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\begin{document}
	\begin{titlepage}
		\title{Banks and Inequality: Evidence from a Nationwide Branch Expansion Policy
  }

\maketitle
      
\textbf{Authors}\\
      \author{Ashish K. Sedai: \thanks{Corresponding author: Department of Economics, College of Business, University of Texas at Arlington, Email: \href{mailto: ashish.sedai@uta.edu}{ashish.sedai@uta.edu}}\\  Mahmut Yasar: \thanks{Department of Economics, College of Business, University of Texas at Arlington} \\ William Crowder: \thanks{Department of Economics, College of Business, University of Texas at Arlington}\\  
      Rikhia Bhukta: \thanks{Department of Economic Sciences, Indian Institute of Technology, Kanpur}}





\section{Online Appendix}

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\clearpage
\begin{figure}[h]
		\centering
		
		\scriptsize
		\begin{tabular}{cc}
			\textbf{Panel A: 2005, IHDS} & \ \textbf{Panel B: 2012, IHDS} \\
			\includegraphics[width=0.350\linewidth]{Figures/ihdsineq1.png} & \includegraphics[width=0.350\linewidth]{Figures/ihdsineq2.png}\\
		\end{tabular}
  \caption{District Inequality in Consumption in India: 2005 and 2012 } 
     \label{nssmap0}
		\begin{fignote}
			\textbf{Notes:} Authors' computations using IHDS (2005 and 2012), 371 districts. 
		\end{fignote}
	\end{figure}
\begin{enumerate}
    
Panel (A) and Panel (B) in Figure \ref{nssmap0} show the district-level consumption inequality in India in 2005 and 2012, respectively. Northern regions exhibit higher inequality than southern regions, which aligns with the existing evidence on inequality in India. Panel (A) and (B) in Figure \ref{nssmap0} are not directly comparable across time as the equal intervals used in legends differ slightly across time due to sample selection across the two IHDS survey waves.
\end{enumerate}


\clearpage





\clearpage
\begin{figure}
 \centering
%\begin{tabular}{c}
   

    \includegraphics[scale=0.25]{Figures/LASSO.png}

  %  \end{tabular}\\
   
    \caption{Cross Validation Plot for LASSO variable selection}
    \label{lasso}
    
\begin{minipage}{13cm}
\vspace{0.2cm}
    
    
Note: The output reveals that cross-validation (CV) selected a $\lambda$ for which 34 of the 46 covariates used have nonzero coefficients. The CV function appears somewhat flat near the optimal $\lambda$, which implies that nearby values of $\lambda$ would produce similar out-of-sample Mean Squared Errors. This constancy in $\lambda$ yields the income variable from an adaptive selection model. The model uses 34 variables, as described in Table 7.
    
\end{minipage}
\end{figure}

\clearpage



\clearpage

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{LASSO Linear Model with Adaptive Selection}
\label{lasso1}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}llllll@{}}
\toprule
ID & Description & \lambda & No. of non-zero coef. & Out of sample R-squared & CV mean prediction error \\ \midrule
89 & first \lambda & 1123790 & 0 & 0.0008 & 2.20E+10 \\
160 & $\lambda$  before & 1520.541 & 34 & 0.2552 & 1.64E+10 \\
*161 & selected \lambda & 1385.461 & 34 & 0.2552 & 1.64E+10 \\
162 & $\lambda$  after & 1262.38 & 35 & 0.2552 & 1.64E+10 \\
188 & last \lambda & 112.379 & 39 & 0.2547 & 1.64E+10 \\ \bottomrule
\end{tabular}%
}
\begin{tablenotes}
   \textbf{Notes}: Table shows final adaptive step results with 36,305 number of observations from IHDS 1. No. of covariates is 46, and the number of LASSO steps is 2. CV is the default method of selecting the tuning parameters in the lasso command. CV finds the $\lambda$ that minimizes the out-of-sample Mean Squared Error of the predictions. The mechanics of CV mimic the process using split samples to find the best out-of-sample predictor.    
\end{tablenotes}
\end{table}

\clearpage

\clearpage

\begin{figure}
\centering
\begin{tabular}{c}
    

    \includegraphics[scale=0.25]{Figures/mccrary.png}
    
       

    \end{tabular}\\
   
    \caption{McCrary Density Plot and Histogram}
    \label{mccrary}
    \begin{fignote}
    Source: Authors' calculation, Data RBI MOF. Graph adapted from \cite{cramer2020bank}. Number of observations left of the cut-off, 209, right of the cut-off, 372. Cut-off is set at 14,780. Efficient Observations left, 142, right, 193. Bandwidth: 5,726. Value of t statistics, 0.251, p-value, 0.801. The figure also shows the distribution of the running variable (district population to bank branch ratio) in the RD design.
    \end{fignote}
\end{figure}

\clearpage

\clearpage


\clearpage

\clearpage
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Descriptive Statistics Consumption and Inequality, NSS}
\label{des2}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
 & \multicolumn{2}{c}{Banked} & \multicolumn{2}{c}{Underbanked} &  \\ \cmidrule(lr){2-3} \cmidrule(lr){4-5}
\multicolumn{1}{c}{Variable} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{SD} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{SD} & \multicolumn{1}{c}{t-test} \\ \midrule
National Sample Survey\\ \cmidrule(lr){1-1}
\textbf{2004} &  &  &  &  &  \\ \cmidrule(lr){1-1}
Gini Coefficient Consumption & 0.254 & 0.035 & 0.212 & 0.048 & *** \\
Personal Consumption Exp. Rs. & 895.6 & 169.4 & 714.1 & 124.8 & *** \\
\textbf{2009} &  &  &  &  &  \\ \cmidrule(lr){1-1}
Gini Coefficient Consumption & 0.218     & 0.036 & 0.245 & 0.034 & *** \\
Personal Consumption Exp. Rs. & 1342.5 & 213.1 & 1071.6 & 201.5 & *** \\
\textbf{2011} &  &  &  &  &  \\ \cmidrule(lr){1-1}
Gini Coefficient Consumption & 0.161 & 0.046 & 0.214 & 0.041 & *** \\
Personal Consumption Exp. Rs. & 1622.2 & 208.9 & 1325.3 & 221.6 & *** \\ \midrule
Observations & 202 & &  373 &  \\ \midrule 
National Family Health Survey
\\  \cmidrule(lr){1-1}
\textbf{2015}\\ \cmidrule(lr){1-1}
Gini Coefficient Living Standard & 0.154	 & 0.055 & 0.251 &	0.053 & ***\\
Living Standards (1-5) & 3.657 &	0.587 & 2.615 &	0.623 & ***\\
\midrule
Observations & 206 & & 374 &\\  \midrule
Population Census\\ \cmidrule(lr){1-1}
\textbf{2001}\\ \cmidrule(lr){1-1}
Migration from Other District & 53280.39	 & 81328.89 & 31879.35 &	 76040.95 & ***\\
\textbf{2011}\\ \cmidrule(lr){1-1}
Migration from Other District &  83441.29  &   141525.9 &  45847.19 &    116982.5 & ***\\
\midrule
Observations & 207 & & 374 &\\

