The trade war between the United States and China has become one of the main events in the global economy this year. What could be its consequences for the US and China, and how might it affect other countries – for example, Russia?
Chronology of the trade war
Donald Trump started the war, raising import tariffs on solar panels in January 2018, of which the main supplier is China. In response, on April 2nd, China raised import duties on 128 commodities originating from the United States. On July 6th, the US increased tariffs on Chinese goods by 25 pp., imports worth $34 billion. China responded symmetrically. In August, the United States increased the tariffs on another $16 billion of imported goods from China, to which a symmetrical response again followed. In September, the United States again applied higher tariffs for $200 billion of Chinese exports, and China for $60 billion of US exports. At each stage of the conflict escalation, China appealed to the WTO with complaints about the actions of the United States, pointing to the inconsistency of their actions with the obligations and principles of the WTO. There were several meetings of official representatives from the United States and China – without any significant results.
What are the main reasons for this unprecedented escalation?
Imbalance and intellectual property
The economies of the US and China today are by far the largest in the world, and the trade turnover between the two countries is one of the most important. A remarkable feature of these trade flows over last decades is their imbalance. In 2017, the United States imported $526 billion worth of goods from China, while China’s imports from the United States amounted to $154 billion. Part of this imbalance is offset by trade in services, but it is not enough to even it out: in the same the year the United States delivered $57 billion worth of services to China while importing services of $17 billion from China.
Experts have different views on this imbalance. On the one hand, there is a perception that it is a source of world economy vulnerability, a source of potential crisis. Therefore, it is necessary to reduce the trade deficit. Another point of view is that this imbalance merely reflects the fact that the US economy and its assets are very attractive to investors from all over the world, including Chinese – and that, in turn, requires that the surplus of capital flows biased to US side, was compensated by the corresponding deficit of trade in goods and services. One such investor is the Chinese state itself, which for many years has been pursuing a policy of exchange rate undervaluation in order to promote foreign trade. It led to an enormous accumulation of foreign exchange reserves and as of January 2018, China held $1.17 trillion of US bonds and was the largest creditor of US government.
US President Donald Trump referred to this trade imbalance as one of the reasons for the outbreak of this trade war against China. Trump aims at reducing the deficit by $100 billion from the current $375 billion. The unilateral increase in import tariffs applied to Chinese goods was the first action of the US administration in this direction.
The second, no less important, formal reason for the trade war is the inadequate protection of intellectual property rights in China. China’s production of counterfeit products, the lack of adequate practices and laws to protect foreign technologies from illegal dissemination in the country, is not news to anyone. And although the almost two decades since China’s WTO accession have meant a largely modernized legal framework in this regard, a number of important provisions are still inconsistent with international practices, and the implementation of existing intellectual property rights leaves much to be desired. Established in 2012, The Commission on the Theft of American Intellectual Property identifies China as the most malicious violator of US rights. The exact damage is not known, but the commission assessment of the losses to the American economy due to the forced transfer of technology to Chinese partners – which is an unspoken condition of foreign manufacturers access to the Chinese market – industrial espionage, contradictions in legislation, requirements for the storage of sensitive data in China are in the range from $225 to $600 billion per year (Office of US Trade Representative, 2018).
While both the trade deficit and the intellectual property rights issue were recognized for many years, it was in 2018 that Trump started acting on them. Therefore, in order to discuss the potential impact of the conflict between the world’s largest economies on themselves and other economies, such as Russia, it is important to understand what drives the actions undertaken by Trump’s administration.
Trump won the elections in 2016 with a minimum margin against the Democratic rival. To provide support for his decisions and to increase the chances of being reelected for the next term in 2020, it is crucial to maximize the pool of his supporters. Trade policy measures aimed at import substitution are very effective populist policies in any country. One of the first steps made by the US toward trade war was the increase in import tariffs on steel and aluminum – for all countries. Metallurgy and coal industries are among the most organized and strong lobbyists in any country. The European Union as an economic organization started with the European Coal and Steel Association. By aligning interests with these sectors much can be achieved in relation to trade liberalization, and vice versa – by increasing the level of protectionism, a significant popularity increase can be among voters whose incomes depend on the success of companies in these industries.
