\setcounter{page}{1}
\renewcommand*{\thepage}{A\arabic{page}}


\begin{doublespacing}
This appendix contains eight sections that include additional details about the study. The first appendix provides summary statistics of the cities in the sample and the extent to which covariates were balanced with respect to the treatment assignment (See Appendix \ref{app:DS}). Appendix \ref{app:OPEN} discusses the open rates for the emails and additional analysis. Appendix \ref{app:Power_Analysis} shows the two power analyses completed before implementing the experiment. In Appendix \ref{app:ethics}, I discuss the ethical concerns surrounding asking municipal governments to respond and comply with open records requests. In Appendix \ref{app:TIME}, I report an analysis based on time-to-response. I discuss heterogeneous treatment effects in Appendix \ref{app:het}. I correct p-values for multiple comparisons in Appendix \ref{app:p_value}. In Appendix \ref{app:manipulation_check}, using both interviews and a survey, I present a post-treatment manipulation check to examine how mayors understood the content of the open records requests.


\begin{itemize}
\item Appendix \ref{app:DS} Descriptive Statistics and Balance
\item Appendix \ref{app:OPEN} Open Rates and Analysis
\item Appendix \ref{app:Power_Analysis}: Power Analysis
\item Appendix \ref{app:ethics}: Ethics
\item Appendix \ref{app:TIME}: Time
\item Appendix \ref{app:het}: Heterogenous Treatment Effects
\item Appendix \ref{app:p_value}: P-Value Corrections
\item Appendix \ref{app:manipulation_check} Manipulation Check
\end{itemize}

\newpage

\section{Descriptive Statistics and Balance}
\label{app:DS}
\begin{table}[!htbp] \centering 
  \caption{Summary Statistics}
        \label{table:sum_stat} 
    \begin{threeparttable}
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
Population &    1409        &    93200    &    180870.7    &     1216    &    3792621 \\
Avg. Age of Pop. &        1409        &    35.6        &    05.4        &    19.1        &    73.5    \\
Perc. Female    &    1409        &    51.3        &    01.6        &    39.5    &    59.8 \\
Perc. Black    &    1409        &    12.7        &    16.2        &    00.1    &    93.5 \\
Perc. White    &    1409        &    69.8    &    18.6        &    03.2    &     98.0\\
Perc. Renter    &    1409        &    39.6        &    13.7    &    04.5    &    95.1 \\
\hline \\[-1.8ex] 
\end{tabular}
   \begin{tablenotes}[flushleft]
      \small
      \item Note: This table shows descriptive statistics of the cities in the experimental sample. 
    \end{tablenotes}
   \end{threeparttable}
\end{table} 


Table \ref{table:sum_stat} shows descriptive statistics from the sample. The sample is large and includes a wide variety of cities with differing characteristics from across the United States. This provides leverage in generalizing the results. Exploring the sample further, the average age of the population ranges from 19 to 73 years old. The Lakewood Township in New Jersey has an average age of 19 years old. This township is home to Beth Medrash Govoha, one of the largest Orthodox Jewish yeshivas in the world. New Jersey is also home to the oldest average population at 73. Holiday City-Berkeley, New Jersey, is largely built around a retirement community. The largest white community is Clarence, New York, and the largest black community is the City of Warrensville Heights, Ohio.




\subsection*{Balance and Predicting the Treatment Assignment}


Table \ref{tab:random} shows the results from the randomization check. The probabilities represent the p-value of the residual deviance test from the logistic regression model predicting the assignment of each group given observed covariates. The p-values are generated from the $\chi^2$ distribution. The high p-values indicate randomization was successful (Duty-$\chi^2 (6)=6.0, p=0.423.$ Peer Effects-$\chi^2 (6)= 4.8, p=0.570.$ Control-$\chi^2 (6)=2.6 , p=.860.$). All of the regressions strongly imply that randomization was successful.

\begin{table}[!htbp]
\centering
\caption {Randomization Check}
\label{tab:random}
\begin{threeparttable}
\begin{tabular}{@{}lcc@{}}
\toprule
\multicolumn{1}{l}{Treatment} & Probability & N   \\ \midrule
Duty                         & 0.423       & 469 \\
Peer Effects                          & 0.570       & 471 \\
Control                       & 0.857       & 469 \\ \bottomrule
\end{tabular}
   \end{threeparttable} 
\end{table}

For a more individualistic examination of specific variables, I ran a a series of logistic regressions predicting treatment assignment. As shown in Table \ref{tab:Bal_Logit}, no variable significantly predicts assignment to the treatment groups. 


