Do legislators’ words match their deeds?

↑ constituent facing

↑ behind-the-scenes work

Is a legislators’ constituent-facing communication about a federal agency correlated with their behind-the-scenes work with federal agencies?

Promise-Keeping and Democratic Accountability

Theory on Promise-Keeping

Empirical work on Promise-Keeping

Our Contribution

Link theories of promise-keeping to data on promise fulfillment (requests to federal agencies)

  • Contact with federal agencies is not easily observed by those providing accountability (voters, journalists, etc.)
  • New Data: congressional contact w/ federal agencies
  • New Measure: mentions of agencies in constituent-facing newsletters

Theory

  • H1 - Promise Keeping: Attention Consistency Hypothesis:

    • Legislator’s Newsletter Attention to Agency Correlates w/ Behind the Scenes Work with Agency

  • H2 - Conditional on Ideological Alignment

    • (If newsletters are disproportionately credit-claiming for agency work their constituents support)

  • H3 - Conditional on Ideological Disagreement

    • (If newsletters are disproportionately oversight and hostile position taking)

Data

Dependent Variable: Legislator Requests to Federal Agencies

Primary explanatory Variable: Attention to Federal Agencies in Legislator Newsletters

  • 150,581 newsletters (Cormack 2025)

  • We count # legislator newsletters that mention each agency

Additional Explanatory Variables

Examples: Agency Mentions in Newsletters

Informational

“The U.S. Department of Veterans Affairs VA announced today telehealth video appointments using VA Video Connect increased from approximately 10,000 to 120,000 appointments a week between February and May of 2020. This is attributed to VA providers and Veterans taking precautions against COVID-19.” - Rep. Tulsi Gabbard (D-HI)

Examples: Agency Mentions in Newsletters

Position Taking

“On the Senate Veterans Affairs Committee, I will work to expand the Veterans Choice Program, reduce wait times at VA clinics, and ensure the mental health needs of our veterans are being met” - Sen. Marsha Blackburn (R-TN)

Examples: Agency Mentions in Newsletters

Oversight

“[I] signed a bipartisan letter to ask the US Department of Veterans Affairs VA why it denied New Jersey’s request for additional medical personnel needed to combat coronavirus outbreaks in the state long-term care facilities” - Rep. Mikie Sherrill (D-NJ)

Figure 1: Average Agency Mentions in Newsletters by Percentile

Figure 2: Most Mentioned Agencies in Newsletters

Figure 3: Total Mentions of Selected Federal Agencies Over Time
(a) Department of Veterans Affairs
(b) Department of Health and Human Services

Analysis

\[\begin{aligned} \log (Y_{ijt} + 1) = & \boldsymbol{\beta}_{1} \textbf{log(Newsletter Mentions + 1)}_{ijt} + \\ & \beta_{2:n} X_{it} + \sum_{s = 1}^{6} \eta_{s} \text{I}\left(\text{tenure}_{it} = s\right) + \gamma_{ij} + \delta_{jt} + m_{it} + p_{it} + \epsilon_{ijt} \end{aligned} \qquad(1)\]

Log-log models testing whether newsletter mentions predict requests \(Y_{ijt}\)

  • With and without controls (committee position, tenure, majority, etc., \(X_{it}\))
  • Cross-sectional and within-legislator difference-in-differences (with and without legislator-agency fixed effect, \(\gamma_{ij}\))

Public Attention to Agencies Correlates with Action

Table 1: Newsletter Attention and Requests to Federal Agencies
(1) (2)
† p < 0.1, * p < 0.05, ** p < 0.01
Log(newsletters) 0.066** 0.048**
(0.013) (0.008)
President's party -0.009 -0.013
(0.021) (0.013)
Observations 36,032 36,032
Year x agency fixed effects X X
Legislator x agency fixed effects X
Dependent variable Log(Count+1) Log(Count+1)

Does the relationship depend on ideological alignment?

