# Selective Accountability: Performance Indicators and MPs' Behavior

Tom Buchot, Charles Louis-Sidois, Elisa Mougin

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Tom Buchot, Charles Louis-Sidois, Elisa Mougin. Selective Accountability: Performance Indicators and MPs' Behavior. 2025. hal-04851299v2

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CERGIC Working Papers

No. 0003

July 2025 – Revised version

Selective Accountability:
Performance Indicators and MPs’
Behavior

Tom Buchot

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Charles Louis-Sidois

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Elisa Mougin

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JEL codes: D72, L82, H83.

Keywords: Political accountability, Media, Transparency, Data journalism.
Selective Accountability:
Performance Indicators and Legislators’ Behavior

Tom Buchot, Charles Louis-Sidois and Elisa Mougin*

July 7, 2025

Abstract

We study how data-driven press coverage affects politicians’ behavior. Since 2009, the websites Nosdeputes.fr and Nossenateurs.fr publish performance indicators for French legislators. Compiling a comprehensive dataset of press articles, we track how these metrics are used by the press and estimate their effects on parliamentary activity. We find a positive impact on legislative performance, primarily driven by general press coverage, suggesting that monitoring tools must be visible to be effective. The effects are not driven by legislators who are explicitly mentioned, implying that indicators reinforce collective rather than individual accountability. Coverage of indicators also shape the nature of parliamentary work, though we find no evidence of strategic manipulation. Finally, using measures of media congruence and competition, we show that variation in media pressure is the main transmission channel.

JEL Classification: D72, L82, H83.

Keywords: Political accountability; Media; Transparency; Data journal-
ism.

<footnote>*Buchot: ENS de Lyon, Center for Economic Research on Governance, Inequality and Conflict (CERGIC), France (email: tom.buchot@ens-lyon.fr). Louis-Sidois: WU (Vienna University of Economics and Business), Welthandelsplatz 1, Vienna 1020, Austria (email: charles.louis-sidois@wu.ac.at). Mougin: ENS de Lyon, Center for Economic Research on Governance, Inequality and Conflict (CERGIC), France (email: elisa.mougin@ens-lyon.fr). We did not receive any specific funding for this article. We thank Charles Angelucci, Vincent Bagilet, Cédric Chambru, Mathieu Couttenier, Olivier Gergaud, Sophie Hatte, Daniela Iorio, Caroline Le Pennec, Vincent Pons, Benjamin Ooghe-Tabanou, Camille Urvoy, the participants of seminars at CEU, CERDI, CREM, University of Copenhagen, Lund University, BETA, Medialab and LIEPP (Sciences Po), University of Bologna, and CERGIC (ENS de Lyon).</footnote>
# 1 Introduction

The availability and use of data have transformed media coverage of political outcomes and legislators' work. Civil society organizations, including NGOs and media outlets, increasingly report on statistical indicators, often framing legislative performance through quantifiable metrics. Parliamentary activities such as attendance, amendments submitted, and questions to the government are now regularly scrutinized (Benesch et al., 2018). This data-driven shift often originates from independent platforms—such as GovTrack.us in the U.S., They-WorkForYou.com in the U.K., and Nosdeputes.fr or Nossenateurs.fr in France—which convert raw parliamentary data into standardized indicators accessible to citizens and journalists (François and Rozenberg, 2019; Parasie, 2022). In public debate, this change is frequently portrayed as a turning point for legislative accountability. While praised for promoting transparency, such websites have also been criticized for narrowing the scope of parliamentary work to measurable outputs. Yet, the academic literature has paid limited attention to how data-driven coverage may reshape legislators' incentives. This paper addresses this gap by analyzing two French websites launched as citizen initiatives to open parliamentary data to the public: Nosdeputes.fr and Nossenateurs.fr. Both provide standardized indicators that have become new tools for media outlets to report on political and parliamentary affairs. While press coverage can enhance legislative performance by reinforcing accountability mechanisms (Besley and Burgess, 2001; Snyder and Strömberg, 2010), statistical indicators may also reshape legislators' incentives and alter the nature of parliamentary work. To our knowledge, this paper is the first to quantify the effects of performance indicators on incentives and legislative activity.

Combining data on legislators' activity and media attention to performance indicators, we leverage the timing of the websites' creation and exogenous variation in the intensity of performance coverage in the press to disentangle the effects of monitoring tools from those of their visibility in the media. We document a significant effect of general press coverage of the indicators: legislators increase their performance in response to a high volume of articles that draw on these newly available online data. The effect is driven by members of the National Assembly, covered by Nosdeputes.fr, for whom greater media intensity further boosts activity. Yet, somewhat surprisingly, MPs do not react to individual mentions. This suggests that performance indicators operate primarily through collective accountability, while individual-level pressure plays a limited role. Finally, the direct effect of the websites' launch is minimal, underscoring that visibility is essential for monitoring tools to be effective.

Our data come from two main sources. First, we use data from Nosdeputes.fr and Nossenateurs.fr, which report weekly performance on 13 indicators.<fnref n="1" /> Nosdeputes.fr covers members of the National Assembly (the lower chamber), hereafter referred to as MPs, while Nossenateurs.fr focuses on Senators, members of the upper chamber. The two websites were launched by an independent citizen collective in September 2009 and September 2011,

<footnote n="1">¹The indicators include attendance and interventions in committees, interventions in plenary sessions (short and long), amendments proposed, signed, and accepted, published reports, law proposals written and signed, and oral and written questions. A final indicator, weeks of activity, combines several of the above to measure attendance.</footnote>
respectively, and retrospectively computed indicators starting from the beginning of the
current legislatures.<fnref n="2" /> This provides us with pre-website observations. The analysis spans
from June 2007 to the first French COVID-19 lockdown in March 2020. We also collect data
on press coverage of these indicators. Using several press databases, we compile a dataset
of articles mentioning the websites, yielding 885 articles for Nosdeputes.fr but only 95 for
Nossenateurs.fr, reflecting the latter's lower visibility. In these articles, we identify 3,920
instances of MP performance being reported, along with 434 mentions of Senators. In each
case, we record the legislator's name and the indicator mentioned.

We propose two identification strategies to measure both the effects of visibility mediated
by press coverage and the direct impact of monitoring tools. Our first strategy focuses
on MPs only, as they are the ones receiving substantial media attention. To account for
heterogeneity, we include MP-legislature fixed effects and control for political cycles using
various time fixed effects. To estimate the effects of general press coverage, we assess the
impact of a high number of articles mentioning Nosdeputes.fr within a given period. We also
use continuous measures, such as the number or share of articles citing the website relative
to total coverage of parliamentary issues. Additionally, we test whether MPs personally
mentioned in the press change their performance. Residual performance shifts following
the website's launch—especially during low-coverage periods—reflect the tool's direct effect.
Our primary outcome is an aggregate indicator of legislative activity, constructed as the
standardized sum of the normalized indicators from the websites. Results show that during
periods of high press coverage, aggregate performance rises markedly, by around 0.09 standard
deviation. This corresponds to an average MP moving up 30 positions in the performance
ranking of the 577 legislators when the number of articles mentioning Nosdeputes.fr in the
previous three months exceeds the median. Additionally, a 1% increase in the number of
articles citing Nosdeputes.fr is associated with a 0.03 standard deviation rise in activity.
However, MPs explicitly mentioned in the press do not show additional performance gains in
the three months following their mention.

Our second identification strategy exploits the staggered timing of the websites’ implemen-
tation, using a difference-in-differences approach that jointly considers MPs and Senators. In
order to isolate the role of press coverage from any direct effects of the platforms themselves,
we take advantage of the existence of a low-visibility monitoring tool, Nossenateurs.fr. Its
limited public exposure provides a useful contrast to the more prominent Nosdeputes.fr. While
Senators are not a perfect control group for MPs, both are similarly exposed to unobserved
shocks, such as major events boosting parliamentary activity, which we control for using
time fixed effects. The results reinforce those of the first strategy. General press coverage
significantly boosts MP activity, as compared to Senators, with high-coverage periods raising
performance by about 6% of a standard deviation, consistent with the first specification.
Moreover, individual mentions have little impact, and the websites’ direct effect appears
negligible.

These findings are also robust to alternative specifications. Controlling for general media

<footnote n="2">²Indicators are available from June 2007 for Nosdeputes.fr and from October 2004 for Nossenateurs.fr.</footnote>
attention to the National Assembly or for direct traffic to the website does not alter the
estimates, confirming that the effect is specific to the visibility of the indicators in the press.

To unpack this aggregate effect, we estimate our two models on each performance indicator
available on the websites, in order to assess whether some are prioritized over others and
contribute more to the overall rise in activity. We find that while legislators increase effort
across most activities during periods of greater scrutiny, the introduction of the websites
has led to a reallocation of efforts. For instance, we observe greater effort on activities that
attract more media attention—such as attendance, committee interventions, and questions
to the government—and a decline in those less frequently covered, such as amendments or
proposal writing.

While these outcomes allow us to test whether MPs respond to the specific performance
measures promoted by the websites, our analysis also considers outcomes not directly tracked.
These may reflect more subtle transformations in the nature of parliamentary work following
the introduction of metric-based monitoring. Indeed, a common criticism in countries where
such indicators are used (Hurst, 2006; Acatrinei and Quénel, 2019) is that they invite
manipulation, a concern that has thus far been supported mostly by anecdotal evidence. To
address this question, we build a dataset of text-based outcomes, using corpora of written
questions to the government and transcripts of interventions in plenary sessions, two activities
that expanded following the creation of the websites. Using text analysis methods, we identify
identical or highly similar questions and find that indicator visibility increases the share of
copy-pasted written questions. Although we cannot distinguish whether MPs copy-paste
simply to boost their statistics or to better emphasize priority issues when attention is greater,
our findings suggest a shift in how written questions are used after the websites' introduction.
Moreover, MPs are sometimes criticized for giving unnecessary oral interventions to inflate
scores. Analyzing the distribution of words per intervention, we find that press coverage is
associated with an increase in short interventions and a decrease in very long interventions in
plenary sessions. Finally, we examine whether the nature of parliamentary work shifted in
terms of the themes raised. We find no significant effect on the number of topics addressed
in questions to the government, suggesting that the scope of the political agenda remains
unaffected by the introduction of performance indicators.

Our final section investigates the mechanisms behind the results, examining why coverage
prompts collective responses but elicits few individual reactions to personal mentions. We
first explore whether the effects stem from peer influence or media-driven accountability.
One possible channel is that a few MPs respond to coverage and, through peer effects, shift
collective behavior, in the spirit of Canen et al. (2023). However, we find stronger evidence
supporting a media accountability mechanism. In districts with less competitive media
markets—where a single outlet dominates—or where the outlet's circulation closely matches
the district's electorate, as in Snyder and Strömberg (2010), MPs show significantly larger
increases in effort. Overall, when local media are more likely to reach a broad share of voters,
legislators respond more strongly to local coverage. These findings highlight media-driven
accountability as a central mechanism behind our results.
Given the key role of collective accountability, we next examine how it operates at the group level. We find that performance tends to decline among MPs from a group when its members receive high media coverage. We also observe a reallocation of activity from high-performing MPs to lower-performing ones, especially for constrained activities, such as oral interventions where speaking time is limited.

We also investigate individual factors that may lead MPs to respond differently. First, we test whether this result masks heterogeneity across districts with different levels of electoral competition. Results offer limited support: while mentions in local outlets are somewhat more influential, effects do not vary by electoral margin. Moreover, MPs who are directly mentioned in articles and publicly respond to those mentions do not exhibit any change in behavior. By contrast, positive-toned mentions tend to have a greater impact than neutral or negative ones, though the effect varies. Lastly, left-wing MPs, those elected in districts farther from Paris, and female MPs tend to respond less to media coverage, suggesting that individual characteristics also shape how legislators react to new types of incentives.

Our paper contributes to the debate on performance indicators and their media coverage. The example of Nosdeputes.fr and Nossenateurs.fr offers a compelling case, as both platforms use identical methods but differ sharply in visibility, allowing us to examine how public attention shapes the impact of monitoring tools. Over the period of study, they were the only systematic sources of standardized data on legislators' activity in France.<fnref n="3" /> Proponents argue that such indicators enhance transparency and accountability—claims supported by most of our findings. The strong collective responses to coverage that we document, combined with the limited effects of individual mentions, align with this argument. Although focused on France, our results speak more broadly to the effectiveness of transparency tools, which must reach the public to influence behavior.

**Contribution to the literature.** Our paper contributes to the broad literature on the determinants of politicians' performance (Besley and Larcinese, 2011; Bernecker, 2014). This literature highlights, among other factors, the positive effects of electoral competition (Galasso and Nannicini, 2011; Bełdowski et al., 2022; Gavoille and Verschelde, 2017; Gavoille, 2018) and legislative term length on parliamentary attendance (Dal Bó and Rossi, 2011). Network effects and political connectedness have also been shown to influence legislative behavior (Canen et al., 2023).

Our work is particularly related to studies on the role of information and transparency in shaping political performance, as reviewed in Finan et al. (2017). Banerjee et al. (2011), Humphreys and Weinstein (2012), and Grossman and Michelitch (2018) study the distribution of performance scorecards to voters; only the latter find an effect, and only in competitive districts. Moreover, Bidwell et al. (2020) show that candidate debates can stimulate political engagement at the local level, while corruption audits have been found to reduce corrupt practices and improve political accountability (Olken, 2007; Ferraz and Finan, 2008, 2011; Bourlès et al., 2025). Nosdeputes.fr and Nossenateurs.fr represent a new type of monitoring

<footnote n="3">³Other initiatives, such as mon-depute.fr, were short-lived or received limited attention.</footnote>
tools that can directly influence politicians' behavior. Indeed, related evidence on the effectiveness of performance metrics shows that they can improve legislative attendance (Benesch et al., 2018) and enhance environmental compliance (Anderson et al., 2019; Buntaine et al., 2024). Yet, several studies highlight adverse or ambiguous effects due to strategic adaptation: politicians may over-invest in visible public goods at the expense of more valuable ones (Mani and Mukand, 2007; Johannessen, 2019), delay corruption (Bobonis et al., 2016), or increase vote buying (Cruz et al., 2021). Our paper adds new evidence on how information, through coverage of statistical indicators, influences legislative behavior.

Information often reaches the public through media coverage, and our paper contributes to the literature on the media's role in political accountability. In line with Snyder and Strömberg (2010), who exploit congruence between newspaper markets and congressional districts to show that press coverage boosts parliamentary activity, we use the structure of the local press market to examine different accountability mechanisms. Adserà et al. (2003) and Brunetti and Weder (2003) document a negative correlation between press freedom and corruption, which Besley and Burgess (2001) interpret as evidence that information enables voters to monitor and discipline politicians. More broadly, greater media access has been shown to improve access to public services and aid (Strömberg, 2004; Eisensee and Strömberg, 2007). Voter attention also matters: Balles et al. (2024) and Kaplan et al. (2025) show that when major events capture public focus, legislators shift their efforts toward special interest groups.<fnref n="4" /> Several other studies highlight the press's role in promoting accountability (Garz and Sörensen, 2017; Larreguy et al., 2020; Cagé, 2020; Djourelova and Durante, 2022). We add to this strand by analyzing the use of statistical performance indicators by the press, a recent and salient form of media coverage. Unlike most prior work focused on general coverage, we examine the visibility of specific performance dimensions.

In our setting, new data resources have become available to journalists, contributing to the literature on the effects of data transparency and open data policies (Parasie, 2022; Louis-Sidois and Mougin, 2023). While transparency often has positive effects, it can also misalign the interests of principals and agents (Groseclose and McCarty, 2001; Prat, 2005; Fehrler and Hughes, 2018; Hansen et al., 2018). In some contexts, it may inhibit expression or encourage conflict, as shown by Malesky et al. (2012) in authoritarian assemblies and Fasone and Lupo (2015) in legislative debate. In France, Cloléry (2023) finds that vote transparency reduces turnout, while Harden and Kirkland (2021) show that U.S. transparency laws did not undermine political efficiency. By combining the existence of monitoring tools with their media visibility, our empirical framework allows us to deepen the understanding of the channels through which such data can enhance collective accountability, while offering little evidence that it undermines political functioning.

The remainder of the paper is structured as follows. Section 2 presents the context and the data. In Section 3, we study the effect of the websites and their media coverage on aggregate legislative activity. We then investigate the heterogeneous effects on different types

<footnote n="4">⁴Our objective is to study how politicians respond to changes in the visibility of their actions. We do not analyze voter behavior directly, though existing research shows voters respond to political information (Chong et al., 2015; Kendall et al., 2015; Le Pennec and Pons, 2023).</footnote>
of activities and explore changes in the nature of parliamentary work in Section 4. Section 5
discusses the mechanisms behind our results, and Section 6 concludes.

2 Context and Data

**2.1 French Parliament**

The French Parliament comprises the National Assembly (the lower chamber) and the Senate (the upper chamber). Legislators of both chambers are responsible for debating and voting on laws, scrutinizing the government's work, and representing the interests of their constituents. They participate in plenary sessions, serve on committees, ask questions to the government, and draft or amend legislation. More details on the French political landscape are provided in Appendix A.1.

**National Assembly.** The 577 MPs are elected for five-year terms in single-member districts using a two-round runoff system. Our study period includes the legislatures from 2007–2012 (right-wing majority), 2012–2017 (left-wing majority), and 2017–2022 (centrist majority). Performance indicators are available for 1,381 MPs, including substitutes who replace MPs vacating their seats due to reasons such as illness or appointment to government positions.

**Senate.** The Senate consists of 348 members elected for six-year terms, with half of the seats renewed every three years. Senators are chosen by an electoral college composed of local officials, including municipal, departmental, and regional representatives. They have duties similar to those of their counterparts in the National Assembly, but the Senate plays a relatively weaker role, as the National Assembly has the final say in case of disagreement. The Senate is less scrutinized by citizens, partly because its members are not directly elected. Performance indicators are available for 771 senators during our study period.

