicxBM0gQ7nc5rr732pZdeGhsbe+655/7lX/5lYmLiS1/60g9/+EOjQ0ZHR8u8IB1U06rJx87OzpIHVn/JFTTpn/7pn37yk58sW7bssccem5ycfP311++6666SJ7S32ykUihYSjURG2Xp7ewFgampKOzQjzWIGg2/6vHnzyqqXvi4oFUAdrMbzhS98wel0/vu///vevXtVu3ie/81vfgMFJ2zJkiWZTGbjxo3yMs8++ywALF261Epd3/rWt4gnFwwGzznnnB/+8IcvvPACADzxxBNSGfls4d27d4+Pj1d2XXKefvpp+cennnoKCm9Jc6q/5Aqa9MgjjzQ3Nz/++OMXX3wxke6npqaqqctKt1MoFHNGRkbuuusup9P5qU99CgCWLVsGAJs2bZKXOXTo0PXXX/+LX/xC2qL7TSfHWoe+LiiVUO+gL4oeN998MwBEo9FHH300m82SjUNDQx/72McA4IwzziBzDMkEvYGBgaGhIVKGTKlzOp07d+7EFqLUBwYGotHojh07pL3kFyFJFr969WoAeOCBB8gujuOkYALtqSRAE5EKmiD3jo4OaVLMa6+91tLS4nA4pGaQMhzHaWup/pK1lGxSV1eX1+uV5vgkEgmSKmLBggWqk5A2V9ntFApFjvaVIooiyeQOADfddBPZSH539fX1SXHuyWSSBEvdf//92Pib7na79+3bR7aoXj5a6OuCUjHUwZoR5HK566+/nrgyHo/npJNOGhgYIIFHixYtklJkiaJ41VVXAQBJOrV27VqSFOoHP/gBKVDyq3vfffcBAMMwq1atuuyyy9asWYMQ8nq9r732Gsb4Zz/7GQA4nc5LL730uuuuW7hw4ZlnnknCHUzOb8XBWr16tcfjOfvssz/0oQ+RJDH/5//8H6n8iSeeCAAf+chHvvKVr6hqqf6StZRs0r/+678CQG9v7w033HDVVVd1dnaedtppLS0tAPClL32JvEnlba6y2ykUihxQrkW4evXqcDhMXo+f+MQnpAnLGOOvfvWrAMCy7Jo1az7+8Y+TwKZLLrkkl8vhwnfwvPPOU33Tf/zjH0tnUL18tNDXBaViqIM1UxBF8emnn77iiivISs9dXV1r16697bbb5G8TUuzee+9du3ZtNBqdM2fO+vXrn332WWmvFW/j8ccfP+uss+bMmeN2u+fNm3fFFVdIaRdEUbzzzjv7+/s9Hk9ra+uNN944PT2tnWCsOr8VB4vjOPJmCYVCa9euffDBB+Xln3rqqfnz57tcrvb2dm0t1V+y7l6TJvE8//3vf3/RokVer/fEE0+85ZZbeJ6/8847g8FgS0sL+e0rb3OV3U6hUORoR1oikchZZ511//33S7mmJB5++OFzzz2XJGo/+eST77nnHp7nyS7yHUwkEjfccENzc3MkEjnnnHOefPJJ+eGql48W+rqgVAzCx9IqwpT6s3TpUjKW1+iGFJmBTaJQKDMT+rqgVAwNcqdQKBQKhUKxGepgUSgUCoVCodgMdbAoFAqFQqFQbIbGYFFqy/DwsCAIJPHxDGEGNolCocxM6OuCUjHUwaJQKBQKhUKxGTpESKFQKBQKhWIz1MGiUCgUCoVCsRnqYFEoFAqFQqHYDHWwKBQKhUKhUGyGOlgUCoVCoVAoNlNvB0sQBLI8p4pcLpdIJOrcmA82U1NTdIqojaRSqWw22+hWfHDIZrOpVKrRrVDD8/zXv/517XZBEJLJZP3b8wGGvqDshb6g7MWWF1S9HaxcLnfXXXfpbs9kMnVuzAcbjuPo+8tGeJ4XBKHRrfjgMDO/8tls9u6779ZuF0UxnU7Xvz0fYGagez2roS8oexEEgef5Kk9ChwgpFAqFQqFQbIY6WBQKhUKhUCg2Qx0sCoVCoVAoFJuhDhaFQqE0DBomWTdoV1PqDHWwKBQKpWFsHqN2v07QrqbUGepgUSgUSsN44RC1+nWCdjWlzlAHi0KhUBrGC0Nio5twrLApjpM0jwGljlAHi0KhUBpDUoC/x6msUidGOLxtnPY2pX5QB4tCoVAawwiHxzIwlKJWvx4Mc7CVOliUOkIdLAqFQmkMIxwAwJaxRrfj2CDO4S00zp1SR6iDRaFQKI0hzmEA2EJlldqTycEkD9TBotQT6mBRKBRKYxhNg9dJrX49iKex10l9WUpdoQ4WhUKhNIbhFKxuR9TBqgMjHCwLo5wIca7RTaEcM1AHi0KhUBpDPI3P7mR2TuIcdbFqTJyDNg/0RxEVsSh1gzpYFAqF0hjiHMxrgk4f2jNFrX5tGeFwuxfFImgr1Qsp9YI6WBQKhdIYRjjc5kWxKB0lrDlxDtq8EKMKFqWOUAeLQqFQGkOcg3YvxCI0zr3mxNO4zYv6I9SXpdQP6mBRKBRKY4incZsH9UfRlvFGN+WDDonBikXR9gksUheLUheog0WhUCgNQBBhkodmD8SorFJ7SAxWwAXNLBqcpr1NqQfUwaJQKJQGMJqGKAsOBItC6HCKrkNcW0gMFgDEojQbFqVOUAeLQqFQGkA8jVs9CAAcCJaEEV2HuKbE07jVAwAwEEWb6dpElLpAHSwKhUJpACMcdPjyf/dHEF2HuHZggCNpaPciIF1NB2QpdYE6WBQKhdIA4hxu8yDydyxKrX4NOZqGgAtcDADpaurLUuoCdbAoFAqlAYxw0O7N/x2L0PxMNYREuJO/l4TQvgRO5xrbIsoxAXWwKBQKpQHEOdxasPr9NBVWLYmn8xHuAOBiYEEQvTtBe5tSc6iDRaFQKA0gni4qWF1+JNB1iGvGSKo4GgsA/VQvpNQF6mBRKBRKA5DHYAHAChrnXjPkviwA0LWJKPWBOlgUCoXSAOQxWECtfi2Jc7jNW/RlaWZXSn2gDhaFQqE0ACn1JYGuQ1w75DFYkM812rjWUI4ZqINFoVAoDWC0kGiUQGWV2kEWIpToaUKJLB7LqIvRZPoUe6EOFoVCodSbCR7cDPicxS10HeLaIU/TAAAIYEVEJ3X+yyM4R/ufYh/UwaJQKJR6o4oKAgCyDvH7CWrh7WdEORoLBnrhvgTeM0X7n2Ib1MGiUCiUeqM1+UBig+goYQ2IKxUsAOjXi3gb4Wj/U+yEOlgUCoVSb0bTihwNhFiErkNsPykBchgCLsVGXQVrNI2pg0WxEepgUSgUSr1R5Wgg9NNl8mrAiEa+gsKKhFhdks4upNgJdbAoFAql3oxwWOtg0YmEtUDXl42y4HeiA8qItzhHFSyKnVAHi0KhUOpNnIM2jayyNEzXIbafOIe14W6glw1rhIO905gma6DYBXWwKBQKpd6MpqHVo95I1iHeQdchtpXRNLRqwt0AYECTOn80jTu8aDsdpaXYBHWwKBQKpd4Mc7jDp2P1+/XyM1GqYZiDDj0Fq1+5+GMOw1gGzuqkYXAU26AOFoVCodQbVW5xCboioe1oU44RVF09moaIG45rpv1PsQ3qYFEoFEq9MbL6/RGgKxLai2ohQollYbR7CvNioRiH272oP0JXhKTYBnWwKBQKpa5kcpASIMLq7IpF0BaaCstWRlI6aRoAwOOAHj/aPZl3p8ja27EIzTVKsQ3qYFEoFEpdGU3jFg/SsfkAvQE0xeNxzTrElIrRnU9AGIiizQV3Kp7GrR7U5UdZEeJc/Zp3jHMk3egW1BLqYFEolA8+f/rTn5YvXx4Oh5cvX/7ss882tjG6mZkIZB1iGmdtI8Mc7tBTsECZ2XWEgw4fgCb4nVJT/jwkli40a6EOFoVC+YCTy+WuvPLKn/3sZ2NjY9/73veuvvrqxrbHxMECGuduKzkMkzw0GyhYsQhIA7JxLr94Ee3/evLnoQ9yVzsb3QAKhUKpLYIg3H///WeffXYikWBZNhwOmxQWRfEnP/mJ9LG9vf3iiy/meZ7juFQqlX7xEW7jo0bHetdd7ll1ofRx+te3CPt35ned+w+e084jfx+cZKIulErx3AsPpF/+X9VJviWCiNEhR+WGx//R69wrz8DJqYn/+GLFJzHC0dYT/ML3zcskH76T3/53xVFz5gev+X/lW0h/ItlIaeKB27O73pSXcfYsCXz22wCQ/P1/8LvfUuzqXR74zL+WbO1IGoXdjgyXyr6/I3HfD1R7T8KwVEBjzis8p51/eBJf99xnDj0A/yyCUF3/W4QJtYS+fLv0kd/29+Qjd+Z3RdpCN/679VOJ46OJn30tqT/srEPTZV9zLT5BPDI0ede3TIo5uxYG/vE7AJB44CfZXW/olon8P78GpkKlJv3Xx7/y3MOHnqphVzP+UOjrdwAA9+xv06/8yaSk/yPXuI8/U/rIcRzP826326i8x+NhSl04dbAoFMoHHJZlL7jggkQiEQwGEUJ/+9vfTApjjLdu3Sp9TCQSWRl3bc99lksaHSskp7LZrPRRnJ7EhcI52a6RlCvqgmw2KySnsOZs5I1ejc3JJqdQNiumEtqTV484PS6/xuzrz2ee/61z5Zme8z8rbcwlJlRVi4kJ+VEAQPpT7mCJmt4QpyfJUcK05oRJ9Ql1OZxgWtxMNpsV9HoDAQQAUtPTjmx2MpVzZZIYwAXgqq7/LSIyTvklyFsoOhVXJ7yzMf2nXzlXnO656FoASN7xdZyY8F//IxRuIQVyqQSkU9bbLCQTkM3mktPmT0iucNfE5KRRySyXBLeBQliK1PRUQEjgWqbOz+Vy5BJyet81OeRbg6fHk3f+M/IGHF+4lTyiRuVZVm+WihLqYFEolGOCpqamRCLx05/+9Ctf+cprr71mVMzhcPziF79QbeR5nmGYUCj050WXrPnEJ1a3q6WCxIuPTj1zP+tgQqGQtDEtCjkA78BqbvNLrAMFC7smca4njEIhn8igLED441/wnXyudNR4Bo5/NDt4ucuqHCFj6o+/TvztSY/TEQiFhPRUEsDZ2tX2tZ+WfyYdclNjI//fdSiXlV/jNBbSXNKV4+Ubs4AFgOZ/vJldOJAbGx657UZGFOQFAIDjuFAoJHeweDEnALRc92/uecuE+IH4T74mHZXBuRxAy/Xfd/csyQ6/P/qf32DEnOqEuiQTuLMpFwqFOLeLA/CsODX6D9+U9ib+8vDUc7+bzqC5oVAiMwoATFOo6Zv/vewP2X2Xu5gKboA1MM8d/u5VICg6LeFk0oWnRdXJCRA5LunMZsjGZGISc0k/63IVymSnR1MAjjnz2r90m3nVE3+4I/XmBq/L4QuF+DF3CsA9b2nLdbeoiglHhuL/8WUml+9/HosCQMt133PPWy6VGf7+P4rJqYDXwzSVvhG6jIvICTC+5rMrzruosjOYIeaGvn0ZCPmeHEeYBwh/6ou+E85SFZx6+r7Exsfz3xo+keCSIIrBYDCTyVh5xkwo28F65JFHbr755oMHDw4MDPziF79YvHgx2T4+Pv6Zz3zmpZde+tCHPvTrX/86EolU0ywKhUKxi717995999233nqr3+///Oc//4MfqIeKrLOkmX2HQ2f41EMDjK8JAHBWMf2PfHQEIgCAs7y0fYSDgSgAAObTAMD4guRwQrMPch5hCDu6/WVbeOT1S/WS/xHrkZ+8KrAIyguRPqouXOQzAMA0hRhfE86qL9/w9NkMADCBEONrYprC8qMKPRlmfE0Osou3NNNSyjeGs2kAYDw+eW8gXxMAHE2kAWAqxQMAcrPBUJPLL+wXHfMDtfKwMOsBbU/y+k8LKG8oFB4bEIplSIcjt7fkvUYen3R+k6McAWX/529oWNF7LjcAYKH0nTUizaWbAA6IgZhdj6gKxgFiDsQcMA6RPF3+oPZiGfm3hld0dbX1l1V6cHDwc5/73L333nv48OGLLrpIHit666239vb2Hj58uKen50c/+pEtjaNQKJTq6ezsvOeee/76179ijB944IHjjz++4lP1R/QjoJGLBQO7yPgCoHxly6w+MerqOI9YFDZXlA2LNAP4otOT32IHhWvUcSJ1L5wY4LwZtmCx5A1WHUXMnnKXJbsuZRnNd7VL0dXkhBMpHgBSXLGKWBQ21zLOHTmcRdtfIN9CXxMghIUsYKzalb9kjHFOAGWXyju8RNWym1joE50nRHWvdc+v+8yXRSaTAYB9aVfFZzBH/qhgvuTFyjpZFEknV0l5CtbevXuvvPLKU045BQA+97nP3XrrrdKuRx999PHHH2dZ9sYbb7z44ot/+MMfVtageOrIcGLEaG+7v63d32r9bMPJeDw5Kn1s8TV3NnXIC2DAW+LbTc7QHeyKePRDYgVR2H5kp9GBy1oWuxjFczOSHB1Jxo3KOxjnipYlJi2Rs3/q4ER6Ur7F4/Qsji4wP+r9yQNTmSn5Fp/LuzAy32KlFjkwPTTOKRap72hqb/O1YIy3jOa7OsAG+kI9qgO3H9kpiIbPdLu/td3fJt+SFYUdxv0PAE7GtbxlsXb7eHrycGJ4ueXe1uW9ifeTvOGIfm+oO8QGtdtFjLeOFp836a7tmzowmZ6SHd4TYgPVNI9g/m0CALeDXdq8ULXR/NLCnnBPsEu7fSgxfCR11KSuzsCcFm/UtL21wuPxPProo1/96lcHBweXLl167733VnyqWBT9Zo/OxHJdNyL/Ts+LWwoFqz1v9fV9oP4I2jqGL+wuX8GSNUPXpagG5HIXbX9haE9XwZJbbutmWN5g1VF6uyxpDCNcPsuo3EVTXBHAdCo9lQU/FKvoj6CtY/CxXis1VAhyuXGGw1kesfkJpfmHwc0ipwtneSxkpXsnV7BkTk+xS/N/O604WDKfI1+jzlEqL1bXFcuXsSYl6sJnMgDwXtq2R1RFoZMziPWa+KDKb42se5GjygaU52CtW7du3bp1AJDL5W6++ebLLrtM2nXo0KHe3l4AIDqWyUlyuVw0WnzP9vf3P/TQQ9lsNpFICILw1tEtr8XfNjr2xNaVJ7YMWG/wqyNvvjO2Tfq4IrJ0dcfJisaIucd3ms0sOHPO6UvCRQsk4FwO55zI4UCOlMA9vtvw2KZFPr/TJ9/y5pEtr48aXprP6W1ZpOPJHU2PH0geavZEu/2d0sa/Hdr03tQ+ebGIOxRaoFA+R0dHRVGUT3P468GX358+IC/T4mkO9PmNmlQZmw6/unNij3zLyW3HHd8cE7H4+Lv57upp6vJ1q43K07tfSAopo9Oe2DpwYstK+Rbz/geAJqe/eZF6BF3AuZcOv3IgOdQs6+2xzMT+xMGIO9wbmAsAOyZ2Z3KZZeHFrKP4bZycnGRZ1uPJh3P++f2Nw5yhu3xO15r5QZ3Xcw7npE4AgCgbCc1vAoAXD7y8L1G8NR+eu3ZeoNvk0iwyOL3/9fjb4/ykUYEIG47MV3ty5pe2KNTHdn5Iu/3VkTe2jO0waczqjlNWRIpObSaTSafTPG9od6PRqMtl20/btWvXvvGG/jSosohF0dZxjAFUvk/e6vM6Qo6OgpXGZCFCIx8oFkXPHapEQVH+FrdZwQKEkMOJhaye7VfeR7mC5XRp3TJdzBQsnV3WFCwOFgUBDLqafExx/EgKz3EVfYhYFD2+r7Zh7nLbT7ZITgxyuXGWx9mMrJN51f+gq2AZT3mT1VuOgiUo6rJdwcpmeADYzbm1XyhbkH8rLch1iu4FgQeXcTIVa1QS5P7ss89+61vfWr9+/S23FCPjMMYkXBFjnMvljI8Gh8Px7rvvSh9dLlcoFOJ5nmXZ5ubmRa4FyKPjNhL5JxgMtLaqFax3j+7ZM753cXSBVsIJpoOelKezqaPF1wwA3YHO1mbF4RjwKdyJJq3ta57XGige8ud9f31rZMu6eWuOa+1PZlOeAx4X41ymp4V0tHV4HIp7uci1gPEaesSsw629NAAYHj2y+fCOGLvshNaie+GfbPLwnt5gd8gTBAAX4+wNdreGFYeLotja2ip3sHzjPk/W0xfqCbCBtJDeNfaez+cllQ5O7JvmEwsifX6Xb5pPvD2y1eNiT+6oZCRlKSwKBfNuTTw5OpyMB4LB1tbWHM553vcwCPW3Lm/1RbUXe2L6uHTO8MdQX6inNaI4hM9lT8kY3ju3w3Vm9yrt9mQ2te/9Q36vT96A+JGjm4d2LGtefFLr8QDw2KGnp/jpk+edEPYU/TOXy8WyrNeb/8qtzPX3pHUcl6PcWIJPNkebWyOtgpib5qedjDPgzvu+GPApqRMBICNkdo7t8Xnz/d807vcInr5wT8AdAIB5bT2tvhajS7NOa2trS7TlvYn3jQo0uXzaG2F0aYQOf5vug3pB84cXji04lND/ceVinIujC1qbigem02mO40yCNUtOgW4IYTeE3GjfNJ6nDNDRUbDIUA5CjFehYOoCvyIAACAASURBVGGAo2lozQ8R6vtAsQj6jy2VJGBU/BbnbVawAAC5WCxkcZbX2H5DBQsQ0koyusgbrHLLdBQsa6E/Ixxu9zJg0NXkoxdn3h7D7c5iFbEIuuWt2ibA1HrkkhODXCxAQiFQ8XoKluJYQ+9BU69GwdK9KQyDHE6cE3BOQA6nmYJVRbgSiYtyuNn9CdzbZL+LpZWmylOw6uxgYYy/9a1vbdq06YEHHpDC2wmdnZ0HDhxYtGjRoUOHurp0RhDktLW1qbYwBRZE+xZE+8pq1URmYtf4ey2+qPaNjAEjhJa2LDqxY6XusQCwfr56ToEJDsaBEMKAGYZxMI45Te1el9fiGRZG+xaWeWkA4GAYhBAgpLg6BAih4ztii6KGA3xSl8o3IoROnLOyL9wrYpEXswzkC7wZ37Jv8kDEFw6wTVwu/drwWx3+tlM7zVxPIwbaVwzACvL3pkOvj6RGATDDMDlRRAg5GadRd53Zq+MPmeBh2LLuHYHcQUBK+42QYiMChBBiin2eFjLvTuz2e5pW+vOXdkrnCSXrmkyP/XrLA62+ls/GilovafNRbmzX+HvF24oAIXRCx8r5YZuHJSp46qxcGgBgwLvG3kOAyA8bN8P0ty3tb1tqfhQnpB9+90mvy3Nh77na53NWEIvClnGtg6X+NV+0l27FrqNpaHKBmwEo2kX1S395BO2ZwlkRXGV2j575tE/BIufnyJkLvxlMYrAKgopWktFF0WDJLcvyyM0Sdyp/uNL2mzeYrDAIBl1NPjY7so8N4VZn0YdYGkb7EjiTA7baMSJDtN6JXMHS3VVSwQJnacW38DSWULBIC3FOwNkMcjhrpGCRY7tC7q3j0FuDMHetNGWmYPGG3Vsx5X19N27c+MQTTzz55JOdnZ2JRCKRSADAhg0bAOCiiy669957Mcb33nvvxRdfXH3LrMDnsgCAEAMAuB6JSxT4XN5P91/yySUfqWktDGIAQMSKn1MYYwBAltPK5Y8CchRDTutxsG6HS7ELo2KNYMMPOJQ/OWmzKNXeQBiEAEDEyqcFYyj0KhR628EUX67JbHLDoZdfHVZkQSwJOZ3uTVLd1spuaGPBGD+5+5n/3VPesjM5MTecjI+aRmvNcGIRtHVcvbG0vSyEqsTT+Qh3aaP2pe9xQLcf7Z4q+52mNwBks4IFoPIjSylYFi1xQfCTfCbpWnBOAFHMB4bnd1nVTuJpKIzG6ilYbhYAwkzmhSHcyhS7y8XA/AB6d7KWce4mHrnBLvk0N00BuxUsudsh3RqlA1e9goWEDAD0Rjw1Sp2vaGGZChYSSmdZK0l51m7Dhg07d+6MRCKBAgBw1llnAcDNN9+8efPm7u7ubdu2ffvb366+ZVb4/fZHbnvlztHUEdCaTAAoGDDGPqOu6+7UGLmXkifvD1U0bM3oWXG5gc/XZ8cDn/d9yexussWGs1YJGchW3EERMGhuq0MW4ZjvjDI7xbpPSZ5e1a353z3P/GbrQ2lhhq6GKoUElHUUcdyZ2bxIl+5SKkZGUXSq7eUoV1x72MQHikXR5qMVOFi1V7D0tBNF8BmZgcUwMleptCUuGvvCt0C6Fq0foBvxpot6wqaeghWA7O5JHGEU3RWTrQNdC3R6kq9WwSp3FiEYT6yTtxBnecCYDNrqnapyBcsh8ADQF3XXzMEqR8Ey7t6KKW+I8Dvf+c53vvMd1Ubyhg2Hw0899VT1DSoLck8K/lM9FCzi09RILeOE9P9981es033DCf8obdRVXFq8zYKY87t96lOY8qmlHzXaJffY5F6RdUaS8VeH3mr3t8oHmLxONsQGWScrnbCBCtbm+LZnBzcsbV4EmmeFdbg9DtZVsAdav7yyxuu6TQS1gqXnMR+cPpzgk4JoFtHYQMg4KsZYir+0BAYAyBgH2818+iPo1nfU3w5tTiDyps4wrGqXfO3hwsiXzku/siWH5cORdVKwZCkh8luIsXdq/CFTS6y1f0VXgHFodlnSTiZ5cDHgcwIYdDX56MM8AEQcWZB1F5nIaX7+atDpk8IwqFF4lsksQih7FmEWin1icFShhUaeevUKFnGwFkY9Ww7UXMEySokCyi+vfvdWyuzO5I5lhlBXwWIdbIgNepz2ZYKp6Fe71ZMD5HBOcyE6CtYZ3afZW7WOglXmGab5xM6xPTmlWzbQtmKgLR+01HAFq+Du6LiPS5sXEceLoB2wq6zxpkKj4kHSdeBq6s3bAgLAAGXNACI+JRncn6UsC6O905gX83FUBG3uAPKCToFbtUuKCpLKGChY8OvdZbdNoWAZe28Vo+dHqmOwsKDwVFStMkLbFYW6suDQ9JK1OHdJvgJJH3LrKFisyDMIQoyiu2JRuGtH7RUsTU8WFSyNs06kQQMFy6ozrRxENtM482cTeO0NzRdwV6VgZUVwi1kAWNzMvjel/kLZgnw+hMnFagfWARR5XCtmljtYgAHAYRyD9aHuUz/UfaqNNepH8NgFsejKk3NCyrzGHM4dnBpyIMfcYGfFNctdisqGQUu6ILYP15o2Bj83+GJOFM5fcI5ic/FpKXE4KB2jyhQsk8iqINt07XFXSbsuW/5xbZnqH7a/Hvj7OyNbz+g5fWXBzbUXhBjAOYxFQI6dY3tGEqOLmxd0+NVTWARREHDOiZxOxpH/ns6AoeKKYR0wrwm9O4EHosXLIJZbqzpMg1vlXozKY7BMHKwI2jJWdihCzWOwiE3l1aZdypcNepbMSuYqs6PITAiNggWlTLsUgKV7/uJHIdMXQEGk6K5YBG2pKNerRYw8clkMliaGXaYnASidWsvDwcpBZLMnpBgDV0LBqtARGU1jL84AAOthe5vQzgkci9r8Xih+KzXD1opimoF1AEsD0CWZNZEQB6aHnh3c8O5RxW86pSYx64cISWCKKro8Q7xv4xrTQuahd594cs8zZdWVFjIb9296ZSgfta2NjCn3Gkv6Tx4ne8nSj160cH1Zp60QDJvj27Yf3SXfRp4WErpu7j5etuzjn+6/RJ4n1mSwT8XbI1seeveJPeN7wbRPEKAQGwy6zbOJViYmFuFzfDqXqV3IoLx9e8fff/Xwm0c5Tfg3wGuH377j9V+8MvQ6FL6nZGCxRq2qA/1R9fidkb2cENX2cljKMpoTQMzJY7flLAii0TSeKlPpU6auzieuLO8Uls9P0Np7Qy2qXAVLso46J7SUa1TKMqp7fnnD+iOoCRQKVm8ATfF4rGZD2UYeuSwGS6NgQSEiSlZeUcDKLEIdBcvIwZJi4PT9sLIyvmoZ4cAj5s9c2YB4SYo+IlFzDYZQdRWsBswibCBHU2Ob49sOTg9pd9Uz8Fwen3SUG7tv64PlzqIyOzkAaMxpYQqe4cNX2eVnxeyrh998e2QLyM4vV7DKtn+lJCwHcvSGuquR2ayDUCE8SNad5G9LV4cgI2SyYtG4WZ9VMJ6e3Dd5YDIzDVWPitZWLrUD+dfBNOCseCGidEdms4gV0wToGHkeYyKbdSh2xTkwzzJKYBAsDaHtZVqd2s8i1I+/lm/UOnZVKlh6uyxpJ1LGfN3zy6uIRRWZ3AEAAayIoG01sPqqqqUtdihY1mOwSsR9g2n/a09VAaNJ0YHzqlJM84vFFkr6iPliMme3qM7WfxZhA9E1NnKFoD5WSO4D5bA4khwd0/vJXuHJ9cKDyEeP06N/TKUqh0qKUwa5fyBmh2majwt6Z2+ouyc01+TQ5wdffOjdJ45yxREC62kU5L1XdVx/tQpWrYdlFWk4SgWc5QtKuTDEek7FtRmSCkuxiXGoVpcj73QOuXYlnPLV5eLS4i2lhnUqsDq1n0WoP1kSZGKMbQqWsXW0qJ2UDHeT0pmeEAU2p/YLa2T181UbeOTVKFjWhgituuCS26EbvgaW74IRo4m0dJJYFGoxIFvSR1QVA7sVrFkTg2UwtQ0DQNgT7A11Rw1WDLSXxdEFEW+42RORGmPjtDgDBQsDgO7CdvmjLE/6e/nQqwk+dXrXSQF3k8qLkssPDuQIscGot7z+tD6IVh8YxORwTsSilG1B8gAuMZ5NmT+WYUDpAVj3VOSea5V9Ur2CVeuJBfJBbRMfVKFgSXH9NWtVHYhFdQJ0kHJ1OfKCzjDs5nFYK0tlHk/n0zSUVJh080GYUy8Fq2D7McaFH/oyZcXmGCzsYNS7NONruoym8ZKQNERoKMNgPrOmRdAORFbQ/9bRkakKfoxeoFvhb0MFy/oQodVB5ForWOPJYofHImjLuP2/uKwqWLoxWFm++jfn7HGwAEDzUiYv64WR+ce3l7FAYTVEvZGoN7+4h+3WS2vXwcBIP777T/HkkY8tPr/V12Jd5dg9NjiaOnJ8e3/A3aRSsD4Xu1zEIrkav8t37XFXld98nf4YnNz/+uG3+0I9J805rvwTVgVCABjki8aZuDt/H3r99cNv94V6L1x4LgAgIrXIClq/13nPFTAAdAXmXLbsY2ylk1irD/ireWoM2bQMEwVLfiEeF8sgRsQitiOTbaPoC6CxDJ7gISx7YyMXK19djryv/V731nF8lovFhVTmIxxu9xUVLDBWHfoj6Ml95fWSfIWZWipYBSNUkOVA8dPfbgWLpGlwl61gjXCwpsPw/NKpMJ+JMJkJTXf1R9ADe2sWv2iDgiUrYDnergwFq8YxWOMyBasvgI6k8VQWgratPgoga2GJ/pHJz8foLEL9hOalImM2HXr94PTQ6V0nzQ3YH/pj//iLnhkviEyKrVOZxGRmSpSZTysKltzcatWRKi9E131J8sl9kweklfjqCQIGIIdBBCgoWMbehijitJCZzEzlj2XUXdoT7LohdjXLWnh/yfIvuB2u7qD+slEiFl8fftuBHGQRp5cPvpbIJld1ndzkLq69XVlCMjm1VrCuWflpQMAybjD1X+VyadAdcDIOXpuNZFaBAJaF0fZxvKrdcCIheae3BT0Pj2HJVqUEEMSCFbGiYFWQCsvlxnym5K/2ylDZfoNod40WpZFktJgpWIxGwbIag5VfiFD3/KpTabtrIIq2jOks7G0Lau+ETHNDCDmc5l6sKtZN2g7W7rX1QeRaK1iTqWKbGQTLwmjbOD69zc7OtqhggUx+tjcP1oyJmKmIc/vWfrhvrYlCEE+O7ps8kMpytajd9uVNyHp4BqHZqlqKptP6QFL12URNz06m3yvaKXeL+Vx259iewYn9ttVoCmmIvFvMvA2k6PAkn4JKpSOLspOI8cb9m/56YBP5uGtsz+b4NtVa133hnljrMpPwu5LUWsHyOFmPgy34T4Z1qfpEd7bsrEPr/ajtYpYHgLlhdst4cZdyXlsJhanDCwhguMy3l8W4k8rQXKNutPusicEC0+6KsuBzwsFkTX4KqF1VWdIyjRerGCs08W4txWApNM5GKlhTKUWbYxH7B2StfxdkJY/JGCxdBWtBeJ75UTh/bE1+w1ezXo0R6+adwSBGnrpRVyeTqwXWB5KURwFYG1i0iN/t7w11t3ijRgWm+Okndz/T4o32hXvsq9YQYsXl3dIX7vG7vG3+Vm1hUSThQSIAiKJIwtsr01gYBkknNEGV8ozMrWOUz9Ka7tMraICcWitYyroMvw6q3wBzmtpFwAwwudnsY2ntga5d7AyyE0ew6MzviouWsoxK9EfQljHc0VXGPbT+q70CKlSwrMRgGc89rE7BKpFyzLy7SDasbj/Yjq47TmrX3ZX/m+dlaxFWkqahuIq2kG2sgpXkFB1ei4i3MhQsKaJf6lU7ZhHOGgerMpegpr/gaxHWfVx7zEox+XVVNunPdgVrfrh3frhXtVHuFtd7qRxN1tYOf5s2ByZBKRlKf1X2bbf0oBJHRLbYc006p67JXc0EXUWffHLpRQCQTqc5EOrQsBoRi6JH3ld8fVR2kc/wAODxuJdFUHqYdRMFi8fmiQO0tWwZw+eW52B94BSs/FI55SlYvAhJASIsAJilHDPvLtL/F3Tb/yNF1x23pGAJCtFLe7iFqvMRgY1VsJKcUsGKosfKjDgsSXUK1rE0RFjZjCrbRaZpPrFv8kAhNUM9BAKfywsAu8bek29UDPaRJeGgdOZGubm1XcEyoFhPPdUUAGj3tc4NdOpWeN/WB3/+9n1pQfbaIpP+yGy4gqwiVtQ9Fh9UlSNSo85Z2rxoTc/pbb4Wu0+sg9flDbFBN6PzG3rmJ/SqgAGdIUKFXUwV7EcsghIo//s4noY2T0FTMZj9LqeCMKy6Klja5VwqVrDszoMV53CrJ/81M+mKEgpWRWFwVihHwZIl5BOyBvJhGfdadskNU7AwQCajyN010FgFS1kSbHKwZo2C5XF6OvxtutkKBif2Pf/+xvnheevmnaHaZXuY1ODEvmcHNwy0rfhw39r6JCboCc4FzdioquprVn4aLF2mPHKLCbFBSdvYeGBTNpc9o/t0t8OVw7lHdj6FsXjpso9V2Xi5Za2zgnXJMsNcDNN8IpXl5F1aSFsF0v/SxnJp9kSXRBe2+KIAMJ6eGEkeCbGBOU3tqmKqlZJtf1AJCyN9AH32ntOICxSrEinoDXVftGh92DjVyGykxQNuBg4lcZc/f9dUdpFL834A5HLHgmgCu6NEwcqUSH2pIhZBd+8ocyJhXRUs3SHCGaFglcwyqjqVvoIVQbdvqclAttpVlY2QlorB0pUPMwCWFnsG6ZL5tBRWb95CQwXLwtwFIyYyEFCmzm/1gJOBoRTu9NkX1lyJgqXTvRUzaxysnmDXp/sv0d2VFYXJzFQym9Tusl3Bko+shdjAyrYVzcZRR7Zg8OtfIXmYZMmSI3fLGMTIczFsG303mU2d1nWS2+ECgH2TB6T0UdUgjw+rs4JlgjZqLd9CEoNVULDkBUaSo5sOvdYZ7Dil+wTzky+Kzl8UnU/+PpwY+eN7zy9vWaJ1sADyKyVDfiSzJg5WrXnt8FtcNn1y5/Fe00j8EBuUHtF9Uwce2vFEb7D7or4P16WNNSQWRVvHoasQoKOyi5lM2k8UrCgay7nnA+AsP5rGvU0looLk9EfRjgmcw+Cw/GjUVsHSmymZ/7t2ClZFMVgWF9U2767lEbR7CmdFcNVoHeJyY7B0JRaMzV0lTdVuABCT04AxcrNg8Nop9ExNFKwRDrc51ApuLIK2jkOnr4Lz6SOLrCpbwTq2Mrnrsn/y4HAiLs88pMJ2kUk+shbxhM/tW3tCR61TcOmM5lV2XSbupnyXjUsPyePD6hkPZI42ai2vYBU+AIDHwc4LdksFxtOT28d2HkoMVVKdYTOKac9mXB58a2wZ3fHq4Te5cmbpzrSEtNUQiyjGj3RjsJDLHYuguJC3VRZlFQm/E9q9aO9UGWJqQZ/IYCFLIprLuSZrJ6+PgmVsHSXbb3LCeBoXR2ONU46ZixweB3T70e5y+t8ialfVegyWJn0DzvKAMThdRq6SumoXCwBiahpMHz8pa38tYrDiaWhxKBQsqEGceyUKlqDT7RUzy17oKv6w88nfbHtIWj5WW8D2Yalar3t4hBsbTsblzqKuglWZMtfua+3wtzn1VpatkdlTKFiNFmmm+cRwMp7KcloFS5S5gPmuYBiXQ26ZKhHgRNPHT+4463bOY7v+dPur/3ff5IGyKq0nlU08AQCLlmCG06+0Byq7KPD5d3qLB6TlCOOc3OpbUpj6ywwDyusTqWnAGFk2umWd3MD264x2FY6yJQZLO0Ro5rHJfVmTlGMlBb/+2uRzr17BktLolzsWTHy7goNlRdSpiYIVdag7vN/uTA3lxmCJXBIKib6PrSB3XaTV5cDA6Zk5w1IWeWjH47/Z+pA8cZduFoaVbf2nzDnB7ShP/P/4kgs/3X+JQV6lYldZj5qX8/bI1p+/fd9rh9+SbwywTUuiCzubOqRLsDerRVm8OvTmb7Y+tHNsj9ZnnR/u6Q11k4E83XbmXaUyG2/++MmHm3UrzWEhh2d0Rs4K5qKSqQMHpw5N84laNateqDI1qOyiyBff6U1eKQ9WeQoWAMQi5S3TZlGfqIzGxGBl1VKHxSD3Ngspx6RAIiPBrxb5mcDAVS2tYPEZ7RI6ZeRoyFdtTcGSesZoLUJnaR3RiDgHUUZ9U2yfUlCugkX6JM8xlcldF2KWHMaLPXcHO/0un8/p1dtZCbVWsLTo2rBTO0tEApWLevyufM80LaQnM1OZnOKhbPO1XLRofaGKmTM2pFZe+sK9fYUcE7piUmWeurmA6nV4HMCQZ7jF28y7ec34abVzPRN8ModzfpdfV7asngqmB5LuzYqCIApOqEmr6saKCHp3EgsiOBkAyS7yOn5GyJe3VUqrzwNAySG8WBQ9NFiRgmVBHisb3TxYCAHGqvxMyKkVnMwVLMNsotihCXK3EF4d5yBWiI8tGYOVF/xcbq3gF4vCr3eb1FMh+gqW01jBIp0s5RGQ+twXKFvBkj8hxo9f0cEVim1TwDDI6cJCFgvZckei4xzuQVlVA1ZE0M4yIw7NKVvBIg6W8nmuhlnsYEkG2+Rn9Oq5p9pdbW3zG2gduLdGtkApG/bm8GYBCye0r6zYjqocCIZhcmJOFEWH5RNa8J/qqic+sfvpMW78Y4svCHtCZIvkRJq7BbrhUJWNNZtf8HXHf0b6+9JlF2sLVJ/d4I/vPb9/6uClyy4mc1FtRy6vPv/+i+PpyXPmnRkpdLhEKsuNpo74XN5WX4ukxc5cXc4yPid0+dCeKbw0jECyi4Ufvkgomr1oEwsAuSw/wUNzwQ7m7aK7RKb+WBTd/EYZv+iI81FTBQuUM60Yj1/kErLgFV5qRuEoKwqWYS6GwlqE5Z3Qyjo50kaT7opF0JaxGq5DrGghmUWoF56V7+TCgJ28z6tSsIwfv6KDa7yQH3K6sZDFAl+2g5WGAFI//34ndPjQnqniEt1VYuUS5CVJn6ie52qYNQ7WSHL0ucENbf7WD/etLWzL2696pXQCUNo8EeOaBi0R0kIaAMyv7+9Dr6eyXH/LMidThlb32K4/ZkXhY4svcDFOlQNRSZeiEv7T3EDnjSdd46hXkPsYN36EGxNwTtoia5/Z9QXdgW+c+kXVRuu+4dbRHRv2vdTftmxtz+oq4/rtWuy5dk6tfLrA0PRwPHUkK+pMvTk0ffjx3X9aFJ1/8aLzVblVZztkUKPgYClMJiMU3+mtATcApNJ8hM3LXVLJkiLT4iA6mMQpAXzW3tY1VbB0M7kzvgCx/YWNts8irCRNQzwNbQXbbZJyrGR3LQii0VquQ5xvYakYLKmTtX1elYJVRQwWkC5NJzHPg6e8bPcjnJSmQZl4LIK2jNnnYBXWBdKG8alLyvqE9O2xNYtQEIXhZJysYUKQrJf9y+oZI7d5O8d23/bKnU/tec7O8+ukZc87cxZbZZ2D00P7Jg/kxBxoHIhKYmvEEgoWgxiPg3XpJaKsBdpLkJzIksrQ4OT+Vw+/OZo6oj22ZL0Y43QuoxoqrYzKcvQrGgMAtRyWla8qaOKDKi6kcDUzObbMOrEIbC0E6KjGrZy5ov3oCHoAIJHKSBHuULSLJXwgJwOLgmjHhNXuqm8MVgYAGF8ADHyF/FFKSUYX0xisyhKNlpEHy6S7GARLQ2i73elG5WsCqlqoK25JnUyUFcbXJO2qVQyWMrm5wQBrhRMJ4xz2KfNgEWJR2GpjV5OgOoxFLqGtS1FQrmD5muS3phpmjYOlRXqhkzdWfd7XyrwD9gcVaYcISS2LowsstqocZGnWq1awGh7DrkKzUk7xgRloW3HKnBM8xmuE7xnbu3H/pqHEiPZYC/XalpmiegWr1qkx5BdrcbFn3TRjs5dYFG0Zz/8tt4s5DO5c0X64WTcAjExn2mQSszZ226QW61ZHoU+YpomvAH0Fyx8AjUNQtoJlshahcXiW4dkAjqRxa6mFCEHVXQbObln9b5WC7S/MBJS1kHEA4wAxB2IOAIAvdrKYToIoIocTsR4o+KxlZRkFa5cMYObgastYrFpihAMvNkidX86UjpKUdbFSMXJrIFftWl6zZohQ63lIL/QQGzxlzgkhTz3yRPeGuq897ionQ/qtHkFFxMHqCnTIN6aFDCBgGTcqZ73nX235vRM5/qH/U4V8V0Uhp9XXKmJRupLF0YUA6kBvc2aagyUXVwjSA3Nix0pV4Sl+GmPsc3mJwKbn5lr1VKqXnYqXMONXmFE4WGSLXjFlQv/8RozxjHlYKqdfFqAjt/qjaYX9ILtGpzPS2sNgeYgQypy+nv8tnqyNguVwAuMgS/sB4yiIK0U1BUy1KJMzW8jkXoaCNZYBvwvcxdHYUgqWaXfZnj6gULWbDPkhl1vVQuRy4wyHszxivfJOltopf9jKzShr8QmptYLlETOCpgH9EbRl3M7BKORiARKlL9at6BNUWK6xytpnjYNllLuIQSjIBtb0nF6fZrgYp5STuj4Klq7luvutX2VF4SsnXUdyNVk06mPcmIgxxnl1R96lVymz5J83/+xyW647+W44Gf/Djifam9ouWWq4cE3dMPEAXtz/8s6jeyKe0OdXflpWqtifPqe3zdsScDeVrAUBmdMqAsCJHStXtCwtuOMVUG14Ya0VrCaXP8QGyUwIk7rkw7VzmtpCbHAyMyX3fWcvi0JoKIWTAvidCqsf53AYy+aFud0AMJHItMpCiksG3krEouinW612V21nEaptfz4eCEA/Z2YeuSRjMG/G3hgseb4xsK5gGQh+sSj63/21iHNnARI4mwFoUrUQuVic4XA2o+pkqZ3yh61WCpYUwMSnAfS9k8oUrHQO0jlw5nhB04BFQXSonIjDklSoYJGSVYdhzR4HyyA4yVxAun/bH8a5iX/o/1TEE7a9SbVYXE9rUXXtViG8HxWOsqRgqTxCG7UWqXaVgoUA0rmMfFnlulGWCkU6IZlNFY5VS0dLmhe2O1pYtrQtVA1NmgxEPv/+Ri7LrZu3xufy3rf1IQQgiYtGl1AutZZYP7Lww1aKyZ/qiCfc5PZPZqbsevAaiwPBkhDaPo5PbkVyqx9PClEsIocTZMu8OHK8PRjoGQAAIABJREFUQsEymv2uIRYF6/mB8voECTqxbHSto7T9GQBgvCVisACAcbNiOiXyGcajvxIKGSlTTEZjGORw4pwgZlKqa1EEMOn9xFVkGTXtaivdVaMln01UKMbNigAin2GkGCxvQN7OwrFp2bFlxmDlw5LMnhDkYjGfNilZWDYgbbFqAklWglM6z4mTgcUhtH0Cn9Ri50TCkl8HeTFwuSu7Li2zJgZLa/PcDvba46663HRB4rSQSedqZd1rYb10QrP1alH5ClbGkrR6W/UhPtomaUYViQ8nAsBo6sjG/Zt2HN1lS3UVULDoOndMvtjzNJ/YN3UQKnU9tUOTRrw/uX/n2B4+x2OMR5LxkdSoyj1dEl24bt6aOU0dRmcoST0X2LaoYIHdnn3Dkayv/Nf8kWnFj36yyyPyCqtvWcHq9iNOgFFrb/u8oRVFeQNsRKGdkNl5pWKwAAr6ivHUd4OjCteiSQFqrp3I842BaVdb6a4OLyCA4TJWhLKEsieVLSRuR7aYhQH5mqR2gostHJuVjtVdCMik3sIlmzpYTpd5yXwzylR6iPtrdFPszeyad6rIJZikaZAXK3TvMRSDpfUGGIQsLnJcI2ox/nL+gnUiFsMyvc3ARip8BWuT/tTuhb12ric414mcHf42+UYmv0wkAMBRbvzVw28ubV60rHmxLTWawzDFlf4Ia3pOP7nz+LAmSxMUnisyrpfMpo6kjgJU6Hpa79WCrmPo9y2IzKugAXLqmXnMZCkklS67MNzX6e8IsE2Q05adfUgLqMkFibFpRV4AsssHGZ9OkLslkak/iraN47VzSt9MbaySvWgHp0rOIgQoMZEQ5wQSu00EP1ldLM5wUJCs1Lv4DM5mdK2mWsEqFYMlvzRdSBhWR5edXyZtT6o8cm0nS3uri8FSD7aWLkkGeXUKVBKDFU9Dm9fwpti7IqE2qs9KsUL/HzsxWBXlYpg5CwxbpM3XotpClKepzJR2Y3GwDwBKhepou8JeBWthpG9hpE+1UX7X6nwvSFouLHsfhtgg8cj5HC8CZh1uSTGSK1hSf9Tcwcp3Dq6dzlRPBctkloNqrPOkOccBQDqd5ji7NYFGEIugpw8Uf+ITazeR0lGwwoh36aRpsCQ8DETR5jGLDpY8P0ItFCy1aS84WDxAfgViXX8IjC2W4Wp3Sg9VeUIzBWs0jVvLicEyqkVioBltHsPn2uxgGTpJRp0s7a0uBsuqC656gPUKVBKDNcrhVg8yuimxKHr2kG0Rb9p5qfrFlMF/hYS6x04MFgAY+BAixrzIM8C4HepBaNsXGJ7mEzuO7mpy+Ze3LKmPx7A4On//1MEN+18eaFtBtmgtGetgPY4Sb1KtmFGHSWr2+nBlsSDS1+JrDrh00t89svOpg9NDly//+NxAJ9kiQjGrQjGPQEU9Y/2SGUCk6trpTOfNX5fJZULuQOmiVdMX7s0IGZfmOwgfuDFBFVLmHrlRnEzq2MsA8OGKFawIevuopQ5srIJV2cLAhqvdGRt4c49thIPjm2XnN045Zl3BennE7lRYZSlY/gBZwgUMFKxy8mCVrWAZFatMwRrmoMNEwZLllqseqwqWW0fBqn45wlnjYLFOdqBthd+lk6l8PD3+y82/a/ZGrx64QrXL9twB03xi4/5NnU0dy1uW2HVOc1a0LH3+/Y1yg631Gq9c8cmS59GKGfPD8xJ80sU4+Vz2wR2PuR1uacGW+7Y+OJo6+o8DV+oOqFlHmfi+rgoWkUlKtqoABlD7OJX5hc3eyJqe08NsCAD2jO89ND3cF+7tCXbpFC3Ew9dOZ+poaitdyCZkSyyoafe3XnvcVQ7kAIBUlpvip/0un2v2vHzM6fQhQYQ4B80yozid0rGXXuAVgUHlKFixCLpvt6Wf9dbNZ2XoiSvFxIxGXmOFCpaxgS8Vg6UaIjRMOWZR8ItF0N07bJ5IWJaChVwscrqKfxs7ZxbrLfxtTcEymF9ZmYIV53CX31DB6vKjrAijaWgtsYiUJRT3t1SQu/R3of+PGQfL7/IZvcFNJlvZbtTl+sS8UM+H+9a2+JpLHVRdjZpf/5VdlDbI/azeD5E/OCE9nIx7ncXHmc9lRSzaoTzJcpnKPjeaYug9gaShx2S4jsQ5ApIvR7g5vu3V/W+ubF9xcneJZbYjnvApc/JlDidGXjv8lsfp0XWwJD9vJvVMebx86LVD04dXdZ3cFZhjUsyBHFK45PYjOzfsf+mkOced1nZiXdpYD/qjaOs4XhsuGsUEp2MvPWKmOPkcY5wTACHksPQSJrkusYXnpJ4KFhSWUyS2X7YmSZkKlqlbZrLLWMHCbd7i97dkJiejWiRWRNB2W9chBmWflFSwiFNVWOexXgqW8RCtfHsFMVjHt5j5hSsiaOs4PsvCgHhJil2qifBTFquJgjVrgpO0pHOZ1w6/tfPonnpWKhc/mr2RgbYVnVVM8rJWoybDKgBUYo8Nj9MJz7JpTEeZYbJ+8UDmyEPvCRgVPqF82NbcwJxTOou+1DSfiHNHUkJ5s3bNM6VJzrru8tIAkBbSR1JHU9mZG6h0JHV03+QBKb2FFWZaQlpbiEXQ5jEsN4pcWsdeOmULKOEsDxgjl1s3y4CWkBvCbvT+tIXYvrorWLKgYH6GKFgq/cN0FqGl7mpyQYcXvTdl5yhhmQqWW/Y42adgmeb6LxmDBeRw00WQtMQ53MYCFrLayaGEWBRttjYgXpKSo5zavTYqWI23dhXDZbkX97/8t4Ov1FPBArNgMBsYnNi/Ob4twSdNaqzMU3Egx3HtsRXWRjal8CDr58/msulchqxsKGMGKVh7J/aRvtXJhVGYbCiKIvkbKX/rVNZ4c2ei6MUSR1ZjaDcdev1XW37fwMQWJanAEceFcLdq8nvNNIiCJU/OxKUN7SWhXKMIAP3WlmmruYIlmw8oE1cKSUFrpGAZhGcZeWzDHO7wWQxyt9pd/XYvmFOBgiXtndUxWCMcdLj1J0MQ+iO2dXXJUc5CMapgKZHJA4ZOj+1B7rUODH/t8FvPDm4YS49LW7Q1OhnnRYvWX7BgXVlndjlc58xbIw0LytFxQ7Ur+ZXi2cENd7z+i13j78k3ep2eNT2nnzznBJgBCtbr+b6d0D4u5/StPbXzxFPmnMAwDFGzEFY8M5U13vzxkzJmGQldMz82vIJJDORy3hh+Z3Byf62aVXfymXsKq8tNprJ+rPQzGAY5nCCKuJBZp9yp9YVaShejChYngCBCUOZvmLmz5NYY1CInFgGbl8mTrQ5upmDximFBslfXzbVab6NnEY5wuM2ZNTmtjZldK1Kw3Plbc+zEYGmR5AETp+f0uSfncM5V+VolairLFmEd7bXwuSwChAFjwIU1BJkl0YU2Vqo18BX4kbpqjdvhkgKS6q9gCaKAADkK6VukBmglz2Zv5Izu08jfJB6LYRQtJV1RbuPzyVcNjutoanM5XKyDdTCOgbYVrENjk6qeg7lnfDCHcwvC86pYrseMip8TqO66ZhqxKNo+gUUMyMXiLD86nW7V2A/kcuOcgLM8MecVKFixKHrqwAxQsCSbSvxFhkEOZ1HBMhiMq2cM1ogyy2jx/AYxzuTW6NYiJxZFDw3WWsFyG+5yuuV7ybUoFSyr33HljLnqFCxZMywiYhjLQJTJjBqftj+Cto1jEQNTtcEoPcpJ9hItLT9Jk7VLwZrNDlZBVDCxQ9plfauk1nkHtOf/3fZH8mkwMTbSQl4++OpRbnzV3FOavZGKqlU7P9bTkRdPUUosJMs4+t36C2XYzpO7n9k5tueiReslZ1Qm1JmN87JOd4e/TbW2kvXIIUEUktmUAzma3H5SgVEM1tm9Z0h/607gqF4ufW5wQzKb+qcTrq6RgyVfePGuN3+ZE3P/dMLVDk1CwrSQ3jy63eNgB9pWEP8VZrYyVy4BFzSzaHAa+1xuABhN8C1Oklxb9k53ugFSOJsBjw+KJt/qsA4AxKLo1ncsfCULK8xAjRSsgk2VDL9sI290XeaZG/PbdbwoEwXLBQYemyrLKGjcF82pWEindGuRE4ui77xh6zrEGidJcgGL3SWbDKGMwSpKLLNLwTqagaALHDkzlzfkhiiL3k/g+YFqPSyLChaRn1XhbseQgpUW0ne88d8ep+fGEz9Ptki6Sz1HUuQ2bzIzxQnpgLvJ77LNadBei6SWiVg0CiY7mDi8f/LgyrYVZTlYD+988vD0yKeWfZRoJ8ohQlJ3GS0vGe7W37qsv3VZGWesDr2ezLuA5o5LZ1PHp5VLX4MF91FiJDn6u+2PdAXmXLH8E1WOilYvl9Y6NYa8k1NZDgNm9ObpcEJ64/5NEU94oG0FoA+gggWFbFinutwAMJ5MRxl1XoD8mI75ejKmLA2hvdOYF8Fd6n5alGQqQ3c0UHIIjAf7zCyWURqF0goWr+OxxdO4TTnD3yRNAyhssFl3LQ6iA0nMCeC1dR1iMvsSMJZPc5O6q+gaIqSMwdIqWLMjBmuEw+1eZO7yQiEblh0OliUFixQoTNK0TcGaRTFYWpuf113qm82y2IzN8e2/2frQ1tEdtp5dfS1Y84eVo6zA64elA1SmYEElg2i1Q68nS48pEwYn9z+5+xn5nbWuYMl9jipHRaufT1HrYVmLv23kY7JSt9c0w239IQFS5G0+nuAjjNp+qEyRNN/eehWsA+Y1oZ0TZYwS1jQGS+5LSTErxoN9pRUso6NA70JMtJM4B+3qIUJTc26tu5wMLAqiHRb63yImMwPyV8dn5K6hoYLFl5fJXb7uTf0VrDhH1skxc3khH4Zl/ayGWFWwlBeb/7vMNRa1zBoHS2sUJQWrDhnJJRyI8ThYF+OCcoyudbSiBTFLFy8+36m3FBTB4mLPKl9KMnu2pGkoaDwz5YkyeWBO6Fh50aL1XQHD/BoT6YmdY3tGkke0x5auV+abNlzBqvXEgmIyeowxYISQwddBNpm0cEdwOe77zKc/iraM5zM1TKYyYSAxWJqxmCoULKmWksUsSjKVUUrBMtOi7Few9GOwoE0+RCiNshloPNa7y2L/W0Tdk5rQKIWCpWynVsEq615bDv0upWC5y1awyDrcJRWsfpuWfC5LwZIOMf89YJ1ZM0RoIki4He6LFq131MW0B9nAjSddQ/6uxfiLkavU4o1KpksQc4emh1wOlywFV2mxYzIz+d/v3B/xhD+/8h/yxxS8KNbBDrSt8Mmy5K9sW7G0eVFZadxJTgdGaV8x4KnMNAIUZOuxWosSw/QWnX51fvPd43tFLHY2dTS5/QiQPLRIfqwVMUjumwbYQMQT0kavW76AaqVZ635hZaD84yqaP4CKdGhSkPsHTsG65S2RvM2nk3wQWVOwynSA8tMVF5QqV1MFy63UXdxEXJF8BTMtSndED2xXsNK4t0mZo8E05Zh1wS9mk9WXV2emYGWNFCxFkFC5axGSU+VX0a67gkUi5KTBOKNisSj6wds2/AYrmSu1WFKrYB1DaxGahdQYzqqbzEwBgJRF2l5qYSG0NlXrxiWzyYfefSLEBq897iqyxaKCBUpbK9XV5PargqwrWghIZzxKEHM/f/s+F+P8yslfKP+EVaHtE5PxspcOvHKEGwOA64//XJPbjzTHdgU6k9FUs6d0iJv8Dq7qOnlV18k2XkL51HaQsN3ftiS6MOwJkfT3ugFYoOyT49tjY9zEnvG9HzAHa0kYvZ/A2OkGgCSX8YMmBktpiipTsGJR+Pm75ShY5QxBWsRAwZJ8hUoULJKpUucot6GBN1GwRjk4uaX4sWRXW1ewYlH00602rkNsmNuiqGDxugpWIU1DxQqW29Il1yIGi6zDXVLBWhpGgwmcyQFrOHJjCapgWUL31zzrcJsISBjwz9++DwH651NvqEWTSGPsVbD0xubUNlJnRM+C1KEd0LR3aFU3u0Fts7KaopNN1Hi8TEqpWkiqru7P5S2LO51tLFvaHFrv1WcHN+ybPLB+/ll+l/+Xm3/b7I1cPXCl8hKqnb1RawUr1ros1roMAAQxB8Z+nELVczeRFUXLSmM783EzsCCAEhzrVjhYditY1gJTGhCDpVSwQCcvaP1isIY53G5tnRzVyUsrWFGwdYiwTAXLrZZY8gqWUF6Qe/GEDicYR54AlPZOKlOwTm4FnCpxU9wMzA+gdyfxymhVr6/GxmDNHgdLYy+7AnO+dNK1JoeY/6qunlpYLz0Fq3QtViyxnnthp/+jm59THt28dXTHjqO7V7Qsqc862YZzIvQKS88VxnhwYt/fh16Hij0byzlaU9nUZGaKz2V9Tn3Pb0l00dxAZ9Bd+ehq3dalMQ/2Uj3VMz+BamXEomhi2NUGwKUzXrfWwSJjZAUFi69EweoLoIkMnuAhbOqYNSIGKz+nbybEYMWVMVilFSxJzikl+HX7ESfYvA5xCQVLNwbLXZRYsJAFUSzpKulWXVrUKfaMjbMIod0LeLL08x+Loi1j1TtYVMGyQAW+QK2nUNWihvMXnHPhwnOVdahtpMlgn8mZbckmasLpXSevbFuhWfq6eN/G05P7Jg/oLntcC7TZRK8euMKocHGqJsYpgZvMTEOlHoD1VYaKaxEa+NAhNhCqLnbN9pUMDCsCAONvgupJW96ypDvYFXIEPlhh7hCLoiM5tg0gk+ZZZyan1iRsULAQwLII2jaOV7eb/uIi9comi9mIFQWrHrMI3YbaCUkEIDu5bQoWAPRH0bZxvNa+dYiL2Vl1knoYxmDlb66Yw/kMXmU+SMoTmhczOb/qZ4MV4mnc5mWsPP8xWxbMKVfBkqccO3ZmEZIJSmSyksVDaj2FqhYKlvZsxEbuOLorK+ZvttFgXymHQG0E7U1v0R3oXBJdKI+UB7Vlrb3Hq8DQJx/jxvdNHpjmE9IWyQ+T3B2ACqe5WZ/6Jwk5NdKZSs3ss5P80Krhd01xLzqbOpZEF4bZMqZQzApiERQXXACAchmyrrOOglVdDBZYszrEPNdCvgKZTc0v4WJHDJaNaxHmMEzw0Cyr38YYLLDL6hMqVrBkTq2YmobyHySrClYNYrDiRMGyoODGolD9lIJiFaXkyWKfSCnHjqlM7gghjLFuQvN9kwcAoDfULd9YC3ueFYWn33vByTjPX7CuPovrndu39tnBv7y4/+XF0YUh1gV6jmOrr4UTMl6lc6NCR/cydsuS2VRayPhdPo+z2hgOBiERY/IPahkPpGJp88KOpjbZRMsib45seXtkyznz1hzXHstvKvRBcellS6N8OliXWosOVm10JoTQl0+6rqxkZhXjRM41Pac7kf77xMEUk5t8gIlF4QGBBYBmJguyGXYEWxQssDZ93aI+URkVKliyoGwtNq5FeCQNYTc4ZW/lkinHyhL8+iPoHZsmElYzi5B8xBmu4GBVomBB9QoWWeA8J4AogrVonDiH2zxItPD891tbfNOccmOw5P8fQ5ncAeDcvrUYi7ou08M7nxQx/vopN8jtdyFg2U4HCGNx59get8N9PqyrjyCzMNL3l31/A+Akq62t99TOE0/tPNH8PFqZZEXLkjlN7S2+6MHpod9vf7Qn2HXpso+RXc8NbtgzPvixxRcsjPRVfQUIAANg84X5bKcrMKcrMMeoQaD0gSQvRASsCngvlyZ30yVLP+pyuABgw/6XRlNHV889RdfPKy72XIMHleB21MmncTIOadFJLS7GJSU3OTg9NMaNzw10+pDZ74HZSE8TmsZuAIg6MnqahE0KVhQ98n6JJzNvgGswhRBA7kvpKVhG8wGrVLAsx2DFdRYitKRgWfRRYlH0mz32/GgpaoGVKVjuooJV0lXSrbp6BYuUwXwGCzxylw5MS2RBBGhywaSF539eAE3ypSMOzSk3Bkvx3TmmFKyY8UIrCDGAcxiLgGQ/QYgEYW8QumwAaPXcU45r7w/XJgeEHFU4UWXKmVYmWRiZT/6Y5pOqwjaOHjKIkXKZqhrQKLThWcXsl1iUJru1eIvxZE/sfnrr8I6LFq6PeZebn9zJOCQllYxFntA+oFtSioavW6SU7bwy9MZrQ2+d2nXiyXOOt3jI1tF3t47uOG/+2QsD1fvuMwsEEPSzABBheJzWahL2KFgDFnJdzkQFy0oMlsES0aB3LUYKlt5ChJZisCx2VyyKto5jbMdPa5NxVbWC5dZXsABATFauYFUfg0XKkMkNVhyseDofHmfl+UcAyyNo6xj+UEflnV2GgiXrZLsUrFkTg6VlOBl/cvcz78S3gYFDUJtZfgAF8SPgburwt3mcdswnKYfKrstUQFKLYloXpGKkHqv1unjVIMt+mQ+9WhiZf3rXSaoC5cpv5KRGd0pKZ2rkYE1mprYd2XlweqisSutJDovpXCabKyMUdPZ6k1Zo9rsBIISyOEsyuWsUrELYLJlaX4EP1OIBNwOHkmY+VlmSTLlIF6KrYIHBdZVSsNTdJT/KZJf2hPF0pQqWNcEv7IaQG+2btuHHZyHQJ6v1SsuIweISUP6DZK+CBZbDsCT31+LzH6s6db40v7I8BcumGKyZaO0s8v+z997xcVV3+vD33DqjaZrRaNQlF9mSbUm2MdhgmsGUUEzA4BBaAgF203Y32WTDJtlf8r67yWaz2U02Lym/DSUJJCHUbIAEMMYYU4yNbWy5ypItq3dpernlnPePOzO6c+fOnSqB2Dx/6KO59ZwzZ+557rc8X38s0DXdo0Rf6YYTzUXscAmZhy66pnq29+7q9w2m3jQlCauwflWYXR9bcrmupUGHseWftDkSHOvzDQiydkYmbX7zHOKejjf639neu0uQxfQvcbZAHmAcZwAp5xZGC7KIFyRjsBAAJIsgz6LfP/TS6R3HJk7mddP5RAGJqAp/nQrP+IXAXDXrg0OV3QQANqQXVcPFhQyUj3FXWkFevA4X6jSMTZknC5ZqjUyEWImZnX0Zk/6gpDFYExFIq/Ssz940Pcp9uDpc0FmSKi40AxRNZCkhqq6aLUpskySqg8HVaW7Jj8XEYJXKggU5G3smIkRRuMjRglsS6Xz1O4DhYaoxiSdpYqVoesFYSC5CDdS0QNeCNTfvynOrnTkcHO0cP1ZhdjY66lW3TOmdp8x9/5q7aJRfAnYZa27L4GPVSUvMv9jz9t5dE+HJT7ffWlnmVm+/qeU6jGWWZucnJ8AAxyZPhsXIRfUb0mnB5865u983JGCh3OQY9A9DmqWtGAtWVvlNgjEAoLQQ0Q9QpjVHFOBKVkZ938hBLOHVziz+1gWHGjsHAFbQjaopTQwWALQ50dEZck1Dxtk45xYshNJExjPGayfOMko3K2EM1miqRgPkMNT5DlebEx2dgRuacjzcCIjlSCyiWKFS0twQQgxLRAFHQ5oWKuOf/FiUBSu3xDrj6xtTZw1GI1AddxHmNP/bXeip3mLNGcog52XBSp4Fkgh04TRpARMs9fql+yZtYvhtrTcwVCn7OJ+FpRX0+wdFLAHMZv4zFFPa4j/pTDRnsUz1RfT5U4OtNn6AcuXCm5kfgkIoIATLWHNyrFSMXEtdaEQvLm9MtFOHSxXmljXmlEvKm8y0qdLitrKWqxZvsnJWzQFFmktFLL49uI+juI31hZfrMUaSI2KCA0KQQpQtrRcaqFj7h5k6FoiGclMMwAK6eWGlicECgHYX2jVi7CKcQwuWYkEhkhhf+7W1CDNQpYRJBghJj4stoQVrPAJLU5+OOcRg5WThSKLdhf48UKpEQk6VCchrd4mCeldaLqHKgpWnKXQOLFg5uQiTGrA5WnA7XOjIdLERb3l1dvYwhoNYhIgxxBeejrOQCNZEeFIm2FPmVi88CUuDzts+QzEa4YaSIKk7MBeKA+lr6tMnnleWfIMf9DuD+45Ndm2sX78qH5H0sBgRsWhmTOnhWenVjrMCx3lJRlRZKudT/ahz/Pg7Q/s21p+3sW59Yluc4xmzZLe5osOzqtaWkveXe7FnTHD3zBkaUc3OJXFvY4YjlzmXLEvkGXR4VmW9cr4QZWn/yKEy1jyHBCthwfLF/JpS4hq82LOdEHL9squSfvz5fEuZN9jKuBhAZd9+IolJV44C5cEdOfru6L/cAwCyfwoAUD4FepNod6EHjxn9NuOMoaCL5wLE8UQSI4ffnr0XywOAOHAqbo5NX48TJpnR79yj/CZkWR6j42Z4OejVbbBCy+KlmjW7lHVdiCrjmcTfRIidRaOqlS2rjUcd0pQL2l3o+4dLlEjI8QCgHsnUVgVTBznVxJJybkEWrKzVdSgK0QyRJSOCxfEAMPXov+Qy326KAkPB6EsgB6YBsheodvFQxsBQiNRbiohzj78DZOlsYnjjh1EcL4cAC7FifC4LiWA92/ViUAgp5Xg1u+bXsBTXHTjt7QsJoSZHY5Fy26mXTvG5qIVVDbQro3LMF/PHpPx0/d8a3Ns5fuyqxZsUdVC1oaWQ2Jq4jSfjbFztWbV6DmhEJqR3IWmFMnZsLS5vTFqzksjd/CZh+YXuVzia/dtz/6rIapVFWrDmoU5OciSzuuN7ZnolLF2DN88GSn4Uw9yVdY4So6By5SR2KTn5UWlqRHN8vlhZjrp8RMIpak9qmNsvYGsfpMpK9lzSQFn7iRCFZBdSQ/h112PE8kQUpKnR5BZJe4AOLav6yoOEEJ0MtcTarx5PAHDrXTlTkxTwyzo8X3mQMpVlOkCDVgc6EyACBq7oeIckTYS0yZC+i66o9nzlwSQrSj0gPzJtWX8Fv2w1bSvPeqTnyz8mBFMm7ZqraicHALJvKpf7Kq/XyW8nl/mvRBzWZ7x/dlTc83+IGGOrs/h0Tas2eL7yIFUWt8Fb7/4nERPaUWF8ljEWEsHSrItqC1ZpRcmNkdQd6Bw/dnrm7E3LryslwUqN1k/RaspMdwpbiZMURC/IPW8f4byVvcsRBsWes4qtDwdH/9Tzap2t5tqlV2jOzX5f5fj4WUVGARYVhTUP+XooPoWwYr+kMn/7Gtl6AG1KykcD3OKVv9ry8N3LqeoyBKmzhV/aXv1/fqk5ni6vLOAuZQzUlqFCYwF4AAAgAElEQVQeP2kt1x9wqszGzRm7AgDPl35EsKz8T9tcAMC4a9W9012Pq776U0Lk5MeJiQm3243SOKgGbH1zpmZUfePh9F/HJS/Kv72MakgzeFCWjGEVlMnCNSzLtDcdPA2LrKjLS9qLK5MHAJWf+x5JJOHS1hS6U/nF7ycjrJVdiGHV7Sy/4T771fHy8JTJ4hfzeP5T1nLOmp1dAQBbv9T4AOcn/56I0Rzve/vr8t+spC5IFHqiHW7j4yERcXht5ojDrGCrtS/MutD8ahhPgxyLoSICsGBhEay0ZDqAxBI0n+VjG+11MsEIoXko9qys6zRFf/m8zxZ55bAY8ccCJoYvNyWddPH1Wy/IHUFuBfWS+LCpMKRzk+SEWVvVvtK9nKczvvPJWPbF/HYVb87dgqVmb0WSziLtsvNAeRM1mhJcPPNvQWPrgvl6HZpnIJZrbKytrKWYtBQUxHJMhb7ybQFQ0tczEay5Bu30aLYgmsnaO9qVchYSEVNRVcwLAFOho997iIieWpaZ45WtzYWOzJSAYNHlGRlGVvJNWR2UVRVx4fUW2ZjCQNuduR98DCRnDc048xi3Nid6bXihPisWEsHSLJpqo8Ltq24mhFi4XG28xWBry/WJf0sft40QmoplUWSYiXq7Z86U847lrvi7RS4WrMHA8PPdLy93Lb1h2ccSZ8XX76qyyqsWb1IHzq+rXr3S3eI05fSKA4m2AugMRq+3T8DiEkcTO1+q4gr0tBjiFNDEMJoSQK+dfZOlmCZHvcdSaWZM6QbR3Omjeo4uLV9k52zmQpXSirTLzocFKxGrl/VlY9ZcCh9lggUAF1cjvvQVlrXocMGRabJt8ZzfaGEhIAIFYJn7ZS0uH5DFuPMXaDEeJR5Tfk+kdhf6r6MLtSz8QiJYWkFzAEisZ3ZOxx4ek4Wzvn6e5hfNQag7zI2Q6R/OwokpsmVpihtFs0ZORWZ29+9pdi5OEqzEom60YunUIoS4f6fc5FCZtQAAXOY8Xkri189Q72XH2d2+mP/+NXc55pdgpSMT4SBA3h/rBIB9Iwdvbt2y2NGYbhA9r2Ztk6kul+RNtQXrfJVUaTokLEtEYhAtEdkb9ZkZk+b6Rdpl58GmWGutvqTxgkqzW/n2Da1l8Ul6c8v1B0YPv9731kfSRQgAi23zYVVqc6Lfnf5oDmAxGEvTaJgjtLvgoZN/Gf/8IGHwClCR5/vmynLU7SciBvbD4h3JAwuJYGlgYcvqbbUOU8Y1zxf1v9D9isfiXuS4dS4aQEpdXG/nMHmsGy8yEV03qOq+6aoKFGRfibUXK61tIxN9mX9hi8R9NfWFCAGCEhHuGaHqhXo8m52L3cjJ5xCSmXvY1J6h9/YOH7i44fxy3vFCzystFc1bmq826EK+mAddDI/F7bG4AWA0OA6GM0o9DT6oKfFRQpsTHZleqK/1c4f0OjlzhHYXOjIzHzf6KGEiChU80Hk+kswM1FtQt5+s/IAc4sVgIREszXrT7FxsXIp47mroKogvYCWiKQMh8smdkkDKJmKVVKIOrq4RQidkKocVK/1SpVWlzxzxE6ccfz796pmZvmubr1xSXgqFvmzQhukjqLZ4dOlGihsRyO6BPfuGD0KhxZ4VDhfnc4b0ZjYsKcPQLXI0fOm8zzJUgQ6n+VR2zfpbU7s7CxAB+Qs0WOZAI2ESkubDHbaAkF7peY6w2Ia8sWLrEP9vQ8H2xXYXOjL9F4I1x8g7pWqOX+FL6IKJyXDLDnkiCgArfHgFmQ3R1OmDnmuykHSzolLU0nDHqm0AJL0yY5L8CbIYlWNzV2VIA80qjgDd2bZN90g1MU0We4a5z5mgKAQAGOtlcSoHIOWQAjGfyq5Z473URsHlrqWLHA1EIiD+xYhVIGgEyx3o+Aw5r3LhrTpzh3mzYCGAFU50fIZsrPrL+OeK8WhcZTRftDvR0RkyJ36oOcZC8mpmza7XYM4tWKVbgP9mj7xvgiyxoZsXUwBwcDJFjFHC0vbeXZORZPkx7dJZmAUr3/E0hoO3OXh7OktImi7mOc3QYEzGw5P7Rg6e9Q0kNqgIljqFs1DymatpMFGDkMQDmEo8MhSiHLzdXlLR/0zITrBUFqwy1lxuchQc+/8XKCi+Du5HDwUv4QWg3fmX8c8P44VbsOCIYfHNDy0+IhasIxMnIlKkzb1C0cxUMEdZVL879mxIDN++6uYiZSSTeLgLP3QSlzHw7BX0u+Pk2V44kCBYJsZ0/5q7Xj7zeuf4saXli9xmF+hRpVprzfqac+psRmnS6fYM5X9MSFiM+IWAlbUkFVwnI9NjofHKMrenLLtOiTE+qGLPy1xLa6xVFlZHj2ckOLa7f89qzyol+yFVjBQniVHBQUIIARDAhBhHGyQtWHM0UZ0mx/1r7irtNTPBbanY1noDz2SMUWuw14XE8IdHxeMjAMVv8kG34sOFiQhZ5pinZ8yHbfxHo6hpbmojlQrjEags6K1q4UYcLiSCdV3zlSKWdLUDDo52ToQnFzsa54FghcSQLxaQsKysFkVmEb43Qb74jgwA//ciek0FEjAAwMGp+O+WQsjB2094GStlZFBptNc12uuMb5QekdNWuaLeVltucnRN97x2dvfaqvbNiy5Rdp2c7H53eP9FDRuKJ1hJS9I8F3s2M6ZMNhINU08ZWKIK21IRrMnI9HRkuoatyuXWn117DyBgEP3QocdjUuyu9lt1pWhzl0H/0ELEYliMsBRbxpqNy1IlJVv3Dh/0xfwbas/h4S/RK0WhzYleGVyQq87cYSwCF+loY80J2pzo2aLrEJcQu8boT89THbICUXAM1lI7GouQgAi2DzgTPW8sJIJVYXZl2qXrD0p4YErekPgCfdvKrUVeaCIKN++QYzL8zSrqrmYKADpciKXghJdEJDAnvpx3xuGSylmPZGG+tnQDkoO3Z9IdSBpX8rpFBiTlTLUN+KCg8Y2qXb0YsOKwW+RoWKwKxv/jqZfG/OP3tN9mg+wC2SqRLRKVM9YvmnWeZpNB/9DijLfvhe5XWlzNW5Zdnf1oAADonjk9GhzvqFzJz1mlvP8laHfCh8qC8mHAWIRUmefpFa7jQ+ai7fKj0QhaPF8e0gIwHoWWfKQVk6ARtJaj4zNkg2eBPSQXsMX+/bEjjxz+7eHxYwCgLNyaoKh42VGq5KEtpUkylzB8cqc0ECIXVqH/3BDPFDPRsKIcSRg6E49OEUO3Dyaj6tgggPyZioEBSYexlY6cflAWLANoLFipYqRxe9Iy19J11auLvJGxUtps9ZgMFqyJSOCzr7+xb+Rgkc2YO+SeiPrDI3gkDJD4kYp4vh+Uzz33XFtbW3l5+SWXXHLq1Kl5vvtcoM6CRAzjkQ+6HR8mTEQLdEIVALcJeAoGQx8WjjUlUEe9H2r+MR7JW2U0iQ4X6lyArxMf/GpXMCJiZCbqDYlhSJbsAI0FiwAAIiWfc6VJv/vGfnnnMKkpg6c3M0kJtZ6ZM0u5Z13UgWQY1t5xEsNoMjorjkUhZKJ5LnOlF12sdLd+qv3WDbXn5HJw3IKVD4l8b+T9fSMH0/2YqiD30uuy5o6oHPvN0aefPvk8pNECClHusorEeOrbkwq1GmaSrgBQuwgzBLl/ZU9gz3Bn9/SZvG46n8jxl7BrhDzwnqw4vgmQGQH+dPbEae/ZOW7dLHp7e+++++5HH310ZGRky5Yt99xzz7zdek7xlzh3DeZNpkFBuwt9eIKvx6Nw7IMplpMrxiOFpyAoFQlL2pz5wEJyEWqgXrB136QVD1dRme56KIkF65le/B+dmKXgqcuZGlWBn4gUczATLHIlw7B2jhBC0GQMkgvZKnfrKner5oJZhW45ms0UUKWTYKhnETTG7oE9hJDzqtdq6MQtrVswEJ7mPthq0DKWR0PjFlanmJKZMd3d/smwGBGxaGZMg/5hgPSyeoXYDY2Dq5IWLF3q+d8n8dO90MDATOxDFOehQS6JqIMhcutOScJwcJJc14AwIUMhMhzq9DCLamtyimkrHmfOnLn99tvXr18PAHfffff3v/99g4NlWf72t7+d/FhbW3vHHXcIghAKhXie3zd6cO9oRpvihbXrz/F0JD8+1/OnoeCI8v/FdeevqWzTHL9nZP/+sUMAUGep3rrses3ex0887Y35Mt1rrae9xXrhgRFxgz1eQVmQhf8+8hgAXNl0aatTW714R//uE9MZTXc1lqpblm3RbPxd13NTES2DcJsrbmu5KflRJrKIJRrRLDW7mrzS9/qpmdPqs6otnm3LblBvCYVCwWBQ/ev489kdU5GZu1boyKm8eGZ7r7//hiVXN9kzhvqJGPwiz4vBoKSzdzg0+mz3i+ot66vXbqhel/xIgPz08KPKb9bE8Pe3aRNEXu7b2T2T8rZjEajfHIZDfRgA6q21NzVfqznF+Bu0c7ZPr5wVHzg1c+aVvp3K/+W8/a4Vn0jukrEsEokGSik4FpMFAoSnOfXjdDRMvT8+9b23nsp0OwDY2nxdnXU2F2om6v3NyWeU/5vLF1+zaLPBuRooPwSE0BdWfybHp/pImLfhUDCos6s/MPTH0y8ZnLvEfdvzk+XB4KzBdv/YoT0j+zMdb2JM97fdqd7y9vC+g+Odyv9XNW1qcWYsIq4gEokIgsBkrmpZVlZGZfOPLWCClXXBbnLUf/Hc++hS+6SKrBAHAF0+cu9umQD88Hz6ouqU9iMAtwlBQqkhKkV39B3hqJnJKDFwxByclJ7vF7+5hlU/43JH+kjm20fFz4UQSicTSUvbPIdy+2L+M96zNs7a7FyiubsuHS9jzQBmyODXK8z8ZjxFl5Q3WTmL01RuYcvqbDVq8rd3nPzdHlnhc8PhAmfaaGj8zYF3K8sqNjVeWNgVsiJZFDwohIaCI1bWokllFTF8cqesuLHi4iOEDIeAp0umcJsLNm/evHnzZgCQZflb3/rWrbdmkdQJh8PJ/6PRKEmFwYmCLKgPiKki8GKpu5Ibk//nK/tCCFnpwO9PU+nKbVExVsDV0k/RvUhMTrl458Txt4b3rnQtv7zhYtUxQtpZ2u6n3zEmC76YfzAwrCYB6gsaj/94BCp4gjIMZFTSRkNqrqbuV46j4eTIaAQZHGAAhqKbbA3qU4TMs+XYVNcbQ+8sK19yddNlAPD4iaciUvTO1m3lqiDaKYHuDtJrrEY3TeuykGlXUAy9MbjHzPDqr1VzKeVvTBL43NwpkzGo4PQHKevQtdjxUS/KfYR1Z1emvT2+3l5ff3P54sX2Rs0xBnfMpTELiWCd8fYFheCS8kWKmkDWJZNClIkufd5qkfJRfhE+vl32i3BnM/XFlVryRyGqggcK4WMzJCrDRCQy7n+XB/ALEBT1LzgZhdtePV7HvnGhs/3KxZtyb8lM1DcennDy5Xq1d/K0YOXgK1pd1dbsWqxbNXIuMB6afO3sm8tcS+IEK4XrGDV3VWVrna26NvURn5f5be/wgZgkbKhbZ0zLnKbyZEqsml2NR+CW1+SYDLUmBDIMhQqcaYIk9PkG5ramckIvfyQ09kL3K8ucSzQE64vvyG+PkUoTsOJLZ8ZnJiNbZCDDYWJjP4BSz9u3b3/ggQeuvvrq73znOwaH0TT9gx/8QLNREAQAsNlsm22Xbl52afpZo6HxocBIjbXKZlXNcBqxLLuuenX3zBm7xWazaSc/xSKWZa9desVKd0v6NT93XhZXpmOMPNEv22yzYUcXNZ2/d/gAY2LS73XjymtvBK2JxRh/fe6n1R+jUvQnBx4BGqkvTvtplmUpllZvvHzpxf5YoN5WW8aag0Lo/77/K6BB06RQKGSz2VIeOzRiWKbcVp4yhgoYxLKsx+mxlWV8gIQFUmWW0zsev0CMYVl2RcXy65qv1D2AxIBlWQdvu3/Np3QP2Nb2cc2W/ZPkr96Uv35RxmU06zeY0sIgq8yWc6o7eJpXF6RnggzLsoillN7xHC8hmTWzNstsZycFYVp0fe3CL+Vei2aaeFmWbXI0bGu9QbOLCDAYGbZxVuWOL/ZsDwjBjy25PPnI2my7tCd4NiAEuTLOlsNT3ScAR4kep/6Rq2ytq2q1bhkNKCSGGFt1wsm4yXbRpqUXZb3v7OksYln2+uarWiu0xt2wL3Im2FddXmWz2SYj08+dfLHKUnlVw6ZYLJZpOuWIhUSwDo4ePusbuKX1hjjBUq15pRUlN0bSRfh631sili5uOD93yUQC8OldcpePrKlA/32RbgkUxFDQaIURL7zQjxGWk5l8J33y+jShK5nA7a9LYxHCy3l3/6yvX1FnsHBlUJwFK5cQpVV6S8jcASEk4FmOSFIcykZO3kZ7XYO9NiYLMXn2zSyvd9PO8WO+WGB1VVtWx2J6qQ0Jw607pcEQubgafaqZ+fe9MBIutBbhXMUgziI5kjiewZByr0e68C9OYjMDL32M+eSfwxHJPxGRhoJYwDAjgIjnz4JFCHnggQf27Nnz5JNPLl++fC5uUW3xVFs8mo3KS/OG2nWXNemvBIIsAgBXaB30Nhc6NkMwgWQchJKbGZMEAJCwLMgCT3N0odWWNFCs0TEpppirlY3KvWKp2bLq0eASXq2s11cuonRBwtL+kUOY4I316wFAkAUA4Kj4QImyKGBR4/E3lnFXGmAw1Mrd2XxiW1eWo5M+ImFgSuEmEbAAADzDpyd3K4MsJMbQZSoPiWFBmh1SvwgUQJUJTvvJ8pyVwJTLJkdVDY7iQPW1joUmZqJezWOQp7kAgCAJuSiuFF+Hu82Jjk6T6roCL6J0VtfYlpzYABCVon4hYOMNLYE5YyEFuWtMR+pFvbSi5FkbAgAApHvmTOf4MVHOYFnSw/cO4f/pwy4enr2CLtMjt8qKdaEHAODhk/jNERkAEDAxUtnj17HG/dN++dUhQoCKyjAZkfPvC4Ce/wvFF85cx1P53X2ohJw6p2HPGEmTZUcAwFCMieYNavxFxOhP9j/8yOHfJLfkGUAWnyHGFqwX+8m/HtJ+ZQ+8J+8aIbVl6KnNzDkVFBThIpyjGEQ1FPZGUHx01QRr/2RC4O1Cep0b1VooAOicxmeDBAAwgaF5TH/bvXv3888//8ILL9TW1gaDwaBuGMgcQHlkGzhQEqt+gYZ2OwsuHilDqoCn+eRlz/r6f3bw0ee7Xy7s4umgENVgr1tU3qB+MkhYggRT1AVLsRRCoixmfWFLjAYHAAjQW4N73x0+oN6lKNmKsvjj/b946NBjmtONI9wVdsJnHmqDBTgTyhioK0M9/tK82hvQnXj3E21TxkHNWccjxGOCVeUkL+UO9ahqwDMcQkjEkvKtCXpHKh8FnNMKWHwVo7biUjqMOktzkOhjAdPAAAvJgmVgpuJpzkTz8xNA3V7Z2mSvL2PL8g0q2j5EvnVAphD8ZhOzxJbpLAQAGzxgPgM7hkm3GTMAy53OF8dvPh3SsuHnzuLvH8YsBbVllByD3qDR5BsOjo6HJmusnqrEm2XSr5ouoBDPysx5Ms9RsZeCMRaBr+4lbjSbeqnmOkvKm7547n0Gp6ebuPKKwTIY2CR6/OSuXdLaipQLPnkG//AI5ih45gq62gwcongaAiIZCUONTnR+VhQm6JEHFA0UQoimLNVkFG7eIUdl+MJK6lPLKACot1AjQTgyLfcG4pxy0HC6lha7du3q6upyOp3JLXNdaBIAlMWJoWgDA1JWBpYVihpW8nmiWGiUpSKxKJZSb+zWFTdqtqyt7jg8fszAQIUQaq1YzlCMjLFx5fKEPY8DAJqiGYqRsCRiiaUY9UCxNIsASViWsaweW+MlXHc0fLHA4bGjFq5sXfVqNb3LHUoiZ2sp6hAbMABRFgCgpSIelx0nBHh2zMci4OZxmxMdnYGbFxvdZcA/dHj8aL2tdk1Vu8H0Q4BYihVkQZBFnuYUU5aG/CkfY2nBbbrQpb/Pd7/sjfquWbq5MgdF63Yn2jOe38/2wOjhycj0+bXrHLxdyPz9KrRbmX7xnv4vJFiaQCu1BeuGZR+bt2asqWpX/snLqnE2QG7fKckE/nkdfU1DxlOUJdxEk1sWUY/34JEIWcTCBVXUi+Oz9XN6Zs6cmOxm2EV3v7GEAPz7erpnhtp+BvoCRgans97+d4be21i/PkmwkoR1tadtqXOxVVVVpsOzapW7NfdJZsA/jk6cmI542ypbXWZn+t65gIThE69JYxFgGRASHlYDruON+l7u3Wmi+VpbdaOtvtrq0SPzhZCVTU0XxeRY+qISkmDrDtkrwPtThCQuenSG3LtbBoAfnU9f4Ik7vit4CEpwcIpcV5b3E3wedDEQgaCoiNEDJEyYMoFP7pT6g2RjFfrh+fG+11riqRu9QSciFIt8g+GMly05vv3tb6sTA+cHWU0mkIPfKisUpYCPN8U/qi1YiaVibuunKMtzLLOgLqh0/A0gE1nCEoWoZKYOT3MSlgRZAEIUqpoMQuAZPipFBSyaVT8uYwtWZZl7lbtFk0kdEkP7Rg7WWKvWVa+OOyjzJVhOODJNthlymhxhQHc0FizlO42pXITHJ3td6HQNv2j31BLjuwSE4MmpHgpRa6rajSk4T3OCLMTkGINoCcsIEJs6UTnFH52D8xcy0N/pqHcyPJXL6QDQ7kK/OJmfk6p7+sxgYHilu8XB2w1+a4mOxCDRncHA8FR0xooKea9VYyERrHxDrcZDkyenuj0Wd3pQW0mQuwUrIsHNr8lTMdjSSP3TWiMzTzL46f5W6vEeDECqzFBvoXgauv3xWgGT4emjkz1PDtgC4pLbllJfaqN+egRtB+gzNAkoy21KoFXCr2rlZqsQKqARTdN5xG0YcM3u6TOnvWdrbdXzRrC+tk/ePUrKECIAA8GEXDsAZOBHIhYVaYaemd5LGzdWWz3pOROfaL3R6/fmyDiTp6/2rNI94P435SPThIawIE4fHLfXW+Qz3onPvO0KSe5PL6M+n0h9oBDlNqG+EFYEDnLr/SzmQRfj/RnXfv91n1tbplQiV0yY39wvvzZMqs3w9GaaS0z2BgsFAG+OykF8rYsZqoAXBgt1fS4U8DR/c+sWYztwLiTMGO0u9Mc+tYtwNpqktM6OTDAzpksbN6prlBWG9NZyNBsSZxMz1aPE01xUisakmDr+dSIKKzM/Y1orlqUvBGo+qhgw8iZYLvTbnhK5CDPTnXpbLUMx5Xy8FI5yjNqC1R8YY8nxCtZyZDoL1+Pi7jAREt49JdxK70jFGiqylAh6pjX16GWFrgasIOUx/9uc6ISXyARyj+JPhCTOMifdeymmuPiYyCIABIVQUAhZ+f9NBEvjuGly1NMU7c5cP2cyMr1v5OBK9/K5Ilg5L2Cfe1s+OEmWOdDjm7LMjWT63kXVqLUcnfXhOgtiKardifZPkkNT5OJqRAD2TZDBELS70EMX0wCwopwCgL6gEbtPlKZWR7Ir20sAA66ZMBqRgBDEBNs465xW/P39afyjo5ijoMNJTflhIJTdgpVqpyIzUW9YjGi2eyxuTmRytAYZD+x/HcVPnMZ2FpZZh/3BHS+eadlc5/jBwffGQ+euraj8+YWzvJZGdKOtfM+UPWm8zAtzTbB6A+Tu3TRH1VVZ2InwFAAghJ7txf9+GLMUPLWZqVVZ3TwmxFAgSwAANy6m3zoLY2EsfHgVvvIGAeKN+kRZ8ljiNhKGohc7Go3PMlhTc0S7E333/dlxTHER4lmP29yBoZjzataqt4iy2OvrV8RHcr+OYj8ISmqCFV+/lceLuiM8w0FMu7SPRUh1nnVy1MNVZ6vevOiSClN+74HtLnR0ppB5/NLp17qmu69deuVy11Jly9aW62NyjKe57ukzo6HxFe7lydXt3Jo1Kc2O++Zmuz8VFU00LLJygyESlkA3wDd+Lj1rdqq31QJAtVWbnKEg4TgTGESDHvVUs/msGIuQVU7ts0jhiFxu89/KgseMzvjzqOetpoAX1q+PSlFdC5Y6pi2ZSaDmrwXjwxI0kws0BKvZuWRT44UNmYscz3VgUI4umJ8dx7/uxhYGnruCdmSbSA7e1uFZtcjRgADubaEQkLoyBADnuGfFsQjB/UFCAD10EW1hAABqLRRPg18gBgHReloMJcsMIISYaN6kl02Z/NaeOvHHhw497ov5i79dJhyZJve9KQPAf11A37SIAoDBBOnkGb7Ds6rFFQ9i2K+iLGpLFSFkZ99bTxx/DooYGYOB3T1KvrZPRgC/vJS+tJoGgLNBmQDpCxIA9MgltFn1ZLRyls+uvWNYuuZgYQRrLoXHIhJs3SFPx2A0AsPhuFrMSATu2S0TgB+spy9OFXijKaqCBwQYAK6opR0cLwJ7wreQnj/GkLD8yOHf/u74s3q7pGe7XlSqCGjw+XPuuX/NXTQqPMuvtRydDZJYIlmCo3kESJmBxQd4FQa/EHy+++XtvbvyOkuQRUxgODy7/iXND+k0NL72p4ZXj+UvFM6r2EZlmXttVXujoz6vKzTb0UiYhPSkTY2BEJKwrOaIDEVb2DKGYnpmzuwdPjAemtA98cTUqT1D79k5m9M0W9vZF4uaaDAz7DI7OuE1elyoXbrNzsWbGi9UaJbOkYnxV8Y5/TWgjDXbeRtL52SmGY9qXYSKhhZCKPcp2u7KT89dubISwbauevWF9Rt0XzjVYzJLsPJJX8uEhWTBytfmMtcxKLkU13t3nHz5XRkAHrmEbkvj7+moLHNfldCyunsZ9eMjUGkGClFxgjVFAGA4hAMi2Fh0XmX8ghQgNw9TIjk4CbUZ3pn11ESV7SVAGWvOFDauKpVTSLWZ3OEVYOsOOSTBnc3U51ZQfz5LAcBQgnHaOGtyYP/zCH57jDx3hc6qRoAkh6TgkUmIQ2m3j0bgkzslEcPXV1NbF1FTIXgBoD8AAyEclqCcRWsqtDNkmQM5OBgIkQKKrM0pwfr8O/KhRLGBg5PQYCIihgePkoAIty2l/q5N+y0jgEoTggBwFGxZVPunoXvf68GdvtimuWjcBwGWYihESVjSRF4DAE3RZ739gC+Cw6AAACAASURBVAATonkcmRiT7mtJPveFJTZ00kdWuxAAOHjbVzZ8XtlVfIBXYUjEfuV3XwLEzFZM+WarASfZT2WZ++oll6t1oXTDq8ejeRMsllLi5SUCpDBbL42gpRwdnyHJp3GOMDD/sCral46IGBWx1OZc3KGKQPALQjVDeJpTKMg6d8bGxFUzpOzmmeT4MzILep7E82rWaoyXBhiLEE+qfVHEIgHCIjb3kW93wpEZuGlRjofnKhHCq2LaktGEQm6uT2MspDfI3CvLxjE34gEiFqNyDBOcNex5NAI375AFDH/fTt26JO+hdpvg6+2UmeanItNe/zN26tTbY4QAHJ4hALDaRScT8CmE3CYEQJIFdtKRzm/yHs+CoNIsncOkNkzgrl1Sj5+sc8fdpi3lFIXQaISOpL5cvjFCvrV/ont8+ztD+xLnphR7xqlx8QVASaZTEuuSEDBsfVUaCcOVdehfzqUBYIWTBoD+oHx4igDAGreO+xgBKAvn22N5N8bB21tczbXW6sJ6YYCfHMe/OoWtLNzQRAHAwSlCCHl/igyFUYcLPXyxDnNFiHKbAAHe4EFWFs6pQABweGYhPX+ygsuwLiJAHM0SQkryyE5He4Y6uAZBJwXjvZH3Hzr0+KGxo8pHTPCLPdt3nN2td1/temws01Bt8Vy06NZhaTYcXlnRBVkoY83tlSuWOWfDt+NBSGptboCJCKnMs5YwQohnOEJIjn4uXbQ7C6lDnIij0jGT6HKvl8/sfLTzd2OhCV23ckCImWhgEdvmRMZKDbkHTlVbqlpczVbWUhJT6ERE+4pYgH88a+80SAhcZeksSzF1tpp6W41iVFM2fmAES5bl1tYU0dU33nhjzZo1NpttzZo1u3fvznRikTCwuBwc7Xyh+5WhwIh6o27V3uLxfPcrP9n/cJ9vwNiCJWK49TVpOEw21aDvn1egC+Dz7fVfPPe+9soVFJmoMoVP+8mbo6RzCgPA6orZ+1Zbqi5ZdJWXrMkrWCdJfYYCI53jx6YiM8ldE+HJXf1vHxk/Xliz1UjSuDk1KH7nEH6xn1Tw8OwVtIkGAFhaXsNbPzsgXqd+9il18QgWYmLP8alBvSsRRZ8TirBg9QYtdt5OATURnky6RL+0R94zTpqs6InLGYVJVZspMwNhmewakQFgbYX+RNrSRAHAL0/l3Zxqq2fLsqvXVa8utB/6eHuM/P27MgJ49BL69qUIAA5MknbPitPSPRPyBf+xQV/grcW19OL6c0Ri21xLQcLlfdj7kSJYajUdDTJxr5JAEWBM3y5kIDrFQMSiL+YPi+HkLU5OdZ+cTClxGNdzUt33ncF9P9z3s33D7xtffCwCaqEjPnOSWoIlzFKQmRiUMWDK/KDNRO94VdB3YWjL02+lgM2scaDLvQKx4HRkJixGNJqrCoKSaKYJx3DtrmwEK42bZsK5NWu2LLu63l5r52xrqtoWlzdlPcUAoxFSnZrjmcgqyOMFIGvvNEjXs9AFQui2lVtvbt2CEGqpaG6010PO+l7GyPsB9+Mf/3jjxo1dXV3qjXfeeec3v/nN6enpb3zjG3feeWemc4vENUuv+OqGL+gKgo+Gxrqme3yxgHrjHLlIkkFFNdaqaosnE4H7h33y7lFSb0FPXs4UrfOLEMBV9QAAvziJlZhKtYSSlbN8rHFZBFcZBOukW7Cayxd/8dz7Prbk8uOTXdt7dw0GhpO7piPe/SOHzvoHimy3GnMXc/3nAfL/HpRpBE9czjRZZ6+v9qsCQEyGba/JYxEggABmS9BoLFgkoTBOSFynlBDSOX7s+HTKnM+E417y711XXrLoTqfJ8esjT/7qyO8B4Fen8M9PYBMNz15BVySeJwghNw9ASG+AJFubjk8vozgK/jyAC1YcLSFGwrDtNUnE8JV2attiqr1cslKnj0z0ewX60BTH08xFVfq9aKtccVvrRppxcuIffnP06XYnRgDHfJT0EYpz1xCCych0z0yvwrDVsSAlR7sLdAUYi4+gT0fcqpRYe5RbMDQzFpqISlFlo5Cmd0AhCudgwBuPkMkojCYUaC1smYO3M3r1VdMtE8YaDQDww70///F7v0g32LOq+JvC0J6nWUWBER2ndCxYydmlK5cVFgUTDTzNZ5oMSdCIZigGEyziXAPHPBb3FYsuXVOlLVWeO2IyRCQo5zUb83YltzjQQIhEco54S9ezyIqV7pYVFcsg5+B9Y+Qdg9XR0bF06dItW1Lqrtvtdp/PFwwGA4GA1ZpdY97r9Sb/p2naYrHgBHJviSiLJPH1EAKEEIxl9RUwkQkhQFBel80OAoQQGcu3tMTrN6Vf/4kz5MdHMU/DU5chN1/s/REAIWRzNfl5NzxxGrsQbuChwUKpr7vEBg4OBkPklFdutiOlVeoDkqUrkxsRIA6xoLAKZYeaW8Q3FDt0CBAhRJIljLGmASXBaT+5cxfGBP71XGpzzayDDwDOqYBfAeyfwLgFAOCJ0+TdcbLIhja4qIPDMBiK907GOCk7iTHGQAghF9ZvcJocWMYIIUzwK2del0RpdVWbceP9Imx9FQdFODCJF1mU6Qf7x+XPvY0B4GcbqbUuVQsxcZsAggQIsXNQbdafqBUc3NCInjlLHu3C31j9QYrlixg+sROPhOGyGvjuOoQxdvP+JvbVoOB6oqcRE7jAAzyVcYQogAsqgYPxkaBsY/BfL8ceVoxKTKaMp6yV6j9s0EReHxk/fmD08KWNG8+rWZsuvV1CtDvRkWmd7VcuujQohhy8Q2dfodB4r5SlKyiEHj/61HXNV66oWA5Jy4SKARjYotQYiwAAJMuhbKhdt6F2ne6RK9zLq62eSnOF+lwDlVFF9JUATo8BTfRImIxMI4By3pFvZaF2FxTgIuRU4pYAEBYje4cPlJsca6vaNcM1E/U+cvi3SqtEWUjX6xIxiFjgKeAotsaCIpK+JoLq1qwiMCbKIkezuhS2tBiPkkqz1tRRgIwIQ0GzHZ3wkkyvoxok9SyiUjQqxcrYslz4HJfZe5sv8h7Zyy67LH3jr3/96/POO+/+++8HgPfee8/4CrIsNzQ0JD+2t7f/4Q9/EEUxGAzmtfTuGNx9JtB3Rf0lS2xNoWAoGo1Oz0xPkNnMi+mZmWg0Ggj4Jyb00zEKQzAYjEaj09PTE5I+lTzup//qLTsA+pdVoSUoVvzN/f5ANBrlBe+lleLr4yyioMoke73eCSrl0tdUWX4/wP/kUOibKyIAMDU1BapVKhgIxqIxv88/gbQN8vm80WjUp9rlDXij0ajfX4KhC/j90Wh0xusNR8JRKTo1ORVmQkVeM4mIjG56yz4To6+tEe+pCWgau5hiAOzvjUkTEz4A+NMZKwB3b2OIhdBBgD6fqPRuOjwVjUYJAYTA6/MGI8FoNGoRTS7WMTk5CQASlqLRqCzKk5OTJlPG5xYB+Mx7ti4fCwBvDYQvLvNGo9EIIlt3SFGZ+vSi2LXlIXULZ0JeOyUiQhDgak72ebVfjYTllwZeI0Buqb7+mbO2h09I99Z457LyTRZ882jZW6OmWhP+SYd/ZgoDgE/wOzkcjOD/7JQAqPMdkYkJowo49zRwfTMRTPDkxMTXF0nRaDQ048g0G1wuF8vOd4B2MdBEXsdzr2gOZsWHUhjGaHC8Z6a3zlZdpPOlyYb8ApmJgTPVQpCXSkKO0MRfqw0/Sa4Qz71X+bASCf9ZTAITUcJS0DlNrshWb85tdmkEeiaiRgFYBnpjTlN5TIpRFPWHrj/5Yv57V9+RrGecI2rLECZ5l4LhmRTLWVAMHRg9rGQyJuoApgQDyVhWNqr17hVMREkZLSAUNzcoce6X1WQcjSZ7gyALmOBfHHoMCPz9+s/NUSpMEuMR8KQ9OG28dV316nxHu8OFOqdzJVhljLna4innHV3Tp1/t3dXhWZVMdTJAwoJYgteh0lDXBx544Gtf+9qXvvSlH/3oR//4j/+4Y8cOg4Npmg4EApqNgiCYzeaKigrdU3Rh89vMkrnCVVHlqnKE7OaY2elyVVVWJQ8YwCNmv9lZ7qyqqjK4Tr5w+B1madJV4apy6VzWK8BfvSGFZfKZ5dQ/rM9v6gDASHDst8eeqbPV3LZya3KjU3Kag2ZHuePz7abXX5NFYu+oWryourHKmdKAv11Lfj8gPT1k/s9L7CwFAFBZWZkkWDdWXXcjXKd7U3vIYRbMFarRC3Bh85TZZrflOHRBIfR631tWzpJe1LZGqPGhYJXbYwqaQULVVVVFpk2pcdcu+ZgftzjQE1eV2VmtKNxlFUC/I3YF6HJ31VhoqHvq0Xqm9sbWrd6o8MgpmBAopXdMhF2HV78xNNNQ5nc7K4JU2EzMlZWVVdZ430VZNPebMSN7PB6zOeND9HuH8UujMo1AJnAybPZ4WNOA+aVBbiBMne9Bv7jcylEpjDzqF2rtPIwRANLoYFwuV5UnZbRFWfT2+1mKuXeV6+vHpN4AdUz2ZF175gi/6cGP9Mo8DX+4mltZWals5KOmqjJ6MErOhigA+PhyW5VHW6dWjTuq4Ef7TJiQA6EjPHDnVa5Rl69Z6NAYHtQiirrZTEPBkXeH959T3VEkwUIAq5zo6AxRpDEOjnYOBUbOq1mbSd+oGGgcW+q3/CRXSI/ByjHWeCwC6yt14pkmI9OSLJWbHabM8TqjEajOrAppUCblY0suVx9TWE6AEoaVVzFjXkNVVYHkmkFWTxtBFpTtr5zZKRP8hXWfQYBGw9jEVLrNYcUWpcTkGRCs65qvBICoHCOEmBi+YHYlyOILPa8ggK0t1xsfqcs+3WZXpiLoBmhzomM5R7zVWKvubNsGAO+NvA85W8vsvK3ZuaScMXqU5YjSGOH37t375S9/uaam5oEHHti7d29JrpkV6qBpXeUhnuYcvN1cuuVcQXqhOlWT4M7X47lsP72wcG0bTaBA8o43NFEeM/hw29+dc0OzKqdGwQUetMqJRiPwYn9+Prj08CxV6l9OiMqxrumePp9OzNbFDeffv+au1opliW+nZPzgwWP4Nz3YxsJzV9J2PUuHhYGVTiRg2DVCTvvlsARWllrlRM12xFAwEyMzMQCACrPr2qVXdgs3f3zFvRvr12vK6kEyesxQYGL7EPk/+2UKgZJD9/4UkQkZi0BfEFWa4BmVpnkSiCAbCxaWIERqy1B6MF/8WwCgEHxmOQUAD3V9MCFLh6fJX78lA8CDF9DrVenoCCG3KZ6u6+DAIDk8fnwidaBrqqfPr5tksIChsdOoRRR1U7cSxX1LECOl1gcaCox0TffMkeCchkSqOVPy//QgobgBz9Dn8ubAnsGp5y5wD6fHM+0ZfO83x54+6zUKCZ2IEAOnWC51BosJWWvPkGdgAD7VRai+u3aQVdMmJgsKFQuJ4agUFWUJAMajlMN+0yeaPx5vTG5B90LR049CVK+3r8+X/Yc8FsmPfRqgvaCSz+mk3wAVZueNy6/ZUHNO3o1LQ7EEa9euXQDQ0dHxyCOPBIPBxx57bPXqEmctZYJ6wdYlPe2elfevuSuTF78IZExn/Jf38Z8GiNs0m8uWLzQdGQ6OvtD9yompbuWOHAWfXkatKEe1qZXpolKszzcwFhq/r4UCgIeLXobP+OG4l+CcE+lykQRTrlWqLMK3RslX9sZz2VbqVVoNieFXel+/zL0HAB7qwu+MYQBYbkcIgKGoCh4A8PuJ+HeZwK4RHE8RIAAAUXn2UlnzH5OFJr99Dn33cqrOgnwCnAng4RABQJ9YQtVZdM6ttdXcv+auRtcVNY5N/7TxC+2elZoD1Doa9yxHDAX/cxZPRHMdovfHOh/c/9DugT25npABMzHY+qocluC+Fur+1pSvGCHkNiEEpAwNXejcNxjIuASKshiVYxKWZsPd5lgfZP7BpeajJfgTCxkS7xUGVpIgdHX6OpfqeyotNExRuYuSuaJrjEmclb1J4+GpiDh6rls+7iVy6oMnHnVkOFBjETBYwrMKDUhYxgQzFFOY6Gtb/qu+lbNc2rjx/Lr48qROqeMZ3spZktWHlMYnB1kZ58THGACMp9KXHLUM4jHmmUd1Mjz10wOP/O7Ys2IGZqwUMpeJLBNZ94AkxvPXgM2ENifoRhwaI2tA/bvD+x869Hjn+LGAECyJxKiCYl2El112GSHk0Ucfvffee//t3/5NYVolaVk6Tk719PsHWxNZlInaL8oTv4SqmVmQST7qTwPkn9+XaQRPXJaSy5YnUjoSiAW7pnuU/5Vl/t4WKiprbz0dnXn65PO11uq7lt389ffkVwbJQIhkfWx7o77O8ePlJrvGguUT4Et7SBMHuY9nLopjNy6/lhDMUCWIqhkJwyd2SiKGf+igblmsz+oEWTwyfnxJmYOhzn+hD48FMAAsL6cBAAFym9BAGA5Okctr4xL5MzE4MEk+3gTXLN0ckaQ/D5X/1WxwsJGCV0qhyTUUALRb90Fs7M2hjqEwIYA21+qfyFC0g7evq5QzZ9LN2hHrLOiaeuqFfvxYN/5Ke07vRYRATBaknHOFdIEJ3LFLOhMg51WiBzdq1x4KKAcHHI1NeKSSfX84yC5yNOhe59Wzbxyf7Lo64Y6BbKpICxEdnpXLXUtsnE35mKAFPABUWzytFcs05TgLK36ni3YXeqo3Po3ioWBzE1CvdRHKAgCUseaQGE7mSF7XfGVMFrh0gmXoIhRkISLBEgdXlVYOJZeBGo/qRPkkkdU6VZg4ahLtTvRonq+1PM2pVToFVQPsnO2za+9WtW12kAVZWF+7NixGjk92+WKBmCzY0iTsFQsWyeYsUL8A6IKm6IgUNUmRZ7teHPQP377q5nRRPY5iI1iOSYJxMcrxKKnJv1y9LhqsKCyRqRhU5OPLzTqFYpLgi/mHg2Pbe3c5TeX3rr6j+KZCwRas5Guo8k9ra+vbb78dCATefvttjURWCTESHO0cPzaRKL6tVmEwcNuVHMpyKGLpl52/+92xeGWMHj+583UJE/juuXQxUTKajqgXIYUGtTiQwfpawcNNiyiZwKNd2YciIIb2DB88MXlKTSAIwKffkM+GYCKakpFnjHS3Wjoa7XVNjobiLVgihm2vSSNh2FyLvpdZYEy5URlDrm+gBAzvT2EAaC2Pzxa3CQBIUtVi5zCBRCWiCrOrP1y5a3CszzegBJYaW7CShSYf2xSXfq03T5Whwf0T4bEIIECbaoyG5ZwKpEhDZepCcg7c14IgH/NkSaTz/5+D8ksDRPFyphtlEUIIwGMiCEFdGQKS8ctNfy3J3QG9UODg7VUWz6zhQSVD1VLRfH3zVc3OxerjE2tqiVyE0/EBVSidN+rrmTkzEhwv/uJqaILJFIJl46zqjQzFWNgyVpWblosMWFQWIjLUmPmOhG5qVIo+2/Xi/5z6c1wMQuXMkonc5xs4PXM2ucVYpiFrfFWRmmFtLnTcS3J+XuogkzorJMhBcpDXVnVcWL/BwlogYdzSBPiXc1DOobOBLK3RlXtQI/mtKVSMQTrmmER6bBZzqW6Qe2FAir0wT4ds1s4q5DsgBKGk5TsXUiK05hmt1lVK1mOZh2b89jQERcAET0VmFLYXkuDmHbJXgK2LqK+tLmpINR1RlnanydHhWVWTeHtIN4+pR0Zx4jx6Kjs72j9BOqcJSSUQ/3oI/7EPA0F+ASLFPDDmDH//rvz2GGlUKXbqIjmS98W9WsTOQQVPAQBLsW3uhiipSuqyvjaMQaWY9dow6Znc/vTJ56NyDAwVvNSFJssTv8oGKwKAVwYlTKC6DDkN37TWVyJN2T411IT72gaqtgyd9JK3RnP6Xor/ObzQj797CDMU/P5yplHPKKuMSZUZnBxx8kBlTnFUdny0CZYGWXUUC0hTz4QKHswMDIZI8oL9/qH/OfXS77uKdRBrwFLseTVrN9TGw1OsnKXOVlNlqQRD3aBcqgIHBIFBYOO4NiccnQEAQIjq9fb1+4eU9fuUb9bWIsri0yeff+n0bDaVcRJf1qpBMSkLAzOGnYUKHvVm4zQGMAjDV3ZVWSrXVnUk5ezVYqRjEahO7XubC7KGYcU91JmnX9LlbeCizdFcqhuDFZOF3LW41Ghz5q3smrWzylfvjwWMD8sXC6kWoca6o6YFLrOzydGgcPw5xSNd+PmhlZdtaKyzupJ3v/9NuXOarChHv7rUYMXPCZpofaVedY21OpfkUgWbalCzHfX4ya4J7lZVFtH+kUPTUW+HZ2W1xQMAw2HypT1kuQkwwUkC8fIg+dYBmUJQzrMx2ekVc02jmOs6g0k83oN/chybaHhmM21cmy8Zp/+xelRvQd4wqStTDC5g5SyfX/PxfzwsTviJTwCehrfGCACMhGEkDDVl8NownoqBhOMkIJNi7Z5EocmHL04pNNlopQAgImMLBU3WLGOyyGY0ZRCigMiEYEA0Q8E9y9F3D5GHuvBF1dkjRQhR+9DzRrePfGqXjAn823n05Rm8nMr8rzSTdhrAUEUWpVmwNKWEPmLAhIhYRIAMSuFmfavOC4oaVoMlvi4GhSAADIS4sASZlMYKAELo0saNyY9rqzrWVnWMhsYPjx8zWGVpit686GKWYg1K/nljgpkBpZreM70EADiaRYBELBKCRQyHp7mLEroTyqAJWCCEKD9M4zBqA/vQcHD0jf534gysiHg4ReFzqb3AFcCghcouj6Vytar4YFzxVRIgbr1L+Zkrk2FLhrq0R8aPvzP0nhI8YEAmkuU1I2Ik05E56uDrxmA93/1yn2/gltYbMsUVZEK7Ex3O2YL1QvcrE5GpkBACw4h+hXwrv5oSavMuJAtWWqDVrGNrtWfVttYblruWzunt35sgX3xHjpLKwehSO28FAISo/zqKnziNbSw8dwVtKzq+SOsi1FsjX+9766FDj/fM9M6epSp+hwDubaEA4Lf9KQ/u4eBo5/gxhaELGLa9Jo9HYTJKAODjy6756oYvMOziO16XMIF/XkdvWVTdJ96KmUuL7Q/AkYkTT598/mQ8Tr8oHJqK57L9ZCOdtbRq0oJFI/jMcgSI1FlmGQBLwQYPwgSeO4uf7TlVhx5uYp+soV/9Q8/hsATvjhNCqKlY3Aika8EajcC21+KFJj+5NOULcvGUmQGJmAekrTctv7KYLmssmve2UBSCZ3qxN4cAm2Kk80MSbN0hewXYtpj6aofBUwIBgMcEHa7Z1uqCSvDdDs+qle7l8FG3YAmyQAhRKILBMVA6f0SyikhcNR5LABAQ2RPeOR9nPgcP4NqqjrbKFZlGgxDiFwQzAzzDJzui0FNCiIilqISOe2dfKhAglmYxIRKRACAigYDBnnkgm8sXX7V4k64choTlocDIZHgKikupyyT3miMS6lY6bDtdGR9SUxkmgycPDT95ePLYbGMMg+4JkIAQjEhRyDb91HNJ900gznSzuQjHIiTdvlhwicO8Cub4BP90ZCZrEF5KT2n+qZN//NnhX05Fi/hGAeAjY8HSxZGJEyenutsrV7RWLCv+7pNRuOU1WUkuOzhFlLsPh9F/HJYRwK8upVv1ctnyRwqJ1LWdhMSwL+ZXBy8r+5ML1t3LqW8dkLePsWMRqLHEN6qH68vvyu+MERNFhSTwCxiUSO0d8nQMPt5EfWMN9fBJDAC5VzY0sGB5o74+30CjvS7HS2XCdAy27pAjEtzfSikM0hhqpYnPtFD/XyepTViwFNyznHpjRH64C68rlxFIJjTDUTOdk9yboyQmA7BoMhr/lm2c9asbvqAuP6AUmhwK6ReaRIDcJjgTo4DyXN+UkXSHxcjRyRNljFkmeCw0saaqzVPmNugFACy2oc216NUh8tse/IWVWQahmFJR970pH50hK8vRo5cYGWV5mvviufdJMrzat//0tPG9EAAwFH3V4k1hMXJ88tRHL8hdDRPD/82594uGb/ZZ/VZ5od2FXh2aJVgKgjLfOU2yymcUCTtvu23lVuMwZ2OIWAxL2MxwCNByOyjlUMwM8DSvMA+RsEdmUk7hKFaUxZgksByrBCEZdNJjcXss2h9X/Dq0WhC1GAsW+sPZ/KZ0SAwHhVC5ycHTnJkxZRIV6vCsqrfVKn7YJNS+Ob8QEsSpqDzrsGh3oe8fzmghTsk/MLTWcDSn8DCWYnTJcS4WLAIwFQN3mgxswfO/3YWO5RDFn2ihVi5EF2puzdGsjGVRFovPFFlIFizN2/zG+vXXLL3CQAd2Jurt8w34Y1pR0wIgE/jkTqk/SFY540lnBIiE4cV+ImJ4YDW1dVFpRlI3yD1tZmuT2jSOxWozXN9IiRh+3U3Sz3qsG//sODbR0GgBABgKEwD4+n75/Smy3IF+fSmNklX8ciZYBll2So9kgrume05Nn87xghpgAne8LvUGyPpK9OAFOeVRq2dLkxXd2YxMdMpIbltMOTh4Z4z8eZAAQLOdAoD+kPz8mVMO6jgCaTIKkIEEfC1RaPL3eoUmEUKVJoSAXOBB5syvMGExvLt/z/7Rw73e/s7xY3494aJWV3OHZ5WauOSuxFFwde0fHcW/P43tLDx3JW01fPohhEw0b+V4Jc4sqwVL3aT5SUmZT0hYPjJxIlkinac5K2cxON7CllnYsoLjfjRISjGpLQ2+GFtAHeJ8QSO6zlajPIoFWdjV/7ai65g7BFmMSGBlWABgKFhmR4rhLcl4BMwdmU6ZMerw6vQgpNyh3MLGWe9s25YUTSgAOapPqfHymZ2PH31q0D8MAJc1XXT/mrsWl8e9eu8M7Xvy+B+GAiMA0GivX1PVrgxvVIp5o76YLCRMRwIBUIx/rCoIvdWBzgSIkOEhoXS52uLZsuzqdD3F9CMhMzVhczBeTkXBxkK6EGDByq7lHNg51B/MabSVLlxUv+GW1hsMRDHV3mGe5rjcXJ9ZsZAsWBoakdUoUsJiz994T35tmFSbYfs1dMez0lgEhkN4NAJBCbU50XfOLVxTVAMzY7pq8abkbNZdI9M3pmtU3NdC/eEsfrSbPLAmJb74lA999m0ZAH56IX1ogn65J17z+NleNUuiYQAAIABJREFUAgC/vpR2cAAAbU7EUdDlIyEJLDnMEZ7mmhwN6QYYSCy6MpZe6H6FoZjC3LjfPii/PEgqTfDMFTSf22BrVvFvnlM1HtpUrqrLVsbA7Uupn5/AExG5hoGOCvqUD/cFiDfyjocOEqAnokRXq+mJ0/i/jmKOgqc307pxtQiQmwcAsrnOkHYnDI8GKmJqXQMFNy6iKk3yoSmyf5Kcm8UyYaQukQlvjJCv7ZMRwK830S2OXM/N+ltT810K0Y32OoQX0ttdLpCw+MqZnSaaT5c0C4uRRw//lqKoz5/zmeRGRWO6VFjpRN1+ImIoY8wNtlpvzB8QgjMiW0Ad4mIQkaL7Rw7ZeZtagyArYrIQkcHKxddaxcN1jhslyWIUs14BhkKkPiEppzbhjBUhs6Qs8BKWlODUgtHiQH1BEpUhdwXEuPkH67CTyfD0QGA4IGjrSPX5Bl7oeaXF1by2up1q2FBnrZmJgYkWaJRa/JGGRVbU5SXtLj2zE8MDAIWoFldzLi2EzASrxloVlaLlJqN6l+NR/QRPoRhlVxccmYamHIKulZZbOItxpJfaeMnRXC7KbblgIT3j0mmEMYqJQVHj2V78g07MUvDUZqa2DK11IwA4NI2HwwQIurYBFRvZrgJHcx2eVUmfJomHVaV8TekEKz0H/up6VGvC3T7yxsistzEqw5f2kogEf91KfWY5pfz2hsPkpJcMhojHDBs88WvyNKxyIpnA4amcxttT5t7WeoM6+lUFBAASlqFQsvt8H/7u+5ih4MnNTIOeYqc+UMp8qbaUd3hWNTrq1YckZTM9ZvCYKY6CaQGPRQiNACHKK0AsTT+vc5rc/6YMAD++gD7fo98YCiG3CQGQTApY8QYCAACGuKBrupK7LjgKPrWMAoCHTmYxYhVgwRoMkVt3ShKGr6+hbmzK4+GQ470UHsbR7CdW3Lhl6VW5X39BQB15rdnF0WxUjpWkulkmmGiot6BuP7HztltX3tTkaACAGYEvOcESsaQIMunuLSwdj6NZRK9scMTfvpJSmcrqeOXiTU7b1QApCpNxyVMlyjvDEp4L+BJV9mUpWGJDJ/OJeDOQB4uHBKUOMgESFEMAwDFcva32/NpzG+x14xHi4HQUrQy0T3UrY+q3MEHaMjlPV7lbrm++arEjQzg9AGRI8MQEi1hCgHTVH7KizZmrsiufW2fVM5ZPEKzCkhzVWFAES68ejgFw3DBQFP054SX37JYJwH9soJV0+g6nvwwN7h/3DYUIIJTFSlEcbJy1ydEwGhr7j70/VeUkp7kI01QqaAS3N8VAtQwTIO+Ok6EgbPCgH19AA0C7iwaA0TBWVKAur01Jso97CXMjWAagqVnyVwDZPeUjn3pDJgDfO482qK6lc1+gqq2eeltt+q6YLDx06PHHjjy5tgKtcyMEuK4MMYiqMAEiBIBUmcHFUZjA0ZkUhuUT0c075JAE9yynPrsi4/eOEGVlod6SxcJExeczyUmnVQXFS/j70zhouCgscTZd0niBosqbC2IybHtNHovAVXXon9flZ5Q1M7yDt/N0Rgt8vLMf6bgrBIilmGTktRoMxSia10Xqvhqjw4U6Ez9YZUWZjrF+EXJX/88Fu/re+vnBXx6fPAUADx/+za+PPKn+WjOl9IfE8GhoPJhmklFg46wx5tK1VeuVj5qAfZ7mfFJ5GQOdKrKYIEZKGl3hMks0omlEy1hWXgKLQVLBK0dwmS1YCQdoyq7Hjjz1et9bkMqlJqJgY3RUNNsz67nrVsbURVtla3vlCoCicl0nIjp1uJPCY4Ut0LnHueciEQIJ3VfFD8vTfOJl6X+Ti9DGWaotHkVgTYNDY0ffG3l/TVWb2i6tLFvFKFv6Rdi6Qw6IcEcz9ber4guqmz5RxxzcM7xyMgoUQhdVzWEAabNzSbNzyfHJrpHgWHI2pUeU23nb/Wvu0sSY39YQ+9Ep83Nn8XSMdvEgEzIQIoDQwxfHvWzL7DSi7JOxsidPHfTQvgsrzwGIK037Yn4r3lnNcAcmry66E0jV5vzGKijC1h2yT4BPLKFylC9PgqXZO1dldMH4Yn7lh3dfC/WPe6CuDFGIcptQd5AAkDoLCkj0dAyOTJMrEvwEE7jnLaQUmvyZYaHJaotHlMXbaO7UdJeVs2SiOElanK+pqbUcXVyN3hwlT57BBvH+tdbqdOVlA/zdHvndcbLIhn5nKDCmi4316zfWrzc4YJW7pdrqqTTnUc19IYKjORFLSuS1ZhdPc2EcickCQ83VU1etDxSTBRGDDNw6Nzo6Y1T6N18k135RFr1RH0PR6hcnZU1KN3jsHT5wcLTzsqaL1lXr11Ibi5CqhNaAInkAKq3LsQhcXJ0S5BS3TEiKi5AY27aHg6MsxVSYK3R/ZTzDhcVITI4xVOZ60TmgLc8wrHjNQT0LlkKhNNwrSVvVdGc0QuysAABsqgWr3QUPndRvTKLeUXb/V7NzSbWlqsFeZ2YKz2AY1YuQK1IXo92J/j1zFL8audTBBACaoi9t3LiiYrlfCFRbPDNRL+Rm5DPGQrJgrXS33Nm27Zzqjv+fvTcPj+Mq08W/U9V7t3pTq7VLlrXaUstLHMdL4thZgZAEEgwkBAiTZJZMBpg781zuHX7A3PvcmWdmYAaYIUxIAgGyASYJJJAQx87iLHZsx5sky7Jky5KsrVvqfaleqs7vj2q1qrurqqv3VqL3H8tdyzl16lSdt77l/dI3RZiIJ+wNRZO+1Hj9a9KBAe59iz7nxhvM6NGrlxdUVulk2KNwMf1NVZ0F1JgRQprCKkByZA2BCINSnyID1qhmbmwEioYnRxkAmAsyoRhY1URCsalabaw1fWGa/uRF97iBOHulJZQ4NsrEFHhaCU7pce7CnV8mWFnFA2GA+96mh1y414R+ek0B3bBJvua7OwijkrGqgUCERQUIYQDcqEHsu37ARQOAL+LfP/7m//5g8E8zhJRCkxtr+27tvPkLnYaXLxx4e+qI0G6J24oRBgAkLIOejkIVnUzgZ+eZn5xj1DJ47noyqxoUb08dPjR5OKPmgkVT3W3uIAnye+8//NipJ/PqawVjiRCEx92ThyYPT/lmUjYVsMxZOmxmSKTahWPhEA1mpTKrnHYpiK/9dGSJSykBYMIzdWp+wBP2ClWwzlgthyuV1KxFoRg4KGjQ1a6r7jKq9PMhfH0Dwb0Q7sKZsdTdb8+99IuBXwvV1Fuqu5zvaipiNBJpl9eSpFgaLgYzM/45dr1PGK64/NUeAg3Bo3dgM6OUvEvOyeUIoSgTk2JR1im06y3diej7HMArsp+nsus6o1gUPxcSLVgsrFpLh6lNp9AWyoK1kghWCmb8c5e9M/HgHj4l9zyD3P/lNPPCJcakhOdvJLksql5LKghgsNxB77hhzbac+58NUrQbMldWZsFdhs97MABsTw4bYv2ACLBeDs0cSUwCkFkJBAFn3Xgxvzg/LjvMyoL1HwPMby4yBgU8f0OGXLZswfU16+XwtV6SQCzBQgBYRWCLCu7ovu1S7O7TTiMABKOhd6eHXp2eyqrQpJLIJL66dFcxwwAAIrJ4GPe2EUYFHLHjgqydHyzgv36XBoD/3kluzjKl/+jMyaOzJyQy55IJ0pYLCUWoaf/s0dkTbIIYd1NGxaB8wF3gP7f+U3vavlytrrZlL3stjrjdhY6wixZLBU7ODx64dMgeXBByEYq4w1ikKIX2mdGQC/fVrLul48ZWfbM9BNc1oFEvThTu3N645cHNf8Zqb4qrjCZEX4UUAdhbc3ZhhIrl5UzlElwpYPsTpSPBaOjpod++NPbqcpeWzIT+aOCZoed+M/x74NARRRLBwipZFABSyry2VSF3GPMK5rG+bIzxBdelEnjt7RQP/ZVSwFsEShJadWhEQsQbayRbDLlm/HPZnL8wT+sKftO9evGNXw2/4Al7QKAAc1axwynYP42/dZwmEDy1W7Y2WWubALAs+fuFFK5zBoPxXMBuDywktcgnACal4U82ozo1DLrwYTu+EKgO45prapO+GDZXxxf5Bi3i2vkRImQEtOuYGANPjRbATEJnGYP1xiz+X8doBPCLa8kuyblsIghGQ3MBO1tqKiVbYlcdBgASEUYFKAjcoMUEgqusegb0gy4iwgAAng5gBqNbmnA+hSZTkJCHZU1XiO9d8dPTT3/v/Yc9aVIjahl8oaMwRqwFCu48QFM0PLie+HJn1i8EguB59D6yWEqej6aXwUkx4Yy7J38z/PsP5k4XsPV2PXJQ2BcFAJATcm9MY1XLCm7BWrL3hOMWLFk8TAoAwrFwXD1VyIIlYCVKVwpN4YXzIdyiQ2wUP/uLSqbSyNWsv5V3CU+AFX2Vk/xKTgCw1rRGSSoOTx8XChGTiBYdCkSxU/KinBgTKkbN+ue5r/1EaDa3SvGyi5BUYIz9kYCL8syHQEXwqMAjgHUmNCTArbfWb0YI/e78yyV4cu18Qe41murbuz6+vWFLzqcVF1NNoFptbtY3zvjnTs8PSj+54iMY5J6CzF/DWcYOJ3DJh+9+PUZj+M5m8hPNqYcjRFhUiLX6bM2kJ54tYkzsqcF9vxp+nvtjCsHqqe7YWr/ZoMxcx0ZOwJe7CAB4ZJh5z7ljMnbnbW1JanVL5grcqOHRfdhoQQDwmIQlfNw98dipJw9cOpS+yawydps7LGozSLYmTgXw51+PxRj4h43E7dnksong3OLoU4P7WHmeFDreqm+6rvUam3X9GkNzo87SrAUAqJJDlwFFGBh0Ygbj6SAAoOvqslioMs7PhIioiKl1qZM87bLmyafGGCqP2Fwaw11vxCb8eEct+v62XNRG4hNI2sAkxuSnp596YvDZD5+YeyK7Oxwv5LxsVEgpeOyN+Ca9lxdD2Vg8MoFAsM6Ihlz4lQsHHjn584ue2RoV9JniqoyFQkqJOpZLJeKmhXLvWVoWFSBYc8FoilJogheOeybfnzkVjrmqlYJR5I4QFimcFc6kt7Sj8Ur2ovIplQMACKA3G3uhIp4IGU7XGU/MFq7cf4K2EoQixsQeOfnzXww866Cg09zXb+1NicECgH5hbr2lfhPGmA3wz+oaU+CL+H/0weOPnvylyD52viB3lUzVaVrbmmWRHC4Swm/isKjNrDaQPBsV2RpN9dWNV603d+fcPRYrmGBxwWvBsmprus0d4hId6QjF4M6D9GIYbm0h/r+NPOPD6nQDwrvqUbrIZJ4QkKJI+rnf2rurZbvE67qvi0AAT40xvij0GFFjcijoOiPSyIAA3KBBXKMYO57tVbhODUMufNieYR5H6Kgn7A3FQumb2oytt3be3FvTA9IsWGEaPnOAtofgY01Z57KJIKk0eLI8ukVTvbmuf2v95r09t22u39akZekOwepfvz6LaczMBDEA2l2XxddeRhEqBEhFKhWkwqQy1mmtvInQIpqcG6vRlTXIGYbnxvl75Yv4PWGveNDPN4/TB6ZxnRr2XU+mKwFKARvjiKUVFkyMiZvyeiO+vBVUKg4J8pEuopgIz2L/m8iiKmwH2Dh3Khb2RwJzAapOAwYFmJToUh51iFOQWPu5Vrols1aUa3FJPkosc+3V8UMtxH8PLYxwL4QlB+cWzr86/q5VYScQ9Jp4ahgzGBbDYE1bwhOIShjquMcqb9HXPmmrPotlVsqkzpaERZBbTybBwBbDCjslQ4BiDD0fjG1v2n5T2+70AIxeoyDbk+ihizG0i3K7KI/QDnJSTsXCIp5fyFSHO2f0LWVCZITEmjxROhqIBtlAPYNSf0XthtYqqSnYQlhJWYS/m2DWGVFC/DCjhWBTrW1TrS3bVh58jz6xgDsN6Je7SYLvmY2rHGF8fUPh6WmKFMUHc6dPzJ0xqQwg6oWJMbET82dIRKZn6HQa0O569MYsBoB0WSYSwQYzokOgliUHSCEAAALg3i7iX04zj59jtlvFuA5bBpEQ5uvSE+W+epg+6sBtVejpPTLe8ZeOJwf3UTHq/o33IEBcKxFvxB6LTzQTw7NAM4AA9rahp8bgZyPMJhMOxaBKjrqkFr8GkBAqp5GrH9pyf6bTiKm/3d9NHHPQj48wrLswBUemj5+2D93YtptbI5aL5y8x/3Z6WeAtU08E+ocAAN69fNQRXNzWuKWZTxcDALxhn4tys+srQgRBEEADw3zYHItKUsHWx03nTylFRVh2km51yBMsL7nZqgCAhXCkvwoBQJ8JBpy4TbSsuHQsr/2cYtVx0QQ6opIpDUq9Rp6ajqdI9pCmYJGKqGUgI5ZfMqwwOmY1SGNgVCoBwGZCT46lPgsLFBgUIPKtm7GoNgYcZWIICQZpSYeI+lQ6qlWmu3vvVMtUjnglxOXWNXJNt7nDrDZyjVuJ6eQOy11RUMqUVIyyU9FaNT91sJnR85f4HzGJKupz/vlfDb9g1Vg+u+5TKt5ahKQCIRSOLVfdTod4hFzOkF78kevLFsGBS4eGFs5ta9xyddNV+XePxYohWCMe/BdvXfjSmpN3tK/d3sg6bpdtBLwWrBzw38PMz88zWhk8dz1pFLgdCCGDAjQy5rr6wjs4UpbTUJTyhL1swQ2RxhiMD00eVpBy3hTo+3uIN2ZpSCNYVCz80tiraxQyLEulAonwoPu6iX89zfz6IvP97aRe+OWTkT/plbqHttyf0YL10xHmUTaX7QbSnHcFkYXgIo1pBjMkIlPoOIEQgzGDcUqfb25CH2v6S/bvTzRDg4YZduP/GowBwFoJqsEs3ps+emL2TENVHeSnEgKiFiwAuKud+Lv36bdm8agHd6ZFqonHIJ5z43vfojHAd7fGBd5y7CEQAGAPLEx6LwsxOQAYcY69Nfke6xFA4rRxJWN36849rVcDwKBjGJLf6V3VHdVqU2NVPfvfPIN8hWAzoz9MMrfVKwHARYXZyCQ2oey21sI0kbC7cA0DCbPWtS27d/KtT9zKNulwRSIqEnHXe6MCDAo04cNsOmS1UhG/EGfqS95OYRHzFUgoqs3yA/HK3BJhM6FfXZC6DMlJOSukctk3C8kU0KCsurXzZgA4bR+CJWci6+Rqqmrw0bpxNzaSCipGLYYoq5r/3dQvzPbSYwR5wc5Pe3Bh1j/Pm0jIKoVGcTTKRHlHOBgDBqCqwN8RAABtVWgxjN0REFqpE4hnY2Sq5M1O7CHHuQISrJXhIvRF4Y7XaCpGjXvsnqWSbdxFvSDv68N2/PXDNAA8fg3JW2GABQKEAGwG+/6xR54a3Jdfm2knT7ZgLZUWyaCwmlLsOQV3rCHMSiAR7K5Put0Y8IRnyiCbadMv23WWThh3onXo0e56FIjBsxneGhkcYqw7TPx5Pr6AH3qPBoBHdpKbqgvwxcNlJ2n9458yiR3OLY6emD9xT4cfAPZPYwDoqJL63ozRNEWHlwIk87oQcXHdKjl8bi2BhULdhWMQfUsCb3e3E1/ry+slgDgyHCIxdsnJpERWjsUVhMRDxFqqFBzG0FzVsLHWVrNUTkrIlZYn2MhfdqlzhRxseny28gHiSIgaRNKMKyJZV+JB7h4qrCZT6/72m+H9ec9l32yIxma1AgDa9Wg+hFl9XSpGvTd97P2ZExk1GjIWFU730OWMhOEtK4j4sCKczicchc6wbMCJFaSCxhBhYgaBSWRRgZKAywGe7rCXnNFilxgTkS+BRFYp71beAKyCgEDQKxzFz0V6iBsv2Ist7CO5AggWBvizQ/RZNwYgFjjl4bg2iWxF3tMxH4K9B+kIA3/bR3y+XWxY2BbZ1LOCJ5ynLPusY8uiNt/UtnuzgEAfZIqDUZHwxU5iswWZkl8g7HJYr4Zmbar9ietEY+vJiBdmybmucAKJXLa/Xk98KftcNl5wZ0WKw25rw2Y2j0bo2DP2s4cmD9/e7CMQIGAAoF2aOgMsDSzN0JC3BUvEm8mCDXX/xSgTTbs/QjeFFXgbduN+M3r0mnyj3LjMKaMBIPHwLnXqQxeEtYT0IPe0HTL4rXJDnRoQQICWA4Av4q1NWLAKR7DUctXtXR+/tfNjTfqGrfWb67V1IEHmSlwGzBuNqGWpa1ufCY14ZRhwKAa1GiUAkAh6jPE1NcbQ710+emLudEYPVMYg94g0c44UmJWglaEpaXWIlzsgzAC4umJ1WusnO27a1rBlLgQDLqwkFaEY1KiiIhffZ0aDfHkUoRgFyR8AvEhMYJHRE1HzAgGVUQCY8c9NeKby1MXok5ZSEBbIbE1B/FOhoI/kCnARfu8M89txpkoOmIYFin+pqdFUb63fXKur4duYGVEGPnswNh3A19ajf9uaYcnpq1nXYWpzBBd/d/7lglSS5gIhxAYMYcAIELuy6pVV/RznS4SOMsAoCEVi7RS3YAHA/d2Ey/P7Jwcjt3V+LJF+yC6HJiWu15r9cgXJCYCQEWR3dYdOrgWAT68hqpX0Bwv45CIWMixJl+biBY3h86/HJv14Ry36j5xy2XjBZaspbINrBPZHAlEmqparVZyXCLtngwaub0DvzoBRAQa51JdmvL51rtWBuBB3EQLANitil8+XJpk71qSMP79Z8d9OM8+zAm83kFIqeUuBRAtWLE46iYQPujDNVx7EXTDvTL1vDzpEdsgHfSY0FZADgDcqZ31nPUZ0yY/DNEgslC4OBKjTtJb9O1GlgLe0CxcKQn5P396U6z02e5KKhQHAHwkatamruM2M/jAp36KFUAy6VEl1oK+yInY5pGLhk7NHVDQ+NLk8+buq27mVmzPGOC9SLijcysqqYbVIjigAAGdcR5SPYHFcyTqFli1T6wjRw24sJxQhGqyygRFnWKhsM2u8/FhT6oPpDLlBkotQiRDCGIvsyW46OnNCJ9duqrOl6F2PuebNxNihydSZN+q66KLcn+r6RIepTbwPIpBomnWGnCDhYhUcZzcA+KJ+b8hXpa/irozZotIJ1sEZ/L+P0QSCp/eQX3+HjETBHoxnpXMtWNVq644ma845ff/zKH1oDjdq0a+vk2U8CVsJcklRqfCf4OyEZmMGeQP5nz37vCO48GXb5xLuhowhaH0mtKk6MB9wc/dJLN6f6vp4yv4KUnFrR7xIDmsA+8Eg8/gI8/AO/qkm4iAccY69dvHNruqOm9p2C3XvH47RB2fyymXjRbJdU7CPp+yDR6aPd1d3zPntFo350123AMd0dH838faMrM1gNSqlpqOyA9tuWnPX+k/nqeOXkvDIi/u7ia8dph87l0qweC1YB6bxN4/TBIInd8vYmgR5wqgyyAgZlqY5Z1BWbbCu18g1r158HTJd18oFxrjdtOaC+5JQVZzjcydZrpnxqzoH2MxonrLurNvsGDZUqwAAFAS0VaFzHrxBOPIhT7AB2okIs3QghLikh8WA4yyboRaMMmqSSDHh2MzoX0+Tf925+X0n06CNb0roY8mQjECIxvSE+wSJ4Ojs8qUZVXpuW/21vWuNrSqZoJCDRW3eWr/ZlGW+uRDYVf+WNH0fEQSjQRCwd7LfwynkYD4EYRrcUUUoBlriwkuj45esU1eZNqUfbjOjN2d5nrLmqoat9ZsbqmrFO4YAXdVwBcMwIqPHdvvswggAdJrXphCsCd+inDnFvTtc5ElqbWb0wkTmj7TNdRsCkWDGvPt6Xe3W+s0WTbyc13nnhUg00mxtIuFDSrCmAviuuB4VcWsL8d8GNLYAk0seZe6C+ffv0/d2ESn2lT+M7b/knryl4yZxmf9nLzA/GGTkBPxqDyk9mzR/p5gQMnqFBM1Fon2pkkNy5eIsEuz/vIf4wSDz9Bjz3a0kb2kgkYxOjDFFh0XKUPxugvnuGUZOwK/zyGXjhYgFK7mHDLufJ+xNZMokmM2n1hBVyrq7evdeY/BKbXeJ2IlXncOA/zC6HxDE6FggGryt62N6RVXaJWSYDADwpU7ifx2j90/jCT/mqsynp3ZO+vHdb8RoDP+4mcxqARDBXevvAIDnzr0EIKY5x84NtUzF2mL3SyCOKxcIoe2NW0TYxtVN29i7Y1Blk5gqDTYzem++5v71dQp5NFFhipVEKh7B0il0u1q2JyxbErGlbmOYiQDAs1P0VY2EDCU9Lz0GdNGHr2rcHjgbq9XEFzmbGf1xKm4u3dW8gwHmhJfpMqBdnPlcr0viDSpSqdKILeTVavOulu1Z9VwENjNiozalo9/au8bY0siXgdtuWsMAk1JU1EHhaiVEiU6rwTxH410tRJ3Wyvsit5nRfw3xbGjSNzTp+RN+U5Ax4nuDta/FEDdkprArAAjjmkbjtl0t/N/NZpVRSh+EINH3LVT+MgVWjcXaYlk+qnZDOBzO08ZcuQSLouHOA7SDgk80o29vIgGgx0iMLcCELz5dvtT32SgTVZDKZy4w/znE9JtRCsEK0xGKDovHvg848QNv0wDwn9vJq7PJpcrTKSYCAiEaA4MxiRK0ICU4HSCZTSFAXLsX72nTOVBGx2IC64xouxUdtuN940z2Yt8IALxh36HJw1VKXYpwxrAbf+lNGgN87ypyVx65bLzgGva4OlgpwJydo0xs//ibMoJMHKsg4N4u4to6AnhEvvghNeUCw4hzDCFkUOrdlIdXs2BTrc1Ws06vTCVeXBgV8OlW4pkLzBPnmX/czPnYSp4oFA13HKAdFNzSjL61qcDzNuP3RsqYSCGOKxo1GkvCwJyOLfUbi9e0zYR+MsykRCZlJYCZA1Qy5db6zdke1W/tZd9X0+Ho7mZ5yvRRkrBGh0bcmCuk1Gda1hplh9E/QG+qRVvrC/8qzg19JvTvA9n5vlnHHy9MKmP6wM6FYE8DMUM1a+QtbUa8tZ4EALfbnX74eiM658ExRkzGIk90msVYtSdW3V1tKdLdqVGBnICZIC7sl3kBUSmTMh0PvUcfc+B2PXpqd1wPaZ0RAcDEkgVLr6yqVpsHXcAypBOL6a+PDKlt7gjccYAOxOArXcRfrstuKDLKSOaMu3rvvKdv75IkDM87kd9clLlDqXtkpW0hHupeq63ZWr95DZ+lkF10A9HA0dkTQwvnuJvY5v03AAAgAElEQVS8S7lsX+ggvtpbrKnImkk21dru6dvbV7MufQfueMbo2Bn70JDjHJcSfHMjacrGki0x5WI53k44gGm9pbvf2qtNUxVKwf09BAD8bATTnPmSMk8efJf+YAF36NFTeQuMpSNjveqUMdlQ27eldqO4hW8VuaHXhM668UwgSd3RZoLCFswpIBgMzvBy/TEu2IgrewisS1vrNYAA5jlfO0WSWcoZ601o1CupDnHOsIfw9Q1owMlfR5kLjQwaNWjMW7ZbL17FKH/0SVbDKgsqlGA9eo756QijlsFvrlte29aZCACY8i/PXHcE7jxAB2MAACcW0hR+GQwAhMBiwiYnjnnxxmokFFokguJZsKwaS53Wyn7is2skzdBzAftiSGwexUvCCSs35unT/OxaQi+Hd+fxWb76mg26ul0t29uNa9I3CUV8Y4D7DtHn2Fy2qwsW2M5FXN4CMADoFNo6rTXBVFjrJruJZWDsANI4XjucG10ulAUthGxFQ+hMEeIZsbsedRrQVADvv7zcLJdQPzLMPHGeUcvgN8ICb/kgY73qlDHZ0XjlzsatikLLbK4CAHRyqFWjI3aGqw5lM1fuOsQqhcr55g5rr3JQSTSiNzm0udhLeLZQkdCiRaOeYnGaKAO+KFxbjwZceD6TRAVIrtlXJMwHi0t/y3t1GVGJBOuoA3/1MA0Aj15NLhXLAwCoVxMqEgIxPLmUBPvV9+gxL+43IwKYARcViiXlAIt/Vf/fE8wLlxiLCn53I6nO5kN6IeQ8OnvigvsSFCcGi4s1xpat9ZvlpPypwX1/HHuN/ZHXgqUilSpSKeJzSU+kjzsWAUsJhdHK4O4OAgB+mmV14WTNguU+/8tp5rfj8Vw23riu/LHe0nVl/SZesewnzjzzo+OPByJBWGJgJFduAKF8fFjSTYPsnvHByeNhRAD3dRGQXDjy2pYde3tua9DVv2/HXz8SF3griMBYOpr1jd3mDo1wJGz+QiqrkA6bGR2YwVwL1poq5Ilgt1hFk7JBRCnUZoa357CKBBXJ/TFpTbWHMgiNlh4FL7DNBRuA1WVAc0F80ZeZvpTXeGmnlq2PxUBRhzp/VBzBsofgMwfoMA1/00vck1wAxKDSW6vWB5lm1hsYY+DFSQYA9l1PdusvN8ATPxt4lbu/yFf1y1P4/56kSQTP7JG1StY3YjEfcByaPDy8cB6Kk0XIRadp7a6W7Q26WkiyiPC4A/9q81ce2nJ/Rp9Lipmky9zebe74zbnfPzW4jytRgzH+z+OP/vvRH3N3ZiWXfjnKhLOpLrwYJiJMKtt4bRp/6zhNIHiqQLlsvLimefu1LTt4Kzxw+RM30G1ZCTOPKOymqoZdLduVpPLgpUPnnRdE9mTvRkaNAyn4chchJ+APk8zckvfEpDK0Gpp9MfVnDtJhGr7WR9wtKvCWD3Y0bb218+ZqtVlohy7z2gc2fnFb45VzAbuL4gkWWUUB0W+GYw5co+Z+TaUafioHDmETlM2MjjlSlSq5yfms6KiuwiyhxTCr+JZez/YQWNWIlQT7YEGsyvVyZ8pnvHRQSfOw4CisiG7BUVkxEDSGr/1pts5hv8GI/rmGDF9M2loFcLvc6vUzM2eHwjQx5MJdbrpZi1oXyRvQ7Acxl2NGGTYMJfaPOufpkCs6ORpWJ0mtzQXx99+mr4jCX/YQu8JESisZEfNN0l6XWVW9Xt+mjarDF4cyH5MfohEv7XVFqRjbVtTloGOh6MRIWCZmHWaczrDPTizxy6jbQdOR6MRImJMZfhPRBACPTP2BoqMh5RDm5AlTLgcGTF0cTJDIPoC7ZfSoEx88TEiswxhj4MdHZqxGp0otoxmaiZLhi0PzIfxvb9ObI3BfN3F9NOvxLwgYzyIdDYYvDStkmujCFO11McQM7XVFCZJmaJoMElGF3O+PzYyHPZFLwbnDznO1SL/T0ksoM0djmQBMoBzyTRyyn6D0ra01gmJ6jMdN4xjbaOTSOVkeWtImgK/qmENzzMtvE4nShDQD336frl/EN5rQ/7OkPlAFwUHHKU8scEPNRr1MK76nCuBCYPaluSPt2vpb67ZFIxEwS8pjWkW26DOhKJNaYbfPjAZdOJ+ySEXCnLAXqa0KqcjUC7GZUcJSW2kBWCxsJvj5aIHPeWguLv2QCPnvM6EPFnBdRguWGQ26ymM5pjG4wpCRAuaDXlPRo/jzQWURLH8Utk2//t2LTwKA+wTPDp8G+DQAXAT7qzBnQP/EwLqL2DEAd6tRtZVomseO155fPls9SSnAffBdBZXEcEmAJ9i/LoIj+056tIiyEEwA1ywwALmcIVu45UA1kIEoOF54BQACTSRFgvNP71J0Bua+yPk72EyGCVh85R1l2rMWaJFFEXa+8o6cs4lqITECx8vvIE4j/8r+k824PahGf7ISfgwYQZDCjvnfEdzxf0XyiQqKUCNJycDx6jtUDDzVBKVDfs97lAGFMWAEJA39l+l+AIA3HACzOjRRTcgD2LuQxXvKpUNUNeHzv+dYfFpon3AzGSGAzXR1vvJOIO30QRIFZNgQBYWElr8O8HVIvTvfWvrD/YH0vmeBiw3Eohytn3khHM38HenSIKqG8AfB4dgHAOoHvwvWDEo8q8gBbKUva5rhp6iJhDmDmySYAgSw3oRSKFSfCQ27MYOBQBUXgMWCt2Zinjg4zdzSTAKHU9rMCAF/cgAXHXo0G8SBGBRKVVg6HBSYlEAWkwCzUfwXfLg7rRhrJaCyCJZBAfdtrXVGelUCQc8xBo4tYIxhkwUdUM1HaWjS1VargGIiDplLrlYo2kyJnQm1iyCjcrlJQS8bZs57sIMCtQw2mFFuN14uowiVl1QrFVWFEaZLwagsFEPQGVXJlkxHSoImNIuIIRXKagAgtAsEYhStFgUWI+3RaFQuX75wmyIQQoy6tSr9lhNaO4FAuaZGxolXk2ntNAJZWw3J+ZHGcNSBGQxXWBD3HsUAaIRJnKRj46DgvAe7lBGfym1QIAQg0yomYgZ7CFQkbKhGshI+EbNExE3SdbTcxMgAQKZZIAhGJqtWMCSp9BJySqnWEvIAACAAGUMq5NWJY0lZiFD5SKVCptUTwnHcKSBkIULlU6jVCr2gyAKpcRAEqx8BijU1irR4wdfV7gky8rGQoZXObNzCAB8s4DANfSbEBuYPubA7Al0GVLyPSLnaSZAxmcykYDL7aUhZmFB55BolodNHMcMUutTxKlh06ZEyzfDTZ0b7xisxBs5BiZWrs5lQinFCJ4caFbrowx16VJkWrLVVyEFhbxT0hfNdvjWHIwwoiGVOaTMjs5I/OYALEkG3EZ114StrSj1Q9pLcHTYMa5VgSYJu6w26rTeI7PDrN+hnLzDf6MevnPsJDeR3HnjQooL5hcv7//gCjRq/8cU7ErRJM7RP7bfX9O616uKqvj8YZP72CK2Tw5HbZPWmHO+H23lBPfonnbnd2vmx3M4gjt+d/KU34tu06UsJ2Uk55VafflqrMlo3fAEAbneN05huNrWRSCz5bn5+3lJTkyAEtwjvqTr2ExkTs175F9wQLu2xn0SZmOXKv5Anx3W98Bb9i1HmHzYS/7RlufX3po++d/nYjsatO5quZH8548TbX4wFq0GGfOuqLnx3i2/AMXyGav4z4uNaGRy5XdaQ6/jnhlPjb56xD93Utttq7QUA7emnI5TbsuELJpXxprBvW9irkau1jpEoEz05P6BV6q0bv5g4dt4xrL74ulLbami/Xq2W+r08Mz+gvnRIX2uzrtkltI/2g58SS9W4arf8ebqUs/78y2rXuKnrE1ZpBSWOnqD/zwnm7nbi6T1khIFbfxkNxmDmC/LifeVrh/ap/fZqzlMmApdzTD36qt7csZ9yzfvsn1HLcyxutQpRyAhYZ0xl1RUbrWIPwZXC88BmRgtUardtZnTGiTv0yBEqrgcqNxAI1hnRkAtvtxbsLTcTwCNubDMjx1L5ZJsJiWs0JGAzoTPOshCsUtwdNor/M5JekKVGRfotRcEKMv1gkKExWJRxA6lRSVTJIUIz5znJsZtqbbtatlcp49qyb8/h/3mURgA/30X25rG6ZywPlye44dVROkotFahPxIl3mNq6zR3i7CorCOiw86sNsOP/8/M4xvkYTtGAcIbh06/RwRjc20WQRNWQb2Ojvj3KwPOXMAA8fg3ZVyp2RcXCbN9SZDWSVBiUVc36RlbNeXNdP6TFm+cmySFFF6NZ39hqaLbVrOu39pJ8trFsY+3v6yZIBM9fYhbD8PuLE4i5YDOHeYutFgpZVRVkLwQhVOyHaBV9aZ41iwpUJFwOVNyYi7gIge9CAKDPBGwNY/Fjy4g+ExosHJ2lMTgoYD28iUuu14DEhYwNvytUZ6SjNPbFPjMa4CtoXQmoOAtWRuyuRx16dNHLAAlNujjJQAAWFbKH4cQCZvVIAaDX0pM4aiaIP3swFmXgGxuIO9vypJXZ6hxlB2462ysXD553XtjTenXGFl8bf5OKhW9su1akaJQweNIShZbAnbVovRGddeM/TjG3t8ZHMrFwAgCD4Z43Yxd9eGsNemQnOejExxfwqE/mieopRnuVFX2+aLlsKfjlwK/twYUv2T5n1VhSrjCeRZimGkASZJ3WmiDlLLIVlY3QkVCMCkZDkCnP9LZMRtBs1SKatejmJvTyFH5ylDk9c7SenNtpvQMgm8KzWYK96fvOvUgCcXfvnWa1iXc3N+U577pgDywAKzP2YS/2XHZst6J09Rk2oawpQzZCqTEfwrVqwXdCvxk5w2kWLBN67hIGADuF26sq0TdU2ETCRQpoDANOfFd70nDtqZdqwfrTVBmetdLQX5sJ/UOhI94KhZVkwZr1z3/v/YefHtzH6gUAQLM2Pr0IRFhUAMDw6blDhIG9B+m5ENzQiLiOrdzAEqBANDjjn/OGfXmeTRzJCgJij+u4e3LEORZhoiL7pOOnp5/+0fHHeW0tIkv7fd0EADzOkVziFqL5xxP0K1O4RgW/vYFUknCFhdXfr48p77bT117fULq3IVd7KcUKhTgapFzoFVX39O29vTOp+nW2Fqwx1/hjp548MnMc8hZfyMHSc//S3RnzAgAU0EnBC/YCuaZWXrjCnkOTh88tjgJ7UcX9SFkF3NDIc98rU5VRPFDdooL+tBKKCfUjKUqbZUFhHbJ2CgMAe++4w8V7l3k6Y4Yz5fAO26kMQvMFQachHsVfgVhJBItdbHBc8ochEDRpl/tfo0IIwUG+Kpv/4wj93jxu0aFn98jyz2hgTzDrn39m6LnD08fyPV0akmUqlwmWuBkjN92mSLxcI4+hReSEX+oklCS8MoUT7oaEk/GlSeafTjEyAn51nYylv6xU7IkFfHAGA4BEfYeCgLuIsxwlcZEZicugY/il0VfH3RPpx2bZhzwnXNZM5JMtRJ0ahlx40k8TCLZbi2ul5iaZirDJJIVbRKxasIqNLr6Y38oMw0oEFQkh/Vq6DGgqgEMxcGSqFVMuFFYA0x6CZi1ivaLcwCbeu5yOBg1icFJ9odKgNBFybBT/UOV9OcDKIlgJg0SdGj7RjGrVIF8qg0MgokEDWhlz2omPJ9fM+eUo8/BZRkXCczeQGTNapYAkZCpSyYZ+F6NUTjItYABAK1ff07f3zu5Pih2ViWBxdURTjgK+pVFEjtyigk+1EjSGJ84nNTcdwF98k2Yw/PMW8rolSxVLsN6YxWecWC2DHbWlexumF3tOxJntat5+a+fNBpVe6FhHcHHEOeak3JBsn5Pe7tLfpbZgyQm4tyvegRpV0TUYuUK+IkPEHYdKXA8/GrCVKRZHBL4oYICqLGepnIAOPRpOrgNdUahVA0nAbLAwZ5sP4W1WtEhhTwQcVC6BTWUJw5oLZZbpKgj6KlWCZEURLABYYh5f7kCNGpR4axtVhtu7br6+dRskFyQ+tYj/8l0aAB7eSW6xFOZOt+gbH9py/+7Wq6E4S0WyYwsDAEnI6rTWGo2F3WHSe3nCM5XCpcSDdRiM//P4o/+RrMwOSxHK66q7Nlh7UzbtaNp6U9tuBcmfSM+Guv/sPFvvERjMxBj4PyfAE4G9bcTf9y/Pq/VGRkN65wJ+BsMOKxIS4CgGuKSTGyUGAK2G5m5zh4pUAsAlz9TZhRF/JEDRYSrGIwqacqyUlgFARSotmmq1qBisxFNl60u7r5tAAICYRi3KpwKPFHBLUaXlSXB242xCiMhYOnMVxcB6IzrvSUpPKTtyzuRnyWJlyjSwKOCqPx+Ceg2sM6J35lMLB0mEraBB9xJhL5UD12Yuw9VJwYoiWBzmsacR2vWJUA5Qkcpuc8dDtlYE8OwFhq2f4Iv4fzDgpmLMfd3En3UV+EqLV+yZu6Ly2k5+e+7FfedeTJlN4hYswd4iAIBrmrddnyYlsMHa22/tlQvU3rmuAa2tQpd8+LXpeLWZyQAe90O3Af1sV5IbNhBx9WueaZC9DADXN5Z0viXXw+HNlAQAODU/8PKFA9O+2R8df/yRk79gf+Ravyya6n5rb50mswzB0rEIAFoMTffaPr+lfmPG/UWcv9LLGnLRoUe76xFgaNDkGQOWGc36hm5zR3yeCLfFbrFqLPf07d3WeEXcsShQiH0VRYJaBk1aNOqtoKXITuVogrKZ0MlF7ImCOffyB8VFAb2E9hCuUSGbGR2cZnJziZYl/K7YhQgTsJkqMbgQVhjB4qyXJIJatTKl9F6PEe2sQ74o/GacAYBnzz4/MPOUDAIPrS/8ZWabWSYdXB7JG37O+yMBCAAYgaVaqLe5rd/sqZJD3fF0ADCgL3cRKT4phIgaFULAAMANJYxwh2T/msj9insPiaRAN+5kW2Novqltd7thjcR2paf+7Rt+8bFTT/77+z/+/rFH+E+Va0nE+3sIBcHUqovyDcDFtsYtt3berJQpQbReNTvTZISsTmvVK6pWaz+XC5VWHHc+1yCqPjN6fQZXF1koPB/0FW7VZ2mozYQOJtfwzq4zZbBglShCrq+sBa1FsJIIFne91Mo1D225/wGOGiSLB7oJWPISuijwRcHMl4SSP0QsInkiyYKVZnkSjAdC/LoDIHCe5LZymZpf6SJkBLw4wdhDwGA8E8QA6Lq0tGEEyKIEGfJ3KP9AR07m0FAeWB5LEYtjSrHnpwb3/eni6/kUe5Z+bCAa8IS9IMzUt9Zvuqdvb4d5bbZ9uGMN0W1EJIoz72IjWy9qtvITqygU+kxQUd/6OcdBs/KSlRnhzqKAKQWsr40lx+IJAULoM6OzbsykdcdetMh3bxRkBGhKogTVqEVRpojXkjNWng6WOPa2EV87TB+x4wEnHvfTAHBNrawYjojs43Kk4p6+vcutAEDy6ivUbjzFUuBxFrLfLBkScnkL1Gvglmbi9xPML0aZG+rWnQvVqWQ1W9LEggmELCpEQHSNZtodLkpxISFwGfmnu0Wk7DmpmhjPBewEInRyLWQZXZ6AdPawLBsh8KljUhlz6AAAqEi4sRGisaJM0XRkzANIsZVub9rSY+owkzle3Spyhs2EnhyrIII1F4LchHCbdcigqNAIdxZ9ZnTOjWlcABsbq33VrkcYchwuvRyqlWjch9v1Sb15fYYpkjDhvHAN72KAjXi7rsII98qyYGX2Z6ll8IUOAgB+ep6Z8GMAuLY4ugDZZpblhsTFnrEPDTiGQdhyJp73LnyUmGMxIxKSSydcNX6m/ep6I++rxKwEAkGjttTpYyJ+qPmAY8IzxYa0xy1YsBzSRyBiSRMkl5GRnvqXGJBisCCtrBRTlEVG2fqUDU1VDZ3GtZp8MwBWkTUqzUWYsxcJAfSakDUnc05poJVBrRpdKETEG6t9VaMCqzr3sHGbmcd4eWwBeyL5do8XJU7wrLSJzWJlWbAEM6piDD3tm5GT8gZd3f3dxMNnmSdHmXrEAMDu+gITLE/Ye2jy8CLlgryT8DPi6qarQjRlUhl/dfYFAhG2mnVCtqg6Xa1SplQKJP0J2rDY/vNRAV/EH4yGqtUmmUCcOwB8vBk1adF5D/7XMwwAXMcXYkUgQkaAUQGNWlTseKAUbK7r76vpsWp56py9c/nIuHvyzu5b24wt6VwZiZIzCZCa+pegvMXI9fuK7a4ojglPiUJiZ9PWKBMVmSoEIlSkUp5WbHEVJUa7Hs2HsD9adP0OiXBQsKM2x2P7zUhRwqzkHNBvRmecWKJalQjsS1Jh/WaUm4twqTPwqdakH+dDMOTCxVDPEa/hXXDYzOiYo+II1kqyYGnkqpvadl/dvC19UyAa2HfuxT+OvQYAG6vRFgtyhiFEY50cOvQFvsYwHRlxji0EF6E4WYRctBiaus0dGrkaluUG+GOJbliza2/PbUIeJSEDw5a6DVc1XPHzgV89cvLnKZueO/fSk4O/cYe9It0jEXylCwHAqAeDoKwwAoAWHTIqis5HU9BmaOk2d2jlGqFeLYVnxb2uCbqDEJGLOsIS6nXWvT23aeXaX5194bzzgpRDiuHIIwlSRSpLY8HaVGvbWr9ZhGDVaCwPbbl/T8vVZ+xDcwF7Cbq0Cl6QCNYZK0g0KB+dBd4yhRUFmxnyH2o2KZ6VCrOZUM5mId7yiPYQLlJMnrhAf8FRmSK6K4lgyQl5v7V3XXVX+qaUsGJWpQlh3KhBBfe+sKczq0339t+10dpX2JMLtIgQQhgwxlhKCeF0CDk0+2rWbazt490k0YRzXzfBkpEGDeox8lqwEAAsVXeulLch14uX8J8mRpVAyKgytBqaDUo9AByaPPy99x8+4Tgj8eQqmarV0KwkFZd9M76IX7QbCUpXKSOTLfaPv/mj449L5JHjnon942+OOi8Wu1erEEGfuYJkr/NxJNnMubON0qDPFJdfzwdcDtpnzp1T8srMzoegSApSc8Ecw8Vyg00gir+8WEkEiwtvxPfS6KtvTx1h/5sSVnxXO6GTs0KLhXe+sMyDAGRRm3WKEtVN5RhUchGIEAnJF2JsEk04rTp0YyMCgOsa+DkCe2vYXJISW7BEIBCHjhJbe6o79/bcZqtZB7nG22WVZyp0ckdw4Yx9yBFcyKrpUiLGRCk6HGNoKTsvFR2qlGnw0URFiQblk8lvy4NtlAYFCQziclCbCeVsFuo2oAk/ppKf1PkiWrBKmuNZJYdqJbrkr5SJzWKlEiwqGh5xjo17Jtj/psS/V8nhs20EQFGEFvNxHknBibkz+8ffXAg5kxpdsiflZsFSyVSf7LhpV/N2vo38jE26RNb93YQSLV5huhyM8qTJcm9AGVfWozMn9o+/6aY88Z5wTJ7pFqyUSVM8SY7lGCyBk59bHN0//uZF92TBmy4U2PxHLK2q4FLBIuQMuSa9l4Oxysur/gigr2LCgWMMeKNQnatSqFEBm6oL2qFCo1OPLgdwML86xNxgpt48vKJyAtr1aNi9fOsZDIvhYilI2UMlUhlNoFw1rUWwUglWRqPCAz1EtZLRyAQT4HNGzuKcEnHJM3nGPuQN+7g/JiwuKpnqgY1f/ELvXoGj+SEjyJ7qzvWW7vRNXBWo3HB7K7FGedzt+8O0fzZ9q4pU7u25rd/aC2V1hI25xs/YhxIrOteC9YXezzx0xf11OuvH22/Y2rAZCkQEpdCyRDNCjLnyBTmzqpaYiCA8PH38hbGXpwM8E2YVxYbNhCpkHbJTuFqZl55/g6aiLVgyAjqTOU0O4FqwNDIw5pG1klK9ZzEMBjnICJgJFn4+lL6KUZ8JDToz71ZKrKwswmWk+LzSSc82K7pr3Sc/vQaRxAojkdy1f8Y/F6WjDbq6hMWFIBAbGFQ48D9a0nmknIDbWpFOzs9LSIJsNTTPBxxQHCOQCDxhrzfsM6tNWrkmhe6k0AKVTAkAHaY2djRScghys2BJceWus3TV6+q0Co1