% =====================================================================
%  Teaching slides — "Teacher Preparation, Pre-College Human Capital and
%  Student Learning: Evidence from Enseña Chile"
%  De Gregorio, Neilson and Gallegos (2026)
%
%  THESE SLIDES ARE MEANT TO BE ADAPTED. They are released so that
%  instructors can drop the evidence into a class on teacher quality,
%  education policy, or applied microeconometrics without rebuilding the
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%
%  Every number here traces to the paper. If you change a number, change
%  it in the paper first: the source of truth is reproduce_numbers.py.
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\title{Teacher Preparation, Pre-College Human Capital\\and Student Learning}
\subtitle{Evidence from \ech{}}
\author{Soledad De Gregorio \and Christopher A. Neilson \and Sebasti\'an Gallegos}
\date{Teaching slides · 2026}

\begin{document}

% =====================================================================
\begin{frame}[plain]
  \titlepage
  \begin{center}
    {\footnotesize\color{eChGray}
     Slides released for classroom use and adaptation.\\
     Figures and numbers correspond to the August 2026 working paper.}
  \end{center}
\end{frame}

% =====================================================================
\section{The question}
% =====================================================================
\begin{frame}{The puzzle these programs pose}
  \begin{itemize}\setlength{\itemsep}{0.7em}
    \item Teach For America and the \textbf{Teach For All} network recruit high-achieving
          graduates, train them for a few weeks, and place them in hard-to-staff schools.
    \item Evaluations repeatedly find they do \textbf{about as well as} traditionally
          prepared teachers --- sometimes modestly better in mathematics.
    \item That result is puzzling. Traditional preparation takes \textbf{four to five years};
          the alternative route compresses pre-service training into weeks.
  \end{itemize}
  \vspace{0.6em}
  \begin{block}{The question we can now ask}
    Is the parity explained by \textbf{who these programs select}, or by \textbf{what
    recruits learn on the job}? The two are almost always tangled together.
  \end{block}
  \teachnote{Good place to poll the room before showing any results. Students
  usually split, and the split itself motivates the decomposition.}
\end{frame}

% =====================================================================
\begin{frame}{Why the question stayed open}
  To separate selection from experience you need, for the \emph{same} teachers:
  \begin{enumerate}\setlength{\itemsep}{0.6em}
    \item a measure of \textbf{academic ability taken before any teacher training};
    \item \textbf{student test-score panel data} good enough to estimate value-added; and
    \item \textbf{years of experience} measured accurately.
  \end{enumerate}
  \vspace{0.8em}
  Almost no setting has all three. US studies use post-training subject knowledge,
  licensure tests, or pre-hire screening batteries --- all administered to an
  \emph{already-screened} pool.
  \vspace{0.5em}
  \begin{center}
    \color{eChNavy}\bfseries Chile has all three, for a Teach For All affiliate.
  \end{center}
\end{frame}

% =====================================================================
\section{Setting and data}
% =====================================================================
\begin{frame}{Setting: \ech{} and the PSU}
  \begin{columns}[T]
    \begin{column}{0.5\textwidth}
      \textbf{\ech{} (eCh)}
      \begin{itemize}
        \item Chilean Teach For All affiliate.
        \item Four weeks of intensive pre-service training.
        \item Then \textbf{two years} of in-service coaching, peer learning, tutor visits.
        \item Recruits from selective universities.
      \end{itemize}
    \end{column}
    \begin{column}{0.5\textwidth}
      \textbf{The \PSU{} entrance exam}
      \begin{itemize}
        \item National, high-stakes university admission test.
        \item Taken \textbf{before} any teacher preparation --- a clean pre-program
              ability measure.
        \item Mean 500, SD 110 among all test-takers.
        \item Among \emph{teachers} the combined math--language SD is
              about \textbf{80 points}.
      \end{itemize}
    \end{column}
  \end{columns}
  \vspace{0.8em}
  \teachnote{The 80-point teacher SD matters for every effect size below.
  Units are the single easiest thing to get wrong here.}
\end{frame}

% =====================================================================
\begin{frame}{Data: a testing campaign, then two administrative links}
  \begin{block}{We ran the tests ourselves}
    SEPA mathematics assessment (MIDE-UC), administered by external proctors at the
    \textbf{start (May)} and \textbf{end (November)} of the 2016 school year, grades 9--11.
  \end{block}
  \vspace{0.4em}
  \begin{center}
  \begin{tabular}{lr}
    \toprule
    Students & 3{,}756 \\
    Classrooms & 169 \\
    Mathematics teachers & 114 \quad (37 eCh, 77 traditional) \\
    Schools & 59 \\
    \bottomrule
  \end{tabular}
  \end{center}
  \vspace{0.6em}
  Linked to \textbf{DEMRE} \PSU{} scores and the Ministry of Education
  \textit{Docentes} panel for years of service.
  \teachnote{Worth dwelling on: value-added estimates are rare in middle-income
  countries precisely because this repeated classroom-level testing usually does not exist.}
\end{frame}