**2.2 Performance Indicators**

Both Nosdeputes.fr and Nossenateurs.fr were founded by *Regards Citoyens*, a collective of citizens aiming to simplify access to public data. Nosdeputes.fr was launched in September 2009. It utilizes content published by the National Assembly, which, while already publicly available, was difficult to navigate for citizens and journalists. Nosdeputes.fr simplifies this information into statistical indicators that are accessible to all. In September 2011, *Regards Citoyens* launched Nossenateurs.fr. This website essentially mirrors Nosdeputes.fr, providing the same information in a similar format for the members of the Senate. Appendix A.2 provides more information on the two websites and Appendix Figures A.1 and A.2 illustrate the similarity between them.

The websites are practically the sole providers of statistical indicators for the two chambers of the French Parliament. However, there is a stark contrast in visibility between them. Nosdeputes.fr receives significant attention from both citizens and journalists, averaging 400,000 visits per month during the period studied. Additionally, press coverage of Nosdeputes.fr further enhances the visibility of its indicators. MPs frequently discuss the impact of these
indicators on their work, as evidenced by their regular comments in the press. Furthermore, the website's founders report being regularly contacted by MPs regarding their indicators. By contrast, Nossenateurs.fr receives considerably less attention, with only 16,000 visits per month, and the number of press articles mentioning it is nine times lower. This discrepancy is illustrated by Appendix Figures A.3 and A.4, which show the number of monthly visits and article coverage for the two websites. The difference can be linked to the Senate's weaker legislative role or because its members are not directly elected and less well-known to the public.

Both websites report 13 identical indicators. Three relate to oral interventions: long interventions count those exceeding an arbitrary threshold of 20 words made by a legislator during plenary sessions; short interventions count those under 20 words; and interventions in committees refer to those made in specialized working groups. These statistics are established from the transcripts of debates. Additionally, one indicator tracks the number of committee meetings attended, recorded by an attendance sheet. Two indicators pertain to questions: the number of oral questions asked to the government during a dedicated weekly meeting, and the number of written questions, which legislators can send at any time to any member of the government. Three indicators relate to amendments: the number of amendments proposed (i.e., personally written), signed (i.e., written by another legislator but supported), and adopted. Two other indicators pertain to law proposals: the number they proposed and the number they signed. One indicator tracks the number of parliamentary reports written. Finally, the number of weeks of activity indicates whether a legislator was active in a given week. This last indicator combines oral interventions and attendance in committees; a legislator is considered active if at least one of these activities is recorded during the week.

Two of the indicators are not suitable for our analysis. First, the number of accepted amendments is influenced by joint decisions, limiting the control of individual legislators. Second, the number of weeks of activity combines multiple other indicators, making it difficult to interpret on its own. Therefore, our results focus on the remaining 11 indicators.

The websites are easy to navigate, allowing users to view statistics for specific legislators and to list them by indicator, as illustrated in Appendix Figures A.1 and A.2. When listed by indicators, the websites display legislators' total performance over the last 12 months. Although the websites are not designed as a ranking tool, indicators on which a legislator ranks among the top 150 are displayed in green, while those in the bottom 150 are shown in red.

We collect all available indicators from the websites and aggregate them at the weekly level. Although the websites were launched in 2009 and 2011, the indicators were retroactively computed from the June 2007 legislative elections for Nosdeputes.fr and from 2004 for the Nossenateurs.fr. On average, MPs (respectively, Senators) attend 0.76 (0.77) committee sessions per week, submit 0.74 (0.30) written questions to the government, make 2.92 (2.78) short interventions, and 1.84 (2.33) long interventions in plenary sessions. Descriptive statistics for all indicators are displayed in Panel A of Table 1.
<table><caption>Table 1: Descriptive Statistics</caption><thead><tr><th rowspan="3">Indicators</th><th colspan="6">Panel A: Performance of Legislators</th><th colspan="6">Panel B: Press Coverage</th></tr><tr><th colspan="3">MPs</th><th colspan="3">Senators</th><th colspan="2">MPs</th><th colspan="2">Senators</th></tr><tr><th>Mean</th><th>SD</th><th>Max</th><th>Mean</th><th>SD</th><th>Max</th><th>Number of mentions</th><th>Number of articles</th><th>Number of mentions</th><th>Number of articles</th></tr></thead><tbody><tr><td>Total press</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>885</td><td>.</td><td>95</td></tr><tr><td>Citing indicators</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>3920</td><td>480</td><td>434</td><td>58</td></tr><tr><td><strong>Interventions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Interv. committees</td><td>1.32</td><td>7.54</td><td>894</td><td>1.62</td><td>7.69</td><td>685</td><td>273</td><td>148</td><td>30</td><td>18</td></tr><tr><td>Short interv. plenary</td><td>2.92</td><td>24.85</td><td>2,632</td><td>2.78</td><td>20.06</td><td>759</td><td>260</td><td>133</td><td>4</td><td>2</td></tr><tr><td>Long interv. plenary</td><td>1.84</td><td>9.58</td><td>673</td><td>2.33</td><td>11.41</td><td>548</td><td>434</td><td>196</td><td>16</td><td>10</td></tr><tr><td><strong>Attendance</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Attendance committees</td><td>0.76</td><td>0.82</td><td>7</td><td>0.77</td><td>0.83</td><td>5</td><td>532</td><td>212</td><td>62</td><td>21</td></tr><tr><td><strong>Questions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written questions</td><td>0.74</td><td>4.48</td><td>967</td><td>0.30</td><td>1.38</td><td>100</td><td>254</td><td>147</td><td>28</td><td>17</td></tr><tr><td>Oral questions</td><td>0.05</td><td>0.22</td><td>3</td><td>0.04</td><td>0.21</td><td>4</td><td>175</td><td>115</td><td>23</td><td>16</td></tr><tr><td><strong>Amendments</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Signed amendments</td><td>12.31</td><td>34.25</td><td>1,557</td><td>5.79</td><td>17.93</td><td>556</td><td>236</td><td>118</td><td>28</td><td>17</td></tr><tr><td>Written amendments</td><td>1.30</td><td>14.86</td><td>1,551</td><td>0.83</td><td>5.29</td><td>556</td><td>147</td><td>81</td><td>0</td><td>0</td></tr><tr><td>Adopted amendments</td><td>1.42</td><td>5.53</td><td>373</td><td>0.99</td><td>3.73</td><td>216</td><td>75</td><td>44</td><td>9</td><td>5</td></tr><tr><td><strong>Proposals</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written proposals</td><td>0.02</td><td>0.18</td><td>19</td><td>0.02</td><td>0.14</td><td>7</td><td>175</td><td>95</td><td>22</td><td>16</td></tr><tr><td>Signed proposals</td><td>0.49</td><td>1.15</td><td>22</td><td>0.20</td><td>0.54</td><td>16</td><td>132</td><td>76</td><td>12</td><td>8</td></tr><tr><td><strong>Reports</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written reports</td><td>0.02</td><td>0.16</td><td>9</td><td>0.04</td><td>0.21</td><td>7</td><td>177</td><td>95</td><td>31</td><td>17</td></tr><tr><td>Activity (overall)</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>1050</td><td>349</td><td>135</td><td>48</td></tr></tbody></table>

**Notes:** Panel A: descriptive statistics on the performance of legislators. A performance is the number of activities corresponding to the indicator in the row performed by an MP or a Senator in a given week. There are 490,608 observations for each indicator between 2007 and 2020. The minimum value for all indicators is 0. For example, on average, MPs (resp. Senators) made 1.32 (resp. 1.62) interventions in committee per week. Panel B: descriptive statistics on the press mentions of indicators for MPs and Senators. We identified 3,920 mentions across 885 articles between 2009 and 2020 for MPs and 434 mentions across 95 articles for Senators. *Number of mentions:* total number of mentions for the indicator in a row. *Number of articles:* number of distinct articles that mention at least once a legislator for the indicator in a row. *Total press:* distinct number of articles that mentions “Nosdeputes.fr” or “Nossenateurs.fr”. *Citing indicators:* distinct number of articles that mention at least one legislator.
## 2.3 Press Coverage

We compile all articles referencing Nosdeputes.fr and Nossenateurs.fr from Europresse, Factiva, and Nexis, covering the period from the websites' launch until March 2020. The dataset includes all media types available in these databases, encompassing 50 local newspapers, 43 national outlets, and 5 magazines, for a total of 98 distinct offline or online outlets.<fnref n="5" /> In total, we identified 885 articles mentioning Nosdeputes.fr, from which 3,920 mentions of statistical indicators were manually extracted. By contrast, only 95 articles and 434 mentions were identified for Nossenateurs.fr.<fnref n="6" /> For each mention, we recorded the indicator and the legislator mentioned. Summary statistics of our press database are displayed in Panel B of Table 1. Coverage varies notably among indicators: attendance in committees, for example, is mentioned seven times more frequently than adopted amendments.<fnref n="7" /> The bottom line indicates the cases where articles mention legislators' general performance according to the websites but without referencing any specific indicator.

Additional details on data collection and further descriptive statistics are provided in Appendix A.3, including an example article that illustrates our coding process. We also display the distribution of the number of articles published over time on both websites in Appendix Figure A.4, highlighting that Nossenateurs.fr consistently receives significantly less coverage than Nosdeputes.fr. Furthermore, for each mention, we coded the tone of the coverage, noting whether the performance is reported positively, negatively, or presented neutrally. Additionally, some journalists report statistics for groups of indicators, either by summing them intentionally or due to a lack of precision. We reflect this approach in Appendix Table A.3, where we aggregate the main legislative activities. Also informative is the distribution of mentions per legislator: 53% of MPs are mentioned at least once, and the average number of mentions per MP is 2.82. By contrast, only 16% of Senators are mentioned, and their average number of mentions is 0.55.

One limitation of our approach is that we only consider articles that explicitly mention the websites. Some articles may have gone undetected, but this would likely result in an underestimation of our findings rather than undermining our conclusions. Another important concern in analyzing press coverage of legislators lies in the risk of endogeneity in the timing of article publication. Since our empirical goal is to isolate the effect of press coverage on legislators' behavior, we need to ensure that any observed response is driven by the coverage itself and not by a correlation between legislative activity and press attention. Such a correlation would compromise the interpretation of observed effects as causal responses to visibility. Yet, media coverage and the parliamentary agenda are likely to coincide along multiple dimensions.

<footnote n="5">⁵Our set of daily outlets comprises both national and local French press, following the classification provided by the *Alliance pour les Chiffres de la Presse et des Médias*. We also examine references to Nosdeputes.fr and Nossenateurs.fr in television newscasts using data from the *Institut National de l'Audiovisuel*, but find that the websites are rarely cited outside the printed press.</footnote>

<footnote n="6">⁶The annotation was conducted by one of us, and another coauthor independently checked 100 articles using the same guidelines, achieving an agreement rate of 91%. We also used OpenAI for this task but found the results less consistent.</footnote>

<footnote n="7">⁷We use this variation in the visibility of indicators to study redistribution in parliamentary activities in Section 5.</footnote>
To address this concern, our main empirical strategy, presented in Section 3, includes a comprehensive set of time fixed effects to account for political cycles that may influence article publication. We conduct a series of empirical exercises, detailed in Appendix A.3, to validate the assumption that coverage does not tend to follow periods of unusually high or low legislative activity once political cycles are accounted for. In Appendix Table A.4, we show that, conditional on time fixed effects and across different specifications, neither the number nor the probability of articles mentioning performance indicators is statistically correlated with MPs' preceding activity.<fnref n="8" />

In Subsection 5.1, we leverage differences in press market structure at the department level and compile a series of statistics to describe press circulation patterns. To this end, we collect data on daily outlets—both local and national—and their circulation figures using data from the ACPM (Alliance pour les Chiffres de la Presse et des Médias). Each local outlet is linked to its corresponding circulation département. While MPs are elected in specific districts, circulation data is only available at the department level (with one department containing on average 5.7 electoral districts). Appendix Table A.5 provides summary statistics: on average, 12 outlets, including 3 local outlets, are available in a department, with substantial heterogeneity in local outlet counts ranging from 1 to 7.

# 3 Effect of Statistical Indicators on Aggregate Performance

Our objective is to assess how performance indicators influence overall legislative output, and to determine whether general activity levels intensify accordingly. The possible effects of indicators could be driven by several channels. First, performance is likely to depend on the press coverage of the indicators. Increased coverage could influence all legislators through collective accountability. Additionally, individual coverage may hold legislators personally accountable. Legislators mentioned in the press might react to all indicators or focus only on those specifically mentioned in the article. Finally, the mere implementation of the websites could have an effect, as legislators may react to the creation of new monitoring tools. To highlight the main findings, we first focus on an aggregate performance indicator and then provide a detailed analysis of each individual performance metric in Subsection 4.1. Our aggregate measure of activity is constructed to have a mean of zero and a standard deviation of one, assigning equal weight to all component indicators:

$$ \text{Aggregate Indicator}_{itlc} = \frac{1}{\sigma_{lc}^A} \cdot \sum_s \frac{y_{itlc}^s - \bar{y}_{lc}^s}{\sigma_{lc}^s}. \quad (*) $$

$\bar{y}_{lc}^s$ denotes the mean of indicator $s$ over the legislature $l$ in chamber $c$ (Senate or National Assembly), and $\sigma_{lc}^s$ represents its standard deviation. The term $\sum_s (y_{itlc}^s - \bar{y}_{lc}^s)/\sigma_{lc}^s$ reflects the sum of the normalized performances on all indicators $s$ of legislator $i$ serving in chamber $c$ during week $t$. It assigns equal weight to all indicators, which are standardized to have the same

<footnote n="8">⁸More precisely, using both OLS and Poisson models, we show that the probability of an additional article being published is not significantly associated with changes in overall activity, controlling for legislature-year and either week-of-year or month fixed effects.</footnote>
distribution (mean of zero and standard deviation of one). Therefore, Aggregate Indicator$_{itlc}$ has a mean of zero. To ensure that the final aggregate indicator also has a standard deviation of one, we compute the standard deviation of the sum, $\sigma_{lc}^A$, and divide the result by this standard deviation.

We begin the empirical analysis by proposing two identification strategies to estimate the effect of the websites and their media coverage on the aggregate performance indicator. The first strategy focuses on variation within the National Assembly (hereafter Model 1), while the second exploits differences between the two parliamentary chambers (Model 2).

## 3.1 Nosdeputes.fr and MPs' Aggregate Performance

Given the limited attention paid to Nossenateurs.fr, MPs experience a much higher treatment intensity. Hence, we first focus on MPs only, and their coverage by Nosdeputes.fr, and estimate the following equation:

$$y_{it} = \beta_1 \cdot \text{HighCoverage}_t + \beta_2 \cdot \text{MentionMP}_{it} + \beta_3 \cdot \text{PostWebsite}_t + \text{MP} \times \text{Legislature}_{it} + \gamma_t + \epsilon_{it}. \quad (1)$$

The dependent variable, $y_{it}$, represents the aggregate performance of MP $i$ in week $t$. The variable HighCoverage$_t$ captures general press coverage and is defined as a function of the number of articles mentioning the website that were published recently. In our main specification, HighCoverage$_t$ is a binary indicator equal to one if the number of articles published during the 12 weeks preceding week $t$ exceeds the median of this distribution ($p50 = 11$). This serves as a measure of periods characterized by high visibility of indicators from Nosdeputes.fr in the press. We explore specifications using a continuous measure of press coverage in subsequent regressions. Individual coverage is measured by MentionMP$_{it}$, which reflects the articles referencing the performance of MP $i$. In our main specification, MentionMP$_{it}$ equals one if MP $i$ is mentioned on at least one indicator in the press during the 12 weeks preceding week $t$. Accordingly, $\beta_2$ captures the individual accountability effect of press coverage, while $\beta_1$ can be interpreted as the effect of collective accountability.

Furthermore, PostWebsite$_t$ is a binary variable that takes the value of one after the creation of Nosdeputes.fr in September 2009. Therefore, PostWebsite$_t$ and HighCoverage$_t$ measure average effects for all MPs. The coefficient $\beta_3$ measures the change in performance following the creation of the website when press coverage is low. $\beta_1$ shows whether this effect is amplified by high press coverage.

We also introduce MP-legislature fixed effects (MP × Legislature$_{it}$), which control for each MP's average performance during a given legislature. We specify one fixed effect for each legislature in which the MP serves because an MP's role can change after reelection. Specifically, changes in the majority party alter roles and induce new committee appointments. Consequently, we cannot include legislature fixed effects separately; however, differences in average activity across legislatures are captured by the combination of all MP-legislature fixed effects.

Finally, we introduce time fixed effects to control for the political cycle $\gamma_t$. For the main
results displayed in Table 2, we focus on two specifications. First, we include week-of-the-year ($WeekOfYear_t$) and legislature-year ($LegislatureYear_t$) fixed effects, which together are expected to capture most political cycle effects. $WeekOfYear_t$ consists of 52 fixed effects, accounting for seasonal trends such as reduced parliamentary activity during the summer. $LegislatureYear_t$ includes five fixed effects, one for each year within a legislature, controlling for within-legislature trends—for instance, MPs may become more active toward the end of a legislature to enhance their reelection prospects. In the second time fixed effects specification, we introduce one fixed effect for each month of our study period. These fixed effects flexibly control for the political cycle, news pressure, and any events that could affect MPs' performance. However, a drawback of this approach is that we cannot estimate $\beta_3$, since $PostWebsite_t$ captures all months following the website's launch.