China works hard raising the technological level of its economy. In recent years the Chinese government and Communist party launched a number of ambitious programs aimed at achieving a technological breakthrough, lessening the dependence on imported technologies by substituting them with ones produced by domestic innovation centers. These programs specify the priority sectors, in which state subsidies are provided for the acquisition of foreign technologies by Chinese companies and their adaptation. One of the common arguments was that the United States believes that powerful state support for technology sectors in China, along with the existing problems in protecting intellectual property rights, increases the risks and potential losses of American companies.
However, while these concerns seem reasonable at first, they should not be taken at the face value.
China’s ability to push out American companies in the high-tech sector on the world market seems rather limited. So far, China has only succeeded in increasing its share in the middle and low technology segments. Instead, in recent years, China is rapidly increasing its defense spending, which in 2017, for the first time, reached a level of 1 trillion yuan (about $150 billion). China’s defense spending is the second highest in the world after the United States. Moreover, it’s growing very fast. While in 2005 the Chinese nominal defense expenses were only 10% of American expenses, in 2018 they are already around 40%. The dominance of state enterprises in the defense industry in China implies that the real purchasing value of these expenditures is quite comparable. New and existing Chinese industrial policy programs target military and dual-use industries among others. Therefore whilst addressing the intellectual property rights problem in China now, Trump’s administration also aims at preserving US leadership position in the military sector, which finds widespread support in Trump’s main voter groups among Republicans.
Historically, trade wars implied tariff escalations to protect domestic industries from foreign competition. Today, the Trump administration behaves in a similar manner. However, the circumstances now are fundamentally different from those in the first half of 20th century and earlier. Firms not only trade in final goods, but more and more they trade in intermediate products and within firms themselves (Baldwin, 2012). The distribution of the production process to many companies across different countries of the world leads to two important effects, which were not observed in previous trade wars.
First, it is the effect of the escalation of tariff protection in the framework of the value chains. The import tariff is applied to the gross value of the product crossing the customs border. However, the exporting firm’s contribution to the gross value might be quite small. So the effective level of the tariff will be higher than the nominal level of the tariff, known as a so called amplification effect (World Bank, 2017, page 98). It means that the effective growth of the tariff by 25 percentage points in relation to Chinese imports will significantly exceed 25 % and in some cases can even become prohibitive. So, the tariff warfare will result in significantly greater losses for the sectors involved in the value chains, compared to the sectors less exposed to them. It means that foreign investors and multinational companies in China will suffer bigger losses compared to purely domestic Chinese companies. The Peterson Institute for International Economics made an assessment and confirmed these observations (Lovely and Yang, 2018).
Second, China’s participation in international multinational companies most often occurs in the assembly segments, while developed countries’ companies contribute at other stages, such as with innovation, design, financial and consulting services, marketing, and after-sales services. Then, the protectionist measures against goods produced in China by multinational companies will hit an American economy, generating losses in the service segments. A similar episode happened, for example, in 2006, when the European Union introduced anti-dumping duties on imported footwear from China and Vietnam, which in turn lead to a decline in the services sector in Europe – imported footwear contained a significant share of the value added created by European designers and distributors (World Bank, 2017). Obviously, we will observe the same consequences in the United States now, since the role of the American services sector in creating and promoting Chinese goods on the American market is significant and according to World Bank estimates in 2011, the contribution of value added generated by foreign services in China’s gross exports amounted to about 15% (World Bank, 2017).
Thus, not only the economy of China, but also the US economy itself will suffer from the growth of import tariffs in the USA. The USA is not an exception here – the governments of most countries continue to live in the paradigm of trade policy, which suits the structure of the world trade as at the beginning of the 20th century, while trade has gone far ahead and requires much more elaborate effective regulatory tools than tariffs on imported goods.
Consequences for Russia
The consequences of the US trade war with China for the Russian economy depend on what the main goals of the war are. If the motive is primarily electoral – to secure enough support in 2020, one can expect that the protective measures will be short-lived, and the geographical distribution of investment flows will remain almost intact and that China will remain an important location for global value chains transactions. The trade war will in this case lead to some economic slowdown in the short term. The main effects will be related to the redistribution of income within economies, where protected sectors will benefit on the expense of all other sectors. In these circumstances, Russia would suffer direct losses from the growth of tariffs on their exports to US (now it is predominantly steel and aluminum), but for the economy as a whole, the losses will not be significant, especially relative to the losses Russia bears because of sanctions.
