
\appendix
%===============================================================

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Mountain areas delimitation}

\renewcommand{\thefigure}{A.\arabic{figure}} % 
\setcounter{figure}{0} % Remet à zéro le compteur de figures
\begin{figure}[!ht]
  \caption{\small\textbf{\color{blue}Map of Mountain Areas}}
  \centering
  \includegraphics[width=1\textwidth]{Input/Figures/Carte1.jpeg}
  \label{carte1}
  \vspace{-1em}
\end{figure}
\vspace{4mm}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Definitions of the variables}
\label{sec:def}
\begin{table}[!htbp]
\centering
\caption{Variable descriptions}
\label{desc}
\scalebox{0.55}{
\begin{threeparttable}
\begin{tabular}{@{}p{4.2cm}p{8.1cm}p{5.0cm}p{8.0cm}@{}}
\toprule
\textbf{Variable name} & \textbf{Variable description} & \textbf{Source and period} & \textbf{Link (URL)} \\
\midrule

\multicolumn{4}{@{}l}{\underline{\textbf{1. Variable of interest}}} \\
\addlinespace[0.3em]

LFA (Yes/No) & Classification status: Mountain Area, Ordinary Disadvantaged Area or Specific Handicap Area & \textit{Data.gouv (1962--1990)} & {\url{https://www.data.gouv.fr/fr/datasets/communes-de-montagne-30383014/}} \\
LFA exposure index & Share of neighbouring municipalities that are treated (queen or rook contiguity) & Authors’ calculation & -- \\
\addlinespace[0.5em]

\multicolumn{4}{@{}l}{\underline{\textbf{2. Dependent variables}}} \\
\addlinespace[0.3em]

Number of farms & Economic units engaged in agricultural production with independent management & \textit{Agreste -- Agricultural Census, 1970--2020} & {\url{https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/searchUiid/search/}} \\
Utilised agricultural area (UAA) & Arable land, permanent crops, permanent grassland, vegetable/flower areas, etc. & \textit{Agreste -- Agricultural Census, 1970--2020} & {\url{https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/searchUiid/search/}} \\
Average farm size & Ratio of UAA to the number of farms & \textit{Agreste -- Agricultural Census, 1970--2020} & {\url{https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/searchUiid/search/}} \\
Total agricultural employment & Jobs located in agriculture, forestry and fishing & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Salaried agricultural employment & … (salaried positions only) & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Self-employed agricultural employment & … (self-employed positions only) & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Industrial employment & Jobs in manufacturing, extractive industry, etc. & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Employment in trade, transport and miscellaneous services & Jobs in wholesale/retail trade, transport and other services & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Construction employment & Jobs in building and civil engineering (BTP) & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
Agro-food employment & Jobs in food, beverage and tobacco manufacturing & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/information/2880845}} \\
\addlinespace[0.5em]

\multicolumn{4}{@{}l}{\underline{\textbf{2.1 Demographic characteristics}}} \\
\addlinespace[0.3em]

Population density & Inhabitants per km$^{2}$ & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698339}} \\
Population aged 0--19 & Number of inhabitants aged 0--19 & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698339}} \\
Population aged 20--64 & Number of inhabitants aged 20--64 & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698339}} \\
Population aged 64+ & Number of inhabitants aged 64 and over & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698339}} \\
Total population & Total number of inhabitants & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698339}} \\
Dependency ratio & $\frac{\text{Pop }(0\text{--}19) + \text{Pop }(64+)}{\text{Pop }(20\text{--}64)}$ & Authors’ calculation & -- \\
Net migration & In-migrants minus out-migrants & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698340}} \\
Natural balance & Births minus deaths over an intercensal period & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698341}} \\
No diploma / $\le$ lower-secondary & Persons with at most the BEPC or no diploma & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698342}} \\
Upper-secondary -- Baccalauréat & Persons who completed upper-secondary education (BAC) & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698343}} \\
Tertiary education & Persons with higher-education qualifications & \textit{INSEE/Quetelet -- Population Census, 1962--2020} & {\url{https://www.insee.fr/fr/statistiques/3698344}} \\
\addlinespace[0.5em]

\multicolumn{4}{@{}l}{\underline{\textbf{2.2 Physical soil characteristics}}} \\
\addlinespace[0.3em]

Sandy soil & Mostly sand; porous, low water retention & \textit{Joint Research Centre \citep{panagos2012european}} & {\url{https://doi.org/10.1016/j.landusepol.2011.07.003}} \\
Clay soil & Mostly clay; heavy and compact & \textit{Joint Research Centre \citep{panagos2012european}} & {\url{https://doi.org/10.1016/j.landusepol.2011.07.003}} \\
Loamy soil & Mostly silt; medium porosity & \textit{Joint Research Centre \citep{panagos2012european}} & {\url{https://doi.org/10.1016/j.landusepol.2011.07.003}} \\
Humic soil & Rich in organic matter; good drainage & \textit{Joint Research Centre \citep{panagos2012european}} & {\url{https://doi.org/10.1016/j.landusepol.2011.07.003}} \\
\addlinespace[0.5em]

\multicolumn{4}{@{}l}{\underline{\textbf{2.3 Geographic characteristics}}} \\
\addlinespace[0.3em]

Urban-proximity index & Total municipal population divided by distance-weighted sums to nearby cities & Authors’ calculation & -- \\
Neighbouring-municipality density & Distance-weighted population density of contiguous neighbours & Authors’ calculation & -- \\
Neighbouring-employment density & Distance-weighted employment of contiguous neighbours & Authors’ calculation & -- \\
\bottomrule
\end{tabular}

\begin{tablenotes} 
\footnotesize
\item All variables are defined at municipal level. RA = Agricultural Census. INSEE = National Institute of Statistics and Economic Studies. Population-census data for 1962--1990 are accessed via Quetelet ({\url{https://data.progedo.fr/}}).
\end{tablenotes}