We estimate Model 1 in Columns 1 and 2 of Table 2, presenting the results for both specifications of time fixed effects. We find a positive effect of $HighCoverage_t$ that is statistically significant at the 1% level. Specifically, performance during high-coverage periods is 0.09 standard deviations higher than during low-coverage periods. While this magnitude should be interpreted with caution, the impact is sizable: among the 577 MPs, a 0.09 standard deviation increase would translate to a gain of 30 places for an MP with average performance (i.e., an aggregate indicator of 0). Interestingly, we do not find an additional increase in performance for MPs specifically mentioned in the press; the point estimate for $MentionMP_{it}$ is close to zero and not significant. This suggests that performance indicators primarily influence legislators through collective rather than individual accountability.

In Column 1, the estimated effect of $PostWebsite_t$ suggests a positive impact of the website during periods of low coverage. However, it should be noted that even during periods of low press coverage, the number of articles is not zero, and MPs may still anticipate media attention. As a result, $PostWebsite_t$ also captures the effect of coverage when it is low, providing an upper bound for the direct effect of the website.

Model 1 relies on two identification assumptions. First, the coverage of indicators must not coincide with political events that independently affect MPs' activity. If such events were correlated with coverage, omitted variable bias could lead to an overestimation of the effect of press attention. We address this concern in Section 3.4, demonstrating that our results remain robust when controlling for general press coverage of the National Assembly. Together with the consistent results obtained with the second identification strategy and the additional empirical exercises shown in Appendix A.3, these elements support the exogeneity of the coverage of indicators.

The second main assumption relates to the identification of the website's impact: it assumes that the simple difference in MP activity before and after the website launch, as shown in Column 1 of Table 2, captures only the effect of the website. The fixed effects $WeekOfYear_t$ and $LegislatureYear_t$ account for recurring political cycle patterns that affect all legislatures in the same way. However, they do not control for potential trends occurring around the time of the website's implementation. In particular, the website's creation may coincide with a period of heightened scrutiny of politicians in general. If this were the case,
our estimate of PostWebsite<sub>t</sub> would mistakenly attribute this effect to the website. To address this concern, we turn to our second identification strategy, which leverages the staggered timing of implementation of the two websites.

## 3.2 MPs' and Senators' Aggregate Performance

Our second identification strategy includes both MPs and Senators. We estimate the following model:

$$y_{itc} = \beta_1 \cdot \text{HighCoverage}_{tc} + \beta_2 \cdot \text{MentionLegislator}_{itc} + \beta_3 \cdot \text{PostWebsite}_{tc} + \text{Legislator} \times \text{Legislature}_{itc} + \gamma_t + \epsilon_{it}. \quad (2)$$

The dependent variable, $y_{itc}$, represents the performance of legislator $i$, member of chamber $c$, during week $t$. For MPs, HighCoverage$_{tc}$ is as before: it equals one if the number of articles published during the last 12 weeks exceeds the median. For Senators, a sensible approach is to set HighCoverage$_{tc}$ to zero, as the number of press articles on Nossenateurs.fr is most of the times very low. We also test a specification allowing Senators to react to lower numbers of articles below. MentionLegislator$_{itc}$ is defined identically for both Senators and MPs: it equals one if the performance of legislator $i$ was mentioned in the press during the last 12 weeks. Crucially, PostWebsite$_{tc}$ now equals one for MPs after the launch of Nosdeputes.fr in September 2009 and equals one for Senators after the creation of Nossenateurs.fr in September 2011. Hence, $\beta_3$ is identified using a staggered difference-in-differences approach.

The legislator-legislature fixed effects remain the same for MPs. For Senators, they are defined based on the legislatures of the National Assembly. This approach reflects the fact that the National Assembly's terms align with the presidential mandates, and together, they determine the French legislative agenda.

We display the results in Columns 3 and 4 of Table 2, mirroring the time fixed effects approach of Columns 1 and 2. In Column 3, we include week-of-the-year (WeekOfYear$_{tc}$) and legislature-year (LegislatureYear$_{tc}$) fixed effects, specific to MPs and Senators. For Senators, LegislatureYear$_{tc}$ represents the number of years since their election. There are 6 year fixed effects for most of the sample, and 10 for the earliest cohorts, as Senators were elected for 10-year terms before 2003. This specification controls for the respective political cycles of the two chambers.

The results for press coverage are generally consistent with those of the first specification. We observe a significant effect of general press coverage, albeit with a smaller magnitude (+0.06 standard deviations), and limited effects of individual mentions. We also obtain similar results in Column 4 with the second time fixed effects specification, which consists of introducing month fixed effects common to both chambers. This specification accounts for a potential trend in legislators' performance and does not preclude the estimation of the effect of the websites' launch, thanks to their staggered implementation. We obtain identical results in Section 3.4 with week fixed effects.

Moreover, it should be noted that the effect of PostWebsite$_{tc}$ is close to zero and not
<table><caption>Table 2: Statistical Indicators, Press Coverage and Legislator's performance</caption><thead><tr><th rowspan="2"></th><th colspan="2">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.085***<br>(0.005)</td><td>0.095***<br>(0.009)</td><td>0.063***<br>(0.010)</td><td>0.031***<br>(0.009)</td><td>0.071***<br>(0.010)</td><td>0.028***<br>(0.010)</td></tr><tr><td>Mention Legislator</td><td>-0.002<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>0.008<br>(0.014)</td><td>0.021<br>(0.015)</td><td>0.008<br>(0.014)</td><td>0.019<br>(0.015)</td></tr><tr><td>High Coverage Senators</td><td></td><td></td><td></td><td></td><td>0.018<br>(0.011)</td><td>-0.005<br>(0.010)</td></tr><tr><td>Post Website</td><td>0.057***<br>(0.013)</td><td></td><td>0.009<br>(0.012)</td><td>0.016<br>(0.018)</td><td>0.008<br>(0.012)</td><td>0.014<br>(0.018)</td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>490,600</td><td>490,600</td><td>490,600</td><td>490,600</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.20</td><td>0.19</td><td>0.19</td><td>0.19</td><td>0.19</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Columns 1 and 2: estimation of Model 1. Columns 3 to 6: estimation of Model 2. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *High Coverage Senators*: dummy variable equal to 1 if at least one article covering Nossenateurs.fr in the previous 12 weeks was published. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.

significant. This second identification strategy likely provides a more accurate estimate of the direct effect of the websites for two reasons. First, the staggered difference-in-differences approach accounts for potential trends affecting legislators around the time of the websites' implementation, particularly with the month fixed effects in Column 4. Second, the very small number of articles on Senators makes it plausible that the effect of PostWebsite$_{tc}$ is less confounded by the residual effect of the press when the number of articles is low.

Nevertheless, some limitations must be acknowledged. First, the differing legislative agendas of MPs and Senators suggest that their performance may follow distinct trends. While Senators are not a perfect control group, the consistency of results across our two time fixed-effects specifications is reassuring of the comparability of political cycles between the two chambers. Additionally, the press coverage of Nossenateurs.fr is not absent, meaning that our estimate of PostWebsite$_{tc}$ remains an upper bound that still captures some influence of press coverage. This reinforces our conclusion that the direct effect of the websites is small.

We investigate further the perception of coverage for both groups in Columns 5 and 6 of Table 2. We simultaneously introduce HighCoverageMP$_{tc}$, defined as usual for MPs, and HighCoverageSenator$_{tc}$, which takes the value of one if at least one article was published on Nossenateurs.fr during the past 12 weeks. This specification accounts for the possibility that Senators, who are less accustomed to their website being covered, may be more sensitive to coverage and therefore react to a lower number of articles than MPs. This threshold also
ensures an approximately balanced number of weeks with high and low coverage for Senators. We find that the effect of HighCoverageSenator$_{tc}$ is very close to zero and not statistically significant, which confirms the hypothesis that the coverage of the Senators' website is indeed low and is perceived as such by the Senators themselves.

In summary, our findings based on the aggregate indicator reveal that legislators exhibit a moderate response to the creation of the websites. However, general press coverage of these platforms has a substantial impact on performance, an effect that does not appear to be driven solely by legislators explicitly mentioned in articles. This supports our interpretation that legislators respond collectively to heightened scrutiny, a phenomenon we attribute to collective accountability. In the next paragraph, we refine our primary approach by initially treating coverage as a binary variable and then turn to an examination of variations in the intensity of coverage, focusing specifically on MPs in the National Assembly, the chamber for which such variation can be effectively leveraged.

## 3.3 Continuous Measures of Coverage

To enable a more accurate comparison between MPs and Senators, and considering that Senators receive substantially less media attention, we initially estimate Model 1 and Model 2 using media coverage as a binary treatment. However, given the positive effect of increased media coverage observed for MPs across both models, we refine our measure to capture more granular variations in media intensity. Specifically, we now model indicators press coverage using the number of articles, treated as a continuous variable, to account for smaller variations in media exposure, more likely to be considered as random. To this end, we define different continuous treatment variables and estimate the following augmented version of Model 1:

$$y_{it} = \beta_1 \cdot \text{Articles}_t + \beta_2 \cdot \text{MentionMP}_{it} + \beta_3 \cdot \text{PostWebsite}_t + \text{MP} \times \text{Legislature}_{it} + \gamma_t + \epsilon_{it}. \quad (3)$$

The dependent variable, $y_{it}$, remains unchanged, measuring the performance of MP $i$ in week $t$. The variable Articles$_t$ is now defined as the number of articles published in the preceding 12 weeks. The definitions of MentionMP$_{it}$, PostWebsite$_t$, and the fixed effects remain the same as in the earlier specifications.

Table 3 presents the results. We consider different functional forms of media coverage: the logarithm of the number of articles (Columns 1 and 2), the log-plus-one transformation (Columns 3 and 4), and the inverse hyperbolic sine transformation (Columns 5 and 6). In Columns 7 and 8, we use the share of articles citing Nosdeputes.fr out of the total number of articles published in the press mentioning the National Assembly. In the even-numbered columns, we replace WeekOfYear$_t$ and LegislatureYear$_t$ fixed effects with month fixed effects. This specification prevents identification of the PostWebsite$_t$ coefficient, which is also excluded in Column 1 due to its definition being restricted to the post-website period with at least one
article.<fnref n="9" />

Across all specifications, we find that increased media coverage of Nosdeputes.fr positively impacts MPs' performance. In Columns 1 and 2, we focus on the intensive margin by using the logarithm of article count. A 1% increase in coverage intensity is associated with a statistically significant increase in performance of 3% of a standard deviation, with coefficients significant at the 1% level. These specifications are estimated using only the weeks in which at least one article was published. The exclusion of non-covered weeks makes this a conservative test for identifying the effect of an additional article at the purely intensive margin. Using either the log-plus-one or inverse hyperbolic sine transformations, we find similar positive and significant effects across the entire sample period. The magnitude of the coefficients varies slightly depending on the fixed effects used: between 0.02 and 0.03 in models with WeekOfYear_t and LegislatureYear_t fixed effects, and around 0.1 in models with month fixed effects—the latter do not capture political cycles, which likely explains that the coefficients are larger.

Finally, in Columns 7 and 8, we find that an increase in the share of Nosdeputes.fr-related articles relative to overall Assembly coverage also improves performance. In our preferred specification (Column 7), which includes political-cycle fixed effects, a one percentage point increase in this share, equivalent to two standard deviations of the variable, is associated with a 0.05-point increase in MPs' performance.

As a robustness check, and for the sake of comparison with the empirical models presented in Subsections 3.1 and 3.2, we also estimate an augmented version of Model 2 using continuous coverage.<fnref n="10" /> The results, reported in Appendix Table B.1, remain consistent with a positive and statistically significant effect of additional articles about MPs on their overall activity.

A likely explanation for the positive effect of continuous measures of press coverage is that the publication of one or more additional articles prompts legislators to update their expectations regarding future media attention. As a result, when a greater number of articles have recently been published, legislators anticipate increased scrutiny and respond by exerting more effort. In Section 4, we further show that media coverage affects different types of parliamentary activities in distinct ways. This suggests that MPs engage in a trade-off between various forms of activity in response to changes in visibility, reallocating their effort toward those that are more likely to be rewarded or noticed. While this belief-updating may occur at the individual level, the evidence on collective accountability suggests that group dynamics may also be involved. Section 5 discusses the mechanisms that may drive these dynamics, notably highlighting how political groups can shape these responses and how the structure of local media markets triggers different forms of accountability.

<footnote n="9">⁹This variable is only defined after the website's introduction and for observations with at least one article.</footnote>

<footnote n="10">¹⁰We estimate: $y_{itc} = \beta_1 \cdot \text{Articles}_{tc} + \beta_2 \cdot \text{MentionLegislator}_{itc} + \beta_3 \cdot \text{PostWebsite}_{tc} + \text{Legislator} \times \text{Legislature}_{itc} + \gamma_t + \epsilon_{it}$. As in Equation (3), Articles_t denotes the number of articles mentioning Nosdeputes.fr published in the preceding 12 weeks. All other variables, including the dependent variable $y_{itc}$ and the binary treatment variable High Coverage Senate_t for Senators, remain identical to those in Model 2.</footnote>
<table><caption>Table 3: Alternative Measures of Press Coverage and MP's performance</caption><thead><tr><th></th><th colspan="8">Model 1</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Articles (ln)</td><td>0.030***<br>(0.004)</td><td>0.096***<br>(0.006)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Articles (ln+1)</td><td></td><td></td><td>0.027***<br>(0.003)</td><td>0.110***<br>(0.006)</td><td></td><td></td><td></td><td></td></tr><tr><td>Articles (asinh)</td><td></td><td></td><td></td><td></td><td>0.023***<br>(0.003)</td><td>0.092***<br>(0.005)</td><td></td><td></td></tr><tr><td>Share articles ND/Assembly</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.049***<br>(0.006)</td><td>0.148***<br>(0.011)</td></tr><tr><td>Mention MP</td><td>-0.000<br>(0.014)</td><td>-0.005<br>(0.013)</td><td>0.005<br>(0.013)</td><td>-0.007<br>(0.013)</td><td>0.006<br>(0.013)</td><td>-0.007<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>-0.006<br>(0.013)</td></tr><tr><td>Post Website</td><td></td><td></td><td>0.048***<br>(0.014)</td><td></td><td>0.045***<br>(0.015)</td><td></td><td>0.087***<br>(0.013)</td><td></td></tr><tr><td>Observations</td><td>260,948</td><td>260,948</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>322,580</td><td>322,580</td></tr><tr><td>Adjusted R2</td><td>0.21</td><td>0.21</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td></tr><tr><td>MP × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

**Notes:** * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Estimation of Model 1 with continuous coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *Articles (ln)*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks. *Articles (ln+1)*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one. *Articles (asinh)*: inverse hyperbolic sine (asinh) of the number of articles covering Nosdeputes.fr in the previous 12 weeks. *Share articles ND/Assembly*: number of articles covering Nosdeputes.fr divided by the total number of articles covering the National Assembly, in the previous 12 weeks, multiplied by 100. *Mention MP*: dummy variable equal to 1 if the MP is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1.

## 3.4 Robustness Checks

We present the results of robustness checks in Appendix B. Unless otherwise specified, each results table replicates the baseline findings of Models 1 and 2, using both binary and continuous coverage measures.

First, the press coverage of statistical indicators might align with a broader increase in scrutiny of the National Assembly overall. As a result, the observed effect of general press coverage could stem from heightened pressure to perform due to overall parliamentary coverage, rather than from the coverage of the indicators themselves. To test this alternative explanation, we use Europresse to collect data on the number of articles mentioning various keywords related to the National Assembly across all French newspapers, excluding those in our sample specifically focused on Nosdeputes.fr.<fnref n="11" /> In Columns 1–4 of Appendix Table B.2, we estimate Model 1 with an added control for general parliamentary coverage, consisting of a binary variable equal to 1 if the number of articles on the National Assembly in the past 12 weeks exceeds the median. When estimating Model 2 in Columns 5–8, this variable is zero for Senators. This control does not alter our main estimates, indicating that the observed effect is indeed driven by specific coverage of the indicators. Interestingly, periods of

<footnote n="11">¹¹We selected the keywords “National Assembly” (*Assemblée Nationale*) and “MP” (*Député*, *Députée*, or *Parlementaire*), yielding 813,002 articles.</footnote>
high general parliamentary coverage tend to negatively affect MPs' performance, which may suggest that MPs deprioritize performance on observed activities to focus on other activities not captured by the indicators, such as working with their staff or intervening in the media. Similar results are found when controlling for the weekly number of articles mentioning the National Assembly (not reported).

Additionally, we estimate Models 1 and 2, controlling for the number of visits to the website Nosdeputes.fr.<fnref n="12" /> Website visits offer an alternative proxy for the visibility of performance indicators to the broader public, including journalists. Appendix Table B.3 presents the results, with estimates for Model 1 in Columns 1–4 and for Model 2 in Columns 5–8. Across all specifications, the coefficients on HighCoverage$_t$ for MPs and on the logarithm of the number of articles remain positive and statistically significant, indicating that the effect on activity is not driven by temporary spikes in website traffic. Interestingly, we find that an increase in visits to Nosdeputes.fr in the past 12 weeks is negatively correlated with MP activity. One possible interpretation is that website traffic and press coverage reflect distinct forms of visibility, with media coverage of indicators serving as the primary channel through which legislators perceive accountability. Combined with the inverse relationship between general press coverage of the National Assembly and activity that we discussed in Appendix Table B.2, this suggests that coverage of performance indicators captures a specific form of visibility, distinct from both overall media attention to the Assembly and visits on Nosdeputes.fr, which are more likely to move together with broader parliamentary activity.

We also show that the results are robust to sample restrictions. In Appendix Table B.4, we focus on observations after the launch of the second website in September 2011. While this specification does not allow us to measure the websites' effect, we find stronger results for press coverage after the introduction of the indicators. Similarly, Appendix Table B.5 shows comparable results when restricting the sample to legislators directly affected by the website's implementation—specifically, those already in office in September 2009.