However, if the main reason for the trade war has a long-term perspective, the investors will be forced to adjust the geography of their investment plans and China will face a significant outflow of foreign investments, which will significantly affect Chinese – and global – economic growth. In this case, both for Russia and for the whole world, the indirect effect of the US-Chinese trade conflict will be quite noticeable and it will take years to create new trade links and restore world trade and global value chains.
- Baldwin, Richard, 2012. “Global supply chains: why they emerged, why they matter, and where they are going”, CTEI Working papers 2012-13, The Graduate Institute, Geneve
- Lovely, Mary E., and Liang Yang, 2018. “Revised Tariffs Against China Hit Chinese Non-Supply Chains Even Harder.” PIIE Policy brief, Peterson Institute
- Office of the US Trade Representative. March 22, 2018. “Executive office of the President findings of the investigation into China’s acts, policies, and practices related to technology transfer, intellectual property, and innovation under section 301 of the trade act of 1974.” https://ustr.gov/sites/default/files/Section%20301%20FINAL.PDF
- World Bank, 2017. “Measuring and analyzing the impact of GVCs on economic development”. World Bank, Washington DC.
A longer version of this brief has been published in Russian by Republic: https://republic.ru/posts/92217
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Whistleblower reward programs, or “bounty regimes”, provide financial incentives to witnesses that report information on infringements, helping law enforcement agencies to detect/convict culprits. These programs have been successfully used in the US against procurement fraud and tax evasion for quite some time, and were extended to fight financial fraud after the recent crisis. In Europe there is currently a debate on their possible introduction, but authorities appear much less enthusiastic than their US counterparts. In this brief, we discuss recent research on two commonly voiced concerns on whistleblower rewards – the risk of increasing false accusations, and that of crowding out other motivations to blow the whistle – and the adaptations these programs may need to fight more general forms of corruption. Research suggests that the mentioned concerns can be handled by an appropriate design and management of the programs, as apparently done in the US, and that these programs can indeed be a cost effective instrument to fight corruption, but only in countries with a sufficient quality of the judicial system and administrative capacity. They may instead be problematic for weak institutions environments.
Corruption and fraud seem to remain highly widespread in almost all countries. For example, a recent survey of over 6,000 organizations across 115 countries shows that one in three organizations, both worldwide and in the US, experienced fraud in the past 24 months, prevalently in the form of asset misappropriation, cybercrime, corruption, and procurement and accounting fraud (Global Crime Survey, 2016).
Whistleblower (protection and) reward programs are a possibly effective tool to combat fraud and corruption, at least in the light of the US successful experience, where for a long time whistleblowers reporting large federal fraud have been entitled to up to 30% of recovered funds and sanctions under the False Claims Act. The US Internal Revenue Service (IRS) also allows whistleblower rewards in the tax area, and the Dodd-Frank Act introduced them for financial and securities fraud, apparently also with success (c.f. Call et al., 2017, and Wilde, 2017).
In Europe and the rest of the world, instead, rewards are absent and whistleblowers are still poorly protected from retaliation from employers. Some countries have taken encouraging legal steps to at least improve protection, and a discussion is ongoing at the G20 level on how to further improve the situation (G20 report, 2011).
Although many praise whistleblowers, there has been a large range of objections raised against introducing rewards (and even against improving whistleblower protection); mostly by corporate lawyers and lobbyists, but also by regulatory and law enforcement agencies (see Nyreröd and Spagnolo, 2017, for an overview).
In the rest of this brief, we focus on two often voiced concerns, the risks of eliciting false/fraudulent reporting and of crowding out of non-financial motivation, on which recent research has shed light that should be taken into account in the current policy debate. We then discuss some problems linked to the use of whistleblower rewards programs in a more general corruption context.
One concern commonly raised in the discussion of whistleblower rewards is that they may create incentives to fraudulently report false or fabricated information in the hope of receiving a reward. Although clearly an important concern to take into account, we only know of very few anecdotal cases of malicious or false reporting, and fraudulent reporting does not appear to have been a major problem in the US (see again Nyreröd and Spagnolo, 2017 for an overview of the empirical evidence).