\end{threeparttable}
}
\end{table}






\newpage
\renewcommand{\thefigure}{B.\arabic{figure}} % 
\setcounter{figure}{0} 

\section{Natural Handicap Compensation Allowance (LFA) exposure index}

\begin{figure}[H]
\centering
\caption{\small{\color{blue} LFA exposure index based on contiguity}}
\includegraphics[width=0.75\textwidth]{Input/Figures/IE_bon.jpeg}
\label{fig:figA1}
\vspace{-1em}
\end{figure}

\begin{figure}[H]
\centering
\caption{\small{\color{blue} LFA exposure index by distance bands}}
\includegraphics[width=0.75\textwidth]{Input/Figures/IE_dist.jpeg}
\label{fig:figA2}
\vspace{-1em}
\end{figure}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Covariate Balance}
\label{balance}

The first step in entropy balancing matches the means of all covariates, using one-year-lagged municipal variables to minimise potential reverse causality. Panel A of Table \ref{tab:covar} compares unweighted sample means for every regression covariate. Observations in which a municipality receives the LFA form the treatment group (column [1]); observations without LFA support constitute the control group (column [2]). Column [3] shows mean differences and the corresponding $p$-values for the null of no difference. Treated and untreated periods differ significantly on every pre-treatment characteristic except the dependency ratio. Treated municipalities experience heavier precipitation, more episodes of effective rainfall and higher neighbouring-employment density; by contrast, they have lower population density, lower average temperatures, weaker migration and natural balances, lower education levels, a poorer soil-texture index and less urban proximity. These differences highlight the need for an appropriate control group to obtain unbiased treatment-effect estimates.

Panel B of Table \ref{tab:covar} reports covariate means for the treatment group (column [1]) and for the synthetic control group produced by entropy balancing (column [4]). Column [5] lists the mean differences and their $p$-values. None of the covariate gaps remain statistically significant—all $p$-values exceed the conventional 0.10 threshold. In short, weighting eliminates all pre-treatment imbalances, achieving perfect covariate balance and confirming the effectiveness of entropy balancing. The resulting weighted control group therefore provides a credible counterfactual based on observables, so that any residual outcome differences can be attributed to the treatment alone.

\begin{table}[!htbp]\centering
\caption{Descriptive statistics: covariate balance before and after entropy balancing}
\label{tab:covar}
\scalebox{0.75}{
\begin{threeparttable}
\begin{tabular}{@{\extracolsep{20pt}}lccccc}
\toprule
& \multicolumn{1}{c}{\textbf{[1]}} & \multicolumn{1}{c}{\textbf{[2]}} & \multicolumn{2}{c}{\textbf{[3] = [1] -- [2]}}\\
\cmidrule(lr){2-2}\cmidrule(lr){3-3}\cmidrule(lr){4-5}
\multicolumn{5}{l}{\textbf{Panel A: Before balancing}}\\[0.3em]
\textit{Variable} & LFA & No LFA & Difference & \textit{p}-value\\
\midrule
$\log(\text{Population density})_{t-1}$              & 2.977 & 4.023 & -1.046 & 0.000\\
Soil--texture index                                 & -0.392 & 0.012 & -0.404 & 0.000\\
$\log(\text{Weighted urban proximity})_{t-1}$       & 8.499 & 8.850 & -0.351 & 0.000\\
$\log(\text{Neighbour pop.\ density})_{t-1}$        & 3.327 & 4.251 & -0.925 & 0.000\\
$\log(\text{Neighbour emp.\ density})_{t-1}$        & 0.289 & 0.263 &  0.026 & 0.000\\
Effective rainfall$_{t-1}$                          & 1.753 & 0.815 &  0.939 & 0.000\\
Mean precipitation$_{t-1}$                          & 3.234 & 2.175 &  1.058 & 0.000\\
Mean temperature$_{t-1}$                            & 8.616 & 10.164& -1.549 & 0.000\\
Dependency ratio$_{t-1}$                            & 0.917 & 0.918 & -0.002 & 0.475\\
$\log(\text{Active population})_{t-1}$              & 5.001 & 5.673 & -0.672 & 0.000\\
$\text{asinh}(\text{Net migration})_{t-1}$          & -0.411& 0.281 & -0.692 & 0.000\\
$\text{asinh}(\text{Natural balance})_{t-1}$        & -0.715& 2.022 & -2.737 & 0.000\\
$\log(\text{Education index})_{t-1}$                & -0.473& 0.141 & -0.614 & 0.000\\
\midrule
Observations & \textbf{23,285} & \textbf{74360}\\
\bottomrule
\end{tabular}
\end{threeparttable}}
\vspace{0.3cm}

\scalebox{0.75}{
\begin{threeparttable}
\begin{tabular}{@{\extracolsep{20pt}}lccccc}
\toprule
& \multicolumn{1}{c}{\textbf{[1]}} & \multicolumn{1}{c}{\textbf{[4]}} & \multicolumn{2}{c}{\textbf{[5] = [1] -- [4]}}\\
\cmidrule(lr){2-2}\cmidrule(lr){3-3}\cmidrule(lr){4-5}
\multicolumn{5}{l}{\textbf{Panel B: After balancing}}\\[0.3em]
\textit{Variable} & LFA & Synthetic control & Difference & \textit{p}-value\\
\midrule
$\log(\text{Population density})_{t-1}$              & 2.998 & 3.004 & -0.006 & 0.401\\
Soil--texture index                                 & -0.389& -0.388& -0.001 & 0.896\\
$\log(\text{Weighted urban proximity})_{t-1}$       & 8.513 & 8.514 & -0.001 & 0.964\\
$\log(\text{Neighbour pop.\ density})_{t-1}$        & 3.349 & 3.353 & -0.004 & 0.464\\
$\log(\text{Neighbour emp.\ density})_{t-1}$        & 0.289 & 0.289 &  0.000 & 0.875\\
Effective rainfall$_{t-1}$                          & 1.760 & 1.757 &  0.003 & 0.571\\
Mean precipitation$_{t-1}$                          & 3.239 & 3.236 &  0.004 & 0.541\\
Mean temperature$_{t-1}$                            & 8.580 & 8.587 & -0.007 & 0.661\\
Dependency ratio$_{t-1}$                            & 0.916 & 0.915 &  0.000 & 0.928\\
$\log(\text{Active population})_{t-1}$              & 5.039 & 5.043 & -0.004 & 0.549\\
$\text{asinh}(\text{Net migration})_{t-1}$          & -0.384& -0.372& -0.012 & 0.645\\
$\text{asinh}(\text{Natural balance})_{t-1}$        & -0.683& -0.667& -0.016 & 0.438\\
$\log(\text{Education index})_{t-1}$                & -0.434& -0.429& -0.005 & 0.531\\
\midrule
Observations & \textbf{18,862} & \textbf{74,360}\\
Total weights & \textbf{18,862} & \textbf{18,862}\\
\bottomrule
\end{tabular}
\begin{tablenotes}
\footnotesize
\item \textit{Notes}: Panel~A reports raw means; Panel~B reports weighted means after entropy balancing. Column labels correspond to treatment, unweighted control, and synthetic (weighted) control groups. \textit{p}-values test equality of means.
\end{tablenotes}
\end{threeparttable}}
\end{table}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newpage
\section{Identification strategy}
\label{strategie_annexe}