\bottomrule
\end{tabular}%
}\begin{tablenotes}
   \textbf{Notes}: Stars indicate a significant mean difference between the district-level average of households in banked and underbanked districts in a given year based on a t-test. \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\). SD implies standard deviation. Variables in monetary terms are winsorized at 10 and 90\%.
\end{tablenotes}
\end{table}

\clearpage
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Bank Presence and Per Capita Monthly Consumption Inequality: NSS and RBI}
\label{nss}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lcccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{Pre} & \multicolumn{4}{c}{Post} \\ \cmidrule(lr) {2-3} \cmidrule(lr){4-7}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{Gini} & \multicolumn{1}{c}{Log Gini} & \multicolumn{2}{c}{Gini} & \multicolumn{2}{c}{Log Gini} \\  \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & 2004 & 2004 & 2009 & 2011 & 2009 & 2011 \\ \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
\textit{Polynomial 1} &  &  &  &  &  &  \\ \cmidrule(lr){1-1}
Treatment & 0.004 & 0.018 & -0.014 & -0.013 & -0.065 & -0.083 \\
 & (0.015) & (0.068) & (0.011) & (0.015) & (0.047) & (0.085) \\
 &  &  &  &  &  &  \\
 Untreated Mean & 0.253 & -1.38 & 0.218 & -1.534 & 0.160 & -1.873 \\

 Bandwidth & 4,858 & 4,934 & 4,308 & 4,201 & 4,233 & 4,001\\
 
Observations & 575 & 575 & 575 & 575 & 575 & 575 \\
\midrule
\textit{Polynomial 2} &  &  &  &  &  &  \\  \cmidrule(lr){1-1}
Treatment & 0.009 & 0.039 & -0.016* & -0.017 & -0.081* & -0.125* \\
 & (0.017) & (0.078) & (0.011) & (0.016) & (0.052) & (0.090) \\
 &  &  &  &  &  &  \\
Observations & 575 & 575 & 575 & 575 & 575 & 575 \\ \bottomrule
\end{tabular}%
} \begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$ for RD robust p values. Standard errors in parentheses clustered at the district level. Data: Cross-Sectional, National Sample Survey, 2004, 2009, 2011 and MOF, RBI, district level data. Six districts, out of the 581 in RBI data are missed when using the NSS data due to the balancing of districts across time. 
\end{tablenotes}
\end{table}

\clearpage
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% Please add the following required packages to your document preamble:
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% \usepackage{graphicx}


\clearpage




\begin{figure}
\centering
\begin{tabular}{c}
    \includegraphics[scale=0.4]{nlw.png}
    \end{tabular}\\
   
    \caption{Bank Branch Expansion and Non-linearity in Wealth Inequality}
    \label{nlw}
    \begin{fignote}
    \scriptsize
      Source: Authors' calculation, Data: RBI MOF and district-level data on wealth inequality NFHS-4, 2015/2016. 95\% confidence intervals are used. The coefficients are calculated using an ordinary least squares regression with robust standard errors, separately for treated (Underbanked in 2005) and non-treated (Not underbanked in 2005) districts. Estimates are derived from differences in bank branches (x-axis) between 2015 and 2005. Y axis shows the Gini coefficient of the wealth index. 
    \end{fignote}
\end{figure}



\clearpage



\clearpage


\begin{figure}
\centering
\begin{tabular}{c}
    

    \includegraphics[scale=0.3]{Figures/nonlinear wealth.png}
    
       

    \end{tabular}\\
   
    \caption{Bank Branch Expansion and Non-linearity in Wealth Inequality}
    \label{nonlinear_wealth}
    \begin{fignote}
    \scriptsize
       Source: Authors' calculation, Data: RBI MOF and district-level data on wealth inequality NFHS-4, 2015/2016. The coefficients are calculated using quantile regression with robust standard errors, separately for treated (Underbanked in 2005) and non-treated (Not underbanked in 2005) districts. Point estimates are derived from individual quantile regressions, and show the impact of branch expansion on Gini of the wealth index. 
    \end{fignote}
\end{figure}




\clearpage

\begin{table}[htbp]
\centering
\scriptsize
\caption{Bank Expansion and Consumption Inequality in Rural and Urban Areas, IHDS, 2012.}
\begin{tabular}{lcc}
\hline 
                  & (1) \textbf{Rural} & (2) \textbf{Urban}\\ 
\hline \\
Treatment Effects: Consumption Inequality & -0.010*** & -0.052***\\ 
& (0.002)   & (0.004)  \\ \\
Untreated Mean                             & 0.352     & 0.337    \\
Bandwidth                                  & 1,248     & 1,191    \\
Observations                               & 55,477    & 24,490   \\
\hline
\end{tabular}
\label{tab:rural_urban}
\begin{tablenotes}
\item \textit{Notes:} *** p$<$0.01. Standard errors are in parentheses. Observations are at the household level. The dependent variable in each column is the measure of consumption inequality in rural and urban areas. "Untreated Mean" represents the average level of consumption inequality in untreated districts. The bandwidth refers to the optimal neighborhood around the cutoff, as estimated using the method of \citet{calonico2014robust}. Data: Reserve Bank of India (RBI), and India Human Development Survey (IHDS), 2012. Rural and urban classification follows the IHDS survey.
\end{tablenotes}
\end{table}

\clearpage

\begin{comment}

\begin{table}[h!]
\centering
\scriptsize
\label{a5}
\caption{Test for $\beta$ Convergence in Consumption and income for Households Above and Below Median Income level}
\label{convergence1}
\begin{tabular}{lcccc}
\toprule
          & (1)                 & (2)                  & (3)                 & (4) \\ \midrule
        \multicolumn{1}{c}{} & \multicolumn{2}{c}{Above Median Income} & \multicolumn{2}{c}{Below Median Income} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-5}
Variables & Gr. Rate of Cons. & Gr. Rate of Pre. Income & Gr. Rate of Cons. & Gr. Rate of Pre. Income \\
\midrule
Treatment & 2.655***            & 2.967***            & 3.993***            & -1.758 \\
          & (0.993)             & (0.932)             & (1.517)             & (1.985) \\

