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Keywords:

  • Living organ donation;
  • state policy;
  • tax;
  • United States

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

In an effort to increase living organ donation, fifteen states passed tax deductions and one a tax credit to help defray potential medical, lodging and wage loss costs between 2004 and 2008. To assess the impact of these policies on living donation rates, we used a differences-in-differences strategy that compares the pre- and postlegislation change in living donations in states that passed legislation against the same change in those states that did not. We found no statistically significant effect of these tax policies on donation rates. Furthermore, we found no evidence of any lagged effects, differential impacts by gender, race or donor relationship, or impacts on deceased donation. Possible hypotheses to explain our findings are: the cash value of the tax deduction may be too low to defray costs faced by donors, lack of public awareness about the existence of these policies, and that states that were proactive enough to pass tax policy laws may have already depleted donor pools with previous interventions.


Abbreviations: 
ESRD

end-stage renal disease

OPTN

Organ Procurement and Transplantation Network

UNOS

United Network for Organ Sharing

USRDS

United States Renal Database System

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

The shortage of transplantable organs remains a major health care policy issue in the United States. At present, there are over 100 000 individuals on the organ waiting list, a number that has grown over the course of the past decade (1). A variety of different strategies have been developed to address organ shortages, including utilizing organs from marginal donors, improving desensitization techniques for blood group incompatible transplantation, sharing best practice techniques between organ procurement organizations (the Organ Donation Breakthrough Collaborative), large scale education and media campaigns to raise awareness among potential donors, and, for kidney transplants, developing more efficient donor–recipient match algorithms such as paired-kidney exchanges (2–13).

While these strategies have likely been helpful in expanding living and deceased donor pools, growth in demand for organs continues to outstrip growth in supply. In fact, after a steady growth from 1990 to 2004, the number of living donors have become stagnant over the last 5 years. As a result, there has been increased attention on financial incentives to increase donation, particularly those that target living donors (4,14). Outright payment for organs is currently prohibited in the United States: perhaps a reflection of the vibrant debate over the ethics of such “organ markets” (15,16), public support for such policies has consistently been marginal (17). However, there does appear to be support for more limited financial interventions, such as reimbursing living donors for the travel, medical costs, and foregone wages associated with the act of donation (18).

One example of a policy designed with these objectives in mind is Wisconsin‘s 2004 law allowing living donors to deduct up to $10,000 in travel, lodging and lost wage costs accruing from the donation act. While some argued that the policy was unethical in that it had the potential to coerce individuals into donation, tax code breaks for expenses related to living donation have gone on to gain wide support (19,20). Indeed, the policy has diffused to other states and by 2009, 14 other states had passed tax deduction laws with one (Iowa) passing a tax credit law. In addition, another four states are actively considering such legislation (21).

At present, it is unknown whether these tax policies actually do enough to reduce barriers to living donation enough to increase available organ supply. Only one study has attempted to examine the efficacy of these laws on living donation (22). However, this study was limited because outcomes were assessed for only the years immediately before and after the policy was enacted, thereby ruling out the possibility of lagged impacts (i.e. a potential time delay between the legislation's enactment and its eventual implementation and subsequent changes in citizens’ behaviors). Furthermore, the study did not assess differential policy impacts within key subgroups, such as race, gender, and donor–recipient relationship.

This study attempts to quantify the returns to tax policies on organ donation rates at the state level. We improve upon past research by utilizing a large panel data set extending through 2010 that allows us to include more years before and after enactment, thereby yielding more precise estimates of policy effects. We also examined differential impacts by the aforementioned subgroups to test whether the policies have a stronger effect among certain populations.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Data

We obtained yearly data on the total number of living organ donors in each state between 2000 and 2010 from the United Network for Organ Sharing (UNOS) and Organ Procurement and Transplantation Network (OPTN) online database (23). We also gleaned these data stratified by race, gender and relationship to donor.

