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Honolulu Star Advertiser columnist, Lee Cataluna, recently asked: “How many tourists is too many tourists?”1 Apparently, she already knew the answer. To her, the 8.5 million plus tourists coming to Hawaii each year is way too many. She laments that nobody seems to be talking about limiting the number “…like maybe we’ve all become accustomed to being crowded. Like maybe we lost the fight.” Tourists used to stay in designated tourism zones like Waikiki, but now they are everywhere. Indeed, Hawaii Tourism Authority’s (HTA) 2015 study of vacation rental units “show that there were vacation rentals available in almost every zip code across the state.”2 We are receiving record number of tourists, but 2016 visitor spending per Hawaii resident is expected to fall 31% below its 1988 peak, after adjusting for inflation. Cataluna concluded: “It would be one thing if the explosion in tourism meant better living for everyone, nicer schools, cleaner parks, spiffy roads, but we’re getting all the tourism problems without the tourism benefits.”
Recent surveys show a growing percentage of Hawaii’s residents agree with Cataluna. Still, most people in Hawaii “strongly/somewhat agree that tourism has brought more benefits than problems.”3 HTA’s 2015 Resident Sentiment Study noted that 66% of Hawaii’s residents surveyed felt that way. But the percentage of residents who agree with the quote has been slipping in recent years. The percentage used to be in the 70s, going as far back as 1975. However, the percentage of respondents who perceive “Tourism has been ‘mostly positive’ for you and your family,” has slipped quite a bit from 60% in 1988 to 40% in 2015. The less positive responses at the individual/ family level might be explained by the fact that we are much less dependent on tourism than we were 25 to 30 years ago as tourism’s imprint on Hawaii’s economy—measured by its share of the state’s gross domestic product (GDP)--has declined. Tourism’s (direct) share of Hawaii’s gross domestic product peaked in 1988 at 24.7%4; by 2010 it had fallen to 12.3% (16.4% in 2010 if tourism’s indirect effects are included, and 16.7% in 2015).5
The surveys show a more disturbing trend; the majority (58%) of the respondents to HTA’s survey in 2015 agreed with the statement: “This island is being run for tourists at the expense of local people.” The first year this happened was in 2005. Yet, no follow-up studies have been done to find the reasons for the response and hence how to reverse the perception. If residents in growing numbers feel their wellbeing is not the state’s priority in developing tourism, how might that affect the “Aloha Spirit” which is so important to the industry? HTA conducts a resident sentiment study almost every year; I wonder how many people pay any attention to them.
Cataluna is not the first to inquire about Hawaii’s tourism carrying capacity. In the late 1960s and the entire decade of the 1970s, many in Hawaii felt that tourism was growing way too fast.6 The average annual rate of increase in visitor arrivals in Hawaii was 20% in the 1960s and nearly 9% in the 1970s. In response, the Legislature passed Act 133 (The Interim Tourism Policy Act) in 1976 which required the State to craft a 10-year strategic plan to chart the course of tourism development for the next ten years. It became part of Hawaii’s first (overall) State Plan in 1980. In 1980 Hawaii had 3.9 million visitor arrivals compared to less than 300,000 in 1960, and the number kept rising. As the count of visitors approached 7 million in 2000 the 2001 Legislature directed the Department of Business, Economic Development and Tourism (DBEDT) to conduct a tourism sustainability study “to begin looking to how Hawaii can better monitor and manage future growth in tourism.” The $1.2 million study was completed in 2006.7 By then the number of visitors had increased to 7.5 million. Except during the Great Recession (2007-2009), even more visitors would come. Hawaii Tourism Authority (HTA) announces the ever-increasing numbers of tourists with pride since HTA is charged with promoting tourist travel to Hawaii. With the Great Recession, attention has focused more on how to bring more tourists in, and not how to keep them out.
Even if we wanted to, Hawaii has few weapons available to control the inflow of visitors. We can raise taxes on tourism to make it more expensive to visit Hawaii; spend less on tourism promotion; tighten and enforce land use and zoning laws; or make Hawaii a less attractive place for tourists (and, unfortunately, for us as well!) Unlike in some countries, Hawaii cannot (on our own) limit the number of people who can travel to Hawaii. An entry tax, imposed in many countries, would not be legal here.
