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


This study is the first to measure the impact of federal regulations on consumer prices. By combining consumer expenditure and pricing data from the Bureau of Labor Statistics, industry supply-chain data from the Bureau of Economic Analysis, and industry-specific regulation information from the Mercatus Center’s RegData database, we determine that regulations promote higher consumer prices, and that these price increases have a disproportionately negative effect on low-income households. Specifically, we find that the poorest households spend larger proportions of their incomes on heavily regulated goods and services prone to sharp price increases. While the literature explores other specific costs of regulation, noting that higher consumer prices are a probable consequence of heavy regulation, this study is the first to provide a thorough empirical analysis of that relationship across industries. Irrespective of the reasons for imposing new regulations, these results demonstrate that in the aggregate, the negative consequences are significant, especially for the most vulnerable households.

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  1. 1.

    All estimates of the regulatory burden are from the RegData database of the Mercatus Center at George Mason University (North American Industry Classification System [NAICS] 2212—natural gas, NAICS 2213—water sewage, and NAICS 2373—highway and street construction).

  2. 2.

    This cost estimate is in constant 2014 dollars, as evaluated by Crain and Crain (2014). The actual estimate cited in OMB (2014) is $68.5 billion to $101.8 billion, in 2010 dollars.

  3. 3.

    This estimate is in 2014 dollars.

  4. 4.

    For other examples detailing the relationship between regulation and economic growth, see Ardagna and Lusardi (2010) and Benson (2015).

  5. 5.

    For a breakdown of federal regulations by type, see the Mercatus Center’s “How the Top Ten Regulators of 2012 Changed over 10 Years” (

  6. 6.

    For example, if regulatory compliance raises fixed costs, larger producers would benefit over smaller rivals by spreading said costs over larger volumes of output. Likewise, if newer and more efficient producers are better able to comply with new directives, they may push for tougher standards to squeeze the operators of older facilities who would be forced to make significant investments to stay in business. Regardless, the result is larger market shares and less competition for the advantaged producers.

  7. 7.

    For related research detailing the effects for specific industries, see Becker and Henderson (2001). Additionally, Becker (2003) examines how local community attributes predict the level of investment in pollution abatement.

  8. 8.

    They estimate that while the Clean Air and Water acts created the largest burden for the energy sector, every production sector experienced some rise in costs, many through secondary effects. The insurance and finance sectors, for example, do not bear direct cost increases from environmental regulations. However, production costs in even these sectors rise as a result of increased factor prices.

  9. 9.

    See Poterba (1991) for an analysis of gasoline taxes, Wier et al. (2005) for an analysis of carbon dioxide taxes, and Borren and Sutton (1992) for an examination of cigarette taxes.

  10. 10.

    Department of Energy, Direct Final Rule: Energy Conservation Standards for Residential Dishwashers, RIN No. 1904-AC64, May 30, 2012,

  11. 11.

    We place no restrictions on the precise form of the demand function, though for simplicity we assume that it generates constant own-price and income elasticities.

  12. 12.

    Data available at Given the minor variation in categories by year, we select those categories with consistent coverage over the sample period.

  13. 13.

    We use the 2010 Diary stub file (Dstub) to map UCC codes onto the aggregate categories. Documentation pertaining to the CES and the stub files are available at For missing or sparsely covered expenditure categories, we included additional, related UCC mappings.

  14. 14.

    For each year, we use the year-end annual price averages from the December “CPI Detailed Report”, available at

  15. 15.

    Five words are coded as restrictions in RegData: shall, must, may not, prohibited, and required.

  16. 16.

    For details on the methodology of calculating measures of regulation, see

  17. 17.

    For a detailed description of the methodology mapping regulations from the NAICS space onto goods and services in the UCC space, see Appendix 2.

  18. 18.

    For a complete list of the top-20 expenditure categories and their corresponding direct and total regulation ranks for each of the five income quintiles, see Appendix 2.

  19. 19.

    Note that spending for each quintile is reported as a percentage of overall total expenditures for each income group. The level of total spending in most categories is greatest for households in the top quintile.

  20. 20.

    RegData contains no direct federal restrictions for the nonalcoholic beverages expenditure category, so we include no corresponding graph of changes in regulation for this category.

  21. 21.

    In practice, we transform the price and regulation data by taking their natural logarithm and first differencing each series. This calculation effectively yields the growth rate of each series.

  22. 22.