Data on state level tax policies specific to living solid organ donations were taken from a variety of sources. We consulted published reports by UNOS on state-specific policies targeting organ donation, with information supplemented by a recent article by Janette and Sanoff (21), which identifies states that successfully passed or were considering laws through 2009. We then verified and augmented these data by examining online tax code legislation and state tax forms and instructions, particularly for those states that were reported to either have enacted a law or have a bill under consideration by a state governing body. We also verified these details by telephone discussions with representatives in the state tax and Donate for Life campaign offices. For each state, we collected information on both the year the policy was passed as well as the year the law was first enacted, with the latter being the variable of interest in our analyses (using date of law passage did not change our substantive results).

Data for time-varying control variables came from a variety of other sources. State income per capita and population were taken from the Bureau of Economic Analysis website (24). Aspects of the health care system relevant to accessing transplantable organs, such as the percentage of uninsured individuals, percentage receiving Medicare, and number of transplant centers, were obtained from the Area Resource File (25). To control for the demand for organs, we obtained estimates of the number of individuals with end stage renal disease in each state for each year from the United States Renal Database System (USRDS) (26). Finally, we included a measure of state political orientation, as both donation rates and policies may both be related to underlying political preferences (27). The measure, developed by Barry and Busch (2007), assigns −1 to each chamber of the state legislature and/or governorship if they are controlled by Democrats and +1 for Republicans. The final index score ranges from −3 to 3 (28).

Statistical analysis

We used the following specification to estimate the impacts of tax legislation on donation rates:

  • image

where ln(Dit) is the logged living donation rate (per 100 000 population) in state i in year t and Taxit= 1 if a tax deduction or credit was available in state i during a particular year t. The objects λi and ηt represent dummy variables for each year and each state, respectively. We estimated this model using ordinary least squares. (For some of the subgroup analyses, see further, we utilized Poisson regression models given the predominance of zeros in the stratified data. Using Poisson and negative binomial models in the main analyses yielded substantively similar results).

The above specification represents a differences-in-differences model that compares changes in donation rates between states that enacted policies against those that did not. States that enacted a tax policy serve as the “treatment” group and those that did not were used as the “control” group. No state had already adopted a law at the start of the time period, so all states started in the control group. States that adopted a law moved to the treatment group at the time of enactment. No states repealed laws during the time period. Time fixed effects (a dummy variable for each year) help account for national policy changes (such as the Organ Donation Breakthrough Collaborative) as well as secular trends in attitudes toward donation. We included state fixed effects to control for any unobservable time-invariant state characteristics that may jointly impact public policy changes and living donation rates. A sensitivity analysis considered alternative timing of the laws (using legislation dates rather than enactment dates), and found the substantive results to be unchanged.

As with differences-in-differences in general, our model does not account for time-variant state characteristics that may jointly influence living donation rates and the presence of donation-promoting legislation. To address the issue of omitted variables, we assessed the sensitivity of our results to the inclusion of state-level control variables for logged state per capita income, logged state total and ESRD population, percentage of individuals uninsured or on Medicare, and the number of transplant centers. In addition, we also estimated a specification with state-specific linear time trends, which help control for secular state-specific changes in attitudes toward organ donation and the sequential institution of other organ donation policies that may jointly be correlated with living donation rates and tax policies.

We also estimated several extensions of our core model. In order to account for possible lagged policy effects (i.e. additional time between the dates of policy enactment and potential changes in donation rates), we included 1 and 2 year lags as additional independent variables. We then explored the possibility of differential policy effects by estimating separate models for key subgroups. First, we examined whether policy impacts by donor relationship to the recipient. In particular, we assessed whether individuals with a close personal connection (such as spouses or siblings) were less sensitive to tax incentives than nondirected donors, as they are more invested in the donation act. Second, we also explored impacts within gender and race subgroups (African American vs. Caucasian). The former is motivated by recent evidence suggesting women may actually reduce altruistic behavior in the face of payments or incentives while men may be more inclined (29). The latter is important given the increasing focus on raising donation rates among African Americans, as well as the fact that this group may benefit more from tax laws that help defray donation costs given the correlation between race, access to health care, and socioeconomic status (30). Finally, we also examined whether the tax policies had any impacts on deceased donation rates, based on the hypothesis that increases in living donations could crowd out deceased donations or conversely increased policy attention to specific types of donations may increase other types of donation.