A recent paper by Buccirossi, Immordino and Spagnolo (2017) analyzes this concern within a formal economic model and shows that it is not a ground (or an excuse) for not introducing appropriately designed and managed protection and reward programs in countries with sufficiently effective court systems. In these countries, stronger sanctions against lying to the court can (and should) be introduced to balance the incentives for manipulation that may be generated by large bounties. Most legal systems already have defamation and perjury laws, which means that a whistleblower is already committing a crime by fraudulently reporting false information, that can easily be strengthened where necessary without giving up whistleblower rewards. According to this study, the balancing of incentives is what allows the US to effectively use large financial incentives for whistleblowers, besides a very strong protection from retaliation, with little problems in terms of fraudulent reports.
However, the study also shows that this is only possible if the precision (effectiveness, independence) of the court system is sufficiently high. Where court systems are imprecise, the interaction between courts’ mistakes in the legal case based on the information reported by the whistleblower and in the following case for perjury/defamation against the whistleblower if the first case is dismissed, incentives for fraudulent reports, and courts’ adaptation of the standard of proof to account for these incentives, make it impossible to appropriately balance the two incentives. Therefore, whistleblower reward programs should not be introduced in environments where the law enforcement system is ineffective, independently from why it is so (bureaucratic slack, incompetence, political interference, corruption, etc.).
Crowding-out non-financial motivation
Another concern is that whistleblower rewards may have a “crowding out” effect on intrinsic motivation. The problem is that “the commodification of whistleblowing via the provision of bounties may render would-be whistleblowers less likely to come forward by reducing the moral valance of the wrongdoing” (Engstrom, 2016:11). Recent experimental evidence suggests that this concern is overstated. In particular, Schmolke and Utikal (2016) investigate the effects of whistleblower rewards in an environment where one subject may increase his payoff at the cost of harming the group, and find rewards to be highly effective in increasing the number of crimes reported. Data from that experiment suggests a little role for crowding out of non-monetary motivation, if any. Another recent study by Butler, Serra and Spagnolo (2017) investigates if and how monetary incentives, expectations of social approval or disapproval, and the salience of the harm caused by the reported illegal activity interact and affect the decision to blow the whistle. Experimental results show that financial rewards significantly increase the likelihood of whistleblowing and do not substantially crowd out non-monetary motivations activated by expectations of social judgment. The study also finds that public scrutiny and social judgment decrease (increase) whistleblowing when the public is less (more) aware (aware) of the negative externalities generated by the reported crime. All in all, most the recent studies we are aware of suggest that crowding-out of non- financial concerns is not a first-order problem for whistleblower reward schemes as long as there is a clear perception of the public harm linked to the illegal behavior reported by the whistleblower.
Whistleblower rewards and corruption
Although whistleblowing can occur in any sector, firm, or government, an area of particular interest is corruption. Corruption in public procurement is estimated to cost the EU 5.3 billion Euros annually. Hence, corruption deterrence through increased whistleblowing could save the EU significant resources annually (EC Report, 2017).
Contrary to fraud, corruption always takes at least two parties, a bribe taker, typically a government official or politician, and a bribe giver, which may be a firm or an individual. The fact that at least one additional party is involved than in the standard case of fraud, should make whistleblower rewards programs even more powerful since they may deter corruption by increasing the fear that a (potential or real) partner in crime may blow the whistle, even when no third party witness observes the illegal act (Spagnolo, 2004).
When the reported wrongdoer is an individual, as is often the case with corruption, there may be an issue in the use of rewards for whistleblowers linked to the funding of the rewards (c.f Nyreröd & Spagnolo, 2017b for an overview).
In the current US schemes, rewards for whistleblowers are ‘self-financing’, as they constitute a fraction of the funds recovered thanks to the whistleblower or/and of the fines paid by the culprits. An individual and a government official involved in a corrupt deal may, however, not be wealthy enough for the fines and the recovered funds to amount to a sufficiently strong incentive to blow the whistle, given the loss of future gains from the corrupt relationships and the various forms of retaliation whistleblowing may lead to. This problem is of course also relevant for fraud when an individual with few or well-hidden assets is the culprit, rather than a corporation, but it seems particularly relevant for corruption.
