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Economic Currents

Keep up to date with the latest UHERO news.

Hawai‘i’s Environmental Response, Energy, and Food Security Tax (aka Barrel Tax)

The one-dollar increase in Hawai‘i’s environmental tax from five-cents since its inception in 1993 to $1.05 effective July 1, 2010 was a stepping stone in Hawai‘i’s clean energy progress. While in theory it serves to discourage fossil fuels (internalizing the negative externality), its major impact has been as a funding source for energy and food security initiatives. Act 73 temporarily created three new funds—the Energy Security Special Fund, the Energy Systems Development Special Fund, and the Agricultural Development & Food Security Fund. Providing support for the Hawai‘i Clean Energy Initiative (HCEI) and the Greenhouse Gas Emissions Reduction Task Force (GHGRTF) as well as instrumental research conducted by the Hawai‘i Natural Energy Institute (HNEI) are just several examples of how the barrel tax has contributed to advancing the State’s energy goals.

What does the barrel tax apply to and how much has been collected?

As of the end of fiscal year 2013, the $1.05 per barrel tax on petroleum products—excluding jet fuel (aviation fuel) and any fuel sold to a refiner—totaled $80 million dollars statewide; on an annual basis this translates to approximately $27 million dollars. The petroleum products taxed represents roughly 2/3 of the barrels of oil imported each year.

Ongoing/Current Issues

Originally, of the $1.05 tax, forty-five cents was allocated to supporting environmental response, energy, and food security, while the remaining sixty-cents was apportioned to the general fund. During the 2013 Legislative session, though unsuccessful, it was proposed that the tax be distributed according to its intended purpose, rather than given to the general fund. As such, increasing the amount allocated to environmental response, energy and food security funds, along with re-establishing the energy systems development special fund, and extending the barrel tax to 2030 have been proposed under SB2196 in the current Legislative Session.1 The barrel tax is set to sunset in 2015, and Hawai‘i’s energy industry hopes to extend the repeal of the barrel tax to 2030, the same year as Hawai‘i’s ultimate renewable portfolio standards (RPS) target.


Table 1 below shows the original breakdown under Act 73, SLH 2010, and the allocation as of July 1, 2013.


-- Sherilyn Wee and Makena Coffman



1  The bill text and status can be found here: http://www.capitol.hawaii.gov/measure_indiv.aspx?billtype=SB&billnumber=2196&year=2014

Is inequality actually lower in Honolulu?

Posted February 5, 2014 | Categories: Hawaii's People, Blog

The outlook for inequality and poverty in Honolulu is not as rosy as it might seem at first glance.

On the 50th anniversary of the ‘War on Poverty’, poverty and income inequality are major policy issues facing President Obama’s administration and driving public policy analysis and debate. The Business Journals, parent of Pacific Business News (PBN), took a look at several measures of inequality and PBN followed up with a Honolulu specific look at the findings.

The PBN story characterizes Honolulu as among the most “equal” cities of the 102 examined in the study.1

However, several inequality measures used in The Business Journals’ analysis are not ideal for cross-city comparisons. The measures are constructed using data from the American Community Survey, making them available for all cities, but not necessarily appropriate for cross-city comparison.

A major concern is that the inequality measures use dollar amounts as cutoffs. One measure looks at the ratio of the number of households earning less than $50,000 to the number of those earning more than $200,000. These data for Hawaii are certainly interesting. To see them for your neighborhood or county, visit our new income distribution map to see how these income groups are situated geographically. However, while these dollar cutoffs can help us identify differences in economic fortune across our state where the prices for goods are relatively constant, they are much less useful for comparing differences across the country where prices can vary dramatically.

The cost of housing, food, transportation, etc. is not equal across all cities. Differences in regional prices, and the need for regional price parity to make proper comparisons makes any comparison using a fixed-dollar poverty line substantially less meaningful.