In all models, we clustered all standard errors at the state-level in order to account for serial correlation in the outcome variable (31). We also weighted all of our observations by state population; our substantive results were unchanged when weights were not used.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Table 1 lists the states that passed tax policy laws targeting living organ donation during between 2000 and 2010, as well as the year the policy became effective. Sixteen states passed policies during this period, starting with Wisconsin in 2004. With the exception of Idaho, which offered a $10,000 tax credit, the listed states all allow for up to a $10,000 tax deduction in that year for travel, lodging and lost wages related to living organ donation.

Table 1.  States that passed tax deduction/credit legislation and year policy became effective
StateYear enacted
  1. Source : Jennette and Sanoff (2009) as well as state tax and Donate for Life offices and published tax forms and instructions. See main text for details. All states, except for Idaho which passed a tax credit law, passed $10,000 tax deductions for expensives related to the act of living donation.

Arkansas2005
Georgia2005
Idaho2007
Iowa2005
Louisiana2005
Massachusetts2008
Minnesota2005
Mississippi2006
New Mexico2005
New York2006
North Dakota2005
Ohio2007
Oklahoma2008
Utah2005
Virginia2007
Wisconsin2004

Table 2 compares descriptive statistics for the 16 states that enacted tax policies against those that did not for years 2000 to 2004, which is the period prior to when the laws were enacted. States with tax policies were similar to those without on all dimensions except income per capita, with states passing tax laws poorer by a small but statistically significant amount.

Table 2.  Descriptive statistics
 Passed living donation tax policyp-Value
NoYes
  1. Means with std. dev in parenthesis. All statistics computed for the 2000–2004, just prior to when the majority of states with tax laws made these effective. Results are stratified by those states that, at some point between 2004 and 2009 passed a tax deduction law, and those that did not.

  2. Donors per 100 000 Population refers to the number of living donations in a given year.

  3. Per Capita Income and Population were taken from the Bureau of Economic Analysis website.

  4. ESRD Population reflects the number of individuals with end-stage renal disease and was taken from the USRDS.

  5. % of the state population uninsured, on Medicare, and the number of transplant centers was taken from the Area Resource File.

  6. Conservative Ideology Scale was taken from Barry and Busch (2007). A −1 is assigned for each legislative chamber and governorship controlled by Democrats and +1 for Republicans. A score of −3 reflects full Democrat control and a score of +3 reflects full Republican control.

Donors per 100 000 population2.47 (2.95)2.64 (1.81)0.65
Per capita income31 759.10 (5039.66)29 531.77 (4830.49)0.00
Population (thousands)6240.31 (7077.95)5640.97 (4565.44)0.50
ESRD population8802.98 (10078.31)8048.19 (6846.26)0.56
% Uninsured15.49 (4.30)16.69 (5.84)0.08
% Medicare14.91 (1.88)14.59 (1.94)0.23
Transplant hospitals (Number)7.46 (8.63)6.80 (4.97)0.54
Conservative Ideology Scale (–3/3 scale)0.14 (2.06)−0.19 (1.98)0.24
Number of states3215
Number of observations16075

Figure 1 provides a graphical representation of our differences-in-differences regression modeling strategy. The two lines represent the average living donation rates for the states in the control (no law) and treatment (law enacted) groups. Because states in the treatment group enacted laws at different calendar years, the time period was shifted so that the X-axis represents the number of years before and after enactment. Prior to policy enactment, donation rate trends among treatment and control states were similar, though there may have been a slight widening in the gap between the two groups in the 2 years just prior to when the tax laws went into effect. This supports the use of a differences-in-differences strategy for these data, which assumes similar pre-existing trends in the outcome for treatment and control groups. Second, there was no sharp uptick in donation rates the year the policies went into effect (year 0). Although treated states diverge from the control states slightly 2 years after the policy was enacted, this remains smaller than that of the pre-intervention period.