AJH5e5tK3Jt9DgHY0bZURxUy1QggAgtGgJ+zNODMTYyKcvbqKoqNeA6jkWfS8sIegkpVCCwLWS3hFHpVwC3inUlyWrH+2QQMDTmjgfwPljpIVIkzAZka/n6isd8pKIh8Y8FzAbg8uwJLgUyK+ilc7+2sbWloNzQV3SxXbgsU1Wrxy4eC+cy/6o4GM2pWvXDj42KknL3tnsmrLHwl4wj7IOzpqWy2AqOHHrDZ1mztqNCU16L97+eivh3834Zni28hPXfRKfb+1t83Ykk+7FB0ecY65wx7IlGfab+3d1bL9iroNPdWdvDvkY8E6bR88OnuieBOVBSul9vbUkcdPPSWyWyhGvTT6KhsLjxCRlWNxFQVHb2WkXFUCySs28i8CWMCCMympduz4F6lMYektWBWYSLiSLFgxhn5qcJ+ckH3tyr9IsWDx8o+i3VsEAOFY+LR9qEFXW6OxFPbsvD6XjI6YYCzkCXujTDSrtl6+8Nqkdxr4uFFWPBJnigzrMLV1mNqy6lv+4F5CmgWL3ZQ6nlaN5aa23Sk/ZmvB8lDel0ZfZf/OcxJm5YBLAXtQsb2yEt2+MSY24hyLH5Lfda0if7BrqoCuSulQ4jjossBmQgem8/qQsFOwM1epsNTOmGGAk9XIUjebGb01W+AnMcJAIAam0tbh7jHGo/hVFaOOtpIsWFybQ5qLsHTva7Ytig6/Nv4my04KCy5Z5CztGZxFuY0A4ig/pXVDtL1U5JLbWGwkTRjOLxwIXt+5xdHHTj3Jpqlme21copmnUlo+NftY+1DBYxBTkDi/OAHlbiUQseojLC8q5Fv/o2HByjeKfD6EawsUataiQ4EoXgwnzhyvIV1wXTRWGbXE64GcgLVV6Fx+EW+FxUqyYHENEmlB7sXO7VuGnJC1GpqdIZcv4i9Kseckd2d8cX/oivsyHCW6EkeZ6LzfoZarq9Um7u/LeXM83cjCOZVbbmOxwb2EFIHW3S07dzZtVcsEs1yiTMwT9gajwfRjM7ebLAqfD/KpOV0azpu459JvPkIozxpNq8gTNjN6bKT8/tkFCiq51k1B0KRFEQYclKSa1uM+3FaVOiAFTMdDAOtNaMBx6uEAACAASURBVMiFd9UhALBTuEaFek1opEA1ExMoF3VmTbMbqytlUq0oCxZnsUmxYMkQ+cDGL97bf1cJuqGSqfb23NZuaoPirF7Jjq1U4kLFqKOzJ87Yh1KOQqK181whz6+GX/jjhf1pWxAAbKq1bW+6MmXDtS077unbW6eVZJsWSerEgEecYxKFKAsLEQuWRq42KPUKUgEA70y9/9bke1SMmvHPjbsnonRU/NjM7SICAFQy5XpLl1GVV33rfCxYDMMAAFHkJI9lrVTRlwn3UwQBKnYg4yrE0WdCwxWgyjhf8jjosqDXJFXZ9cA0z24FjMECABtHz90eglo1aGRQr0FjhaiZmEDpI9xZ2Mw8avVlxIoiWJzFBqOkFR0hZFDquUlMDMYjzrFRV7E0o7O1akgH90lKj/4JREOHJg9/MJeqKh4fHAFThxABYqnbGkNLp2ltyqZabU2d1qqQVjxORMgUY3hp9NU/jL0q5TyFBdeCJRJHNbx4/tjsyTAdeeXCwedG/uCPBiCZ5vbW9NzUtrtOY5XYLjuqapn6E+03NujqRPaM0tFANDgXsC+G+NOL8yIiJbHqrjWu2dG0FTLZL9ltKlL591f99ea6/krzJn/UoJNDjQpd9JV5KfooxGBBNnKjB2dSd8tTKiwdfZyQ9kQ1m4JXSrZTuCy2yT4TT0HrMmIlESzgRBqpSGWrodkqHGBO49hLo6++PHagSD0pnv8Fia6pS1Qp/ShRgiVAgPIxkHAhQl/KGM6cbIUSEU7LoKnWXNXQb+01KqXboqRSmz9dfP2/Tzzx1OC+Vy4clHxyScAYY4wRQsXWHmvRN26u64dM4mFxsrs0JkuzYtWCVTbYzOVXwyq9FmVZ0CdZOn/AiR1U0i8OCsz5SYWlgBt+l6ghbTMVWG60bBYsExqoJCmsFUawEktXrda6t+e2a1t2CO1X7Kig4lmwDEp9ndaqkqmAj7gItcsqVjACee9MPN65WARLHOxd+NHxx0/ODxS1obR2l6lqtcpUp7XyViNmkos9//T006+Ov5GP6Ug6p1xmPwLzdGNt399tfXBP69VZd6J0QYli9ssEeMekxMKzNE339PSUssVKRp8J8i+Tlyc+CkHukE1KwVwIp8SbF1zsgK1IiOMnj5cL7DOjgYJOhtJrNLBorULeCHaGM+9ZGqykIHcAIBDBYIbBTMbo8tzqx0lH8SxY2xu3bG/cEm8lbelaquaWCvGsrCUZhbRUwTxiqLm4teNmGtPClRkRAKbocBktFp/uvkVoU2KqJEaVAJQPQ0GSj03MT6GKmSTKMeG4eOV90iGFYKWM57bGLb2mbhwtnQXlhz/84TPPPDMyMlKyFiscNhN67lI5LVgYwPGRcREOuTDOtF5EGXCFYcCJ99Qv71hwDmpWglaGpvy4RYfsSzTIZkL/4Czky9keApu5gOeTCrQU8XZNXUXMqxVmwZJOCKS99HNH8SxYSa0srf3vXn5///ibgWiQV2oclkKMhZQbBV2EBSJYOoXWoNQLsYESC7intMvL6iY9l4/Onpjz24EzOBzRCpSPZxNJPraoeZc1GotRVVjRf35I+ZhJGU+1TKVXVCkISRF+BUF/f/+3vvWtkjVX+Sh42E228EYJFVlBkkXFg0EBRgW6lCnizUFhDJBiwSpGmBqrhuWPAgOgkwMAdBrQbBAH8quZyEW5LFhQARObixVmwRJxaR2fPcUAc2X9Jnaf4n3B0wz9szPPsIWNiz2Dvmz7PAasIOUjzgvOkGtL3QYh12e7aY1WrrGo+b8aBIPcgQAAd9jjojym5Hy3Gf/cQnCx1dCcf2WelIpGJcMVdRv6revVMp4PwIueieOzp3a37KzTWdMtWAiIfJyn0o9NULqC5/qRiPyy7XOFPacQ1DLVZ3tuz6iD1WpoJsoX275nzx6Je9I0/dWvfjXx39bW1j//8z+PRCJ+v1+hyKMOXIWhnoBJv9Lu8qnLtAhMuoLVCqPP58u868rHer386HTY0ihmJRp3IRIpTzliPl8w8eNlt8xAgM+Xmfv4/f5IJBKLZd6zRyc7PhttldMWhSIx/h1VimPTgSvMhbFjzQUUWibs85XBZdGllZ20I19zdprb6QiFQpFIhCQFvwA0Go3IVhYrjGB9ft2nGWBkfB++704fjdLRTVabXFriW84gCMIT9gIAsRS1UzzolVXsHwn/ihBx7DK3d5nbhc4jZMG6qvEKi6b67anDl30zd3bfyt10Yu7MucXRT3bcVACCVaZlVdhlya+Fwa1UyLXtuSh3mI4QDFKCpGQerULz0Jb7Xzz/yg+OPXJH960t+kahPZcpXaV8cWUNKhYO02Gr1qISFhUDAARob89tA47h/eNv2mrW1esKJE1dBCCEamuXu2cymRAHZexYYaEgob0KRv3EBlN5Jt9iVFarxh+mIRXBeiMe9pK3NIkNtSNMbDQxwx7EfVPbKahVS5p40qfoegN+fZ60h7FVtTz+vUY45yW2VBdmMjjCRK2qPDd3vQE/N0nk33TG8ZTSxAojWFatYNpgSthLsWNQEKD/sfXBIp2cp7k8atKBsAXLojb7wuwXTLqCQ8GUiogyWbBEkDxbUgPqEEImlWFr/eZqjQkA3ph456J74saGa6vUOmknRypSCYBiDC0eiSWipL9ScNo++PbUkasarrimeVvGnSc8U+cWR1v0jZVMsAiC+OY3v5nyYyQSwRjrdJImwErBBgt9gZLt1JVn7vmJSL1WptOVtpxKmbC5lvnjFNbpxD5CvMCsM2NHBC9gbUJu1EXT/QYkfiCLWCymVCrV6swRW1c24IdHaR/IG3Q4Mf4ba5jzAazTFcBliwEWw9E1Fp2iHDPrKjmcfSeq1enyZFgkSYbD4Twf+ZX6Wh9zjT926slDk4cTv6Sk0RUvyB0BQghhwPmHLmXVKABgwLkRR5GcSqFNBc1CQ0n/VACSJbJSLVgIULXavKtle6+lB3KN55MSpbccVi9w8vmAY//4m4OO4ayaLiWy9KWWRl5+FZLQV9aCOQth4qOgMspCSmAQq0rVl6yYUIwYrPVGNObFlwNJSgoFLPnsDINWDmVhVwBgVoJGBpcDFeEUWKkEK0JHPWGvP+pf/ik5j66oQe5FTYH/YO70U4P7hhaS0p0SMcKsdURJZhcL0qRv+Er/Xde1XpO+SYiJZmUzO2MfOjp7gopRvFuXC/KUz07z6+HfPTW4L1EMm+0QS61qNNUmlYHbt5TRyI2sS6ESXErHu8NiyHnGPlSMkpeFQlZ5EgkuO+OfOz5/aiYwV9zOrUIUBVxTc8BCBFlVFbEKlgA9BnTRh8O02D72ELaqkC1ZMaEYShZKEpq16J15zD1zAaWw2AspyKlyQ+WoYa1UgpVeyDYlTam4BCs/h504ApHgXMAeiAaSW4wvY036hoe23P+prk9kdU45IatWm00qY/omYQ1SAMkk8tjsyUOTh0MxfvmR+zbc01PdCWU1XMz57XMBe+K/S3QKA8Bd6++4b8M9ckJ2W+fHrqzfBGmjkZummjRbY4J68p+88kvKZGXBSlDVKe/0u9NHJ32lJo6ltDpXPmwmKOM6tBAmaj7shQgTUJKwtgqd84hNP1acM0U0yxGSVMQwW/Sb0RszDHf8G7UoyoA9VICTl0tlNAEhe2HpH/4VFoOVQDotSHnRG1T6h7bcX6Tw6qW2ioIEl8KAj82eRIASqZEiy1iMoWM4JkMkr5amCApiwRK31ihJBYkIKLkFyx5YmPBO1WmtzfrGlD7yqjDoFFqlTAE8o4FBWGMsH9hq1jXp69WkqkrJ7+nPWUcjGA39fvSVKoXukx035dtLUbA9XAy5Lnmm1hiaxXdeVsQoicLtKsTRrEPBGF4MF7ISi3Q4wsRHQWU0gT4TGnTiDWbBJWk+hGvVRJMW/vlUItAF7FRR9A76zGjfeKptrM+EBl34urybK6NGA4s+E0ovOgQAg05sEx7/YmCFWbDOLY6esQ9F6AgPwUpeinJzpUnEUltFsSssh1sxzKHJw+9cfh8kWDLeuXzkR8cfPzU/mFVb9uDCjH8O8o7Bymit6anu2tWyvVZbk1X38sSEd+qtyfcuuicgzQoltMC3GVpvatvdaW7j/pitBQsDPjR5eCnVVOwRs2ot3eaOFkMTr3FRpJ8ZEcOxad/sjK/oPjh2WMbdE29Nvie+5y8Gfj3hmYKlKMZid2wVGcGqMparOK4jjMq7DJcYGR2ybAxWjxFd8sedie4wFEkqzGYCAEiJ7iqUglTZBfqFLuQAH+sqKlYYwXp76sj+8TdDMYpJs7uI14opLNhWX7v01mKo8MUmEsaVrEJ/cvMlDTqGj8+eAoB8swgzBRy1GVu21m8WkukqEriKaCkUUMgyVKut6bf21mqtGDBFhyN0JP3YjMAYH509EYgGM++aCTnrnRZbaDeBhJs+Y0u+sC/KxIBVwVi1YFUGyhiGtRglPzpB7gBgM4E4l2V5iYKAtiVnYvGKYbOGnBQaxFbRyf/kZa/hvd6IRr04lrx2YYA3VgmWOBKrPk43KhQ18pyvG2cXRvwRf8ads0XCN8ddI6vVZqFSevGjACD7FWu5VEt+zjuW1wrVeykXROiFnJCrSKWMFBzPWf/8j44//ttzL+XWcuKvPAc252i/0hEs6RocHCXXyo8t+4jAVj4L1gIFH50YLIhzWcGtGGCBipde7l8ywDiKVgx7bRXSylLH3ya5KLU4ihQ3Jh1qGTRr0Xlv0rV4InB8odQvnBUbg5UW5K4ilREyUprWN9duGHQMeyO+YgQVJYgi1yp0U9vuDEfFV2L+x2Mx5Bp3T1g1lhZDU/JRgi+4rJbAki3nWYFLxzHgROgPAGyqtW2qtYkcyzWxZGvBIpJCA/NCzpaeYtfiTEC6ODtHDg2Jl85cRcnQZ0ZPXygDzaVoCNHI9JHQwIpjTRVyh7E7Aka+uBWuN7DXFLckzRUtmIlAYDMjSzIN6jOjIRdmMBD5tVl2FyEA9JnRoBOvNy5fyVwQzwbBQZWU/K0wgsVZMgG4n8QAXypVYRAA2NF05ZRv2hvxFWPuJ4wWvOv6jH/ug9nTDVV1V9Rt4P4u7kuaDzjenHx3vaUrhWCxJ99c17+lbmPKIdc0b9vTerXEPosv58dnTwWjoS31GzXykj52yzZNYesmzdDPnn1eJVN+pue2884LLsrTYVpTrTZzxzMH7SYECAPe1bxdKxdUk5d0nlyD3Jm4ClfxKa9kCxY7PWo0FiWpFC+duYqSgc1ZwyXP8LWHsEWJK+uDrMhAAOtMaMiFd9byXPd8aNmtZjPBT85hKHI63p56RCZ3RC8HsxJd8uO1VXndGTZaP6/O5Y0+Ewy48Gc5v9gpAIAhF95dX7p5t8IIVmKhzGhU8EX8J+cG9ErdRlFDhTiGh4d//OMf82664LoUjAbPmk5o5Zqcz88LR3Bxzj9/RPOWVWM5uzBCEuSI5XRiq5vyTnkvG1T6Fn0SVZoPOOwBx1Ht288uBZKHQiGVSsUusS7Kfdk7847K+Ir+Re5RcwG7I7BwTPfOCxpBiXwpOLswQjP0RcsQSfAEZJ53XgjHwp3V7SqypF+si5Rrxjv7nvqN3+l+M+gYRghdqklV7GQwM+Q4hxDxVs1rE54pb9jXamjWK6tCMWrMeVEtVx01HaLoMMZ4hD4ll8kTxad++MMfihQQRAhhjLfUbxSnHZ6wd9Zvj9Bho8rIW1En9xgs9vDir5tthpY9rVe/MfFOxj3ZqXhn9yd1Cq096IBca2mvCEQikb/7u78rdy8kQXGB+bPDhK50dbcBABYoQLP0V9//CJR65oCaw9/+DTzyna91dnambLJTy1Yfmzku42SncPFMQdc18LyaWDWsPAmWnSqWZ1M6bCb05FjS62U+hAFgwLlKsISRsO7Uaa1b6zc3VAkW3PBF/EdnTzTo6vIhWJcuXTp48OBf/dVfpW/qgq6cTysO7nnXwbr0HbbClhz6cxV/W4W5iq4usfMUb6yko7u7W2hTT3cP+0dKPzdAv9AhX//617///e+LECwCEQxmGMyIE6whx8h700cBoNfSw0uw5IS8Tms1qrIuBylFR74g0Cm0bHKoRAsW19j5IQ5yj0ajjz766Pe+971ydyQzvlOOp7MLYIfg4/WhRVcX/McP/2tq6o50guUI4URE1Joq5IlgdwTsIeg1FYsN8BrS+s3ojBNub83rzI4Qril3fqjNjAacSQZyRwhUJJQ4pWOlESwAAMAATfqGJn1DCVpsbW39m7/5mxI0tIoVhL/9278V30FixgU3LIl3hwZd3T19e7PrHNt0mg+9eJDoQuW6O0mCVBIKEn2YDRgymWz11bGKFDz5mxd4f58LQd2SsQoBrDehQSeeD8F1RVvl1HyLf58Z/X4iLwoSikGE4Y8zKyXa9Wg+hH1RqFoyzc6H8M7aUqd0VFbaV0ZIj7wudrHnVaxCFJIoViJGquCmplLmdUq0lnGtVust3X+58d5dDZnrQ69iFR8m0AJmW3sIp1cG5AZmlQa2vMtTlr7PvCAR9BjRWY69yk7B9Y3EoKukcQkrjH9Ij/ldLSq7ijJCYuxUIi2g4BNVI1P3Wrpbk3MaigSJGYs5x5OtYhUfGiCABYrnEbBTwK3fZzOhQRfmBmaVBj1GNOHPUDNRHJWQQsgiReNtPgSdejAq0ISvdK+gFeciXJaOTMHbU0fclOfq5qtYUeySxaCsYhXpMCj1juCim3JbtTUizCNF+LSA0CurPt5+Q2HPKYRWffNXtzyQ8RI2WPuC0VCRiiusYhUrAiQBl3w8v8+H4HqON9BmRvvGmdJXTZYT8ZqJIiV9xGGncNkj3FnYTEm6qfYQrlUTNjMMuPCa/KL4pWOF8Y+7e+/8+6v+urGqPn3TlHd6xDkWilHsf9kP5RLoAK1iFen4ku1zGrn6ycF9waik6qlopT2JCfgjgRHn2ELIKScy5KFtqd/ojfieH/mDi/KUpm+rWEWlgUBwkY9gOZLVz21mdGoRh2JgLLlUmM2MzvB5CWPSNFUcFJQ9wp1FSsEcVgHLZkZnSljgfKW+1mMMTdFhrikrxXvIui2k149bxSpKD44Fq7wdyR3zQcdLo6++P/OBlJ0XgotzATuN8/BArGIVKxkkgnE+F1WKZ61aCTo5WNVleDH0CYj7v++Q5FmbCy5H65cXfaYkpjgXxHUa1GcqTDkgiVipBOvw9LEfHX/82OzJxC+pBKsitcVX8dGBlDQLIpNKp4tyPz302z+OvVbw7hUKBGQRWVUyfflVrKIyIUiwgqmi7TYTKkutRtaJlvJjMAZH7JKe8bIXIkygXgMIYD4EAEDREGbAoChYQWuJyIVg0TTd09PD/SUWiz344IM1NTU7d+6cnp4uUN/EEH9Tc2uSYACAhDbw6qt8FeWFNIqfCHLn3y3GxGb98wuhEhq1s0YWRUCZpRKi8wH7yxcPnFoYLGbHVrGKigOBYCqQWoc4sfxz0WdGZYkWt5niMqdczIewRF5iL1r9xBzQu5QUaQ/hGhVCAD0GdNGXVxR/VsiaYP3whz/csWPHyMgI98cf/OAHXq93YmJix44d3/nOdwrXPUHgtBArRBAAgJn4zJURpEGp1ynyKlSyilXkDCkUn1iWaeDfLedizyUDQSAAYBiJH4Xx7F5/NDjqvjjjnytex1axigoEAqhRpdYhtoewRZX6CrCZUFmKYbdWIW8Eu8JJP9pDUiU67ZUh08CifymezE7Fiw4pyXgUf2k6kHUWYX9/f3t7+6233sr98ZlnnnniiSc0Gs23v/3t8+fPi58BY3z8+PHEfzUaTWdnZ3QJErsRo2MMw9AxOnEIE2MYhonS8ZM0qOvu7f08AEg/J08rsVjOx67iw41oNCouFxKjaYZh6Fgsygi+btr1a+7t/byckGvkat6JGotGGYahGSbbaXzJO/Xi6J/WGls/2X5TVgdmCzpKMwzjDDkH54e7zR0ie0boSDT+2MaYGM0wTJSOiVyXTCZbdfGv4sOHtqrUOsTzIZ64JZsZnXWXQdMELVWbvqaO20M87MY0BjLTE1k5Mg0A0GdCh+0YknvFBpnlnCaZFbImWHv27En/cWJi4tlnn92zZ8/atWufeOIJ8TMwDHPvvfcm/tvT0/PII49EIhG/3y+TZejPoHN4kXLbzD2BQCASiQSDQY8nnpEUiYQjkYjH4/UwBctRCgaDOdTZrSiwRfE+fG2Vt1EA8Hg8ItP1jel3Q+Egg7HH45UTGWZ1FCIeiPBuCkQCkUgkCMvzXCJ8fl8oHPIFfNkemC38wUAkEnFEFodmz9WRNSJ7/m78lYXgIgB4vT5fxB+JRMJAiXTPaDRmfCGsonhYfXUUCW1VMJhWhzg93Gq9ETVoyvOBwQYqcQmWnYJQDC54cZchQ5cqJwYLAGxm9NgIA6z86ZI50GYuXZx7Yd5fXq8XYzw0NPTwww8/8MADR44cEdmZJMnBwdTYi0gkolQqq6urxRtyO33jockN2j5tTKcKqExGo8USr1KsXdSpoiqTyWgx5lW3mAu9Xr/Sv6G/8Y1vlLsLAAA33HDDgQMHPkwdsFgsIsu/d9qvUCoBwFpTk09BGJKSqVQqjUqTmOcS4ZH5VfOqKl1VtgdmC8obUalUAKCvytCWzqFTMV4AqLFYSIpUzahUSnWxu7eKnLH66ihSB9qq0OnkIKd5Pr0rtQy2W8tGsFIoCBsqPuDMQLBoDK4wWEouLSGEPhMadmMGg51jwbKZ4NFzJSJYhckirKmp+fr/396dR0Vxpo0Cf96mF3boZhdFFhUVmtGJYxLjHo2RiLjE+UI+x2TufObovZqDY+7np4wm5+qIRmdEk5NkZoxM1GgSs02MSzQuZGLUaJQAggRxQ6C7gWan1+q6fxQ0TS/VDRTdXfD8jieBquqqt4vut59+66nnzc6OiYlZs2aNbfDEKXNGrXWp9s460YN3+ti+2bFjh6ebAABw7ty5IdWA7uSq/t1m4frcUFbcVmjX9dl+LMrWE8LcjcLXm5iHBOw6BqgBCUHW+Ux1GjsjWADwWLiHAiwpsW6hlg4RO0/DatBCiBiEXvO+DhRBpC+pbKHrtN3zT6fKSHGjmxrQ3xGsixcvzpw5c968ef/85z//+Mc//v3vf580aRInLbPLPNuG7T3wUQERRhPl6+Om4HnmCeM11+qCuEfBAuEke+9GZhicEHL06NHc3NxHjx5t3rw5Ozu7vr5+1apVBQUFMpls+/btS5cuVavV2dnZZ8+eJYTMnTt3z549MpmMELJx48b8/PzVq1ebTKYjR44olcoNGzZs2rSJELJz587du3cnJycfOnQoPj6eOaJCoVi7dm1BQUFgYODUqVN37ty5evVqAJgwYcLp06etVsXExFg21dGxvvrqqy1btty/f9/Pz2/9+vWvvfYaIeTw4cPZ2dnMY+vq6p599tm1a9cy/7U6xKJFi5gGFBYWDvQfovO5mIOJ/o2Auj43lBW3TRXVHWC5viUhVrekDCkFtfSP3tR1zIwhv4nArsN9XUesP1E00G0GCLSYhzg2wM6fwFORCnOJkLZ4Uys1MDNGYHt3oRWlxrrYhMcx+WRKDUzoujyWEESadHST3h0zUvf3D8ikZOXm5p4/fz4qKurcuXP79+/nomH2dQdYnbdodZsSO3nZ2IV2i7wPBI0ROrzpH+Xso+rhw4eFhYXHjh3btGkTAKxbty4xMbG2tnb//v2vvPKKTqfLzs4Wi8V3796trKwUi8Xr169nHiiXy8+cOfP666+HhoaWlJR8+umnW7duZVa1tbXV1NRMmzZt3bp15gOtXLny+eeff/DgwY0bN5KSklauXPnll18CQGFhoe0qq0Y6OtbmzZuXL1/e0NBw8uTJnJwcZuNLly5duHABABQKxdy5c1977bWXX37Z7iHMDejnH911zHDOMwkz+zmCZTlBcq+4bbJz83zSzkewaAIACaEjRQKhVVGVIaXNCCoN7T3/OpzdyYNdB7ddh4BAcmiPBHa7OVgeJJOAvxAetfeYZ+bpYdbDWra8KsOdIZdBcWOPyI8AjJOSW+5Jw6LdS6vVisVi2+U6na6+vt7pw49XfLPryttl9b+crjy368rbRapSR1vebXrwSdm/rtXc7E9rT548+eyzz9pdpTHSbQYv+keZ7D8F6Br/YPLkzEvCwsLMJ7yhocFoNIaFhSmVSmaJQqGIjIxkNtbpdCaTCbrum2N+ZlZVVlbSNF1XVyeTycx7DgjoURojIiKCfZVlUx0di6KoK1euvP/++ytWrDAfXaVSMT9MnDgxPDyc+dXRIbh9qfv4+DAtdOSDoo92XXlb2V7Hvh+1pvHCg++/uXvhbuMDuxu06dt3XXn7nZ8O9LaFZfW/7Lry9lcVp3v7wN6iTKbrtYW7rrx95u4F9i0/vf3VritvM8+0urV2x6W9+68dGujm9VZbW5u/v7/tchc7KKf74RHsOiyfNVdmzZp17ty5ly4a99+mzAvnnDR888hBD+6yxsZG5q4sTjxz0nDiYXeTUj413Kg3+efr29i6PfrDO9QL541ctYETR+9Qz39rTPvMcLO+++n813fGd0splkfRNN3R0dHY2NjPo3vNxVLXmFNSfhWZmjF63nDH41WtutYHzVVqbdMAtcTXBwKEXvRP4GwwICgoyPJXiqIEgs6/vkKh0Gq10PMiDkV11mITi8XMcian2/aal0AgMG8MAFKplOk9aZpua2uzLMnBsor9WL/97W/37t0bERGRm5tr3jgiovOetX379r344ovMmL/TQ7iHi/Wr6jvU12sLi1S36jUNdjcQCoTyiHHjw5N724DOqaIGPstJQAjzrnRhtKwXJUmR98Cug3NyWY/RIK8qzsmwqniu0tDD/MmYEFLGWjnCK0ewSLGaVmpoyzFCq/M/cHgWYJlFB0Ymy0ZJfUMdbeC2HBSemjNnzq5duyiKunr16uTJk/V6/fz583NycrRarUajycnJSU9Pd7qT/Px8o9G4j2+nNwAAIABJREFUZ8+e6dOnmxcuWbIkNze3o6NDpVJlZmaa+zWDweBolVNnz57NyclZsGDB6dOnwaY+2dSpU//85z9funTp1KlTLIfoT0W03jJfy2bfjDjLhZf4iOclzp4RN6W3DTDXTO/tAweO5W0ovj6SuODh4X5O7hpGXgi7jv6zmYeYjvDCAKsrBKFoaNRDmATSnM0zU6ehPVIclcWYEFLVTqt1YNkwt02Yw7MAy/Wbqtx2FxVPvfXWW8XFxZGRkVlZWfn5+VKpNC8vT6PRxMfHJyYm6vX6vLw8pzsxGAzR0dHnz5/fu3eveeG2bdsoikpISEhJSYmPj9+9ezcApKenJyUl2V3liu3bt8+cOVMulzc0NMybN8+yjhojMDDwvffeW7Vq1YYNG+wegmmAi4frPxdzp8wBFufJUu58/buY79V5TmgaAGR+0sWj0qcPe8INzUPcwq6j/+TS7g94Ew31WvCe2lEMucWUz3VakElAKACnMyV74QiWSACjgkmwCEQW/ZPcXVM+u7vSmk6nCw4O1ul0Vsv1en1ra6vTOljf3D1fXFc2L3G2PGIc+5Y3lcXn7n83MUr+dPx09i1ZnDp1at++fadOnerzHgYxT5Xp8wZCoVCr1bLUwTpa+nl1a23W+CXsd13ca3rwWfnXAPB0/LSJUWkctrBR21TZeD/MT5oQOpLD3dpVWl9+6dGP8ohxT8Sy3UH8r19OVTTezRwzf7Q0EQCYAQ+pVDrQzeuV9vb2yMjI9vZ2q+UudlBO94NgaHcds2fP/tOf/jR79uyIw4ZbS0WRflCnhXHHDPW/Ezl/MKumpiaJROLnx02Ao6Ug7JChaYVIJIAiNf2fF6jipcJTVfSeEurMfIf9XsYZ48pkwcKR3jWusfwidaOeLn2+R7NjPjRcXyS0e/MmQ6PR6HS60FCHV8lcwbNCySGS4OiASLu1GL57eLlcfWd63JPMfB1uu4sKIVvDg2KqW2sftdY4u63VfImQ4xeq1Dd0UswEbvfpyPjwZFeyxH4zbOLY8NHRAZFuaBJCXi5FSoob6af9iNKbJu8z8/WB4QGkooUeH0qUXWW65DJgv7Km1IC3lWkAALmUVLdbN5uphhU7wJMV8yz+eCJ20vLUZaNlibarNJS2WdeiN9qfcgRx7ujRo55ugvf6TcxEALhW6+TubqeTPXs/rVH73cPLP9bccLrlsMDoE3fO/O3mB05T09Dghl0HWMxDXKcFb0vAYqTJSFED08LOEHB4ANGZoE7r8CF1WojwskuEAJAqszNttvn8DyieBVhmdR31D5qrtFT3pUYB9KjkjiNYA+2FF17wdBO8l4s55ubcdgFv78fQU4Yfa28Uqlyav8ELU++R+2HXAV1TDgOAsoOO9tCcg+zMGVfWMyU7zl7ywkKjACCX2skMS7VIMhs4fI0/vq+6euz2V9WtteYlVjWv6c5KpF73x0ZDgYsvv+6BKwcxBw30X66+89cf3+W0dZxyufYCRlcImZlv0/PCxHAGU6ITAFQ9Z0p2dJWwxQA+BAK8L+1oRCCxnULRPZUa+BpgmTrL/HSfNSaLhYbOGwyZSIu/V14Qr7n48usewXK8JQ20N2cEM6W2TODKpDfdtVNa9K35JUf/dRdvH0FDVPc8xFqvK23AMN/qqOwxU7LDAEtlb8pqb0AAZg+zblhKKPmlmTYO8GRdfA2wgPnIsfhYYn40p3fI/KTJslER/lhoh3seCVv5FSszAYfTESyhj9DXRxIg8vcX2v8OS4AQQvoQY7UbOtSaRq3RcboER1yfLbHHVXsaWvStLYa2AW4d8i7YdZgFiSDCl9xtpeu8bJ4cs6RgotLQrYYeZbpYBn5UDqas9gbjpdavAT9hZxb/gB6XrwGW7eUGQWft7M7zNVqamDF63tiw0R5p3pA1Z86cId6ATp3xv5OePTogcs2k/1r969+zFFPoW/nz0vryA0VHrrqQe95PTBCppXRlDb+wb6nWNIHFbZMAWNQddfP4O9f9DWDymRQdXnqJUEBgXCgp7Wxh5xs3VUpKG+1/nfLOBCyG3WY5LevVfzwLsG4qiw6XHCupK6OJdY5Ln6fFRRw6d+7cEG8A499VV4CjeyxcnHXHittyEJkvOZSJatE5GY76WVUCXU+nNxcW0ZDg8Xeu+xsgl0GxGlRa741LmPEqlbY7BAwRg1RC7rfa+Zz1timrnXJadaL/vC8hjVWbvkPRrmo3dNAm60LVrl+q4IT6g1x9zT33HMsVYS9vEsXE2y6vr69ftWpVQUGBTCbbvn370qVL1Wp1dnb22bNnCSFz587ds2ePTCYjhGzcuDE/P3/16tUmk+nIkSNKpXLDhg2bNm0ihOzcuXP37t3JycmHDh2Kj+88ikKhWLt2bUFBQWBg4NSpU3fu3Ll69WoAmDBhwunTp61WxcR0l4NiOdZXX321ZcuW+/fv+/n5rV+//rXXXiOEHD58mJksDADq6uqeffbZtWvXMv+1OsSiRYuYBhQWOqmPMNAomgKOZmrq2zcH9+Ugkh5jUqwbEvNmbn63epXW85+2Xz3j6VZ0C5r9fMDjz9gux65joMml5PP7tBdORGjGpLSbyzQwUqVQrKYTgqzf8V6bre9IqpR8eAdHsCyYp3ijCQAAsTg5bh7BMjYojMqH3vOP1tvPtlm3bl1iYmJtbe3+/ftfeeUVnU6XnZ0tFovv3r1bWVkpFovXr1/PbCmXy8+cOfP666+HhoaWlJR8+umnW7duZVa1tbXV1NRMmzZt3bp15j2vXLny+eeff/DgwY0bN5KSklauXPnll18CQGFhoe0qq1Y5OtbmzZuXL1/e0NBw8uTJnJwcZuNLly5duHABABQKxdy5c1977bWXX37Z7iHMDeDu79xHzAuVk5lq+haLuG8Eq6sPcWGqHAF0nZOhPN5samvyeHdh+c/U3mK3ndh1DLRUZnzIi6+spUrJJSXtQ8DfYihGLiPM3YVWvDbJ3RE33EjIs6lyfnj04w/V16YMn3y/6WFNm+LFlKXDAqOZVfebqx42P4oPGREXMpyr1rJMlWNUK2m99bPwIGFYNBGJbZeHh4eXl5czJ1atVoeEhERFRZWWlkZGRgKAUqlMS0tTKpWEEJ1OJxKJBAKBwWAQCoU0TQsEApqmCSGVlZWJiYn19fXJyckNDQ3MTBeBgYGWM4FERESoVCqWVeZfWY5lMpmuXbt269atgoKCgwcPMkdXqVQRERGEkIkTJ1ZVVZWWlkZERDg6hHtm4XA6Vc7Xd87cbqgI85P+Pu3Ffh5r3/V/6Cn9q5NWin3s/H0d+aH62g+PfpwS+5spwyf3swHsDCbD3mt/B4BZI6c+Fv0rli3PP/j3DUWRv8jvf//6f2mM2n0//t2H9nntqf8zoM3rLTdMlWNqa6La7Mc0HuETFCoICLZdjl3HQDBPlQMABhMEf2AgBDpe7u88OcD1VDkMhQZijxjiA0nlf3T3dR/eMR1/SH8028dq4/84Ty0eSV5I4s2oDUVDyAcGxX+KAnuefrUOZJIhOVUOSz5KfMiI+JARbmuJUBbltmP1B0VRAkHnK16hUEgkErC4ckQIoSiK+Vks7vz8ZuIG26tLAoHAvDEASKXSoqKixMREAGhvb29oaHBlFfuxfvvb34rF4qysrNzc3IMHDzILIyIimB/27dt37Nix7OzsDz/80OkhPIsZzpkUM5GLXXWO2vbycd01EQYUASL2Eekpg9PRMuacxIfEQc/LhUONIDBUENivXts9sOsYaCIBjA4hrQZPHd+5aD8Ik1hf+JPLyPZCOx/Big46yp830RUA+BBIDiWlTfTkiB6v2As1piUJ3DwRPp0OsLj3yGmh9n9XXXn7+v5CpUsFpgexOXPm7Nq1i6Koq1evTp48Wa/Xz58/Pycnh5lqNycnJz093elO8vPzjUbjnj17pk/vnjl7yZIlubm5HR0dKpUqMzMzNzeXWW4wGBytcurs2bM5OTkLFiw4ffo0ABiNRsu1U6dO/fOf/3zp0qVTp06xHMJg8JYey8fZVbM2ffuhkmOHS47VtCkcb9WXO+7cVtVTKBCODx/ryrGYCIypnIJJ7t4Puw43kEuJ1yZgMVKlxGqqxLGh5H4braOst7TMhecLu2W97rTAwzZuxjL5FmB1jWAtGpO+csLvwv0cjtgbTAYtpTP18t6rweett94qLi6OjIzMysrKz8+XSqV5eXkajSY+Pj4xMVGv1+fl5TndicFgiI6OPn/+/N69e80Lt23bRlFUQkJCSkpKfHz87t27ASA9PT0pKcnuKlds37595syZcrm8oaFh3rx5L7/8stUGgYGB77333qpVqzZs2GD3EEwDXDzcwGFCf6cvP4PJqGxXKdpVOsdzaM4Y8eQzCTOFgt4NNru30K5Lo2WWyWRMu4ZkjjtvYNfhBnIZ8doELIZcRqzCJrEAEoLI7Wbrd6+SbzlYACCX2anUoNLSxWpu9s+zHKwfa258V3X5NzETZ8RNYd/y2/vfFSqL58RPnxAl73NrWXKwhg73pCbwi9McrG/uni+uK5uXOFseMY5lP826ln8UHgKAZeMWjgzm8gL3d1WXf6y5MX3Ek5OH/ZrD3dp15t7FItWtZxJmpkWmsGz276orV2t+mjbiiceHPWaiTRfufm/QG54d9/RAN69X3JCDNXRg12HLMgcLAE5U0Z/fM70/3TqfqQ8GIgcLAPaXm+630tsm9Wjhixeo9BFk+ajuARqDCQL+adD+XiTgVYh1ppp+82fq2/QePfmKi9TYULIuWTfkcrA6q4m6dLnETTkoCNlysXiVOW+J0By/UN052XmvJl6kO69dCp6KnazRaNzQPIS8llwKPyg93QhWcinRGK0XptiU6FRp6HBf4Fd0BQCpUiiyuUSo0NBGjr4X8CzAYrll3WgyGmlKSIRCgQ/gzLLcOXr0qKebwD8upk11pwwLOI6EHov+1ShpQrAkiNvd2jUsMFpv1If4hrBv1tdsfcRX2HU4ZXceYq+SKiNV7dbvWbkU/na7Z4ClBd5dHwSAYf7ERFtP8qPSQG0HN90UzwKsCL+wtMgUc2kGS4XKkosPL02KmTAz7inoGjxwQx2gQe+FF17wdBP4JyogAgCU7XXsm1mMYHHcgCBxYJA4kOOdOiCPGMd+JZTxq8jUMD9ZoCjADU1C3gC7DqcIwKwYr/6QChCC1U120FmAtMcSlQYi+JbhzpDLSJGanhPb/RzrtFCnpfVc5G/zLMk9LmT4Mwkzk8NG2a4ind+PO88KbbEQITeT+kkBoFFjrx6fBcub3ge8TQPm8/Kvj93+iilez0Jr1B2v+ObMvQvuaRVCvBAX6O3vfdsWxgeRZj3dZHFnjlJDR3t3tr4jVjMS0gD1WjoukFS0cPB0eBZgmf2sunWt9qae6r6rVtB59bDzV3fmoCBkpXMqJ2cX/rpHsBwHWGpNo6JdZTTZ5EF4jaqWmgfNVU5vmXRbcXmE0IAiAOOlpMQie4l38+SYMdMBmX9t0EKgCB4LJyVNHOycr/HH1ZqfCh7+oDV2zw/DTMRBd1fWwSR3AJ4PjfBX52TkzlLXzTmCAsfvxC8rTh0uOdaia+WwedxyMbmq86p913cerVGnM+nxLjOvhV0HYmE1z4xKQ0fwMAcLbJ+Ilo70I3IpKW0awiNYtuGTVf47JrkzNmzY4OkmDEUmEw0AAuc31RCr/zvawpvDEBcnFmTekuYn+n7J4fdvfUgN+Up1Xgu7DsRC3vPKGh+rjDJSpaSsiTZ1PRVmKE4uA05GsHiW5G7G1IC2/N4vAAIApq5efmzY6MiAcKZs9FC2Y8cOTzdhaHJpANVH4DMyZAQBCBD5O9rGxYoPVqpaa1p1bcODYtxwI6HL01EzJUY737MWQ84cFAFCnMOuA7GQy8ixe92dkkpDR/rxcrwmSAThvuReK50UTKBrymq5jNxq4uDp8DXAslOoumdx6FHSBICEgWvAhQffqx2nMMeHxtlOfPt5+dcsH0ITo9MSQ0daLmnWtXx7r4ClDbPjp0l9nZRBY2r9EUKOHj2am5v76NGjzZs3Z2dn19fXr1q1qqCgQCaTbd++fenSpWq1Ojs7++zZs4SQuXPn7tmzRyaTEUI2btyYn5+/evVqk8l05MgRpVK5YcOGTZs2KRSKtWvXFhQUBAYGTp06defOnTExMeyNGToMJiO4MIAqEgiXjV3Ivo2L40NWilWlpfXl6UlzxkuSe/XAPmAyHWvblFYvYCut+jawvCo6VKs2FNeV/dJwx9HaQHHAvMTZVgv/XXVZ1V7v6CHDg2Mftykn+1XFaQPlcOIXeeT4MTInVcux60AsrGqgq7QQ4d1z/rBIk5EidWeAVaeFCD9ICCKNemgxkH5OGsqzAKu0vvxk5bfjw5NtE2bd3F9Xt9Yq2lWO1gaK7dyLfr/5IUvzEqXxVkv0lP5e80OWNmiN1gXxWTx8+LCwsPDChQsLFizIzs5et25dYmLiRx99dPny5UWLFjELxWLx3bt3AeDVV19dv359fn4+AMjl8jNnzqSlpe3Zs6ekpKSgoGDhwoWbNm1auXLl8uXLDx48qNPp9u7du3Llyq+//tr19gxudR31wFFCt5dP9tx9PGctZAKs7nNCM/8ZcgGWWtPI8r62+5Wptk35sKXa0UPEQrHtwvvND/WOA6y4kOHOmtkNuw5kK0wCvj7wqJ0eHkAAQKmBaIej8N4uVQoljbA4HgBAqaGj/AgBGBtC324RxEX0a888C7DMU7zZjmDZJLkPrNnx03SO45sge9dlFo9ZwJJLE+Yvs1oSIglemryApQ0yP6mzZnZbvXo1IWT27NlM+exTp06Vl5cLhcJp06ZVVFQIhcKTJ0+WlpYyMy1s27YtLS2NeeDSpUtFIhEArFmzRigUzpkzR6vVAsCFCxcsu0XzxPUIui6EcRJguXwBrgd35iAy70oXjkXApjLFEExyT4scHxcc62ityEdku3B63BSNwWHVe7tf5zJHz2e5rxO7DtR/qVJSrIbhAUAD1PE2yR0AUqXki/udHZFSA4+FAwCkhMKtZvJM//bMswDLnPJrJ8By7/SxdoudsksIjevV9mIfcQLrNZdeCQrqEfNRFCXoKiKgUCgkEgn0/PCjqM6yRmJx5/djZuo98zZSqbSoqCgxMREA2tvbGxoauGrqIEB6BhP931VvR3rcWROh6yhOb5nsEXQO2QBL6hvq9Mq+leiAyN4eZWQIZ1NbYteB7GKurM0fQZp04CcEX97mUspl5P/d7Pw2otJ0XuuUh9I17f3tP3mWlWa+XGKb5D48aFjG6HkTo/s+tfOQMmfOnF27dlEUdfXq1cmTJ+v1+vnz5+fk5Gi1Wo1Gk5OTk56ezr6HJUuW5ObmdnR0qFSqzMzM3Nxc97ScF5gXqlDAwRcY86htrx5lJ0lxwDBHcTqCxdyGYt6sb4Ej8jjsOhDDXKKTuazm6eb0XXIIedBGM1Mumiumrk425aQ4vMjuIp4FWOZOmfnea9mlB4kDk2WjzANL7YaOZl2L0eSkuvSQ9dZbbxUXF0dGRmZlZeXn50ul0ry8PI1GEx8fn5iYqNfr8/Ly2Pewbds2iqISEhJSUlLi4+N3797tnpbzAhNzuDItzABVEHXnCBZzVcv5sZhz0jWBz5AdweI77DoQw1yiU9lzLj/eEQlgVDC53UwDWM9L2E88u0Ro7pQlQjGhOvOu7DpZ+e2D5qplYxdyOFTOR8wHmOXHGPNzdHT08ePHLbcMCws7dOiQ3Yfb3UNQUNCBAwcGptW85/rwzNs/vW80GV+ZuCJYbL+egvePYPkJfV05FjN2ZY7DmOFnk7uSJlFvYdeB2I0PJRUttNEEKi2/R7AAQC4lxWp6YhhRauhI7p4L3wKsrs+tVRNfZt8SJ3tGHuR6VMR85LBUcp8bP0NvMsh6mbXjzhEsl6el6lEzFUewEOI1PyGMCCC/tNB1Gh7XaGAwVSc6jEDREGznPpM+4lmA5fot6zjZM+IFASEUzfZC7dUNX2ZeOJOB1Zt31oinWjVt/kI+X1pAaGhLlZESNa3U0Dwt424ml5G3b5k4TybjWYDlelVrnOwZeZDrI1gcFnToyX11sFx8r1ldE4wPidOINXarEiCEeCFVCsWNdJ0GfhXmRd/l+iBVCsWNHCdgAf8CLABwUEtKa9T+oq4sV1cyvzZo1G5rFUJWXJ9AkNnSlaGmRy01l2uus2yQOXq+uCteWZqcoaP0Ii5uY3TqNzETm3TNTufk8RH4+PpIxAKMqBAaJORScugOLSAQyfNLhHGBpN1AlzVxPBTHtwCLCAJE/tNHPGm7StlRd+beRauFOIKFPCJJmiD1DQ3qumOOxaLk50wmSuxjpxi3lQ6j5kFzFcsGJpoC6A5fJC7skxNOJ11hjJImrJn0XwPdGISQ28hlpFhtivYHnk5EaEYAxkvJ+Ro6ktNyqTwLsCIDwheOfjYqwE7l3yj/CNtp3cL8rMujI+QGQeJAV6IrABgRNMzFfQ4PGsY+caErURpCCHElKZgoNbSG4neZBkaajBx/SC8fNYQDLF8fSWyQ/WlBfYW+Q7wiAxrc/EV++ApHCHkPHwJjQ8lP9bwv0wAAqVLyXpkpitOhOH4P6yGEEELIU+QyIvGBEP6PnstlBACGdA6W++n1eoVC4elWIIR4hqZp7DqQFb1e7+kmcEwuJed8vakeTF/JpQQAhnQOlpulpqY2NzdPmDDB0w3pC5qmsQwYhyzPZ3h4OJ5bxEIikcyfP5+nXYcligYtBQGOPyhaDRAkAgDoMIJIACKXL4oM2Q6KmR570JDLSAT/E7AAQCqB4QEcPxcMsNiMGDHi+nW2G+O9mVKpjIiIMM97j/qpqalJIpH4+Q2KvgQNMKFQ+Nlnn3m6FRzQGCH8sKFqhchu5PSwjZ5ynHqUJQSA/3uVenaE4OlhrsZMCoUiKipqaMZYg0mqlEQPlk4xVQrRnCaT4acvQggh+/yEMDyAVLTYr+mm0nbnrKTKCN+LIaE+iPGHFOkgiZLTZCSM0+FFDLAQQoNfY2NjRkaGTCZbuHBhY2Ojp5vDJ3IZKVY7CLA03RUm5VIyCG4lQ30wK2aQBBIzYwRCTp/KIDkvCCHEYufOnSNHjqytrY2Li3vzzTc93Rw+kUtZAiw6oispeLyUhOEI1pA0PWaQBNacPxEMsBBCg98XX3yxZs0aiUSyZs2azz//3NPN4RO5DEocDPkpNRDt3/mzrw/4DJLPWdQ7LPdA8AvnT2SwnBiEEHKsurp65MiRAMCMY7FsSVHU7373O/OvCQkJ69ev1+v1bW1tQuFQ7DDjheTnet/m5jbbVVXNomg/aG429GG3LS0tvr6+mOTOlZaWFolEMvjKQHiKVqvV6XQsr8/AwEAfHx/2nQzF/gIhNNSYiwLQNE1RFMuWhJApU6aYf42MjBSJRDRNi0QikWgozlQ9Rgr1eqIj4kCh9YXCer3PhDC6b6eFOZ8YYHFFJBIJhcKh+RIdCEaj0WQysZxPV166GGAhhAa/YcOGVVVVjR49urq6OjY2lmVLgUCwevVqq4VCoZCiKH9/f7sPGfTGhhjvaX0fj7T+RFEbjCNCfPz9+xIk+fn5+fv7Y4DFFb1ej3VkOEQI8fHx6edbHnOwEEKDX0ZGxoEDB2iaPnDgQGZmpqebwzOpMlLcaCfPXaUZDLP8IjRAMMBCCA1+W7ZsKSoqGjFixK1bt/70pz95ujk84+hGQqWG5nbuNoQGE7xEiBAa/EJDQ0+cOOHpVvCVXEa+fmiyWkgDNOgggtO52xAaTHAECyGEEBu5vUuEDVoIEvVi8kGEhhp8cyCEEGIT7QcEQKHpsVCpobF0O0IsMMBCCCHkRKpNGpZKixnuCLHBAAshhJATchkp6XmVUNlBR2ICFkKOYYCFEELICbsjWHgLIUIsMMBCCCHkhFxmE2Bp6EjMwULIMQywEEIIOZEqJaVNNGURYmEOFkLsMMBCCCHkRKAIovzI3ZbuCEupwUuECLHBAAshhJBzVtWwVBpMckeIDQZYCCGEnJNLoVjd/asSJyJEiBUGWAghhJyzqtSgwkKjCLHCAAshhJBzljcSdhjBBBAk8myLEPJqGGAhhBBybkwwqWqnNUYAACUmYCHkDAZYCCGEnBMKYFQwKWuiAW8hRMgFGGAhhBBySZqMFKlpAKjT0hG+nm4NQt4NAyyEEEIuSZV25rkrOiDaHy8RIsQGAyyEEEIuMee5q7QQiSNYCLHCAAshhJBL5DJgao3iRIQIOYUBFkIIIZeMCCAaI9RrscooQs5hgIUQQshVqTJyq5HGKqMIOYUBFkIIIVfJpaS4kcYcLIScwgALIYSQq1KlpFhNK3EECyFnMMBCCCHkKrmMFDbQTXoIwxEshFhhgIUQQshVchm50UBLxeCDA1gIsep1gEVR1NixY+2uKikpCQgI6HeTEEIIealQMUT7Ebw+iJBTvQuw9u7dO2XKlPLycttVTU1NL730UkdHB0cNQwgh5I3kMpyIECHnhL3aOi0tLSkpKSMjw2q5yWR66aWXNm3a9PzzzzvdCU3TH3/8sfnX0NDQGTNm6PV6nU6n1Wp71R7EgjmfAgFeBeaGTqejaZoQ/OLODa1Wy/6WF4vF+Or1TnIpqWr3dCMQ8nq9C7BmzZpld3lubu6YMWOWLl3qyk5omj58+LD514SEhMmTJ+v1eo1Go9FoetUexEKr1Wo0GvyI4opGozGZTJ5uxeCh0+k0Go1EInG0gUgkcmd7kOvkMqKlaE+3AiFv17sAy65vv/323LlzZ86ccXF7gUBw/Phxq4V6vV4oFEql0v63BzH0er1UKsUAiyuEEIlE4ueHl0a4odVqJRIJvuX5SC7DESyEnOPg0/fbb7+9cOGCSCRirp4QQr7//vv+7xYhhJAXGhtCYv093QiEvF6/AqyLFy8CwI4dO+guAEDT9NSpUzlpHEIIIW8j8YGnojAZESEn+hVgOUrJ6oPq6uqPPvqIq70tV9ocAAAJB0lEQVQhAHj33XfxpgEOnTp16tatW55uxeBx+/Zt21QBr/Xw4cNPPvnE063wIknB/Q2w3nnnHb1ez0ljEACcOHGirKzM060YPMrKyk6cONHPnfQlwGJGqix/sF3VWw8ePDh48GDfHovsysvLw5sGOPSvf/2rsLDQ060YPEpLSz/77DNPt8JV9+/ft7w1B/XfX//6V51O5+lWDB5ffPFFUVGRp1sxeBQVFX3xxRf93AlmQCOEEEIIcQwDLIQQQgghjnFQpqEPFAqF1ZLGxkaDwWC7HPWZyWRSqVQGg8HTDRkktFptS0sLvkS50tTUpNPpWM5neHi4UOiBDoqmadtWqdVq7KC4RdO0UqnEyT+4otPpmpub8SXKlebm5v53UKTPWVN9YzKZli5devnyZdvler3e1xfnZ+eMRqPx9fXFyuNc0el0QqHQx8fH0w0ZJCiKMhqNLIVGT58+PWHCBHc2CQCMRuOyZcuwg3IDjUaDVeU4hB0Ut5x2UOfOnUtJSWHfibsDLIQQQgihQQ9zsBBCCCGEOIYBFkIIIYQQxzDAQgghhBDiGAZYCCGEEEIcwwALIYQQQohjng+wGhsbMzIyZDLZwoULGxsbPd0cvpoyZQrpsmrVKsAT21cURY0dO5b52fYc4lntLcvzCTx8oXp58/iCd393r4UdFLcGtIPyfIC1c+fOkSNH1tbWxsXFvfnmm55uDi/RNF1eXl5bW9va2tra2pqXlwd4Yvtk7969U6ZMKS8vZ361PYd4VnvF6nzy8YXq5c3jBT7+3b0TdlDcGvAOiva0MWPGlJWV0TRdVlY2ZswYTzeHl6qrq4ODg3/9618HBgZmZmYqlUoaT2yfnD9//vjx4+b3he05xLPaK1bnk48vVC9vHi/w8e/unbCD4tZAd1CeD7ACAgI6Ojpomu7o6AgKCvJ0c3jp+vXrs2bNunnzZkNDw4oVK1544QUaT2w/mN9vtucQz2ofmM8nH1+oXt48XuDj392bYQfFrYHroDxfyT0gIKChocHX17ejoyMiIqK9vd2z7eG72tralJQUtVqNJ7bPCOl8X9ieQzyrfWA+n5b48kL18ubxDl/+7t4MOyhuDVwH5fkcrGHDhlVVVQFAdXV1bGysp5vDSz/99NMPP/zA/CwWi5npk/DE9p/tOcSz2h98fKF6efN4gY9/d17ADopbnL9QPR9gZWRkHDhwgKbpAwcOZGZmero5vNTe3r548eKysjK9Xr9169ZFixYBnlgu2J5DPKv9wccXqpc3jxf4+HfnBeyguMX9C7X/1y/7qbGxMT09PTY2NiMjo6mpydPN4SWTyfTWW28lJSWFh4evWLGiubmZxhPbD+b3he05xLPaB+bzyccXqpc3jxf4+Hf3ZthBcWvgOijP52AhhBBCCA0ynr9EiBBCCCE0yGCAhRBCCCHEMQywEEIIIYQ4hgEWQgghhBDHMMBCCCGEEOIYBlgIIYQQQhzDAAshhBBCiGMYYCGEEEIIcQwDLNQXt2/fJhZ8fHySk5N37dpFUZSLeyCE3L59u2/HNZlMvX0gQmjowA4KeQOhpxuAeOzQoUPMD1qt9urVqxs3bvT19V27dq0rj92wYYNMJuvbcSmKEgjwuwFCiA12UMizcKoc1Be3b98eN26c1Ytnx44dhw8fLikpGejjajQaX1/fgTsKQojXsINC3gCjbMSZp59++t69ewBQUVHx3HPPyWSy6OjoF198saKiAgAIIT///PPy5cujoqLAZgT+448/fuyxx4KCgiZNmvTxxx9b7vYf//hHSkqKTCZbuHBhTU0NABiNRgC4e/fusmXLIiMjg4KCZs2adfPmTXc+WYQQv2AHhdwMAyzEmVu3bkVFRVVXV0+ePFkmk73zzjsbNmy4ePHi448/XllZCQDr1q2rr6//4x//aPXAY8eOZWVlTZs27f333586dWpWVtann37KrDp8+PArr7wyY8aMffv2hYSELFmyBAAoiqJp+rnnnlOr1W+88UZeXp6vr29mZiamPiCEHMEOCrkbjVDvlZWVAUBZl5s3b/7tb3+TyWRvvPHGq6++umrVKvOW9+/fDwgIyMrKAoDFixebTCZmOfNw5ufU1NQtW7aYH7J58+a0tDTm57Fjx/7P//wP87PJZFq2bBkANDQ0VFVVAUBNTQ2zqq2t7Q9/+ENjY+NAP3GEkPfDDgp5AwywUF8w/ZelqKio//7v/9br9SkpKQUFBZYbZ2RkxMbGAsDx48fNC839V0dHBwBUVlaaV1VUVBBCtFptW1sbANy5c8e86vjx4wCgVCq1Wm1kZOTMmTM///xztVo98M8YIcQb2EEhb4B3EaK+o+3dIXHv3r0ZM2ZYLWRSPuPj4223VyqVADBs2DDzktjYWJqma2trzb9argIAiqIkEsnFixffeOONFStWtLe3T5s2befOnU888UQ/nxFCaNDADgp5FgZYiGPBwcEffPBBamqq1fJx48YJhXZeb0xKaU1NTWJiIrOESRSNiooihABAdXV1UlKS5SqmmM24ceM+/vhjg8Fw/fr1v/zlL+np6TU1NXjzDkKIBXZQyG0wyR1xbMKECYWFhWO7xMXFZWdnHzlyxNH2fn5+KSkpBw8eNC9huj8/Pz9fX1+5XL5//37zKmYzo9FYV1eXlJTU0tIiEomefPLJd999t7GxUa1WD+hTQwjxHXZQyG1wBAtx7PXXX3/qqaeqqqrmzZtXU1Nz5MiR27dvb926devWrY4esmXLlqysrJaWlieeeOLy5ct79+795JNPmFUbN2588cUXW1tbn3zyyW+++eb69esAQFFUeHi4UChcvHjxihUraJo+cuTI+PHjY2Ji3PQkEUL8hB0Uch9PJoAh3mJySB2tLSgoeOqppwIDA6OiojIzM69du0b3vCvH9tejR49OnDgxICBg4sSJn3zyieXe9u/fP378+NDQ0PT0dObenPLycpqmS0pK5s6dGxoaGhISsmDBgoqKCu6fJ0KIh7CDQt4AK7kjhBBCCHEMc7AQQgghhDiGARZCCCGEEMcwwEIIIYQQ4hgGWAghhBBCHMMACyGEEEKIYxhgIYQQQghxDAMshBBCCCGO/X87l4bzLTP4yAAAAABJRU5ErkJggg==" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Random.seed!(42)\n", "N_simul = 150\n", "cp = ConsumptionProblem()\n", "\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "\n", "c_path, debt_path, y_path, s_path = consumption_incomplete(cp, N_simul=N_simul)\n", "\n", "plt_cons = plot(title = \"Consumption paths\", xlabel = \"Periods\", ylim = [1.4,2.1])\n", "plot!(plt_cons, 1:N_simul, c_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, fill(c̄, N_simul), label = \"complete market\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, y_path, label = \"income\", lw = 2, alpha = 0.6, linestyle = :dash)\n", "plot!(plt_cons, legend = :bottom)\n", "\n", "plt_debt = plot(title = \"Debt paths\", xlabel = \"Periods\")\n", "plot!(plt_debt, 1:N_simul, debt_path, label = \"incomplete market\")\n", "plot!(plt_debt, 1:N_simul, debt_complete[s_path], label = \"complete market\", lw = 2)\n", "plot!(plt_debt, 1:N_simul, y_path, label = \"income\", lw = 2, alpha = 0.6, linestyle = :dash)\n", "plot!(plt_debt, legend = :bottomleft)\n", "\n", "plot(plt_cons, plt_debt, layout = (1,2), size = (800, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the graph on the left, for the same sample path of nonfinancial\n", "income $ y_t $, notice that\n", "\n", "- consumption is constant when there are complete markets, but it takes a random walk in the incomplete markets version of the model \n", "- the consumer’s debt oscillates between two values that are functions\n", " of the Markov state in the complete markets model, while the\n", " consumer’s debt drifts in a “unit root” fashion in the incomplete\n", " markets economy " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using the Isomorphism\n", "\n", "We can simply relabel variables to acquire tax-smoothing interpretations of our two models" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": "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" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt_tax = plot(title = \"Tax collection paths\", x_label = \"Periods\", ylim = [1.4,2.1])\n", "plot!(plt_tax, 1:N_simul, c_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_tax, 1:N_simul, fill(c̄, N_simul), label = \"complete market\", lw = 2)\n", "plot!(plt_tax, 1:N_simul, y_path, label = \"govt expenditures\", alpha = .6, linestyle = :dash,\n", " lw = 2)\n", "\n", "plt_gov = plot(title = \"Government assets paths\", x_label = \"Periods\")\n", "plot!(plt_gov, 1:N_simul, debt_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_gov, 1:N_simul, debt_complete[s_path], label = \"complete market\", lw = 2)\n", "plot!(plt_gov, 1:N_simul, y_path, label = \"govt expenditures\", alpha = .6, linestyle = :dash,\n", " lw = 2)\n", "hline!(plt_gov, [0], linestyle = :dash, color = :black, lw = 2, label = \"\")\n", "plot(plt_tax, plt_gov, layout = (1,2), size = (800, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Tax Smoothing with Complete Markets\n", "\n", "It is useful to focus on a simple tax-smoothing example with complete markets.