L.Log Consumption*Treatment & -0.234*** & & -0.353** & \\
                            & (0.0873) & & (0.135) & \\

L.Log Predicted Income*Treatment & & -0.259*** & & 0.157 \\
                                 & & (0.0810) & & (0.182) \\

Lag Y & -0.574***            & -0.347***            & -0.692***            & -0.828*** \\
      & (0.0602)             & (0.0548)             & (0.137)             & (0.175) \\
\midrule
Observations & 277 & 277 & 94 & 94 \\
R-squared    & 0.365 & 0.598 & 0.606 & 0.833 \\
\bottomrule
\end{tabular}
\begin{tablenotes}
\small
\scriptsize \textbf{Notes}: Table \ref{convergence1} reports the results from $\beta$ convergence DID model, the impact of RBI bank branch expansion policy (2005) on convergence in consumption and predicted income in India for households above and below median income level. $* p < 0.1$, $** p < 0.05$, $*** p < 0.01$. Standard errors in parentheses are clustered at the district level. Data: IHDS panel, 2005 and 2012, and MOF, RBI, district-level data. Both variables are winsorized at 10\% and 90\%. Here, $\beta$-convergence implies conditional convergence in mean values of consumption and predicted income across districts. Additional control variables include average district poverty in the pre-policy period and Gini coefficient of consumption. Regional fixed effects are included.
\end{tablenotes}
\end{table}
\end{comment}





\begin{table}[]
\centering
\caption{Treatment Effects on Log Consumption and Log Income}
\label{cla}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lcccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)}\\ \midrule

\multicolumn{1}{c}{} & \multicolumn{2}{c}{Log Levels (DID)} & \multicolumn{2}{c}{Log Levels (Differences-in-Discontinuity)} \\ \midrule

\multicolumn{1}{c}{Variables} & \multicolumn{1}{c}{Log Consumption} & \multicolumn{1}{c}{Log Income} & \multicolumn{1}{c}{Log Consumption} & \multicolumn{1}{c}{Log Income }\\ \midrule
 \cmidrule{1-1}
Treatment & 1.984**	& 1.144** &	2.726*** &	1.179* \\
& (0.854) &	(0.536) &	(0.986) &	(0.621) \\ \midrule
 L. Log Consumption & 0.167* &	 &	0.211** &	\\
 
& (0.0858) &	 &	(0.0945) &	 \\

L. Log Income  &  &	0.283*** &	 &	0.219*** \\
 
&  &	(0.0603) & &	(0.0631) \\

\midrule
L. Log Consumption*Treatment & -0.173**	 & &	-0.239*** & \\
& (0.0759)	& &	(0.0875) & \\

L. Log Income*Treatment & & -0.100** & &	-0.103* \\
& & (0.0467) & & (0.0543) \\ \midrule
 
Observations & 371 & 371 & 264 & 264  \\
R-squared & 0.778 &	0.866 &	0.769 &	0.841 \\ \bottomrule
\end{tabular}%
}
\begin{minipage}{16.5cm}
\vspace{0.2cm}
\scriptsize
\textbf{Notes}: Table \ref{cla} reports results from the DID and Differences-in-Discontinuity models examining the effects of treatment on log levels of consumption and income. $* p < 0.1$, $** p < 0.05$, $*** p < 0.01$. Standard errors in parentheses are clustered at the district level. The dataset includes IHDS panel data (2005 and 2012) and MOF, RBI district-level data. Variables are winsorized at 10\% and 90\%. Additional district-level controls include (i) household poverty, (ii) consumption Gini, (iii) household wealth, and (iv) household head's education. Regional fixed effects are included for the seven major regions in India.
\end{minipage}
\end{table}

\clearpage

\begin{table}[]
\centering
\caption{DID and Difference-in-Discontinuity \(\beta\)-Convergence: IV model}
\label{convergenceiv}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lcccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} \\
\midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{DID \(\beta\)-Convergence} & \multicolumn{2}{c}{Differences-in-Discontinuity \(\beta\)-Convergence} \\
\midrule
\multicolumn{1}{c}{Variables} & \multicolumn{1}{c}{Growth Consumption} & \multicolumn{1}{c}{Growth Income} & \multicolumn{1}{c}{Growth Consumption} & \multicolumn{1}{c}{Growth Income} \\
\midrule


Treatment          & 3.390*** & 0.991* & 4.009*** & 1.180* \\
                  & (1.036)  & (0.572) & (1.058)  & (0.624) \\
                  \midrule
                  L.Log Consumption & -0.641*** &  & -0.607*** &  \\
                  & (0.119)  &  & (0.122)   &  \\
                  L.Log Income       &  & -0.744*** &  & -0.781*** \\
                  &  & (0.0758)  &  & (0.0779)   \\ \midrule

L.Log Consumption*Treatment & -0.298*** &  & -0.353*** &  \\
                            & (0.0921)  &  & (0.0939)   &  \\



L.Log Income*Treatment &  & -0.0872* &  & -0.103* \\
                        &  & (0.0498)  &  & (0.0545)   \\
\midrule
Under identification Kleibergen-Paap rk LM statistic & 47 & 59 & 32 & 52 \\
Weak identification Cragg-Donald Wald F statistic   & 499 & 680 & 384 & 616 \\
First stage       & -22.441*** & -37.513*** & -22.766*** & -40.267*** \\
                  & (1.670)    & (2.575)    & (1.861)    & (2.949) \\
Sanderson-Windmeijer F test excluded instrument & 180 & 212 & 149 & 186 \\
Observations      & 371 & 371 & 264 & 264 \\
R-squared         & 0.530 & 0.633 & 0.558 & 0.680 \\
\bottomrule
\end{tabular}%
}
\begin{minipage}{16.5cm}
\vspace{0.2cm}
\scriptsize
\textbf{Notes}: Table \ref{convergenceiv} reports the results from $\beta$ convergence DID model, the impact of RBI bank branch expansion policy (2005) on convergence in consumption and income in India. $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses are clustered at the district level. Data: IHDS panel, 2005 and 2012, and MOF, RBI, district-level data. Both variables are winsorized at 10\% and 90\%. Here, $\beta$-convergence implies convergence in mean values of consumption and across districts. The baseline district level control variables include (i) household poverty (ii) consumption gini, (iii) household wealth, and (iv) household head's education. Additional regional fixed effects are included for the seven major regions in India. In columns (3) and (4), following \cite{khanna2023political}, we use the difference-in-discontinuity design, within the $\beta$-convergence framework. The choice of bandwidth for this model is taken from our main result on the Gini coefficient,$\pm 6,459$, as shown in column (3) of Table 2. \textit{Instrumental Variables (IV)}: For consumption regressions, the IV is calculated as the logarithm of the mean consumption within a region-year, excluding the district's own value. For income regressions, the IV is the logarithm of the mean income within a region-year, similarly excluding the districts's own value. \textit{Interpretation of Tests}: The Kleibergen-Paap rk LM statistic tests whether the model is under-identified, ensuring that the instruments are relevant for the endogenous variables. The Cragg-Donald Wald F statistic assesses weak identification, ensuring the instruments are strong enough for reliable inference. The first-stage coefficients and their standard errors indicate the strength and significance of the relationship between the instruments and endogenous variables. The Sanderson-Windmeijer F-test for excluded instruments evaluates the joint significance of the instruments in the first stage.
\end{minipage}
\end{table}
\clearpage