Our target parameter is the Average Treatment Effect on the Treated (ATT), defined as

\begin{equation}
\label{eq:att1}
ATT(\chi) = \mathbb{E}\!\big[Y_{i}(1) \mid LFA_{i}=1\big] 
           - \mathbb{E}\!\big[Y_{i}(0) \mid LFA_{i}=1\big].
\end{equation}



Here, \(LFA_{i}=1\) is an indicator equal to 1 when municipality \(i\) receives the LFA transfer (i.e.\ is classified as a mountain area) in period \(t\), and 0 otherwise.  
\(\mathbb{E}\!\left[Y_{i}(1)\mid LFA_{i}=1\right]\) is the observed outcome—total agricultural employment, salaried agricultural employment or self-employed agricultural employment—after treatment.  
\(\mathbb{E}\!\left[Y_{i}(0)\mid LFA_{i}=1\right]\) is the counterfactual outcome that the same municipality would have registered had it not received the LFA. Because a given municipality cannot be simultaneously treated and untreated, the counterfactual is unobservable.  
If treatment were randomly assigned, the average outcome for untreated units,
\(\mathbb{E}[Y_{i}(0)\mid LFA_{i}=0]\), would be a valid proxy.  
As mountain classification is likely endogenous, a naïve difference would yield a biased estimate.

To overcome this difficulty we mimic random assignment by comparing LFA municipalities with non-LFA municipalities that share similar \emph{pre-treatment} characteristics related (i) to the probability of classification and (ii) to agricultural employment.  
The identification assumption is that, conditional on these observables, treated and control municipalities were indistinguishable before treatment, so that any post-treatment gap can be attributed to the policy.  
Formally, we rewrite \eqref{eq:att1} as

\begin{equation}
\label{eq:att2}
ATT(\chi) =
\mathbb{E}\!\big[Y_{i}(1) \mid LFA_{i}=1,\, X=\chi \big]
-
\mathbb{E}\!\big[Y_{i}(0) \mid LFA_{i}=0,\, X=\chi \big].
\end{equation}


where \(X=\chi\) is a vector of pre-treatment covariates that jointly influence both the likelihood of receiving the LFA and—potentially—agricultural employment.  
Conditional on \(X\), assignment to the LFA (mountain classification) is assumed to be independent of the outcome.

\vspace{4mm}

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


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newpage
\section{Additional results}
\label{addresults}
\begin{table}[!htbp]
  \centering
  \caption{Main results: Average effect, deviation relative to 1962}
  \label{EMT}
  \scalebox{0.7}{
    \begin{threeparttable}
      \begin{tabular}{@{\extracolsep{12pt}}lccc}
        \toprule
        & (1) & (2) & (3) \\
        & $\log(\text{total agricultural employment})$ 
        & $\log(\text{salaried })$ 
        & $\log(\text{self-employed })$ \\
        \midrule
        Mountain area (Yes/No)   
          & -0.165$^{***}$ &  0.094$^{*}$   & -0.200$^{***}$ \\
          & (0.025)        & (0.052)        & (0.032)        \\[0.5em]
        $\log(\text{population density}_{t-1})$
          & -0.055$^{***}$ &  0.126$^{***}$ & -0.088$^{***}$ \\
          & (0.018)        & (0.029)        & (0.023)        \\[0.5em]
        Effective rainfall$_{t-1}$ (mm)
          &  0.146$^{*}$   &  0.952$^{***}$ &  0.154         \\
          & (0.081)        & (0.138)        & (0.099)        \\[0.5em]
        Average total precipitation$_{t-1}$ (mm)
          & -0.191$^{**}$  & -0.895$^{***}$ & -0.187$^{*}$   \\
          & (0.079)        & (0.129)        & (0.099)        \\[0.5em]
        Mean temperature$_{t-1}$ (°C)
          &  0.059$^{***}$ & -0.021         &  0.073$^{***}$ \\
          & (0.009)        & (0.015)        & (0.012)        \\[0.5em]
        Dependency ratio$_{t-1}$
          & -0.042         & -0.115$^{***}$ & -0.033         \\
          & (0.028)        & (0.036)        & (0.031)        \\[0.5em]
        $\log(\text{pop.\ 20–64}_{t-1})$
          &  0.066$^{***}$ & -0.185$^{***}$ &  0.072$^{**}$  \\
          & (0.023)        & (0.039)        & (0.028)        \\[0.5em]
        $\log(\text{net migration}_{t-1})$
          & -0.013$^{***}$ &  0.020$^{***}$ & -0.017$^{***}$ \\
          & (0.003)        & (0.005)        & (0.003)        \\[0.5em]
        $\log(\text{natural balance}_{t-1})$
          &  0.017$^{***}$ &  0.020$^{***}$ &  0.017$^{***}$ \\
          & (0.003)        & (0.006)        & (0.004)        \\[0.5em]
        Soil-texture index$_{t-1}$
          & -0.016         & -0.040$^{*}$   & -0.002         \\
          & (0.012)        & (0.021)        & (0.015)        \\[0.5em]
        Education level$_{t-1}$
          & -0.006         &  0.011         & -0.015         \\
          & (0.023)        & (0.038)        & (0.029)        \\[0.5em]
        $\log(\text{urban-proximity index}_{t-1})$
          &  0.007         & -0.025$^{***}$ &  0.018$^{**}$  \\
          & (0.005)        & (0.008)        & (0.008)        \\[0.5em]
        $\log(\text{contiguity density}_{t-1})$
          & -0.072$^{***}$ & -0.059         & -0.074$^{***}$ \\
          & (0.016)        & (0.037)        & (0.021)        \\[0.5em]
        $\log(\text{total population density}_{t-1})$
          &  0.763$^{***}$ & -0.691$^{***}$ &  0.629$^{***}$ \\
          & (0.155)        & (0.244)        & (0.181)        \\[0.8em]
        \midrule
        Municipalities / Observations
          & 23\,583 / 93\,174 & 23\,583 / 93\,174 & 23\,583 / 93\,174 \\
        \(R^{2}\)
          & 0.208          & 0.195          & 0.184          \\
     Year fixed effects   & YES & YES & YES \\
     Municipality fixed effects & YES & YES & YES \\
        \bottomrule
      \end{tabular}
      \begin{tablenotes}
        \small
       \item \textit{Notes}: The table shows the average treatment effect (LFA beneficiary municipalities) estimated with weighted OLS (Entropy Balancing).  
        Only coefficients for the treatment and covariates are displayed.  
        ***, **, * denote significance at the 1 %, 5 % and 10 % levels.  
        \(t\)-statistics clustered at the municipality level are in parentheses.  
        The reference year is 1962 (coefficients equal zero).  
      \end{tablenotes}
    \end{threeparttable}%
  }
\end{table}

