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%	TITLE PAGE
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\title[]{Quantitative Macro-Labor:\\ Inequality in Heterogeneous Agent Models} % The short title appears at the bottom of every slide, the full
% title is only on the title page

\author{Professor Griffy} % Your name
\institute[University at Albany, SUNY] % Your institution as it will appear on the bottom of
% every slide, may be shorthand to save space
{
UAlbany  \ % Your institution for the title page
}
\date{Fall 2022} % Date, can be changed to a custom date

\begin{document}

\begin{frame}
  \titlepage % Print the title page as the first slide
\end{frame}




% ----------------------------------------------------------------------------------------
%	PRESENTATION SLIDES
% ----------------------------------------------------------------------------------------

% ------------------------------------------------
\section{Introduction} % Sections can be created in order to organize your presentation into discrete blocks, all sections and subsections are automatically printed in the table of contents as an overview of the talk
% ------------------------------------------------

\begin{frame}
  \frametitle{Announcements}
  \begin{itemize}
  \item Today: heterogeneous agents with frictional labor markets.
  \item How does this affect inequality? (Griffy, 2021)
  \item Start writing down and solving your model.
  \item Due in less than 2(!) weeks!
  \item Today: last class that I'm teaching
  \end{itemize}
\end{frame}

% ----------------------------------------------------------------------------------------
%	PRESENTATION SLIDES
% ----------------------------------------------------------------------------------------

% ------------------------------------------------
\section{Introduction} % Sections can be created in order to organize your presentation into discrete blocks, all sections and subsections are automatically printed in the table of contents as an overview of the talk
% ------------------------------------------------

\begin{frame}
  \frametitle{Wealth and Borrowing Constraints}\label{wealthandborrowingconstraints}
  \begin{itemize}
  \item Low wealth limits ability to borrow early in the life-cycle.
  \item Feared or were denied credit (ages 20-30):
    \begin{itemize}
    \item 1st quartile (Survey of Consumer Finances, 2013): $50\%$
    \item Rest of population (SCF, 2013): $33\%$
    \end{itemize}
  \item Less likely to be able to borrow in the future (ages 20-30):
    \begin{itemize}
    \item 1st quartile (SCF, 2013): unsecured $80\%$ of total debt
    \item Population Average (SCF, 2013): unsecured $41\%$ of total debt
    \end{itemize}
  \item Wealth and earnings are correlated:
    \begin{itemize}
    \item Low wealth, lower initial earnings;
    \item Lower slope over life-cycle.
    \end{itemize}
\end{itemize}
\end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Wealth and Earnings}\label{lcprofilesbywealth}
% \begin{figure}
% \includegraphics[width=\linewidth]{./Empirical_Regularities/LifeCycleProfiles_MedbyEdPSIDLiq.png}
% \end{figure}
% \hyperlink{UIElast}{\beamergotobutton{Search Behavior}}, \hyperlink{Training}{\beamergotobutton{Human Capital}}
% \end{frame}

% % ------------------------------------------------

\begin{frame}
  \frametitle{Question}\label{question}
  \begin{itemize}
  \item How do {\bf differences} in {\bf wealth}, {\bf human capital}, and {\bf learning ability} at {\bf labor market entry} impact life-cycle
    \begin{itemize}
    \item job search behavior?
    \item human capital accumulation?
    \item consumption?
    \end{itemize}
  \item What channels are quantitatively important?
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{What I Do}\label{whatido}
  \begin{itemize}
  \item Construct quantitative general equilibrium life-cycle model:
    \begin{itemize}
    \item search and matching in the labor market;
    \item risk-aversion and borrowing constraints;
    \item endogenous human capital accumulation.
    \end{itemize}
  \item Estimate model using indirect inference.
  \item Consider counterfactual initial conditions.
  \item Decompose effect into interaction between wealth, search, and human capital.
  \end{itemize}
\end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Preview of Findings}\label{question}
%   \begin{itemize}
%   \item How do {\bf differences} in {\bf wealth}, {\bf human capital}, and {\bf learning ability} at {\bf labor market entry} impact life-cycle
%     \begin{itemize}
%     \item job search behavior?
%       \begin{itemize}
%       \item Early: low-wealth causes workers to search for low-paying jobs.
%       \end{itemize}
%     \item human capital accumulation?
%       \begin{itemize}
%       \item Learning changes return to accumulation and earnings growth.
%       \item Low-wealth causes slow accumulation to mitigate cons. risk.
%       \item Initial HC causes permanent change, but no accum. change.
%       \end{itemize}
%     \item consumption?
%       \begin{itemize}
%       \item Learning causes largest change in life-cycle consumption.
%       \item Wealth causes larger change than human capital.
%       \end{itemize}
%     \end{itemize}
%   \end{itemize}
% \end{frame}

% ------------------------------------------------

\section{Model} % Sections can be created in order to organize your presentation into discrete blocks, all sections and subsections are automatically printed in the table of contents as an overview of the talk

% ------------------------------------------------

\begin{frame}
  \frametitle{Model Environment}\label{environment}
  \begin{itemize}
  \item Life-cycle model: age discrete, indexed by $t$; retire at $T+1$.
  \item Agents:
    \begin{itemize}
    \item Employed and unemployed workers.
    \item Matched and unmatched firms.
  \end{itemize}
  \item Technology:
    \begin{itemize}
    \item Frictional matching in labor markets.
    \item Endogenous human capital accumulation.
    \item Borrowing constraints.
    \end{itemize}
  \item Initial heterogeneity:
    \begin{itemize}
    \item Initial wealth ($a_{0}$), human capital ($h_{0}$), and learning ability ($\ell$).
    \end{itemize}
  % \item Timing: \hyperlink{timing}{\beamergotobutton{here}}
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Agents}\label{agents}
  \begin{itemize}
  \item Risk-averse workers indexed by $(a, h, \ell, t)$:
    \begin{itemize}
    \item Employed ($\mu$), unemployed w/ UI ($b_{UI}$) or w/o UI ($b_{L}$).
    \item Search on and off job.
    \item Consume \& save s.t. borrowing constraint $a'\geq\underline{a}_{t}$.
    \item Emp.: portfolio allocation (HC inv. \& precautionary savings).
    \item Unemployed \& employed: stochastic HC depreciation.
    \end{itemize}
  \item Continuum of profit maximizing firms:
    \begin{itemize}
    \item Risk neutral. Produce using human capital.
    \item Post vacancies that specify piece-rate $\mu$.
    \end{itemize}
  \item World risk-free rate $r_{F}$; common discount rate $\beta$.
  \item Type-distribution $\phi' = \Phi(\phi)$ (suppressed throughout).
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Search and Matching Technology}\label{searchandmatching}
  \begin{itemize}
  \item Directed search (Moen, 1997):
    \begin{itemize}
    \item Submarket: homogeneous workers $(a, h, \ell, t)$ and firms $(\mu)$
    \item Workers apply to job in submarket w/ known piece-rate $\mu$.
    \end{itemize}
  \item Matching technology:
    \begin{itemize}
    \item \# of matches in submkt $(\mu, a, h, \ell, t)$: $M_{t} = M(s_{t},v_{t})$ (CRS).
    % \item Definitions:
    %   \begin{itemize}
      \item Submarket tightness: $\theta_{t}(\cdot) = \frac{v_{t}}{s_{t}}$
      \item Worker finding rate: $q(\theta_{t}) = \frac{M(s_{t},v_{t})}{v_{t}}$
      \item Job finding rates: $p(\theta_{t}) = \frac{M(s_{t},v_{t})}{s_{t}} = \theta_{t}q(\theta_{t})$
      % \end{itemize}
    \end{itemize}
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Firms}\label{firms}
  \begin{itemize}
  \item States: $s_{J} = (\mu, a, h, \ell), s' = (\mu', a', h', \ell), s_{J}' = (\mu, a', h', \ell)$
  \item Matched firms:
    \begin{itemize}
    \item produce $(1 - \tau)h$, pay $\mu(1 - \tau)h$
    \item separate exog. w/ prob. $\delta$; endog. w/ prob. $\lambda_{E}p(\theta_{t}(s'))$
    \item continue w/ value $J_{t + 1}(s_{J}')$
    \end{itemize}
  \item Value of filled vacancy with age-$t$ type-$s_{J}$ worker:
    \begin{flalign*}
      \hspace{-5mm}J_{t}(s_{J}) &=  (1 - \mu)(1 - \tau)h + \beta E[(1 - \delta)(1 - \lambda_{E}p(\theta_{t}(s')))J_{t + 1}(s_{J}')]\\
      h' &= e^{\epsilon'}(h + H(h,\ell,\tau)\\
      \epsilon'&\sim N(\mu_{\epsilon},\sigma_{\epsilon})
    \end{flalign*}
    \item Worker decisions: $\mu', a', h', \tau$.
  \end{itemize}
\end{frame}

% ------------------------------------------------


\begin{frame}
  \frametitle{Free Entry and Equilibrium Job-Finding Rates}\label{freeentry}
  \begin{itemize}
    \item Unmatched firms:
      \begin{itemize}
      \item Pay $\kappa$ to post (profitable) vacancies.
      \item Match w/ prob. $q(\theta_{t}(s_{J}))$.
      \end{itemize}
    \item Value of vacancy with age-$t$ type-$s_{J}$ worker:
      \begin{equation*}
        V_{t}(s_{J}) = -\kappa + q(\theta_{t}(s_{J}))J_{t}(s_{J})
      \end{equation*}
    \item Free Entry ($V_{t}(s_{J}) = 0$):
      \begin{align*}
        q(\theta_{t}(s_{J})) &= \frac{\kappa}{J_{t}(s_{J})}\\
        \theta_{t}(s_{J}) &= q^{-1}(\frac{\kappa}{J_{t}(s_{J})})
      \end{align*}
      \item Eqm. job finding rate: $p(\theta_{t}) = \theta_{t}q(\theta_{t})$ determined by $J_{t}, \kappa$
      \item Eqm.: $\der{P}{\mu}<0$
  \end{itemize}
\end{frame}


% ------------------------------------------------

\begin{frame}
  \frametitle{Unemployed Searcher's Problem}\label{unemployedworkerssearching}
  \begin{itemize}
\only<1>{  \item States (w/ UI): $s_{U} = (b_{UI}, a, h, \ell)$, $s_{E}' = (\mu', a, h, \ell)$}
\only<1>{  \item States (w/o UI): $s_{U} = (b_{L}, a, h, \ell)$, $s_{E}' = (\mu', a, h, \ell)$}
  \only<1>{\item Unemployed searcher's problem:
    \begin{itemize}
    \item Apply for job w/ piece-rate $\mu'$.
    \item Transition to employment w/ prob. $p(\theta_{t}(s_{E}'))$.
    \item Continue w/ value $W_{t}(s_{E}')$ if offered job.
    \item Continue w/ value $U_{t}(s_{U})$ if no offer.
    \end{itemize}}
  \item Value of searching while unemployed:
    \begin{equation*}
      R_{t}^{U}(s_{U}) =\max_{\mu'}{\only<2>{\color{red}}p(\theta_{t}(s_{E}'))}W_{t}(s_{E}') + (1 - p(\theta_{t}(s_{E}'))){\only<2>{\color{red}}U_{t}(s_{U})}
    \end{equation*}
  \vspace{-5mm}\only<2>{\hypertarget{unemployedworkerssearching2}
  \item Competitive labor market:
    \begin{itemize}
    \item Paid marginal product $\rightarrow$ inc. inequality because of diffs in HC
    \item Idiosyncratic shocks $\rightarrow$ consumption risk. Insurance via $a-\underline{a}$.
    % \item Precautionary savings only explicit insurance.
    \end{itemize}
  \item Frictional labor market:
    \begin{itemize}
    \item Frictions $\rightarrow$ $\mu < 1$.
    \item Employment risk $\rightarrow$ consumption risk.
    \item Precautionary savings (\& UI) only explicit insurance.
    \item Alternative: decrease $\mu$. $\rightarrow$ (low) wealth can impact earnings.
    % \end{itemize}}
  \end{itemize}}
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Unemployed Worker's Problem}\label{unemployedworkersconsumption}
  \begin{itemize}
  \item States:
    \begin{itemize}
    \item Unemp. w/ UI: $s_{U} = (b_{UI}, a, h, \ell)$, $s_{UI}' = (b_{UI}, a', h', \ell)$
% \only<2>{\vspace{-5mm}}
    \item Unemp w/o UI: $s_{U} = (b_{L}, a, h, \ell)$, $s_{L}' = (b_{L}, a', h', \ell)$
    \end{itemize}
    \only<1>{\item Consumption and savings problem:
      \begin{itemize}
      \item Consume \& save s.t. $a'\geq\underline{a}_{t}$.
      \item Lose benefits w/ prob. $\gamma$.
      \item Human Capital depreciates: $\epsilon'\sim N(\mu_{\epsilon},\sigma_{\epsilon})$.
      \end{itemize}}
  \item Value of unemployment (w/ UI)\only<2>{\hypertarget{unemployedworkersconsumption2}}: % \hyperlink{woUI_valuefunction}{\beamergotobutton{w/o UI}}
\begin{flalign*}
      U_{t}(s_{U}) &= \max_{c, {\only<2>{\color{red}}a'\geq\underline{a}_{t}}}u(c) + \beta E[(1 - \gamma)R_{t + 1}^{U}(s_{UI}') + {\only<2>{\color{red}}\gamma R_{t + 1}^{U}(s_{L}')}]\\
      \text{s.t. } c + a' &\leq (1 + r_{F})a + b_{UI}& \\
      h' &= e^{\epsilon'}h \\
      \epsilon'&\sim N(\mu_{\epsilon},\sigma_{\epsilon})
    \end{flalign*}
  \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Employed Worker's Problem}\label{employedworkersproblem}
  \only<1>{\vspace{-3mm}\begin{itemize}
% \only<1>{\item Value of on-the-job search: \hyperlink{otjs_valuefunction}{\beamergotobutton{Bellman Eqn.}}}
  \item States:
\begin{itemize}
  \item Emp.: $s_{E} = (\mu, a, h, \ell)$, $s_{E}' = (\mu, a', h', \ell)$
% \only<2>{\vspace{-1mm}}
  \item Unemp. w/ UI: $s_{U}' = (b_{UI}, a', h', \ell)$
\end{itemize}
% \only<2>{\vspace{-2mm}}
\item Employed Worker's Problem:
  \begin{itemize}
  \item Portfolio alloc.: $(a'\geq\unerline{a}_{t},\tau)$, $\tau$ to HC inv. \& $(1 - \tau)$ to work.
  \item Stochastic HC depreciation $\epsilon'\sim N(\mu_{\epsilon},\sigma_{\epsilon})$
  \item Lose job w/ prob. $\delta$, receive $b(1 - \tau)\mu h$.
  \end{itemize}
\item Value of employment:} % \hyperlink{otjs_valuefunction}{\beamergotobutton{Value of on-the-job search}}}
  % \end{itemize}
\vspace{-3mm}\begin{flalign*}
  W_{t}(s_{E}) &= \max_{c, a'\geq\underline{a}_{t},\tau} u(c) + \beta E[(1 - \delta)R_{t + 1}^{E}(s_{E}') + {\only<2>{\color{red}}\delta R_{t + 1}^{U}(s_{U}')}] \\
     \text{s.t. } c + a' &\leq (1 + r_{F})a + {\only<2>{\color{red}}(1 - \tau)\mu h}&  \\
     b_{UI} &= b(1 - \tau)\mu h \\
      h' &= e^{\epsilon'}(h + {\only<2>{\color{red}}\ell(h\tau)^{\alpha}}),\hspace{3mm} \epsilon'\sim N(\mu_{\epsilon},\sigma_{\epsilon})
    \end{flalign*}\vspace{-8mm}
  \only<2>{\begin{itemize}\hypertarget{employedworkersproblem2}  \item Human capital inv. is risky:
    \begin{enumerate}
    \item Rate of return uncertain: stochastic dep., unknown ex-ante.
    \item Illiquid: no consumption smoothing value when unemployed.
    \end{enumerate}
  \item Rate of return risk determines allocation for ``wealthy-enough.''
  \item Separation while low-wealth $\rightarrow$ take low-$\mu$ job.
  \item $\rightarrow$ {\color{red}Exposure to unemployment risk distorts allocation.} % \hyperlink{humancapital_mechanism}{\beamergotobutton{sketches}}
  }
\end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Equilibrium}\label{equilibrium}
A {\it Block Recursive Equilibrium} (BRE) in this model is a set of value functions, $U_{t}, W_{t}, R_{t}^{E}, R_{t}^{U}, J_{t}, V_{t}$, associated policy and market tightness functions, $a', c, \mu', \tau$, and $\theta_{t}$, which satisfy
\begin{enumerate}
\item The policy functions $\{c, \mu', a', \tau\}$ solve the workers problems, $W_{t}, U_{t}, R_{t}^{E}, R_{t}^{U}$.
\item $\theta_{t}(\mu, a, h, \ell)$ satisfies the free entry condition for all submarkets $(\mu, a, h, \ell, t)$.
\item The aggregate law of motion is consistent with all policy functions.
\end{enumerate}\vspace{5mm}
\end{frame}