\clearpage


\begin{landscape}
    \begin{figure}[h]
		\centering	
		\scriptsize
		\begin{tabular}{cc}
			\textbf{Panel A: Pre Treatment, 2004} & \ \textbf{Panel B: Post Treatment 2011} \\
			\includegraphics[width=0.5300\linewidth]{Figures/bimap0.png} & \includegraphics[width=0.5300\linewidth]{Figures/bimap1.png}\\
		\end{tabular}
  \caption{Gini coeff. of consumption inequality in India by underbankedness, district level.} 
     \label{nssmap1}
		\begin{fignote}
			\textbf{Notes:} Authors' computations using NSS (2004 and 2011), 575 districts. The figure shows a reduction in inequality in treated districts. 
		\end{fignote}
	\end{figure}
 
\end{landscape}
\clearpage
	\begin{figure}[h]
		\centering
		\scriptsize
		\begin{tabular}{cc}
			\textbf{Panel A: Overall} & \textbf{Panel B: Treated} \\
			\includegraphics[width=0.400\linewidth]{Figures/nfhsineqd.png} & \includegraphics[width=0.400\linewidth]{Figures/nfhsineqdt.png} 
		\end{tabular}
  \caption{District Level Inequality in Living Standards in India. }     \label{nfhsmap2}
		\begin{fignote}
			\textbf{Notes:} Authors' computations using NFHS, 2014/2015. Panel (A) shows the inequality in living standards for the full sample of districts, 580 districts. Panel (B) shows the inequality in living standard for the districts that were treated with the Bank Branch Authorization Policy, 374 districts.  
		\end{fignote}
	\end{figure}


 \begin{fignote}
     
 
Figure \ref{nfhsmap2} shows the district-level map of inequality in wealth in India. Panel (A) shows the overall inequality in living standards and Panel (B) shows the inequality in treated districts. As evident, there seems to be high inequality in living standards in the BIMARU states of India (Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh). In treated districts, we find a significantly lower level of inequality. However, in the BIMARU states, the impact of treatment on living standard inequality seems to be smaller.

\end{fignote}


\clearpage



\begin{figure}
    \centering
    \includegraphics[scale=0.30]{Figures/Night light.png} \\ 

    \caption{District level change in mean total night-lights luminosity for treated districts.}
    \label{night1}
    \begin{fignote}
   \scriptsize  Source: Authors' calculation, SHRUG Data. Total light is the sum of the luminosity values (0-63) of all pixels in the unit of analysis  \citep{asher2021development}. The base year is 2005.
    \end{fignote}
\end{figure}

\clearpage
\begin{figure}[]
    \centering
    \scriptsize
\includegraphics[width=0.700\linewidth]{Figures/middle.png}
\caption{Bank Presence, Consumption and Income by Income Tercile}
\begin{fignote}
  Source: Authors’ calculation using IHDS household data, 2012, and RBI, MOF. Variables deflated using survey deflator and winsorized at 10th and 90th percentile.  
\end{fignote}

\label{middle}
\end{figure}


\clearpage
\begin{enumerate}
    \newpage
\subsection{\textcolor{red}{Impact of Financial Access on Institutional Confidence}}

\textcolor{red}{In this section, we examine if the bank expansion policy had broader societal effects in terms of household confidence in political institutions. 
Given the structural transitions in employment and increased economic opportunities in treated districts, it is interesting to examine if bank expansion could have broader social repercussions. To this extent, we examine if the policy led to higher confidence in government and local leadership.
Central to our hypothesis is the idea that the expansion of banking services acts as a catalyst for substantial economic activity within these districts. Such economic enhancements are expected to bolster trust in governmental capabilities, especially in delivering necessary infrastructural developments. This expectation is supported by findings from \citet{JHA2025105303}, who observed that even temporary participation in financial markets could significantly boost generalized trust among individuals. Therefore, we posit that similar increases in trust could arise from expanded banking services, which, like financial market exposure, involve entrusting assets to institutional frameworks perceived as capable and reliable. This potential increase in trust could lead to strengthened public confidence in governmental efforts, thereby promoting collaborative community initiatives and reducing local inequalities. Additionally, as economic growth and institutional effectiveness are intertwined, the anticipated outcomes suggest that the expansion of banking services will not only stimulate local economies but also reinforce public confidence in governmental efforts.} As shown in Figure \ref{confi} and in columns 4, 5 and 6 of Table \ref{confidence}, we find that bank presence increased confidence in state government to look after people (14.4 percentage points), local leadership to implement public projects (11.5 percentage points) and politicians to fulfill their promises (6.2 percentage points). Columns 1, 2 and 3 of Table \ref{confidence} show pre-policy smoothness in institutional confidence variables and provide robustness to the post-policy estimates. Panel (a) of Figure \ref{confi} shows the discontinuous jump in district-level aggregated confidence of households that the state takes care of people, panel (b) shows the discontinuous jump in the confidence that local leaders implement public projects properly, and panel (c) shows the discontinuous jump in the confidence that local politicians fulfill their promises.  As banks establish themselves within a locality, residents may perceive an improvement in the capacity of the state government to attend to the welfare of its people. The accessibility of banking services often signifies economic development, fostering trust in local leadership to effectively implement public projects. Moreover, the financial infrastructure provided by banks can enhance the perceived reliability of politicians to fulfill their promises, as economic growth and stability become more tangible. This newfound confidence in the efficacy of governmental institutions and leaders can catalyze collaborative efforts, encouraging the equitable distribution of resources and opportunities across the community, and ultimately contributing to a reduction in local inequality.
\end{enumerate}