\begin{table}[!htbp] \centering 
  \caption{Logistic Regression Predicting Treatment Assignment} 
  \begin{threeparttable} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & Duty & Peer & Control \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 Population & 0.00000 & $-$0.00000 & $-$0.00000 \\ 
  & (0.00000) & (0.00000) & (0.00000) \\ 
%  & & & \\ 
 City\_Age & $-$0.004 & 0.014 & $-$0.010 \\ 
  & (0.013) & (0.013) & (0.013) \\ 
%  & & & \\ 
 City\_Female & 0.035 & $-$0.00002 & $-$0.035 \\ 
  & (0.042) & (0.042) & (0.041) \\ 
 % & & & \\ 
 City\_Black & 0.004 & $-$0.007 & 0.003 \\ 
  & (0.005) & (0.005) & (0.005) \\ 
 % & & & \\ 
 City\_White & 0.003 & $-$0.005 & 0.002 \\ 
  & (0.004) & (0.004) & (0.004) \\ 
 % & & & \\ 
 City\_Renter & 0.008 & $-$0.003 & $-$0.005 \\ 
  & (0.005) & (0.005) & (0.005) \\ 
 % & & & \\ 
 Constant & $-$2.924 & $-$0.649 & 1.470 \\ 
  & (2.043) & (2.037) & (2.012) \\ 
 % & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,409 & 1,409 & 1,409 \\ 
Log Likelihood & $-$893.358 & $-$895.376 & $-$895.089 \\ 
Akaike Inf. Crit. & 1,800.717 & 1,804.751 & 1,804.178 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
   \end{threeparttable}
     \label{tab:Bal_Logit} 
\end{table} 




\FloatBarrier

\section{Open Rates and Analysis}
\label{app:OPEN} 

Table \ref{tab:open_rate} shows the percentages and numbers of emails that were opened in each treatment condition. The overall open rate was 78.6\%. Though the open rates differ by treatment, the differences are not statistically significant. For the analysis, I used robust standard errors to account for the differences in open rates across treatment groups. According to \cite{GerberGreen:2012}, one should use robust standard errors when the treatment and control groups are of different sizes (see their footnote 5 on pg. 103)

\begin{table}[!htbp]
\centering
\caption {Email Open Rates}
\begin{threeparttable}
\begin{tabular}{@{}lcc@{}}
\toprule
\multicolumn{1}{c}{} & Open Rates & N       \\ \midrule
Control              & 77.6       & 364/469 \\
Duty                 & 80.2      & 376/469 \\
Peer Effect                 & 77.9      & 367/471 \\ \bottomrule
\end{tabular}
  \begin{tablenotes}[flushleft]
      \small
      \item Note: This table reflects the percent and number of emails that were opened in each treatment condition.
            \end{tablenotes}
   \end{threeparttable}  
\label{tab:open_rate}
\end{table}




Since the open rates are similar across treatment and control groups, I provide results conditional on receipt of the treatment in Table \ref{tab:itt_open}. The \textit{Peer} and  \textit{Duty} coefficients indicate the effect of being assigned to each treatment conditional on receiving the email. I find similar results to the main analysis. Being assigned to the \textit{Peer} prime decreases the likelihood of responding by 7 percentage points while being assigned to the \textit{Duty} prime has no distinguishable effect.



\begin{table}[!htbp] \centering 
  \caption{Conditional on Receiving Email} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{1}{c}{\textit{Dependent variable:}} \\ 
\cline{2-2} 
\\[-1.8ex] & responsive \\ 
\hline \\[-1.8ex] 
 Peer & $-$0.070$^{**}$ \\ 
  & (0.027) \\ 
  & \\ 
 Duty & $-$0.002 \\ 
  & (0.027) \\ 
  & \\ 
Email Opened & 0.658$^{***}$ \\ 
  & (0.027) \\ 
  & \\ 
 Constant & 0.025 \\ 
  & (0.029) \\ 
  & \\ 
\hline \\[-1.8ex] 
Observations & 1,409 \\ 
R$^{2}$ & 0.297 \\ 
Adjusted R$^{2}$ & 0.295 \\ 
Residual Std. Error & 0.420 (df = 1405) \\ 
F Statistic & 197.606$^{***}$ (df = 3; 1405) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} \label{tab:itt_open}
\end{table} 

\FloatBarrier

\section{Power Analysis}
\label{app:Power_Analysis}


Before implementing the experiment, I conducted two sample size/power analyses to see the extent to which the number of mayors in my sample would allow me to find a reasonably small treatment effect.

