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Economic Currents

Keep up to date with the latest UHERO news.

Tariff Peril

Posted May 8, 2019 | Categories: Hawaii's Economy, Blog

In a Sunday Tweet, President Trump threatened this week to increase dramatically the tariffs he has placed on imports from China. The aggressive trade policies of the administration and retaliation by foreign countries are already having an adverse impact on the US, China, and other trade partners. Further escalation could potentially lead to a broad global slowdown. How large are these effects likely to be?

So far, there have been several rounds of US actions. To date these include tariffs on imported washing machines and solar panels and levies on imports of steel and aluminum. The administration has imposed 10% bilateral tariffs on $50 billion of imports from China, later expanded to another $200 billion of Chinese goods. Levies on imported autos and parts are also under consideration. In this week’s announcement, the President threatened to go ahead on Friday with a previously announced hike to 25% tariffs on Chinese goods, citing the unwillingness of China to move forward with structural changes demanded by the US in ongoing negotiations.

In each of these cases so far, other countries have retaliated in kind, often choosing targets for maximum political effect, including Harley Davidson motorcycles and Kentucky bourbon. In some cases, the possibility of levies has forced partners into trade agreements or at least negotiation. Threatened tariffs on auto imports from the European Union have been held off for now pending trade negotiations. Under threat of further trade restrictions, Canada and Mexico have agreed to a new deal to replace NAFTA. And President Trump has suggested that an expansion of tariffs to the remaining $267 billion in US purchases from China might be avoided if a trade agreement can be struck soon.

There is an overwhelming consensus among economists that these restrictive trade measures are harmful to many countries—including the US. Immediate costs include higher prices on imported consumer goods, higher production costs from imported inputs, and decreased access to foreign markets. While there will be some US jobs created as some production is re-shored, the number will be small, and other jobs will be lost as US companies are forced to abandon highly efficient global production arrangements or move offshore. Potential losses could hit business expectations and investment, and financial markets could also be affected, as recent volatility illustrates. In the long run, the US will lose out on opportunities by locking itself out of new trade liberalization agreements.

How large are these costs likely to be? The direct effect of the measures taken so far is fairly small. For example, the New York Times estimates that the appliance and solar panel tariffs and the 10% levy on $250 billion of Chinese imports will cost the average American family $127, about two-tenths of one percent of income. The International Monetary Fund estimates that the threatened hike in tariffs on China to 25%, on top of measures already implemented by the US and Chinese, will still take just two-tenths of a percent off US GDP and half a percent off Chinese output.

But estimates of adverse effects grow much larger if all threatened additional actions are carried out and equivalent foreign retaliation occurs. Throw in knock-on effects on confidence and investment and adverse stock market reaction, and the IMF estimates 2019 losses of nearly 1% of GDP for the US and 1.6% for China. The global economy overall would take a hit of nearly 1% of GDP. The United Nations separately estimates that spiraling trade barriers could cut world growth by more than 1%.

It is unclear how likely it is that all of these actions and reactions will occur, particularly the potential falloff in capital investment and equity markets. But the bottom line is that Trump’s trade policies are already hurting the US. And a full-blown trade war would certainly have enough juice to edge us toward the next global downturn.

Effects of trade policy relative to baseline GDP levels

- Byron Gangnes, Peter Fuleky, and Carl Bonham


The Impact of Medicaid on Health Care Utilization among Hawai‘i’s COFA Migrants

Posted April 16, 2019 | Categories: Blog

The Compacts of Free Association (COFA) are treaties between the United States (US) and three Micronesian nations: the Republic of Palau, the Republic of the Marshall Islands, and the Federated States of Micronesia. Collectively, citizens of these nations are often referred to as “Micronesians.” COFA guarantees Micronesians free entry into the United States, the ability to work, and access to health care. Because of these provisions, many citizens of these nations have immigrated to the United States. Most COFA migrants reside in Hawai‘i and Arkansas. Currently, Hawai‘i is estimated to have about 28,000 COFA migrants. While precise motives for migration vary, economic considerations and threats from climate change throughout Micronesia provide strong incentives to leave COFA nations to come to the US. In addition, many citizens of COFA nations come to the US due to the lack of adequate health care in Micronesia.