\begin{figure}[]
    \centering
    \scriptsize
    \small
    \begin{subfigure}[b]{0.30\textwidth}
    \includegraphics[width=\textwidth]{Figures/stategovt.png}
    \caption{Confidence in State Govt.}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.30\textwidth}
    \includegraphics[width=\textwidth]{Figures/local_leader.png}
    \caption{Confidence in Local Leaders}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.30\textwidth}
    \includegraphics[width=\textwidth]{Figures/polparties.png}
    \caption{Confidence in Pol. Parties}
    \end{subfigure}
    \caption{RD plot on Bank Presence and Confidence in Institutions}
    \label{confi}
    \begin{fignote}
    \centering
    Source: Authors' calculation, RD plots, Data: IHDS, 2012, and RBI, MOF. 
    \end{fignote}
\end{figure}

\clearpage
\begin{table}[]
\centering
\caption{Bank Presence and Confidence in Institutions}
\label{confidence}
\resizebox{14cm}{!}{%
\begin{tabular}{@{}lcccccc@{}}
\toprule
 & (1) & (2) & (3) & (4) & (5) & (6) \\ \midrule
 & \multicolumn{3}{c}{Pre Policy} & \multicolumn{3}{c}{Post Policy} \\ \cmidrule(lr){2-4} \cmidrule(lr){5-7}
Confidence & \begin{tabular}[c]{@{}l@{}}State \end{tabular} & \begin{tabular}[c]{@{}l@{}}Local Leaders \end{tabular} & \begin{tabular}[c]{@{}l@{}}Politicians\end{tabular} & \begin{tabular}[c]{@{}l@{}}State \end{tabular} & \begin{tabular}[c]{@{}l@{}}Local Leaders \end{tabular} & \begin{tabular}[c]{@{}l@{}}Politicians \end{tabular} \\ \midrule
Treatment & 0.073 & -0.028 & 0.006 & 0.144** & 0.115** & 0.062* \\
 & (0.058) & (0.068) & (0.030) & (0.061) & (0.059) & (0.032) \\ \midrule
Untreated Mean & 0.24 & 0.36 & 0.10 & 0.26 & 0.29 & 0.08 \\

Bandwidth & 6,692 & 6,303 & 6,861 & 5,753 & 5,485 & 6,128 \\

Observations & 371 & 371 & 371 & 371 & 371 & 371 \\ \bottomrule
\end{tabular}%
}
\begin{tablenotes}
\scriptsize
\textbf{Notes}: Table \ref{confidence} reports the results from the RD robust regression of the impact of RBI bank branch expansion policy (2005) on the confidence in local institutions in India. $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses are clustered at the district level. Data: India Human Development Survey, 2005 and 2012, and MOF, RBI, district level data. We average the household-level dummy variables of their confidence in institutions at the district level to get the fraction of households in the district having confidence in the respective institutions, which we then use as outcome variables.
%Outcomes are dummy variables and coefficients are interpreted as percentage point changes. 
For more details of the variable, see the data section on IHDS survey.  
\end{tablenotes}
\end{table}

\clearpage


\clearpage
\begin{table}[]
\centering
\caption{Bank Presence and Change in Total Night-lights Luminosity (Base: 2005)}
\label{night}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lccccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)} & \multicolumn{1}{c}{(7)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{4}{c}{Post} & \multicolumn{3}{c}{Pre} \\
\cmidrule(lr){2-5} \cmidrule(lr){6-8}
\multicolumn{1}{c}{Variables} & \multicolumn{1}{c}{2012} & \multicolumn{1}{c}{2010} & \multicolumn{1}{c}{2008} & \multicolumn{1}{c}{2006} & \multicolumn{1}{c}{2004} & \multicolumn{1}{c}{2002} & \multicolumn{1}{c}{2000} \\ \midrule
Treatment & 39.23* & 62.80* & 30.39** & 7.277* & 2.294 & 3.293 & -3.628 \\
 & (23.21) & (33.37) & (15.34) & (4.091) & (2.606) & (6.034) & (4.963) \\
 &  &  &  &  &  &  &  \\
Untreated Mean & 37.77 & 43.49 & 15.22 & 3.70 & 2.48 & 7.09 & 4.64 \\
Bandwidth & 2,436 & 2,526 & 2,446 & 2,710 & 2,552 & 2,497 & 2,951 \\
Observations & 575 & 575 & 575 & 575 & 575 & 575 & 575 \\ \bottomrule
\end{tabular}%

}\begin{tablenotes}
\scriptsize \textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. Point estimate is shown in Figure \ref{night1}. Post-policy estimates show a non-linear trend. 
\end{tablenotes}
\end{table}

\clearpage

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Bank Presence: Household Consumption and Income by Income Tercile}
\label{middle2}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lcccccc@{}}
\toprule
 & (1) & (2) & (3) & (4) & (5) & (6) \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{3}{c}{Log Predicted   Income} & \multicolumn{3}{c}{Log Consumption} \\ \cmidrule(lr){2-4} \cmidrule(lr){5-7}
\multicolumn{1}{c}{Variables} & \multicolumn{1}{c}{Low   Income} & \multicolumn{1}{c}{Middle   Income} & \multicolumn{1}{c}{High   Income} & \multicolumn{1}{c}{Low   Income} & \multicolumn{1}{c}{Middle   Income} & \multicolumn{1}{c}{High   Income} \\ \midrule
Treatment & 0.050 & 0.112* & 0.084 & 0.036 & 0.165** & 0.064 \\
 & (0.085) & (0.060) & (0.064) & (0.129) & (0.081) & (0.070) \\
 &  &  &  &  &  &  \\
Untreated Mean & 10.99 & 11.20 & 11.60 & 11.13 & 11.34 & 11.77 \\
Bandwidth & 3,868 & 4,137 & 5,551 & 3,902 & 4,091 & 4,896 \\
Observations & 11,598 & 13,474 & 14,253 & 11,814 & 13,678 & 14,502 \\ \bottomrule
\end{tabular}%
} \begin{tablenotes}
    \textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. Control Variables include log of district population in 2001, and commercial bank branches in 1997. 
\end{tablenotes}
\end{table}
\clearpage