%%%%%%%%%%%%%%%%%%%%%%%

\begin{table}[!htbp]
  \centering
  \caption{Main results: Dynamic effects, deviation relative to 1962}
  \label{MDR}
  \scalebox{0.75}{%
    \begin{threeparttable}
      \begin{tabular}{@{\extracolsep{12pt}}lccc}
        \toprule
        & (1) & (2) & (3) \\
        & $\log(\text{total agricultural employment})$ 
        & $\log(\text{salaried })$ 
        & $\log(\text{self-employed })$ \\
        \midrule
        LFA$_{1962}$ 
          & 0.000         & 0.000         & 0.000         \\
          & (.)           & (.)           & (.)           \\[0.5em]
        LFA$_{1968}$ 
          & -0.225$^{***}$& -0.340$^{***}$& -0.238$^{***}$\\
          & (0.059)       & (0.092)       & (0.074)       \\[0.5em]
        LFA$_{1975}$ 
          & -0.151$^{***}$&  0.007        & -0.181$^{***}$\\
          & (0.043)       & (0.093)       & (0.060)       \\[0.5em]
        LFA$_{1982}$ 
          & -0.115$^{**}$ &  0.343$^{***}$& -0.167$^{**}$ \\
          & (0.054)       & (0.080)       & (0.067)       \\[0.5em]
        LFA$_{1990}$ 
          & -0.181$^{***}$&  0.210$^{***}$& -0.219$^{***}$\\
          & (0.055)       & (0.055)       & (0.064)       \\[0.5em]
        $\log(\text{population density}_{t-1})$
          & -0.054$^{***}$&  0.138$^{***}$& -0.087$^{***}$\\
          & (0.019)       & (0.028)       & (0.024)       \\[0.5em]
        Effective rainfall$_{t-1}$ (mm)
          &  0.160$^{*}$  &  0.935$^{***}$&  0.166        \\
          & (0.085)       & (0.127)       & (0.107)       \\[0.5em]
        Total precipitation$_{t-1}$ (mm)
          & -0.205$^{**}$ & -0.906$^{***}$& -0.199$^{*}$  \\
          & (0.081)       & (0.122)       & (0.104)       \\[0.5em]
        Mean temperature$_{t-1}$ (°C)
          &  0.063$^{***}$& -0.006        &  0.076$^{***}$\\
          & (0.009)       & (0.014)       & (0.013)       \\[0.5em]
        Dependency ratio$_{t-1}$
          & -0.044        & -0.134$^{***}$& -0.034        \\
          & (0.027)       & (0.034)       & (0.029)       \\[0.5em]
        $\log(\text{pop.\ 20–64}_{t-1})$
          &  0.065$^{***}$& -0.194$^{***}$&  0.071$^{**}$ \\
          & (0.022)       & (0.034)       & (0.028)       \\[0.5em]
        $\log(\text{net migration}_{t-1})$
          & -0.013$^{***}$&  0.022$^{***}$& -0.016$^{***}$\\
          & (0.002)       & (0.005)       & (0.003)       \\[0.5em]
        $\log(\text{natural balance}_{t-1})$
          &  0.018$^{***}$&  0.025$^{***}$&  0.017$^{***}$\\
          & (0.003)       & (0.006)       & (0.004)       \\[0.5em]
        Soil-texture index$_{t-1}$
          & -0.016        & -0.022        & -0.002        \\
          & (0.013)       & (0.020)       & (0.017)       \\[0.5em]
        Education level$_{t-1}$
          & -0.005        &  0.011        & -0.014        \\
          & (0.023)       & (0.034)       & (0.029)       \\[0.5em]
        $\log(\text{urban-proximity index}_{t-1})$
          &  0.007        & -0.025$^{***}$&  0.018$^{**}$ \\
          & (0.004)       & (0.008)       & (0.008)       \\[0.5em]
        $\log(\text{contiguity density}_{t-1})$
          & -0.073$^{***}$& -0.062$^{*}$  & -0.074$^{***}$\\
          & (0.016)       & (0.034)       & (0.021)       \\[0.5em]
        $\log(\text{total population density}_{t-1})$
          &  0.748$^{***}$& -0.746$^{***}$&  0.617$^{***}$\\
          & (0.155)       & (0.241)       & (0.182)       \\[0.8em]
        \midrule
        Municipalities / Observations
          & 23\,583 / 93\,174 & 23\,583 / 93\,174 & 23\,583 / 93\,174 \\
        \(R^{2}\)
          & 0.208           & 0.204          & 0.184          \\
     Year fixed effects   & YES & YES & YES \\
     Municipality fixed effects & YES & YES & YES \\
        \bottomrule
      \end{tabular}
      \begin{tablenotes}
        \footnotesize
      \item  \textit{Notes}: The table reports the average treatment effect (LFA beneficiary municipalities) and dynamic effects, estimated with weighted OLS (Entropy Balancing).  
        Only coefficients for the treatment and covariates are displayed.  
        ***, **, * denote significance at the 1 %, 5 % and 10 % levels.  
        \(t\)-statistics clustered at the municipality level are in parentheses.  
        The reference year is 1962 (coefficients equal zero).  
      \end{tablenotes}
    \end{threeparttable}%
  }
\end{table}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\newpage
\section{Robustness}
\label{robust}