Prior to 1996, COFA migrants in the US were eligible to obtain health insurance from the federal Medicaid program. However, welfare reform in 1996 made this group ineligible for federal funds through Medicaid. Note that Medicaid reform provided provisions for many immigrants to be eligible for Medicaid after residing for five years in good standing in US. However, because most COFA migrants are not classified as immigrants, but as “non-qualified aliens,” this provision did not apply to them. Hence, on one hand, COFA guaranteed this population access to health care in the US, but on the other hand, the 1996 welfare reform prohibited COFA migrants from obtaining it from the federal government.

Despite the lack of federal Medicaid financing for COFA migrants, the State of Hawai‘i continued to provide coverage via state-funded health insurance in various forms for many low income COFA migrants, including through a state Medicaid plan provided by the State of Hawai‘i Medicaid agency (called Med-QUEST). Due to a court ruling in April of 2014, however, state Medicaid coverage for this population was suspended1. As a consequence, most non-disabled, non-infant, non-pregnant, and low income COFA migrants were ultimately denied access to traditional Medicaid benefits in March of 2015. Instead, they were asked to obtain private health insurance through the health care exchanges set up under the Affordable Care Act.

There are several reasons to suspect that this move from Medicaid to private insurance might have reduced the use of medical services. First, take-up rates of insurance may have declined as a consequence of the transition which may, in turn, have reduced the use of medical services. For example, enrollment in private insurance in the exchanges is notably more complicated than enrollment in the State’s Medicaid program. Medicaid has a year-round enrollment period, whereas the enrollment period on the exchanges is only six weeks. In addition, COFA migrants must first apply for Medicaid and get rejected before they can apply for insurance on the exchanges which are also not translated into COFA languages (Hofschneider 2019). Second, cost-sharing is minimal on Medicaid, whereas most private plans have co-payments and co-insurance which can be prohibitive for poorer migrants. While the premiums for this population were covered by Medicaid, payment processes and expectations can still be complex to understand and navigate, particularly, for this population.

In a recent UHERO working paper titled, “The Impact of Public Health Insurance on Medical Utilization in a Vulnerable Population: Evidence from COFA Migrants,” we investigate the effect of the expiration of Medicaid benefits on medical utilization among COFA migrants. We show that there was a sharp reduction in the number of emergency and in-patient medical care admissions charged to the State’s Medicaid program after the expiration of benefits for COFA migrants relative to the non-Hispanic white and Japanese populations in Hawai‘i. In particular, Medicaid-funded ER visits and inpatient admissions declined by 69% and 42%, respectively. This sharp reduction in utilization is consistent with other studies that have investigated the impact of the expiration of Medicaid benefits such as studies on Tennessee after it discontinued Medicaid benefits (see DeLeire 2018, Tarazi 2017, Tello-Trillo 2016). At the same time, there was a substantial increase in the number of emergency room (ER) visits and inpatient admissions charged to private payers, indicating that there was a move towards private insurance among COFA migrants after Medicaid benefits expired. However, the magnitude of this increase was smaller than the reduction in Medicaid-funded utilizations. As a result, net inpatient admissions and emergency visits declined.

These findings can clearly be seen in Figure 1. The figure shows the utilization of COFA migrants in Hawai‘i relative to our control groups for each month between January 2014 and December 2015 for both inpatient and ER utilization. The vertical line corresponds to the official expiration date of March 2015. The left panel shows a net decline of inpatient utilization after the expiration date but no relative impact prior to the official expiration date. In the jargon of the empirical microeconomics literature, this means that there were no pre-trends which is a precondition for valid causal inference. Note that these utilizations could have been charged to any payer and so they include those charged to both private and public insurers. The right panel shows that there was also a dramatic decline in emergency room (ER) visits charged to any payer. By-and-large, this decline took place after the official expiration date. However, and in contrast to inpatient utilization, we do see that the downward trend in utilization pre-dated the official expiration date. This implies that the actual treatment effect is greater than what we have estimated. While it is not entirely clear why the decline in ER utilization pre-dated March 2015, we suspect that the confusion surrounding the policy may have led many providers to believe that COFA migrants could be not enrolled in Medicaid even while they were still eligible.

This confusion can be seen by a commensurate run-up in uninsured visits to the ER by COFA migrants after and just prior to the expiration of benefits. This is shown in Figure 2 which displays uninsured ER visits of COFA migrants relative to our control groups in each month during 2014-15. First, this figure shows an increase in uninsured ER visits after the expiration of Medicaid benefits. This indicates that, in the immediate aftermath of the expiration of benefits, many COFA migrants were not enrolled in private insurance plans. Second, the figure also shows that the ramp-up in ER visits pre-dated March 2015, which is a period when COFA migrants should have been enrolled in the State’s Medicaid plan. Typical practice at most hospitals with emergency rooms is to support the enrollment of eligible, uninsured patients into Medicaid; this guarantees that the hospital will get paid for the visit. That we do not see this in the period just prior to the expiration date indicates a rigidity preventing this from happening2.