\clearpage

\begin{table}[htbp]
\centering
\caption{RD Estimates of the Effects of Bank Expansion on Log Consumption, Rural and Urban Households by Income Class}
\begin{tabular}{lcccccc}
\hline
          & \multicolumn{3}{c}{Rural} & \multicolumn{3}{c}{Urban} \\
\hline
Variables & Poor  & Middle & Rich   & Poor  & Middle & Rich   \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
Treat   & -0.069 & 0.068* & -0.044 & 0.486*** & 0.199*** & 0.081 \\
              & (0.048) & (0.038) & (0.050) & (0.088) & (0.047) & (0.050) \\ \\
Untreated Mean & 10.42  & 10.68  & 11.20  & 10.54  & 10.88  & 11.42  \\
Bandwidth     & 3,130  & 2,705  & 2,881  & 4,313  & 3,968  & 3,338  \\
Observations  & 10,037 & 9,576  & 7,680  & 1,777  & 4,102  & 6,822  \\
\hline
\end{tabular}
\label{tab:rd_estimates}
\begin{tablenotes}
\scriptsize
\item \textit{Notes:} *** p$<$0.01, * p$<$0.1. Standard errors are in parentheses. The dependent variable is the log of household consumption. "Untreated Mean" represents the average level of log consumption for untreated households in each income group. Bandwidth refers to the optimal window around the RD cutoff, calculated following \citet{calonico2014robust}. Observations denote the total number of units within each income class included in the estimation. Data sources: Reserve Bank of India's (RBI) Master Office File (MOF), and India Human Development Survey (IHDS), 2012.
\end{tablenotes}
\end{table}

\clearpage

\begin{table}[h!]
\centering
\scriptsize
\caption{Income Terciles in Urban and Rural Areas, IHDS, 2012}
\begin{tabular}{lccc|ccc}
\hline
\textbf{Terciles of Income} & \textbf{Observations} & \textbf{Percent (Urban)} & & \textbf{Observations} & \textbf{Percent (Rural)} \\
\hline
1 & 10,047 & 36.79 & & 1,782 & 14.02 \\
2 & 9,578  & 35.07 & & 4,105 & 32.30 \\
3 & 7,683  & 28.13 & & 6,823 & 53.68 \\
\hline
\textbf{Total} & 27,308 & 100.00 & & 12,710 & 100.00 \\
\hline
\end{tabular}
\label{tab333}
\begin{tablenotes}
\item \textit{Notes:} The table reports the distribution of households by income terciles across urban and rural areas. The "Percent" column shows the share of households in each income tercile as a percentage of the total sample for urban and rural areas, respectively. Data are sourced from the India Human Development Survey (IHDS), 2012.
\end{tablenotes}
\end{table}

\clearpage
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Bank Presence and Consumption Inequality in India: Polynomial 2}
\label{poly2}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{Pre} & \multicolumn{3}{c}{Post} \\ \cmidrule(lr) {2-3} \cmidrule(lr) {4-6}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{Gini Coeff} & \multicolumn{1}{c}{Log Gini} & \multicolumn{1}{c}{Gini Coeff} & \multicolumn{1}{c}{Log Gini} & \multicolumn{1}{c}{Diff Gini} \\ \midrule
Treatment & -0.002 &	-0.043 &	-0.035** &	-0.103* &	-0.041**\\ &
(0.023) &	(0.086) &	(0.016) &	(0.060) &	(0.0169)
 \\

Bandwidth & 8,713 & 8,643 & 8,926 & 8,576 & 8,775 \\
Observations & 371 & 371 & 371 & 371 & 371 \\ \bottomrule
\end{tabular}%
}\begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. All other characteristics are similar to Table 2. 
\end{tablenotes}
\end{table}
\clearpage

\clearpage
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\scriptsize
\caption{RD Donut Hole Test: Bank Presence and Consumption Inequality in India}
\label{donut}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{Pre} & \multicolumn{3}{c}{Post} \\ \cmidrule(lr) {2-3} \cmidrule(lr) {4-6}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{Gini Coeff} & \multicolumn{1}{c}{Log Gini} & \multicolumn{1}{c}{Gini Coeff} & \multicolumn{1}{c}{Log Gini} & \multicolumn{1}{c}{Diff Gini} \\ \midrule
Treatment & 0.003 &	-0.023 &	-0.025**& 	-0.097 &	-0.037**\\
& (0.019)	& (0.078) &	(0.012) &	(0.066)&	(0.018)
 \\
\\

Bandwidth & 4,703 & 4,624 & 4,779 & 4,610 & 4,095\\

Observations & 367 & 367 & 367 & 367 & 367 \\ \bottomrule
\end{tabular}%
}\begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. Data: India Human Development Survey, 2005 and 2012, and MOF, RBI, district level data. 
\end{tablenotes}
\end{table}
\clearpage
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{\textbf{Placebo Cutoffs: Main Results}}
\label{placebo1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
         & \multicolumn{5}{c}{Placebo Cut-off Main Results} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{-3000} & \multicolumn{1}{c}{-1000} & \multicolumn{1}{c}{0}   & \multicolumn{1}{c}{1000} & \multicolumn{1}{c}{3000} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)}   & \multicolumn{1}{c}{(2)}   & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)}  & \multicolumn{1}{c}{(5)}  \\ \midrule
\textbf{ Table (2)} District Consumption Inequality                             &          &         &         &         &         \\ \cmidrule(lr){1-1}                           Gini Coeff. Cons.	 &-0.146 &	0.110 &	-0.026** &	-0.007 &	0.101
	
    \\
Difference in Gini Coeff. Cons. & 51.87 &	0.102 &	-0.032*** &	-0.016 &	0.0732  
\\ 
\\
\textbf{ Table (3)} District Living Standards       &          &         &         &         &         \\ \cmidrule(lr){1-1}   

Gini Living Standard & 0.002 &	0.062 &	-0.030 &	0.023 &	-0.016\\
Living standards Index  & 3.499 &	-73.17 &	0.311** &	-0.319 &	-0.286
 \\ \\
\textbf{ Table (4)} Poverty  &          &         &         &         &         \\ \cmidrule(lr){1-1} 

District Poverty &-0.106 &	-0.011 &	-0.059* &	2.051 &	-0.152\\ \\

\textbf{ Table (5)} District Income  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
Income  &115,839 &	-33,826 &	13,397** &	31,322 &	-37,471\\
Difference in Income & 634,760 &	6,085	& 8,479* &	49,141 &	-6,526\\
\\