% ------------------------------------------------

\section{Estimation} % Sections can be created in order to organize your presentation into discrete blocks, all sections and subsections are automatically printed in the table of contents as an overview of the talk

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Estimation}\label{estimation}
%   \begin{itemize}
%   \item Indirect Inference (conditional MoM) (Gourieux et al, 1993):
%   % \item Indirect inference approach (Gourieux et al, 1993):
%     \begin{itemize}
%     \item Use reduced-form analogs to structural model.
%     % \item Good analogs: capture mechanisms \& the eqm. Often regs.
%     % \item Estimate analog specifications using data \& simulated data.
%     \item Objective: match reg. estimates w/ data \& sim. data.
%     \item Minimize by changing structural parameters.
%     \end{itemize}
%   % \item Weight coefficients by inverse of empirical variance.
%   \item Outline:
%     \begin{itemize}
%     \item Preset functional forms \& ubiquitous/external parameters.
%     \item Estimate remaining parameters.
%     \end{itemize}
%   \end{itemize}
% \end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Estimation}\label{estimationstrategy}
  \begin{itemize}
  \item Indirect Inference (conditional MoM) (Gourieux et al, 1993):
  % \item Indirect inference approach (Gourieux et al, 1993):
    \begin{itemize}
    \item Select reduced-form analogs to structural model.
    % \item Good analogs: capture mechanisms \& the eqm. Often regs.
    % \item Estimate analog specifications using data \& simulated data.
    \item Objective: match coefs. for regs. w/ data \& simulated data.
    \item Minimize by changing structural parameters.
    \end{itemize}
  % \item Weight coefficients by inverse of empirical variance.
  \item Basic approach:
    \begin{itemize}
    \item Estimate effect of wealth on job search behavior.
    \item Match age-earnings regs (eqm. outcome) by initial heterogeneity.
    \item Match observable marginal distributions.
    \end{itemize}
  % \item Full auxiliary model (moments): \hyperlink{auxiliarymodel}{\beamergotobutton{here}}.
  % \item Further details: link
  % \item Outline:
  %   \begin{itemize}
  %   \item Discuss strategy.
  %   \item Show external moments.
  %   \end{itemize}
  \end{itemize}
\end{frame}

% ------------------------------------------------


\begin{frame}
  \frametitle{Empirical Preliminaries}\label{empiricalpreliminaries}
  \begin{itemize}
  \item Quarterly model, ages 23-64, retire at 65.
  \item Model parameters: $\sigma = 2, r_{F} = 0.012, \beta = \frac{1}{1 + r_{F}}$
  \item Power utility + unemp leisure: $u(c) = \frac{c^{1 - \sigma} - 1}{1 - \sigma}$
  \item HC Evolution: $h'=e^{\epsilon}(h+H(h,\ell,\tau))={\only<1>{\color{red}}e^{\epsilon}(h+\ell\times(h\tau)^{{\only<2>{\color{blue}}\alpha}})}$
  \item Natural borrowing constraint: ${\only<1>{\color{red}}\underline{a}_{t} = \sum_{j = t}^{T}\frac{\only<2>{\color{blue}}{b_{L}}}{(1 + r_{F})^{j}}}$
  \item Initial conditions:
    \begin{itemize}
    \item $(a_{0},h_{0},\ell) \sim LN(\only<2>{\color{blue}}{\psi, \Sigma}$)
    \item Correlations $\only<2>{\color{blue}}{\rho_{AH}, \rho_{AL}, \rho_{HL}}$
    \end{itemize}
  \item Full list of preset values: % \hyperlink{presetparameters}{\beamergotobutton{params}} \hyperlink{presetfunctionsanddistributions}{\beamergotobutton{fcns}} \hyperlink{presetfunctionsanddistributions}{\beamergotobutton{dists}}
  \end{itemize}
\end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Estimation Strategy}
%   \begin{itemize}
%   \item Target labor market behavior of low-wealth individuals for inference on $b_{L}$ and $a_{t}$.
%   \item Target earnings growth w/i job over life-cycle for inference on human capital accumulation $(\alpha_{H}, \mu_{\epsilon}, \sigma_{\epsilon})$.
%   \item Target earnings regression (Mincer) by initial heterogeneity:
%     \begin{itemize}
%     \item Intercepts \& slopes by initial wealth quintile ($\rho_{AH},\rho_{AL}$).
%     \item Intercepts \& slopes by standardized test (AFQT) quintile ($\rho_{HL}$).
%     \end{itemize}
%   \item Marginal Distributions:
%     \begin{itemize}
%     \item Wealth: Distribution of pre-labor market wealth.
%     \item Human Capital: Distribution of initial earnings.
%     \item Learning Ability: Slope and variance of earnings growth.
%     \end{itemize}
%   \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Estimation Strategy}\label{estimationstrategy}
%   \begin{itemize}
%   \item Basic outline:
%     \begin{enumerate}
%     \item Use regressions that yield inference on mechanisms.
%     \item Life-cycle eqm. outcomes for inference on correlations.
%     \item Match observed initial conditions for marginal distributions.
%     \end{enumerate}
%   % \item Mechanisms:
%   %   \begin{itemize}
%   \item ``Auxiliary model'' (Reduced-form moments):
% \begin{itemize}
%     \item Impact of relaxing borrowing constraint on application strategy.
%     \item Earnings growth within job.
%     % \end{itemize}
%   \item Earnings slope-intercept estimates by wealth \& AFQT quintiles
%   %   \begin{itemize}
%   %   \item Initial earnings: eqm of application rule \& initial HC ($\rho_{AH},\rho_{AL}$).
%   %   \item Growth: eqm outcome of application rule \& HC growth.
%   % \end{itemize}
%   % \item Run $ln(Y) = \sum_{q=1}^{5}(\beta_{0}^{q} + \beta_{1}^{q}Age) + nuiss. + \epsilon$  by
%   %   \begin{itemize}
%   % \item pre-labor market initial wealth quintiles: $\rho_{AH}, \rho_{AL}$.
%   % \item pre-labor market AFQT score quintiles: $\rho_{HL}$.
%   %   \end{itemize}
%   % \item Marginal Distributions:
%   %   \begin{itemize}
%     \item Dists. of pre-job wealth, initial earnings, \& LC earnings growth.
%     % \item Human Capital: Distribution of initial earnings.
%     % \item Learning Ability: Slope and variance of earnings growth.
%     \end{itemize}
%   % \item Mincer Eqn. details: (\hyperlink{mincerequation}{\beamergotobutton{here}})
%   \item Sample: male, full-time employed, ages 25-54. \hyperlink{data}{\beamergotobutton{details}}
%   \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Target: Borrowing Constraints \& Search Behavior}\label{targetborrowingconstraints}
%   \begin{itemize}
%   % \item Estimate response of unemployed workers to UI generosity.
%   \item Estimate elasticity of earnings at job $j + 1$ wrt UI generosity.
%   \item Model, UI generosity drawn iid $b\sim N(\round{\mur}{Value}{Params}{Param}{Mur}{2}, \round{\sigr}{Value}{Params}{Param}{Sigr}{2})$
%   % \item Classify by wealth quintile $q$ at beginning of spell.
%     \item Elasticity of earnings wrt UI RR by wealth qtile $q$: \hyperlink{UIelasticity}{\beamergotobutton{details}}
%       \begin{equation*}
%         ln(W_{i,j + 1}) = \sum_{q=1}^{5}\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i}) + X_{1,i}\beta_{2} + X_{2,i}\delta_{1} + \epsilon_{i,j + 1}
%       \end{equation*}
%     \item $X_{1,i}$ (included):  tenure, age, prev. earnings.
%     \item $X_{2,i}$ (``nuissance''): state, year, industry, occ, ed.
%     \item $\beta_{1}^{q}>0$: relax borrowing constraint $\rightarrow$ application strategy $\uparrow$.
%     % \item Findings: $\beta_{1}^{1}>0$ \& only $\beta_{1}^{1}$ significant. \hyperlink{allcoefficientestimates}{\beamergotobutton{All estimates}}
%     \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Identification: Borrowing Constraints \& Search Behavior}\label{targetborrowingconstraints}
%   % \item Estimate response of unemployed workers to UI generosity.
%   % \item Elast. of inc. wrt UI generosity by wealth quintile q.
%       % \vspace{-5mm}\begin{equation*}
%       %   ln(W_{i,j + 1}) = \sum_{q=1}^{5}\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i}) + X_{1,i}\beta_{2} + X_{2,i}\beta_{3} + \epsilon_{i,j + 1}\hspace{2mm}\hyperlink{UIelasticity}{\beamergotobutton{details}}
%       % \end{equation*}
%       \vspace{-3.5mm}
% \hspace{-5mm}\begin{minipage}{.4\textwidth}
% \begin{tikzpicture}[scale=0.7]
% \begin{axis}[
%     % axis lines=both,
%     domain = -30000:100000,
%     samples = 100,
%     % x label style={at={(axis description cs:0.5,0)},anchor=north},
%     % y label style={at={(axis description cs:0.05,0.5)},rotate=0,anchor=south},
%     legend style={at={(0.75,0.3)},anchor=north,legend columns=1},
%     legend cell align={left},
%     xtick pos = both, ytick pos = both,
%     yticklabel pos=left,
%     clip=false,
%     xlabel = {Liquid Wealth, $a$},
%     ylabel = {Application Strategy, $\mu'$},
%     ymin = 0, ymax = 1,
%     xmin = -30000, xmax = 100000
% ]

%     \addlegendimage{blue}
%     \addlegendentry{$b_{L}^{2} > b_{L}^{1}$}
%     \addlegendimage{red}
%     \addlegendentry{$b_{L}^{1}$}

% %% application rules
% \addplot [thick, blue, domain=-20000:100000] {0.7*(x + 30000)/(x + 32000) + 0.2};

% \addplot [dashed, blue, domain=-20000:10000] {0.86047 + 0.00000184429*x};

% \addplot [thick, red, domain=-8000:100000] {0.7*(x + 8000)/(x + 13000) + 0.2};

% \addplot [dashed, red, domain=-15000:7000] {0.65647 + 0.00003139053*x};

%        \node (source) at (axis cs:-5000,0){};
%        \node (destination) at (axis cs:-5000,1){};
%        \draw[dashed](source)--(destination);
% \node[anchor=west] (source) at (axis cs:-20000,0.95){\footnotesize $\epsilon_{Y,b}\approx 0$};
% \node (sourceA) at (axis cs:-15000,0.9){};
%        \node (destinationA) at (axis cs:-5000,0.85){};
%        \draw[->](sourceA) -- (destinationA);
% \node[anchor=west] (source) at (axis cs:10000,0.4){\footnotesize $\epsilon_{Y,b}>> 0$};
% \node (sourceB) at (axis cs:12000,0.45){};
%        \node (destinationB) at (axis cs:-5000,0.5){};
%        \draw[->](sourceB) -- (destinationB);


% \end{axis}
% \end{tikzpicture}
% \end{minipage}\hspace{15mm}
% \begin{minipage}{.35\textwidth}
% \begin{tikzpicture}[scale=0.7]
% \begin{axis}[
%     % axis lines=both,
%     domain = -30000:100000,
%     samples = 100,
%     % x label style={at={(axis description cs:0.5,0)},anchor=north},
%     % y label style={at={(axis description cs:0.05,0.5)},rotate=0,anchor=south},
%     legend style={at={(0.185,0.225)},anchor=north,legend columns=1},
%     legend cell align={left},
%     xtick pos = both, ytick pos = both,
%     yticklabel pos=left,
%     clip=false,
%     xlabel = {Application Strategy, $\mu'$},
%     ylabel = {Job-Finding Rate, $P(\theta)$},
%     ymin = 0, ymax = 1,
%     xmin = 0.2, xmax = 1
% ]

%     % xmin = -10000, xmax = 100000

%     \addlegendimage{blue}
%     \addlegendentry{$\eta_{2}>\eta_{1}$}
%     \addlegendimage{red}
%     \addlegendentry{$\eta_{1}$}

% \addplot [thick, blue, domain=0.2:0.965] {-x^(5) + 0.9};

% \addplot [dashed, blue, domain=0.4:0.6] {-0.3*x + 1.025};

% \addplot [dashed, blue, domain=0.75:0.9] {-2.3*x + 2.425};

% \addplot [thick, red, domain=0.2:0.935] {-x^(1.75) + 0.9};

% \addplot [dashed, red, domain=0.4:0.6] {-1.06*x + 1.15};

% \addplot [dashed, red, domain=0.75:0.9] {-1.5*x + 1.45};

% \node (source) at (axis cs:0.49,0){};
% \node (destination) at (axis cs:0.49,1){};
% \draw[dashed](source)--(destination);

% \node (source) at (axis cs:0.815,0){};
% \node (destination) at (axis cs:0.815,1){};
% \draw[dashed](source)--(destination);


% \node[anchor=west] (source) at (axis cs:0.565,0.9){\footnotesize $\epsilon_{P,b}^{1}>>\epsilon_{P,b}^{2}$};
% \node (sourceA) at (axis cs:0.575,0.91){};
%        \node (destinationA) at (axis cs:0.4825,0.88){};
%        \draw[->](sourceA) -- (destinationA);
% \node (sourceB) at (axis cs:0.775,0.85){};
%        \node (destinationB) at (axis cs:0.8,0.625){};
%        \draw[->](sourceB) -- (destinationB);


% \node[anchor=west] (source) at (axis cs:0.5,0.2){\footnotesize $\epsilon_{P,b}^{1}\approx\epsilon_{P,b}^{2}$};
% \node (sourceC) at (axis cs:0.525,0.225){};
%        \node (destinationC) at (axis cs:0.5,0.61){};
%        \draw[->](sourceC) -- (destinationC);
% \node (sourceD) at (axis cs:0.7,0.2){};
%        \node (destinationD) at (axis cs:0.8,0.225){};
%        \draw[->](sourceD) -- (destinationD);

% \end{axis}
% \end{tikzpicture}
% \end{minipage}
% \begin{itemize}
% \item Two specifications:
% \begin{enumerate}
% \item re-employment wage elasticity wrt UI by wealth.
% \item re-employment hazard elasticity wrt UI by wealth.
% \end{enumerate}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Identification: Correlations}\label{targetborrowingconstraints}
%   % \item Estimate response of unemployed workers to UI generosity.
%   % \item Elast. of inc. wrt UI generosity by wealth quintile q.
%       \vspace{-1.5mm}
% % \begin{center}
% \hspace{-3mm}
% \begin{minipage}{.4\textwidth}
% \begin{tikzpicture}[scale=0.7]
% \begin{axis}[
%     axis lines=both,
%     % axis line style={--},
%     x label style={at={(axis description cs:0.5,0)},anchor=north},
%     y label style={at={(axis description cs:0.1,0.5)},rotate=0,anchor=south},
%     % xtick=\empty, ytick=\empty,
%     width=9cm, height=8cm,
%     % ymajorgrids=true,
%     % grid style=dashed,
%     legend style={at={(0.8,0.3)},anchor=north,legend columns=1},
%     legend cell align={left},
%     xtick pos = both, ytick pos = both,
%     yticklabel pos=left,
%     clip=false,
%     xlabel = {Age},
%     ylabel = {Labor Earnings (logs)},
%     ymax = 12.5, ymin = 6,
%     xmin = 19.5, xmax = 31
% ]