In an analysis of regional prices by the BEA, the state of Hawaii had the highest price level among states, and Honolulu had the second-highest price level among cities.2 The Business Journals analysis does not take these price differences into account. Honolulu’s high prices mean that a household earning $50,000 in Honolulu will be worse off than a household earning $50,000 in most other cities. Also, a household earning $200,000 in Honolulu is not as wealthy as households earning $200,000 in most other cities. Both of these effects mean The Business Journals’ ratio of low-income households to high-income households in Honolulu is too low.

Regional price differences also mean that estimates of the number of poor households in Honolulu using a single poverty line for the entire US are deceptively low.

Comparisons of inequality across the US are made difficult by regional price differences. We’ll do our best here to keep you informed. Watch for more updates, data, and visualizations from UHERO on issues of poverty and income inequality.

---Jonathan Page and Tim Halliday



1  In such markets, competition between firms does exist as firms try to attract more customers, but it is realized via incentives rather than changes (decreases) in prices. Instead, prices are kept relatively constant, but firms engage in fierce advertising highlighting the differences across products to attract customers.

However, G. Scott Thomas, author of The Business Journals post, does mention the nationwide trend towards greater inequality found by this Congressional Budget Office (CBO) study. But there is no city-level analysis of the trends in inequality in either the CBO study or The Business Journals post.


2  See http://uhero.hawaii.edu/news/view/235 for a summary of this report as it applies to Hawaii.

Hawaii's Minimum Wage, Poverty, and Job Creation

Ten different bills have been introduced at the legislature this session to raise Hawaii's minimum wage. According to proponents, raising Hawaii's minimum wage is necessary to help the working poor whose buying power has diminished. In the past, UHERO briefs and blog posts have argued that the minimum wage is not an efficient tool to fight poverty and pointed to the earned income tax credit as a more effective tool. On the flip side, we have pointed out that, unlike other anti-poverty programs, raising the minimum wage is politically attractive because it involves almost zero administrative cost and no new government expenditures. This post focuses on the growing body of economic evidence that small minimum wage increases reduce poverty and have little or no adverse effects on employment levels.

For example, a new paper by Arindrajit Dube from the University of Massachusetts Amherst finds that raising the minimum wage by 10 percent (for example from $7.25 to $8.00) would reduce the number of people living in poverty by 2.4%. Dube's paper makes use of data from the Current Population survey for the period between 1990 and 2012 to examine the impact of minimum wages on the distribution of family incomes for non-elderly individuals. This line of research generally compares areas with minimum wage changes to a control group of areas with no minimum wage changes. A key contribution of Dube's work is demonstrating that estimates from earlier research suffer from serious limitations due to their failure to adequately control for state-level economic performance unrelated to minimum wage changes. When he accounts for these factors, he finds larger anti-poverty effects and "robust evidence that higher minimum wages moderately reduce the share of individuals with incomes below 50, 75 and 100 percent of the federal poverty line." To put these findings in perspective, SB 2828  introduced on behalf of Governor Abercrombie would increase the Hawaii's minimum wage to $8.75 dollars/hour in 2015 and could help reduce Hawaii's population living below the poverty line by more than 7,000 persons. The additional increases to $10/hr in 2010 may reduce poverty even further, but note that the estimated 2.4% reduction in poverty is based on the small minimum wage changes observed historically and such estimates should generally not be extrapolated to reach conclusions about much larger changes.

While the conclusion that an increase in the minimum wage helps to reduce poverty is fairly widely accepted (even if there are more efficient but less politically acceptable means of attacking poverty), many economists will still point to the potential negative effects of raising the minimum wage on employment levels. Any Econ 101 student should know that imposing or raising a minimum wage in a competitive labor market reduces the quantity of labor demanded and leads to unemployment. But this theoretical prediction is subject to empirical verification, and this is where much of the economic debate has occurred during the past 20 years. A recent paper by Dube, Lester, and Reich (2010) compares changes in restaurant employment across contiguous U.S. counties with different minimum wage levels using quarterly data from 1990 to 2006. Their rich data set provides them with significantly more experimental variation than most of the literature, and they find "strong earnings effects and no employment effects of minimum wage increases." Using the same statistical techniques common in the earlier literature, they largely replicate the literature's finding of job losses associated with minimum wage increases. But when they control for regional and local differences in employment trends that are unrelated to the minimum wage, they find "no detectable employment losses from the kind of minimum wage increases we have seen in the United States."