Indirectly, Cataluna recognizes that there is no magic number of tourists that’s best for Hawaii. Eight million visitors might be o.k. “if the explosion in tourism meant better living for everyone, nicer schools, cleaner parks, spiffy roads.” We might welcome more tourists if we can increase tourism’s benefits and reduce its problems. In my 2004 book, I discuss some of the tools that can be used to achieve this.8 However, the burden is on us collectively to get it done. Hawaii doesn’t have nice public schools, clean parks and public bathrooms, and spiffy roads not because tourism has failed us, but because we have, for too long, come to accept that nothing works here. Ainokea! A term (meaning “I don’t care”) that columnist David Shapiro once described as “our official state attitude—not only in popular culture but also in officialdom.”9 What state and local governments everywhere are supposed to do well (i.e. the core functions of government), they are not done well here. Limiting the number of tourists won’t change that.
Money, or lack of it, is frequently given as the main reason why things don’t get done in Hawaii or why it takes so long to get things done. The U.S. Census Bureau reports that in 2014 Hawaii’s state and local governments received $14.5 billion in general revenues, or $10,239 per resident.10 (That amounts to nearly $9,300 available to spend on every man, woman, child and tourist present in Hawaii on a given day.) Hawaii ranked 10th among the 50 states and the District of Columbia.11 As a group, Hawaii’s state and local governments are not poor when compared to other states. According to the Tax Foundation, Hawaii has one of the highest state-local tax burden as a percent of state income among the 50 states and the District of Columbia. Why aren’t we getting more for our tax dollars? Some residents who find the “price of paradise” too high choose to leave. Even as more tourists are pouring into Hawaii, there is a net (and growing) exodus of Hawaii residents to the mainland even though the local economy is humming along and structural unemployment is non-existent.12
Lee Cataluna reminds us that in developing tourism the wellbeing of residents must come first. What should be done about tourism’s problems? Rather than trying futilely to limit the number of tourists, a better strategy is to attack the problems directly. That requires leadership and effort.
2Hawaii Tourism Authority, 2015 Visitor Plant Inventory.
3Hawaii Tourism Authority, 2015 HTA Resident Sentiment Study.
4Andrew Kato and James Mak, “Technical Progress in Transport and the Tourism Area Life Cycle,” in Clement A. Tisdell (ed.), Handbook of Tourism Economics: Analysis, New Applications and Case Studies, 2013.
5Eugene Tian, James Mak and PingSun Leung, “The Direct and Indirect Contributions of Tourism to Regional GDP: Hawaii” in Clement A. Tisdell (ed.), Handbook of Tourism Economics: Analysis, New Applications and Case Studies, 2013; also 2015 State of Hawaii Data Book.
6James Mak, Developing a Dream Destination: Tourism and Tourism Policy Planning in Hawaii, 2008.
7DBEDT, Planning for Sustainable Tourism, Project Summary Report, 2006.
8James Mak, Tourism and the Economy: Understanding the Economics of Tourism, 2004, Chapter 11.
9Honoluluadvertiser.com, “Why you should care about ‘ainokea,’” January 4, 2010.
10U.S. Census Bureau, State and Local Government Finances by Level of Government and by State: 2013-14.
11DBEDT, 2015 State of Hawaii Data Book, Table. 9.11.
12Honolulu Star Advertiser, “Census: Growing exodus of residents to mainland,” December 22, 2016.
Kīlauea volcano is the largest stationary source of sulfur dioxide (SO₂) pollution in the United States of America. The SO₂ that the volcano emits eventually forms particulate matter, another major pollutant. In a recent project, we use this exogenous source of pollution variation to estimate the impact of particulate matter and SO₂ on emergency room admissions and costs in the state of Hawai‘i.
To accomplish this, we employ two sources of data. The first is measurements of air quality collected by the Hawai‘i Department of Health taken from various monitoring stations across the state. The second is data on emergency room utilization due to cardio-pulmonary reasons which we obtained from the Hawai‘i Health Information Corporation. An important feature of our study is that our cost data are more accurate than the cost measures used in much of the literature. We then merged these data by region and day to obtain a comprehensive database of air quality and medical care utilization in the State of Hawai‘i. Importantly, we employed coarse geographic information on the patients’ residence (as opposed to the hospital in which they were admitted) when computing the utilization time series by region to ensure that our utilization measures corresponded more accurately with the pollution exposure. Using the merged database, we then employed regression techniques in which we related ER utilization and charges to measures of exposure to particulates and SO2 while controlling for comprehensive seasonal patterns and regional effects.