\textbf{ Table (6)} Agri. and Non-Agri. Employment  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
Agricultural Employment  & -0.175	& -0.006 &	-0.061**	& -0.212 &	-0.125\\
Non Agricultural Employment  & -0.849	& -0.051 &	0.055*	& 1.354 &	0.023\\
\\
\textbf{ Table (7)} Earnings from Employment  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
 Earnings Inequality & -145.0 & 	-0.003 &	-0.032 &	0.161 &	-0.109\\
Total Work Earnings  & 132,906 &	446,773 &	6,416* &	2,194 & 9,505\\


\bottomrule
\end{tabular}%
}\begin{tablenotes}
\textbf{Notes}: p-values of respective regressions with different (placebo) cutoffs are shown. For details of the regressions, refer to the
respective main table. The structure of the table is drawn from Cramer (2020). 
\end{tablenotes}
\end{table}

\clearpage

\clearpage

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{\textbf{Robustness to Different Bandwidth Multipliers: Main Results}}
\label{bandwidths}
\resizebox{\textwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
\multicolumn{1}{c}{}                                   & \multicolumn{5}{c}{\textbf{Bandwidth Multiplier}}    \\ \midrule
\multicolumn{1}{c}{} &
  \multicolumn{1}{c}{\textbf{0.50x}} &
  \multicolumn{1}{c}{\textbf{0.75x}} &
  \multicolumn{1}{c}{\textbf{1.00x}} &
  \multicolumn{1}{c}{\textbf{1.25x}} &
  \multicolumn{1}{c}{\textbf{1.50x}} \\ \midrule
\multicolumn{1}{c}{} &
  \multicolumn{1}{c}{(1)} &
  \multicolumn{1}{c}{(2)} &
  \multicolumn{1}{c}{(3)} &
  \multicolumn{1}{c}{(4)} &
  \multicolumn{1}{c}{(5)} \\ \midrule
\textbf{ Table (2)} District Consumption Inequality                             &          &         &         &         &         \\ \cmidrule(lr){1-1}                           Gini Coeff. Cons. &                                   -0.032* &	-0.027** &	-0.026** &	-0.023** &	-0.020**\\ &
(0.016) &	(0.013) &	(0.011) &	(0.010) &	(0.009)
 \\
Difference in Gini Coeff. Cons. &          -0.041** &	-0.036** &	-0.032***	& -0.027***	& -0.024***\\
& (0.018) &	(0.014) &	(0.012) &	(0.010) &	(0.010)
\\ \\
\textbf{ Table (3)} District Living Standards       &          &         &         &         &         \\ \cmidrule(lr){1-1}   

Gini Living Standard & -0.046 & 	-0.038 &	-0.029 &	-0.022* &	-0.0159*\\
& (0.039) &	(0.027) &	(0.021) &	(0.018) &	(0.015)
\\
Living standards Index  & 0.509***	& 0.422** &	0.311** &	0.235** &	0.149**\\ &
(0.258) &	(0.198) &	(0.163) &	(0.140) &	(0.124)

 \\ \\
\textbf{ Table (4)} Poverty  &          &         &         &         &         \\ \cmidrule(lr){1-1} 

District Poverty & -0.068 &	-0.069* &	-0.058* &	-0.049* &	-0.040* \\
& (0.050) &	(0.038) &	(0.032) &	(0.027) &	(0.0255)
\\ \\
       
\textbf{ Table (5)} District Income  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
Income  & 20,229** &	17,677** &	13,398** &	9,958** &	7,334**\\ &
(9,864) &	(8,439) &	(7,198) &	(6,203) &	(5,639)
\\
Difference in Income & 6,987 &	8,965 &	8,479* &	7,229* &	5,830*\\ &
(6,818) &	(5,623) &	(4,738) &	(3,998) &	(3,608)
\\ 
\\
\textbf{ Table (6)} Agri. and Non-Agri. Employment  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
Agricultural Employment  & -0.583* &	-0.062* &	-0.061** &	-0.056** &	-0.053**\\ &
(0.316) &	(0.036) &	(0.029) &	(0.024) &	(0.0213)
\\
Non Agricultural Employment  & 0.079 &	0.067* &	0.055* &	0.056** &	0.0550**\\
& (0.052) &	(0.040) &	(0.033) &	(0.029) &	(0.026)
\\ \\
\textbf{ Table (7)} Earnings from Employment  &          &         &         &         &         \\ \cmidrule(lr){1-1} 
 Earnings Inequality & -0.045	& -0.042 &	-0.032 &	-0.016* &	-0.004 \\ &
(0.040) &	(0.033) &	(0.028) &	(0.023 ) &	(0.021)
\\
Total Work Earnings  & 9,465* &	7,635* &	6,417* &	3,870** &	2,069*\\ &
(5,237)	& (4,579) &	(4,227) &	(3,924)	& (3,555)
\\


 \bottomrule
\end{tabular}%
} \begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$, $*** p < 0.01$. Standard errors in parentheses. For details of the regression, refer to the respective main table. The structure of the table is drawn from Cramer (2020).
\end{tablenotes}
\end{table}
\clearpage

	% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Robustness to Different Bandwidth Selectors: Main Results}
\label{mse}
\scriptsize
\resizebox{\textwidth}{!}{%
\begin{tabular}{@{}lcccc@{}}
\toprule
& \multicolumn{2}{c}{MSE Optimal}   &               \multicolumn{2}{c}{CER Optimal} \\ \cmidrule (lr) {2-3} \cmidrule (lr){4-5}
Variables                                        & {Common}         & Two-sided   & Common         & Two-sided      \\  \cmidrule (lr) {2-3} \cmidrule (lr){4-5}
    &            (1) & (2)         & (3)            & (4)            \\ \midrule
\textbf{ Table (2)} District Consumption Inequality                             &          &         &         &         &          \cmidrule(lr){1-1}                          
Gini Coeff. Cons. &                                   -0.026** &	-0.034** &	-0.027** &	-0.039**\\ &
(0.011) &	(0.014) &	(0.013) &	(0.017)

 \\
Difference in Gini Coeff. Cons. &          -0.032** &	-0.040*** &	-0.036** &	-0.046** \\ &
(0.012) &	(0.015) &	(0.014) &	(0.0183)

\\

\textbf{ Table (3)} District Living Standards       &          &         &         &         &         \cmidrule(lr){1-1}   

Gini Living Standard & 	-0.030 &	-0.026 &	-0.038 &	-0.031 \\ &
(0.021) &	(0.022) &	(0.028) &	(0.0285)

\\
Living standards Index  & 0.311** &	0.235* &	0.433** &	0.326*\\ &
(0.163) &	(0.176) &	(0.202) &	(0.217)