\subsection{Robustness Checks}

\textit{Alternative sample}

We use an alternative sample to ensure that our results are not driven by a specific subgroup within the dataset. First, we consider an expanded sample that includes all disadvantaged areas in the treatment group—that is, municipalities classified as mountain areas (MA), as well as those designated as simply disadvantaged (SDA) or areas with specific handicaps (SHA). Panel A of Table \ref{table:ro1} presents the results obtained from estimating Equation \ref{eq:entropy} using the entropy balancing method with this new sample. The findings confirm the robustness of our baseline conclusions: a positive and significant effect on salaried agricultural employment (column 2, Panel A), as well as a negative and significant effect on non-salaried agricultural employment and total agricultural employment (columns 1 and 3 of Panel B, respectively). Finally, we observe a persistent decline in non-salaried agricultural employment in municipalities benefiting from the LFA payments, relative to non-beneficiary municipalities, alongside a rise in salaried employment. This indicates a substitution effect between salaried and non-salaried employment.

\textit{Sensitivity to outliers}

We also examine the sensitivity of our results to outliers.\footnote{This strategy is used by \citet{acemoglu2019democracy}.} We exclude these outliers to obtain a new sample. Specifically, outliers are defined as observations with standardized residuals (in absolute value) greater than 1.96 and a Cook’s distance exceeding the empirical threshold of 4/NT, where NT corresponds to the number of observations in a balanced panel. This approach assumes a 95\% confidence level that non-outlier observations are part of the true data. The observations considered outliers are thus removed from the sample. The results are presented in Panel B of Table \ref{table:ro1}, using the same estimation method as before, with standard errors clustered at the municipal level. The findings are also consistent with our baseline conclusions: the LFA payments have a positive and significant effect on salaried agricultural employment (column 2, Panel B), and a negative and significant effect on non-salaried and total agricultural employment (columns 1 and 3 of Panel B, respectively) in beneficiary municipalities compared to non-beneficiaries. Moreover, these results reveal a persistent decline in family labor (non-salaried employment) in LFA beneficiary municipalities, relative to the 1962 baseline and to non-beneficiary municipalities. This trend is accompanied by an increase in salaried agricultural employment in these same beneficiary municipalities since 1975. These findings reinforce our initial conclusions.

%   Disadvantaged vs. non-disadvantaged areas
\begin{table}[!htbp]
  \centering
  \caption{Robustness to alternative sample and outliers}
  \label{table:ro1}
  \scalebox{0.65}{
   \begin{threeparttable}
    \begin{tabular}{@{\extracolsep{20pt}}lccc}
      \hline\hline
      & (1) & (2) & (3) \\
      & \texttt{log(agricultural employment)} & \texttt{log(salaried)} & \texttt{log(self-employed)} \\
      \midrule
      \\
\multicolumn{4}{c}{\textbf{PANEL A: Including all disadvantaged areas}} \\\\
\hline
LFA (Yes/No) & -0.141*** & 0.0761*** & -0.171*** \\
              & (-7.55)   &  (2.68)   &  (-7.51)  \\[0.5em]
\hline
LFA1962  & 0.000 & 0.000 & 0.000 \\
          &  (.)  &  (.)  &  (.)  \\[0.3em]
LFA1968  & -0.0933*** & -0.177**  & -0.100*** \\
          & (-3.88)    & (-2.41)   & (-3.79)   \\[0.3em]
LFA1975  & -0.0957*** & -0.0621   & -0.0934***\\
          & (-3.49)    & (-1.39)   & (-3.21)   \\[0.3em]
LFA1982  & -0.139***  &  0.187*** & -0.188*** \\
          & (-5.78)    &  (4.16)   & (-6.09)   \\[0.3em]
LFA1990  & -0.168***  &  0.0918***& -0.197*** \\
          & (-5.90)    &  (3.33)   & (-6.12)   \\[0.3em]
Municipalities / Observations & 32\,644 / 129\,048 & 32\,644 / 129\,048 & 32\,644 / 129\,048 \\
\(R^{2}\)  & 0.149 & 0.124 & 0.135 \\
Covariates & YES & YES & YES \\
Municipality FE & YES & YES & YES \\
Year FE & YES & YES & YES \\
\midrule
\multicolumn{4}{c}{\textbf{PANEL B: Excluding outliers}} \\\\
\hline
LFA (Yes/No) & -0.107*** & 0.0889*  & -0.143*** \\
              & (-6.57)   & (1.83)   & (-6.46)   \\[0.5em]
\hline
LFA1962 & 0.000 & 0.000 & 0.000 \\
         &  (.)  &  (.)  &  (.)  \\[0.3em]
LFA1968 & -0.168*** & -0.340*** & -0.191*** \\
         & (-3.53)   & (-3.82)   & (-3.06)   \\[0.3em]
LFA1975 & -0.0631*  & 0.109     & -0.0977*  \\
         & (-1.86)   & (1.46)    & (-1.81)   \\[0.3em]
LFA1982 & -0.0965*  & 0.295***  & -0.162**  \\
         & (-1.77)   & (3.79)    & (-2.44)   \\[0.3em]
LFA1990 & -0.108*** & 0.170***  & -0.124*** \\
         & (-5.74)   & (3.28)    & (-5.89)   \\[0.3em]
Municipalities / Observations & 23\,484 / 86\,513 & 23\,484 / 86\,513 & 23\,484 / 86\,513 \\
\(R^{2}\) & 0.336 & 0.280 & 0.286 \\
Covariates & YES & YES & YES \\
Municipality FE & YES & YES & YES \\
Year FE & YES & YES & YES \\
\hline\hline
    \end{tabular}
  \begin{tablenotes}
    \footnotesize
   \item  \textit{Notes}: The table tests robustness to the alternative sample (Panel A) and to outliers (Panel B) using weighted OLS (Entropy-balancing weights).  
    Only the coefficients of interest are reported.  
    ***, **, * indicate significance at the 1 %, 5 % and 10 % levels.  
    \(t\)-statistics clustered at the municipality level are in parentheses.  
    The reference year is 1962 (coefficients equal zero).
  \end{tablenotes}
    \end{threeparttable}
    }
\end{table}