Whistleblower reward programs are also malleable to the concerns at hand. If the priority is to combat higher-level corruption, then setting a monetary threshold for when a claim is to be considered is appropriate to limit administrative costs for the program. Indeed, a concern with utilizing whistleblower rewards programs for combating lower-level corruption is that the administrative burden required looking through the whistleblower claims and the costs of limiting abuses may outweigh the benefits gained in detection and deterrence. This concern is also valid for small fraud and tax evasion, which is why all the US programs have a minimum size for cases eligible to whistleblower rewards, but the problem is likely to be more relevant to the case of ‘petty’ corruption. These programs are more suited for ‘large cases’ in which the amount of funds recovered is large enough to pay for rewards and administrative costs, making these programs self-financing even without calculating the benefits for the deterrence/prevention of future infringements. However, when focusing on large corruption cases, other issues become relevant.
An issue particularly important for the case of ‘grand’ corruption is how independent the judicial system is from political pressure, and how able it is to protect whistleblowers against politically mandated retaliation. If corrupt politicians can importantly influence courts, the police or other relevant administrative agencies, then protection can hardly be guaranteed and inducing witnesses to blow the whistle through financial incentives may put their life at risk, although sufficiently large rewards can partly compensate for this risk and help escaping part of the retaliation.
On the whole, whistleblower rewards, in general and in the corruption context specifically, remain a promising tool to detect and deter crime. Careful design and implementation are necessary, because as for any powerful tool, these programs can be well used to do great thing, but also misused to do great damage. As the US experience has shown, along with sufficiently independent and precise courts and an effective administration of law enforcement, well designed and administered whistleblower reward programs hold the promise of greatly improving fraud and corruption detection and of being self-financing through recovered funds and fines.
Of course, even in a very good institutional environment, a poor design and/or implementation can lead to poor performance and do more harm than good (c.f. the case of leniency policies in China discussed in Perrotta et al., 2017). Moreover, in poor institutional environments, where the court system is not sufficiently precise and independent and other law enforcement institutions are not effective, even well-designed and implemented whistleblower reward schemes may bring more problems than benefits. Whistleblower rewards, as any other high-powered incentives, need good governance to ensure that the potentially very high benefits they can generate will be realized. Third parties like international courts and organizations could potentially provide for some low institution environments, the independent safe harbor necessary to protect whistleblowers and a check on court effectiveness for the award of financial incentives.
- Global Economic Crime Survey, 2016. Available at: https://www.pwc.com/gx/en/economic-crime-survey/pdf/GlobalEconomicCrimeSurvey2016.pdf
- Buccirossi, P., Immordino, G., and Spagnolo, G., 2017. “Whistleblower Rewards, False Reports, and Corporate Fraud”. SITE Working Paper No. 42, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2993776
- European Commission Report, 2017. Estimating the Economic Benefits of Whistleblower Protection in Public Procurement, Milieu Ltd.
- Engstrom, D., 2016. “Bounty Regimes”, in Research Handbook on Corporate Criminal Enforcement and Financial Misleading (Jennifer Arlen ed., Edward Elgar Press, forthcoming 2016)
- Butler, J., Serra, D., and Spagnolo G., 2017. “Motivating Whistleblowers.” Unpublished manuscript. Available at: https://www.aeaweb.org/conference/2017/preliminary/1658
- Schmolke, K.U., Utikal, V., 2016. “Whistleblowing: Incentives and Situational Determinants.” FAU – Discussion Papers in Economics, No. 09/2016. 2016. Available at: https://ssrn.com/abstract=2820475
- Call, A.C., Martin, G.S, Sharp, N.Y., Wilde, J.H., 2017. “Whistleblowers and Outcomes of Financial Misrepresentation Enforcement Actions.” Journal of Accounting Research, forthcoming.
- Wilde, J.H., (2017). “The Deterrent Effect of Employee Whistleblowing on Firms’ Financial Misreporting and Tax Aggressiveness”, The Accounting Review, forthcoming.
- Nyreröd, T. Spagnolo, G., 2017a “Myths and evidence on whistleblower rewards”, SITE Working Paper No.
- Spagnolo, G., 2004. “Divide et Impera: Optimal Leniency Programs.” CEPR Discussion Papers 4840, 2004.