In the first analysis, depicted in Figure \ref{power1}, I pre-specified three equal sample groups. I would be able to find an effect size of 10\% with an 80\% probability. I chose an effect size of 10\% because it is reasonable in the experimental and responsiveness literature. The analysis suggests the optimal size of each group should be 323 individuals. Under this scenario, the optimal full sample size should be 969 mayors. In the second analysis, depicted in Figure \ref{power2}, I pre-specified the number of mayoral email addresses collected (around 1,400). With each group having approximately 467 mayors, I would be able to find an effect of 8\%. 


\begin{figure}[!htbp]
\centering
\caption{Power Analysis: Sample Size to Find a 10\% Effect}
\label{power1}
\includegraphics[scale=.5]{C1}
\end{figure}


\begin{figure}[!htbp]
\centering
\caption{Power Analysis: Expected Effect with Current Sample Size}
\label{power2}
\includegraphics[width=0.8\linewidth]{C2}
\end{figure}













\FloatBarrier
\section{Ethics}
\label{app:ethics}

With the expansion of experimental fieldwork in political science, there has been a growing concern over (1) the use of deception, and (2) the cost-benefit considerations. I designed my experiment with both of these concerns in mind. 

With regard to the first concern, the experiment reported in this paper does not use any form of deception. The email sent to all mayors identify the researcher, the school affiliation, and the purpose of receiving the records.\footnote{ The experiment was approved by my university's Institutional Review Board: \#201804001.} I also sent a report to mayoral offices that detailed my experience of sending 1400 open records request across municipalities and highlighted best practices for making email retrieval more efficient.

Local governments routinely field open records requests for email archives. One mayor explained, ``[p]ublic emails to elected officials are produceable [sic]. Our IT staff routinely fills similar requests" (See Appendix \ref{app:manipulation_check}, pg. A20). I made the request for email archives for two reasons. First, emails fall within the definition of public records under most, if not all, state open records laws. Second, the request had to be more than a simple service-related request like producing zoning records or council meeting minutes. The request should have some level of political risk such that it allows the mayor or their office to use discretion in different ways. In a survey, I asked whether mayors would respond to a similar request, one mayor questioned whether this information will be used for political reasons (See Appendix \ref{app:manipulation_check}, pg. A20). This statement underscores the calculation mayors make in responding to open records request and the potential risks of compliance. As such, email archives fit both the producible and non-trivial risk category.

With the nature of the request, I was concerned about placing too large of a burden on the city governments who complied with the request. I study \textit{responsiveness} to requests instead of \textit{compliance} in part because of this concern. Responsiveness examines the extent to which the request received a response. Compliance is the extent to which the city government fulfilled the request. There are many reasons why cities would fail to comply with the request: (1) the request is deemed as overburdensome under the law, (2) different definitions of \textit{specificity}\footnote{ Most states require request to articulate exactly the records to be produced. Municipalities have interpreted this require in different ways. Some argue that a request for 3-months of mayoral emails is not specific enough because it does not provide a topic discussed in emails.} of the records request, and (3) researcher-induced bias (i.e., my response time to email inquiries, the willingness to work with the city on amending the request, and the use of a budget to offset cost). With these concerns in mind, I only study responsiveness. Responding to an open records request is substantively different (i.e., easier) than actually complying with the request. 

I made an effort not to push cities to comply if they expressed concerns about the potential cost of and time needed for the request. When asked to do so, I worked with municipalities to minimize the time and effort it took to fulfill the requests. For example, a few cities who were currently involved in a lawsuit expressed concern with the amount of time it would take to review email between the mayor and city attorneys. In this case, I amended the request to exclude all email content from the city attorney.  Another city expressed concern about a personnel matter, which led to a staff member being fired. In this case, I accommodated the city by giving a blanket exemption from all emails to and from the human resource manager.

The overall project had a budget to help cities offset the cost of producing public records. In most cases, if the estimated costs were above a few hundred dollars, I swiftly withdrew my request, citing financial cost. If the city expressed the overall time and expense could not be offset by paying the price of records, I withdrew my request. In short, I acknowledge that there are costs to research like this and took steps to minimize the burden by paying the cost or by modifying or withdrawing the request. 

An important contribution of this project is not only the test of theories of social pressure on responsiveness, but the data retrieved through the open records requests. Mayoral email data opens the door to a more extensive research agenda: I plan to (1) explore patterns of internal and external communication, (2) create text-based measures of latent concepts (i.e., management style), and (3) examine differential responsiveness in email communication. I will make the database publicly available after the emails are organized and cleaned. 



\FloatBarrier

\section{Time Analysis}
\label{app:TIME}




Timing matters when it comes to responsiveness. Most states require public officials to respond to public records request in a ``reasonable amount of time." Though many states do not specify the exact number of days that are considered ``reasonable," the plurality of states have decided that officials must respond within at least two weeks of receiving a request. Most laws allow cities to ask for an extension if the request disrupts the day-to-day operations of the government. 

