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Economic Currents

Keep up to date with the latest UHERO news.

State Government Revenue Sources

Posted November 1, 2017 | Categories: Blog, Visualizations

State governments raise revenue from a variety of sources, with most revenue coming from personal income taxes and general sales taxes.

According to the Pew Charitable Trust's "How States Raise Their Tax Dollars" personal income taxes are the greatest source of tax dollars in 28 of the 41 states that impose them. General sales taxes are the largest source in 17 of the 45 states that collect them. States that rely heavily on sales taxes, like Texas (62% of revenue) and Florida (59%) generally results in overall tax collection systems that are more regressive meaning lower income familes pay a larger share of their income in taxes than do those at the top of the income distribution. This visualization shows the source of each state's tax revenue. Select a state to highlight and compare to other states or the 50 state average.

   

For example, Hawaii raises 30.6% of its revenue from general income taxes, a bit lower share than the 37.2% for all 50 states combined. In contrast, Hawaii's General excise tax contributes 46.3% of state revenues vs 31.6% for general sales taxes for all states combined. While the property tax does appear in this visualization, most states do not levy significant taxes on personal or business property. When including taxes levied by counties in each state, using data for 2015, the Institute on Taxation and Economic Policy's 5th Edition of "Who Pays" finds that Hawaii's ranks 2nd among all 50 states in the share of family income going to taxes for families in the bottom 20% of the income distribution. To hear about other features of Hawaii's tax system, comparisons with other states and ideas for reform, join us for a tax conference this Thursday, November 2:

Hawai‘i Tax Structure & How Tax Systems Work 101


Cost-Effectiveness of Herbicide Ballistic Technology to Control Miconia in Hawaii

UHERO is working with Dr. James Leary (CTAHR) to assess cost effectiveness of Herbicide Ballistic Technology (HBT) operations to control invasive miconia (Miconia calvescens) plants before reaching maturity. Based on studies in Costa Rica, Tahiti and Australia, we can interpret spatial and temporal implications of management driven by miconia’s fecundity, dispersal, seed bank longevity and recruitment. We find that the dispersal kernel of miconia in the East Maui Watershed is closely matched to a similar probability density function developed from miconia naturalized in North Queensland, Australia (Fletcher and Westcott 2013). In this spatial model, 99% of recruitment was within 609 m with rare stochastic events (i.e., 1%) extending out to 1644 m. Based on these biological features, one autogamous, mature plant can impact up to 850 ha (i.e., 2100 acres) of forested watershed with hundreds to thousands of dispersed progeny germinating asynchronously over several decades (Fig. 1).

Figure 1. The dispersal kernel displays as a raster layer creating an 850-ha area calculation with corresponding probability density function (color shades).

Effective management is achieved when target mortality outpaces biological recruitment. Cacho et al. (2007) coined the term ‘‘mortality factor’’ described by the simple equation: m=Pd x Pk, where the probabilities of detection (Pd ) and kill (Pk)are equal determinants of the “mortality” product. Our current Pk is 0.98 for all HBT treatments. With this effective and reliable treatment technique, management outcomes largely depend on detection (Leary et al. 2013; Lodge et al. 2006). Koopman (1946) introduced the mathematical framework for estimating the probability of detection: Pd=1-e-c, where the probability of detection asymptotically approaches 1.0 with increasing coverage (Fig. 2). In operations, imperfect detection can be compensated by frequent interventions compounding coverage levels over time, but with obvious diminishing returns (Leary et al. 2014).

Figure 2. Probability of detection (blue) and the inverse for the equally important confirmation of no targets (orange). Note gray dash connotation of a theoretically “perfect” sensor, where coverage is equal to detection and confirmation.

The variable costs for HBT operations (e.g., flight time and projectiles) are driven by target density (Leary et al 2013, Leary et al. 2014). With that knowledge, we estimate the cost to manage the area (i.e., 850 ha) impacted by the dispersal of new progeny created by a mature plant. A new mature miconia with two panicles may produce ~300-400 progeny. With a single, incipient target being such a high risk, intensive efforts should be matched to comprehensively search the entire impact area over the several decades with a level probability of detection (and equal confirmation of no targets) of all progeny recruits. For instance, with 320 propagules dispersed, Pd would need to exceed 0.9968 with coverage at 5.77 s per 100 m2 pixel totaling ~136 hours of effort over the entire impact area over four decades (Fig. 3A). Any level of coverage less than that (including 99%) would be prone to missing a target that ultimately reaches maturity and newly replenishes the seed bank (Fig. 3B). Furthermore, an overwhelming majority of search effort would actually be dedicated to the confirmation of no targets, where, for instance 87% of effort is invested in looking for 1% of the targets dispersed out to the perimeter.