image

Figure 1. Trends in living donations in states that enacted tax policies versus those that did not. Source: UNOS & OPTN Database. For tax deduction states, the data point at time 0 represents the average donation rate across all states in the year the policy became effective. Time 1 and 2 reflect the postpolicy period, and Time –5 to –1 represent the pre-intervention period. For states that did not pass tax laws, Time 0 reflects the year 2005, the median and modal year tax policies became effective in states with tax legislation.

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Figure 2 displays time trends in living donation rates for each state that enacted a tax law. (Control states are not included in this graph.) As in Figure 1, time zero represents the year the policy went into effect, rather than the calendar year. Similar findings emerge. Only a few states show even modest upticks in donation rates the year the policy was enacted, with little evidence of any impacts accruing in later years.

image

Figure 2. Trends in living donations for each state that enacted tax legislation. Source: UNOS & OPTN Database. The data point at time 0 represents the average donation rate across all states in the year the policy became effective. Times 1–3 reflect the postpolicy period, and Time −3 to −1 represent the pre-intervention period.

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Table 3 presents results from our core differences-in-differences regression model. In the model with state and year fixed effects only (column 1), enactment of a tax policy is associated with a 2.91%decrease in the number of donors per capita, although this was not significant at the p < 0.05 level (95% CI ranges from a 13.3% decrease to a 7.5% increase). The inclusion of the time varying state specific characteristics (column 2) and state specific linear time trends (column 3) continues to produce negative and statistically insignificant point estimates for the effect of the tax legislation.

Table 3.  Differences-in-differences estimates of the impact of tax legislation on logged living donations per capita
 (1)(2)(3)
 State, Year FE(1) + State characteristics(2) + State specific trends
  1. Robust standard errors, corrected for clustering at the state level, in parentheses.

  2. 95% confidence intervals in italics.

  3. Dependent variable is logged number of living donors per 100,000 population.

  4. All observations weighted by state population (unweighted regressions provided similar results).

  5. State, Year FE refers to fixed effects (dummy variables) for the state and year.

Tax legislation (=1)−0.0291−0.0372−0.0502
 (0.0516)(0.0462)(0.0672)
  (−0.133, 0.075) (−0.130, 0.056) (−0.185, 0.085)
Ln (Per capita income) 0.5521.251
  (0.703)(0.801)
   (−0.864, 1.967) (−0.362, 2.864)
ln (ESRD population) 0.07860.293
  (0.731)(1.493)
   (−1.393, 1.551) (−2.712, 3.297)
Conservative ideology scale 0.001570.0112
  (0.0118)(0.0103)
   (−0.0222, 0.0253) (−0.00957, 0.03203)
% Uninsured −0.001090.00228
  (0.00709)(0.00680)
   (−0.0154, 0.0132) (−0.0114, 0.0160)
Transplant hospitals (Number) −0.00605−0.00188
  (0.00473)(0.00538)
   (−0.0156, 0.0035) (−0.0127, 0.0090)
% Medicare 0.005980.0127
  (0.0141)(0.0164)
   (−0.0224, 0.0343) (−0.0203, 0.0457)
Ln (Population) −0.5241.560
  (1.051)(2.080)
   (−2.639, 1.592) (−2.626, 5.747)
Number of observations466466466

One issue with our differences-in-differences specification is that it combines the states that passed tax deductions with the one state (Iowa) that passed tax credits. In principle, these two policies differ in terms of their cash value and could have markedly different impacts on donation. To get at this, we estimated models that did include Iowa, and found markedly similar results. This is not surprising given that Iowa did not experience any bump after passing their tax credit law (see Figure 2).