The dependent variable in the time analysis is the number of \textit{Days to Initial Response}. In analyzing the \textit{Days to Initial Response}, I use a survival model. The majority of the responses were received in the first couple of days (See Figure \ref{fig:time_1}). As time went on, I was less likely to receive a response. This generally follows the pattern as expected. Fifty-nine mayors or mayoral offices responded on the same day my email went out. The highest number of responses came after two days (with 134 responses). The second mode in the distribution occurs after ten days. This corresponds to the fact that many states are required to give a response after ten days. 




\begin{figure}[!htbp]
  \caption{Days to Initial Response}
  \includegraphics[scale=.6]{Days1.png}%width=0.5\linewidth
\caption*{Notes: Depicts the distribution of initial responses by days. There were 59 mayoral offices that responded the day the email request went out. The highest day of initial responses was two days after the email date (134 initial responses). }
  \label{fig:time_1}
\end{figure}




Figure \ref{fig:d1} depicts the survival curves, which predicts the survival proportion at a given time. Everyone has roughly the same probability of responding to the email on the first day. The number of responses on the first day by treatment group are the following: 20 people responded from the control group, 22 from the duty group, and 17 from the peer group. As time moves on, the probability of responding by treatment groups diverges. By day 25, individuals in the peer condition were significantly less likely to respond than both the control and public concern prime. By day 25, 244 individuals in the control group have responded, 253 in the duty group, and 210 in the peer group.  


\begin{figure}[!htbp]
 \caption{Survival Curve for Initial Responses}
  \includegraphics[scale=.55]{d1.png}
  \caption*{Note: Depicts the survival curve for initial response.}
  \label{fig:d1}
\end{figure}









Table \ref{tab:time_1_results} shows the results from the proportional hazard model for the time-to-initial response variable. The \textit{Duty} prime shows no statistically significant effect. The parameter estimate for the \textit{Peer Effects} treatment is in the negative direction. There is a 0.184 unit decrease in the expected log of the relative hazard for each day that goes by under the peer effects prime. For substantive interpretation, I compute the hazard ratio by exponentiating the parameter estimates. Receiving the \textit{Peer Effects} prime decreased the expected hazard by almost 17 percentage points (1-0.8317=0.1683). In other words, mayors under the peer effects prime responded 17 percentage points slower compared to the baseline. 

The results of the time analysis follow the pattern of the main results. I find no evidence that invoking a sense of duty increases responsiveness. Similarly, I find counterintuitive negative consequences for the peer prime. Not only do mayors in this group respond less likely, but they also responded at a slower rate.


\begin{table}[!htbp] \centering 
  \caption{The Effect of Social Pressure on Time to Response} 
 \begin{threeparttable}
\begin{tabular}{@{\extracolsep{5pt}}lc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
\\[-1.8ex] & Initial \\ 
\hline \\[-1.8ex] 
Duty  & 0.070 \\ 
  & (0.089) \\ 
  & \\ 
 Peer Effect & $-$0.184$^{**}$  \\ 
  & (0.092)  \\ 
  &  \\ 
\hline \\[-1.8ex] 
Observations & 1,409  \\ 
\hline 
\hline \\[-1.8ex] 
\end{tabular}
   \begin{tablenotes}[flushleft]
      \small
      \item Note: $^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01. The model shows the coefficients for the Cox-Hazard Proportion Model with \textit{Days to Initial Response} as the dependent variable. 
    \end{tablenotes}
   \end{threeparttable}  
\label{tab:time_1_results}
\end{table} 





\FloatBarrier

\section{Heterogenous Treatment Effects}
\label{app:het}

One of the more puzzling findings of my experiment is that signaling peer accountability leads to a lower and slower response rate. This finding is contrary to the positive effect I expected. Heterogeneous effects or alternative theories could drive the results. I examine \textit{Strong Time, Strong Mayor, Partisanship, Term Limit,} and \textit{Term Length}.

Though all states have laws that require a response to open records requests, the laws vary in their language. The Better Government Association and the National Freedom of Information Coalition created a report card that classified the strength of open records laws. The response time variable measures the ambiguity of the law concerning the time at which a request requires an official response. I measure \textit{strong time} in a binary form: a ``1" indicates whether the law requires a specific response time between 1-30 days; a zero indicates otherwise.\footnote{ BGA's original variable consists of a 5 point scale: 0 represents the failure of the state to provide a definition of reasonable response time; 1 represents states that only offer ambiguous language for response time; 2 represents states that must provide a response in 16-30 days; 3 represents states that require a response in 8 to 15 days; and 4 represents states that require a response in 1 to 7 day(s).} I expect having strong language in the open records law will increase the likelihood of mayoral response. City governments are traditionally divided between strong-mayor systems and council-manager systems. I expect cities with a strong mayoral system -- a single elected executive that has almost total administrative powers -- will respond at a higher rate compared to cities with a city manager. Mayors under council-manager systems serve a more ceremonial role. The majority of scholars in the area of urban and city politics suggest that elected strong mayor is more responsive to the public \citep{Svara:1990, Ruhil:2003}. 