Figure 3. (A) Search effort (EFT; hours) over a 43-year period to match the level of coverage with the probability of detection from a random search effort. (B) is the reproduction of 2nd generation progeny by undetected targets of the 1st generation shown as Base 10 log scale.

Based on this model, we estimate accrual of future management costs ranging from $169,000-337,000 for every mature target detected, with the increase from the base cost dependent on increasing propagule loads and the static cost to treat each those individuals detected.

- James Leary, Kimberly Burnett and Christopher Wada


 

References

Cacho, O.J., Hester, S. and Spring, D., 2007. Applying search theory to determine the feasibility of eradicating an invasive population in natural environments. Australian Journal of Agricultural and Resource Economics, 51(4), pp.425-443. 


Fletcher C. S. and Westcott D. A.. 2013. Dispersal and the design of effective management strategies for plant invasions: matching scales for success. Ecological Applications 23:1881–1892. 


Koopman, B.O. (1946). Search and Screening. Operations Evaluations Group Report no. 56, Center for Naval Analyses, Alexandria, VA. 


Leary, J.J., Gooding, J., Chapman, J., Radford, A., Mahnken, B. and Cox, L.J., 2013. Calibration of an Herbicide Ballistic Technology (HBT) helicopter platform targeting Miconia calvescens in Hawaii. Invasive Plant Science and Management, 6(2), pp.292-303. 


Leary, J., Mahnken, B.V., Cox, L.J., Radford, A., Yanagida, J., Penniman, T., Duffy, D.C. and Gooding, J., 2014. Reducing nascent miconia (Miconia calvescens) patches with an accelerated intervention strategy utilizing herbicide ballistic technology.

Lodge, D.M., Williams, S., MacIsaac, H.J., Hayes, K.R., Leung, B., Reichard, S., Mack, R.N., Moyle, P.B., Smith, M., Andow, D.A. and Carlton, J.T., 2006. Biological invasions: recommendations for US policy and management. Ecological Applications, 16(6), pp.2035- 2054. 



The Role of Policy and Peers in EV Adoption

Electric vehicles (EVs) can be a cleaner means of transportation compared to cars with traditional gasoline engines. They have the added benefit of being able to provide support to the electric power grid—an increasingly important attribute in states like Hawaii with high levels of intermittent renewable energy. To achieve widespread deployment of EVs, we need to know why consumers choose to buy an EV rather than a traditional car. Towards this end, we have conducted two studies that evaluate the effects of state-level policy incentives in the United States and that estimate “spillover effects” from geographic peers in Hawaii who purchase EVs. Preliminary results are presented below.

State EV Policies

Though EV battery costs have fallen rapidly in the last several years, the upfront cost of EVs still remain a barrier to rapid adoption. States have implemented a range of policies to encourage consumers to purchase EVs—financial and otherwise—but it is unclear how effective these policies are at achieving additional EV uptake. We estimate the effect of policy on EV adoption using semi-annual new vehicle registrations by EV model from 2010 to 2015 and a rich dataset of consumer-oriented state-level policies designed to promote EV purchases. We focus our policy analysis on EV vehicle purchase incentives and a range of other policies like home charge subsidies, reduced vehicle license taxes or registration fees, time-of-use rates, emissions inspection exemptions, high occupancy vehicle lane exemptions, designated and free parking, and an annual EV fee (that discourages EV purchase). As a rough indicator capturing the overall number of policies that states have used to incentivize consumer EV adoption, we add the number of policies up by state, illustrated in Figure 1. We separate the “policy index” (ranging from 0 to 9) by battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) and show how it has changed over time (as shown in Figure 1 for the second half of 2011, 2013, 2015). Overall, there are more BEV policies, where California and Arizona are leaders in the number of EV policies adopted.

Figure 1. State Policy Index: BEVs (top) and PHEVs (bottom)


Our econometric estimates show that state policies positively impact EV adoption for both BEVs and PHEVs. The vehicle purchase incentive has a pronounced effect on BEV uptake. A $1,000 increase in the purchase incentive leads to an approximately 15% increase in sales of BEVs. We test these results by examining states that have ended large purchase subsidies, and find that BEV adoption declines. Other policies—aggregated together into a policy index—likewise increase EV uptake, though more so for PHEVs. This suggests that policies related to usage are perhaps more relevant for PHEVs. Each additional policy increases PHEV sales by 18%. The contrast between the effectiveness of different types of incentives for BEVs and PHEVs offers some guidance for policymakers evaluating current state policies or considering adopting new state EV policies. In sum, we find that state policies have driven additional EV uptake—extending EV purchases to consumers who would not have otherwise entered the market.