This implies that the reduction in insured COFA migrants’ ER visits was offset by an increase in the number of uninsured visits to ER. The corresponding increase in uninsured ER visits was about one-third of the decline in Medicaid funded ER visits. These were visits that ostensibly should have been covered by private insurers.

Another important result from our study is that the expiration of Medicaid benefits appears to have increased ER visits of Micronesian infants that were funded by Medicaid. This can be seen in Figure 3 where we show the effects of the transition from Medicaid by age on Medicaid-funded ER visits. We break the treatment effects down by age into five year age bins, except we used a separate category for infants. The figure shows that Medicaid-funded ER visits declined for most ages under 65 except for infants who experienced a dramatic increase. While the hospital data that we use makes it hard to pin down the precise mechanism, we suspect that the expiration of benefits for most COFA migrants may have led many to believe that their newborns were also not covered by Medicaid contrary to fact (infants remained eligible for Medicaid after March 2015). This may have led to a decline in ambulatory care for newborns that was made up for by an increased use of the ER for Micronesian infants.

In some sense, this can be viewed as a reverse woodworking effect. Benefits expired for a large swath of the Micronesian population in Hawai‘i. Many COFA migrants were actually still covered by the State’s Children’s Health Insurance Program (CHIP) program. However, it appears as if the salience of the expiration of benefits for the majority of migrants led many eligible migrants to believe that they were not covered. In a similar vein, but in the opposite direction, Frean, et al. (2017) found that the expansion of Medicaid under the ACA increased enrollment in Medicaid among people who were previously eligible for Medicaid benefits.

Our findings indicate that medical utilization declined for COFA migrants after Medicaid benefits expired. This could be good or bad. It could be good if the transition to private insurance increased the use of preventative services that reduced the need for ER visits and hospitalizations. However, many aspects of our analysis are not consistent with this story. Notably, the increase in uninsured ER visits after and just-prior to the expiration of benefits is not consistent with increased prevention. It also indicates low take-up rates of private insurance which is a pre-condition for better use of preventative care. In addition, the increase in ER visits by Micronesian infants after benefits expired appears to be consistent with a substitution of ER visits for ambulatory care, which is also not consistent with increased prevention. Our interpretation of our findings then is that the decline in utilization is probably not a reflection of increased prevention. We suspect that it is a reflection of low take-up of private insurance and the effects of costs sharing on utilization. This most likely reflects a decline in needed care.

Figure 1: Event Analysis, Inpatient and ER Utilization

Figure 2: Event Analysis, Uninsured ER Visits

Figure 3: Effects by Age

- Timothy Halliday and Randall Q Akee



DeLeire, T. (2018). The Effect of Disenrollment from Medicaid on Employment, Insurance Coverage, Health and Health Care Utilization (No. w24899). National Bureau of Economic Research.

Frean, M., Gruber, J., and Sommers, B. D. (2017). Premium subsidies, the mandate, and Medicaid expansion: Coverage effects of the Affordable Care Act. Journal of Health Economics, 53, 72-86.

Hofschneider, A, “Micronesians in Hawaii Still Struggle to Get Health Care” Civil Beat, April 3, 2019.

McElfish, P.A., Hallgren, E. and Yamada, S., 2015. Effect of US health policies on health care access for Marshallese migrants. American journal of public health, 105(4), pp.637-643.

Tarazi, W. W., Green, T. L., & Sabik, L. M. (2017). Medicaid disenrollment and disparities in access to care: evidence from Tennessee. Health services research, 52(3), 1156-1167.

Tello-Trillo, D. S. (2016, June). Effects of Losing Public Health Insurance on Healthcare Access, Utilization and Health Outcomes: Evidence from the TennCare Disenrollment. In 6th Biennial Conference of the American Society of Health Economists, Ashecon.

1For details, see McElfish, et al. (2015).

2Private communication with physicians working at Queens Medical Center in Honolulu indicated that just prior to the expiration of Medicaid benefits, there was a sense that it would be difficult to enroll uninsured COFA migrants in the State’s Medicaid program so many providers may not have put forth the effort.

