Barriers to prosperity: the harmful impact of entry regulations on income inequality

Chambers, Dustin; McLaughlin, Patrick A.; Stanley, Laura E.

doi:10.1007/s11127-018-0498-4

Cited by 39 publications

(37 citation statements)

References 38 publications

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“…When considering the differences between the ease of starting a business scores, a one standard deviation decrease in the starting a business score would equate to a 1.9-point rise in the Gini coefficient (12.74*0.15 = 1.9). This supports the findings by Chambers et al (2019) and further confirms that barriers to entry increase income inequality. In order to ensure that no endogenous relationship or reverse causality exists between the ease of starting a business and income inequality, I ran a similar regression, this time estimating the values using a two-stage least squares method.…”

Section: Baseline Model and Resultssupporting

confidence: 90%

“…In my final iteration, the observations drop to 318, but I maintain a strong sample of OECD nations while still producing similar results to the previous ones. My results are also closely in line with those of Chambers et al (2019), providing further confirmation that my results and techniques are valid. While the number of observations in my second model may fall dramatically as a result of using only a cross-sectional subset of my data, I am still able to maintain a dataset spanning over 130 countries for six out of the seven iterations of the model.…”

Section: Validity Of Findingssupporting

confidence: 84%

“…This article adds to the findings by Chambers et al (2019) and further empirically establishes the relationship between business start-up regulations and income inequality. While Chambers et al (2019) specifically studied the impact of the number of procedures required to start a new business on income inequality, I focus on a broader measure of an individual's ability to start a business and its impact on the Gini coefficient, which is a measure of income inequality. I use the World Bank's Doing Business dataset, using the 'Score -Starting A Business' statistic.…”

mentioning

confidence: 63%

“…While there may be many indicators that influence income inequality, I base my independent variables on past studies on the subject. I use similar variables to those used by Chambers et al (2019), but differ on several to help minimise the possibility of multicollinearity.…”

Section: Income Inequalitymentioning

confidence: 99%

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The negative impact of barriers to entry on income inequality

Overstreet

2020

Economic Affairs

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Barriers to entry such as fees, licensing, or educational requirements make it more difficult to start businesses. Problematically, many barriers to entry are due to regulatory capture and serve only to benefit incumbent firms. These regulations, which are created by government, often make it exceedingly difficult for low-income individuals to start new businesses, denying individuals access to higher-paying occupations. I estimate two models and find that barriers to entry increase income inequality. A one-point decrease in the World Bank's ease of starting a business score equates to a 0.15-1.25-point increase in the Gini coefficient.

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Section: Baseline Model and Resultssupporting

confidence: 90%

Section: Validity Of Findingssupporting

confidence: 84%

mentioning

confidence: 63%

Section: Income Inequalitymentioning

confidence: 99%

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The negative impact of barriers to entry on income inequality

Overstreet

2020

Economic Affairs

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“…However, in high oil revenues regime, income inequality would be increased by an increase in oil revenues [16]. According to Chambers and Mclaughlin, the phenomena of income inequality are more serious if the entry regulations of the country are stringent [17]. Above all, income inequality differs from different countries.…”

Section: Income Inequalitymentioning

confidence: 99%

Income Inequality: How Do Racial and Gender Differences Influence the Incomes in US

Liu¹,

Yang²,

Wang³

2019

JHRSS

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Income inequality is a serious social economic issue in many countries. The issue is deteriorated due to the complex social environment and demographic composition in the Unites States. This study focuses on the racial and gender factors which influence workers' income. And the easy-qualified year of schooling and working-year are chosen as the other two factors. Basing on the Ordinary Least Square (OLS) analysis, the selected factors are qualified to understand their influence degree in income inequality. By comparing the coefficients of the factors, we find the inner relationships between the factors and income inequality. The precise results not only provide a chance for people to examine their income levels, but also help the government to develop fair policies.

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Farm‐Level Cost Drivers of Salmonid Fish Health Inspections

Engle¹,

Senten

Schwarz

et al. 2021

J Aqua Anim Hlth

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Regulatory costs on aquaculture farms have been shown to be of a magnitude that warrants additional analysis. The drivers of farm-level costs of fish health inspections were identified in this study from national survey data on U.S. salmonid farms. The greatest costs identified were related primarily to state fish health requirements for inspection and testing to certify that fish are free of specific pathogens prior to approval of necessary permits to sell and/or transport animals. Fish health inspection costs included laboratory testing, farm personnel time, veterinary fees, and shipping samples to laboratories, with laboratory testing and the value of farm personnel time being the most expensive components. Principal cost drivers were the number of tests and whether required sampling was farmwide or for each lot as identified by the collector. Farmers who primarily sold into recreational markets had greater fish health costs than farmers who primarily sold food fish because of the greater numbers of species and size-/age-classes of salmonids on their farms. Regulatory requirements to test all species and size-/age-classes on farms increased inspection costs by increasing the total number of tests, the total value of fish sacrificed, and shipping costs. Consequently, for farms with more than one species or more than one size-/age-class, annual farm-level testing was less costly than annual lot-based testing. Increased numbers of tests in a given year, although reported by only a few respondents, can increase costs dramatically and turn profitable farms unprofitable, even food fish farms. Smaller salmonid farms experienced disproportionately greater inspection cost burdens than did larger farms. The fish health inspection scenario of only one annual inspection of only the most susceptible species and size-/age-class showed a cost burden that did not generate economic distress, even on smaller salmonid farms. Other scenarios modeled (based on survey data) that included lot-based surveys of multiple species and size-/age-classes resulted in substantially greater fish health inspection costs that led to unprofitability for various farm sizes and business types. Study results suggest that implementing Comprehensive Aquaculture Health Program Standards might allow for risk-and pathogen-based reductions in the total number of inspections and fish sampled while maintaining equivalent or greater health status compared to current methods. American Fisheries Society-Fish Health Section Blue Book inspection methods are interpreted and applied inconsistently across states and generally yield lot-rather than farm-level health attestations because the

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Barriers to prosperity: the harmful impact of entry regulations on income inequality

Cited by 39 publications

References 38 publications

The negative impact of barriers to entry on income inequality

The negative impact of barriers to entry on income inequality

Income Inequality: How Do Racial and Gender Differences Influence the Incomes in US

Farm‐Level Cost Drivers of Salmonid Fish Health Inspections

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