Does Ideological Alignment or Disagreement Explain the Relationship between Public Communication and Private Action

\[\begin{aligned} \log (Y_{ijt} + 1) = & \boldsymbol{\beta}_{1} \textbf{log(Newsletter Mentions + 1)}_{ijt} + \\ & \boldsymbol{\beta}_{2} \textbf{Ideological Aligment}_{ijt} + \\ & \boldsymbol{\beta}_{3} \textbf{log(Newsletter Mentions + 1)}_{ijt} \times \textbf{Ideological Aligment}_{ijt} + \\ & \beta_{4:n} X_{it} + \sum_{s = 1}^{6} \eta_{s} \text{I}\left(\text{tenure}_{it} = s\right) + \delta_{jt} + m_{it} + p_{it} + \epsilon_{ijt} \end{aligned} \qquad(2)\]

  • Only cross-sectional (ideological alignment does not vary over time)

Figure 4: Predicted Requests to Agencies by Newsletter Mentions and Ideological Alignment
(a) Model 1
(b) Model 2

Takeaways

The more a legislator talks about an agency, the more requests they make to that agency

  • Unconditional on ideological alignment/misalignment

Big Picture: Evidence supports promise-keeping

Thank you! jemunson@wisc.edu

Extra

Figure 5: Chamber Average Mentions of Selected Federal Agencies Over Time
(a) VA House and Senate
(b) DHHS House and Senate

Table 2: Newsletter Attention and Requests to Federal Agencies
(1) (2) (3) (4)
† p < 0.1, * p < 0.05, ** p < 0.01
Robust standard errors in parentheses, clustered by legislator.
This table shows how the number of requests changes as legislators acquire more experience and power. Column 1 shows the average differences across legislators. Column 2 presents the difference-in-differences estimates. Column 3 adds covariates. Column 4 presents the difference-in-differences estimates with covariates. All coefficients represent the average effect on the log of requests per year per agency per legislator plus one.
Dependent variable Log(Count+1) Log(Count+1) Log(Count+1) Log(Count+1)
Log(newsletters) 0.066** 0.048** 0.070** 0.044**
(0.013) (0.008) (0.012) (0.008)
Committee chair 0.326** 0.088†
(0.056) (0.046)
Ranking member 0.312** 0.091*
(0.073) (0.038)
Prestige committee 0.094** -0.016
(0.026) (0.026)
First year -0.060* -0.142**
(0.029) (0.039)
Second year -0.016 -0.083*
(0.029) (0.035)
Third year 0.038 -0.005
(0.028) (0.031)
Fourth year 0.017 -0.005
(0.027) (0.029)
Fifth year 0.030 -0.012
(0.028) (0.024)
Sixth year -0.011 -0.021
(0.029) (0.025)
Majority 0.004 0.022
(0.019) (0.014)
President's party -0.009 -0.013 -0.011 -0.021
(0.021) (0.013) (0.021) (0.014)
Observations 36,032 36,032 35,997 35,997
Year x agency fixed effects X X X X
Legislator x agency fixed effects X X

Table 3: Newsletter Attention and Requests to Federal Agencies
(1) (2)
† p < 0.1, * p < 0.05, ** p < 0.01
Robust standard errors in parentheses, clustered by legislator.
Dependent variable Log(Count+1) Log(Count+1)
Aligned with agency -0.013
(0.023)
Log(newsletters)*aligned 0.026†
(0.016)
Ideological distance -0.036†
(0.021)
Log(newsletters)*distance -0.007
(0.011)
Log(newsletters) 0.047** 0.067**
(0.013) (0.017)
Committee chair 0.317** 0.316**
(0.055) (0.055)
Ranking member 0.303** 0.302**
(0.072) (0.072)
Prestige committee 0.082** 0.081**
(0.026) (0.026)
First year -0.044 -0.044
(0.028) (0.028)
Second year 0.001 0.000
(0.028) (0.028)
Third year 0.044 0.045
(0.027) (0.027)
Fourth year 0.022 0.023
(0.027) (0.027)
Fifth year 0.033 0.034
(0.028) (0.028)
Sixth year -0.006 -0.006
(0.029) (0.029)
Majority 0.002 0.004
(0.019) (0.019)
President's party -0.016 -0.017
(0.020) (0.020)
Observations 32,919 32,919
Year x agency fixed effects X X

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