How high is too high? What’s known and unknown about minimum wage increases

In 2014, while the legislature was debating Senate Bill 2609, which eventually raised Hawaii’s minimum wage from $7.25 to $10.10, we wrote about the growing body of evidence that small minimum wage increases reduce poverty and have little or no adverse effects on employment levels. At the same time, we cautioned that findings of research based on the historically small increases in minimum wage levels might not apply to the much larger increases now being contemplated in Hawaii. Back in 2014, no state or city had even a $10 minimum wage. Since then, ten large cities and seven states have adopted minimum wage policies in the $12 to $15 range.

The legislature is currently advancing two bills that would raise Hawaii’s minimum wage. Senate Bill 789 and House Bill 1191 would both increase the minimum from $10.10 today to $12 per hour in January 2020 and $15 per hour in 2023. This blog discusses results from two recent studies that have examined the impact of large minimum wage hikes that states and cities around the country have launched over the past five years. Based on this research, we argue that there is good reason to proceed with caution. While much of the new research continues to find that minimum wage increases to the $10-$15 range may have very small negative employment effects, some studies provide tentative evidence of much larger negative impacts that warrant additional careful analysis.

Economists studying the impact of minimum wage policy changes are inevitably faced with two important challenges. First, we are rarely able to conduct actual experiments. We simply do not observe policy changes that raise the minimum wage for a randomly selected group of workers while leaving a control group unaffected. Instead, they use quasi-experiments where statistical methods are used to identify minimum wage impacts. Much of the debate in the literature has focused on which method is most convincing for accurately identifying the causal effects. In fact, the growing consensus that modest minimum wage increases help reduce poverty and cause little or no negative employment effects was based in large part on the argument that researchers were using better methods for identifying control groups.

A second important problem facing researchers is that we don’t actually observe employment, hourly wages, and hours worked by low-wage workers. To address this second problem, the vast majority of research on the minimum wage has studied total employment and total wages paid in a low-wage industry such as the food service industry. But using total employment and wages paid to an entire industry group means that some of the workers being counted should actually be considered part of the unaffected control group. By lumping all workers in an industry together, we are including workers who are unlikely to be affected directly by minimum wage changes because they were already earning a wage above the new higher minimum wage. In other words, the use of aggregate data may lead to biased estimates of the impact of the policy change. The research we discuss below addresses these problems differently and, unsurprisingly, reaches different conclusions.

Research by Allegretto et al (2018) examines citywide minimum wage policies adopted in Chicago, Washington DC, Oakland, San Francisco, San Jose, and Seattle from 2009 to 2016, where minimum wages were raised to the $10-13 per hour range. They compare data on total employment and average earnings in the food service industry in each city with a minimum wage change (the treatment group) to the employment and average earnings for synthetic controls that closely matched the treated group prior to the policy change1. Their results are very similar across the cities and for alternative statistical methods. They find that a 10% increase in the minimum wage increases average earnings in the food service industry by 1.3-2.5%, with no significant negative impact on employment. Specifically, for a 10% increase in the minimum wage, they find average employment effects that range from a 0.3% decrease to a 1.1% increase.

Recent analysis by Jardim et al (2018) at the University of Washington focuses on the impact of Seattle’s minimum wage increase from $9.47 to $11 in 2015, then to $13 in 2016. Most minimum wage research uses data on total employment and wages paid to a low wage group most likely to be affected by policy changes, such as teenagers or restaurant workers. In contrast, Jardim et al (2018) use individual-level data on hours worked and employee earnings to compute hourly wages for the entire state of Washington2. This allows the authors to identify actual individuals who would be affected by minimum wage policies. While Allegretto et al. (2018) calculate average weekly earnings for the food services industry, Jardim et al (2018) study the effect of minimum wage changes on hours worked, since employers may reduce working hours in response to a minimum wage hike. They find that the increase in Seattle’s minimum wage from $9.47 to $11 in 2015 led to reductions in employment or hours that approximately offset the impact of the higher minimum wage on total worker earnings. And, the subsequent increase to $13 led to large reductions in hours for low wage workers of 6-7% that completely offset a 3% rise in the average hourly wages in such jobs. The overall impact was a $74 per month average reduction in the amount paid to workers in low-wage jobs in 2016. So Jardim et. al (2018) not only find evidence of significant and large negative employment effects associated with Seattle’s minimum wage policies, but they also find evidence that the negative effects may increase as the wage floor increases. Bigger minimum wage hikes have larger effects than smaller ones.