Additionally, we test alternative time frames for defining the press coverage variables. In Appendix Table B.6, we show that the results remain consistent when the general coverage binary variable accounts for articles published in the past 16 weeks, instead of the 12-week window used in the main specification. However, an 8-week window appears too short to fully capture the effect of general coverage, leading to larger estimates for the effects of individual mentions and websites. In Appendix Table B.7, we estimate Model 1 including separate binary variables for high coverage within successive four-week intervals over the past four months (Columns 1 and 2). We also estimate a version with continuous coverage measures for each of these intervals (Columns 3 and 4). This specification yields coefficients that are more difficult to interpret due to the combination of narrowly defined treatment periods and a restrictive set of time fixed effects. Nevertheless, the results remain overall consistent with a surge in legislative activity in response to more intensive media coverage in the three months preceding the week of observation.

Furthermore, our findings remain consistent across different fixed-effect specifications. In

<footnote n="12">¹²Website traffic data is provided by the NGO *Regards Citoyens* (available [here](#)).</footnote>
Appendix Table B.8, we first reestimate Models 1 and 2 using legislator fixed effects instead of legislature-legislator fixed effects in Columns 1-4. The results remain similar. In Column 5, we estimate Model 2 with week fixed effects common to both chambers. This specification is even more flexible than the month fixed effects previously used and yields very similar results. Moreover, the specific election timing of Senators suggests using different specifications for their individual fixed effects. We reestimate Model 2, redefining Senator-legislature fixed effects after each reelection, and find similar results in Columns 6 and 7.

Moreover, reversion to the mean is a common concern in similar research designs (Mattozzi et al., 2024), but it cannot explain our results. A positive effect of individual mentions could have resulted from a focus on legislators performing exceptionally poorly, who then mechanically revert to average performance levels. This is not a concern since we observe a limited effect of individual mentions.

One initial assumption regarding the coverage of performance indicators by the press is that legislators might adjust their perception of being individually mentioned after an article mentions them. This adjustment would imply a positive effect of individual mentions, which is not supported by our results. One possible explanation is that when legislators are mentioned negatively, they respond by publicly criticizing the performance indicators, which increases their resistance to these indicators and results in lower performance. We find mixed support for this possibility in Subsection 5.3. Another interpretation is that legislators may not use individual mentions to update their beliefs about the likelihood of future mentions. In this scenario, the information from new articles would be similar for all legislators, which would explain why the results are mainly driven by general coverage. Additionally, some legislators might believe that once they have been personally mentioned, they are less likely to be mentioned again, as journalists might shift their focus elsewhere. They might also assume that their reputation is established after a mention, possibly because their voters form opinions based on the initial performance revealed, thereby reducing their incentive to improve their indicators further. However, a lack of attention to their own coverage is unlikely to explain the limited effect of individual mentions: in Subsection 5.2, we show that legislators who comment on their performance—making limited attention implausible—exhibit a similarly small reaction.

Modeling this as a formal game would require a precise understanding of how performance affects coverage, which is challenging due to the uncertainties involved. In particular, legislators do not know in advance which indicators will be covered or the baseline to which they will be compared. Moreover, the timing of coverage is uncertain, and indicators are displayed on the website over the last 12 months. Thus, a perfectly rational legislator would need to continuously track all indicators for all legislators, which is unrealistic. Legislators likely decide their effort allocation based on more straightforward reasoning, such as the simple updating we propose.
# 4 Disaggregated Effects on Parliamentary Activity

In the previous section, we examined the effect of the introduction of performance monitoring on parliamentary activity in both chambers and found that the launch of the website—and its subsequent media coverage—induced measurable changes in MPs' activity, whereas no comparable effect was observed among Senators. Our outcome variable captured aggregate activity, which we interpreted as a general increase in effort by MPs following the website's introduction, and especially in periods of heightened media scrutiny.

In this section, we investigate the nature of these effects by examining different types of parliamentary activities separately. Indeed, if aggregate activity increased, it is important to understand whether this reflects strategic choices and potential trade-offs. To do so, we consider specific indicators directly measured by the websites, as well as other dimensions of parliamentary work, adopting a more qualitative perspective based on the content of written questions and intervention transcripts.

## 4.1 Disaggregated Outcomes from the Websites

To investigate how the different performances measured by the indicators were impacted, we estimate our two identification strategies using each indicator displayed on the websites as a dependent variable. The results for Model 1 are presented in Appendix Table B.9, while those for Model 2 appear in Appendix Table B.10. We employ the same specifications as in Columns 1 and 3 of Table 2, with one key modification: we introduce the coefficient $MentionLegislatorIndicator_{it}$, which equals one for individual mentions related to the specific indicator under consideration. This coefficient tests whether the mentioned legislators respond by improving their performance on the particular measure reported. Hence, it reveals whether the limited effect of individual mentions on the aggregate indicator conceals an increase in the mentioned indicators, offset by declines in others.

We find a positive effect of $HighCoverage_t$ on nearly all performance indicators, confirming our main conclusion. Additionally, the results for the post-website coefficients reveal notable heterogeneities. We observe positive and significant effects on the number of committee attendances and written questions. However, there is a decline in the number of long interventions in plenary sessions, as well as in written and signed amendments and signed proposals. Both empirical models yield consistent findings in this regard. Furthermore, legislators respond to specific mentions by reducing their participation in committee work and submitting fewer amendments, although they tend to sign more proposals. When examining the effects of mentions on individual indicators, the estimates indicate that only written questions and signed proposals exhibit a significant and positive response. Overall, this provides limited evidence of a selective reaction on some activities to being explicitly mentioned in the press.

The combined effects of $PostWebsite_t$ and $HighCoverage_t$ suggest a stronger reaction for indicators that receive higher press coverage. In particular, we observe positive effects on the number of attendances in legislative committees and the number of written questions to the
government—two indicators that attract significant media attention, as shown in Panel B of Table 1. Conversely, the number of amendments, that receive less media coverage, shows a negative combined effect. This suggests that legislators prioritize efforts on frequently covered indicators. We find further support for this hypothesis in Appendix Table B.11: we reestimate the specifications of Columns 1-4 of Table 2 using an aggregate indicator that focuses on the three metrics included in the weekly activity indicator computed by both websites—also the most discussed in the press (long and short interventions in plenary sessions, attendance in committees). The results show a positive effect of the website's launch in all specifications. However, individual mentions still have no significant effect.

A complementary explanation for the strong impact on committee attendance and written questions is the relative ease with which these activities can be increased. In contrast, indicators such as oral interventions are inherently limited by the total speaking time available. Therefore, the magnitude of the disaggregated estimates should be interpreted with caution. In Appendix B.12, we turn to continuous measures of press coverage, following a similar approach to that used in Subsection 3.3. Specifically, we examine the logarithm of the number of articles and the share of articles mentioning Nosdeputes.fr out of the total coverage of the National Assembly. At the intensive margin, we find that increased media visibility tends to significantly boost certain activities, such as attendance and plenary interventions, while reducing the likelihood of engaging in written activities.

We also conduct several robustness checks using disaggregated outcomes as the dependent variables. Results shown in Appendix Tables B.9 and B.10 capture effects on the intensive margin. To assess effects on the extensive margin, we consider an alternative dependent variable: a binary variable equal to one if a legislator engaged in at least one activity of a given type during the week. The results for all indicators are presented in Appendix Table B.13 for Model 1 and in Appendix Table B.14 for Model 2. For most indicators, we find comparable effects of general press coverage, mentions of specific indicators, and the website's launch. Additionally, the effect of individual mentions on the extensive margin is positive for most indicators, albeit not always statistically significant. This suggests that while individual mentions have a mixed effect on the intensive margin, they may encourage previously inactive legislators to become active.

Moreover, the distribution of performance across disaggregated indicators, illustrated in Appendix Figure B.1, is skewed to the left. To address the large and plausibly not random number of zeros, we estimate a Tobit model, which is specifically designed for such distributions (Wooldridge, 2020). Although a direct comparison of coefficient magnitudes between the Tobit and OLS models is not possible, the results reported in Appendix Tables B.15 and B.16 show consistent signs for Model 1 and Model 2, with some estimates becoming more statistically significant. We also replicate Table 2 with a Tobit model, as shown in Appendix Table B.17, and obtain robust results. Similarly, we estimate Models 1 and 2 with a Poisson model in Appendix Table B.18 and Appendix Table B.19, and observe consistent findings. In Appendix Tables B.20 and B.21, we trim the distribution of the variable by excluding the top 1% of values and obtain similar results for Models 1 and 2 with disaggregated
indicators, indicating that the findings are not driven by outliers.

## 4.2 Outcomes not Measured by the Websites

In the previous subsection, we examined disaggregated activity measures tracked by Nosdeputes.fr and Nossenateurs.fr, which shed light on the specific outputs monitored by the NGO and to which MPs may align their behavior. A related and important question is whether this alignment with monitored indicators affects other untracked forms of parliamentary activity. Exploring activity beyond the websites' scope is challenging—first, because such outputs do not exist by definition. Yet, by constructing alternative indicators that capture other forms of legislative engagement, we can assess whether the introduction of monitoring tools influences not only targeted behaviors but also broader involvement and the quality of less visible work.

This issue is particularly relevant given that legislators are often accused of manipulating performance indicators, raising concerns about the unintended consequences of transparency on political efficiency, a risk well documented in the literature (Prat, 2005; Hansen et al., 2018). This subsection explores whether the introduction of performance metrics may have transformed not only the quantity, but also the nature of parliamentary work.

### Content of Written Questions

First, we focus on written questions to test for the possibility that legislators inflate indicators for written activities by submitting identical texts, a claim made by Acatrinei and Quénel (2019).<fnref n="13" /> Given the limited effect observed on amendments, we focus on written questions to test whether this strategy is employed. Written questions can be submitted at any time, on any topic, to any member of the government, who is obligated to respond. It is, for instance, possible to ask the same question to different members of the government, or to copy a question sent by another legislator.

We collect the text of all written questions submitted during the study period from Nosdeputes.fr and Nossenateurs.fr, totaling 262,283 questions from MPs and 64,476 from Senators. For each question, we compute its cosine similarity with all questions asked in the same or preceding weeks. A question is classified as copy-pasted if its cosine similarity score with at least one previously submitted question exceeds 0.9.<fnref n="14" /> For each legislator who submitted at least one question in a given week, we calculate the share of copy-pasted questions. The data collection process, descriptive statistics, and methodology are detailed in Appendix C.2.

In Columns 1-4 of Table 4, we focus on MPs and estimate Model 1. In Column 1, we consider the weekly share of copy-pasted questions as the dependent variable. We find positive and significant effects from both the website's launch and general coverage, but no significant

<footnote n="13">¹³This concern is not unique to France, as similar criticisms have appeared in international media coverage (e.g. Hurst, 2006).</footnote>

<footnote n="14">¹⁴Due to computational capacity constraints, we focus on questions asked by members of the same chamber during the current legislature of the National Assembly. Consequently, we cannot detect copy-pasting of questions from previous legislatures or by legislators from the other chamber. Identifying such copy-pasting would likely result in larger shares and higher estimates.</footnote>
<table><caption>Table 4: Statistical Indicators, Press Coverage and Similarity of Written Questions</caption><thead><tr><th rowspan="3"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr><tr><th>Ratio copyp.<br>(strict)</th><th>Ratio copyp.<br>(extended)</th><th>New written<br>questions</th><th>Length</th><th>Ratio copyp.<br>(strict)</th><th>Ratio copyp.<br>(extended)</th><th>New written<br>questions</th><th>Length</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.020***<br>(0.003)</td><td>0.022***<br>(0.003)</td><td>-0.007<br>(0.014)</td><td>0.672<br>(0.868)</td><td>0.016***<br>(0.005)</td><td>0.019***<br>(0.006)</td><td>-0.006<br>(0.018)</td><td>-0.466<br>(1.796)</td></tr><tr><td>Mention Legislator</td><td>-0.002<br>(0.007)</td><td>0.002<br>(0.008)</td><td>0.010<br>(0.036)</td><td>3.750<br>(2.612)</td><td>-0.005<br>(0.007)</td><td>-0.003<br>(0.008)</td><td>0.009<br>(0.034)</td><td>4.108<br>(2.574)</td></tr><tr><td>Mention Legis. Indic.</td><td>0.001<br>(0.016)</td><td>0.009<br>(0.018)</td><td>0.060<br>(0.117)</td><td>-4.817<br>(4.972)</td><td>0.008<br>(0.015)</td><td>0.016<br>(0.017)</td><td>0.043<br>(0.103)</td><td>-7.237<br>(4.815)</td></tr><tr><td>Post Website</td><td>0.076***<br>(0.008)</td><td>0.091***<br>(0.008)</td><td>0.173***<br>(0.037)</td><td>3.961*<br>(2.353)</td><td>0.070***<br>(0.007)</td><td>0.087***<br>(0.007)</td><td>0.076***<br>(0.028)</td><td>7.561***<br>(2.111)</td></tr><tr><td>Observations</td><td>80,941</td><td>80,941</td><td>326,617</td><td>80,941</td><td>104,455</td><td>104,455</td><td>490,600</td><td>104,455</td></tr><tr><td>Mean DepVar</td><td>0.18</td><td>0.21</td><td>0.53</td><td>221.98</td><td>0.16</td><td>0.19</td><td>0.43</td><td>226.15</td></tr><tr><td>Adjusted R2</td><td>0.22</td><td>0.23</td><td>0.12</td><td>0.19</td><td>0.20</td><td>0.21</td><td>0.14</td><td>0.20</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Columns 1 to 4: estimation of Model 1. Columns 5 to 8: estimation of Model 2. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations: Columns 1 and 5: share of questions asked by the legislator during the week classified as strict copy-paste, i.e., identical to another question submitted in a previous week. Columns 2 and 6: share of questions classified as extended copy-paste, i.e., identical to another question submitted in a previous week or during the current week. Columns 3 and 7: number of original written questions asked by the legislator during the week. Columns 4 and 8: average length of questions asked by the legislator during the week. All columns except columns 3 and 7 only include observations for which the number of written questions is non-zero. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median ($p_{50} = 11$). *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Mention Legis. Indic.*: dummy variable equal to 1 if the legislator is mentioned on written questions in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
effect from individual coverage. The magnitude of these coefficients is substantial: with a mean share of 0.18 for copy-pasted questions, the sum of the high coverage and post-website coefficients suggests an increase of 0.10 in this share under high general coverage.

Since the submission date of questions is recorded weekly, it is impossible to determine which of two identical questions submitted in the same week is the original. In the first column, we consider a question as copy-pasted only if an identical question had been submitted in a previous week. Consequently, if a new question is submitted multiple times within the same week, we treat all instances as original. This approach provides a lower bound on the share of copy-pasted questions. In Column 2, we classify identical questions submitted in the same week as copy-pasted: we estimate Model 1 under this assumption and obtain larger estimates.

Hence, the surge in written questions observed after the introduction of the website can be partly explained by an increase in copy-pasting. However, the rise in copy-pasting does not rule out a simultaneous increase in original questions. To investigate the effects of indicators on original questions, we calculate the weekly number of original written questions submitted by MPs, subtracting the copy-pasted questions from the total. We estimate Model 1 with this number as the dependent variable in Column 3 of Table 4. We then compare these findings with the disaggregated results presented in Appendix Table B.9, which includes all written questions and where we observed a positive effect of the website's launch, but no significant effects from the coverage measures. While the effect of the website's launch remains significant, the magnitude is halved when focusing on original questions, suggesting that about half of the increase in written questions is attributable to copy-pasting.

In addition to the increase in copy-pasting, there may be a corresponding decrease in the quality of the questions. Although quality is difficult to measure, length can serve as a proxy. In Column 4 of Table 4, we observe that indicators and their coverage had a positive but non-significant effect on question length, which does not provide evidence for a decrease in quality. Furthermore, we consider all legislators and estimate Model 2 in Columns 5–8 of Table 4, using the same dependent variables as in Columns 1–4. The results are very similar and confirm the previous findings. In response to the surge in the number of written questions, a cap of 52 weekly submissions per MP was introduced in June 2015. This cap likely restricted the use of copy-pasting, thereby reducing the observed effect. As a robustness check, we restrict the sample to the period prior to the implementation of this regulation in Appendix Table C.1 and test the same specifications as in Table 4. We obtain similar results, indicating that the cap had a limited effect on how legislators respond to indicators regarding copy-pasting.

It should be pointed out that copy-pasted questions do not necessarily indicate manipulation of performance indicators. While such behavior may reflect strategic inflation of activity, an increase in the number of similar questions sent to the government can also be interpreted as an effort to boost the visibility of a policy topic and to leverage media attention to raise its salience. In this context, it may be appropriate to ask the same question to multiple members of the government if heightened coverage translates into a willingness to highlight specific issues. It is difficult to determine whether the increased use of copy-pasting is purely
a strategy to inflate statistics or rather a deliberate method to spotlight important policy
topics as perceived by legislators. Nonetheless, a more cautious conclusion from this analysis
is that the statistical monitoring of written questions alters the nature of parliamentary
activity. To further explore this transformation, we assess whether the observed increase in
question volume is also associated with changes in the diversity of topics. Specifically, we
examine whether it reshapes the diversity of themes addressed and the range of ministries
questioned. To do so, we classify each question by policy topic and recipient ministry, and
use the number of distinct categories as dependent variables in our analysis. Overall, while
media coverage influences the volume and form of parliamentary activity, it does not appear
to reshape its substantive focus. In Appendix Table C.2, we find no significant effect of media
coverage on either the number of distinct themes (Columns 1 and 3) or the number of distinct
ministers to whom legislators direct written questions (Column 2). This suggests that press
coverage does not affect the scope of legislators' political agendas, at least as reflected in
written questions.