Partisan elections serve as a useful heuristic to hold elected officials accountable. In the absence of party labels, \cite{Hansen:1975} finds that cities have weaker representation. Following this logic, cities with \textit{partisan} electoral systems (as opposed to non-partisan elections) are expected to have a higher response rate. I expect mayors under a system of \textit{term limits} will have a lower response rate, given that these systems free the mayor to act without electoral consequences. Lastly, \textit{Term Length} refers to the number of years a mayor serves in one term. The length of mayoral terms ranges from 1 year to 4 years. I expect lower term lengths to be more responsive. I also examine the interactive effect of the treatments and partisan characteristics of the mayors. 

Figures \ref{fig:Het1} and \ref{fig:Het2} depict the first-order interaction effects for the \textit{Peer} and \textit{Duty} conditions. None of the interactive effects reach statistical significance. In general, I find no substantive heterogeneous effects. Furthermore, all models with interactive effects had a larger root mean squared errors compared to the full model with no interactions.\footnote{In an analysis not shown in the paper, I ran a \textit{F-Test} individually comparing the interactive model and the full model.} This suggests that the interaction models are not better at fitting the given data than the full model without interactions. 


\begin{figure}[!htbp]
\center
 \caption{Heterogeneous Effects of Peer Accountability on Responsiveness}
  \includegraphics[scale=.7]{PeerHet}
 \caption*{Notes: Results of a linear probability model that examines the effect of the \textit{Peer} treatment and other variables on responsiveness. The coefficients correspond to interactive effects between the \textit{Peer} prime and covariates. No interaction is statistically significant at the 0.1 p-value cutoff.}
  \label{fig:Het1}
\end{figure}

\begin{figure}[!htbp]
\center
 \caption{Heterogeneous Effects of Duty on Responsiveness}
  \includegraphics[scale=.7]{DutyHet}
 \caption*{Notes: Results of a linear probability model that examines the effect of the \textit{Duty} treatment and other variables on responsiveness. The coefficients correspond to interactive effects between the \textit{Duty} prime and covariates. No interaction is statistically significant at the 0.1 p-value cutoff.}
  \label{fig:Het2}
\end{figure}




\FloatBarrier

\section{P-Value Corrections}
\label{app:p_value}


To address concerns with multiple comparisons, I present two corrections to the p-values reported in the paper. The first is the Bonferroni correction. Because this correction is the most conservative, it drastically lowers the probability of a false positive but raises the likelihood of a false negative. The second correction follows the Benjamini and Hochberg (BH) standard. Table \ref{tab:pv} shows that the results are robust to the 0.1 p-values cut off. 

\begin{table}[ht]
\centering
\caption{P-Value Corrections}
\label{tab:pv}
\begin{tabular}{rllrrr}
  \hline
 & Model & Treatment & Raw P-Value & Bonferroni & BH \\ 
  \hline
1 & ITT & Duty & 0.647 & 1.000 & 0.647 \\ 
  2 & ITT & Peer & 0.037 & 0.073 & 0.073 \\ 
  3 & CACE & Duty & 0.650 & 1.000 & 0.650 \\ 
  4 & CACE & Peer & 0.039 & 0.077 & 0.077 \\ 
   \hline
\end{tabular}
\end{table}














\FloatBarrier

\section{Manipulation Check}
\label{app:manipulation_check}

To better understand how mayors interpreted the content of the primes, I conducted two types of manipulation checks. The first was a post-experiment interview with three mayors. The second was a survey of newly elected mayors fielded in October 2019.\footnote{ The survey was approved by my university's Institutional Review Board: \#201910047.}  

\subsection{Over-the-Phone Interview}

I conducted three over-the-phone interviews with mayors in the experimental control group. Before the interview, I sent each person a copy of the duty and peer primes and asked them to think about the contents of the email requests. When asked what they thought of the email that included references to ``duty," they saw the wording as a way to increase responsiveness and compliance. One mayor, in particular, stated that the purpose was to remind them that (1) the public cares about this issue, and (2) his constituents will hold him accountable.\footnote{ Transparency in government is one of the planks this mayor ran on in his most recent election.} 
 With regards to the peer effects prime, all of the mayors thought the report was going to be used to pressure them to respond and comply with the request. One mayor brought up the potential for this report to be used by their opponents in the next election. Another mayor suggested the media might write an article about their lack of response. All discussion centered on the potential to increase responsiveness based on the text of the request. No one brought up the potential to decrease responsiveness. 