In terms of the control variables, we do not find any statistically significant coefficients. The signs on state income, ESRD population, political preferences, and% Medicare population are positive and, counter-intuitively, the estimate on the number of transplant hospitals is negative. The signs on the other variables vary depending on the specification. The nonfindings with respect to the control variables were likely driven by our use of state fixed effects—if there is only a small amount of year-to-year variation in these variables within a given state, state dummy variables will soak much of this up and leave little variation with which to estimate precise coefficients on the controls. To address this we estimated specifications without state fixed effects. The substantive results around the tax policy laws were unchanged. However, we found now that percent uninsured and percent on Medicare were both negatively and statistically significantly associated with donation rates. State per capita income, in some specifications, was positively associated with donation rates (these results are available upon request). Given our focus on the impacts of the tax policies, we opted to focus on the specifications with state fixed effects given our desire to better recover causal effects.

Table 4 includes both contemporaneous enactment as well as one and two year lags of the tax policies in order to test whether there is a time delay between enactment and changes in donation rates. The rows display the estimated coefficient for the tax policy main effects variable. We do not find any evidence of lagged effects. The point estimates for the contemporaneous and two year lags continue to be negative and insignificant. The point estimate on the 1 year lag is positive, implying a 3% increase in living donations per capita as a result of tax policies, although it is not statistically significant.

Table 4.  Differences-in-differences estimates allowing for lagged policy effects
 (1)(2)(3)
 State, Year FE(1) + State characteristics(2) + State-specific trends
  1. Robust standard errors, corrected for clustering at the state level, in parentheses.

  2. 95% confidence intervals in italics.

  3. Dependent variable is logged number of living donors per 100 000 population.

  4. Tax Legislation, 1 Year Lag allows for policy impacts 1 year after the tax laws went into effect and likewise for Tax Legislation, 2 Year Lag.

  5. All observations weighted by state population (unweighted regressions provided similar results).

  6. State, Year FE refers to fixed effects (dummy variables) for the state and year.

  7. State Characteristics refers to the control variables shown in Columns 2 and 3 of Table 3.

Tax legislation (=1)−0.0422−0.0463−0.0613
 (0.0377)(0.0382)(0.0590)
  (−0.118, 0.034) (−0.123, 0.031) (−0.180, 0.057)
Tax legislation, 1 Year Lag (=1)0.03520.03070.0244
 (0.0345)(0.0347)(0.0448)
  (−0.0343, 0.1047) (−0.0391, 0.1005) (−0.0658, 0.1146)
Tax legislation, 2 Year Lag (=1)−0.0271−0.0290−0.0328
 (0.0530)(0.0551)(0.0627)
  (−0.134, 0.079) (−0.140, 0.082) (−0.159, 0.093)
Number of observations466466466

Table 5 presents results stratified by relationship to donor and Table 6 presents results stratified by race and gender. We do not find any significant effects for any of these subgroups. We also found no impacts on deceased donation rates (last column, Table 5), suggesting that this was not crowded out by reducing barriers to living donation. Finally, we examined whether the impact of tax policies were higher for states with lower income per capita and did not find any evidence of a significant interaction effect between the tax policy dummy variable and state income per capita. We also assessed whether restricting the control group to states that bordered those that enacted tax legislation would produce changes in our substantive conclusions, and our results remained very similar regardless of how the control states were chosen. (These results are available upon request.)

Table 5.  Differences-in-differences estimates stratified by donor relationship
Panel A—Donor Relationship Living, RelatedLiving, UnrelatedAnonymousDeceased
  1. Robust standard errors, corrected for clustering at the state level, in parentheses; 95% confidence intervals in italics.

  2. Each column represents a separate regression, stratified by the donor relationship type in the column header.

  3. With the exception of Anonymous donors, we used logged donors per 100 000 population as the main dependent variable.

  4. For Anonymous donors we used a Poisson regression model given that over 50% of the state X year observations showed donations = 0.