Length of Interventions

Another common criticism of statistical monitoring of legislative activity is that it may
incentivize legislators to increase the number of oral interventions—often through short,
inconsequential remarks—solely to boost their performance statistics. This concern is echoed
by Hurst (2006), who observes that British legislators “will say just about anything to notch
up their hit rate on sites such as TheyWorkForYou.com, where even the briefest intervention
is classified as a speech.”

We analyze whether monitoring indicators altered the structure of legislative debate by
examining the distribution of word counts in oral interventions. Using transcripts from
the National Assembly's website covering 2007-2020, we identify 1,428,892 interventions in
plenary sessions and 400,675 in committee meetings, calculating the number of words per
intervention. Details on data collection and methodology are provided in Appendix C.3.

To assess potential effects, we focus on MPs who made at least one intervention and
compute the share of their speeches falling within different word count ranges. We then
estimate Model 1 using this share as the dependent variable, with results shown in Appendix
Table C.3. The analysis is conducted separately for plenary sessions and committee meetings.

In plenary sessions, media coverage is associated with an increase in short interventions (fewer than 20 words) and a decline in very long ones. The overall rise in plenary interventions reported in Appendix Table B.9 thus appears partly driven by a growth in short interventions. This pattern is consistent with a reallocation of limited speaking time across MPs, as speech time in plenary sessions is strictly regulated. We provide supporting evidence for this interpretation in Subsection 5.2. By contrast, in committee meetings—where speaking time is less constrained—media coverage is linked to fewer short interventions and more medium-length ones, a shift that coincides with the increase in attendance we document in Appendix Table B.9. While we cannot establish intentional strategic behavior, our analysis suggests that press coverage shapes the nature of oral interventions, supporting concerns that visibility
metrics may influence how debates unfold.

A related concern raised by Acatrinei and Quénel (2019) is that MPs might aim for the 20-word threshold to ensure an intervention qualifies as “long.” To test this, we examine the distribution of word counts before and after the websites’ launch, focusing on the 2007–2012 legislature (Appendix Figure C.2). Separate plots for plenary and committee settings reveal no clear evidence of bunching at the 20-word mark, suggesting that this strategic behavior is limited.

Strategies aimed at inflating the number of interventions are often cited as contributing to the deterioration of legislative debates, and remain a key concern for critics of statistical indicators. While we cannot assert that such strategies are deliberately employed, our analysis indicates that press coverage affects the characteristics of interventions.

# 5 Mechanisms

We now turn to the mechanisms underlying our results. First, we focus on the possible channels through which media visibility might operate, aiming to distinguish the effects of mere accountability from potential spillover effects caused by greater visibility of some MPs. To do this, we use measures of media circulation and indicators of congruence. Next, we investigate the potential for collective adjustment at the party level, as a more organized response to increased scrutiny. Finally, we explore individual-level reactions to coverage through the lenses of electoral competition, tone of coverage, and individual reactions to mentions.

## 5.1 Visibility in the Media or Peer-effects

The findings discussed in Sections 3 and 4 suggest that the introduction of monitoring websites influences MPs’ behavior primarily through press coverage channels. Specifically, while the availability of performance indicators affects directly elected MPs who receive press coverage, the less visible and indirectly elected Senators appear minimally influenced by activity statistics. Moreover, we find that the accountability effect observed is driven primarily by collective rather than individual media coverage.

Canen et al. (2023) highlight the existence of peer effects among U.S. legislators, demonstrating that connections between lawmakers can generate significant externalities; for instance, a non-trivial portion of behavioral incentives stems from the activities and decisions undertaken by politically connected peers. In our setting, this peer-effect channel would suggest that increased activity by certain MPs triggers positive reactions and adjustments among their connected colleagues.

A second possible mechanism operates through press coverage, as described by Snyder and Strömberg (2010). Using exogenous variation arising from the mismatch between newspaper circulation areas and electoral district boundaries, measured by the congruence between newspaper circulation areas and electoral district boundaries, they find that greater local press coverage of MPs strengthens accountability mechanisms.
We argue that the second mechanism—the accountability role of the press—dominates the first, and that the increase in MP activity is primarily driven by this channel. To test this hypothesis, we conduct two empirical exercises. First, we use the degree of media competition within districts as a proxy for the potential influence of local media. Second, we construct a measure of congruence to test for heterogeneous MPs’ responses depending on the extent to which media coverage overlaps with electoral constituencies. We define a department’s congruence for an outlet as the share of the outlet’s total circulation occurring within that department, and calculate the average congruence in a department as the mean congruence across all local outlets circulating there.

In Table 5, we estimate an augmented version of Model 2. Specifically, we interact our variable High Coverage<sub>t</sub> with an indicator for the level of local press competition in the department where the MP’s electoral district is located. We define a market as a monopoly if only one local outlet circulates in the department, and as a duopoly if exactly two outlets circulate. The reference category includes departments with three or more local outlets, which we interpret as competitive markets.<sup>15</sup> Our assumption is that in monopoly or duopoly markets, the concentration of readership increases the likelihood that press coverage reaches a broad audience, due to the reduced fragmentation of journalistic production across competing outlets.

In Column 1, we present results estimated on the full sample and find that the effect of high media coverage is significantly larger in monopoly markets. This result remains robust when controlling for local press readership per capita in Column 2, addressing the concern that market concentration may be correlated with overall audience size. In Columns 3 and 4, we further split the sample into departments with low and high readership (based on whether press circulation per capita is below or above the median). The results confirm that the effect of media coverage is amplified in monopoly and duopoly local markets.

Next, we implement a second empirical test to explore the accountability mechanism more directly. Following Snyder and Strömberg (2010), we construct a congruence index at the department-year level. For each department-outlet pair (j, d), we compute congruence as the share of outlet j's total readership that is concentrated in department d. We then take the average congruence across all outlets circulating in the department. In cases where only one outlet is present, the mean equals that outlet's own congruence score. As shown in the summary statistics in Appendix Table A.5, there is substantial heterogeneity in congruence across departments: the mean is 0.43, with values ranging from 0.14 to 1. We interpret higher congruence as reflecting stronger alignment between an outlet's readership and the department's population, thereby increasing the outlet's incentive to cover local political affairs. Accordingly, we assume that MPs elected in departments with higher congruence are more likely to be subject to stronger media-driven accountability pressures.

We present the results in Columns 5 and 6 of Table 5. In Column 5, we interact the binary media coverage variable with the continuous congruence index and find that a 1% increase in congruence amplifies the effect of high coverage by 0.14, significant at the 1% level. In

<footnote n="15"><sup>15</sup>In total, including changes in the structure of the media markets (N=192), 52% are competitive (more than 3 local outlets), 37% are duopolies, and 11% are monopolies.</footnote>
Column 6, we interact media coverage with the market share of the top local outlet and find that a 1% increase in its share enhances the effect of coverage by 0.10, significant at the 5% level. In Appendix Table C.4, we use a continuous measure of media coverage, defined as the logarithm of the number of articles, and test for its effect in both low- and high-congruence departments (Columns 1 and 2). We find that a 1% increase in the number of articles has a larger effect in high-congruence departments (defined as those above the median congruence), and the result is robust to using a binary variable for high coverage (Columns 3 and 4).

Taken together, both exercises provide consistent evidence that MPs respond more strongly to media coverage in environments with lower competition and greater congruence. These findings suggest that the observed behavioral responses reflect an accountability mechanism, where press visibility alters incentives in districts that are more central or more coherently covered by the local press.

We also address the potential role of peer effects and positive externalities through the lens of politicians' connectedness. One hypothesis is that MPs from the governing majority may be more closely linked to institutions or fellow politicians, which could shape their responsiveness.<fnref n="16" /> In Column 1 of Appendix Table C.9, we show that the response to media coverage is similar for MPs from both the majority and the opposition. Combined with the absence of measurable effects from individual-level mentions, discussed in Section 3, this result suggests that peer mechanisms are unlikely to be the primary driver of the observed changes. Instead, our findings point to an accountability mechanism triggered by press visibility as the main channel.

## 5.2 Collective Reactions

Our main results suggest that collective accountability plays a key role in how indicators affect legislators' behavior. In this subsection, we further investigate the collective dynamics. These mechanisms are likely to be specific to each chamber: in particular, Senators are indirectly elected and thus face different electoral incentives and group dynamics. Therefore, this analysis focuses on MPs, allowing us to leverage complementary data that is not always available for the Senate.

### Political Groups

To test if political parties coordinate MPs' responses, we examine the behavior of political groups in the National Assembly. These groups typically align with political parties, except for small parties that lack the 15 members required to form a group. We assess how these groups react to their press coverage by creating a dummy variable that equals one if the number of articles mentioning their members during the last 12 weeks exceeds the median value. This variable captures periods of heightened scrutiny for a particular political group.

We incorporate this variable into Model 1 and present the results in Appendix Table C.5. The findings reveal that when members of a political group receive a high level of

<footnote n="16">¹⁶In Subsection 5.2, we also explore the dynamics of parliamentary groups and find that MPs' activity decreases with media coverage targeting their own group.</footnote>
<table><caption>Table 5: Effects of Press Coverage by Levels of Media Competition and Congruence</caption><thead><tr><th rowspan="2"></th><th colspan="4">Competition</th><th colspan="2">Congruence</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.065***<br>(0.008)</td><td>0.053***<br>(0.016)</td><td>0.064***<br>(0.010)</td><td>0.069***<br>(0.012)</td><td>0.023*<br>(0.013)</td><td>-0.006<br>(0.039)</td></tr><tr><td>... × Monopoly</td><td>0.097***<br>(0.020)</td><td>0.100***<br>(0.020)</td><td>0.089***<br>(0.022)</td><td>0.101<br>(0.065)</td><td></td><td></td></tr><tr><td>... × Duopoly</td><td>0.030***<br>(0.011)</td><td>0.032***<br>(0.011)</td><td>0.024*<br>(0.014)</td><td>0.037**<br>(0.017)</td><td></td><td></td></tr><tr><td>Monopoly</td><td>-0.030<br>(0.021)</td><td>-0.036*<br>(0.021)</td><td>-0.035<br>(0.023)</td><td>-0.006<br>(0.051)</td><td></td><td></td></tr><tr><td>Duopoly</td><td>-0.018<br>(0.011)</td><td>-0.019*<br>(0.011)</td><td>-0.029**<br>(0.013)</td><td>0.009<br>(0.021)</td><td></td><td></td></tr><tr><td>High Coverage MPs × Readership per capita</td><td></td><td>1.012*<br>(0.556)</td><td></td><td></td><td></td><td></td></tr><tr><td>High Coverage MPs × Congruence</td><td></td><td></td><td></td><td></td><td>0.142***<br>(0.027)</td><td></td></tr><tr><td>High Coverage MPs × Share top 1% outlet</td><td></td><td></td><td></td><td></td><td></td><td>0.102**<br>(0.042)</td></tr><tr><td>Mention MP</td><td>-0.003<br>(0.014)</td><td>-0.003<br>(0.014)</td><td>0.012<br>(0.016)</td><td>-0.032<br>(0.026)</td><td>-0.003<br>(0.014)</td><td>-0.002<br>(0.014)</td></tr><tr><td>Post Website</td><td>0.067***<br>(0.014)</td><td>0.064***<br>(0.014)</td><td>0.095***<br>(0.016)</td><td>0.022<br>(0.024)</td><td>0.065***<br>(0.014)</td><td>0.065***<br>(0.014)</td></tr><tr><td>Observations</td><td>307,119</td><td>307,119</td><td>185,198</td><td>121,921</td><td>307,119</td><td>307,119</td></tr><tr><td>Mean DepVar</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.03</td><td>0.01</td><td>0.01</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td></tr><tr><td>MP × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Readership</td><td>All</td><td>All</td><td>Low</td><td>High</td><td>All</td><td>All</td></tr></tbody></table>

**Notes:** * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Estimation of Model 1. Standard errors in parentheses are clustered at the MP × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each MP from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. Column 3 reports results for *départements* with low newspaper readership per capita; Column 4 for those with high readership per capita. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Monopoly*: dummy equal to 1 if the MP is in a *département* with a monopoly in local media coverage. *Duopoly*: dummy equal to 1 if the MP is in a *département* with a duopoly in local media coverage. *High Coverage MPs × readership per capita*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11), interacted with local press readership per capita (total local circulation by inhabitants of the *département*). *High Coverage MPs × Congruence*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11), interacted with the continuous congruence index. *High Coverage MPs × Share top 1% outlet*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11), interacted with the market share of the top 1% local media outlet. *Mention MP*: dummy variable equal to 1 if the MP is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009.

press mentions, the members of that group exhibit a relative decline in performance. One possible explanation is that when a party faces increased scrutiny, other groups intensify their efforts to emphasize that party's shortcomings. Alternatively, when members are criticized for low performance, the group may collectively push back by questioning the validity of the indicators, rather than making efforts to improve their performance.

To ensure that this result is not driven by the specific behavior of larger political groups, we aggregate performance at the group level and replicate the analysis in Appendix Table C.6. The results confirm that group-level scrutiny leads to a collective relative reduction in
activity.

## Activity Levels Among Legislators

The collective response to performance indicators at the group level may affect legislators differently depending on their performance prior to the introduction of the monitoring tool, potentially leading to a new allocation of tasks within political groups. Before the introduction of indicators, some MPs may have specialized in unmeasured activities and could face stigma following the indicators' implementation. As a result, the internal distribution of roles within political groups may be adjusted, allowing some MPs who were previously less active to improve their relative rankings. Testing the effect of ranking on MPs' response is challenging because performance —and therefore rankings—are likely correlated with MPs' sensitivity to media coverage. To address this endogeneity issue, we focus on the 2007-2012 legislature that was subject to the implementation of the indicators and use the ranking of MPs immediately before this implementation. We base these rankings on the number of weeks of activity, as retrospectively calculated by Nosdeputes.fr, which we interpret as reflecting a performance ranking in the absence of the indicators. We then divide the MPs into three equal-sized groups corresponding to low, medium, and high pre-website performance. We estimate Model 1 separately for these three subgroups and present the results in Appendix Table C.7.

Looking at the aggregate indicator shown in Column 1, we observe similar effects in all groups. For plenary session interventions —both short and long— we find a positive effect in the low-performance group and a negative effect in the medium- and high-performance groups. The low-performance group also shows a stronger response in terms of committee interventions and oral questions to the government, although the difference is less pronounced. For other indicators, the website's launch tends to have similar effects across the three groups, with coefficients of comparable sign and magnitude. Interestingly, the indicators where we observe differential responses are those for which total performance is constrained or capped: while MPs can freely increase the number of amendments or law proposals, the total number of oral interventions is limited by the fixed length of plenary sessions. One interpretation is that, following the website's launch, speaking time was reallocated from high- to low-performing MPs. Supporting this interpretation, Appendix Table C.8 shows that the effect of press coverage on intervention length during plenary sessions, discussed in Subsection 4.2, is driven by high-performing MPs. Results are presented separately for low-(Panel a), medium-(Panel b), and high-performing MPs (Panel c), based on the 2007-2012 legislature. While no significant effect is found among low performers, high-performing MPs increase their share of short interventions and reduce the share of very long ones.

## 5.3 Individual Reactions

Although we found in Section 3 that individual mentions in the media had little effect on overall activity, heterogeneous responses may still arise depending on MPs' individual characteristics. As discussed in Subsection 5.1, we find no significant differences between members of the majority and the opposition. However, when we disaggregate by political orientation in
Column 3 of Appendix Table C.9, we find that right-wing MPs respond more strongly to media coverage. One possible interpretation is that these differences reflect ideological attitudes: right-wing politicians may place greater emphasis on individual performance, while left-wing MPs might be more oriented toward collective forms of engagement. Column 4 also reveals that female MPs respond less than their male counterparts, suggesting that they are relatively less sensitive to media pressure.

Finally, we examine whether geographic distance from the National Assembly affects responsiveness. It is reasonable to assume that increasing attendance is easier for MPs elected in or near Paris than for those whose districts are farther away.17 We classify MPs as “close” or “far” from Paris based on whether travel time from their district’s main city is below or above two hours by train. As shown in Column 2, MPs located farther from Paris respond significantly less to media pressure than those based nearby.

We now investigate two additional mechanisms that could influence individuals’ reactions: first, that responses may be driven by electoral motives; and second, that the limited impact of individual mentions might stem from a backlash, whereby MPs criticize the indicators instead of improving their performance.

## Electoral Motives

The electoral implications of performance indicators and their press coverage may affect MPs' responses. Although assessing the electoral consequences is inherently challenging—for both researchers and MPs—we can hypothesize that certain mentions carry more weight, particularly when an MP is cited in a local outlet or faces electoral uncertainty. In this context, and building on the findings from Subsection 5.1, where we show that MPs' reactions were stronger in areas with limited media competition and higher media congruence, we interpret this as evidence that local press may be more effective in fostering individual accountability, as it directly reaches voters within the MP's electoral district. To complement the previous analysis, we test whether being individually cited by one's local outlet influences individual effort. To that end, we use geographical data on newspaper distribution to identify mentions in local newspapers covering the MP's district.18

To isolate the specific effect of local mentions, we extend Model 1 by interacting the individual mention variable with a dummy indicating whether the mention occurred in a local newspaper. In Appendix Table C.10, we find a positive coefficient of 0.07, significant at the 5% level. This suggests that local mentions have a stronger effect on individual responses, possibly due to electoral incentives. Columns 2 to 12 report the estimates for each type of activity separately. The results indicate that the response to local mentions is primarily driven by increased attendance in committees and plenary interventions, as well as by a rise in the writing and signing of amendments. The effects of indicators may also be more important for narrowly elected MPs, as they are more likely to affect the outcome of the next election. In Appendix Table C.11, we consider MPs elected in 2007 and 2012 and divide

<footnote>17MPs' responsibilities are national in scope and there is no formal requirement regarding time spent in their constituency. Nonetheless, many MPs regularly commute from their district to Paris.</footnote>

<footnote>18Mentions by local outlets account for 68% of all individual mentions.</footnote>
them into two groups: those elected with a win margin above the median and those below this threshold.<fnref n="19" /> We then separately estimate Model 1 on the two subsamples. We observe minimal differences between the two groups. Additionally, individual mentions may have a stronger effect closer to election time. However, we do not find support for this hypothesis in Appendix Table C.12, where we interact MentionMP<sub>it</sub> with a binary variable indicating whether the mention occurred during the year preceding the election. It should be pointed out that these results do not imply that electoral concerns play no role in MPs' reactions; rather, they suggest that their responses do not vary based on the specific electoral incentives they face. Indeed, Appendix Table C.13 reveals that the effect of general press coverage is greater at the beginning and end of legislatures, consistent with the idea that electoral motives influence the collective response to the indicators.