Geographic Peer Effects for Teslas

We also examine the role of geographic peers in EV uptake in Hawaii. Hawaii provides an excellent case for studying peer effects because it has strong EV adoption, the second highest amongst U.S. states in EVs per capita (IHS Markit, U.S. Census Bureau, 2010 – 2015). Although federal and state governments offer a variety of consumer incentives, the decision to adopt EVs may also extend beyond economic and policy motivations to include behavioral and social components. Social networks, also called “peer effects,” could have a potentially large influence on vehicle choice if people are influenced in their decision to adopt an EV by peer decisions to adopt EVs. Our second study examines peer effects defined by geographic networks, i.e., by visual observations of EVs registered in one’s neighborhood. Using zip code-level EV registration data from 2013-2016 for Hawaii, we exploit a three-month gap between adoption decisions and deliveries of Teslas to estimate presence and size of peer effects. Tesla EVs were important for reigniting interest in EVs more generally and amount to 13% of registered EVs on Maui, Oahu, and Hawaii Island. Our econometric analysis identifies statistically significant neighborhood effects. Figures 2 and 3 illustrate EV and Tesla uptake, respectively, by zipcode on Oahu, Maui, and Hawaii Island; Kauai is omitted due to data limitations.

Figure 2. EV Adoption on Oahu, Maui and Hawaii Island

 

Figure 3. Tesla Adoption on Oahu, Maui and Hawaii Island

We find that geographic-based peer effects generate one additional Tesla sale for every 26 Teslas sold in a zip code. How meaningful the magnitude of these peer effects may be is likely contextual. If for example policy focused specifically on marketing to peers and social networks, this may not provide much gain. However, as a pure spillover effect, peer effects can be meaningful. If, for example, Hawaii were to offer a second round of vehicle purchase subsidies, the peer multiplier effect estimated in our analysis would increase the additional Teslas purchased by 4-5% over each year of the vehicle’s life. As a lower bound, this amounts to roughly 1 additional Tesla per zipcode as a result of peer effects. One note of caution: whether the peer multiplier for Teslas—a very high-end vehicle—will translate as the peer multiplier for other lower-priced EVs, such as the Nissan Leaf or Chevy Volt, remains an open question.

- Sherilyn Wee, Makena Coffman and Sumner LaCroix


References

IHS Markit. (2016). Dataset of New Vehicle Registrations by state 2010-2015.

U.S. Census Bureau. (2010-2015). 2010-2015 American Community Survey 1-Year Population Estimates.


Science and Community Engagement to Improve Water Management in Hawaii

‘Ike Wai (from the Hawaiian ‘ike, meaning knowledge, and wai, meaning water) is a five-year National Science Foundation project. The multidisciplinary research team from UH Manoa and Hilo will collect new geophysical and groundwater data, integrate these data into detailed groundwater models, and generate an improved understanding of subsurface water location, volume and flow paths. Data and outputs from ‘Ike Wai will be used to develop decision making tools to address challenges to fresh water scarcity from climate variability, increasing population demands, and water contamination.

UHERO Project Environment researchers will work with stakeholders to develop land-use scenarios, with a particular emphasis on potential areas for watershed restoration. Recharge values and restoration costs will be estimated for these scenarios and used as inputs to the groundwater model. Assumptions about development and population growth will be used to project consumption on the demand side, and the groundwater model will then allocate pumping spatially to minimize declines in water levels and deterioration in water quality due to seawater intrusion (SWI). Results from the pumping simulations can then be compared with current estimates of sustainable yield. We will also estimate the return on investment in watershed restoration for each of the scenarios.

The new field data and groundwater modeling efforts will help to improve current sustainable yield estimates. With recharge likely to change in the future due to climate change and land use decisions (e.g. watershed restoration), sustainable yield should also be variable. Although, current estimates of sustainable yield do not account for ecological and customary uses, several stakeholders have shown interest in developing a framework to do so. We will therefore look at how submarine groundwater discharge (SGD) along the coast varies with pumping and simulate the effects of different SGD constraints. We will also estimate the costs, in terms of restricting groundwater pumping, of enforcing those constraints. That is, we will: (1) compare projected groundwater consumption under each scenario to new sustainable yield estimates that account for both SWI and SGD, and (2) estimate the potential costs of maintaining pumping below sustainable yield.


Michael Roberts Receives Agricultural & Applied Economics Association’s Quality of Research Discovery Award

UHERO congratulates Michael Roberts, recipient of the Agricultural & Applied Economics Association’s Quality of Research Discovery Award for his article, "Who Really Benefits from Agricultural Subsidies? Evidence from Field-level Data." Michael will be recognized during the AAEA 2017 Annual Meeting this summer in Chicago.

Michael Roberts is a UHERO Research Fellow, Professor of Economics and co-founder of UHERO's Energy Policy and Planning Group


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