    The annual civilian unemployment rate and the annual real gross domestic product (billions of chained 2009 dollars) are from the St. Louis Federal Reserve FRED Database ( Using the Hodrick–Prescott filter (\(\lambda = 6.25\)), real output is split into a trend and cyclical component. The cycle is then normalized by the trend component and expressed as a percentage, thus yielding the output gap.

  23. 23.

    The lagged selection model is a simple AR(1) time series framework with common intercept term: \(p_{t}^{h} = \alpha + \beta \left( L \right)reg_{t}^{h} + \rho p_{t - 1}^{h} + u_{t}^{h}\). Without exception, current regulatory growth and the two-period lag of regulatory growth are statistically insignificant in every variant in which they appear.

  24. 24.

    Arellano and Bond (1991) specify the use of all predetermined lagged endogenous variables, whereas we follow the common practice of using less than the full set of lagged variables (i.e., we use periods t  2, t  3, and t  4 inflation rates, but not period t − 5 or before). We did experiment with larger instrument sets that included more lags, but the results (not reported in this paper but available on request) were nearly identical.

  25. 25.

    We use White (period) robust standard errors throughout unless otherwise specified.

  26. 26.

    The original Arellano and Bond (1991) estimator involves two steps, whereby an initial consistent estimate of the dynamic panel yields residuals that are used to construct a GMM weighting matrix, that is, used to more efficiently re-estimate the dynamic panel. Our software package, Eviews, iteratively repeats this process, each time updating the GMM weighting matrix until convergence is achieved. The result is a more efficient estimator than that proposed by Arellano and Bond. If the weighting matrix is not invertible, the 1-step matrix is used instead.

  27. 27.

    Source: RegData (

  28. 28.

    The unrestricted model is similar to Eq. (5), wherein the current growth rate of regulations is regressed on a constant, a lag of itself, current and one-period lags of both unemployment and the GDP gap, and one- and two- period lags of the inflation rate. The restricted model sets the coefficients on the lagged inflation terms equal to zero. The resulting F-stat equals 0.077, which is not significant at any standard level of significance.

  29. 29.

    As noted by Nickell (1981), all dynamic models with relatively short time dimensions, including autoregressive time series and dynamic panel models, suffer from “Hurwicz type bias,” regardless of the estimation procedure used. That said, our estimation procedure is biased but consistent.

  30. 30.

    See Table 3 for a list of the detailed expenditure categories.

  31. 31.

    For a description of the methodology used to construct RegData, see

  32. 32.

    Consumption-based weights equal to each industry’s market share for a given commodity would be preferable to weights based on the overall relative size of the industries that produce said commodity. Unfortunately, to our knowledge, no such data exist.


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We thank Fabrizio Iacone, Diana Thomas and the participants of the Regressive Effects of Regulation conference at Creighton University for their helpful review and comments. Dustin Chambers and Courtney Collins also thank the Mercatus Center (at George Mason University) for their financial support in drafting an earlier Mercatus Working Paper (February 2016) which motivated the current paper.

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Corresponding author

Correspondence to Dustin Chambers.


Appendix 1: Proofs of predictions from theoretical model

Proof of Prediction 1

Consumer i’s spending on the regulated commodity as a share of her income is:

$$S_{i} = \frac{{px_{i} (p,m_{i} )}}{{m_{i} }}$$


$$\ln (S_{i} ) = \ln (p) + \ln (x_{i} (p,m_{i} )) - \ln (m_{i} )$$
$$\frac{{\partial \ln (S_{i} )}}{{\partial m_{i} }} = \frac{1}{{x_{i} }}\frac{{\partial x_{i} (p,m_{i} )}}{{\partial m_{i} }} - \frac{1}{{m_{i} }} = \frac{1}{{m_{i} }}\left[ {\varepsilon_{m} - 1} \right]$$

where \(\varepsilon_{m}\) is the income elasticity of demand. Therefore, if demand with respect to income is inelastic, i.e., \(0 < \varepsilon_{m} < 1\), then \({{\partial \ln (S_{i} )} \mathord{\left/ {\vphantom {{\partial \ln (S_{i} )} {\partial m_{i} }}} \right. \kern-0pt} {\partial m_{i} }} < 0\)