## Potential Backlash from Individual Mentions

A potential explanation for the limited impact of individual mentions is that they provoke opposition from the MPs mentioned, who criticize the indicators rather than improving their performance. This backlash would be less likely for positive mentions. To test this hypothesis, we incorporate the tone of coverage into our analysis and extend Model 1 by interacting the indicator mention variable with dummy variables representing positive and negative tones of each mention. As journalists typically comment on both the positive and negative aspects of each MP's performance, our tone annotation was performed at the indicator level, which precludes an analysis of the aggregate indicator. In Appendix Table C.14, we find that positive mentions, compared to neutral ones, have a greater effect on long and short plenary interventions and written questions, but not on written law proposals. In contrast, there is no clear pattern for the effect of negative mentions compared to neutral ones. These findings suggest that positive mentions may correlate with increased (or less diminished) performance for certain indicators, potentially because they do not provoke any backlash.

However, this pattern is not consistent across all indicators, and the interpretation is not supported by the analysis of MPs' comments on the press mentions of their performance indicators. In some cases, journalists contacted MPs before publication, and their comments were included alongside the mention of their indicators. Additionally, newspapers sometimes published MPs' comments in follow-up articles. MPs typically comment on their overall performance, implying that we cannot associate comments with specific indicators. We identified comments from 123 MPs, accounting for 5% of all mentions of indicators. In Appendix Table C.15, we estimate an extended version of Model 1, interacting the individual mention variable with a dummy variable equal to one if the MP provided a comment. We do not find that comments are associated with different reactions, whether for the aggregate indicator or for separate indicators.

Hence, we find mixed evidence for a potential backlash. Moreover, the results on comments confirm that the lack of effect of individual mentions is not due to MPs being unaware of

<footnote n="19">¹⁹Legislative elections have two rounds, and we focus on the win margin in the second round. The median win margin was 55.1% in 2007 and 55.5% in 2012.</footnote>
their press coverage. A comment from an MP guarantees their awareness of the article, yet we still observe no additional effect of individual mentions.

# 6 Conclusion

This paper investigates how performance indicators shape legislators' behavior. Focusing on the cases of Nosdeputes.fr and Nossenateurs.fr—two French websites launched between 2009 and 2011 to publish statistics on the activity of members of both chambers of Parliament—we examine how statistical monitoring and its visibility in the press affect legislative activity. We document a positive impact of these indicators on legislators' performance, primarily driven by collective accountability, while individual mentions do not lead to further improvements. Activity increases both during periods of greater media scrutiny and at the intensive margin in response to additional articles.

Examining the indicators one by one, we find a reallocation of effort after the introduction of the monitoring tools, mostly in favor of activities more frequently covered by the press, at the expense of those receiving less attention. While the tools alter the nature of some parliamentary behaviors—such as increased use of copy-pasted questions or a redistribution of speech time in plenary sessions—we find limited evidence of strategic manipulation of indicators. Leveraging variation in local media market structures, we show that MPs respond more strongly in districts with higher levels of media penetration or outlet congruence, suggesting that the main transmission channel operates through media-driven accountability.

As journalists increasingly rely on quantitative performance metrics, this paper contributes to broader debates on emerging regimes of political accountability. Although the analysis is based on the French case, our findings have wider implications for contexts where similar indicators are used. In particular, they highlight the importance of visibility in the effectiveness of monitoring tools, with potential relevance for other transparency initiatives such as lobbying disclosures or vote-tracking efforts.

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For Online Publication — Additional Materials

# A Additional contextual information

## A.1 French political landscape

The French political system is characterized by a semi-presidential representative democracy, where the President of the Republic holds significant power. The French Parliament consists of two chambers: the National Assembly (lower house) and the Senate (upper house). We focus on the two chambers and distinguish between legislators elected as members of the National Assembly (referred to as MPs) and legislators sitting in the Senate (referred to as Senators).

### A.1.1 Electoral system: National Assembly

Legislative elections in France are held every five years to elect the 577 members of the National Assembly. These elections occur shortly after the presidential election through a two-round runoff system. French citizens aged 18 and above are eligible to vote. In the first round, a candidate is elected if she secures more than 50% of the votes in her constituency. This happened once in the 2007 elections, 36 times in 2012, and 4 times in 2017. If no candidate wins outright in the first round, a second round is held. Candidates who receive the support of at least 12.5% of registered voters in the first round qualify. The candidate with the highest number of votes in the second round is elected.

We summarize the key information on each of the 3 legislatures covered in Table A.1.

<table><caption>Table A.1: French legislative elections (2007-2020)</caption><thead><tr><td>First and second round</td><td>Majority party</td><td>Turnout (first round, %)</td></tr></thead><tbody><tr><td>2017: June 11 and 18</td><td>REM (center, 308 seats)</td><td>48.70</td></tr><tr><td>2012: June 10 and 17</td><td>PS (left, 280 seats)</td><td>57.22</td></tr><tr><td>2007: June 10 and 17</td><td>UMP (right, 313 seats)</td><td>60.44</td></tr></tbody></table>

### A.1.2 Electoral system: Senate

Senatorial elections in France are quite different due to indirect suffrage: senators are not elected by the general population but by an "electoral college." This college consists of current senators, members of the National Assembly (députés), regional councillors elected in the department, departmental councillors (or those from similar special-status territorial entities), and delegates from municipal councils. Half of the Senate is renewed every three years, with senators serving a renewable six-year term. The voting system is complex and depends on the number of senators to be elected in each department: if one or two senators are to be chosen, a two-round majority vote is used on the same day, unless a candidate secures over 50% in the first round. If three or more senators are to be elected, candidates run on political lists, and a proportional representation system applies.
We summarize information on Senate electoral results in Table A.2. Note that voter turnout is not relevant, as voting is mandatory for members of the electoral college. Additionally, the total number of Senate seats increased from 343 to 348 in 2011.

<table><caption>Table A.2: French senatorial elections (2007-2020)</caption><thead><tr><td>Election Date</td><td>Number of Seats</td><td>Majority Party at the Election</td><td>Majority Group in the Senate</td></tr></thead><tbody><tr><td>2017: September 24</td><td>171/348</td><td>LR (right, 51 seats)</td><td>LR (right, 146 seats)</td></tr><tr><td>2014: September 28</td><td>178/348</td><td>UMP (right, 62 seats)</td><td>UMP (right, 188 seats)</td></tr><tr><td>2011: September 25</td><td>170/348</td><td>PS + allies (left, 95 seats)</td><td>PS + allies (left, 177 seats)</td></tr><tr><td>2008: September 21</td><td>144/343</td><td>PS (left, 44 seats)</td><td>UMP (right, 151 seats)</td></tr></tbody></table>

## A.2 Nosdeputes.fr and Nossenateurs.fr websites

We collect data on performance indicators available on the two websites, Nosdeputes.fr and Nossenateurs.fr. The data are extracted from SQL dumps of Nosdeputes.fr, available [here](#) (last accessed April 12, 2024), and of Nossenateurs.fr, available [here](#) (last accessed March 19, 2025). However, as we cannot extract oral questions of MPs from the SQL dumps, we collect them directly from the National Assembly website. Additionally, we do not collect further information on oral questions in the Senate. The data are aggregated at the week-individual level, and summary statistics are displayed in Table 1.

The following figures illustrate how the data are displayed on both websites. Figure A.1 presents performance indicators in a list format, allowing comparison of all legislators, with MPs shown in Figure A.1a and Senators in Figure A.1b.

Figure A.2 presents two examples of individual legislator profiles, showcasing their performance along with additional details such as biographical information, areas of expertise, and recent legislative contributions—first for an MP (Figure A.2a), followed by a Senator (Figure A.2b).

Both Figures A.1 and A.2 highlight the strong similarities between the two websites. However, despite these similarities, the audience size differs significantly, with Nosdeputes.fr receiving substantially more visits. Figure A.3 illustrates the number of monthly visits to both websites, emphasizing this considerable gap.
<figure><img src="image_6.png" /><figcaption>(a) List of indicators on Nosdeputes.fr</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(b) List of indicators on Nossenateurs.fr</figcaption></figure>

<caption>Figure A.1: Comparison of indicators from Nosdeputes.fr and Nossenateurs.fr</caption>

Source: https://2012-2017.nosdeputes.fr/synthese and
https://web.archive.org/web/20210613213507/https://www.nossenateurs.fr/synthese.
<figure><img src="image_8.png" /><figcaption>(a) Individual MP profile on Nosdeputes.fr</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b) Individual Senator profile on Nossenateurs.fr</figcaption></figure>

Figure A.2: MP and Senator individual profiles

Source: http://2012-2017.Nosdeputes.fr/francois-brottes and https://archive.nossenateurs.fr/eric-bocquet.
<figure><img src="image_10.png" /><figcaption>Figure A.3: Monthly visits on the websites Nosdeputes.fr and Nossenateurs.fr</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure A.4: Monthly number of articles mentioning Nosdeputes.fr and Nossenateurs.fr</figcaption></figure>
<table><caption>Table A.3: Descriptive statistics — Press coverage indicators grouped</caption><thead><tr><th rowspan="2">Indicators</th><th colspan="4">MPs</th><th colspan="4">Senators</th></tr><tr><th>Number of mentions</th><th>Number of articles</th><th>Share of MPs mentioned</th><th>Avg. mentions by MP</th><th>Number of mentions</th><th>Number of articles</th><th>Share of Senators mentioned</th><th>Avg. mentions by Senator</th></tr></thead><tbody><tr><td><strong>Interventions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Interv. committees</td><td>273</td><td>148</td><td>0.17</td><td>0.20</td><td>30</td><td>18</td><td>0.037</td><td>0.038</td></tr><tr><td>Interv. plenary sessions (all)</td><td>451</td><td>203</td><td>0.23</td><td>0.33</td><td>34</td><td>20</td><td>0.042</td><td>0.043</td></tr><tr><td><strong>Attendance</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Attendance committees</td><td>532</td><td>212</td><td>0.23</td><td>0.38</td><td>62</td><td>21</td><td>0.075</td><td>0.079</td></tr><tr><td><strong>Questions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written questions</td><td>254</td><td>147</td><td>0.15</td><td>0.18</td><td>28</td><td>17</td><td>0.036</td><td>0.036</td></tr><tr><td>Oral questions</td><td>175</td><td>115</td><td>0.11</td><td>0.13</td><td>23</td><td>16</td><td>0.028</td><td>0.029</td></tr><tr><td><strong>Amendments</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Amendments (all)</td><td>336</td><td>166</td><td>0.19</td><td>0.24</td><td>29</td><td>18</td><td>0.037</td><td>0.037</td></tr><tr><td><strong>Proposals</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proposals (all)</td><td>220</td><td>117</td><td>0.13</td><td>0.16</td><td>23</td><td>17</td><td>0.027</td><td>0.029</td></tr><tr><td><strong>Reports</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written reports</td><td>177</td><td>95</td><td>0.11</td><td>0.13</td><td>31</td><td>17</td><td>0.039</td><td>0.039</td></tr><tr><td>Activity (overall)</td><td>1050</td><td>349</td><td>0.44</td><td>0.76</td><td>135</td><td>48</td><td>0.13</td><td>0.17</td></tr></tbody></table>

**Notes:** Descriptive statistics on the press mentions of indicators. We identified 3,920 mentions across 885 articles between 2009 and 2020 for MPs and 434 mentions across 95 articles for Senators (see Table 1). *Number of mentions*: total number of mentions for the indicator in a row. *Number of articles*: number of distinct articles that mentions at least once a legislator for the indicator in a row. *Share of MPs (resp. Senators) mentioned*: share of MPs (resp. Senators) mentioned at least once for the indicator in a row. *Avg. mentions by MP (resp. Senator)*: average number of time an MP (resp. Senator) is mentioned for the indicator in a row. *Activity (overall)*: articles that mention MPs' general performance without referring to any specific indicator. For example, 17% of MPs are mentioned at least once on interventions in committees, with an average of 0.20 mentions on interventions in committees per MP.
### A.3 Press coverage

**Determinants of indicators' press coverage.** A key assumption of our design is that press coverage of statistical indicators is not driven by MPs' level of activity (i.e., reverse causality), nor by unobserved factors that simultaneously influence both parliamentary activity and media attention to these indicators. We provide evidence supporting the validity of this assumption. First, Figure A.5 plots press coverage of indicators and MPs' activity over time, showing that periods of high press coverage do not systematically coincide with periods of high or low legislative activity. More formally, Table A.4 reports regression estimates indicating that, conditional on time fixed-effects, past levels of activity in the National Assembly do not predict press coverage of indicators. We find no statistically significant effect of parliamentary activity over the preceding four, eight, or twelve weeks on press coverage of indicators, across alternative model specifications. In particular, past activity fails to predict the number of articles published (Columns 1-4), controlling for different time fixed effects and employing a Poisson specification. Similarly, we find no effect on the probability of at least one article being published in a given week (Columns 5-7). Notably, the final row of Columns 2 and 6 indicates that both the number of articles and the likelihood of press coverage of statistical indicators increase with the volume of articles reporting on the National Assembly. In Section 3.4, we show that our results remain robust when controlling for this general press coverage of the National Assembly.

**Tone of coverage.** With each mention of indicators, we noted whether it was reported as good, bad, or presented neutrally. We merely focused on what was reported in the article: we coded an indicator as positive if the legislator was reported to perform well in general or better than others. Summary statistics on the tone of coverage are displayed in Table A.6. For MPs (resp. Senators) 39% (resp. 36%) of mentions are identified as positive. Cases of positive reporting include the following examples: "With N written amendments, legislator X is very active", "Legislator X is the champion of long parliamentary interventions", "Legislator X ranks among the 150 best for written questions", "Legislator X is the best in the region for attendance in committees", "Legislator X performs better than legislator Y for reports." Instead, we identified an indicator as negative if the legislator was said to perform poorly or worse than others. Finally, coverage was coded neutral if the article plainly reported a statistic, if it was presented as average, or if it was presented as better than some but worse than others.

**Comments on articles.** We coded whether the MP commented on the article, either within the article if the journalist asked them to comment, or after the publication in a separate article. We found individual comments on 123 articles, representing 5% of all mentions of indicators.

**Mentions per type of legislative activities.** Some journalists report statistics for groups of indicators, either by summing them intentionally or due to a lack of precision. We reflect this approach in Table A.3, where we aggregate press mentions related to different legislative activities. Specifically, we combine mentions of amendments (proposed, signed, and adopted),
mentions of questions (both written and oral), and mentions of interventions in plenary sessions (both short and long).

**Press article example.** The following article was published in a local newspaper in northern France. We provide a translation by ChatGPT and display the coded information below.

*Bataille and Pérat, Notable Mention. La Voix du Nord, Maubeuge, Monday, November 16, 2009*

If you don't know what to do today, take a look at the website Nosdeputes.fr. You can compare the work of two MPs from the area: Jean-Luc Pérat and Christian Bataille. These two elected officials from Sambre-Avesnois are among the top 150 for their attendance in the hemicycle and in committees. Jean-Luc Pérat, who is in his first term in the Assembly, seems more comfortable writing than speaking, with 74 written questions compared to 19 from his colleague from the 22nd district. A parliamentarian since 1988, the latter excels in oral interventions with 74 short interventions and 14 long ones.

From this article, we collected the following information:

* Attendance in committees: Positive for both MPs (indicated as being in the top 150).

* Written questions: Positive for Jean-Luc Pérat, negative for Christian Bataille (one is indicated as performing better than the other).