- Nyreröd, T. Spagnolo, G. 2017b. “Whistleblower Rewards in the Fight against Corruption?” (in Portuguese), forthcoming in the book Corrupção e seus múltiplos enfoques jurídi
- Berlin-Perrotta, M., Qin, B. and Spagnolo, G., 2017. “Leniency, Asymmetric Punishment and Corruption: Evidence from China,” SITE Working Paper. Available at:https://ssrn.com/abstract=2718181 or http://dx.doi.org/10.2139/ssrn.2718181
- G20 Anti-Corruption Action Plan, Protection OF Whistleblowers Study on Whistleblower Protection Frameworks, Compendium of Best Practices and Guiding Principles for Legislation, 2011. Available at: https://www.oecd.org/g20/topics/anti-corruption/48972967.pdf
- Wolfe S., Worth M., Dreyfus S., Brown A.J., 2015. Breaking the Silence, Strengths and Weaknesses in G20 Whistleblower Protection Laws, 2015. Available at: https://blueprintforfreespeech.net/wp-content/uploads/2015/10/Breaking-the-Silence-Strengths-and-Weaknesses-in-G20-Whistleblower-Protection-Laws1.pdf
Health care attracts major attention in terms of hospital and physician reimbursement, owing to the large share of public expenditures and the presence of welfare issues demanding regulation. The focus of this policy brief is quality adjustments of prospective payments in the health sector. Using the data on the 2013 reform in Medicare, we show differential effects of value-based purchasing, where price setting is related to benchmark values of quality measures. The theoretical and empirical evidence indicates that unintended effects appear for acute-care U.S. hospitals at the best percentiles of quality. The findings provide insights into benchmarking within pay-for-performance schemes in health care.
The Russian national project “Health”, which was started by the federal government a decade ago and has expanded to regionally financed hospitals, is an example of a public remuneration scheme targeted at increasing health care efficiency. The project emphasized the role of the primary sector and raised salaries of general practitioners. A part of salaries was linked to patients’ assessment of the quality of health care. The reimbursement was seen as a means to stimulate higher quality.
However, cautiousness is required in introducing such payment mechanisms. Indeed, international experience shows that quality-related pay in health care may lead to heterogeneous effects across different groups of providers. A recent CEFIR working paper uses administrative panels of the U.S. hospitals to analyze the changes in quality owing to the introduction of the quality-pay.
The U.S. Health Care Sector
Pilots of pay-for-performance
In the early 2000s, numerous private and public programs linking quality and reimbursements in health care existed in the U.S., mostly at employer or state level (Ryan and Blustein, 2011; Damberg et al., 2009; Pearson et al., 2008). A nationwide pilot of quality-performance reimbursement started with the Hospital Quality Incentive Demonstration, where quality measures for five clinical conditions (heart failure, acute myocardial infarction, community-acquired pneumonia, coronary-artery bypass grafting, and hip and knee replacements) were accumulated from voluntarily participating hospitals. Some of these quality-reporting hospitals opted for the pay-for-performance project (initially established for 2003-2006, and later extended to 2007-2009). The project provided respectively 2% and 1% bonus payments for hospitals in the top and second top deciles of each quality measure (as of the end of the third year of the project). Hospitals in the bottom two deciles, on the other hand, were to receive 1-2% penalties (Kahn et al., 2006). Overall, the financial incentives helped improving the quality of the participating hospitals, but the improvement was inversely related to baseline performance (Lindenauer et al., 2007). Moreover, low-quality hospitals required most investment in quality increase; yet, they were not financially stimulated (Rosenthal et al., 2004).
The accumulation of the measures within the Hospital Quality Incentive was followed by the launch of the Surgical Care Improvement Project (SCIP) and Hospital Consumer Assessment of Healthcare Providers (HCAHPS). HCAHPS was the first national standardized survey with public reporting on various dimensions of patient experience of care. The measures of the clinical process of care domain are collected within the Hospital Inpatient Quality Reporting (IQR) program. These are measures for acute clinical conditions stemming from the Hospital Quality Incentive (i.e. acute myocardial infarction, heart failure, pneumonia), as well as measures from the Surgical Care Improvement Project and Healthcare Associated Infections.
The 2013 reform of Medicare
The success of the pilot project in the U.S. in terms of average enhancement of hospital quality has resulted in the nationwide introduction of these reimbursement policies. Namely, a value-based purchasing reform started at Medicare’s acute-care hospitals in the fiscal year of 2013. The reform decreased Medicare’s prospective payment to each hospital by a factor α and redistributes the accumulated fund. As a result of this rule, all hospitals performing below the mean value of the aggregate quality are financially punished, as their so-called adjustment coefficient is less than unity. At the same time, hospitals above the mean value are rewarded (See details in the Final Rule for 2013: Federal Register, Vol.76, No.88, May 6, 2011.)