%     \addlegendimage{mark=square,blue}
%     \addlegendentry{5th Quintile}
%     \addlegendimage{mark=triangle,green}
%     \addlegendentry{Median}
%     \addlegendimage{mark=*,red}
%     \addlegendentry{1st Quintile}

% %% earnings profile for top quintile
%     \addplot[mark=square*, blue] table[row sep=\\]{
%         X Y\\23 8.8\\24 9.1\\25 9.4\\26 9.7\\27 9.9\\28 10.1\\29 10.3\\30 10.5\\
%     };
%     \addplot[mark=square, blue] table[row sep=\\]{
%         X Y\\23 10.1\\24 10.2\\25 10.3\\26 10.4\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\
%     };
%     % \addplot[mark=square*, blue] table[row sep=\\]{
%     %     X Y\\23 8.8\\24 9.1\\25 9.4\\26 9.7\\27 9.9\\28 10.1\\29 10.3\\30 10.5\\31 10.65\\32 10.75\\33 10.9\\34 11.0\\35 11.1\\40 11.28\\
%     % };
%     % \addplot[mark=square, blue] table[row sep=\\]{
%     %     X Y\\23 10.1\\24 10.2\\25 10.3\\26 10.4\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\31 10.8\\32 10.9\\33 10.98\\34 11.08\\35 11.18\\40 11.3\\
%     % };
%     % \addplot [dashed, blue] table[row sep=\\,
%     % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\23 10.2\\24 10.3\\25 10.38\\26 10.45\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\
%     % };
%     % \addplot [dashed, blue] table[row sep=\\,
%     % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\31 10.69\\32 10.73\\33 10.8\\34 10.85\\35 10.89\\40 10.95\\
%     % };
%     % \addplot [dashed, blue] table[row sep=\\,
%     % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\31 10.8\\32 10.9\\33 10.98\\34 11.08\\35 11.18\\40 11.3\\
%     % };

% %% earnings profile for median
%     \addplot[mark=triangle, green] table[row sep=\\]{
%         X Y\\23 8.6\\24 8.8\\25 9\\26 9.15\\27 9.3\\28 9.42\\29 9.55\\30 9.67\\
%     };
%     \addplot [dashed, green] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\23 8.6\\24 8.8\\25 9\\26 9.15\\27 9.3\\28 9.42\\29 9.55\\30 9.67\\
%     };
%     \addplot [dashed, green] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 9.85\\32 9.95\\33 10.05\\34 10.12\\35 10.25\\40 10.35\\
%     };

% %% earnings profile for bottom quintile
%     \addplot[mark=*, red] table[row sep=\\]{
%         X Y\\23 8.5\\24 8.65\\25 8.75\\26 8.85\\27 9\\28 9.1\\29 9.2\\30 9.3\\
%     };
%     \addplot[mark=o, red] table[row sep=\\]{
%         X Y\\23 8\\24 8.25\\25 8.4\\26 8.55\\27 8.7\\28 8.9\\29 9.0\\30 9.1\\
%     };
%     % \addplot[mark=*, red] table[row sep=\\]{
%     %     X Y\\23 8.5\\24 8.65\\25 8.75\\26 8.85\\27 9\\28 9.1\\29 9.2\\30 9.3\\31 9.3\\32 9.3\\33 9.32\\34 9.325\\35 9.33\\40 9.3\\
%     % };
%     % \addplot[mark=o, red] table[row sep=\\]{
%     %     X Y\\23 8\\24 8.25\\25 8.4\\26 8.55\\27 8.7\\28 8.9\\29 9.0\\30 9.1\\31 9.2\\32 9.3\\33 9.32\\34 9.325\\35 9.33\\40 9.3\\
%     % };
%     % \addplot [dashed, red] table[row sep=\\,
%     % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\23 7\\24 8\\25 8.5\\26 8.7\\27 8.9\\28 9.1\\29 9.2\\30 9.3\\
%     % };
%     % \addplot [dashed, red] table[row sep=\\,
%     % % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\31 9.4\\32 9.5\\33 9.65\\34 9.7\\35 9.8\\40 9.93\\
%     % };
%     % \addplot [dashed, red] table[row sep=\\,
%     % y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     % %input table
%     % {
%     %     X Y\\31 9.3\\32 9.3\\33 9.32\\34 9.325\\35 9.33\\40 9.3\\
%     % };

% \node[anchor=west] (source) at (axis cs:23,11.25){\footnotesize \begin{tabular}{c} Intercept of Wages \\ if $\sigma_{HL} \rightarrow 0$ \end{tabular}};
% \node[anchor=west] (sourceA) at (axis cs:24.5,11.05);
% \node[anchor=west] (sourceB) at (axis cs:26.5,11.05);
%        \node (destinationA) at (axis cs:23,8.8){};
%        \node (destinationB) at (axis cs:23,8.55){};
%        \draw[->](sourceA)--(destinationA);
%        \draw[->](sourceB)--(destinationB);
% \node[anchor=west] (source) at (axis cs:19.25,9){\footnotesize \begin{tabular}{c} Intercept \\ of Wages \\ if $\sigma_{HL} \rightarrow 1$ \end{tabular}};
% \node[anchor=west] (sourceA) at (axis cs:22,9.5);
% \node[anchor=west] (sourceB) at (axis cs:22,8.5);
%        \node (destinationA) at (axis cs:23,9.85){};
%        \node (destinationB) at (axis cs:23,8.1){};
%        \draw[->](sourceA) -- (destinationA);
%        \draw[->](sourceB) -- (destinationB);

% \end{axis}
% \end{tikzpicture}
% \end{minipage}\hspace{15mm}
% \begin{minipage}{.4\textwidth}
% \begin{tikzpicture}[scale=0.7]
% \begin{axis}[
%     axis lines=both,
%     % axis line style={--},
%     x label style={at={(axis description cs:0.5,0)},anchor=north},
%     y label style={at={(axis description cs:0.1,0.5)},rotate=0,anchor=south},
%     % xtick=\empty, ytick=\empty,
%     width=9cm, height=8cm,
%     % ymajorgrids=true,
%     % grid style=dashed,
%     legend style={at={(0.8,0.3)},anchor=north,legend columns=1},
%     legend cell align={left},
%     xtick pos = both, ytick pos = both,
%     yticklabels={,,},
%     clip=false,
%     xlabel = {Age},
%     ylabel = {},
%     ymax = 12.5, ymin = 6,
%     xmin = 22, xmax = 62
% ]

%     \addlegendimage{mark=square,blue}
%     \addlegendentry{5th Quintile}
%     \addlegendimage{mark=triangle,green}
%     \addlegendentry{Median}
%     \addlegendimage{mark=*,red}
%     \addlegendentry{1st Quintile}

% %% earnings profile for top quintile
%     \addplot[mark=square*, blue] table[row sep=\\]{
%         X Y\\23 10.2\\24 10.3\\25 10.38\\26 10.45\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\31 10.69\\32 10.73\\33 10.8\\34 10.85\\35 10.89\\40 10.95\\45 11.15\\50 11.05\\55 11\\60 10.9\\
%     };
%     \addplot[mark=square, blue] table[row sep=\\]{
%         X Y\\23 10.2\\24 10.3\\25 10.38\\26 10.45\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\31 10.8\\32 10.9\\33 10.98\\34 11.08\\35 11.18\\40 11.3\\45 11.4\\50 11.5\\55 11.45\\60 11.3\\
%     };
%     \addplot [dashed, blue] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\23 10.2\\24 10.3\\25 10.38\\26 10.45\\27 10.5\\28 10.55\\29 10.6\\30 10.65\\
%     };
%     \addplot [dashed, blue] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 10.69\\32 10.73\\33 10.8\\34 10.85\\35 10.89\\40 10.95\\45 11.15\\50 11.05\\55 11\\60 10.9\\
%     };
%     \addplot [dashed, blue] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 10.8\\32 10.9\\33 10.98\\34 11.08\\35 11.18\\40 11.3\\45 11.4\\50 11.5\\55 11.45\\60 11.3\\
%     };

% %% earnings profile for median
%     \addplot[mark=triangle, green] table[row sep=\\]{
%         X Y\\23 8.6\\24 8.8\\25 9\\26 9.15\\27 9.3\\28 9.42\\29 9.55\\30 9.67\\31 9.85\\32 9.95\\33 10.05\\34 10.12\\35 10.25\\40 10.35\\45 10.5\\50 10.48\\55 10.47\\60 10.4\\
%     };
%     \addplot [dashed, green] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\23 8.6\\24 8.8\\25 9\\26 9.15\\27 9.3\\28 9.42\\29 9.55\\30 9.67\\
%     };
%     \addplot [dashed, green] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 9.85\\32 9.95\\33 10.05\\34 10.12\\35 10.25\\40 10.35\\45 10.5\\50 10.48\\55 10.47\\60 10.4\\
%     };

% %% earnings profile for bottom quintile
%     \addplot[mark=*, red] table[row sep=\\]{
%         X Y\\23 7\\24 8\\25 8.5\\26 8.7\\27 8.9\\28 9.1\\29 9.2\\30 9.3\\31 9.4\\32 9.5\\33 9.65\\34 9.7\\35 9.8\\40 9.93\\45 10.08\\50 10\\55 9.98\\60 9.75\\
%     };
%     \addplot[mark=o, red] table[row sep=\\]{
%         X Y\\23 7\\24 8\\25 8.5\\26 8.7\\27 8.9\\28 9.1\\29 9.2\\30 9.3\\31 9.3\\32 9.3\\33 9.32\\34 9.325\\35 9.33\\40 9.3\\45 9.3\\50 9.3\\55 9.25\\60 9.1\\
%     };
%     \addplot [dashed, red] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\23 7\\24 8\\25 8.5\\26 8.7\\27 8.9\\28 9.1\\29 9.2\\30 9.3\\
%     };
%     \addplot [dashed, red] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 9.4\\32 9.5\\33 9.65\\34 9.7\\35 9.8\\40 9.93\\45 10.08\\50 10\\55 9.98\\60 9.75\\
%     };
%     \addplot [dashed, red] table[row sep=\\,
%     y={create col/linear regression={y=Y}}] % compute a linear regression from the
%     %input table
%     {
%         X Y\\31 9.3\\32 9.3\\33 9.32\\34 9.325\\35 9.33\\40 9.3\\45 9.3\\50 9.3\\55 9.25\\60 9.1\\
%     };

% \node[anchor=west] (source) at (axis cs:22,11.8){\footnotesize \begin{tabular}{c} Slope of Wages \\ if $\sigma_{AL} \rightarrow 1$ \end{tabular}};
% \node[anchor=west] (sourceA) at (axis cs:36,11.7);
% \node[anchor=west] (sourceB) at (axis cs:36,11.6);
%        \node (destinationA) at (axis cs:43,11.2){};
%        \node (destinationB) at (axis cs:42,9.25){};
%        \draw[->](sourceA)--(destinationA);
%        \draw[->](sourceB)--(destinationB);
% \node[anchor=west] (source) at (axis cs:32,8.5){\footnotesize \begin{tabular}{c} Slope of Wages \\ if $\sigma_{AL} \rightarrow 0$ \end{tabular}};
% \node[anchor=west] (sourceA) at (axis cs:42,9);
% \node[anchor=west] (sourceB) at (axis cs:44,9);
%        \node (destinationA) at (axis cs:48,10.95){};
%        \node (destinationB) at (axis cs:48,9.85){};
%        \draw[->](sourceA) -- (destinationA);
%        \draw[->](sourceB) -- (destinationB);

% \end{axis}
% \end{tikzpicture}
% \end{minipage}
% % \end{center}
% \begin{itemize}
% \item Slope-intercept by wealth \& AFQT quintiles (separate).
% \item Intuition: learning ability ($\approx$ AFQT) determines slope. % \hyperlink{UIelasticity}{\beamergotobutton{details}}
% \item Sep. coefs for ages 25-40 (job ladder) and 41-54 (HC accum.)
% \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Key Target: Borrowing Constraints \& Search Behavior}\label{targetborrowingconstraints}
%   % \item Estimate response of unemployed workers to UI generosity.
%   % \item Elast. of inc. wrt UI generosity by wealth quintile q.
%       \vspace{-5mm}\begin{equation*}
%         ln(W_{i,j + 1}) = \sum_{q=1}^{5}\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i}) + X_{1,i}\beta_{2} + X_{2,i}\beta_{3} + \epsilon_{i,j + 1}\hspace{2mm}\hyperlink{UIelasticity}{\beamergotobutton{details}}
%       \end{equation*}
%       \vspace{-3mm}\begin{figure}[h]
%         \centering
%         \includegraphics[width=0.7\textwidth]{UI_identification.eps}
%   \end{figure}\vspace{-3.5mm}
% \begin{itemize}
% \item $X_{1}$ (included):  ten., age, prv. inc.;
% \item $X_{2}$ (empirical controls): st., yr., inds., occ., ed.
% \item Find: $\beta_{1}^{1}>0$ and significant at 5 percent lavel.\hyperlink{allcoefficientestimates}{\beamergotobutton{All estimates}}
% \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Inference on Borrowing Constraints}\label{targetapplicationstrategy}
%       \vspace{-7mm}\begin{align*}
%         ln(W_{i,j + 1}) &= \sum_{q=1}^{5}\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i}) + X_{1,i}\beta_{2} + X_{2,i}\delta_{1} + \epsilon_{i,j + 1}\\
%       \end{align*}\vspace{-15mm}
%       \begin{figure}[h]
%         \centering
%         \includegraphics[width=0.85\textwidth]{UI_identification.eps}
%   \end{figure}
% Findings: $\beta_{1}^{1}>0$ \& only $\beta_{1}^{1}$ significant.\hyperlink{allcoefficientestimates}{\beamergotobutton{All estimates}}
% \end{frame}