* Short and long interventions: Positive for Christian Bataille (positive comment: "excels in oral interventions"). No information for Jean-Luc Pérat.
<figure><img src="image_12.png" /><figcaption>Figure A.5: Indicators press coverage and activity in the National Assembly. The left axis indicates the number of weeks in a month with high coverage for Nosdeputes.fr equals to 1. High coverage for Nosdeputes.fr is computed based on the number of articles over the last 3 months.</figcaption></figure>
<table><caption>Table A.4: Determinants of Indicators Coverage</caption><thead><tr><th rowspan="2"></th><th colspan="4">Number of articles</th><th colspan="3">Articles > 0</th></tr><tr><th>(1)<br>OLS</th><th>(2)<br>OLS</th><th>(3)<br>Poisson</th><th>(4)<br>OLS</th><th>(5)<br>OLS</th><th>(6)<br>OLS</th><th>(7)<br>OLS</th></tr></thead><tbody><tr><td>Activity (4 weeks)</td><td>0.338<br>(0.497)</td><td>0.371<br>(0.488)</td><td>0.294<br>(0.212)</td><td>0.072<br>(0.201)</td><td>0.036<br>(0.049)</td><td>-0.003<br>(0.063)</td><td>0.044<br>(0.048)</td></tr><tr><td>Activity (8 weeks)</td><td>-0.477<br>(0.378)</td><td>-0.467<br>(0.383)</td><td>-0.157<br>(0.312)</td><td>-0.150<br>(0.133)</td><td>-0.038<br>(0.043)</td><td>-0.038<br>(0.040)</td><td>-0.036<br>(0.041)</td></tr><tr><td>Activity (12 weeks)</td><td>0.300<br>(0.254)</td><td>0.293<br>(0.263)</td><td>-0.084<br>(0.279)</td><td>0.129<br>(0.129)</td><td>0.008<br>(0.041)</td><td>-0.020<br>(0.072)</td><td>0.007<br>(0.043)</td></tr><tr><td>Coverage Assembly</td><td colspan="3">1.601*<br>(0.795)</td><td colspan="3">0.371**<br>(0.141)</td></tr><tr><td>Observations</td><td>430</td><td>430</td><td>428</td><td>428</td><td>430</td><td>428</td><td>430</td></tr><tr><td>Adj./Pseudo R2</td><td>0.12</td><td>0.13</td><td>0.37</td><td>0.28</td><td>0.10</td><td>0.17</td><td>0.10</td></tr><tr><td>Mean Dep. Var.</td><td>1.26</td><td>1.26</td><td>1.28</td><td>1.26</td><td>0.45</td><td>0.45</td><td>0.45</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Year FE</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Month FE</td><td colspan="3">✓</td><td colspan="3">✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Estimation of: $y_t = \beta_1 \cdot Activity_{t-4} + \beta_2 \cdot Activity_{t-8} + \beta_3 \cdot Activity_{t-12} + \beta_4 \cdot \ln(CoverageAssembly_t) + WeekOfYear_t + LegislatureYear_t + Year_t + \epsilon_t$. Standard errors clustered at the year level. Observations are the weeks after the creation of Nosdeputes (September 2009). All columns are estimated using OLS, except Column 3, which is estimated using a Poisson model. Adjusted R2 is reported for all Columns except Column 3, for which Pseudo R2 is reported. Activity (4 weeks): average value of the aggregate activity indicator over the preceding four weeks. Activity (8 weeks): average value of the aggregate activity indicator over the preceding eight weeks. Activity (12 weeks): average value of the aggregate activity indicator over the preceding twelve weeks. Coverage Assembly: logarithm of the total number of articles covering the National Assembly in the week. Dependent variables: Articles: number of articles covering Nosdeputes.fr in the week (Columns 1 to 4). Articles > 0: dummy variable equals to 1 if at least one article covering Nosdeputes.fr was published in the week (Columns 5 to 7).

<table><caption>Table A.5: Press Circulation — Descriptive Statistics</caption><thead><tr><th></th><th>Mean</th><th>sd</th><th>Min</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td><strong>Number of daily outlets</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Local outlets</td><td>3</td><td>1</td><td>1</td><td>7</td><td>1,631</td></tr><tr><td>All outlets</td><td>12</td><td>1</td><td>9</td><td>17</td><td>1,631</td></tr><tr><td><strong>Circulation</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Local outlets</td><td>45,265</td><td>35,499</td><td>3</td><td>209,796</td><td>1,631</td></tr><tr><td>All outlets</td><td>65,454</td><td>60,234</td><td>1,846</td><td>628,199</td><td>1,631</td></tr><tr><td><strong>Circulation per capita</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Local outlets</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1,631</td></tr><tr><td>All outlets</td><td>0.10</td><td>0.04</td><td>0.01</td><td>0.45</td><td>1,631</td></tr><tr><td>Share top 1 local outlet</td><td>0.91</td><td>0.15</td><td>0.41</td><td>1.00</td><td>1,631</td></tr><tr><td><strong>Congruence</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean congruence of local outlets</td><td>0.43</td><td>0.19</td><td>0.14</td><td>1.00</td><td>1,631</td></tr></tbody></table>

Notes: An observation is a *département* x year between 2007 and March 2020. Categorization of daily outlets and circulation data come from the *Alliance pour les Chiffres de la Presse et des Médias*. Circulation is the mean circulation per month of outlets in a given year, circulation per capita is the total circulation divided by the number of inhabitants in the *département* in a given year. The market share of the top 1 local outlet is the ratio between the circulation of a local outlet in a department and the total circulation of all local outlets present in the *département*. Congruence is the share of an outlet's circulation out of total circulation in the *département*.
<table><caption>Table A.6: Descriptive Statistics — Tone of Coverage</caption><thead><tr><th rowspan="2">Indicators</th><th colspan="4">MPs</th><th colspan="4">Senators</th></tr><tr><th>Number of mentions</th><th>Positive (share)</th><th>Negative (share)</th><th>Neutral (share)</th><th>Number of mentions</th><th>Positive (share)</th><th>Negative (share)</th><th>Neutral (share)</th></tr></thead><tbody><tr><td>Citing indicators</td><td>3,920</td><td>0.39</td><td>0.33</td><td>0.29</td><td>434</td><td>0.36</td><td>0.27</td><td>0.36</td></tr><tr><td><strong>Interventions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Interv. committees</td><td>273</td><td>0.40</td><td>0.37</td><td>0.22</td><td>30</td><td>0.47</td><td>0.27</td><td>0.27</td></tr><tr><td>Short interv. plenary</td><td>260</td><td>0.40</td><td>0.37</td><td>0.24</td><td>4</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>Long interv. plenary</td><td>434</td><td>0.41</td><td>0.33</td><td>0.26</td><td>16</td><td>0.38</td><td>0.25</td><td>0.38</td></tr><tr><td><strong>Attendance</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Attendance committees</td><td>532</td><td>0.28</td><td>0.47</td><td>0.25</td><td>62</td><td>0.26</td><td>0.24</td><td>0.50</td></tr><tr><td><strong>Questions</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written questions</td><td>254</td><td>0.56</td><td>0.16</td><td>0.28</td><td>28</td><td>0.46</td><td>0.21</td><td>0.32</td></tr><tr><td>Oral questions</td><td>175</td><td>0.44</td><td>0.33</td><td>0.23</td><td>23</td><td>0.35</td><td>0.30</td><td>0.35</td></tr><tr><td><strong>Amendments</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Signed amendments</td><td>236</td><td>0.41</td><td>0.18</td><td>0.41</td><td>28</td><td>0.50</td><td>0.21</td><td>0.29</td></tr><tr><td>Written amendments</td><td>147</td><td>0.42</td><td>0.37</td><td>0.21</td><td>0</td><td>.</td><td>.</td><td>.</td></tr><tr><td>Adopted amendments</td><td>75</td><td>0.40</td><td>0.25</td><td>0.35</td><td>9</td><td>0.22</td><td>0.11</td><td>0.67</td></tr><tr><td><strong>Proposals</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written proposals</td><td>175</td><td>0.45</td><td>0.19</td><td>0.37</td><td>22</td><td>0.55</td><td>0.14</td><td>0.32</td></tr><tr><td>Signed proposals</td><td>132</td><td>0.36</td><td>0.21</td><td>0.42</td><td>12</td><td>0.42</td><td>0.17</td><td>0.42</td></tr><tr><td><strong>Reports</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Written reports</td><td>177</td><td>0.46</td><td>0.20</td><td>0.34</td><td>31</td><td>0.52</td><td>0.13</td><td>0.35</td></tr><tr><td>Activity (overall)</td><td>1,050</td><td>0.37</td><td>0.36</td><td>0.29</td><td>135</td><td>0.27</td><td>0.42</td><td>0.31</td></tr></tbody></table>

Notes: Descriptive statistics on the press mentions of indicators, by tone of coverage. We identified 3,920 mentions across 885 articles between 2009 and 2020 for MPs and 434 mentions across 95 articles for Senators (see Table 1). *Activity (overall)*: articles that mention MPs' general performance without referring to any specific indicator. *Number of mentions*: number of mentions for the indicator in a row. *Positive (share)*: share of positive mentions for the indicator in a row. *Negative (share)*: share of negative mentions for the indicator in a row. *Neutral (share)*: share of neutral mentions for the indicator in a row. For instance, 39% of mentions on interventions in committees for MPs are positive.
## B Robustness Checks: Tables and Figures

<table><caption>Table B.1: Continuous Measure of Coverage of Nosdeputes.fr – Model 2</caption><thead><tr><th></th><th colspan="4">Model 2</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Articles (ln) × MP</td><td>0.012<br>(0.010)</td><td></td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td>0.029**<br>(0.011)</td><td></td><td></td></tr><tr><td>Articles (asinh) × MP</td><td></td><td></td><td>0.027***<br>(0.009)</td><td></td></tr><tr><td>Share articles ND/Assembly × MP</td><td></td><td></td><td></td><td>0.052***<br>(0.011)</td></tr><tr><td>Mention Legislator</td><td>0.005<br>(0.014)</td><td>0.013<br>(0.015)</td><td>0.014<br>(0.015)</td><td>0.012<br>(0.015)</td></tr><tr><td>High Coverage Senators</td><td></td><td>0.020<br>(0.014)</td><td>0.023*<br>(0.014)</td><td>0.022**<br>(0.009)</td></tr><tr><td>Post Website</td><td></td><td>-0.012<br>(0.013)</td><td>-0.014<br>(0.013)</td><td>0.042***<br>(0.012)</td></tr><tr><td>Observations</td><td>329,869</td><td>490,600</td><td>490,600</td><td>485,166</td></tr><tr><td>Mean DepVar</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.03</td></tr><tr><td>Adjusted R2</td><td>0.21</td><td>0.19</td><td>0.19</td><td>0.19</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Notes:** * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Estimation of Model 2 with continuous coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *Articles (ln) × MP*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, interacted with a dummy equal to 1 for MPs. *Articles (ln+1) × MP*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one, interacted with a dummy equal to 1 for MPs. *Share articles ND/Assembly × MP*: number of articles covering Nosdeputes.fr divided by the total number of articles covering the National Assembly, in the previous 12 weeks, multiplied by 100, interacted with a dummy equal to 1 for MPs. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *High Coverage Senators*: dummy variable equal to 1 if at least one article covering Nossenateurs.fr in the previous 12 weeks was published. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
<table><caption>Table B.2: Control for Press Coverage of the National Assembly</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.087***<br>(0.005)</td><td>0.096***<br>(0.009)</td><td></td><td></td><td>0.065***<br>(0.010)</td><td>0.032***<br>(0.009)</td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td></td><td>0.041***<br>(0.004)</td><td>0.060***<br>(0.005)</td><td></td><td></td><td>0.022***<br>(0.007)</td><td>0.034***<br>(0.007)</td></tr><tr><td>High Coverage Assembly</td><td>-0.047***<br>(0.006)</td><td>-0.029***<br>(0.009)</td><td>-0.007<br>(0.005)</td><td>0.034***<br>(0.008)</td><td>-0.053***<br>(0.006)</td><td>-0.060***<br>(0.007)</td><td>-0.065***<br>(0.006)</td><td>-0.062***<br>(0.007)</td></tr><tr><td>Mention Legislator</td><td>-0.001<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>-0.002<br>(0.013)</td><td>-0.002<br>(0.013)</td><td>0.009<br>(0.014)</td><td>0.020<br>(0.015)</td><td>0.010<br>(0.015)</td><td>0.020<br>(0.015)</td></tr><tr><td>Post Website</td><td>0.064***<br>(0.013)</td><td></td><td>0.139***<br>(0.017)</td><td></td><td>0.016<br>(0.012)</td><td>-0.002<br>(0.018)</td><td>-0.010<br>(0.013)</td><td>0.004<br>(0.019)</td></tr><tr><td>Observations</td><td>322,580</td><td>322,580</td><td>322,580</td><td>322,580</td><td>487,140</td><td>487,140</td><td>487,140</td><td>487,140</td></tr><tr><td>MeanDepVar</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.03</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.21</td><td>0.19</td><td>0.19</td><td>0.19</td><td>0.19</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Columns 1 to 4: estimation of Model 1 with binary and continuous measures of coverage. Columns 5 to 8: estimation of Model 2 with binary and continuous measures of coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median ($p_{50} = 11$). *Articles (ln+1) × MP*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one, interacted with a dummy equal to 1 for MPs. *High Coverage Assembly*: dummy variable equal to 1 if the number of articles about the National Assembly in the previous 12 weeks is higher than the median ($p_{50} = 14770$). *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
<table><caption>Table B.3: Control for Visits on Nosdeputes.fr</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.126***<br>(0.006)</td><td>0.125***<br>(0.010)</td><td></td><td></td><td>0.058***<br>(0.011)</td><td>0.058***<br>(0.011)</td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td></td><td>0.046***<br>(0.004)</td><td>0.064***<br>(0.006)</td><td></td><td></td><td>0.021***<br>(0.008)</td><td>0.049***<br>(0.007)</td></tr><tr><td>Visits on Nosdeputes.fr</td><td>-0.082***<br>(0.005)</td><td>0.013<br>(0.020)</td><td>-0.038***<br>(0.005)</td><td>-0.044**<br>(0.019)</td><td>-0.107***<br>(0.005)</td><td>0.123***<br>(0.016)</td><td>-0.087***<br>(0.005)</td><td>0.135***<br>(0.016)</td></tr><tr><td>Mention Legislator</td><td>0.012<br>(0.013)</td><td>0.003<br>(0.013)</td><td>0.005<br>(0.013)</td><td>0.001<br>(0.012)</td><td>0.023<br>(0.014)</td><td>0.026*<br>(0.015)</td><td>0.027*<br>(0.015)</td><td>0.024*<br>(0.015)</td></tr><tr><td>Observations</td><td>247,630</td><td>247,630</td><td>247,630</td><td>247,630</td><td>369,352</td><td>369,352</td><td>369,352</td><td>369,352</td></tr><tr><td>MeanDepVar</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.04</td><td>0.04</td><td>0.04</td><td>0.04</td></tr><tr><td>Adjusted R2</td><td>0.21</td><td>0.21</td><td>0.22</td><td>0.22</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Columns 1 to 4: estimation of Model 1 with binary and continuous measures of coverage. Columns 5 to 8: estimation of Model 2 with binary and continuous measures of coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from September 2009 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median ($p_{50} = 11$). *Articles (ln+1) × MP*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one, interacted with a dummy equal to 1 for MPs. *Visits on Nosdeputes.fr*: logarithm of the number of visits on Nosdeputes website in the previous 12 weeks. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks.
<table><caption>Table B.4: Excluding Weeks before Nossenateurs Launch</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.079***<br>(0.005)</td><td>0.095***<br>(0.009)</td><td></td><td></td><td>0.086***<br>(0.011)</td><td>0.069***<br>(0.010)</td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td></td><td>0.021***<br>(0.003)</td><td>0.110***<br>(0.006)</td><td></td><td></td><td>0.004<br>(0.008)</td><td>0.075***<br>(0.007)</td></tr><tr><td>Mention Legislator</td><td>-0.005<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>0.005<br>(0.013)</td><td>-0.007<br>(0.013)</td><td>0.024<br>(0.016)</td><td>0.039**<br>(0.016)</td><td>0.023<br>(0.016)</td><td>0.034**<br>(0.016)</td></tr><tr><td>Observations</td><td>271,282</td><td>271,282</td><td>271,282</td><td>271,282</td><td>330,821</td><td>330,821</td><td>330,821</td><td>330,821</td></tr><tr><td>Mean DepVar</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td></tr><tr><td>Adjusted R2</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.20</td><td>0.21</td><td>0.20</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Columns 1 to 4: estimation of Model 1 with binary and continuous measures of coverage. Columns 5 to 8: estimation of Model 2 with binary and continuous measures of coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from September 2011 until 2020 – i.e. after the creation of both Nosdeputes.fr and Nossenateurs.fr – giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Articles (ln+1)*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks.
<table><caption>Table B.5: Focusing only on Legislators in Office before Nosdeputes.fr</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.120***<br>(0.008)</td><td>0.125***<br>(0.013)</td><td></td><td></td><td>0.068***<br>(0.013)</td><td>0.023*<br>(0.013)</td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td></td><td>0.048***<br>(0.004)</td><td>0.111***<br>(0.007)</td><td></td><td></td><td>0.033***<br>(0.009)</td><td>0.018*<br>(0.010)</td></tr><tr><td>Mention Legislator</td><td>-0.013<br>(0.018)</td><td>-0.011<br>(0.019)</td><td>-0.013<br>(0.018)</td><td>-0.017<br>(0.019)</td><td>0.000<br>(0.020)</td><td>0.012<br>(0.021)</td><td>0.003<br>(0.020)</td><td>0.014<br>(0.021)</td></tr><tr><td>Observations</td><td>208,709</td><td>208,709</td><td>208,709</td><td>208,709</td><td>316,498</td><td>316,498</td><td>316,498</td><td>316,498</td></tr><tr><td>Mean DepVar</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.03</td><td>0.04</td><td>0.04</td><td>0.04</td><td>0.04</td></tr><tr><td>Adjusted R2</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.20</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Month FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Columns 1 to 4: estimation of Model 1 with binary and continuous measures of coverage. Columns 5 to 8: estimation of Model 2 with binary and continuous measures of coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for legislators in office before the implementation of Nosdeputes.fr, from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. We only keep legislators who were already in office before the creation of Nosdeputes.fr (September 2009). *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Articles (ln+1)*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
<table><caption>Table B.6: Alternative Time Frame for High Coverage MP (8 and 16 Weeks)</caption><thead><tr><th rowspan="2"></th><th colspan="2">Model 1</th><th colspan="2">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High Coverage MPs (8 weeks)</td><td>-0.007<br>(0.005)</td><td></td><td>0.022**<br>(0.009)</td><td></td></tr><tr><td>High Coverage MPs (16 weeks)</td><td></td><td>0.078***<br>(0.006)</td><td></td><td>0.051***<br>(0.010)</td></tr><tr><td>Mention Legislator</td><td>0.025*<br>(0.013)</td><td>0.003<br>(0.013)</td><td>0.039***<br>(0.014)</td><td>0.016<br>(0.014)</td></tr><tr><td>Post Website</td><td>0.119***<br>(0.013)</td><td>0.061***<br>(0.013)</td><td>0.074***<br>(0.012)</td><td>0.012<br>(0.012)</td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>490,600</td><td>490,600</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.20</td><td>0.19</td><td>0.19</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Legislature Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Columns 1 and 2: estimation of Model 1. Columns 3 and 4: estimation of Model 2. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs (8 weeks)*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 8 weeks is higher than the median. *High Coverage MPs (16 weeks)*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 16 weeks is higher than the median. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
<table><caption>Table B.7: Lagged Coverage in the Past 3 months by Brackets — Model 1</caption><thead><tr><th rowspan="2"></th><th colspan="2">Binary Treatment</th><th colspan="2">Continuous Treatment</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High Coverage MPs (1-4 weeks)</td><td>-0.024***<br>(0.005)</td><td>0.071***<br>(0.006)</td><td></td><td></td></tr><tr><td>High Coverage MPs (5-8 weeks)</td><td>-0.054***<br>(0.006)</td><td>0.007<br>(0.007)</td><td></td><td></td></tr><tr><td>High Coverage MPs (9-12 weeks)</td><td>0.092***<br>(0.007)</td><td>0.099***<br>(0.010)</td><td></td><td></td></tr><tr><td>High Coverage MPs (13-16 weeks)</td><td>0.042***<br>(0.007)</td><td>-0.152***<br>(0.010)</td><td></td><td></td></tr><tr><td>Articles (ln+1) (1-4 weeks)</td><td></td><td></td><td>-0.012***<br>(0.003)</td><td>0.014***<br>(0.004)</td></tr><tr><td>Articles (ln+1) (5-8 weeks)</td><td></td><td></td><td>0.031***<br>(0.003)</td><td>0.013***<br>(0.005)</td></tr><tr><td>Articles (ln+1) (9-12 weeks)</td><td></td><td></td><td>0.027***<br>(0.003)</td><td>0.020***<br>(0.004)</td></tr><tr><td>Articles (ln+1) (13-16 weeks)</td><td></td><td></td><td>0.014***<br>(0.002)</td><td>-0.046***<br>(0.005)</td></tr><tr><td>Mention MP</td><td>-0.004<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>0.000<br>(0.013)</td><td>-0.003<br>(0.013)</td></tr><tr><td>Post Website</td><td>0.061***<br>(0.013)</td><td></td><td>0.044***<br>(0.014)</td><td></td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td></tr><tr><td>Mean DepVar</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.20</td><td>0.20</td><td>0.20</td></tr><tr><td>MP × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Estimation of Model 1. Standard errors in parentheses are clustered at the MP × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each MP from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs (1-4 weeks)*: dummy variable equal to 1 if the number of articles covering *Nosdeputes.fr* in the previous 1 to 4 weeks is above the median. Equivalent dummy variables are constructed for the 5-8, 9-12, and 13-16 weeks periods. *Articles (ln+1) (1-4 weeks)*: logarithm of the number of articles covering *Nosdeputes.fr* in the previous 1 to 4 weeks, plus one. Equivalent variables are constructed for the 5-8, 9-12, and 13-16 weeks periods. *Mention MP*: dummy variable equal to 1 if the MP is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of *Nosdeputes.fr* in September 2009.
<table><caption>Table B.8: Alternative Sets of Fixed Effects</caption><thead><tr><th></th><th colspan="2">Model 1</th><th colspan="5">Model 2</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.087***<br>(0.006)</td><td>0.095***<br>(0.009)</td><td>0.085***<br>(0.011)</td><td>0.044***<br>(0.011)</td><td>0.031***<br>(0.009)</td><td>0.089***<br>(0.010)</td><td>0.034***<br>(0.010)</td></tr><tr><td>Mention Legislator</td><td>0.020<br>(0.013)</td><td>0.024*<br>(0.013)</td><td>0.032**<br>(0.015)</td><td>0.043***<br>(0.014)</td><td>0.017<br>(0.015)</td><td>0.006<br>(0.014)</td><td>0.017<br>(0.014)</td></tr><tr><td>Post Website</td><td>0.031*<br>(0.018)</td><td></td><td>-0.009<br>(0.015)</td><td>0.009<br>(0.021)</td><td>0.011<br>(0.018)</td><td>0.001<br>(0.014)</td><td>0.011<br>(0.018)</td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>490,601</td><td>490,601</td><td>490,600</td><td>490,601</td><td>490,601</td></tr><tr><td>Mean DepVar</td><td>0.00</td><td>0.00</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td></tr><tr><td>Adjusted R2</td><td>0.18</td><td>0.19</td><td>0.17</td><td>0.17</td><td>0.23</td><td>0.19</td><td>0.19</td></tr><tr><td>Legislator FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Legislator × Legislature FE</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Legislator × Term FE</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Week FE</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr></tbody></table>