Like all minimum wage research, the Seattle study has its shortcomings and its critics. While their data set allows them to study the impact on wages, hours, and employment, because of data limitations they are missing an important segment of Washington state employers: those businesses with multiple locations that only report their unemployment insurance information from a single account/location3. Such workers represent 29% of employees statewide. While the authors address many of the limitations of their study, these limitations have also been the focus of their critics. Zipperer and Schmitt (2017), for example, are highly critical, arguing that the Seattle study suffers from fatal data and methodological flaws.

All existing studies of the minimum wage suffer from data and methodology shortcomings, so that there remains a great deal of uncertainty about the employment effects of a $15 or higher minimum wage. Clearly, many more workers in Hawaii will be impacted in a move from $10.10 to $15 than the increase from $7.25 to $10.10 over the past four years. And this larger jump will be harder for employers to absorb through adjustments to prices, productivity, and efficiency. The results could be unintended.

The impact of minimum wage increases may depend in part on how high the minimum wage is relative to the existing median wage in that region, what researchers have dubbed the Kaitz index. Zipperer and Schmitt (2017) argue that studies finding little to no negative impact of minimum wage hikes on employment levels are based on the experience of local areas with Kaitz indices in the 32 to 55% range. They go on to argue that Seattle’s minimum wage increase to $13 left Seattle’s Kaitz index within that range, at 50.7%, and therefore it is unreasonable to expect any negative impact on employment.

How does Hawaii stack up in this respect? The median hourly wage statewide in 2017 was $20.02, so with a minimum wage of $10.10 the Kaitz index for the entire state is 50.4%. At the County level, the 2017 median hourly wage for Hawaii/Kauai was $18.51, while Maui’s median wage was $19.37 and Honolulu’s $20.62. So the Kaitz index in 2017 ranged from 49% for Honolulu to 54.6% for Hawaii/Kauai. The implication is that the statewide minimum wage is highest when applied to Hawaii and Kauai counties where the overall median wage is lower. We calculate Kaitz indices for potential minimum wage increases under HB1191 and assume that the median hourly wage for each county grows at the same rate as UHERO’s county forecasts of average wage growth. The resulting Kaitz indices fall from 2017 to 2019 as the minimum wage remains $10.10 per hour, and the median hourly wages grow. The indices then rise and fall as the minimum is raised sporadically, reaching  a high point in 2023 of 70.6% for Hawaii/Kauai, 67.4% for Maui, and 63.4% for Honolulu.

The point we are trying to make is not that the $15 minimum is too high, but that it is well outside the range that has been studied extensively for US minimum wage changes over the past 25 years. This, along with the changing Hawaii economic landscape with rising unemployment, falling employment, and dramatically slowing job growth all suggest that a cautious and possibly more gradual approach may be called for. At least until we have a more comprehensive view from the economic literature about the impacts of large minimum wage hikes.

- Ashley Hirashima and Carl Bonham


1They use synthetic controls in addition to the more common nearby communities controls. Synthetic controls use algorithms to select data from a large number of locations that did not experience similar minimum wage changes but share similar characteristics necessary to identify the causal impact of new minimum wage rules in the treated economy.

2Washington is one of only four states that, as part of the administration of unemployment insurance, collects data on both employee quarterly earnings and hours worked.

3Their analysis relies on geographic identification of employers at the Unemployment Insurance account level, so they are only able to include employers that operate from a single location, or multi-site firms that choose to establish UI accounts from each location.

Seiji Naya Memorial Lecture: From First Canoe To Statehood, Eight Hundred Years of Economic And Political Change in Hawai‘i

Posted March 15, 2019 | Categories: Blog

UHERO Fellow Sumner La Croix will be giving The Seiji Naya Memorial Lecture on Thursday, April 11th. Sumner will be talking about his new book, "From First Canoe to Statehood: Eight Hundred Years of Economic and Political Change in Hawai‘i"

Reception starts at 5:30 pm
Seminar: 6:00 pm – 7:30 pm
Shidler College of Business, Room A101

Biocultural Restoration Workday Draws Community Together to Plant an Agroforest

Mahealani Botelho of Kākoʻo ʻŌiwi shares the vision for biocultural restoration of agroforestry in Puʻulani in an opening ceremony (photo by Randy Fish).