% ------------------------------------------------

  % \item Job separations (J2J+J2U) + unemployment rate $(\lambda_{E}, \nu)$.
% \begin{frame}
%   \frametitle{Other Targets}\label{othertargets}
%   \begin{itemize}
%   \item Marginal Distributions:
%     \begin{itemize}
%     \item Wealth: Distribution of pre-labor market wealth.
%     \item Human Capital: Distribution of initial earnings.
%     \item Learning Ability: Slope and variance of earnings growth.
%     \end{itemize}
%   \item HC production: w/i job growth rates by age bin $(\alpha_{H}, \mu_{\epsilon}, \sigma_{\epsilon})$.
%   \item Job separations (J2J+J2U) + unemployment rate $(\lambda_{E}, \nu)$.
%   \end{itemize}
% \end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Key Estimated Parameters and Coefficients}\label{keyestimationquantities}
  \begin{itemize}
  % \item Model Parameters:
  %   \begin{itemize}
  \item Parameter Estimates % \hyperlink{allestimatedparameters}{\beamergotobutton{all}}:
\begin{itemize}
    \item Age-23 constraint: $\underline{a}_{0} = -\$6,378$ (2011\$)
    \item HC curvature: $\alpha = 0.5687$.
      % \begin{itemize}
      % \item Browning, Hansen, and Heckman (1999): [0.5,0.99]
      % \end{itemize}
    % \item $\lambda_{E} = \round{\llambe}{Value}{Params}{Param}{lambe}{4}$.
    %   \begin{itemize}
    %   \item Lit: [0.73, 0.9] (Herkenhoff, 2016; Menzio and Shi, 2010)
    %   \end{itemize}
    % \end{itemize}
  \item HC dep.: $(\mu_{\epsilon},\sigma_{\epsilon})$ & $-0.0249,0.0621)$.
    % \begin{itemize}
    % \item Huggett, Ventura, and Yaron (2011): $(-0.029, 0.111)$
    % \end{itemize}
  \item Corrs.: $\rho_{AH} = 0.3253$ & $\rho_{A\ell} = 0.4642$ & $\rho_{H\ell} = 0.6915$.
    % \begin{itemize}
    % \item $\rho_{AH} = \round{\SigAH}{Value}{Params}{Param}{SigAH}{3}$
    % \item $\rho_{AL} = \round{\SigAL}{Value}{Params}{Param}{SigAL}{3}$
    % \item $\rho_{HL} = \round{\SigHL}{Value}{Params}{Param}{SigHL}{3}$
% \end{itemize}
    \end{itemize}
  % \item All parameters: \hyperlink{allestimatedparameters}{\beamergotobutton{here}}
\item Coefficient Estimates % \hyperlink{allcoefficientestimates}{\beamergotobutton{all}}:
 \begin{itemize}
  %  \item Search behavior:
  % \begin{itemize}
  \item $\der{ln(W_{i,j+1})}{ln(UI_{i})}$: Data: $0.4652$; Model: $0.2918$,
  \item $\der{ln(W_{i,j+1})}{ln(UI_{i})}(q>1)$: Data: $-0.4425$; Model: $-0.2731$
  \item $\der{ln(H_{i,j+1})}{ln(UI_{i})}(q=1)$: Data: $-0.8664$; Model: $-0.932$,
  \item $\der{ln(H_{i,j+1})}{ln(UI_{i})}(q>1)$: Data: $-0.4542$; Model: $-0.3336$
%   \end{itemize}
% \item Correlations:
%   \begin{itemize}
  \item $\rho_{AH}$: intercepts by wealth underpredicts higher quintiles.
  \item $\rho_{AL}$: overpredicts slopes by wealth in higher quintiles.
  \item $\rho_{HL}$: slopes by AFQT score quintile close.
  % \end{itemize}
\end{itemize}

  \end{itemize}
\end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Key Estimated Parameters}\label{keyestimatedparameters}
%   \begin{itemize}
%   % \item Model Parameters:
%   %   \begin{itemize}
%     \item Age-23 constraint: $\underline{a}_{0} = -\$3258$ (2011\$)
%     \item HC curvature: $\alpha_{H} = \round{\curvh}{Value}{Params}{Param}{curvh}{3}$.
%       \begin{itemize}
%       \item Browning, Hansen, and Heckman (1999): [0.5,0.99]
%       \end{itemize}
%     % \item $\lambda_{E} = \round{\llambe}{Value}{Params}{Param}{lambe}{4}$.
%     %   \begin{itemize}
%     %   \item Lit: [0.73, 0.9] (Herkenhoff, 2016; Menzio and Shi, 2010)
%     %   \end{itemize}
%     % \end{itemize}
%   \item Human capital depreciation: $(\mu_{\epsilon},\sigma_{\epsilon})$ & $(\round{\MuZ}{Value}{Params}{Param}{MuZ}{4},\round{\SigZ}{Value}{Params}{Param}{SigZ}{4})$.
%     \begin{itemize}
%     \item Huggett, Ventura, and Yaron (2011): $(-0.029, 0.111)$
%     \end{itemize}
%   \item Correlations:
%     \begin{itemize}
%     \item $\rho_{AH} = \round{\SigAH}{Value}{Params}{Param}{SigAH}{3}$
%     \item $\rho_{AL} = \round{\SigAL}{Value}{Params}{Param}{SigAL}{3}$
%     \item $\rho_{HL} = \round{\SigHL}{Value}{Params}{Param}{SigHL}{3}$
%       \begin{itemize}
%       \item HVY: $\rho_{HL} = 0.655$
%     \end{itemize}
%     \end{itemize}
%   \item All parameters: \hyperlink{allestimatedparameters}{\beamergotobutton{here}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Fit - Key Coefficients}\label{fitcoefficients}
% \begin{itemize}
% \item Search behavior:
%   \begin{itemize}
%   \item $\der{ln(W_{i,j+1})}{ln(UI_{i})}(q=1)$: Data: $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq1wba}{3}$; Model: $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile1XLogofUI}{3}$
%   \item $\der{ln(W_{i,j+1})}{ln(UI_{i})}(q=5)$: Data: $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq5wba}{3}$; Model: $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile5XLogofUI}{3}$
%   \end{itemize}
% \item Human capital:
%   \begin{itemize}
%   \item Const. (ages 25-29): Data: $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{_cons}{3}$; Model: $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeCons}{3}$
%   \item Ages 40-44: Data: $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{4.AgeGrp}{3}$; Model: $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp4}{3}$
%   \end{itemize}
% \item Correlations:
%   \begin{itemize}
%   \item $\rho_{AH}$: intercepts by wealth quintile close.
%   \item $\rho_{AL}$: overpredicts slopes by wealth quintile in higher quintiles.
%   \item $\rho_{HL}$: slopes by AFQT score quintile close.
%   \end{itemize}
% \item All coefficients: \hyperlink{allcoefficientestimates}{\beamergotobutton{here}}
% % \item Non-targeted life-cycle profiles: close.
% % \item Overpredicts job separations by 2-4pp.
% % \item Cannot reject matching wage growth variance and unemployment rate (P-Vals > 0.45).
% \end{itemize}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Initial Wealth}\label{wealthdistribution}
%     \centering
%         \includegraphics[width=0.8\textwidth]{RLiqWealthDistCompare_standard.eps}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Initial Earnings}\label{earningsdistribution}
%     \centering
%         \includegraphics[width=0.8\textwidth]{LRIncDistCompare_standard.eps}
% \end{frame}

% ------------------------------------------------

% \begin{frame}
%   \frametitle{Non-Targeted Moments}\label{lifecycleprofiles}
%     \centering
%          \includegraphics[width=0.775\textwidth]{LRIncHVY_3_compare_standard.eps}
% \end{frame}

% ------------------------------------------------

% \begin{frame}\frametitle{Fit: Earnings Profiles}\label{earningsprofiles1}
%             \centering
%             \includegraphics[width=0.775\textwidth]{./standard/LRIncHVY_3_compare_standard.eps}
% \end{frame}

% % % ------------------------------------------------

% \begin{frame}\frametitle{Fit: Inequality}\label{earningsprofiles1}
%             \centering
%             \includegraphics[width=0.775\textwidth]{./standard/LRIncHVY_3Var_compare_standard.eps}
% \end{frame}

% % ------------------------------------------------

\section{Findings} % Sections can be created in order to organize your presentation into discrete blocks, all sections and subsections are automatically printed in the table of contents as an overview of the talk

% ------------------------------------------------

\begin{frame}
  \frametitle{Findings}\label{Findings}
  \begin{itemize}
  \item Mechnisms \& life-cycle earnings growth $w_{t} = \mu_{t}(1 - \tau_{t})h_{t}$
  \item Two sources of earnings growth:
    \begin{itemize}
    \item Movement up job (piece-rate) ladder. $\mu_{t}$
    \item Investment in human capital. $h_{t}$
    \end{itemize}
  \item Consider two experiments, compare Inc., Cons., etc.:
    \begin{enumerate}
    \item Decrease initial conditions of median worker by 1 SD for each $(a_{0},h_{0},\ell)$.
    \item Eliminate initial dispersion for each $(a_{0},h_{0},\ell)$.
    \end{enumerate}
  \item Decompose interaction between wealth, search, and human capital.
  \end{itemize}
\end{frame}

% ------------------------------------------------



\begin{frame}\frametitle{Job Ladder}\label{piecerateprofile}
    \begin{figure}[ht]
            \centering
            \includegraphics[width=0.75\textwidth]{./PieceRateShare_Ave_Standard.eps}
    \end{figure}
\end{frame}

% % ------------------------------------------------

\begin{frame}\frametitle{Human Capital}\label{humancapitalprofile}
    \begin{figure}[ht]
            \centering
            \includegraphics[width=0.75\textwidth]{./HumanCap_Ave_Standard.eps}
    \end{figure}
\end{frame}

% ------------------------------------------------

% \begin{frame}\frametitle{Job Search Channel}\label{earningsprofiles2}
%     \begin{figure}[ht]
%             \centering
%             \includegraphics[width=0.7725\textwidth]{LRIncHVY_3_compareFullPlot2_standard.eps}
%     \end{figure}
% \end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Income}\label{earningsprofiles3}
    \begin{figure}[ht]
            \centering
            \includegraphics[width=0.6\textwidth]{./LRInc_3_Ave_Standard.eps}
    \end{figure}
    \begin{itemize}
    \item Job ladder: important early.
    \item Human capital: important mid/late.
    \end{itemize}
\end{frame}

% ------------------------------------------------

% \begin{frame}\frametitle{Application Strategy: Wealth Effects}
%     \begin{figure}[ht]
%         \begin{minipage}[b]{0.465\linewidth}
%             \centering
%             \includegraphics[width=\textwidth]{ApplicationStrategybyEmpUIStat_standard.eps}
%             \caption{Application Strategy}
%             \label{fig:a}
%         \end{minipage}
%         \hspace{0.5cm}
%         \begin{minipage}[b]{0.45\linewidth}
%             \centering
%             \includegraphics[width=\textwidth]{EmploymentProbabilitybyEmpUIStat_standard.eps}
%             \caption{Employment Probabilities}
%             \label{fig:b}
%         \end{minipage}
%     \end{figure}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Application Strategy}
%     \begin{figure}[ht]
%         \begin{minipage}[b]{0.465\linewidth}
%             \centering
%             \includegraphics[width=\textwidth]{LearnLearnTimeEmpEmpSlides_standard.eps}
%             \caption{Time by Learning Quintiles}
%             \label{fig:a}
%         \end{minipage}
%         \hspace{0.5cm}
%         \begin{minipage}[b]{0.45\linewidth}
%             \centering
%             \includegraphics[width=\textwidth]{WealthLearnTimeEmpEmpSlides_standard.eps}
%             \caption{Time by Wealth Quintiles}
%             \label{fig:b}
%         \end{minipage}
%     \end{figure}
% \end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Sources of Inequality}
  \begin{itemize}
  \item Explore 3 ways:
    \begin{enumerate}
    \item Set $h_{0}, \ell$ to median inital value.
      \begin{itemize}
      \item i.e., resulting variation due to wealth heterogeneity {\bf only}.
      \item Compare to previous figures.
      \end{itemize}
    \item Subject median worker to -1 SD in each $(a_{0},h_{0},\ell)$.
      \begin{itemize}
      \item Same experiment as HVY (2011).
      \end{itemize}
    \item Eliminate dispersion in initial conditions (separately).
    \end{enumerate}
  \item Focus on changes in average outcomes \& by wealth.
    \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Income}\label{earningsprofiles3}
    \begin{figure}[ht]
            \centering
            \includegraphics[width=\textwidth]{./LRInc_3_Ave_HLMed.eps}
    \end{figure}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Human Capital}\label{earningsprofiles3}
    \begin{figure}[ht]
            \centering
            \includegraphics[width=\textwidth]{./HumanCap_Ave_HLMed.eps}
    \end{figure}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Findings: Median Worker}\label{decisionrulesfindings}
\begin{table}[!h]\centering
\resizebox{\textwidth}{!}{%
\begin{tabulary}{\textwidth}{L c c | c c c c c c} \toprule & \multicolumn{2}{c|}{$\Delta$ Consumption} & $\Delta$ Earnings & $\Delta$ $h$ & $\Delta$ $\tau$ & $\Delta$ $\mu'$ \\
Change & (\%) &\text{HVY (\%)} & (\%) & (\%) & (\%) & (\%) \\ \hline
Wealth & $-6.4$ & $-1.6$ & $-5.8$ &  $-2.5$ & $-5.7$ & $-4.8$ \\
Human Capital & $-3.8$ & $-28.3$ & $-3.6$ &  $-4.8$ & $-5.9$ & $-0.4$ \\
Learning Ability & $-15.5$ & $-2.6$ & $-16.8$ &  $-29.1$ & $-96.3$ & $0.3$ \\
\bottomrule
\end{tabulary}
}
\end{table}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Findings: No Dispersion}\label{decisionrulesfindings}
\begin{table}[h]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabulary}{\textwidth}{L c c c c c | c c c c | c c c c } \toprule & & \multicolumn{4}{c}{$\Delta$ Income (\%)} & \multicolumn{4}{c}{$\Delta h\;(\%)$} & \multicolumn{4}{c}{$\Delta\mu\;(\%)$} \\
 \multicolumn{2}{c}{Counterfactual} & 1st & 3rd & 5th & Ave & 1st & 3rd & 5th & Ave & 1st & 3rd & 5th & Ave \\
\midrule
\multicolumn{2}{c}{$a_{0} = E[a_{0}]$} & $5.79$ & $1.09$ & $-2.06$ & $1.03$ & $1.50$ & $0.44$ & $-1.33$ & $0.12$ & $5.44$ & $0.89$ & $-1.84$ & $1.42$ \\
  \multicolumn{2}{c}{$h_{0} = E[h_{0}]$} & $1.74$ & $-0.65$ & $-3.40$ & $-1.10$ & $3.16$ & $0.69$ & $-2.14$ & $0.23$ & $0.69$ & $-0.16$ & $-0.52$ & $-0.01$ \\
  \multicolumn{2}{c}{$\ell = E[\ell]$} & $24.85$ & $1.24$ & $-17.97$ & $-1.07$ & $37.75$ & $11.32$ & $-8.37$ & $9.65$ & $1.26$ & $-0.51$ & $-1.35$ & $-0.29$ \\
\bottomrule
\end{tabulary}
}
\end{table}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Decomposing the Interaction}
  \begin{itemize}
  \item How does interaction between wealth, search, and human capital affect inequality?
  \item Compare outcomes in baseline model to 3 restrictions.
  \item Restrictions:
    \begin{itemize}
    \item R1: exogenous portfolio $\tilde{\tau}_{t}(\mu,a,h,\ell) = \tau_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$ and $\tilde{a'}_{t}(\mu,a,h,\ell) = a_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$.
    \item Bewley model: frictionless labor market, still human capital \& savings decision.
    \item R2: Bewley + exogenous portfolio $\tilde{\tau}_{t}(\mu,a,h,\ell) = \tau_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$ and $\tilde{a'}_{t}(\mu,a,h,\ell) = a_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$.
    \end{itemize}
    \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}
  \frametitle{Decomposing the Interaction}
  \begin{itemize}
    \item R1: exogenous portfolio $\tilde{\tau}_{t}(\mu,a,h,\ell) = \tau_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$ and $\tilde{a'}_{t}(\mu,a,h,\ell) = a_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$.
    \item Bewley model: frictionless labor market, still human capital \& savings decision.
    \item R2: Bewley + exogenous portfolio $\tilde{\tau}_{t}(\mu,a,h,\ell) = \tau_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$ and $\tilde{a'}_{t}(\mu,a,h,\ell) = a_{t}(\mu,\bar{a}_{t},h,\ell)\forall\;t$.
    \item $R1$ - Base: precautionary effect on human capital by wealth in baseline model.
    \item $R2$ - Bewley: precautionary effect on human capital by wealth without frictional labor markets.
    \item Difference between these comparisons: interaction between wealth, search, human capital.
    \end{itemize}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Findings: Exogenous Human Capital Comparison}\label{decisionrulesfindings}
\begin{table}[!h]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabulary}{\textwidth}{L c c c c c | c c c c } \toprule & & \multicolumn{4}{c}{$\Delta \tau\;(\%)$} & \multicolumn{4}{c}{$\Delta h\;(\%)$} \\
 \multicolumn{2}{c}{Counterfactual} & 1st & 3rd & 5th & Ave & 1st & 3rd & 5th & Ave \\
\midrule
\multicolumn{2}{c}{\%\Delta(Base$\rightarrow$R1)} & $33.18$ & $17.84$ & $6.42$ & $16.51$ & $6.01$ & $4.90$ & $1.36$ & $4.09$ \\
\bottomrule
\end{tabulary}
}
\end{table}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Findings: Frictionless Labor Markets Comparison}\label{decisionrulesfindings}
\begin{table}[h]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabulary}{\textwidth}{L c c c c c | c c c c } \toprule & & \multicolumn{4}{c}{$\Delta \tau$} & \multicolumn{4}{c}{$\Delta h$} \\
 \multicolumn{2}{c}{Counterfactual} & 1st & 3rd & 5th & Ave & 1st & 3rd & 5th & Ave \\
\midrule
\multicolumn{2}{c}{\%\Delta(Bewley$\rightarrow$R2)} & $15.15\%$ & $12.49\%$ & $6.80\%$ & $11.16\%$ & $3.29\%$ & $3.75\%$ & $2.16\%$ & $3.19\%$\\
\multicolumn{2}{c}{Effect of Wealth x Search} & $18.03$pp & $5.35$pp & $-0.37$pp & $5.35$pp & $2.72$pp & $1.16$pp & $-0.80$pp & $0.90$pp  \\
\bottomrule
\end{tabulary}
}
\end{table}
\end{frame}