\textit{Alternative methods}

To verify that the findings do not depend on the estimation technique, we apply two staggered-adoption DID approaches:  
(i) the Callaway-and-Sant’Anna DID estimator \citep{callaway_difference--differences_2021} and  
(ii) the staggered Entropy-DID.   Because both methods require an untreated baseline at \(t=0\) (1962), we drop municipalities already treated in 1962, thereby creating a genuine pre-treatment window for the common-trend test.   Results should be interpreted with caution, since not all treated municipalities remain in the sample. If the common-trend assumption is violated, the Callaway–Sant’Anna estimates are biased \citep{cefalu_reducing_2020}.  
Combining DID with entropy weights mitigates that risk by balancing covariates and aligning pre-treatment trends, yielding reliable estimates even when raw trends are non-parallel.

Columns [1]–[3] of Table \ref{table:rob2} present the Callaway–Sant’Anna DID results; columns [4]–[6] show the Entropy-DID estimates. All models pass the pre-trend test, reinforcing their credibility. Columns [3] and [6] indicate a positive and significant LFA effect on salaried employment—average impacts of 0.170 and 0.364 log-points, respectively—and an early rise from 1975, peaking in 1982 (see Fig.~\ref{fig:fig22}). By contrast, columns [1] and [2] show negative but insignificant effects on total and self-employed employment, whereas the Entropy-DID in columns [4] and [5] yields negative and significant impacts for both. From 1975 onward, total and self-employed employment decline, while salaried employment grows (Fig.~\ref{fig:fig22}), confirming substitution. The weaker significance on self-employed jobs likely reflects the exclusion of early-treated municipalities, which form a sizeable share of the original sample (Fig.~\ref{fig1: zm}).  

To restore those municipalities, we introduce a \emph{placebo} year (1954), assuming no change in covariates between 1954 and 1962. Columns [7]–[9] re-estimate the DID with the full sample.  
The positive, significant effect on salaried employment persists, while effects on total and self-employed employment remain negative but insignificant, leaving our main conclusions unchanged.

% --- Table with alternative methods ---
\begin{table}[!htbp]
\centering
\caption{\small\textbf{Robustness: alternative methods}}
\label{table:rob2}
\scalebox{0.65}{
\begin{threeparttable}
  \begin{tabular}{@{\extracolsep{20pt}}lccccccccc}
      \\[-1.8ex]\hline 
      \hline \\[-1.8ex] 
      & \multicolumn{3}{c}{DID (Callaway–Sant’Anna)} & \multicolumn{3}{c}{Entropy-DID} & \multicolumn{3}{c}{Placebo year 1954} \\
      \cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10}
      & [1] & [2] & [3] & [4] & [5] & [6] & [7] & [8] & [9] \\
      & AET & AENS & AES & AET & AENS & AES & AET & AENS & AES \\
      \hline \\[-1.8ex]
ATT   &  -0.00524 &  -0.0309 & 0.170** & -0.330** & -0.382** & 0.364* & -0.0312 & -0.0520 & 0.131** \\
      & (-0.10)   & (-0.60)  & (2.59)  & (-1.97)  & (-2.15)  & (1.69) & (-0.80)& (-1.14)& (2.05) \\\\
\bottomrule
\hline
\multicolumn{10}{c}{Dynamic effects} \\
\cmidrule(lr){1-10}
T\textsubscript{1975} & 0.0569 & 0.0382 & 0.217* & -0.0616 & -0.0262 & 0.124 & 0.0186 & 0.00603 & 0.158** \\
T\textsubscript{1982} & -0.0197 & -0.0531 & 0.164* & -0.570** & -0.734** & 0.639* & -0.0387 & -0.0650 & 0.142** \\
T\textsubscript{1990} & -0.165* & -0.188* & 0.0266 & -0.417 & -0.403 & 0.274 & -0.178** & -0.208** & 0.00343 \\\\
Pre-trend test & 2.7558 & 2.8713 & 5.6361 & 1.7419 & 0.7825 & 6.6964 & 3.3730 & 3.0434 & 4.4002 \\
(\emph{p}-value) & (0.431) & (0.412) & (0.131) & (0.628) & (0.854) & (0.822) & (0.338) & (0.385) & (0.221) \\
\hline
Observations & 78\,700 & 78\,700 & 78\,700 & 78\,700 & 78\,700 & 78\,700 & 99\,341 & 99\,341 & 99\,341 \\
Covariates & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
\hline
  \end{tabular}
  \begin{tablenotes}
    \footnotesize
   \item \textit{Notes}: Robustness checks using staggered DID estimators.  
    Columns (1)–(3) and (7)–(9) use the Callaway–Sant’Anna DID; columns (4)–(6) combine entropy balancing with DID.  
    ***, **, * denote significance at the 1 %, 5 % and 10 % levels.  
    AET = total agricultural employment; AENS = self-employed agricultural employment; AES = salaried agricultural employment.
  \end{tablenotes}
\end{threeparttable}
}
\end{table}