\subsection{Survey}



For a more systematic approach, I surveyed individuals who missed the cut off in the original experiment, in order to examine how mayors understood the language in the request. I previously collected the near-universe of mayors who serve a population of over 20,000. Individual email addresses of the other city executives in my target population were not available. To overcome this challenge, I selected newly elected mayors from cities already in my sample. Using information from the U.S. Conference of Mayors and individual city websites, I collected email addresses for 112 mayors who were elected after the conclusion of my experiment. I asked the city executives to complete a short survey on open records requests. I fielded the survey in October 2019. Out of the 112 mayors contacted, 16 participated in the survey (14\% response rate). The average time it took to complete the survey was 6 minutes. 



Once the respondents consented to the survey, an automated system randomly assigned individuals into two groups: (1) the duty group, and (2) the peer group. The groups were asked to read the full open records request and answer questions about its content. Each group saw the request with the respective primes. The language of the request remained on the screen throughout the survey process. I reproduce the survey questions in Table \ref{tab:q}.

All groups answered the same first two questions. The first asked, ``How likely are you to respond to this request?" Respondents were given a three-point scale to answer this question: not very likely, somewhat likely, very likely. The second question asked respondents to write their thoughts about the content of the request in an open response window. 



There are a few takeaways from the survey. Some mayors felt more pressure to respond after reading information about the public's belief; however, the majority were overall indifferent. This is consistent with the positive, but insignificant, results relayed in the paper. Mayors felt sharply divided in their perception about whether the peer accountability language would make them more or less likely to respond. The language surrounding the mechanism for accountability (the report) divided respondents equally. I find evidence against the following alternative hypotheses: (1) others having access to the email content might make mayors less likely to respond; (2) the request coming from a non-constituent researcher made them less likely to respond; and (3) the inclusion of 1,400 other city executives made them less likely to respond. Though I initially hypothesized that peer accountability would unambiguously provide a positive effect, this survey suggests that some mayors do not agree. I theorize in the paper that the theory of reactance may explain this divide. I address the specific findings of the survey by answering the questions below.\\


\textbf{Were mayors likely to respond to this open records request?}

The majority of mayors across both groups stated they were very likely to respond to the request. Out of the mayors who saw the duty prime, 100\% stated they were somewhat or very likely to respond. Out of the mayors who saw the peer prime, 89\% stated they were somewhat or very likely to respond (see Figure \ref{fig:MayorsLikely}). 

When allowed to detail their thoughts about the content of the request, one mayor outlined the process that they would take after receiving this request: ``We forward requests to the city clerk who works with the city attorney. The city clerk's office sends the request back to my office and I have a limited number of days to respond. Once I have responded, the city attorney then reviews all emails forwarded prior to sending them out to the requestor." Another mayor simply wrote, ``we are legally required to respond to requests."\\


\textbf{Did the mayors perceive this request as strange?}

I include text from the open response questions on page A20. One respondent wrote, ``[p]ublic emails to elected officials are produceable [sic]. Our IT staff routinely fills similar requests." Another mayor stated, ``The request seems straight forward. We already prove our email archives via a long-standing agreement with the local newspaper."  Even the mayor who suggested the request might be deemed overburdensome, which allows cities to deny the request, points out that they "are required to respond."\\



\textbf{Did knowing the public's belief increase their likelihood to respond? Overall, what did mayors think about the duty prime? }

Figure \ref{fig:DutyLikert} shows the results from the group that saw the duty prime. The purpose of this prime was to increase responsiveness by triggering thoughts about their duty to the public. Forty-three percent stated that they somewhat agreed that knowing the public's belief made them more likely to respond to the request. When asked whether the duty prime -- overall -- made them more likely to respond to the request, only 14\% stated that they somewhat agreed. The results of the survey are largely consistent with the main results of the paper. The treatment effect for the duty prime was positive but insignificant. The fact that the majority of respondents neither agreed nor disagreed with the idea that the language made them more likely to respond explains the insignificance of the duty treatment.\\


\textbf{Did mayors feel more pressure to respond after reading the peer accountability prime?}
 
Figure \ref{fig:PeerLikert} shows how mayors thought about the peer prime. I find mixed results. When asked whether they would be more likely to respond given the peer prime, 38\% either indicated somewhat agree or strongly agree, which coincides with my initial hypothesis. The findings of the experiment, however, were the opposite. Mayors were less likely to respond after reading the peer treatment. Twenty-five percent of respondents stated that they strongly disagreed that the prime made them more likely to respond. \\



\textbf{What did the mayors think about the ``report"?}

The text of the peer effects prime suggested that a report will be published. Mayors are evenly split on whether knowing a report will be released would increase their likelihood of responding (38\% disagreed, 25\% neither agreed nor disagreed, 38\% agreed). Mayors were similarly split on whether sending the report to other city executives will increase their likelihood to respond. 