% ------------------------------------------------

\begin{frame}\frametitle{Findings: Interaction}\label{decisionrulesfindings}
\begin{table}[!h]
\centering\resizebox{\textwidth}{!}{%
\begin{tabulary}{\textwidth}{c c C c c} \toprule \multicolumn{2}{c}{Counterfactual} & 1st & 3rd & 5th \\
\midrule
\multicolumn{2}{c}{\%\Delta Income (Base$\rightarrow$R1)} & $41.11\%$ & $3.24\%$ & $-26.87\%$ \\
\multicolumn{2}{c}{\% Explained by Interaction} & $6.61\%$ & $35.69\%$ & $2.98\%$ \\ % \hline
\bottomrule
\end{tabulary}
}
\end{table}
\end{frame}

% ------------------------------------------------

 \section{Conclusion}
 % ------------------------------------------------

 \begin{frame}
   \frametitle{Conclusion}\label{conclusion}
   \begin{itemize}
   \item Constructed quantitative life-cycle model:
     \begin{itemize}
     \item Risk-averse agents who face borrowing constraints.
     \item General equilibrium labor market frictions.
     \item Endogenous earnings growth through human capital choice.
     \end{itemize}
   \item Estimated using indirect inference.
   \item Findings:
     \begin{itemize}
     \item Borrowing constraints \& search impact low-wealth individuals.
     \item Wealth dynamically alters the earnings process through search behavior and human capital accumulation.
     \item Initial wealth causes larger life-cycle changes than initial human capital (and sometimes learning ability).
     \end{itemize}
   \item Don't forget to start your model projects!
   \end{itemize}
 \end{frame}

 % ------------------------------------------------

%  \begin{frame}
%    \frametitle{Conclusion + Future Work}
%    \begin{itemize}
%    \item Empirical evidence of strong liquidity effects. Explore persistence using model.
%    \item Findings:
%      \begin{itemize}
%    \item Previous literature attributes most to human capital inequality.
%    \item Three channels:
%      \begin{itemize}
%      \item Direct effect of less wealth.
%      \item change in application strategy.
%      \item change in human capital accumulation.
% \end{itemize}
%    \item Learning ability most important driver of inequality in model.
%    \end{itemize}
% \end{itemize}
%  \end{frame}

 % ------------------------------------------------

% \appendix
% \section{Additional Material}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Re-Employment Wages and UI Rep. Rates}\label{UIElast}
%       \vspace{-5mm}\begin{align*}
%         ln(W_{i,j + 1}) &= \sum_{q=1}^{5}(\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i})+\beta_{2}^{q}\times \mathbbm{1}_{i,q}UIDur)\\ &+ X_{1,i}\beta_{2} + X_{2,i}\beta_{3} + \epsilon_{i,j + 1}\hspace{2mm}
%       \end{align*}
% \begin{itemize}
% \item UIDur: Potential UI dur
% \item $X_{1}$:  dur, ten., age, prv. inc., st., yr., inds., occ., ed., total wealth.
% \item Find:
%   \begin{itemize}
%   \item $\beta_{1}^{1} = 0.634$ p$< 0.5$
%   \item $\beta_{1}^{2} = 0.303$
%   \item $\beta_{1}^{3} = -0.0310$
%   \item $\beta_{1}^{4} = 0.0194$
%   \item $\beta_{1}^{5} = 0.107$
%   \end{itemize}
% \item \hyperlink{lcprofilesbywealth}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Training and Wealth}\label{Training}
% \begin{figure}[!h]
% \centering
%   \includegraphics[width=\linewidth]{TrainingbyWealthNLSY2.eps}
% \label{fig:training}
% \end{figure}
% \begin{itemize}
% \item \hyperlink{lcprofilesbywealth}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Timing}\label{timing}
%     \begin{center}
% \begin{tikzpicture}[
%     node distance = 0mm,
% TN/.style args = {#1/#2}{% as Transparency Node
%     fill=#1,
%     fill opacity=#2,
%     text opacity=1, align=flush left,
%     inner sep=1mm, outer sep=0.25mm, below},
%    TN/.default = gray!50/0.5,
%       L/.style = {% as Line
%     draw=gray, line width=1mm,
%     {Bar[width=4mm,line width=0.4pt]}-{Bar[width=4mm,line width=0.4pt]}
%                  },
%       M/.style = {% as Line
%     draw=gray, line width=0.2pt
%                  }
%                     ]
% \draw[L]    ( 0,0)  coordinate (s)
%                     node[above=2mm] {$t$} -- +
%             (10,0)  node[above=2mm] {$t+1$};
%     \draw[M] (5,6pt) -- (5,-6pt);
%     \draw (2.5,0) node[above=3pt] {$Search$};
%     \draw (5,0) node[above=14pt] {$Job$};
%     \draw (5,0) node[above=3pt] {$Offers$};
%     \draw (7.5,0) node[above=3pt] {$Production$};
% \draw[M] (2.5,-0.01) -- + (0,-0.5)
%         node (e1)   [TN]              {Decisions}
%         node (e1a)  [TN,below=of e1]  {Firms: post vacancies.\\
%                                        Workers: apply for jobs};
% \draw[M] (5,-0.01) -- + (0,-2.25)
%         node (e2)   [TN=blue!30/0.4]  {Shocks}
%         node (e2a)  [TN=blue!30/0.7,
%                      below=of e2]    {Labor market resolves\\
%                                     Benefits lost w/ prob. $\gamma$};
% \draw[M] (7.5,-0.01) -- + (0,-0.5)
%         node (e1)   [TN]              {Worker Decisions}
%         node (e1a)  [TN,below=of e1]  {All: Cons. + savings\\
%                                        Emp.: time allocation};
% \draw[M] (10,-0.01) -- + (0,-2.25)
%         node (e2)   [TN=blue!30/0.4]  {Shocks}
%         node (e2a)  [TN=blue!30/0.7,
%                      below=of e2]    {Separate exog. \\\hspace{2mm}w/ prob. $\delta$\\
%                                       UI RR drawn\\
%                                       HC depreciation\\\hspace{2mm}shock, $\epsilon'$};
%     \end{tikzpicture}
%     \end{center}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Unemployed Worker's Problem}\label{woUI_valuefunction}
%   \begin{itemize}
%   \item States:
%     \begin{itemize}
%     \item Unemp w/o UI: $s_{U} = (b_{L}, a, h, \ell)$, $s_{L}' = (b_{L}, a', h', \ell)$
%     \end{itemize}
% \item Value of unemployment (w/o UI):
% \begin{flalign*}
%       U_{t}(s_{U}) &= \max_{c, a'\geq\underline{a}_{t}}U(c) + \beta E[R_{t + 1}^{U}(s_{L}')]\\
%       \text{s.t. } c + a' &\leq (1 + r_{F})a + b_{L}& \\
%       h' &= e^{\epsilon'}h \\
%       \epsilon'&\sim N(\mu_{\epsilon},\sigma_{\epsilon})
%     \end{flalign*}
%   \item \hyperlink{unemployedworkersconsumption2}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Mechanism: Application Strategy}\label{applicationstrategy_mechanism}
%   \begin{itemize}
%   \item Competitive vs frictional labor market:
%     \begin{itemize}
%     \item Competitive: workers paid marginal product $\rightarrow$ HC determines earnings difference.
%     \item Frictional world: piece-rate $\cancel{=}$ MP + depends on worker state.
%   \end{itemize}
%   \item Unemployed application strategy $(\mu^{*})$:
%     \begin{equation*}
%       \mu^{*}'\text{ solves }\underbrace{\frac{\der{P}{\theta_{t}}\der{\theta_{t}}{\mu'}}{P(\theta_{t})}}_{\leq 0} = \underbrace{-\frac{\der{W_{t}}{\mu'}}{(W_{t} - U_{t})}}_{-(\geq 0)}
%     \end{equation*}
%   \item High wealth: $(W_{t} - U_{t})\approxeq 0$  $\rightarrow\mu^{*}'\uparrow$ so LHS $\downarrow$
%   \item Low wealth: $(W_{t} - U_{t})>> 0$ $\rightarrow\mu^{*}'\downarrow$ so LHS $\uparrow$
%   \item \hyperlink{unemployedworkerssearching2}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{On-the-Job Search Value}\label{otjs_valuefunction}
%   \begin{itemize}
%   \item States:
% \begin{itemize}
%   \item Emp.: $s_{E} = (\mu, a, h, \ell)$, $s_{E}' = (\mu', a, h, \ell)$
%   \item Unemp. w/ UI: $s_{U}' = (b_{UI}, a', h', \ell)$
% \end{itemize}
% \item Value of on-the-job search:
%   \begin{equation*}
%     \hspace{-3mm}R_{t}^{E}(s_{E}) = \max_{\mu'}\lambda_{E}p(\theta_{t}(s_{E}'))W_{t}(s_{E}') +  (1 - \lambda_{E}p(\theta_{t}(s_{E}')))W_{t}(s_{E})
%   \end{equation*}
% \item \hyperlink{employedworkersproblem}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Mechanism: Human Capital Accumulation}\label{humancapital_mechanism}
%   \begin{itemize}
%   \item Human capital depreciates: $\Rightarrow$ risky return on investment $\tau$.
%   \item Illiquid: only ``tradeable'' while employed.
%   \item No arbitrage condition:
%     \begin{equation*}
%       E[(1 - \delta)\der{R^{E}}{h'} + \delta\der{R^{U}}{h'}]\der{H}{\tau}\geq E[(1 - \delta)\der{R^{E}}{a'} + \delta\der{R^{U}}{a'}]\mu h
%     \end{equation*}
%   \item Learning ability:
%     \begin{itemize}
%     \item Changes return to HC investment.
%     \end{itemize}
%   \item Euler Equation:
%     \begin{equation*}
%       u'(c)\mu h = \beta E[(1 - \delta)\der{R^{E}}{h'} + \delta\der{R^{U}}{h'}]\der{H}{\tau}
%     \end{equation*}
%   \item Wealth:
%     \begin{itemize}
%     \item Consumption smoothing: decrease wealth $\Rightarrow u'(c)\uparrow\Rightarrow\tau\downarrow$
%     \item Unemployed cons. risk: decrease wealth $\Rightarrow \der{R^{U}}{a'}\uparrow\Rightarrow\tau\downarrow$
%     \end{itemize}
% \item \hyperlink{employedworkersproblem2}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Preset Functions and Distributions}\label{presetfunctionsdistributions}
% \begin{table}[h]\centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\linewidth}{L l c c c} \toprule \multicolumn{2}{c}{\text{Category}} & \text{Symbol} & \text{Value or Function} & \text{Source} \\
% \midrule $Functional;Forms$\\ & Utility Function & U(c) & $\frac{c^{1 - \sigma} - 1}{1 - \sigma} + (1 - e)\nu$ & Power Utility  \\
% & Production Function &$f(h)$ & $zh$ &  \\  & Human Capital Production & $H(h,\ell,\tau,E)$ & $\ell(h\tau)^{\alpha_{H}}$ & Ben-Porath (1967) \\
%                                   & Human Capital Evolution & $h'$ & $e^{\epsilon'}(h + H(h,l,\tau,E))$ &  \\
% & Matching Function & $M(u, v)$ & $\frac{uv}{(u^{\eta} + v^{\eta})^{\frac{1}{\eta}}}$ & Schaal (2012) \\
%  & Borrowing Constraint & $\underline{a}'$ & $\sum_{j = t}^{j = T}\frac{b_{L}}{(1 + r_{F})^{j}}$ & Natural Borrowing Limit at time $t$ \\
% $Distributional\;Assumptions$\\
%                                   & Depreciation & $\epsilon'$ & $\epsilon' \sim N(\mu_{\epsilon}, \sigma_{\epsilon})$ & \\
%                                   & Initial Conditions & $\Psi$ & $\Psi\sim LN(\psi, \Sigma)$ &  \\
%                                   & Mean & $\psi$ & $\begin{bmatrix}
%      \mu_{A} &
%      \mu_{H} &
%      \mu_{L}
%   \end{bmatrix}'$ \\
%                                   & Variance & diag($\Sigma$) & ($\sigma_{A}, \sigma_{H}, \sigma_{L}$) \\
%                                   & Correlation &  & ($\rho_{AH}, \rho_{AL}, \rho_{HL}$) \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \hyperlink{empiricalpreliminaries}{\beamerreturnbutton{back}}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Preset Parameters}\label{presetparameters}
% \begin{table}[h]\centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\linewidth}{R l c c c} \toprule \multicolumn{2}{c}{\text{Category}} & \text{Symbol} & \text{Value or Function} & \text{Source} \\
% \midrule $Model\;Parameters$\\
%                                   &  Discount Factor & $\beta$ & $\pgfmathparse{1/(1 + \rfr)}\pgfmathresult$ & $\frac{1}{1 + r_{F}}$\\
%                                   &  Risk Aversion & $\sigma$ & $\round{\ssigma}{Value}{Params}{Param}{ssigma}{0}$ & Standard \\
%                                   &  Quarters & $T$ & $\round{\TT}{Value}{Params}{Param}{T}{0}$ & Standard \\

%                                   & Elasticity of Matching Function & $\eta$ & $\round{\eeta}{Value}{Params}{Param}{eeta}{1}$ & Shi, 2016 \\
%                                   & Vacancy Creation Cost & $\kappa$ & $\round{\kkappa}{Value}{Params}{Param}{kkappa}{1}$ & Shi, 2016 \\
% & Separation Rate & $\delta$ & $\round{\ddelta}{Value}{Params}{Param}{ddelta}{3}$ & Quarterly average 1968-2013 \\
% & Scale Factor & $z$ & $\round{\prod}{Value}{Params}{Param}{w0}{0}$ & Mean quarterly earnings (Age 25, PSID) \\
%                                  & Quarterly Max UI (unscaled) & $\gamma$ & $\round{\Maxb}{Value}{Params}{Param}{Maxb}{2}$ & Average UI cap \\
%                                  & UI Loss Probability & $\gamma$ & $\round{\ggamma}{Value}{Params}{Param}{ggamma}{2}$ & Sample max UI duration average \\
% & Risk Free Rate  & $r_{F}$ & $\round{\rfr}{Value}{Params}{Param}{r}{3}$ & Annual rate of $\approx 5\%$\\
% $Distributional\;Parameters$\\           & UI Replacement Rate Distribution & $(\mu_{b},\sigma_{b})$ & $(\round{\mur}{Value}{Params}{Param}{Mur}{2}, \round{\sigr}{Value}{Params}{Param}{Sigr}{3})$ & Approx. sample distribution  \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \hyperlink{empiricalpreliminaries}{\beamerreturnbutton{back}}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Data}\label{data}
%   \begin{itemize}
%   \item Panel Study of Income Dynamics (PSID):
%     \begin{itemize}
%     \item Panel from 1968-2013. Initial wealth \& earnings profiles.
%     \end{itemize}
%   \item National Longitudinal Survey of Youth, 1979 (NLSY):
%     \begin{itemize}
%     \item Standardized test scores \& earnings profiles.
%     \end{itemize}
%   \item Survey of Income and Program Participation (SIPP):
%     \begin{itemize}
%     \item Employment status \& earnings \& wealth.
%     \end{itemize}
%   \item Identical sample restrictions: male, full-time employed (except SIPP), ages 25-54.
%   \item \hyperlink{estimationstrategy}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Auxiliary Model}\label{auxiliarymodel}
%   \begin{itemize}
%   \item Mechanisms:
%     \begin{itemize}
%     \item Wealth effects on job search: elasticity of re-employment earnings wrt UI replacement rates (Borr. Const., $\underline{a}_{t}$).
%     \item Earnings growth rates by age (HC Curv. $\alpha_{H}$).
%     \end{itemize}
%   \item Equilibrium Outcomes (Correlations):
%     \begin{itemize}
%     \item Intercept-slope of earnings by initial wealth qtile: $\rho_{AH}, \rho_{AL}$.
%     \item Intercept-slope of earnings by AFQT: $rho_{HL}$
%     \end{itemize}
%   \item Marginal Distributions:
%     \begin{itemize}
%     \item Pre-labor market wealth (wealth dist.).
%     \item Earnings at first job (HC dist).
%     \item Average and variance of earnings growth rates (learning dist).
%     \end{itemize}
%   \item \hyperlink{estimationstrategy}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------


