How do federal regulations affect consumer prices? An analysis of the regressive effects of regulation

Chambers, Dustin; Collins, Courtney A.; Krause, Alan

doi:10.1007/s11127-017-0479-z

Cited by 34 publications

(25 citation statements)

References 42 publications

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“…Some studies have examined disaggregated economic effects of public policies (Harrington, Safirova, Coleman, Houde, & Finkel, 2014), simulating the distributional impacts of policies on workers in different “skill class” categories. There is also evidence that regulations that affect consumer prices can disproportionately influence low‐income households (Chambers, Collins, & Krause, 2017) and that the distributional effects of policies of household income can vary by policy type (Fullerton & Muehlegger, 2017), though there is less empirical evidence about racial/ethnic differences. While a comprehensive examination of approaches to characterize distributions of costs of regulations (especially across race/ethnicity, to inform environmental justice evaluations) is beyond the scope of this article, it is clearly valuable to develop and apply approaches within regulatory impact analyses that could formally examine the influence of candidate policies on both health risk inequality and economic inequality.…”

Section: Literature Reviewmentioning

confidence: 99%

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities

Levy

2021

Risk Analysis

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There has been increasing interest in accounting for inequality in health risks and benefits within regulatory impact analyses, both given more general interest in the distributions of benefits and growing concerns about inequity (defined as those inequalities deemed unjust or unfair) and environmental injustice (in this context, those health risk inequalities that are correlated with race/ethnicity and certain other sociodemographic factors). Although there has been growing literature on this topic, there has been limited progress in practice, and the lack of quantification limits consideration of inequality in the policy process. Controversy remains regarding the best approaches to formally incorporate inequality, when these approaches should be used, and even whether it makes sense to quantify inequality in this context. The objective of this article is to review the literature on approaches for incorporating estimates of, and concerns for, inequality into regulatory impact analyses, especially those where environmental justice considerations are relevant, and consider the interpretation of these approaches and the implications for decision making. Using the case example of the Transportation and Climate Initiative, a collaboration among Northeast and Mid‐Atlantic states to reduce carbon emissions from the transportation sector, multiple strategies are described that could be used to shed light on health risk inequality and inequity, consider them in pending policy decisions, and evaluate their implications for the policy or instrument choice. Given appropriate contextualization and acknowledgment of the multidimensionality of equity, quantitative inequality indicators can provide meaningful insight about both inequality and inequity in health risks.

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Section: Literature Reviewmentioning

confidence: 99%

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities

Levy

2021

Risk Analysis

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“…More concretely, scholars have argued that legal complexity hampers economic development through several channels, including by the lowering of returns on capital and thus impeding innovation [40][41][42][43]. In recent years, several scholars have attempted to use data on aggregate regulatory levels to draw conclusions about the costs and benefits of various regulatory regimes [9,44,45]. In the realm of political discourse, trade associations representing regulated industry frequently bemoan legal complexity and the cumulative cost of regulations [46].…”

Section: General Characteristics Of Legal Systemsmentioning

confidence: 99%

“…Rigorous work on legal complexity has been hampered by inadequate definition and measurement of the underlying concept (see e.g., [37].) Simple measures, such as the number of pages in the U.S. Federal Register, or counting n-grams that target "command" type language (i.e., "shall" or "must") have been used as rough proxies [45], but their shortfalls are fairly obvious. As domestic legal regimes cope with continued economic growth and global integration, legal complexity (broadly understood) is likely to increase, and social scientific study of this phenomenon will take on even greater importance.…”

Section: General Characteristics Of Legal Systemsmentioning

confidence: 99%

A Multinetwork and Machine Learning Examination of Structure and Content in the United States Code

et al. 2021

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This paper introduces a novel linked structure-content representation of federal statutory law in the United States and analyzes and quantifies its structure using tools and concepts drawn from network analysis and complexity studies. The organizational component of our representation is based on the explicit hierarchical organization within the United States Code (USC) as well an embedded cross-reference citation network. We couple this structure with a layer of content-based similarity derived from the application of a “topic model” to the USC. The resulting representation is the first that explicitly models the USC as a “multinetwork” or “multilayered network” incorporating hierarchical structure, cross-references, and content. We report several novel descriptive statistics of this multinetwork. These include the results of this first application of the machine learning technique of topic modeling to the USC as well as multiple measures articulating the relationships between the organizational and content network layers. We find a high degree of assortativity of “titles” (the highest level hierarchy within the USC) with related topics. We also present a link prediction task and show that machine learning techniques are able to recover information about structure from content. Success in this prediction task has a natural interpretation as indicating a form of mutual information. We connect the relational findings between organization and content to a measure of “ease of search” in this large hyperlinked document that has implications for the ways in which the structure of the USC supports (or doesn’t support) broad useful access to the law. The measures developed in this paper have the potential to enable comparative work in the study of statutory networks that ranges across time and geography.

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“…For instance, RegData can be used to study particular deregulatory events (see Ellig & McLaughlin, 2016) or as a measure of political risk (Hassan, Hollander, van Lent, & Tahoun, 2019). Other studies have examined the relationship between RegData restrictiveness clauses and wages (Adamis‐Császár et al., 2019), business investment in the manufacturing sector (Pizzola, 2018), output in the energy sector (Hall & Shakya, 2019), poverty (Chambers, McLaughlin, & Stanley, 2019a), consumer prices (Chambers, Collins, & Krause, 2019b), entrepreneurship (e.g. Goldschlag & Tabarrok, 2018), employment (Bailey & Thomas, 2017), lobbying and regulatory capture (Clarke & Nesbit, 2018; Lowrance, 2019), inequality (Mulholland, 2019), and productivity (Davies, 2014).…”

Section: Regdata: Australia and The New Regulatory Analyticsmentioning

confidence: 99%

The political economy of Australian regulatory reform

et al. 2020

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The problem of regulatory accumulation has increasingly been recognised as a policy problem in its own right. Governments have then devised and implemented regulatory reform policies that directly seek to ameliorate the burdens of regulatory accumulation (e.g. red tape reduction targets). In this paper we examine regulatory reform approaches in Australia through the lens of policy innovation. Our contributions are twofold. We first examine the evolutionary discovery process of regulatory reform policies in Australia (at the federal, intergovernmental, and state levels). This demonstrates a process of policy innovation in regulatory mechanisms and measurements. We then analyse a new measurement of regulatory burden based on text analytics, RegData: Australia. RegData: Australia uses textual analysis to count ‘restrictiveness clauses' in regulation – such as ‘must’, ‘cannot’ and ‘shall’ – thereby developing a new database (RDAU1.0). We place this ‘restrictiveness clauses’ measurement within the context of regulatory policy innovation, and examine the potential for further innovation in regulatory reform mechanisms.

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How do federal regulations affect consumer prices? An analysis of the regressive effects of regulation

Cited by 34 publications

References 42 publications

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities

A Multinetwork and Machine Learning Examination of Structure and Content in the United States Code

The political economy of Australian regulatory reform

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