\\ \\

\textbf{ Table (4)} Poverty &          &         &         &         &          \cmidrule(lr){1-1} 
District Poverty & -0.058* &	-0.071* &	-0.069* &	-0.073\\ &
(0.032) &	(0.037) &	(0.038) &	(0.0460)
\\ 
\\
\textbf{ Table (5)} District Income  &          &         &         &         &         \cmidrule(lr){1-1} 
Income  & 13,397** &	16,311** &	17,751** &	18,641**\\
& (7,176)	& (7,259) &	(8,422) &	(8,533)
 \\ 
Difference in Income & 8,479*	 & 9,686* &	8,938* &	9,268\\ &
(4,721) &	(5,187) &	(5,612)	& (6,002)
\\
\\ 
\textbf{ Table (6)} Agri. and Non-Agri. Employment  &          &         &         &         &          \cmidrule(lr){1-1} 
Agricultural Employment  & -0.061** &	-0.056* &	-0.062* &	-0.061 \\ &
(0.029) &	(0.035) &	(0.036) &	(0.043)

\\
Non Agricultural Employment  & 0.055* &	0.099** &	0.067* &	0.085\\ &
(0.033) &	(0.042) &	(0.040) &	(0.0589)
 
\\ \\
\textbf{ Table (7)} Earnings from Employment  &          &         &         &         &          \cmidrule(lr){1-1} 
 Earnings Inequality & -0.032 &	-0.035 &	-0.043 &	-0.039\\ &
(0.028) &	(0.034) &	(0.033) &	(0.042)

\\
Total Work Earnings  & 6,416*	& 10,085**	 & 7,662*	& 15,281**\\ &
(4,212) &	(4,429) &	(4,561) &	(6,218)

\\



\bottomrule
\end{tabular}%
} \begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$, $*** p < 0.01$. Standard errors in parentheses. The first and second columns are MSE-optimal bandwidths, initially identical and then different to the left and right of the cutoff. The third and fourth columns indicate CER (Coverage Error Rate)-optimal bandwidths, first identical and then different to the left and right of the
cutoff (Calonico et al., 2020,
2014). For details of the regression, refer to the respective tables. The structure of the table is drawn from Cramer (2020).
\end{tablenotes}
\end{table}

\clearpage

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Bank Presence and Consumption Inequality in India: Theil Index}
\label{theil}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{Pre} & \multicolumn{3}{c}{Post} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-6}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{Theil Index} & \multicolumn{1}{c}{Log Theil Index} & \multicolumn{1}{c}{Theil Index} & \multicolumn{1}{c}{Log Theil Index} & \multicolumn{1}{c}{Diff. Theil Index} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-6}
Treatment & -0.000 & -0.001 & -0.018* & -0.174** & -0.022** \\
 & (0.012) & (0.143) & (0.009) & (0.082) & (0.011) \\ \midrule
Untreated Mean & 0.096 & -2.435 & 0.131 & -2.064 & 0.035 \\

Bandwidth & 6495 & 6573 & 6458 & 6436 & 6290 \\

Observations & 371 & 371 & 371 & 370 & 371 \\ \bottomrule
\end{tabular}%
}\begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. Data: India Human Development Survey, 2005 and 2012, and MOF, RBI, district level data. Untreated Mean is the average outcome value for the non-treated household in the optimal bandwidth. Covariates in pre-policy is the number of non-regional rural bank branches in the district in 1997, and in the post period, we additionally control for the district population as per the 2001 census, lag of the outcome variable and number of household assets in the pre-treatment period.  
\end{tablenotes}
\end{table}

\clearpage



\begin{comment}
    

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
% \usepackage{graphicx}
\begin{table}[]
\centering
\caption{Bank Presence and Consumption Inequality in India: Theil Index: NSS}
\label{theilnss}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{@{}lcccccc@{}}
\toprule
\multicolumn{1}{c}{} & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)} \\ \midrule
\multicolumn{1}{c}{} & \multicolumn{2}{c}{Pre} & \multicolumn{4}{c}{Post} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-7}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{2004} & \multicolumn{1}{c}{2004} & \multicolumn{1}{c}{2009} & \multicolumn{1}{c}{2011} & \multicolumn{1}{c}{2009} & \multicolumn{1}{c}{2011} \\ \cmidrule(lr){2-3} \cmidrule(lr){4-7}
 & Theil & Log Theil & Theil & Theil & Loh Theil & Log Theil \\ \midrule
Polynomial 1 &  &  &  &  &  &  \\ \cmidrule(lr){1-1}
Treatment & -0.003 & -0.020 & -0.008 & -0.006 & -0.110 & -0.137 \\
 & (0.009) & (0.096) & (0.008) & (0.008) & (0.096) & (0.151) \\
 &  &  &  &  &  &  \\ \midrule
Observations & 575 & 575 & 575 & 575 & 575 & 575 \\ \midrule
Polynomial 2 &  &  &  &  &  &  \\ \cmidrule(lr){1-1}
Treatment & -0.001 & -0.002 & -0.009 & -0.008 & -0.137 & -0.195 \\
 & (0.010) & (0.111) & (0.008) & (0.009) & (0.103) & (0.156) \\
 &  &  &  &  &  &  \\ \midrule
Observations & 575 & 575 & 575 & 575 & 575 & 575 \\ \bottomrule
\end{tabular}%
}\begin{tablenotes}
\textbf{Notes}: $* p < 0.1$, $** p < 0.05$,$ *** p < 0.01$. Standard errors in parentheses clustered at the district level. Data: National Sample Survey, Consumption rounds, 2004, 2009 and 2011, and MOF, RBI, district level data. No additional controls.  
\end{tablenotes}
\end{table}

\end{comment}


\clearpage



%\clearpage
%\begin{figure}
%\begin{tabular}{cc}
 %   \centering
  %  \\

   % \includegraphics[scale=0.25]{Figures/cons0.png} &  \includegraphics[scale=0.25]{Figures/cons1.png}\\
    %  Panel (A): Gini Coeff. 2005 & Panel (B): Gini Coeff. 2012\\
   % \end{tabular}\\
   
    %\caption{RD plot on Bank Presence and Consumption Inequality}
    %\label{Giniall}
    %\begin{fignote}
    %Source: Authors' calculation, Data for Panel (A) and (b): panel data IHDS I, 2004 and IHDS II, 2011/2012 and RBI MOF, 371 districts. 
    %\end{fignote}
%\end{figure}




\clearpage

\noindent



\end{document}  