“I ola ʽoe, i ola mākou nei.” A community member recites the pule (blessing), “my life is dependent on yours, your life is dependent on mine”, to a native aʽaʽliʽi shrub as she gently tucks them into the ground. The side of the ridge is a sea of colorful flags marking holes where nearly 200 energetic volunteers ages one through 85 plant a variety of native and other culturally valued trees and shrubs. Here the goal of restoration is not just the final outcome, but the process of bringing community together and restoring cultural connection to place.

Julianna Rapu and her keiki plant an ʻāweoweo (photo by Leah Bremer).

Puʽulani stretches like a finger into the Heʽeia wetland, a ridge rising above the alluvial plane. Restoring the traditional name, heavenly or spiritual ridge, and planting culturally important species are the first steps in the process of reconnecting people with this puʽu. Staff at the local nonprofit Kākoʽo ʽŌiwi (kakoooiwi.org) are working to restore ecological and cultural vitality to over 400 acres here.

Community workday at Puʻulani, Kāko’o ʻŌiwi on February 12, 2019 (photo by Sarah Weibe).

Many native, culturally important plants are only found in remnant forests high up in the mountains. There, it can take significant time and energy to reach them, meaning many people, especially keiki and kupuna, do not have the opportunity to interact with the plants. The plants at Puʽulani are only a short walk or drive from Kākoʽo ʽŌiwi’s entrance past an extensive network of loʽi. As one volunteer reflected, “it is uplifting to see so many people come together from diverse walks of life.”

Kākoʻo ‘Ōiwi loʻi and road to Puʻulani (photo by Sarah Wiebe).

“I’ve done a lot of restoration, but I have never seen ohiʽa next to ʽawa and ʻāweoweo. Why did you put them together?” a volunteer asked. The goal of many restoration projects is to re-establish native forest as it was pre-European contact. Instead, at Puʽulani we are bringing together plants that help us achieve both biological and cultural (biocultural) restoration goals such as strengthening community connectedness to place, producing lei making materials, medicine, and food, sequestering carbon, and reducing erosion in a land use system called agroforestry.

Agroforestry is the intentional combination of trees with crops and/or livestock. At first glance, a multi-story agroforest may look similar to a native forest, but the mix of plants is often different from what might grow together without human intervention and may include native and introduced species.

Agroforests were widespread in Hawaiʽi prior to European contact, yet relatively few remain today. At Puʽulani, we are interested in understanding how we can adapt traditional agroforest models to a contemporary context, designing systems that are resilient into the future.

To do this, we set up a controlled experiment testing two different restoration approaches, or species mixes. The hillside is divided into ten plots in which five plots have one set of species and the other five have a different group of species. In the plots we are tracking plant growth and survival as well as indicators of multiple ecosystem services such as soil carbon, erosion, and surveys of visitors who participate in the project. The project is a collaborative effort led by Kākoʽo ʽŌiwi’s staff and UH researchers from Botany, UHERO’s Project Environment, the Water Resources Research Center, and NREM.

By documenting benefits and costs of our two approaches over time, we hope to provide land managers, farm owners, and others with information that can use to make decisions about adopting agroforestry on their land.

While we are still finishing the initial agroforestry planting at Puʽulani, the keiki plants are already creating space for community to learn and feel connected. As one person expressed about their experience, “I understood the energy exchange of giving back to the land, I felt a part of the community taking care of the land that takes care of us.”

Want to get involved? Join us at Kākoʽo ʽŌiwi every second Saturday of the month to care for the agroforest and loʽi.

- Zoe Hastings, Mahealani Botelho, and Leah Bremer
Zoe Hastings is a PhD student and NSF Graduate Research Fellow interested in collaborative biocultural restoration of agroecological systems. She works closely with Mahealani Botelho and others at Kākoʻo ‘Ōiwi and the UH research team to collaboratively design the restoration and research process at Puʻulani. Leah Bremer is an Assistant Specialist with UHERO’s Project Environment and the Water Resources Research Center and a project PI alongside Tamara Ticktin and Clay Trauernicht. Many others have contributed to this effort including Kanekoa Kukea-Shultz, Nick Reppun, and all the Kākoʻo ʻŌiwi staff, as well as Angel Melone, a graduate student helping with many dimensions of the project. We thank our funders, including the United States Department of Agriculture, Natural Resources Conservation Service, Conservation Innovation Grants program (# NR1892510002G003), the College of Social Sciences Research Support Award, and the Heʻeia National Estuarine Research Reserve.


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