We find strong evidence that particulate pollution increases pulmonary-related hospitalization. Specifically, a one standard deviation increase in particulate pollution leads to a 2-3% increase in expenditures on emergency room visits for pulmonary-related outcomes. However, we do not find strong effects for pure SO₂ pollution or for cardiovascular outcomes. We also find no effect of volcanic pollution on fractures, our placebo outcome. Finally, the effects of particulate pollution on pulmonary-related admissions are most concentrated among the very young. Our estimates suggest that, since the large increase in emissions that began in 2008, the volcano has increased healthcare costs in Hawai‘i by approximately $6,277,204.
These estimates provide evidence of some of the external costs of particulate pollution. Importantly, other studies have had a difficult time unraveling the effects of particulate pollution from other types of pollution such as carbon monoxide because they tend to be highly correlated. In contrast, in our data, the correlation between particulate pollution and other pollutants (aside from SO2, of course) is considerably smaller than the other literature on the topic that largely relies on manmade sources of pollution. In this sense, we provide one of the best available estimates of the pure impact of particulate pollution on human health.
Changes in population age structure have important implications for the economies of all countries irrespective of their level of development. One reason age structure is so important is that children consume but produce little or nothing through their own labor. To survive and prosper they must depend on transfers from adults – their parents, of course, but also tax payers. High material standards of living are harder to achieve in countries with young populations, because the number of productive adults is low relative to the number of dependent children. Fertility decline has led to a demographic dividend as the number of dependent children has declined relative to the number of working-age adults. This phenomenon is captured by trends in the support ratio, a key summary measure shown in the Interactive Data Explorer. Other things equal, output per consumer is proportional to the support ratio, and the rate of growth of output per consumer equals the rate of growth of the support ratio.
The Interactive Data Explorer is based on National Transfer Accounts (NTA) and population estimates and projections for forty countries that vary greatly in their level of development, social, political and economic systems, and demographics. The interactive tool can be used to explore the economic role of age structure since 1950 and to assess the likely influence of demography over coming decades. The support ratio is a useful summary measure, but it is also important to drill more deeply into the data, a task made easier by the data explorer.
The rise in the support ratio or what we call the “first demographic dividend” can be seen by tracing the past of most high-income countries and many developing countries that now have low levels of fertility. South Korea’s support ratio, for example, increased from 0.67 in 1973 to 0.95 in 2006, a gain of over 30 percent. In some countries, like China and Vietnam, the gains are even greater. The Interactive Data Explorer shows that in other countries the first demographic dividend is more modest and that many African countries are just beginning to experience it.
An important question: Why does the support ratio rise more in some countries than others? One of the most important factors is the speed of fertility decline. The importance of this factor can be judged using the Interactive Data Explorer by selecting a country and a year in the future and then by choosing among alternative fertility scenarios. For Ethiopia in 2060, for example, the projected support ratio is 0.90 for the “Medium” fertility scenario as compared with 0.71 if fertility remains constant at the current level.
The economic impact of changing age structure depends on features of the economic lifecycle as measured by per capita consumption and labor income by age in National Transfer Accounts. On average, the gap between consumption and labor income is less for children and older adults in lower income countries than in higher income countries. In higher income countries, spending on the costly education of each child is relatively high, and often consumption by the elderly is much higher than consumption by younger adults. This can be seen by setting the Preview to “Per Capita Profiles” and looking at the thumbnails for 40 countries. (Spending on education, health, and other components of consumption is available in NTA but not shown in the data explorer.) General patterns can be seen in the per capita profiles, but also the importance of country-specific features. In a number of African countries the gap between consumption and labor income is high even among those in their 20s. This results in a depressed support ratio.
The rise in the support ratio is a transitory phenomenon and as populations begin to age the support ratio inevitably drops to lower levels. To see why this happens, pick a country, set Scale to “Percentages”, and press “play”, watching the upper right figure titled “Aggregate Consumption and Labor Income by Age”. Instead of high consumption among children, we have high consumption among the elderly. The transition is particularly strong in rapidly aging societies in East Asia and parts of Europe.
Fertility also plays a role here and given the low fertility scenario, aggregate consumption by the elderly would reach very high levels in many countries at time goes on. This can be seen by choosing some future year such as 2050 and varying the fertility level. The rise in old age consumption has a silver lining, however, to the extent that the elderly fund their own consumption by accumulating wealth or capital during their working years. Under these circumstances, the growth in old-age consumption will lead to a second demographic dividend as higher capital fuels development in the host country and possibly in other countries through higher rates of foreign investment.
- Ron Lee and Andy Mason
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.
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."