Proof of Prediction 2

Application of the Implicit Function Theorem to Eq. (1) yields:

$$\frac{\partial p}{\partial R} = \frac{{\frac{\partial x(p,R)}{\partial R}}}{{\sum\nolimits_{i} {\frac{{\partial x_{i} (p,m_{i} )}}{\partial p} - \frac{\partial x(p,R)}{\partial p}} }} > 0$$

which shows that p is increasing in R. □

Proof of Prediction 3

Consumer i’s budget constraint is:

$$px_{i} (p,m_{i} ) + y_{i} = m_{i}$$

where yi denotes consumer i’s expenditure on all commodities other than xi. Dividing the budget constraint by mi yields:

$$\frac{{px_{i} (p,m_{i} )}}{{m_{i} }} + \frac{{y_{i} }}{{m_{i} }} = 1$$

and differentiating with respect to R yields:

$$\frac{{x_{i} }}{{m_{i} }}\frac{\partial p}{\partial R} + \frac{p}{{m_{i} }}\frac{{\partial x_{i} (p,m_{i} )}}{\partial p}\frac{\partial p}{\partial R} + \frac{1}{{m_{i} }}\frac{{\partial y_{i} }}{\partial p}\frac{\partial p}{\partial R} = 0$$

Recall \({{\partial p} \mathord{\left/ {\vphantom {{\partial p} {\partial R}}} \right. \kern-0pt} {\partial R}} > 0\) that (Prediction 2). Equation (13) can be simplified to:

$$\frac{1}{{m_{i} }}\frac{{\partial y_{i} }}{\partial p} = - \frac{{x_{i} }}{{m_{i} }}\left[ {1 + \varepsilon_{p} } \right]$$

where is the regulated commodity’s price elasticity of demand. The left-hand side of Eq. (14) shows the proportional change in consumer i’s spending on other commodities when p increases. Recall that \({{x_{i} } \mathord{\left/ {\vphantom {{x_{i} } {m_{i} }}} \right. \kern-0pt} {m_{i} }}\) is decreasing in \(m_{i}\) if demand with respect to income is inelastic (Prediction 1). Therefore, if demand with respect to price is also inelastic, i.e.,\(- 1 < \varepsilon_{p} < 0,\) then an increase in p causes lower-income consumers to reduce their spending on other commodities by proportionately more than it does for higher-income consumers. □

Appendix 2: methodological description of the construction of the consumer expenditure survey and regulation datasets

To determine the disparate effects of government regulations on households in different socioeconomic strata, we construct a dataset that maps goods and services from the Consumer Expenditure Survey (CE) onto industry regulations from the Mercatus Center at George Mason University’s industry regulation database (RegData).

The CE provides detailed household spending and price data for a wide array of goods and services by income group. These goods and services are organized using the Universal Classification Codes (UCC) system. RegData 2.0, however, reports the level of industry regulation by the two-, three-, and four-digit North American Industry Classification System (NAICS) code for each year between 1997 and 2012. Therefore, to construct a usable database, we map regulations from the NAICS space onto goods and services in the UCC space. The resulting balanced panel dataset contains 9872 observations, covering 617 UCC-based goods and services over a 16-year period.

To construct the final dataset, the following steps are employed:

  1. 1.

    The RegData 2.0 dataset consists of two-digit, three-digit, and four-digit NAICS-based tables. Each regulation record in the tables contains the name of the government agency imposing the regulation, the year of the regulation, the industry affected by the regulation, the regulatory word count, the restriction count, and the industry regulation index value. For our purposes, we use the industry regulation index value, which equals the regulatory restriction count weighted by industry relevance.Footnote 31

    For each industry-and-year pair, the industry regulation index values are summed across federal regulators. Therefore, for each industry-and-year combination, a single-industry regulation index value is derived, equaling the sum of all regulatory restrictions (weighted by industry relevance) imposed on that industry by all federal regulators for that year. The result is three aggregated datasets, one for each two-digit, three-digit, and four-digit NAICS-based table. Last, the three aggregated datasets are combined (stacked) to form a single dataset.

  2. 2.

    The spreadsheet containing the 2007 commodity-by-industry direct requirements (after redefinitions) table was downloaded from the Bureau of Economic Analysis (BEA) website ( This spreadsheet contains two work sheets, both of which are used below:

    1. a.

      The first work sheet is a concordance that converts the BEA’s input–output (I–O) commodity/industry codes into 2007 NAICS codes.

    2. b.

      The second work sheet is the I–O direct requirements table, which contains I–O weights (\(\alpha_{ij}\)) equal to the amount of input (measured in dollars) from industry (\(i\)) required to produce a dollar’s worth of output by industry (\(j\)). By construction, these weights sum to 1 because, in addition to actual inputs, the BEA includes employee compensation, taxes, and gross operating surplus in the weighting schema.

  3. 3.