  5. All models include State and Year FE, state-specific linear time trends and the control variables listed in Table 3.

Tax legislation ( = 1)−0.024−0.1280.1550.012
 (0.067)(0.094)(0.233)(0.031)
 (−0.150, 0.111)(−0.319, 0.062)(−0.302, 0.613)(−0.052, 0.075)
Number of observations466457466421
Table 6.  Differences-in-differences estimates stratified by race and gender
Panel A: Stratifying by Gender MenWomen
  1. Robust standard errors, corrected for clustering at the state level, in parentheses.

  2. 95% confidence intervals in italics.

  3. Each cell represents estimates from a different regression model, specific to the population listed in the column header.

  4. The dependent variable is logged living donations per 100,000 individuals.

  5. All models include State and Year FE, state-specific linear time trends, and the control variables listed in Table 3.

Tax legislation ( =1)−0.129−0.030
 (0.077)(0.070)
 (−0.280, 0.024)(−0.167, 0.108)
Number of observations386386
Panel B: Stratification by Race WhiteAfrican-American
Tax legislation ( =1)−0.038−0.127
 (0.049)(0.094)
 (−0.134, 0.058)(−0.319, 0.065)
Number of observations345352

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

While the establishment of formal markets for organ sales remains politically unpalatable in the United States, 16 states have now implemented tax policies to help incent living organ donation by reducing travel and other incidental costs faced by potential donors. With the exception of Iowa, which offers a tax credit, these states all now offer up to a $10 000 tax deduction for expenses related to the donation act.

In this study, we assessed the impact these tax policies on living donation rates. We used a differences-in-differences strategy to compare the pre- and postlegislation change in donors in states that enacted legislation against the same change in those states that did not, and found no statistically significant association between the passage of tax laws and living organ donation rates. This finding was robust to the inclusion of a variety of control variables, state specific time trends, and allowing for lagged effects. If there were an effect, our estimates suggest it to be small relative to the nationwide gap between organ supply and demand: the highest upper bound of our estimated 95% confidence intervals implies only a 10% increase in living donations accruing from tax policy enactment. We did not find any evidence of any differential impacts within key subgroups either.

There are several potential reasons why these tax laws may not have impacted organ supply in an appreciable way. First, and perhaps most likely, the actual financial return from these laws is quite small. For example, a tax deduction of $10 000 for a family of four at the median income in the state of Wisconsin translates to an actual cash value of just over $600 (22). In contrast, according to one study, the financial burden for a living kidney donor in the US (including transportation, lodging, lost wages, and medical costs) ranges from $907 to $3089, depending on the type of surgical approach (32). Thus, in most cases, the value of the tax deduction represents only a fraction of the true total costs faced by living donors. Along these lines, policies that do more to reduce financial burdens—such as increasing the value of tax deductions or moving from tax deductions to tax credits, which for the same dollar amount would be more valuable and, if refundable, can potentially reduce a payer's tax liability below zero—may have a larger impact.

Second, it is also possible that few people actually knew about the policy, therefore being unable to take advantage of it. Although we do not have survey data to fully support this hypothesis, there is some anecdotal evidence to support this speculation. One author (ASV) contacted different state Donate for Life campaigns to verify the presence of tax incentives, but representatives in only a few states were aware of the existence of these policies. Another author (AV) noticed a lack of knowledge about eligibility for tax incentives within a clinic population being evaluated for living donation, in particularly among the most educated and informed donors. Even with knowledge of the law, potential recipients may face high opportunity costs with respect to the procedures and paperwork to obtain tax deductions, which may be burdensome. This could also blunt the impact of well-intentioned programs.

Third, it may be that states that were proactive enough to pass tax policy laws had already been active in other ways to increase donation rates. This would have depleted their pool of potential donors prior to the enactment of the tax policies, making the law less effective. Along the same lines, tax policies may be more effective when combined with other policies, such as donor awareness campaigns.