% \begin{frame}
%   \frametitle{Correlations with $\ell$}\label{mincerequation}
%   \begin{itemize}
%   \item Initial earnings: equilibrium outcome of application strategy and initial human capital.
%   \item Earnings growth: equilibrium outcome of application strategy and time allocation (mainly, learning ability).
%   \item Use Mincer Equation:
%     \begin{align*}
%       \label{eq:Aux5}
%       ln(Y_{i}) = \beta_{3} + \sum_{q = 2}^{5}\mathbbm{1}_{q}\beta_{4}^{q} + \beta_{5}Age_{i} + \sum_{q = 2}^{5}\beta_{6}^{q}\times \mathbbm{1}_{q}Age_{i} + X_{4,i}\delta_{4} + \epsilon_{i}
%     \end{align*}
%   \item Earnings regression by initial wealth quintiles: $\rho_{AH}, \rho_{AL}$.
%   \item by AFQT score quintiles: $\rho_{HL}$.
%   \item \hyperlink{estimationstrategy}{\beamergotobutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Approach}\label{UIelasticity}
%   \begin{itemize}
%   \item Estimate elasticity of re-employment wages wrt UI benefits, across net liq. wealth (liquid assets - unsecured debt) quintiles.
%   \item Intuition: UI laws vary across states $\rightarrow$ same income $\rightarrow$ different UI.
%   \item Proxy for reported UI using state-month average weekly UI benefits.
%   \item Data: Survey of Income and Program Participation (SIPP).
%   \item Specification:
%     \begin{equation*}
%         ln(W_{i,j + 1}) = \sum_{q=1}^{5}\beta_{1}^{q}\times \mathbbm{1}_{i,q}ln(UI_{i}) + X_{1,i}\beta_{2} + X_{2,i}\delta_{1} + \epsilon_{i,j + 1}
%       \end{equation*}
%   \item $X_{1,i}$ includes prev. wage, tenure, age
%   \item $X_{2,i}$ industry, occ., ed, other demographics.
%   \item \hyperlink{targetborrowingconstraints}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Estimation Results}\label{allestimatedparameters}
% \begin{table}[h]\centering
% \renewcommand{\arraystretch}{1.25}
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{R l c c c c c} \toprule \multicolumn{2}{c}{\text{Category}} & \text{Symbol} & \multicolumn{3}{c}{\text{Model Value}} & \text{Comment} \\
% \midrule $Model\;Parameters$\\ & Subsistence Benefits  & $b_{L}$ & $\round{\nb}{Value}{Params}{Param}{nb}{4}$  \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{nb}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{nb}{4}$] \\
%                            & Borrowing Constraint & $\underline{a}_{t}$ & & & \multicolumn{2}{c}{\text{Qtrly Period-0 Value (2011\$): }$\$\pgfmathparse{int((\prod/4)*\nb*((1 - (1/(1 + \rfr))^(\TT+1))/(1 - (1/(1 + \rfr)))))}\pgfmathresult$} \\
%                            & On-the-job Search Efficiency & $\lambda_{E}$ & $\round{\llambe}{Value}{Params}{Param}{lambe}{4}$ &  \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{lambe}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{lambe}{4}$] \\
%                            & Human Capital Curvature & $\alpha_{H}$ & $\round{\curvh}{Value}{Params}{Param}{curvh}{4}$ & & & BHH: [0.5, 0.99]\\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{curvh}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{curvh}{4}$] \\
%                            &  Leisure Utility & $\nu$ & $\round{\nnu}{Value}{Params}{Param}{nnu}{4}$ \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{nnu}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{nnu}{4}$] \\
% $Distributional\;Parameters$\\ & Dist. of $\epsilon'$ & $(\mu_{\epsilon},\sigma_{\epsilon})$ & $\mu_{\epsilon} = \round{\MuZ}{Value}{Params}{Param}{MuZ}{4}$ & $\sigma_{\epsilon} = \round{\SigZ}{Value}{Params}{Param}{SigZ}{4}$ & & HVY: $(0.029, 0.111)$\\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{MuZ}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{MuZ}{4}$] & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigZ}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigZ}{4}$] & \\
%                                   & Marg. Dist. of $a_{0}$ & $(\mu_{A}, \sigma_{A})$ & $\mu_{A} = \round{\MuA}{Value}{Params}{Param}{MuA}{4}$ & $\sigma_{A} = \round{\SigA}{Value}{Params}{Param}{SigA}{4}$ & & Mean: $\$\getvalround{Value}{InitTestAupresults}{Comparison}{AAInitBase}{0}$ \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{MuA}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{MuA}{4}$] & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigA}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigA}{4}$] & \\
%                                   & Marg. Dist. of $h_{0}$ & $(\mu_{H}, \sigma_{H})$ & $\mu_{H} = \round{\MuH}{Value}{Params}{Param}{MuH}{4}$ & $\sigma_{H} = \round{\SigH}{Value}{Params}{Param}{SigH}{4}$ & & Mean: $\$\getvalround{Value}{InitTestHupresults}{Comparison}{HHInitTest}{0}$ \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigH}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigH}{4}$] \\
%                                   & Marg. Dist. of $\ell$ & $(\mu_{L}, \sigma_{L})$ & $\mu_{L} = \round{\MuL}{Value}{Params}{Param}{MuL}{4}$ & $\sigma_{L} = \round{\SigL}{Value}{Params}{Param}{SigL}{4}$ & \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{MuL}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{MuL}{4}$] & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigL}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigL}{4}$] & \\
%                                   & Correlations & $\rho_{AH}, \rho_{AL}, \rho_{HL}$ & $\rho_{AH} = \round{\SigAH}{Value}{Params}{Param}{SigAH}{4}$ & $\rho_{AL} = \round{\SigAL}{Value}{Params}{Param}{SigAL}{4}$ & $\rho_{HL} = \round{\SigHL}{Value}{Params}{Param}{SigHL}{4}$  & HVY: $\rho_{HL} = 0.655$ \\
%                            & & & [$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigAH}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigAH}{4}$] &[$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigAL}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigAL}{4}$] &[$\getvalround{LowerCI}{StructCIsestimresults}{Coef}{SigHL}{4}$,$\getvalround{UpperCI}{StructCIsestimresults}{Coef}{SigHL}{4}$]\\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \hyperlink{keyestimationquantities}{\beamerreturnbutton{back}}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Coefficient Estimates \hyperlink{targetborrowingconstraints}{\beamerreturnbutton{UI Elasticity}} \hyperlink{targetcorrelations}{\beamerreturnbutton{Mincer}} \hyperlink{keyestimationquantities}{\beamerreturnbutton{Estimates}}}\label{allcoefficientestimates}
% \begin{table}[h]\centering
% \renewcommand{\arraystretch}{1}
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{r r r r | r r r r | r r r r}
%  \toprule \multicolumn{4}{c|}{Slopes and Intercepts by Wealth (PSID)}   & \multicolumn{4}{c|}{Slopes and Intercepts by AFQT (NLSY)}  & \multicolumn{4}{c}{Re-Employment Elasticities (SIPP)} \\
%   \text{Var.} & \text{Data} & \text{Model} & \text{P-Val} & \text{Var.} &\text{Data} & \text{Model} & \text{P-Val} & \text{Var.}  & \text{Data} & \text{Model} & \text{P-Val} \\ \hline
%    \text{Age} &  $\getvalround{Value}{PSIDestimresults}{Coef}{Age}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{AgePSID}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{AgePSID}{4}$ & \text{Age} & $\round{\ageNLSY}{Value}{NLSYestimresults}{Coef}{Age}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{AgeNLSY}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{AgeNLSY}{4}$ & \text{Q1 x Ln(UI)} & $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq1wba}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile1XLogofUI}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Quintile1XLogofUI}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{AgePSID}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{AgePSID}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{AgeNLSY}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{AgeNLSY}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile1XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Quintile1XLogofUI}{4}$) & \\ \text{Wealth Q2 x Age} & $\round{\agesecondPSID}{Value}{PSIDestimresults}{Coef}{ageXsecond}{4}$ &  $\getvalround{Value}{Estimationresults}{Coef}{Slope30thWealth}{4}$& $\getvalround{Value}{PValestimresults}{Coef}{Slope30thWealth}{4}$ & \text{AFQT Q2 x Age} &  $\round{\agesecondNLSY}{Value}{NLSYestimresults}{Coef}{ageXsecond}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope30thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Slope30thAFQT}{4}$ & \text{Q2 x Ln(UI)} & $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq2wba}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile2XLogofUI}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Quintile2XLogofUI}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Slope30thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope30thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Slope30thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope30thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile2XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Quintile2XLogofUI}{4}$) & \\
%                                   \text{Wealth Q3 x Age}  &  $\round{\agethirdPSID}{Value}{PSIDestimresults}{Coef}{ageXthird}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope50thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Slope50thWealth}{4}$ & \text{AFQT Q3 x Age} &  $\round{\agethirdNLSY}{Value}{NLSYestimresults}{Coef}{ageXthird}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope50thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Slope50thAFQT}{4}$ & \text{Q3 x Ln(UI)} & $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq3wba}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile3XLogofUI}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Quintile3XLogofUI}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Slope50thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope50thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Slope50thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope50thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile3XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Quintile3XLogofUI}{4}$) & \\
%                                   \text{Wealth Q4 x Age} &  $\round{\agefourthPSID}{Value}{PSIDestimresults}{Coef}{ageXfourth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope70thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Slope70thWealth}{4}$ & \text{AFQT Q4 x Age} &  $\round{\agefourthNLSY}{Value}{NLSYestimresults}{Coef}{ageXfourth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope70thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Slope70thAFQT}{4}$ & \text{Q4 x Ln(UI)} & $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq4wba}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile4XLogofUI}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Quintile4XLogofUI}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Slope70thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope70thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Slope70thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope70thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile4XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Quintile4XLogofUI}{4}$) & \\
%                                   \text{Wealth Q5 x Age} &  $\round{\agefifthPSID}{Value}{PSIDestimresults}{Coef}{ageXfifth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope90thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Slope90thWealth}{4}$ & \text{AFQT Q5 x Age} & $\round{\agefifthNLSY}{Value}{NLSYestimresults}{Coef}{ageXfifth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Slope90thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Slope90thAFQT}{4}$ & \text{Q5 x Ln(UI)} & $\getvalround{Value}{SIPPestimresults}{Coef}{int_netliq5wba}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Quintile5XLogofUI}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Quintile5XLogofUI}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Slope90thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope90thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Slope90thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope90thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile5XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Quintile5XLogofUI}{4}$) & \\
%                                   \text{Wealth Q2}  & $\round{\secondPSID}{Value}{PSIDestimresults}{Coef}{secondwealth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons30thWealth}{4}$& $\getvalround{Value}{PValestimresults}{Coef}{Cons30thWealth}{4}$ & \text{AFQT Q2}  & $\round{\secondNLSY}{Value}{NLSYestimresults}{Coef}{secondafqt}{4}$ & $ \getvalround{Value}{Estimationresults}{Coef}{Cons30thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Cons30thAFQT}{4}$ & \text{Q2} & $\getvalround{Value}{SIPPestimresults}{Coef}{_Inetliq_qt_2}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Q2}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Q2}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Cons30thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons30thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Cons30thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons30thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Q2}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Q2}{4}$) & \\
%                                   \text{Wealth Q3}  &  $\round{\thirdPSID}{Value}{PSIDestimresults}{Coef}{thirdwealth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons50thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Cons50thWealth}{4}$ & \text{AFQT Q3} & $\round{\thirdNLSY}{Value}{NLSYestimresults}{Coef}{thirdafqt}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons50thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Cons50thAFQT}{4}$ & \text{Q3} & $\getvalround{Value}{SIPPestimresults}{Coef}{_Inetliq_qt_3}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Q3}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Q3}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Cons50thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons50thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Cons50thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons50thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Q3}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Q3}{4}$) & \\
%                                   \text{Wealth Q4}  &  $\round{\fourthPSID}{Value}{PSIDestimresults}{Coef}{fourthwealth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons70thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Cons70thWealth}{4}$ & \text{AFQT Q4} & $\round{\fourthNLSY}{Value}{NLSYestimresults}{Coef}{fourthafqt}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons70thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Cons70thAFQT}{4}$ & \text{Q4} & $\getvalround{Value}{SIPPestimresults}{Coef}{_Inetliq_qt_4}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Q4}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Q4}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Cons70thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons70thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Cons70thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons70thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Q5}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Q5}{4}$) & \\
%                                   \text{Wealth Q5}  &  $\round{\fifthPSID}{Value}{PSIDestimresults}{Coef}{fifthwealth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Cons90thWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Cons90thWealth}{4}$ & \text{AFQT Q5} & $\round{\fifthNLSY}{Value}{NLSYestimresults}{Coef}{fifthafqt}{4}$  & $\getvalround{Value}{Estimationresults}{Coef}{Cons90thAFQT}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Cons90thAFQT}{4}$ & \text{Q5} & $\getvalround{Value}{SIPPestimresults}{Coef}{_Inetliq_qt_5}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NetLiq.Q5}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NetLiq.Q5}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{Cons90thWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons90thWealth}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Cons90thAFQT}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Cons90thAFQT}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Q5}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NetLiq.Q5}{4}$) & \\
% \text{Cons.} & $\round{\consPSID}{Value}{PSIDestimresults}{Coef}{cons}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{ConstantPSID}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{ConstantPSID}{4}$ & \text{Cons.}  & $\round{\consNLSY}{Value}{NLSYestimresults}{Coef}{cons}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{ConstantNLSY}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{ConstantNLSY}{4}$ & \text{Cons.} & $\getvalround{Value}{SIPPestimresults}{Coef}{_cons}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Constant}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Constant}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{ConstantPSID}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{ConstantPSID}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{ConstantNLSY}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{ConstantNLSY}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{Constant}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Constant}{4}$) \\ \cline{1-8}
%   \multicolumn{4}{c|}{Within Job Wage Growth (NLSY)} & \multicolumn{4}{c|}{Job-Stay Rate (NLSY)} & \text{Age} & $\getvalround{Value}{SIPPestimresults}{Coef}{age}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{Age}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{Age}{4}$ \\
%   \text{Var.} & \text{Data} & \text{Model} & \text{P-Val} & \text{Var.} &\text{Data} & \text{Model} & \text{P-Val} & & ($\getvalround{Value}{DataSEestimresults}{Coef}{Age}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Age}{4}$) & \\ \cline{1-8}
%       \text{Cons.} &  $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{_cons}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeCons}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeCons}{4}$ & \text{Age 25 - 29} & $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{1.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp1}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NLSYsameJobAgeGrp1}{4}$ & \text{Wealth} & $\getvalround{Value}{SIPPestimresults}{Coef}{hh_twlth}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LiquidWealth}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LiquidWealth}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{AgePSID}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{AgePSID}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp1}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp1}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{LiquidWealth}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LiquidWealth}{4}$) & \\ \text{Age 30 - 34} & $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{2.AgeGrp}{4}$ &  $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp2}{4}$& $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeGrp2}{4}$ & \text{Age 30 - 34} &  $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{2.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp2}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{NLSYsameJobAgeGrp2}{4}$ & \text{Q1 x Ln(LstWg)} & $\getvalround{Value}{SIPPestimresults}{Coef}{lastwgXnetliq1}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LastLogWageXNetLiq.Q1}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LastLogWageXNetLiq.Q1}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYGrowthAgeGrp2}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{Slope30thWealth}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp2}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp2}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NetLiq.Quintile1XLogofUI}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LastLogWageXNetLiq.Q1}{4}$) & \\
%                                   \text{Age 35 - 39}  &  $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{3.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp3}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeGrp3}{4}$ & \text{Age 35 - 39} &  $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{3.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp3}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{Slope50thAFQT}{4}$ & \text{Q2 x Ln(LstWg)} & $\getvalround{Value}{SIPPestimresults}{Coef}{lastwgXnetliq2}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LastLogWageXNetLiq.Q2}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LastLogWageXNetLiq.Q2}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYGrowthAgeGrp3}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYGrowthAgeGrp3}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp3}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp3}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{LastLogWageXNetLiq.Q2}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LastLogWageXNetLiq.Q2}{4}$) & \\
%                                   \text{Age 40 - 44} &  $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{4.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp4}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeGrp4}{4}$ & \text{Age 40 - 44} &  $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{4.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp4}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{NLSYsameJobAgeGrp4}{4}$ & \text{Q3 x Ln(LstWg)} & $\getvalround{Value}{SIPPestimresults}{Coef}{lastwgXnetliq3}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LastLogWageXNetLiq.Q3}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LastLogWageXNetLiq.Q3}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYGrowthAgeGrp4}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYGrowthAgeGrp4}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp4}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp4}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{LastLogWageXNetLiq.Q3}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LastLogWageXNetLiq.Q3}{4}$) & \\
%                                   \text{Age 45 - 49} &  $\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{5.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp5}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeGrp5}{4}$ & \text{Age 45 - 49} & $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{5.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp5}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{NLSYsameJobAgeGrp5}{4}$ & \text{Q4 x Ln(LstWg)} & $\getvalround{Value}{SIPPestimresults}{Coef}{lastwgXnetliq4}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LastLogWageXNetLiq.Q4}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LastLogWageXNetLiq.Q4}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYGrowthAgeGrp5}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYGrowthAgeGrp5}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp5}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp5}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{LastLogWageXNetLiq.Q4}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LastLogWageXNetLiq.Q4}{4}$) & \\
%                                   \text{Age 50 - 54}  &$\getvalround{Value}{NLSYJobGrowthestimresults}{Coef}{6.AgeGrp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{NLSYGrowthAgeGrp6}{4}$& $\getvalround{Value}{PValestimresults}{Coef}{NLSYGrowthAgeGrp6}{4}$ & \text{Age 50 - 54}  & $\getvalround{Value}{NLSYJobTransitionestimresults}{Coef}{6.AgeGrp}{4}$ & $ \getvalround{Value}{Estimationresults}{Coef}{NLSYsameJobAgeGrp6}{4}$  & $\getvalround{Value}{PValestimresults}{Coef}{NLSYsameJobAgeGrp6}{4}$ & \text{Q5 x Ln(LstWg)} & $\getvalround{Value}{SIPPestimresults}{Coef}{lastwgXnetliq5}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{LastLogWageXNetLiq.Q5}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{LastLogWageXNetLiq.Q5}{4}$ \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{NLSYGrowthAgeGrp6}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYGrowthAgeGrp6}{4}$) & & & ($\round{\ageNLSY}{Value}{DataSEestimresults}{Coef}{NLSYsameJobAgeGrp6}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{NLSYsameJobAgeGrp6}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{LastLogWageXNetLiq.Q5}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{LastLogWageXNetLiq.Q5}{4}$) & \\ \hline
%   \multicolumn{4}{c|}{Within Job Wage Growth Variance (NLSY)} & \multicolumn{4}{c|}{Unemployment Rate (PSID)} &  \\
%   \text{Var.} & \text{Data} & \text{Model} & \text{P-Val} & \text{Var.} &\text{Data} & \text{Model} & \text{P-Val} & & & \\ \cline{1-8}
%       \text{Cons.} &  $\getvalround{Value}{Varianceestimresults}{Coef}{variance}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{variance}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{variance}{4}$ & \text{Age 25 - 29} & $\getvalround{Value}{Unempestimresults}{Coef}{Unemp}{4}$ & $\getvalround{Value}{Estimationresults}{Coef}{AvgUnemp}{4}$ & $\getvalround{Value}{PValestimresults}{Coef}{AvgUnemp}{4}$ &  \\
% &  ($\getvalround{Value}{DataSEestimresults}{Coef}{variance}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{variance}{4}$) & & & ($\getvalround{Value}{DataSEestimresults}{Coef}{AvgUnemp}{4}$) & ($\getvalround{Value}{ModelSEestimresults}{Coef}{AvgUnemp}{4}$) & & &  \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Consumption and Unemployment Risk}\label{consumptionandunemploymentrisk}
%   \begin{itemize}
%   \item Change model to be consistent with heterogeneous agent framework (Bewley).
%   \item Run same counterfactual simulations and compare changes to human capital.
%   \item Competitive restrictions:
%     \begin{itemize}
%     \item $\mu = 1$, i.e. paid marginal product.
%     \item $\delta = 0$, no unemployment risk.
%     \end{itemize}
%   \item Random ``no-learning'' shocks consistent with unemployment in model with search.
%   \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Findings}\label{hcriskfindings}
% \begin{table}[h]
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{L c c c c c c c c c} \toprule & & \multicolumn{2}{c}{$\Delta$ Earnings} & \multicolumn{2}{c}{$\Delta$ $h$} & \multicolumn{2}{c}{$\Delta$ $\tau$} \\
%  \multicolumn{2}{c}{Wealth Change} & Base & No Unemp. & Base & No Unemp. & Base & No Unemp. \\
% \midrule $Test\;1$ & & & & & & & & & \\ \multicolumn{2}{r}{+1 St. Dev.} & $+ \round{\EWRat}{Value}{RatAupresults}{Ratio}{EWRat}{1}$ & $+ \round{\EWRat}{Value}{RatAupNoSearchresults}{Ratio}{EWRat}{1}$ &  $+ \round{\HHRat}{Value}{RatAupresults}{Ratio}{HHRat}{1}$ &  $+ \round{\HHRat}{Value}{RatAupNoSearchresults}{Ratio}{HHRat}{1}$ & $+ \round{\TTRat}{Value}{RatAupresults}{Ratio}{TTRat}{1}$ & $+ \round{\TTRat}{Value}{RatAupNoSearchresults}{Ratio}{TTRat}{1}$ \\
%                                   \multicolumn{2}{r}{-1 St. Dev.} & $\round{\HHRat}{Value}{RatAdownresults}{Ratio}{EWRat}{1}$ & $\round{\HHRat}{Value}{RatAdownNoSearchresults}{Ratio}{EWRat}{1}$ &  $\round{\HHRat}{Value}{RatAdownresults}{Ratio}{HHRat}{1}$ &  $\round{\HHRat}{Value}{RatAdownNoSearchresults}{Ratio}{HHRat}{1}$ & $\round{\TTRat}{Value}{RatAdownresults}{Ratio}{TTRat}{1}$ & $\round{\TTRat}{Value}{RatAdownNoSearchresults}{Ratio}{TTRat}{1}$ \\
% $Test\;2$ & & & & & & & & & \\ \multicolumn{2}{c}{10th$\rightarrow$50th} & $+ \round{\HHRat}{Value}{RatAQ1050Baseresults}{Ratio}{EWRat}{1}$ & $+ \round{\HHRat}{Value}{RatAQ1050BaseNoSearchresults}{Ratio}{EWRat}{1}$ &  $+ \round{\HHRat}{Value}{RatAQ1050Baseresults}{Ratio}{HHRat}{1}$ &  $+ \round{\HHRat}{Value}{RatAQ1050BaseNoSearchresults}{Ratio}{HHRat}{1}$ & $+ \round{\TTRat}{Value}{RatAQ1050Baseresults}{Ratio}{TTRat}{1}$ & $+ \round{\TTRat}{Value}{RatAQ1050BaseNoSearchresults}{Ratio}{TTRat}{1}$ \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \begin{itemize}
% \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Findings}\label{test2}
%   \begin{itemize}
%   \item Baseline:
%     \begin{itemize}
%     \item 10th percentile of one initial condition.
%     \item Correlated draw of other two initial conditions.
%     \end{itemize}
%   \item Counterfactual experiment:
%     \begin{itemize}
%     \item Change from 10th percentile to median.
%     \end{itemize}
%   \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------