\renewcommand{\thefigure}{G.\arabic{figure}} % 
\setcounter{figure}{0} % Remet à zéro le compteur de figures
\begin{figure}[H]
  \centering
  \caption{\small\textbf{Robustness: dynamic effects}}
  \includegraphics[width=1.08\textwidth]{Input/Figures/Event_did1.png}
  \label{fig:fig22}
  \vspace{-1em}
\end{figure}

% -----------------------------------------------------------------------------
\textit{Additional control variables}

This section assesses the robustness of our findings by testing the baseline model’s sensitivity to extra controls. A key concern is the omission of potential determinants of agricultural employment that are correlated with our treatment—here, the LFA. If such determinants are ignored they are absorbed by the error term, which is assumed to be uncorrelated with the regressors; violating that assumption creates an endogeneity risk.

To limit this bias we augment the baseline equation (\ref{eq:entropy}) with two additional controls: temperature amplitude and the presence of geographical indications.

\emph{Temperature amplitude}, defined as the gap between the daily maximum and minimum temperature, is crucial for agricultural activity at the municipal level and thus for employment.  
\emph{Geographical indications} (GI) link the quality, reputation or characteristics of a product to a specific place; they help differentiate, protect and enhance agricultural and food products. By securing market outlets and raising producers’ and processors’ income, GIs stimulate rural employment.  
We approximate this with a dummy that equals 1 from the year a product obtains controlled designation of origin (CDO)/protected designation of origin (PDO) and protected geographical indications (PGI), and 0 otherwise.

Labelling dates are taken from:  
(i) \emph{eAmbrosia}, the EU’s official GI portal;  
(ii) \emph{Eur-Lex}, for the relevant EU regulations;  
(iii) \emph{INAO}, the French database of CDO, PGI and "Label Rouge" products; and  
(iv) \emph{Legifrance}, for French decrees and ministerial orders.  
Including these controls helps isolate the specific impact of the LFA on agricultural employment.

Table~\ref{table:varsup} reports the estimates after adding the extra controls.  
Columns [1]–[3] include the GI dummy; columns [4]–[6] add temperature amplitude; columns [7]–[9] include both.  
In every case, the results corroborate our baseline conclusions, confirming the robustness of our estimates.

\begin{table}[H]
  \centering
  \caption{Robustness: additional control variables}
  \label{table:varsup}
  \scalebox{0.60}{
   \begin{threeparttable}
    \begin{tabular}{@{\extracolsep{20pt}}l*{9}{c}}
\hline\hline
     & \multicolumn{3}{c}{\texttt{CDO/PDO/PGI label}} & \multicolumn{3}{c}{\texttt{Temperature amplitude}} & \multicolumn{3}{c}{\texttt{Label + amplitude}} \\
     \cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10}
     & [1] & [2] & [3] & [4] & [5] & [6] & [7] & [8] & [9] \\
     & AET & AES & AENS & AET & AES & AENS & AET & AES & AENS \\
\hline
LFA & -0.166*** & 0.0934*  & -0.201*** & -0.164*** & 0.0948*  & -0.200*** & -0.166*** & 0.0941*  & -0.201*** \\
     & (0.0252)  & (0.0507) & (0.0321)  & (0.0246)  & (0.0511) & (0.0314)  & (0.0250)  & (0.0502) & (0.0320) \\
[1em]
\hline
LFA\textsubscript{1962} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
LFA\textsubscript{1968} & -0.208*** & -0.337*** & -0.222*** & -0.227*** & -0.345*** & -0.238*** & -0.210*** & -0.341*** & -0.223*** \\
LFA\textsubscript{1975} & -0.164*** & 0.00496 & -0.192*** & -0.148*** & 0.0162 & -0.180*** & -0.160*** & 0.0131 & -0.191*** \\
LFA\textsubscript{1982} & -0.122**  & 0.342*** & -0.174*** & -0.110**  & 0.355*** & -0.167*** & -0.117**  & 0.353*** & -0.173*** \\
LFA\textsubscript{1990} & -0.180*** & 0.210*** & -0.218*** & -0.185*** & 0.199*** & -0.219*** & -0.186*** & 0.199*** & -0.220*** \\
\(N\) & 93\,174 & 93\,174 & 93\,174 & 93\,174 & 93\,174 & 93\,174 & 93\,174 & 93\,174 & 93\,174 \\
adj.\ \(R^{2}\) & 0.210 & 0.195 & 0.185 & 0.208 & 0.196 & 0.183 & 0.210 & 0.196 & 0.185 \\
\hline\hline
Covariates & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
Municipality FE & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
Year FE & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
\hline
    \end{tabular}
  \begin{tablenotes}
    \footnotesize
  \item  \textit{Notes}: The table tests robustness by adding extra controls.  
    Columns (1)–(3) include only the GI dummy; columns (4)–(6) include only temperature amplitude; columns (7)–(9) include both.  
    ***, **, * denote significance at the 1 %, 5 % and 10 % levels.  
    \(t\)-statistics clustered at the municipal level appear in parentheses.  
    AET = total agricultural employment; AENS = self-employed agricultural employment; AES = salaried agricultural employment.
  \end{tablenotes}
   \end{threeparttable}
  }
\end{table}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newpage
\section{Heterogeneity Analysis}
\label{heterogeneity}

Our baseline estimates indicate that the LFA rais;es salaried agricultural employment but lowers self‑employment. We now ask whether these effects differ by (i) average farm size (AFS), (ii) utilised agricultural area (UAA), (iii) number of farms (NF) and (iv) distance to the nearest urban centre. For each dimension we split the sample at the median—“low” versus “high” groups, following \citet{apeti2023tax}—and re‑estimate the treatment effects separately for salaried and self‑employed labour. All regressions keep the full set of covariates plus municipality and year fixed effects.