\n", "\n", "This example will illustrate how, in a complete markets model like that of Lucas and Stokey [[LS83]](https://lectures.quantecon.org/zreferences.html#lucasstokey1983), the government purchases\n", "insurance from the private sector.\n", "\n", "> - Purchasing insurance protects the government against the need to raise taxes too high or issue too much debt in the high government expenditure event. \n", "\n", "\n", "\n", "We assume that government expenditures move between two values $ G_1 < G_2 $, where Markov state $ 1 $ means “peace” and Markov state $ 2 $ means “war”\n", "\n", "The government budget constraint in Markov state $ i $ is\n", "\n", "$$\n", "T_i + b_i = G_i + \\sum_j Q_{ij} b_j\n", "$$\n", "\n", "where\n", "\n", "$$\n", "Q_{ij} = \\beta P_{ij}\n", "$$\n", "\n", "is the price of one unit of output next period in state $ j $ when\n", "today’s Markov state is $ i $ and $ b_i $ is the government’s\n", "level of *assets* in Markov state $ i $\n", "\n", "That is, $ b_i $ is the amount of the one-period loans owned by the government that fall due at time $ t $\n", "\n", "As above, we’ll assume that the initial Markov state is state $ 1 $\n", "\n", "In addition, to simplify our example, we’ll set the government’s initial\n", "asset level to $ 0 $, so that $ b_1 =0 $\n", "\n", "Here’s our code to compute a quantitative example with zero debt in peace time:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hide-output": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P = [0.8 0.2; 0.4 0.6]\n", "Q = [0.768 0.192; 0.384 0.576]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Govt expenditures in peace and war = [1.0, 2.0]\n", "Constant tax collections = 1.3116883116883118\n", "Govt assets in two states = [0.0, 1.6233766233766234]\n", "Now let's check the government's budget constraint in peace and war.\n", "Our assumptions imply that the government always purchases 0 units of the\n", "Arrow peace security.\n", "\n", "Spending on Arrow war security in peace = 0.3116883116883117\n", "Spending on Arrow war security in war = 0.9350649350649349\n", "\n", "\n", "Government tax collections plus asset levels in peace and war\n", "T+b in peace = 1.3116883116883118\n", "T+b in war = 2.9350649350649354\n", "\n", "\n", "Total government spending in peace and war\n", "total govt spending in peace = 1.3116883116883118\n", "total govt spending in war = 2.935064935064935\n", "\n", "\n", "Let's see ex post and ex ante returns on Arrow securities\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ex post returns to purchase of Arrow securities = [1.3020833333333333 5.208333333333333; 2.6041666666666665 1.7361111111111112]\n", "Ex ante returns to purchase of Arrow securities = [1.0416666666666667 1.0416666666666667; 1.0416666666666667 1.0416666666666667]\n" ] } ], "source": [ "# Parameters\n", "\n", "β = .96\n", "y = [1.0, 2.0]\n", "b0 = 0.0\n", "P = [0.8 0.2;\n", " 0.4 0.6]\n", "\n", "cp = ConsumptionProblem(β, y, b0, P)\n", "Q = β * P\n", "N_simul = 150\n", "\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "\n", "println(\"P = $P\")\n", "println(\"Q = $Q\")\n", "println(\"Govt expenditures in peace and war = $y\")\n", "println(\"Constant tax collections = $c̄\")\n", "println(\"Govt assets in two states = $debt_complete\")\n", "\n", "msg = \"\"\"\n", "Now let's check the government's budget constraint in peace and war.\n", "Our assumptions imply that the government always purchases 0 units of the\n", "Arrow peace security.\n", "\"\"\"\n", "println(msg)\n", "\n", "AS1 = Q[1, 2] * b2\n", "println(\"Spending on Arrow war security in peace = $AS1\")\n", "AS2 = Q[2, 2] * b2\n", "println(\"Spending on Arrow war security in war = $AS2\")\n", "\n", "println(\"\\n\")\n", "println(\"Government tax collections plus asset levels in peace and war\")\n", "TB1 = c̄ + b1\n", "println(\"T+b in peace = $TB1\")\n", "TB2 = c̄ + b2\n", "println(\"T+b in war = $TB2\")\n", "\n", "println(\"\\n\")\n", "println(\"Total government spending in peace and war\")\n", "G1= y[1] + AS1\n", "G2 = y[2] + AS2\n", "println(\"total govt spending in peace = $G1\")\n", "println(\"total govt spending in war = $G2\")\n", "\n", "println(\"\\n\")\n", "println(\"Let's see ex post and ex ante returns on Arrow securities\")\n", "\n", "Π = 1 ./ Q # reciprocal(Q)\n", "exret = Π\n", "println(\"Ex post returns to purchase of Arrow securities = $exret\")\n", "exant = Π .* P\n", "println(\"Ex ante returns to purchase of Arrow securities = $exant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explanation\n", "\n", "In this example, the government always purchase $ 0 $ units of the\n", "Arrow security that pays off in peace time (Markov state $ 1 $)\n", "\n", "But it purchases a positive amount of the security that pays off in war\n", "time (Markov state $ 2 $)\n", "\n", "We recommend plugging the quantities computed above into the government\n", "budget constraints in the two Markov states and staring\n", "\n", "This is an example in which the government purchases *insurance* against\n", "the possibility that war breaks out or continues\n", "\n", "- the insurance does not pay off so long as peace continues \n", "- the insurance pays off when there is war \n", "\n", "\n", "*Exercise:* try changing the Markov transition matrix so that\n", "\n", "$$\n", "P = \\begin{bmatrix}\n", " 1 & 0 \\\\\n", " .2 & .8\n", " \\end{bmatrix}\n", "$$\n", "\n", "Also, start the system in Markov state $ 2 $ (war) with initial\n", "government assets $ - 10 $, so that the government starts the\n", "war in debt and $ b_2 = -10 $" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear State Space Version of Complete Markets Model\n", "\n", "Now we’ll use a setting like that in [first lecture on the permanent income model](https://lectures.quantecon.org/perm_income.html)\n", "\n", "In that model, there were\n", "\n", "- incomplete markets: the consumer could trade only a single risk-free one-period bond bearing gross one-period risk-free interest rate equal to $ \\beta^{-1} $ \n", "- the consumer’s exogenous nonfinancial income was governed by a linear state space model driven by Gaussian shocks, the kind of model studied in an earlier lecture about [linear state space models](https://lectures.quantecon.org/tools_and_techniques/linear_models.html) \n", "\n", "\n", "We’ll write down a complete markets counterpart of that model\n", "\n", "So now we’ll suppose that nonfinancial income is governed by the state\n", "space system\n", "\n", "$$\n", "\\begin{aligned}\n", " x_{t+1} & = A x_t + C w_{t+1} \\cr\n", " y_t & = S_y x_t\n", "\\end{aligned}\n", "$$\n", "\n", "where $ x_t $ is an $ n \\times 1 $ vector and $ w_{t+1} \\sim {\\cal N}(0,I) $ is IID over time\n", "\n", "Again, as a counterpart of the Hall-Barro assumption that the risk-free\n", "gross interest rate is $ \\beta^{-1} $, we assume the scaled prices\n", "of one-period ahead Arrow securities are\n", "\n", "\n", "\n", "$$\n", "p_{t+1}(x_{t+1} \\,|\\, x_t) = \\beta \\phi(x_{t+1} \\,|\\, A x_t, CC') \\tag{15}\n", "$$\n", "\n", "where $ \\phi(\\cdot \\,|\\, \\mu, \\Sigma) $ is a multivariate Gaussian\n", "distribution with mean vector $ \\mu $ and covariance matrix\n", "$ \\Sigma $\n", "\n", "Let $ b(x_{t+1}) $ be a vector of state-contingent debt due at $ t+1 $\n", "as a function of the $ t+1 $ state $ x_{t+1} $.\n", "\n", "Using the pricing function assumed in [(15)](#equation-cs-14), the value at\n", "$ t $ of $ b(x_{t+1}) $ is\n", "\n", "$$\n", "\\beta \\int b(x_{t+1}) \\phi(x_{t+1} \\,|\\, A x_t, CC') d x_{t+1} = \\beta \\mathbb E_t b_{t+1}\n", "$$\n", "\n", "In the complete markets setting, the consumer faces a sequence of budget\n", "constraints\n", "\n", "$$\n", "c_t + b_t = y_t + \\beta \\mathbb E_t b_{t+1}, t \\geq 0\n", "$$\n", "\n", "We can solve the time $ t $ budget constraint forward to obtain\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j (y_{t+j} - c_{t+j} )\n", "$$\n", "\n", "We assume as before that the consumer cares about the expected value\n", "of\n", "\n", "$$\n", "\\sum_{t=0}^\\infty \\beta^t u(c_t), \\quad 0 < \\beta < 1\n", "$$\n", "\n", "In the incomplete markets version of the model, we assumed that\n", "$ u(c_t) = - (c_t -\\gamma)^2 $, so that the above utility functional\n", "became\n", "\n", "$$\n", "-\\sum_{t=0}^\\infty \\beta^t ( c_t - \\gamma)^2, \\quad 0 < \\beta < 1\n", "$$\n", "\n", "But in the complete markets version, we can assume a more general form\n", "of utility function that satisfies $ u' > 0 $ and $ u'' < 0 $\n", "\n", "The first-order condition for the consumer’s problem with complete\n", "markets and our assumption about Arrow securities prices is\n", "\n", "$$\n", "u'(c_{t+1}) = u'(c_t) \\quad \\text{for all } t\\geq 0\n", "$$\n", "\n", "which again implies $ c_t = \\bar c $ for some $ \\bar c $\n", "\n", "So it follows that\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j (y_{t+j} - \\bar c)\n", "$$\n", "\n", "or\n", "\n", "\n", "\n", "$$\n", "b_t = S_y (I - \\beta A)^{-1} x_t - \\frac{1}{1-\\beta} \\bar c \\tag{16}\n", "$$\n", "\n", "where the value of $ \\bar c $ satisfies\n", "\n", "\n", "\n", "$$\n", "\\bar b_0 = S_y (I - \\beta A)^{-1} x_0 - \\frac{1}{1 - \\beta } \\bar c \\tag{17}\n", "$$\n", "\n", "where $ \\bar b_0 $ is an initial level of the consumer’s debt, specified\n", "as a parameter of the problem\n", "\n", "Thus, in the complete markets version of the consumption-smoothing\n", "model, $ c_t = \\bar c, \\forall t \\geq 0 $ is determined by [(17)](#equation-cs-16)\n", "and the consumer’s debt is a fixed function of\n", "the state $ x_t $ described by [(16)](#equation-cs-15)\n", "\n", "Here’s an example that shows how in this setting the availability of insurance against fluctuating nonfinancial income\n", "allows the consumer completely to smooth consumption across time and across states of the world." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": "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" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function complete_ss(β, b0, x0, A, C, S_y, T = 12)\n", "\n", " # Create a linear state space for simulation purposes\n", " # This adds \"b\" as a state to the linear state space system\n", " # so that setting the seed places shocks in same place for\n", " # both the complete and incomplete markets economy\n", " # Atilde = vcat(hcat(A, zeros(size(A,1), 1)),\n", " # zeros(1, size(A,2) + 1))\n", " # Ctilde = vcat(C, zeros(1, 1))\n", " # S_ytilde = hcat(S_y, zeros(1, 1))\n", "\n", " lss = LSS(A, C, S_y, mu_0=x0)\n", "\n", " # Add extra state to initial condition\n", " # x0 = hcat(x0, 0)\n", "\n", " # Compute the (I - β*A)^{-1}\n", " rm = inv(I - β * A)\n", "\n", " # Constant level of consumption\n", " cbar = (1 - β) * (S_y * rm * x0 .- b0)\n", " c_hist = ones(T) * cbar[1]\n", "\n", " # Debt\n", " x_hist, y_hist = simulate(lss, T)\n", " b_hist = (S_y * rm * x_hist .- cbar[1] / (1.0 - β))\n", "\n", " return c_hist, vec(b_hist), vec(y_hist), x_hist\n", "end\n", "\n", "N_simul = 150\n", "\n", "# Define parameters\n", "α, ρ1, ρ2 = 10.0, 0.9, 0.0\n", "σ = 1.0\n", "# N_simul = 1\n", "# T = N_simul\n", "A = [1.0 0.0 0.0;\n", " α ρ1 ρ2;\n", " 0.0 1.0 0.0]\n", "C = [0.0, σ, 0.0]\n", "S_y = [1.0 1.0 0.0]\n", "β, b0 = 0.95, -10.0\n", "x0 = [1.0, α / (1 - ρ1), α / (1 - ρ1)]\n", "\n", "# Do simulation for complete markets\n", "out = complete_ss(β, b0, x0, A, C, S_y, 150)\n", "c_hist_com, b_hist_com, y_hist_com, x_hist_com = out\n", "\n", "# Consumption plots\n", "plt_cons = plot(title = \"Cons and income\", xlabel = \"Periods\", ylim = [-5.0, 110])\n", "plot!(plt_cons, 1:N_simul, c_hist_com, label = \"consumption\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, y_hist_com, label = \"income\",\n", " lw = 2, alpha = 0.6, linestyle = :dash)\n", "\n", "# Debt plots\n", "plt_debt = plot(title = \"Debt and income\", xlabel = \"Periods\")\n", "plot!(plt_debt, 1:N_simul, b_hist_com, label = \"debt\", lw = 2)\n", "plot!(plt_debt, 1:N_simul, y_hist_com, label = \"Income\",\n", " lw = 2, alpha = 0.6, linestyle = :dash)\n", "hline!(plt_debt, [0], color = :black, linestyle = :dash, lw = 2, label = \"\")\n", "plot(plt_cons, plt_debt, layout = (1,2), size = (800, 400))\n", "plot!(legend = :bottomleft)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpretation of Graph\n", "\n", "In the above graph, please note that:\n", "\n", "- nonfinancial income fluctuates in a stationary manner \n", "- consumption is completely constant \n", "- the consumer’s debt fluctuates in a stationary manner; in fact, in\n", " this case because nonfinancial income is a first-order\n", " autoregressive process, the consumer’s debt is an exact affine function\n", " (meaning linear plus a constant) of the consumer’s nonfinancial\n", " income " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Incomplete Markets Version\n", "\n", "The incomplete markets version of the model with nonfinancial income being governed by a linear state space system\n", "is described in the first lecture on the [permanent income model](https://lectures.quantecon.org/perm_income.html) and the followup\n", "lecture on the [permanent income model](https://lectures.quantecon.org/perm_income_cons.html)\n", "\n", "In that version, consumption follows a random walk and the consumer’s debt follows a process with a unit root\n", "\n", "We leave it to the reader to apply the usual isomorphism to deduce the corresponding implications for a tax-smoothing model like Barro’s [[Bar79]](https://lectures.quantecon.org/zreferences.html#barro1979)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Government Manipulation of Arrow Securities Prices\n", "\n", "In [optimal taxation in an LQ economy](https://lectures.quantecon.org/dynamic_programming_squared/lqramsey.html) and [recursive optimal taxation](https://lectures.quantecon.org/dynamic_programming_squared/opt_tax_recur.html), we study **complete-markets**\n", "models in which the government recognizes that it can manipulate Arrow securities prices\n", "\n", "In [optimal taxation with incomplete markets](https://lectures.quantecon.org/dynamic_programming_squared/amss.html), we study an **incomplete-markets** model in which the government manipulates asset prices" ] } ], "metadata": { "filename": "smoothing.rst", "kernelspec": { "display_name": "Julia 1.2", "language": "julia", "name": "julia-1.2" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.2.0" }, "title": "Consumption and Tax Smoothing with Complete and Incomplete Markets" }, "nbformat": 4, "nbformat_minor": 2 }