% \begin{frame}
%   \frametitle{Initial Conditions}\label{test2findings}
% \begin{table}[h]
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{C l c c c} \toprule  & & \multicolumn{3}{c}{Test 2} \\
%   & \text{Variable} & \text{10th Wealth} &\text{10th Human Capital} &\text{10th Learning} \\
% \midrule $Wealth$ \\ & Baseline & $\getvalround{Value}{InitTestAQ1050Baseresults}{Comparison}{AAInitBase}{1}$  & $\getvalround{Value}{InitTestHQ1050Baseresults}{Comparison}{AAInitBase}{1}$ & $\getvalround{Value}{InitTestLQ1050Baseresults}{Comparison}{AAInitBase}{1}$ \\
%                                   &  Median & $\getvalround{Value}{InitTestAQ1050Baseresults}{Comparison}{AAInitTest}{1}$ &   & \\
% $Human\;Capital$ \\ & Baseline & $\getvalround{Value}{InitTestAQ1050Baseresults}{Comparison}{HHInitBase}{1}$  & $\getvalround{Value}{InitTestHQ1050Baseresults}{Comparison}{HHInitBase}{1}$ & $\getvalround{Value}{InitTestLQ1050Baseresults}{Comparison}{HHInitBase}{1}$ \\
%                                   &  Median &  &  $\getvalround{Value}{InitTestHQ1050Baseresults}{Comparison}{HHInitTest}{1}$ &   \\
% $Learning\;Ability$ \\ & Baseline & $\getvalround{Value}{InitTestAQ1050Baseresults}{Comparison}{LLInitBase}{3}$  & $\getvalround{Value}{InitTestHQ1050Baseresults}{Comparison}{LLInitBase}{3}$ & $\getvalround{Value}{InitTestLQ1050Baseresults}{Comparison}{LLInitBase}{3}$ \\
%                                   &  Median &  & & $\getvalround{Value}{InitTestLQ1050Baseresults}{Comparison}{LLInitTest}{3}$  \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \begin{itemize}
% \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Findings}\label{test2findings}
% \begin{table}[h]
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{l c c c} \toprule & $Wealth$ & $Human\;Capital$ & $Learning\;Ability$ \\
%  Variable & $10th\rightarrow50th$ & $10th\rightarrow50th$ & $10th\rightarrow50th$ \\
% \midrule $\Delta$ Cons. & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{CCRat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{CCRat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{HHRat}{1}\%$ \\
% $\Delta$ Earnings & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{EWRat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{EWRat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{EWRat}{1}\%$ \\
% $\Delta$ $h$ & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{HHRat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{HHRat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{HHRat}{1}\%$ \\
% $\Delta$ $\tau$ & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{TTRat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{TTRat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{TTRat}{1}\%$ \\
% $\Delta$ $\mu$ & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{MURat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{MURat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{MURat}{1}\%$ \\
% $\Delta$ $\mu'$ & $+ \round{\CCRat}{Value}{RatAQ1050Baseresults}{Ratio}{WPrimeRat}{1}\%$ & $+ \round{\HHRat}{Value}{RatHQ1050Baseresults}{Ratio}{WPrimeRat}{1}\%$ &  $+ \round{\HHRat}{Value}{RatLQ1050Baseresults}{Ratio}{WPrimeRat}{1}\%$ \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \begin{itemize}
% \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}
%   \frametitle{Findings}\label{averageindividual}
%   \begin{itemize}
%   \item Counterfactual experiment:
%     \begin{itemize}
%     \item Change baseline initial conditions by one standard deviation (i.e., shift distribution).
%     \item Assess impact on consumption, earnings growth, human capital accumulation.
%     \end{itemize}
%   \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
%   \end{itemize}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Findings}\label{averageindividualfindings}
% \begin{table}[h]
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{L c c c c c c c c} \toprule & & \text{Cons.} & \text{Earnings} & \text{Human Cap.} & \text{Learn Time} & \text{Piece-Rate} & \text{App. Strat.} \\
%  \multicolumn{2}{c}{Change} &  (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
% \midrule $Wealth$ & & & & & & \\ \multicolumn{2}{c}{+1 St. Dev.} & $+ \round{\HHRat}{Value}{RatAupMeanresults}{Ratio}{CCRat}{1}$ & $+ \round{\HHRat}{Value}{RatAupMeanresults}{Ratio}{EWRat}{1}$ & $+ \round{\HHRat}{Value}{RatAupMeanresults}{Ratio}{HHRat}{1}$ & $+ \round{\TTRat}{Value}{RatAupMeanresults}{Ratio}{TTRat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatAupMeanresults}{Ratio}{MURat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatAupMeanresults}{Ratio}{WPrimeRat}{1}$ \\
%                                   \multicolumn{2}{c}{-1 St. Dev.} & $\round{\HHRat}{Value}{RatAdownMeanresults}{Ratio}{CCRat}{1}$ & $\round{\HHRat}{Value}{RatAdownMeanresults}{Ratio}{EWRat}{1}$ & $\round{\HHRat}{Value}{RatAdownMeanresults}{Ratio}{HHRat}{1}$ & $\round{\TTRat}{Value}{RatAdownMeanresults}{Ratio}{TTRat}{1}$ & $\round{\WPrimeRat}{Value}{RatAdownMeanresults}{Ratio}{MURat}{1}$ & $\round{\WPrimeRat}{Value}{RatAdownMeanresults}{Ratio}{WPrimeRat}{1}$\\
% $Human\;Capital$ & & & & & & \\ \multicolumn{2}{c}{+1 St. Dev.} & $+ \round{\HHRat}{Value}{RatHupMeanresults}{Ratio}{CCRat}{1}$ & $+ \round{\HHRat}{Value}{RatHupMeanresults}{Ratio}{EWRat}{1}$ & $+ \round{\HHRat}{Value}{RatHupMeanresults}{Ratio}{HHRat}{1}$ & $+ \round{\TTRat}{Value}{RatHupMeanresults}{Ratio}{TTRat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatHupMeanresults}{Ratio}{MURat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatHupMeanresults}{Ratio}{WPrimeRat}{1}$ \\
%                                   \multicolumn{2}{c}{-1 St. Dev.} & $\round{\HHRat}{Value}{RatHdownMeanresults}{Ratio}{CCRat}{1}$ & $\round{\HHRat}{Value}{RatHdownMeanresults}{Ratio}{EWRat}{1}$ & $\round{\HHRat}{Value}{RatHdownMeanresults}{Ratio}{HHRat}{1}$ & $\round{\TTRat}{Value}{RatHdownMeanresults}{Ratio}{TTRat}{1}$ & $\round{\WPrimeRat}{Value}{RatHdownMeanresults}{Ratio}{MURat}{1}$ & $\round{\WPrimeRat}{Value}{RatHdownMeanresults}{Ratio}{WPrimeRat}{1}$\\
% $Learning\;Ability$ & & & & & & \\ \multicolumn{2}{c}{+1 St. Dev.} & $+ \round{\HHRat}{Value}{RatLupMeanresults}{Ratio}{CCRat}{1}$ & $+ \round{\HHRat}{Value}{RatLupMeanresults}{Ratio}{EWRat}{1}$ & $+ \round{\HHRat}{Value}{RatLupMeanresults}{Ratio}{HHRat}{1}$ & $+ \round{\TTRat}{Value}{RatLupMeanresults}{Ratio}{TTRat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatLupMeanresults}{Ratio}{MURat}{1}$ & $+ \round{\WPrimeRat}{Value}{RatLupMeanresults}{Ratio}{WPrimeRat}{1}$ \\
%                                   \multicolumn{2}{c}{-1 St. Dev.} & $\round{\HHRat}{Value}{RatLdownMeanresults}{Ratio}{CCRat}{1}$ & $\round{\HHRat}{Value}{RatLdownMeanresults}{Ratio}{EWRat}{1}$ & $\round{\HHRat}{Value}{RatLdownMeanresults}{Ratio}{HHRat}{1}$ & $\round{\TTRat}{Value}{RatLdownMeanresults}{Ratio}{TTRat}{1}$ & $\round{\WPrimeRat}{Value}{RatLdownMeanresults}{Ratio}{MURat}{1}$ & $\round{\WPrimeRat}{Value}{RatLdownMeanresults}{Ratio}{WPrimeRat}{1}$\\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \begin{itemize}
% \item \hyperlink{mainfindings}{\beamerreturnbutton{back}}
% \end{itemize}
% \end{frame}