There is clear disagreement about how mayors interpret the peer accountability prime. On the one hand, some mayors agree that the report would make them more likely to respond. This is consistent with positive social pressure. On the other hand, some mayors disagreed that the report would increase their responsiveness. This theory leans towards ideas of reactance, which is consistent with the results of the experiment. Future experiments should be conducted to separate the positive and negative social pressures related to peer accountability. \\

 
\textbf{Does the request coming from a non-constituent researcher affect the way mayors respond?}

A potential concern with the experiment is that I identified myself as a researcher. Knowing the request came from a non-constituent researcher does not, on average, make mayors less likely to respond. In the duty group, 86\% of the mayors neither agreed nor disagreed that this fact would decrease their likelihood to respond. Fourteen percent strongly disagreed.  Only 12\% (one person) in the peer group agreed that having this knowledge decreased their likelihood to respond, while 50\% indicated that they disagreed. This evidence is consistent with the fact that most open records laws do not require requestors to be residents of the state.  \\



\textbf{Did knowing the request was sent to 1,400 other city executives make mayors less likely to respond?}

The survey suggests no. The majority of mayors either strongly disagreed or somewhat disagreed that knowing similar requests were sent to others made them less likely to respond. Only 12\% indicated that they somewhat agreed with the statement. \\


\textbf{Did knowing that individuals other than the researcher might have access to the email content make mayors less likely to respond?}

The publication of the report might have cued mayors that individuals other than the researchers may have access to the content of the email, thus decreasing the likelihood of responding. The survey suggests no. I find that the 75\% of respondents neither agreed nor disagreed with the statement, ``Knowing other people outside of the requester might have access to the emails makes me less likely to respond." The rest of the respondents strongly disagreed.















\vspace{.5in}

\begin{figure}[!htbp]
 \caption{How Likely Are Mayors to Respond to the Request?}
  \hspace*{-1.9cm} \includegraphics[scale=.75]{Rplot03}
 \caption*{Notes: Depicts results from respondent being asked, ``how likely are you to respond to the open records request?'' There were three possible answers: not likely, somewhat likely, and very likely. The percent on the left corresponds to the proportion of respondents who stated they were not likely to respond. The percent on the right corresponds to the proportion of respondents who stated they were very likely to respond. The percent in the middle of the corresponds to the proportion of respondents who stated they were somewhat likely to respond.}
  \label{fig:MayorsLikely}
\end{figure}





\begin{figure}[!htbp]
 \caption{What Do Mayors Think about the Duty Prime?}
  \hspace*{-1.9cm} \includegraphics[scale=.75]{Rplot01}
 \caption*{Notes: Depicts results from respondents viewing the duty prime. The scale is a 5-point Likert scale from strongly disagree to strongly agree. The percentage on the left corresponds to the extent to which respondents strongly disagreed and somewhat disagreed. The percent on the right corresponds to the extent to which respondents somewhat agreed and strongly agreed. The percent in the middle corresponds to the respondent who chose neither agree nor disagree.}
  \label{fig:DutyLikert}
\end{figure}




\begin{figure}[!htbp]
 \caption{What Do Mayors Think about the Peer Prime}
  \hspace*{-1.9cm} \includegraphics[scale=.75]{Rplot}
 \caption*{Notes: Depicts results from respondents viewing the peer prime. The scale is a 5-point Likert scale from strongly disagree to strongly agree. The percentage on the left corresponds to the extent to which respondents strongly disagreed and somewhat disagreed. The percent on the right corresponds to the extent to which respondents somewhat agreed and strongly agreed. The percent in the middle corresponds to respondents who chose neither agree nor disagree.}
  \label{fig:PeerLikert}
\end{figure}



\FloatBarrier




\textbf{Survey Responses to the Peer Effects Prime}

\noindent\fbox{%
    \parbox{\textwidth}{% P2
Public emails to elected officials are produceable.  Our IT staff routinely fills similar requests.  
     
We are legally required to respond to requests.

The request seems straight forward. We already provide our email archives via a long standing agreement with the local newspaper. 

As I stated above, we already provide these emails to the media.