\textbf{Average farm size.} When we divide communes by AFS, Panel A of Table \ref{table:hetero} shows that the LFA’s positive impact on salaried jobs is larger, though not statistically significant, in areas with very large farms, and positive as well as significant in areas with small‑to‑medium farms. The fall in self‑employment is steeper in the large‑farm group, implying that salaried positions are created mostly on smaller holdings, whereas family labour declines across the board, especially on the largest farms.

\textbf{Number of farms.} Panel B compares communes with few farms (high concentration) to those with many. The wage‑labour boost is strongest where NF is low, while the negative effect on self‑employment is weaker where NF is high. This mirrors national trends: the INSEE counts a drop from 1.6 million farms in 1970 to roughly 390 000 in 2020.\footnote{\url{https://www.insee.fr/fr/statistiques/7728861?sommaire=7728903}}

\textbf{Utilised agricultural area.} Splitting by UAA, we find that the LFA stimulates salaried employment only where land area is small or moderate; it sharply reduces self‑employment in both groups. In communes with extensive UAA, the wage effect disappears, but the family‑labour decline remains.

\textbf{Urban proximity.} Finally, we distinguish communes close to a city from those that are remote. Prior research suggests that peri‑urban farms benefit from better prices, easier market access and wider use of modern inputs, increasing demand for hired labour while displacing family labour \citep{vandercasteelen2018cities}. Panel D confirms that urban proximity amplifies the LFA’s positive effect on salaried jobs—perhaps because marketing services and labour exchanges are more readily available—yet the programme still cuts self‑employment regardless of distance.

Overall, the LFA generates salaried jobs and displaces family workers through all three structural channels, but the wage gains fade once farms become very large, land is abundant, or market access is limited, suggesting a threshold beyond which additional support no longer translates into formal employment growth.

\begin{table}[H]
\centering
\caption{\small\textbf{\color{blue} Treatment heterogeneity}}
\label{table:hetero}
\scalebox{0.60}{
\begin{threeparttable}
    
    \begin{tabular}{@{\extracolsep{20pt}}lcccc} 
      \\[-1.8ex]\hline 
      \hline \\[-1.8ex] 

      & \multicolumn{2}{c}{Salaried employment} & \multicolumn{2}{c}{Self-employed employment} \\
      \cmidrule(lr){2-3} \cmidrule(lr){4-5} 
                    \textbf{PANEL A: Average farm size (AFS)} \\\\
      & [Low AFS] & [High AFS] & [Low AFS] & [High AFS]  \\
\hline

LFA (Yes/No)    &    0.115**   &      0.143    &      -0.202***  &     -0.206*** \\
                 & (2.31)       &      (1.55)   &     (-6.70)     &    (-2.72) \\
\hline
Observations     & 49\,294      & 43\,880       & 49\,294         & 43\,880 \\ 
\(R^{2}\)        & 0.237        & 0.139         & 0.235           & 0.144 \\
Covariates       & YES & YES & YES & YES \\
Municipality FE  & YES & YES & YES & YES \\
Year FE          & YES & YES & YES & YES \\  


\hline \\\\

\textbf{PANEL B: Number of farms (NF)} \\\\

     & [Low NF] & [High NF] & [Low NF] & [High NF]  \\
\hline

LFA (Yes/No)    &  0.379***   &  -0.0471     &  -0.245***    &  -0.135***  \\
                 &  (4.71)     &  (-1.18)     &  (-3.97)      &  (-5.35)    \\
\hline
Observations     & 47\,628     & 45\,546      & 47\,628        & 45\,546 \\
\(R^{2}\)        & 0.124       & 0.273        & 0.130          & 0.306 \\

Covariates       & YES & YES & YES & YES \\
Municipality FE  & YES & YES & YES & YES \\
Year FE          & YES & YES & YES & YES \\  
%\text{Municipality}\times\text{Year FE} & YES & YES & YES & YES \\\\

\hline \\\\

\textbf{PANEL C: Utilised agricultural area (UAA)} \\\\

      & [Low UAA] & [High UAA] & [Low UAA] & [High UAA]  \\
\hline
            
LFA (Yes/No)    &  0.159**   &    0.00346    &   -0.262***  &  -0.102*** \\
                 & (2.44)     &    (0.08)     &   (-6.66)    &  (-2.67)   \\
\hline
Observations     & 47\,298     & 45\,876     & 47\,298       & 45\,876 \\
\(R^{2}\)        & 0.208       & 0.148       & 0.189         & 0.237 \\

Covariates       & YES & YES & YES & YES \\
Municipality FE  & YES & YES & YES & YES \\
Year FE          & YES & YES & YES & YES \\  
%\text{Municipality}\times\text{Year FE} & YES & YES & YES & YES \\\\

\hline \\\\

\textbf{PANEL D: Weighted urban-proximity index (IPPU)} \\\\

     & [Low IPPU] & [High IPPU] & [Low IPPU] & [High IPPU]  \\
\hline

LFA (Yes/No)    & 0.0844      & 0.107**   & -0.230***   & -0.152*** \\
                 & (1.24)      & (2.10)    & (-4.87)     & (-3.74)   \\
\hline
Observations     & 44\,690     & 48\,484   & 44\,690     & 48\,484 \\
\(R^{2}\)        & 0.234       & 0.150     & 0.182       & 0.200 \\

Covariates       & YES & YES & YES & YES \\
Municipality FE  & YES & YES & YES & YES \\
Year FE          & YES & YES & YES & YES \\  
%\text{Municipality}\times\text{Year FE} & YES & YES & YES & YES \\\\

\hline
   \end{tabular}
      \begin{tablenotes}
      \footnotesize
    \item  \textit{Notes}: *** (**) (*) indicate significance at the 1 %, 5 % and 10 % levels, respectively.  
      Robust standard errors (in parentheses) are clustered at the municipality level.
  \end{tablenotes}
\end{threeparttable}
}
\end{table}
\vspace{8mm}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