% \begin{frame}\frametitle{Findings}\label{averageindividualfindings}
% \begin{table}[h]\caption{Impact on Averages}\label{tab:StudentDebt_Relief}
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{L c c c c c c c c c} \toprule & & \multicolumn{2}{c}{$\Delta$ Earnings} & \multicolumn{2}{c}{$\Delta$ $h$} & \multicolumn{2}{c}{$\Delta$ $\mu$} \\
%  \multicolumn{2}{c}{Threshold} & $Treated$ & $Overall$ & $Treated$ & $Overall$ & $Treated$ & $Overall$ \\
% \midrule $Human\;Capital$ & & & & & & & \\ \multicolumn{2}{c}{25th} & $\round{\StudentDebtLowHLRIncTreated}{LRInc}{StudentDebtbyHLowHTreatedPctChange}{Panel}{StudentDebtLowH}{2}$ & $\round{\StudentDebtLowHLRInc}{LRInc}{StudentDebtbyHPctChange}{Panel}{StudentDebtLowH}{2}$ & $\round{\StudentDebtLowHHumanCapTreated}{HumanCap}{StudentDebtbyHLowHTreatedPctChange}{Panel}{StudentDebtLowH}{2}$ & $\round{\StudentDebtLowHHumanCap}{HumanCap}{StudentDebtbyHPctChange}{Panel}{StudentDebtLowH}{2}$ & $\round{\StudentDebtLowHPieceRateTreated}{PieceRate}{StudentDebtbyHLowHTreatedPctChange}{Panel}{StudentDebtLowH}{2}$ & $\round{\StudentDebtLowHPieceRate}{PieceRate}{StudentDebtbyHPctChange}{Panel}{StudentDebtLowH}{2}$  \\
%                                  \multicolumn{2}{c}{50th} & $\round{\StudentDebtMedHLRIncTreated}{LRInc}{StudentDebtbyHMedHTreatedPctChange}{Panel}{StudentDebtMedH}{2}$ & $\round{\StudentDebtMedHLRInc}{LRInc}{StudentDebtbyHPctChange}{Panel}{StudentDebtMedH}{2}$ & $\round{\StudentDebtMedHHumanCapTreated}{HumanCap}{StudentDebtbyHMedHTreatedPctChange}{Panel}{StudentDebtMedH}{2}$ & $\round{\StudentDebtMedHHumanCap}{HumanCap}{StudentDebtbyHPctChange}{Panel}{StudentDebtMedH}{2}$ & $\round{\StudentDebtMedHPieceRateTreated}{PieceRate}{StudentDebtbyHMedHTreatedPctChange}{Panel}{StudentDebtMedH}{2}$ & $\round{\StudentDebtMedHPieceRate}{PieceRate}{StudentDebtbyHPctChange}{Panel}{StudentDebtMedH}{2}$  \\
%                                   \multicolumn{2}{c}{75th} & $\round{\StudentDebtHighHLRIncTreated}{LRInc}{StudentDebtbyHHighHTreatedPctChange}{Panel}{StudentDebtHighH}{2}$ & $\round{\StudentDebtHighHLRInc}{LRInc}{StudentDebtbyHPctChange}{Panel}{StudentDebtHighH}{2}$ & $\round{\StudentDebtHighHHumanCapTreated}{HumanCap}{StudentDebtbyHHighHTreatedPctChange}{Panel}{StudentDebtHighH}{2}$ & $\round{\StudentDebtHighHHumanCap}{HumanCap}{StudentDebtbyHPctChange}{Panel}{StudentDebtHighH}{2}$ & $\round{\StudentDebtHighHPieceRateTreated}{PieceRate}{StudentDebtbyHHighHTreatedPctChange}{Panel}{StudentDebtHighH}{2}$ & $\round{\StudentDebtHighHPieceRate}{PieceRate}{StudentDebtbyHPctChange}{Panel}{StudentDebtHighH}{2}$ \\
% $Learning\;Ability$ & & & & & & & \\ \multicolumn{2}{c}{25th} & $\round{\StudentDebtLowLLRIncTreated}{LRInc}{StudentDebtbyLLowLTreatedPctChange}{Panel}{StudentDebtLowL}{2}$ & $\round{\StudentDebtLowLLRInc}{LRInc}{StudentDebtbyLPctChange}{Panel}{StudentDebtLowL}{2}$ & $\round{\StudentDebtLowLHumanCapTreated}{HumanCap}{StudentDebtbyLLowLTreatedPctChange}{Panel}{StudentDebtLowL}{2}$ & $\round{\StudentDebtLowLHumanCap}{HumanCap}{StudentDebtbyLPctChange}{Panel}{StudentDebtLowL}{2}$ & $\round{\StudentDebtLowLPieceRateTreated}{PieceRate}{StudentDebtbyLLowLTreatedPctChange}{Panel}{StudentDebtLowL}{2}$ & $\round{\StudentDebtLowLPieceRate}{PieceRate}{StudentDebtbyLPctChange}{Panel}{StudentDebtLowL}{2}$  \\
%                                  \multicolumn{2}{c}{50th} & $\round{\StudentDebtMedLLRIncTreated}{LRInc}{StudentDebtbyLMedLTreatedPctChange}{Panel}{StudentDebtMedL}{2}$ & $\round{\StudentDebtMedLLRInc}{LRInc}{StudentDebtbyLPctChange}{Panel}{StudentDebtMedL}{2}$ & $\round{\StudentDebtMedLHumanCapTreated}{HumanCap}{StudentDebtbyLMedLTreatedPctChange}{Panel}{StudentDebtMedL}{2}$ & $\round{\StudentDebtMedLHumanCap}{HumanCap}{StudentDebtbyLPctChange}{Panel}{StudentDebtMedL}{2}$ & $\round{\StudentDebtMedLPieceRateTreated}{PieceRate}{StudentDebtbyLMedLTreatedPctChange}{Panel}{StudentDebtMedL}{2}$ & $\round{\StudentDebtMedLPieceRate}{PieceRate}{StudentDebtbyLPctChange}{Panel}{StudentDebtMedL}{2}$  \\
%                                   \multicolumn{2}{c}{75th} & $\round{\StudentDebtHighLLRIncTreated}{LRInc}{StudentDebtbyLHighLTreatedPctChange}{Panel}{StudentDebtHighL}{2}$ & $\round{\StudentDebtHighLLRInc}{LRInc}{StudentDebtbyLPctChange}{Panel}{StudentDebtHighL}{2}$ & $\round{\StudentDebtHighLHumanCapTreated}{HumanCap}{StudentDebtbyLHighLTreatedPctChange}{Panel}{StudentDebtHighL}{2}$ & $\round{\StudentDebtHighLHumanCap}{HumanCap}{StudentDebtbyLPctChange}{Panel}{StudentDebtHighL}{2}$ & $\round{\StudentDebtHighLPieceRateTreated}{PieceRate}{StudentDebtbyLHighLTreatedPctChange}{Panel}{StudentDebtHighL}{2}$ & $\round{\StudentDebtHighLPieceRate}{PieceRate}{StudentDebtbyLPctChange}{Panel}{StudentDebtHighL}{2}$ \\
% % $Both$ & & & & & & & \\ \multicolumn{2}{c}{25th} & $\round{\StudentDebtLowHLLRIncTreated}{LRInc}{StudentDebtbyHLLowHLTreatedPctChange}{Panel}{StudentDebtLowHL}{2}$ & $\round{\StudentDebtLowHLLRInc}{LRInc}{StudentDebtbyHLPctChange}{Panel}{StudentDebtLowHL}{2}$ & $\round{\StudentDebtLowHLHumanCapTreated}{HumanCap}{StudentDebtbyHLLowHLTreatedPctChange}{Panel}{StudentDebtLowHL}{2}$ & $\round{\StudentDebtLowHLHumanCap}{HumanCap}{StudentDebtbyHLPctChange}{Panel}{StudentDebtLowHL}{2}$ & $\round{\StudentDebtLowHLPieceRateTreated}{PieceRate}{StudentDebtbyHLLowHLTreatedPctChange}{Panel}{StudentDebtLowHL}{2}$ & $\round{\StudentDebtLowHLPieceRate}{PieceRate}{StudentDebtbyHLPctChange}{Panel}{StudentDebtLowHL}{2}$  \\
% %                                  \multicolumn{2}{c}{50th} & $\round{\StudentDebtMedHLLRIncTreated}{LRInc}{StudentDebtbyHLMedHLTreatedPctChange}{Panel}{StudentDebtMedHL}{2}$ & $\round{\StudentDebtMedHLLRInc}{LRInc}{StudentDebtbyHLPctChange}{Panel}{StudentDebtMedHL}{2}$ & $\round{\StudentDebtMedHLHumanCapTreated}{HumanCap}{StudentDebtbyHLMedHLTreatedPctChange}{Panel}{StudentDebtMedHL}{2}$ & $\round{\StudentDebtMedHLHumanCap}{HumanCap}{StudentDebtbyHLPctChange}{Panel}{StudentDebtMedHL}{2}$ & $\round{\StudentDebtMedHLPieceRateTreated}{PieceRate}{StudentDebtbyHLMedHLTreatedPctChange}{Panel}{StudentDebtMedHL}{2}$ & $\round{\StudentDebtMedHLPieceRate}{PieceRate}{StudentDebtbyHLPctChange}{Panel}{StudentDebtMedHL}{2}$  \\
% %                                   \multicolumn{2}{c}{75th} & $\round{\StudentDebtHighHLLRIncTreated}{LRInc}{StudentDebtbyHLHighHLTreatedPctChange}{Panel}{StudentDebtHighHL}{2}$ & $\round{\StudentDebtHighHLLRInc}{LRInc}{StudentDebtbyHLPctChange}{Panel}{StudentDebtHighHL}{2}$ & $\round{\StudentDebtHighHLHumanCapTreated}{HumanCap}{StudentDebtbyHLHighHLTreatedPctChange}{Panel}{StudentDebtHighHL}{2}$ & $\round{\StudentDebtHighHLHumanCap}{HumanCap}{StudentDebtbyHLPctChange}{Panel}{StudentDebtHighHL}{2}$ & $\round{\StudentDebtHighHLPieceRateTreated}{PieceRate}{StudentDebtbyHLHighHLTreatedPctChange}{Panel}{StudentDebtHighHL}{2}$ & $\round{\StudentDebtHighHLPieceRate}{PieceRate}{StudentDebtbyHLPctChange}{Panel}{StudentDebtHighHL}{2}$ \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Findings: Earned Income Tax Credit}\label{decisionrulesfindings}
%   \begin{itemize}
%   \item
%   \end{itemize}
% \begin{table}[h]\label{tab:EITC_Results}
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{L c c c c c c c c c} \toprule & & $\Delta$ Consumption & $\Delta$ Earnings & $\Delta$ $h$ & $\Delta$ $\mu$ & $\Delta$ $\tau$ & $\Delta$ $\mu'$ \\
%  \multicolumn{2}{c}{Change} & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
% \midrule $EITC$ & & & & & & & & & \\ \multicolumn{2}{c}{Introduced} & $\getvalround{Cons}{EITCPctChange}{Panel}{EITC}{2}$ & $\getvalround{LRInc}{EITCPctChange}{Panel}{EITC}{2}$ & $\getvalround{HumanCap}{EITCPctChange}{Panel}{EITC}{2}$ &  $\getvalround{PieceRate}{EITCPctChange}{Panel}{EITC}{2}$ & $\getvalround{LearnTime}{EITCPctChange}{Panel}{EITC}{2}$ & $\getvalround{AppStrat}{EITCPctChange}{Panel}{EITC}{2}$ \\
%                                   % \multicolumn{2}{c}{First Dollar} & $\getvalround{Cons}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ & $\getvalround{LRInc}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ & $\getvalround{HumanCap}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ &  $\getvalround{PieceRate}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ & $\getvalround{LearnTime}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ & $\getvalround{AppStrat}{EITCFirstDollarPctChange}{Panel}{EITCFirstDollar}{2}$ \\
%                                   % \multicolumn{2}{c}{Doubled Cap} & $\getvalround{Cons}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ & $\getvalround{LRInc}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ & $\getvalround{HumanCap}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ &  $\getvalround{PieceRate}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ & $\getvalround{LearnTime}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ & $\getvalround{AppStrat}{EITCHighCapPctChange}{Panel}{EITCHighCap}{2}$ \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \end{frame}

% % ------------------------------------------------

% \begin{frame}\frametitle{Findings: Universal Basic Income}\label{decisionrulesfindings}
% \begin{table}[h]\label{tab:UBI_Results}
% \centering
% \resizebox{\textwidth}{!}{%
% \begin{tabulary}{\textwidth}{L c c c c c c c c c} \toprule & & $\Delta$ Consumption & $\Delta$ Earnings & $\Delta$ $h$ & $\Delta$ $\mu$ & $\Delta$ $\tau$ & $\Delta$ $\mu'$ \\
%  \multicolumn{2}{c}{Change} & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
% \midrule $UBI$ & & & & & & & & & \\ \multicolumn{2}{c}{\$12,000 (annual)} & $\getvalround{Cons}{UBIPctChange}{Panel}{UBI}{2}$ & $\getvalround{LRInc}{UBIPctChange}{Panel}{UBI}{2}$ & $\getvalround{HumanCap}{UBIPctChange}{Panel}{UBI}{2}$ &  $\getvalround{PieceRate}{UBIPctChange}{Panel}{UBI}{2}$ & $\getvalround{LearnTime}{UBIPctChange}{Panel}{UBI}{2}$ & $\getvalround{AppStrat}{UBIPctChange}{Panel}{UBI}{2}$ \\
%                                   \multicolumn{2}{c}{\$6,000 (50\% decrease)} & $\getvalround{Cons}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ & $\getvalround{LRInc}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ & $\getvalround{HumanCap}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ &  $\getvalround{PieceRate}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ & $\getvalround{LearnTime}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ & $\getvalround{AppStrat}{UBIBidenPctChange}{Panel}{UBIBiden}{2}$ \\
%                                   \multicolumn{2}{c}{\$18,000 (50\% increase)} & $\getvalround{Cons}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ & $\getvalround{LRInc}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ & $\getvalround{HumanCap}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ &  $\getvalround{PieceRate}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ & $\getvalround{LearnTime}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ & $\getvalround{AppStrat}{UBIBerniePctChange}{Panel}{UBIBernie}{2}$ \\
% \bottomrule
% \end{tabulary}
% }
% \end{table}
% \end{frame}

% ------------------------------------------------

\end{document}