Will this information be used for political reasons? 

[Name of City] has a wonderful track-record in fulfilling similar requests. We are dedicated to transparency. 

Interesting topic. Clarity on content and purpose at the front end would increase the likelihood to respond.

Without information about content and purpose it's likely your email will go to a suspense file.
    }
}


\textbf{Survey Responses to the Duty Prime}\\
\noindent\fbox{
    \parbox{\textwidth}{
We forward requests to the city clerk who works with the city attorney.  The city clerk's office sends the request back to my office and I have a limited number of days to respond.  Once I have responded the city attorney then reviews all emails forwarded prior to sending them out to the requestor.

I would respond. We keep emails for 30 days by city policy.

This request is on the edge of being overburdensome. We are required to respond, but not required to fulfill requests that ask for this much information.
    }%
}



\begin{landscape}

\begin{table}[]
\caption{Survey Questions}
\label{tab:q}
 \begin{threeparttable}
\begin{tabular}{@{}llll@{}}
\toprule
Group & Name & Text Wording                                                                                                                                                                                                                                                                                                                                                    & Choices                                                                                                  \\ \midrule
Duty  & Duty     & -How likely are you to respond to this request?                                                                                                                                                                                                                                                                                                                  & 3-Point Likely Scale                                                                 \\
Peer  &  Peer & -How likely are you to respond to this request?                                                                                                                                                                                                                                                                                                                  & 3-Point Likely Scale                                                       \\
Duty  &  Non-Constituent: Less    & \begin{tabular}[c]{@{}l@{}}-Knowing the open records request came from a\\ non-constituent researcher makes me less likely to respond.\end{tabular}                                                                                                                                                                                                             & \begin{tabular}[c]{@{}l@{}}5-Point Likert Scale\end{tabular} \\
Duty  &  Public's Belief    & \begin{tabular}[c]{@{}l@{}}-Knowing that only half of the public believes local governments\\ are effectively sharing information makes me more likely to respond.\end{tabular}                                                                                                                                                                                  &                                                                5-Point Likert Scale                                          \\
Duty  & Overall Pressure     & \begin{tabular}[c]{@{}l@{}}-I feel more pressure to respond because of the following statement, \\ "American believe government have a civic duty to share information\\ with their constituent. However, according to the Pew Research Center,\\ only half of Americans believe local \\ governments are effectively sharing data with the public."\end{tabular} &                                                                   5-Point Likert Scale                                       \\
Peer  & Non-Constituent: Less      & \begin{tabular}[c]{@{}l@{}}-Knowing the open records request came from a non-constituent\\ researcher makes me less likely to respond.\end{tabular}                                                                                                                                                                                                             &                                                                                          5-Point Likert Scale                \\
Peer  & 1400 Peers: Less     & \begin{tabular}[c]{@{}l@{}}-Knowing the open records request went to 1400 other city executives\\ makes me less likely to respond\end{tabular}                                                                                                                                                                                                                  &                                                                                             5-Point Likert Scale             \\
Peer  &   Report: More   & \begin{tabular}[c]{@{}l@{}}-Knowing a report will be released about the open records request makes me \\ more likely to respond.\end{tabular}                                                                                                                                                                                                                    &                                                                                          5-Point Likert Scale                \\
Peer  & Report to Peers: More     & \begin{tabular}[c]{@{}l@{}}-Knowing a report will be sent to other city\\ executives makes me more likely to respond.\end{tabular}                                                                                                                                                                                                                              &                                                                                          5-Point Likert Scale                \\
Peer  &  Email Access, Not Researcher    & \begin{tabular}[c]{@{}l@{}}-Knowing other people outside of the requester\\ might have access to the emails makes me less likely to respond.\end{tabular}                                                                                                                                                                                                       &                                                                                              5-Point Likert Scale            \\
Peer  &   Overall Pressure   & \begin{tabular}[c]{@{}l@{}}-I feel more pressure to respond because of the following statement, \\ "We have sent this request to 1400 other city government executives \\ and plan to publish a report about our results."\end{tabular}                                                                                                                          &                                                                                      5-Point Likert Scale                    \\ \bottomrule
\end{tabular}
 \begin{tablenotes}[flushleft]
      \small
      \item Notes:  The 3-point Likely Scale contains the following values: Not very likely, somewhat likely, and very likely.  The 5-point Likert contains the following values: strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree, and strongly agree. I asked two open response questions. The first one appears the likelihood question. The second appears at the end of the survey. Both were used to elicit additional responses and reactions to the open records request.
      \end{tablenotes}
   \end{threeparttable}  
\label{tab:mc}
\end{table}



\end{landscape}



\end{doublespacing}



