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Investing in Service: Professionalization Effects on the Provision of Constituent Service

Despite the political media’s focus on the various legislative battles taking place in state capitols all across the country, policy and legislative work are only one part of a state legislator’s job description. State legislators are also responsible for helping their constituents navigate the often-complicated world of government.

My research examines the effect of professionalism, a term used to describe the institutional capacity of a legislature, has on constituent service. In other words, what do citizens get in return from their investment in their legislature?

Surprisingly, there is substantial variation in professionalism among state legislatures. For example, in 2014, legislators in California were paid a salary of $121,535 and were expected to work full-time. In the same year, legislators in New Hampshire were paid $200 for the session and only worked part-time. In other words, the California legislature is different from the New Hampshire legislature in important ways.

In theory, good constituent service can inform citizens of government programs and ensure that elected officials regularly correspond with constituents. Bad constituent service can prevent citizens from taking advantage of government programs, and in its most extreme form, lock constituents out of the governmental process.

Past research confirms the assumed divergence between legislatures with high and low levels of professionalization. For example, professionalization increases legislators’ ability to manage more complex policy areas, makes them more likely to consider innovative policy proposals, and results in more effective pension system management. However, no work has investigated the relationship between professionalization and constituent service.

To measure the effect of professionalization on constituent service, I repurposed Butler and Broockman’s 2008 cross-sectional experiment. In the experiment, 4,859 state legislators in 44 states were sent an email inquiring how to vote in that legislator’s district, though the sample size was limited to legislators with valid email addresses posted online. Each email address was randomly assigned to either a treatment group (email signed DeShawn, a name common among blacks, but uncommon among whites) or control group (email signed Jake, a name common among whites, but uncommon among blacks).

I used ordinary least squares (OLS) regression, a statistical technique that produces an estimate of the relationship between two variables by generating a line that minimizes the difference between the observed data and a predicted trend line. This line is the best linear estimate of the relationship between the two variables and is sometimes called a“line of best fit.” The method can also control for additional variables that might affect the relationship, allowing a researcher to isolate the relationship between two variables of interest.

Using this method, I analyzed the data with two questions in mind. First, does professionalization increase legislator response rates to constituent inquiries? Second, does professionalization increase the helpfulness of legislator responses to constituent inquiries?

My analysis found no statistically significant relationship between professionalization and response rates.

The relationship between professionalization and helpfulness, however, is significant.  The model predicts a 56 percent in helpfulness between a legislator at the lowest level of professionalization and the highest level of professionalization. Though not statistically significant, observational analysis of the models show professionalization increases legislators tendencies to racially discriminate in constituent service. There is a one percent difference in the helpfulness rate between emails signed Jake and DeShawn at the low end of professionalization, but a six percent difference at the high end of professionalization.

Two interesting results emerged from the empirical analysis. The incongruence between response rates (not statistically significant) and helpfulness rates (statistically significant) provide evidence that legislators employ a sorting mechanism, only providing service to constituents meeting a particular set of criteria unrelated to the office’s capacity to provide service.

The second is the positive relationship between professionalization and racial bias. As stated previously, at the very low end of professionalization, there is no meaningful difference between the treatment (DeShawn) and control (Jake) response and helpfulness rates. However, as professionalization increases, the gap in response and helpfulness rates widens.

To be clear, there is no statistically significant evidence that professionalization increase the racial bias effect, but from a purely observational standpoint, the analysis raises an important question about racial bias.

Both of these findings have important implications for those concerned with equitable access to government. Professionalization has generally been viewed as a net positive for state governance. In light of this analysis, the relationship between professionalization and constituent service appears to be more complicated. Giving legislators increased capacity does not clearly increase the legislator’s ability to provide constituent services to all citizens. Instead, it gives legislators the ability to provide services to select constituencies.

Policymakers attempting to increase citizen access to government need to take the strategic behavior that is inherent to electoral politics into account when considering increasing legislative capacity.