There is no doubt that the debate over the impact of minimum wage laws is alive and well. But there is some evidence that economists are leaning towards raising the minimum wage and indexing it to inflation. In a survey of 41 leading economists by the the University of Chicago's Booth School of Business, 47% agreed that the benefits outweigh the costs of such a policy, while only 11% disagreed (the rest of the panel had no opinion or were unsure). Research is increasingly turning to the question of how can a minimum wage increase not lead to job losses. While the Econ 101 competitive model of labor markets leads to the invariable conclusions that there will be job losses, there are a wide variety of frictions and costs that are not considered in this simple model. For example the costs of job search may result in lower turnover when minimum wages are higher. It is possible that some employers will be discouraged from creating new jobs as minimum wages rise, but others will be more successful in filling positions and retaining workers. As you might have guessed, Dube, Lester and Riech (2013) find evidence that worker turnover falls sharply following a minimum wage increase while overall employment in low-wage sectors is largely unchanged. A survey of the literature conducted by John Schmitt at the Center for Economic and Policy Research summarizes the research into why minimum wage increases don't lead to employment losses. The evidence suggests that businesses adjust to minimum wage changes in a wide variety of ways, and that reductions in labor turnover; improvements in organizational efficiency; reductions in wages of higher earners ("wage compression"); and small price increases "appear to be more than sufficient to avoid employment losses, even for employers with a large share of low-wage workers."

- Carl Bonham

In Search of the Glass Ceiling: Deciphering Data on Gender and Wages

Posted January 27, 2014 | Categories: Blog

The gender pay gap gets a lot of attention, but what can we learn about it by looking at the wage data?

The Hawaii Equal Pay Dashboard compares female salaries to male salaries over time. It breaks up the workforce into a few dozen occupation groups, and uses American Community Survey (ACS) data from 2005-2012 to try to get a glimpse into our state's state of gender wage equality. The data however, presents a picture that's fuzzy and misleading to say the least. But it also helps to illustrate the complicated reality of wage inequality visualization.

For example, looking at the ACS median wage data we see that there are substantial differences across genders within the same occupation. However, the real big questions are: are these big differences really there? Do men in Legal Occupations really make 171% more than women in the same occupation? And do women really make 18% more than men in Installation, Maintenance and Repair Occupations?* The answers are not straightforward and are much more complex, a testament to the multi-faceted - and sometimes misleading - nature of data.

First of all, the ACS’s median wages for men and women are just estimates and the precision of these estimates varies across occupations. To produce an estimate, the ACS surveys a sample of housing units and people in the population, not the entire population. That’s why in addition to the median estimates, the ACS provides a 90% confidence interval, which is a statistical measure that reflects a degree of uncertainty around this estimate. The larger the confidence interval, the lower the precision of that estimate and the lower confidence we have that this estimate is close to the true value. For example, in the case of male salaries for Legal Occupations for the 2007-2009 aggregate, that confidence interval spans $116K - $180K, a huge range. It means that there is a 90% chance that the median wage falls within this range, but we’re not quite sure what exactly the median male in the legal profession earns.

Imagine telling someone that the geographically average person in the continental US lives in Chicago… but with a confidence interval that extends east to New York City and west to Los Angeles. What would we actually know about the way the population is distributed? It's difficult to tell, to say the least.

The size of the confidence interval is affected by the quality and the amount of data collected. One way the ACS is able to produce more accurate estimates for occupations with a small number of workers is by aggregating survey data over several years. Check out how the one-year aggregation data picture (data from just one individual year) compares to the three- or five-year aggregation data pictures (data created by averaging the results from clusters of three or five years, respectively). The confidence intervals narrow as more time periods are aggregated and more data are included in the estimate’s calculation. However, interesting characteristics of individual years are lost in the aggregation process.