Notes: * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$. Columns 1 and 2: estimation of Model 1. Columns 3 to 7: estimation of Model 2. Columns 1 and 3: Legislator fixed effects, Week of the Year fixed effects (from 1 to 52), Legislature Year fixed effects (from 1 to 5); Columns 2 and 4: Legislator fixed effects, Month fixed effects (from 1 to 146): Column 5: Legislator × Legislature fixed effects, Week fixed effects (from 1 to 568); Column 6: Legislator × Term fixed effects, Week of the Year fixed effects, Legislature Year fixed effects; Column 7: Legislator × Term fixed effects, Month fixed effects. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for legislators in office before the implementation of Nosdeputes.fr, from 2007 until 2020, giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median ($p_{50} = 11$). *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.
<table><caption>Table B.9: Effects on Disaggregated Performance Indicators — Model 1</caption><thead><tr><th rowspan="3"></th><th colspan="3">Plenary Sessions</th><th colspan="2">Committees</th><th colspan="2">Questions</th><th colspan="2">Amendments</th><th colspan="3">Reports</th><th colspan="3">Proposals</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th></th><th></th><th></th></tr><tr><th>Agg. Indic.</th><th>Long interventions</th><th>Short interventions</th><th>Attendance</th><th>Interventions</th><th>Oral</th><th>Written</th><th>Written</th><th>Signed</th><th>Written</th><th>Written</th><th>Signed</th><th></th><th></th><th></th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.085***<br>(0.005)</td><td>0.440***<br>(0.047)</td><td>0.734***<br>(0.139)</td><td>0.084***<br>(0.004)</td><td>0.225***<br>(0.040)</td><td>0.002**<br>(0.001)</td><td>0.005<br>(0.024)</td><td>0.196***<br>(0.063)</td><td>1.798***<br>(0.123)</td><td>0.003***<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.009**<br>(0.004)</td><td></td><td></td><td></td></tr><tr><td>Mention MP</td><td>-0.002<br>(0.013)</td><td>-0.107<br>(0.137)</td><td>-0.147<br>(0.419)</td><td>0.021<br>(0.013)</td><td>-0.203**<br>(0.081)</td><td>0.003<br>(0.002)</td><td>0.006<br>(0.050)</td><td>-0.396***<br>(0.132)</td><td>-0.781*<br>(0.437)</td><td>-0.000<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>0.027**<br>(0.013)</td><td></td><td></td><td></td></tr><tr><td>Mention MP Indic.</td><td></td><td>-0.295<br>(0.239)</td><td>0.052<br>(0.724)</td><td>-0.011<br>(0.019)</td><td>-0.316<br>(0.262)</td><td>-0.005<br>(0.005)</td><td>0.446*<br>(0.249)</td><td>0.097<br>(0.369)</td><td>-1.264<br>(0.853)</td><td>0.003<br>(0.006)</td><td>-0.004<br>(0.006)</td><td>0.118***<br>(0.035)</td><td></td><td></td><td></td></tr><tr><td>Post Website</td><td>0.057***<br>(0.013)</td><td>-0.221*<br>(0.120)</td><td>-0.408<br>(0.375)</td><td>0.316***<br>(0.010)</td><td>0.196***<br>(0.069)</td><td>0.002*<br>(0.001)</td><td>0.422***<br>(0.061)</td><td>-1.140***<br>(0.143)</td><td>-4.960***<br>(0.278)</td><td>-0.001<br>(0.001)</td><td>0.003<br>(0.002)</td><td>-0.093***<br>(0.013)</td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td></tr><tr><td>Mean DepVar</td><td>0.00</td><td>1.73</td><td>2.74</td><td>0.72</td><td>1.26</td><td>0.05</td><td>0.73</td><td>1.23</td><td>11.61</td><td>0.02</td><td>0.02</td><td>0.47</td><td></td><td></td><td></td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.11</td><td>0.23</td><td>0.24</td><td>0.07</td><td>0.03</td><td>0.08</td><td>0.04</td><td>0.27</td><td>0.06</td><td>0.04</td><td>0.19</td><td></td><td></td><td></td></tr><tr><td>MP × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Year of Legis. FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Estimation of Model 1. Standard errors in parentheses are clustered at the MP × Legislature level. Observations are weekly performance indicators for each MP from 2007 until 2020. *High Coverage MPs:* dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Mention MP:* dummy variable equal to 1 if the MP is mentioned in one or more articles in the previous 12 weeks. *Mention MP Indic.:* dummy variable equal to 1 if the MP is mentioned on the corresponding indicator in one or more articles in the previous 12 weeks. *Post Website:* dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009. Dependent variables: *Agg. Indic.:* Aggregate weekly performance of MPs, as defined in Equation (*), giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *Short (long) interventions:* number of short (long) interventions in plenary sessions made by the MP during the week. *Committee attendance:* number of committee sessions attended by the MP during the week. *Committee interventions:* number of interventions in committee sessions by the MP during the week. *Oral (written) questions:* number of oral (written) questions asked by the MP to the government during the week. *Written amendments:* number of amendments authored by the MP during the week. *Signed amendments:* number of amendments co-signed by the MP during the week. *Written reports:* number of written reports authored by the MP during the week. *Written proposals:* number of law proposals authored by the MP during the week. *Signed proposals:* number of law proposals co-signed by the MP during the week.
<table><caption>Table B.10: Effects on Disaggregated Performance Indicators — Model 2</caption><thead><tr><th rowspan="3"></th><th colspan="3">Plenary Sessions</th><th colspan="2">Committees</th><th colspan="2">Questions</th><th colspan="2">Amendments</th><th colspan="2">Reports</th><th colspan="2">Proposals</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th></tr><tr><th>Agg. Indic.</th><th>Long interventions</th><th>Short interventions</th><th>Attendance</th><th>Interventions</th><th>Oral</th><th>Written</th><th>Written</th><th>Signed</th><th>Written</th><th>Written</th><th>Signed</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.063<sup>***</sup><br>(0.010)</td><td>0.092<br>(0.111)</td><td>0.161<br>(0.268)</td><td>0.092<sup>***</sup><br>(0.007)</td><td>0.034<br>(0.082)</td><td>0.001<br>(0.001)</td><td>0.038<br>(0.027)</td><td>0.014<br>(0.081)</td><td>1.534<sup>***</sup><br>(0.173)</td><td>0.001<br>(0.002)</td><td>0.001<br>(0.001)</td><td>0.022<sup>***</sup><br>(0.006)</td></tr><tr><td>Mention Legislator</td><td>0.008<br>(0.014)</td><td>-0.097<br>(0.129)</td><td>-0.108<br>(0.351)</td><td>0.011<br>(0.012)</td><td>-0.081<br>(0.087)</td><td>0.002<br>(0.002)</td><td>0.005<br>(0.046)</td><td>-0.274<sup>**</sup><br>(0.119)</td><td>-0.472<br>(0.381)</td><td>0.003<br>(0.002)</td><td>-0.000<br>(0.002)</td><td>0.025<sup>**</sup><br>(0.012)</td></tr><tr><td>Mention Legis. Indic.</td><td></td><td>-0.049<br>(0.304)</td><td>0.470<br>(0.773)</td><td>-0.013<br>(0.019)</td><td>-0.186<br>(0.416)</td><td>-0.003<br>(0.005)</td><td>0.398<sup>*</sup><br>(0.224)</td><td>0.009<br>(0.354)</td><td>-1.123<br>(0.807)</td><td>-0.008<br>(0.006)</td><td>-0.003<br>(0.006)</td><td>0.111<sup>***</sup><br>(0.032)</td></tr><tr><td>Post Website</td><td>0.009<br>(0.012)</td><td>-0.528<sup>***</sup><br>(0.135)</td><td>-0.560<br>(0.351)</td><td>0.201<sup>***</sup><br>(0.011)</td><td>0.474<sup>***</sup><br>(0.108)</td><td>-0.004<sup>***</sup><br>(0.001)</td><td>0.246<sup>***</sup><br>(0.043)</td><td>-0.927<sup>***</sup><br>(0.107)</td><td>-4.491<sup>***</sup><br>(0.223)</td><td>-0.001<br>(0.002)</td><td>0.003<sup>**</sup><br>(0.002)</td><td>-0.079<sup>***</sup><br>(0.010)</td></tr><tr><td>Observations</td><td colspan="3">490,600</td><td colspan="3">490,600</td><td colspan="3">490,600</td><td colspan="3">490,600</td></tr><tr><td>Mean DepVar</td><td colspan="3">0.02</td><td colspan="3">0.76</td><td colspan="3">1.43</td><td colspan="3">0.03</td></tr><tr><td>Adjusted R2</td><td colspan="3">0.19</td><td colspan="3">0.23</td><td colspan="3">0.09</td><td colspan="3">0.04</td></tr><tr><td>Legislator × Legislature FE</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td></tr><tr><td>Week of Year FE</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td></tr><tr><td>Legislature Year FE</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td><td colspan="3">✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Estimation of Model 2. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are weekly performance indicators for each legislator from 2007 until 2020. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Mention Legis. Indic.*: dummy variable equal to 1 if the legislator is mentioned on the corresponding indicator in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr for MPs (September 2009) and Nossenateurs.fr for Senators (September 2011). Dependent variables: *Agg. Indic.*: Aggregate weekly performance of legislators, as defined in (*), giving equal weight to all indicators and standardized to have a mean of zero and a standard deviation of one. *Short (resp. long) interventions*: number of short (resp. long) interventions in plenary sessions made by the legislator during the week. *Committee attendance*: number of committee sessions attended by the legislator during the week. *Committee interventions*: number of interventions in committee sessions by the legislator during the week. *Oral (resp. written) questions*: number of oral (resp. written) questions asked by the legislator to the government during the week. *Written amendments*: number of amendments authored by the legislator during the week. *Signed amendments*: number of amendments co-signed by the legislator during the week. *Written reports*: number of written reports authored by the legislator during the week. *Written proposals*: number of law proposals authored by the legislator during the week. *Signed proposals*: number of law proposals co-signed by the legislator during the week.
<table><caption>Table B.11: Alternative Aggregate Indicator</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>High Coverage MPs</td><td>0.088***<br>(0.005)</td><td>0.077***<br>(0.009)</td><td></td><td></td><td>0.069***<br>(0.011)</td><td>0.042***<br>(0.011)</td><td></td><td></td></tr><tr><td>Articles (ln+1) × MP</td><td></td><td></td><td>0.038***<br>(0.003)</td><td>0.060***<br>(0.005)</td><td></td><td></td><td>0.035***<br>(0.008)</td><td>0.021***<br>(0.008)</td></tr><tr><td>Mention Legislator</td><td>-0.002<br>(0.013)</td><td>0.000<br>(0.013)</td><td>-0.001<br>(0.013)</td><td>-0.003<br>(0.013)</td><td>-0.001<br>(0.014)</td><td>0.013<br>(0.014)</td><td>-0.007<br>(0.014)</td><td>0.011<br>(0.014)</td></tr><tr><td>Post Website</td><td>0.170***<br>(0.016)</td><td></td><td>0.135***<br>(0.017)</td><td></td><td>0.089***<br>(0.016)</td><td>0.116***<br>(0.022)</td><td>0.036**<br>(0.016)</td><td>0.088***<br>(0.023)</td></tr><tr><td>Observations</td><td>326,617</td><td>326,617</td><td>326,617</td><td>326,617</td><td>490,600</td><td>490,600</td><td>490,600</td><td>490,600</td></tr><tr><td>Mean DepVar</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td></tr><tr><td>Adjusted R2</td><td>0.20</td><td>0.21</td><td>0.20</td><td>0.21</td><td>0.20</td><td>0.21</td><td>0.20</td><td>0.21</td></tr><tr><td>Legislator × Legislature FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Week of Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Legislature Year FE</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Month FE</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

Notes: * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Columns 1 to 4: estimation of Model 1 with binary and continuous measures of coverage. Columns 5 to 8: estimation of Model 2 with binary and continuous measures of coverage. Standard errors in parentheses are clustered at the Legislator × Legislature level. Observations are the weekly aggregate indicator defined in Equation (*) for each legislator from 2007 until 2020, computed using the three components of the “Week of Activity” index developed by Nosdeputes.fr (short and long interventions in plenary sessions and attendance in committees), and standardized to have a mean of zero and a standard deviation of one. *High Coverage MPs*: dummy variable equal to 1 if the number of articles covering Nosdeputes.fr in the previous 12 weeks is higher than the median (*p*50 = 11). *Articles (ln+1) × MP*: logarithm of the number of articles covering Nosdeputes.fr in the previous 12 weeks, plus one, interacted with a dummy equal to 1 for MPs. *Mention Legislator*: dummy variable equal to 1 if the legislator is mentioned in one or more articles in the previous 12 weeks. *Post Website*: dummy variable equal to 1 after the creation of Nosdeputes.fr in September 2009 in Model 1; after September 2009 for MPs and September 2011 (creation of Nossenateurs.fr) for Senators in Model 2.