Problem detection in legislative oversight: an analysis of legislative committee agendas in the UK and US

ABSTRACT

This paper outlines a dynamic problem-detection model of legislative oversight where legislative committees engage in information-gathering to identify emerging policy problems. It is argued that activities of legislative committees are responsive to indicators of problem status across a range of policy domains. This enables committees to react to problems before, or at least simultaneously to, citizens. Our analyses use a new dataset on the policy agenda of UK Parliamentary Select Committees in combination with directly comparable data on US Congressional hearings. Aggregate measures of problem status (e.g., GDP, crime rates) and public opinion on the ‘most important problem’ facing the country are used as independent variables. The comparison between a well-established and developing committee system offers insights into common dynamics across institutional contexts. The findings show that committee agendas in both the UK and US are responsive to problem status for the majority of issues.

KEYWORDS:

• Agenda-setting
• congressional hearings
• legislative oversight
• problem detection
• select committees

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The time lag often involved in publication of official statistical estimates (e.g., economic or crime figures) makes it possible for legislative responses to occur before there has been a formal signal of the deterioration of conditions. Citizens also may, in the aggregate, recognize changes in objective conditions (e.g., prices) before there is a formal signal.

2. The logics of problem detection operate in parallel to the public’s attention to policy issues. Studies show that the public agenda tracks measures of problem status in some policy domains (Wlezien 2005). Therefore, public priorities provide legislators an additional warning of the existence of policy problems in a specific domain (i.e., policy-seeking), combined with a signal of a greater likelihood of political costs for inaction (i.e., vote-seeking).

3. US gasoline prices and domestic fuel costs are highly correlated. However, they are not the same with fuel oil subject to more quarterly fluctuations due to demands based on weather.

4. The correlation between fear of crime and a public priorities (MII) is quite low, 0.32, as crime can be downgraded as a priority as other issues become more important, even if the absolute level of concern with crime does not change.

5. The survey also asks about individuals’ actual experience of crime. Over the period between 2001 and 2012, the correlation of fear of crime and the rate of victimization is equal to 0.83 (P-value<0.0001). Alternative analyses with this measure did not change our substantive results.

6. It can also be interpreted as the full effect that a one unit increase in the independent variable has on the dependent variable if that one unit increase remains indefinitely. Of course, if the data is stationary, we do not expect the one-unit increase to remain indefinitely.

7. As Congressional hearings may be less frequent in all areas in election (even-numbered) years, we also conduct a robustness test where we control for whether the hearings take place in an odd- or even-numbered year. The only issue area for which this control was significant was Health, for which its inclusion produced no substantive changes to the results.

8. Congressional hearings test statistics: Economy, −9.229; Health, −7.024; Energy, −7.525; Transport, −8.193. P-values < 0.001.

9. DF test statistics for MII: Economy, −2.829; Health, −3.278; Energy, −6.391. P-values < .05.

10. Test statistics for Problem status: Economy, −44.601; Health, −0.567; Energy, −1.971; Transport, −3.059. P-values < 0.05.

11. Test statistics for Committee reports: Economy, −6.048; Civil, −5.857; Health, −6.651; Energy, −6.645; Transport, −6.157; Law, −5.998. P-values < 0.001.

12. Test statistics for Problem status: Economy, −4.047; Civil, −2.322; Health, −3.4337; Energy, −1.735; Transport, −2.623; Law, −2.830. P-values < 0.05.

13. Test statistics for MII: Economy, −4.040; Civil, −2.024; Health, −2.493; Energy, −4.460; Transport, −3.794; Law, −2.742. P-values < .05. Note that the test for the economy (as the most important issue) includes a control for a structural break in the second quarter of 2008.

14. Test statistics: Economy, 0.255; Health, 0.001; Energy, 0.361; Transport, 0.8034. P-values > 0.05.

15. Breusch Godfrey test statistics: Economy, 0.126; Civil: 0.018; Health, 0.257; Energy, 0.359; Transport, 0.179; Law, 0.800. The P-value for each is greater than 0.05.