    The I–O commodity/industry code to NAICS concordance described in step (2a) above is matched with the aggregate industry regulations from step (1), to create a new table that lists the aggregate industry regulations by I–O commodity/industry code; the resulting table is further summed over commodity code by year to derive a table with a single total regulation value for each commodity code–year pair. This second round of aggregation after the initial match is necessary because some commodity codes map onto multiple NAICS industries. I–O commodity/industry codes with no associated regulations are assigned an industry regulation index value of 0. The resulting table is a measure of the direct regulations (denoted \(DirectReg_{it}\)) applicable to a given I–O commodity/industry code.

  4. 4.

    To determine the level of regulation that applies to the inputs/supply chain of a given industry, the I–O direct requirements (\(\alpha_{ij}\)) from step (2b) are matched with the direct regulations for each I–O commodity (\(DirectReg_{it}\)) from step (3) by way of their I–O commodity/industry codes. Note that if a commodity/industry is not needed to produce a given output, the associated input value is 0. This produces a large result set with more than 2.4 million rows of data. This dataset is then “grouped by” output industry (\(j\)) and year (\(t\)) and summed over the product of the direct input regulations (indexed by i) and I–O weights, producing an estimate of input–supply chain regulation:\(InputReg_{jt} = \mathop \sum \limits_{i} \alpha_{ij} \cdot DirectReg_{it} .\)

    See Figure 4 for a graphical summary of steps (1) to (4).

    Fig. 4

    Mapping regulations onto input–output (I–O) codes. Note: NAICS = North American Industry Classification System

  5. 5.

    The direct regulations by industry and year are matched with the total input regulations by industry and year. The direct and input regulations are summed to determine the total direct and indirect regulations affecting a given industry: \(TotalReg_{it} = DirectReg_{it} + InputReg_{it} .\)

  6. 6.

    To map regulations onto the UCC codes, a separate set of queries is executed to map the codes onto I–O commodity/industry codes.

    1. a.

      As a beginning step, we import the personal consumption expenditures (PCE) concordance from the Bureau of Labor Statistics (BLS) ( This file maps UCC codes onto PCE codes from the BEA’s national income and product accounts (NIPAs).

    2. b.

      Next, we import BEA table 2.4.5U (I–O, Personal Consumption Expenditures by Type of Product with 2007 Input–Output Commodity Composition). This latter bridge file ( maps NIPA line numbers onto PCE codes.

    3. c.

      The tables from steps (6a) and (6b) are matched by way of their common PCE codes. The resulting table serves as a bridge file that maps UCC codes onto NIPA line numbers.

  7. 7.

    Finally, we import the BEA’s PCE bridge file, which maps NIPA line numbers onto I–O commodity/industry codes (, along with the total value of all purchases of the linked I–O commodity/industry in 2007.

    1. a.

      Matching the NIPA line items from the PCE bridge with the results from step (6c) provides a clear mapping from UCC code to I–O commodity/industry codes. See Figure 5 for a graphic summary of steps (6) and (7).

      Fig. 5

      Mapping input–output (I–O) codes onto consumer expenditure codes. Note: PCE = personal consumption expenditures; UCC = Universal Classification Codes; NIPA = national income and products accounts

  8. 8.

    The resulting table from step (7a) maps a given consumer product from the CE onto all I–O industries that produce that product. In many cases, more than one industry produces a given UCC product. To produce a single regulation value for each consumer product, we derive industry weights equal to a given industry’s 2007 level of output relative to the total output of all industries that supply a given UCC product.Footnote 32 For example, the UCC code for flour is 10,110. This consumer product is produced by seven I–O industries. Assigning each of these industries a weight equal to its total output relative to the total output of all seven industries produces a set of weights that sum to 1 (see Table 8). Although it would be preferable to update these weights annually, the BLS derives these output data from the US Census Bureau’s Economic Census, which is conducted only every five years.

    Table 8 Input–output industries that produce flour (UCC: 10110)
  9. 9.

    Finally, UCC codes, I–O commodity/industry codes, and output shares from step (8) are matched with the regulation-by-industry data from step (5). These matched data are then “grouped by” UCC code and year and aggregated over the product of industry regulation and output shares.

See Table 9.

Table 9 Top 20 expenditure categories by income quintile and corresponding regulations

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Chambers, D., Collins, C.A. & Krause, A. How do federal regulations affect consumer prices? An analysis of the regressive effects of regulation. Public Choice 180, 57–90 (2019).

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  • Regulation
  • Federal regulations
  • Regressive effects
  • Distributional effects
  • Consumer prices
  • Consumer Expenditure Survey
  • RegData

JEL Classification

  • I18
  • D12
  • H23
  • L51