% =====================================================================
\begin{frame}{The sample is purposive --- and that is deliberate}
  \begin{itemize}\setlength{\itemsep}{0.6em}
    \item eCh recruits cluster at the \textbf{top} of the \PSU{} distribution.
    \item A nationally representative comparison group would therefore \textbf{confound
          route with ability} --- the very thing we want to separate.
    \item So we stratified non-partner schools by their teachers' \PSU{} decile and
          invited \textbf{at least one school per decile}.
  \end{itemize}
  \vspace{0.7em}
  \begin{alertblock}{The trade-off, stated plainly}
    We buy the \textbf{ability overlap} needed to estimate the gradient, and give up
    \textbf{representativeness}. Group means are analytic-sample comparisons, not
    population estimates for Chilean teachers.
  \end{alertblock}
  \teachnote{A useful design discussion: when is a non-representative sample the
  \emph{right} choice? Contrast with what a random sample would and would not identify.}
\end{frame}

% =====================================================================
\section{Result 1 — ability}
% =====================================================================
\begin{frame}{Result 1: pre-college ability predicts value-added}
  \begin{center}
    \includegraphics[width=0.86\textwidth]{fig_psu_va_gradient}
  \end{center}
  \vspace{-0.2em}
  {\footnotesize A 100-point higher \PSU{} score predicts \up{+0.51\sig} higher
  value-added (classroom-clustered s.e.\ 0.12) --- equivalently \up{+0.41\sig} per
  standard deviation of teacher ability, since one SD is about 80 points.}
  \teachnote{The units trap: 0.51 is \emph{per 100 points}, not per SD. Students who
  quote 0.51 as a per-SD effect overstate it by about 25\%.}
\end{frame}

% =====================================================================
\section{Result 2 — experience}
% =====================================================================
\begin{frame}{Result 2: the experience profile is steep, then flat}
  \begin{center}
    \includegraphics[width=0.86\textwidth]{fig_experience_by_route}
  \end{center}
  \vspace{-0.2em}
  {\footnotesize First-year eCh teachers average \dn{$-0.47\sigma$}; second-year eCh
  teachers average \up{$+0.13\sigma$} --- the same as traditional teachers, whose own
  profile is flat from early career to veteran.}
  \teachnote{Press on this: the $+0.60\sigma$ difference compares \emph{two different
  cohorts} measured in one testing wave, not the same teachers over time.}
\end{frame}

% =====================================================================
\section{Result 3 — the decomposition}
% =====================================================================
\begin{frame}{Result 3: decomposing the gap}
  \begin{center}
    \includegraphics[width=0.82\textwidth]{fig3_decomposition}
  \end{center}
  \vspace{-0.3em}
  {\footnotesize Reweighting the sampled traditional teachers to the national
  common-support \PSU{} distribution gives a gap of \neu{$-0.16\sigma$}, which splits into
  \up{$+0.25\sigma$} from pre-college ability, \dn{$-0.30\sigma$} from first-year
  inexperience, and \neu{$-0.10\sigma$} of route residual
  (95\% CI $[-0.37, +0.17]$ --- it crosses zero).}
\end{frame}

% =====================================================================
\begin{frame}{Reading the decomposition carefully}
  \begin{itemize}\setlength{\itemsep}{0.55em}
    \item The \textbf{net gap} is itself not statistically distinguishable from zero.
          What the exercise identifies is its \textbf{composition}.
    \item \up{Ability} and \dn{inexperience} are each individually well determined;
          they pull in opposite directions and largely cancel.
    \item The \textbf{route residual is a remainder}, not a structural parameter. It
          absorbs training, motivation, unobserved selection, and any
          ability-by-experience interaction the additive form omits.
  \end{itemize}
  \vspace{0.6em}
  \begin{alertblock}{The honest summary}
    This is a \textbf{failure to detect} a route penalty --- not evidence that the two
    training routes are equivalent. The data cannot rule out a moderate shortfall.
  \end{alertblock}
  \teachnote{This slide is the one to keep if you cut the deck for time. The
  distinction between ``no evidence of a difference'' and ``evidence of no difference''
  is the transferable lesson.}
\end{frame}

% =====================================================================
\section{Benchmark}
% =====================================================================
\begin{frame}{Recruited at the top, delivering at the middle}
  \begin{center}
    \includegraphics[width=0.84\textwidth]{fig_national_percentile}
  \end{center}
  \vspace{-0.2em}
  {\footnotesize Against the PSU-era national teacher cohort ($N \approx 17{,}700$),
  eCh recruits sit near the \up{97th percentile} of ability, while their predicted
  value-added --- pooled across their first two years --- falls at the \neu{42nd
  percentile}. Both are positions in the \emph{model-predicted} distribution, not
  ranks in realized national value-added.}
\end{frame}