Finally, it is possible that any positive policy effects were offset by citizens’ negative reactions to what could be construed as a financial incentive. That is, although some individuals may have been swayed to donate, others were “turned off” by the financial incentive and chose not to donate when they otherwise would have voluntarily done so. The negative point estimates, though statistically insignificant, are suggestive of such a dynamic. Along these lines, it may be that this particular form of altruistic behavior is unrelated to finances: individuals who want to donate will commit to that regardless of cost, especially if they know and care for the recipient. That we find no impacts on anonymous donors either suggests that finances may not matter for any altruistically minded individual, though we cannot exonerate the low cash value of the tax policies as an explanation for this particular result.

There are several limitations to this study, many of which motivate further research. First, it may be that policy effects do accrue, but only several years after the policy is in place. While we explore lagged effects up through 2 years, future research could consider even longer lags. In addition, as alluded to above, tax policies may work interactively with other efforts to increase organ donation, such as provision of paid leave for living donors or public education. We did not include other policies or strategies in our model. Future work that incorporates additional policies would be helpful in designing a multi-pronged package of polices that would have the greatest positive impact.

We do not have data on donation rates by level of education. Educated individuals may have been more likely to have heard about the tax policies and therefore more likely to react. Individual level data with detailed donor demographics and socioeconomic status may be useful in distinguishing who is most likely to respond to financial incentives, which would be useful information to better publicize these tax benefits and improve future policy design. Lastly, and perhaps most importantly, we have only evaluated whether tax policies increased live donations. However, tax deductions/credits may have had other effects among donors, such as improving their well-being. Evaluating additional outcomes such as quality of life and financial hardships would be a promising area for future research.

Despite these limitations, our study makes several important contributions. We utilized a transparent natural experiment study design and a variety of specification checks to recover some of the first estimates of the impact of financial incentives on organ donation rates in the United States. We then extended the scant existing literature on this topic by examining whether policy impacts accrue in the future (if not in the present) or only within key sociodemographic subgroups. Finally, we provided detailed hypotheses as to why we find no policy impacts, each of which inform both future research and policy design.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

We thank seminar participants at the American Transplant Congress 2011 meetings in Philadelphia, PA, two anonymous editors and two anonymous referees for helpful comments and suggestions.

