The weight of service delivery: administrative and rules burdens in street-level bureaucracy

This article explores the factors that shape perceptions of administrative and rules burdens among street-level bureaucrats in Romania. Recent research examining the concept of administrative burden has focused on burdens experienced by a citizen or client. We argue here that national context shapes the features of public service delivery, and that burdens must also be understood from the perspective of bureaucrats in transitioning post-communist countries. Street-level bureaucrats represent an important category of public servants given their main characteristics in implementing policy. Findings of two analyses—one examining broader concepts of administrative burden and one looking at the narrower concept of rules burdens—indicate that rule complexity, autonomy, conformity, job satisfaction, educational attainment, and perceived corruption impact perceptions of administrative burden, while perceptions of rules burdens are impacted by rule-abiding tendencies, discretionary latitude, job satisfaction, organizational commitment, and supervisory status. Points for practitioners A major practical implication of this study derives from our findings on the factors that impact attitudes and perceptions of street-level bureaucrats in Romania. Managers that aim to improve public service delivery in this context will be able to disseminate the fact that reduced rule complexity and increased autonomy, among other organizational variables, positively impact perceptions on administrative and rules burdens. In this sense, our study provides evidence for new and current structures to improve organizational performance and service delivery.

Is the Letter Cancellation Task a Suitable Index of Ego Depletion?

The aim was to quantify ego depletion and measure its effect on inhibitory control. Adults ( N = 523) received the letter “e” cancellation ego depletion task and were subsequently tested on Stroop task performance. Difficulty of the cancellation task was systematically manipulated by modifying the text from semantically meaningful to non-meaningful sentences and words (Experiment 1) and by increasing ego depletion rule complexity (Experiment 2). Participants’ performance was affected by both text and rule manipulations. There was no relation between ego depletion task performance and subsequent Stroop performance. Thus, irrespective of the difficulty of the ego depletion task, Stroop performance was unaffected. The widely used cancellation task may not be a suitable inducer of ego depletion if ego depletion is considered as a lack of inhibitory control.

The Effects of Rule Complexity on Organizational Noncompliance and Remediation: Evidence From Restaurant Health Inspections

Organizational noncompliance with legal rules can be consequential. The antecedents of such noncompliance as well as the remediation of it thus remain enduring subjects of research inquiry. However, prior studies have implicitly treated all rules alike. In contrast, we argue that rules are not all the same and that differences between rules might be systematically linked to variations in the likelihoods of noncompliance and remediation across the range of organizations under the purview of a rule system. We consider the role of two fundamental but distinct sources of rule complexity: components (i.e., sections that compose a rule) and connections (i.e., functional links to other rules in the same system). We analyzed data from 81,266 rule-level observations from 1,011 health inspections of 289 restaurants in Santa Monica, California, conducted from 2007 to 2010. As hypothesized, increases in either source of rule complexity were associated with higher probabilities of noncompliance. Unexpectedly, however, the two sources of rule complexity had divergent effects on remediation such that increases in the number of connections were associated with higher probabilities of repeated noncompliance, whereas increases in the number of components were not. Taken together, we suggest that our understanding of noncompliance and remediation can be enhanced by viewing them both as rule-level phenomena. A range of implications for theory and practice are discussed.

Response of the multiple-demand network during simple stimulus discriminations

The multiple-demand (MD) network is sensitive to many aspects of task difficulty, including such factors as rule complexity, memory load, attentional switching and inhibition. Many accounts link MD activity to top-down task control, raising the question of response when performance is limited by the quality of sensory input, and indeed, some prior results suggest little effect of sensory manipulations. Here we examined judgments of motion direction, manipulating difficulty by either motion coherence or salience of irrelevant dots. We manipulated each difficulty type across six levels, from very easy to very hard, and additionally manipulated whether difficulty level was blocked, and thus known in advance, or randomized. Despite the very large manipulations employed, difficulty had little effect on MD activity, especially for the coherence manipulation. Contrasting with these small or absent effects, we observed the usual increase of MD activity with increased rule complexity. We suggest that, for simple sensory discriminations, it may be impossible to compensate for reduced stimulus information by increased top-down control.

Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning

Rule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.

Who Participates Now… and Why?

Two, I ask how the dimensions of complexity and salience of a policy issue affect the level of participation by different types of actors in the regulatory policy arena. Using Gormley’s (1986) framework of regulatory politics, I develop measures that attempt to capture the dimensional constructs of rule-complexity and issue-salience that might affect different actors’ levels of participation in the rulemaking process. Given the transition to the Regulations.gov platform, I test several propositions implicit to the stated equity-based mission of the George W. Bush administration’s “e-Rulemaking Initiative” (eRI). My findings indicate that these dimensions do, in part, account for the amount of activity of different types of organizations and individuals, despite a “leveling” of access across stakeholder types.