% =====================================================================
\section{Interpretation}
% =====================================================================
\begin{frame}{What this does and does not say about policy}
  \begin{columns}[T]
    \begin{column}{0.48\textwidth}
      \textbf{\color{eChTeal}Supported}
      \begin{itemize}\setlength{\itemsep}{0.4em}
        \item Selection on pre-college ability is a real, measurable margin.
        \item Where strong candidates are scarce, placing an eCh teacher need not
              lower instructional quality.
        \item Experience is the binding constraint: the two-year term ends just as
              early-career returns are largest.
      \end{itemize}
    \end{column}
    \begin{column}{0.48\textwidth}
      \textbf{\color{eChCrimson}Not supported}
      \begin{itemize}\setlength{\itemsep}{0.4em}
        \item That the two routes are equivalent.
        \item That the program \emph{as a whole} is effective --- we evaluate the
              teachers it supplies, not the program.
        \item Any comparison with the \textbf{marginal hire} a vacancy would otherwise
              draw. We never observe that teacher.
      \end{itemize}
    \end{column}
  \end{columns}
  \teachnote{The marginal-hire point is the crux of most policy debates about these
  programs, and it is exactly what the design cannot deliver.}
\end{frame}

% =====================================================================
\begin{frame}{Limitations worth teaching}
  \begin{enumerate}\setlength{\itemsep}{0.55em}
    \item \textbf{Single year of data.} Value-added is cross-sectional teacher fixed
          effects, reported unshrunk, so estimates carry classroom-level noise. One
          $\sigma$ is one SD of the \emph{estimated} teacher-effect distribution ---
          noise-inflated, which makes the gradients conservative.
    \item \textbf{Small cohorts.} The first-to-second-year improvement rests on 20 and
          17 teachers observed in the same wave; learning, cohort composition, and
          attrition cannot be fully separated.
    \item \textbf{Purposive sampling.} Interpretation rests on within-comparison
          validity, not external representativeness.
    \item \textbf{Thin within-school cells.} About 1.9 sampled teachers per school, so
          school fixed effects are infeasible; a leave-out faculty-composition control
          is the placement adjustment the design supports.
  \end{enumerate}
\end{frame}

% =====================================================================
\section{Discussion}
% =====================================================================
\begin{frame}{Questions for discussion}
  \begin{enumerate}\setlength{\itemsep}{0.7em}
    \item The route residual's confidence interval is $[-0.37, +0.17]$. What sample size
          would you need to rule out a $0.10\sigma$ shortfall? Is that study feasible?
    \item The comparison group was chosen to span the ability distribution rather than to
          be representative. What does that buy, and what does it cost?
    \item If selection explains the ability contribution, what policy follows ---
          recruit differently, train differently, or retain differently?
    \item The program's two-year commitment ends exactly when returns to experience are
          steepest. Is that a design flaw, or the price of attracting these recruits?
    \item What would change your mind about the near-zero route residual?
  \end{enumerate}
\end{frame}

% =====================================================================
\begin{frame}{Adapting these slides}
  \begin{itemize}\setlength{\itemsep}{0.6em}
    \item Every block is marked with a \texttt{\textbackslash section} comment in the
          source and can be dropped independently.
    \item \textbf{Short version (20 min):} title, the puzzle, the data slide,
          Results 1--3, and ``Reading the decomposition carefully''.
    \item \textbf{Methods version:} keep the purposive-sampling and limitations slides
          and add the paper's Section 2 for the value-added construction.
    \item Figures live in \texttt{../figures/}; the four used here are
          \texttt{fig\_psu\_va\_gradient}, \texttt{fig\_experience\_by\_route},
          \texttt{fig3\_decomposition}, \texttt{fig\_national\_percentile}.
    \item \textbf{Please keep the units convention:} \PSU{} coefficients are per 100
          points; divide by 1.25 for per-SD.
  \end{itemize}
\end{frame}

% =====================================================================
\begin{frame}{Reference}
  \begin{block}{Paper}
    De Gregorio, S., C. A. Neilson and S. Gallegos (2026).
    ``Teacher Preparation, Pre-College Human Capital and Student Learning:
    Evidence from \ech{}.'' Working paper.
  \end{block}
  \vspace{0.8em}
  {\footnotesize
  Manuscript, online appendix and these slides:\\
  \texttt{https://www.christopher-neilson.com/work/eCh\_Teachers.html}
  \vspace{0.6em}

  Individual-level records are governed by data-sharing agreements with MIDE-UC,
  MINEDUC/DEMRE and \ech{} and cannot be redistributed. The replication package,
  including the aggregate data behind every exhibit, is available from the authors
  on request.}
\end{frame}

\end{document}