Complicating things even further is the issue of important pieces of information not shown in this particular data view. An array of important factors such as education levels, experience, training, industry, job specific duties, and so on affect wages and can all affect this data-drawn picture of the state of equality. In a way, a comparison of the median wages within the same occupation without accounting for differences in qualifications, employment industry, and other factors that are responsible for differences in wages is like comparing papayas to taros.

The takeaway? While a glance at the tilt of the visualization gives us the impression that the data does seem to be skewing wages in the favor of men, the bottom line is that things might not be what they seem. To be able to make an accurate conclusion, the reader should be paying attention to what is displayed, and recognize the fact that visualizing equality is complicated. An enormous amount of additional information would have to be included in a visualization such as ours for it to really be able stand a chance at tackling the gender equality question in a meaningful way.

How do your real life experiences with the issue gender and wages in your occupation group compare? What other information would help you in understanding this issue better?


---Inna Cintina and Natalie Schack 

*This data is from the 3-year aggregate level for the years 2007-2008.

Are Recessions Bad for Your Health?

Posted January 21, 2014 | Categories: Blog

Our work indicates that a bad economy can kill you.

Specifically, we show that over the ten years from 1984 to 1993 that a one-percentage point rise in the unemployment rate increased the risk of dying within the subsequent year by 6% for working-aged men. This translates to roughly 24 more deaths per 100,000 people. A percentage point increase in the unemployment rate increases the number of unemployed by about 1.5 million, resulting in about 360 additional deaths of men between the ages of 30 and 60. Of note is that we find no such relationship for women or the elderly who tend to have weaker ties to the labor market.

Social scientists have long been concerned with the health consequences of business cycle fluctuations and, more generally, changes to one’s socioeconomic status. If public health tends to decline when the economy performs poorly then the costs of recessions are not limited to purely economic hardships such as unemployment and underemployment. Indeed, early work on the topic by Harvey Brenner, a public health researcher at the University of North Texas and Johns Hopkins University, suggested that mortality rates did tend to rise as the economy worsened. This work appears to indicate that poor job market prospects come with increased stressors that pose health hazards.

Subsequent work challenged these findings. Christopher Ruhm, a health economist at the University of Virginia, raised numerous methodological issues with the earlier studies and showed that once these were addressed mortality rates actually declined during recessions. This ushered in an era of work by health economists investigating if recessions were, in fact good for your health. The explanation for this counter-intuitive result was that people tended to lead healthier lifestyles during recessions. Recessions created more time for people to look after themselves and participate in health activities. Having less money also made people less likely to purchase harmful vices.

However, more recent work supports the earlier idea that recessions have a negative impact on public health. For example, research by Till von Wachter and Daniel Sullivan, economists at UCLA and the Chicago Fed, looked at micro-data from Pennsylvania and showed that involuntary job displacements during the early Eighties were associated with much higher mortality risks. Other work by researchers at UC Davis replicated Ruhm’s findings but showed that mortality rates also declined for the elderly and the young. Since those groups should have weaker labor force attachments, this research cast doubt on the healthy living mechanism.

So, what explains the difference between our finding and Ruhm’s? In principle, both of our studies should be identifying the same parameter and so should deliver the same or, at least, similar estimates. However, the one key difference is that our study uses data that are at the individual level, whereas other work uses data that are at the state level. This is noteworthy because higher levels of aggregation have been known to induce biases in primitive relationships that exist for individuals. Our paper argues that this use of individual data supports the case for the negative relationship between recessions and health.

While our work cannot tease out the mechanisms that generated our findings, we can offer some speculation. We believe the relationship between stress under poorer macroeconomic conditions and the risk of dying from cardiovascular-related causes may be a viable mechanism worthy of future investigation.

---Tim Halliday



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