Disclosure

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References
  • 1
    Annual Report of the U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients: Transplant Data 1999–2008. Rockville , MD : U.S. Department of Health and Human Services, Health Resources and Services Administration, Health Care Systems Bureau, Division of Transplantation; 2009.
  • 2
    Boulware LE, Troll MU, Platinga LC, Powe NR. The association of state and national legislation with living kidney donation rates in the united states; a national study. Am J Transplant 2008; 8: 14511470.
  • 3
    Harrison TR, Morgan SE, King AJ, et al. Promoting the Michigan Organ Donor Registry: Evaluating the impact of a multifaceted intervention utilizing media priming and communication design. Health Commun 2010; 25: 700708.
  • 4
    Howard DH. Producing organ donors. J Econ Perspect 2007; 21: 2536.
  • 5
    Rees MA, Kopke JE, Pelletier RP, et al. A non-simultaneous, extended Altruistic donor chain. New England J Med 2009; 260: 10961101.
  • 6
    Richards L. Transplantation: Kidneys from old donors: Tackling the organ shortage. Nature Rev Nephrol 2009; 87: 14371441.
  • 7
    Salim A, Malinoski D, Schulman D, Desai C, Navarro S, Ley EJ. The combination of an online organ and tissue Registry with a public education campaign can increase the number of organs available for transplantation. J Trauma 2010; 69: 451454.
  • 8
    Segev DL, Gentry SE, Warren DS, Reeb B, Montgomery RA. Kidney paired donation and optimizing the use of live donor organs. J Am Med Assoc 2005; 293: 18831890.
  • 9
    Shu J, Fok T, Mussen L, et al. Impact of the Educational Resource One Life … Many Gifts on attitudes of secondary school students towards organ and tissue donation and transplantation. Transplant Proc 2011; 43: 14181420.
  • 10
    Pomfret EA, Sung RS, Allan J, Kinkhabwala M, Melancon JK, Roberts JP. Solving the organ shortage crisis: The 7th Annual American Society of Transplant Surgeons’ State-of-the-Art Winter Symposium. Am J Transplant 2008; 8: 745–752.
  • 11
    Port FK, Bragg-Gresham JL, Metzger RA, et al. Donor characteristics associated with reduced graft survival: An approach to expanding the pool of kidney donors. Transplant Proc 2002; 74: 12811216.
  • 12
    Weber M, Dindo D, Demartines N, Ambuhl P, Clavien P. Kidney transplantation from donors without a heartbeat. N Engl J Med 2002; 347: 248255.
  • 13
    Howard DH, Siminoff LA, McBride V, Lin M. Does quality improvement work? Evaluation of the organ donation breakthrough Collaborative. Health Serv Res 2007; 42: 21602173.
  • 14
    Becker G, Elias J. Introducing incentives in the market for live and cadaveric organ donations. J Econ Perspect 2007; 21: 324.
  • 15
    Chapman J. Should we pay donors to increase the supply of organs for transplantation? No. Br Med J 2008; 336: 1343.
  • 16
    Matas AJ. Should we pay donors to increase the supply of organs for transplantation? Yes. Br Med J 2008; 336: 1342.
  • 17
    Leider S, Roth AE. Kidneys for sale: Who disapproves, and why? Am J Transplant 2010; 10: 12211227.
  • 18
    Boulware LE, Troll MU, Wang NY, Powe NR. Public attitudes towards incentives for organ donation: A National Study of Different Racial/Ethnic and Income Groups. Am J Transplant 2006; 6: 27742785.
  • 19
    Fusco C. Wisconsin Eases Financial Burden on Organ Donors. Chicago Sun-Times. January 23, 2004; Sect 18.
  • 20
    Napolitano J. Wisconsin Senate Approves Tax Deduction for Organ Donors. The New York Times. January 23, 2004.
  • 21
    Jennette C, Sanoff S. State initiatives aim to protect transplant recipients and increase organ donation. ASN Kidney News, May 67, 2009.
  • 22
    Wellington AJ, Sayre EA. An evaluation of financial incentive policies for organ donations in the United States. Contemporary Econ Pol 2011; 29: 113.
  • 23
    United Network for Organ Sharing. State Data. http://optn.transplant.hrsa.gov/latestData/stateData.asp?type=state 2010.
  • 24
    Bureau of Economic Analysis. State Annual Personal Income. 2011 [cited 2011 March 15]; Available from: http://www.bea.gov/regional/spi/.
  • 25
    Bureau of Health Professions. Area Resource File (ARF), 2009–2010. Rockville , MD : US Department of Health and Human Services, Health Resources and Services Administration; 2010.
  • 26
    United States Renal Data System. 2011 Annual Data Report. http://www.usrds.org/adr.aspx.
  • 27
    Grubesic TH. Driving donation: A geographic analysis of potential organ donors in the state of Ohio, USA. Social Sci Med 2000; 51: 11971210.
  • 28
    Barry CL, Busch SH. Do state parity laws reduce the financial burden on families of children with mental health care needs? Health Serv Res 2007; 42: 10611084.
  • 29
    Mellstrom C, Johannesson M. Crowding out in blood donation: Was titmuss right? J Europ Econ Assoc 2008; 6: 845863.
  • 30
    Callender CO, Miles PV. Minority organ donation: The power of an educated community. J Am College Surg 2010; 210: 708715.
  • 31
    Bertrand M, Duflo E, Mullainathan S. How much should we trust differences-in-differences estimates? Quart J Econ 2004 Feb;119: 249275.
  • 32
    Clarke KS, Klarenbach S, Vlaicu S, Yang, Rc, Garg AX. The direct and indirect economic costs incurred by living kidney donors—A systematic review. Nephrology dialysis and transplantation. 2006; 21: 19521960.