The aggregate quality – called the total performance score – is a weighted sum of the scores of the measures in several domains: patient experience of care, clinical process of care, outcome of care, and efficiency. The scores on each measure are based on the hospital’s position against the nationwide distribution of all hospitals. In short, positive scores are given to hospitals above the median, and higher scores correspond to performance at the higher percentiles. The scores are a stepwise function, assigning flat values of points to subgroups within a given percentile range. Hospitals above the benchmark (the 95th percentile or the mean of the top decile) are not evaluated according to their improvement relative to the performance in the previous year.
If one assumes that hospitals are only maximizing profit, then such a linear payment schedule should stimulate quality increases across all spectrums of hospitals. However, the theoretical literature generally separates the hospital management, interested in profits, from the physicians who make decisions affecting the level of quality. In particular, physicians are treated as risk-averse agents, who have a decreasing marginal utility of money; that is, their valuation of monetary gains of a certain size decreases as their income increases. In such behavioral model (Besstremyannaya 2015, CEFIR/NES WP 218) physicians’ decisions about the quality of care is shaped by the trade-off between the potential losses they may incur if fired in case of hospital budget deficit and/or bankruptcy and their own costly effort to maintain and improve quality.
In this respect, the reform introduced two mechanisms: (1) it decreased the level of reward for low-quality hospitals and increased it for high-quality hospitals; and (2) it established a positive dependence of reward on quality. We show that the two forces compete, and the first one may outweigh the second for physicians at hospitals with high quality. Indeed, in these hospitals improved budget financing makes the bankruptcy, and probability of firing, less likely. As a result, physicians may be satisfied with a given sufficient level of a positive reward and not willing to exert any further efforts to raise the amount of this reward. Furthermore, physicians may even become de-stimulated. As a result, in these higher quality hospitals, the quality of care stabilizes or even goes down after the reform.
To sum up, we hypothesize that quality scores increase at the lowest tails of the nationwide distribution, while it may stay stable or fall among the highest quality hospitals. The sign of the mean/median effect is ambiguous.
Data on quality measures and hospital characteristics such as urban/rural location and ownership come from Hospital Compare. The panel covers the period from July 2007 to December 2013, and consists of 3,290 hospitals (12,701 observations). We exploit first-order serial correlation panel data models – longitudinal models where the value of the dependent variable in the previous period (lagged value) becomes one of the explanatory variables (see notations and definitions of analyzed measures in Tables 1-2.) The empirical part of the study evaluates the impact of the reform on changes of the quality scores of hospitals belonging to different percentiles of the nationwide distribution of each quality measure.
Table 1. Patient experience of care
|Comp-1-ap||Nurses always communicated well|
|Comp-2-ap||Doctors always communicated well|
|Comp-3-ap||Patients always received help as soon as they wanted|
|Comp-4-ap||Pain was always well controlled|
|Comp-5-ap||Staff always gave explanation about medicines|
|Clean-hsp-ap||Room was always clean|
|Quiet-hsp-ap||Hospital always quiet at night|
|Hsp-rating-910||Patients who gave hospital a rating of 9 or 10 (high)|
Notes: Score on each measure is the percent of patients’ top-box responses to each question.
Table 2. Clinical process of care
|AMI-8a||Primary PCI received within 90 minutes of hospital arrival|
|HF-1||Discharge instructions (heart failure)|
|SCIP-Inf1||Prophylactic antibiotic received within 1 hour prior to surgical incision|
|SCIP-Inf3||Prophylactic antibiotics discontinued within 24 hours after surgery end time|
|SCIP-Inf4||Cardiac surgery patients with controlled 6 a.m. postoperative blood glucose|
|SCIP-VTE2||Surgery patients who received appropriate venous thromboembolism prophylaxis within 24 hours prior to surgery to 24 hours after surgery|
Notes: Score on each measure is the percent of percent of cases with medical criteria satisfied.
The results of the estimates offer persuasive evidence for a non-rejection of our hypotheses: quality goes up at 1-5th deciles and falls at the 6-9th deciles (see Figures 1-2).
Figure 1. Mean change of scores owing to value-based purchasing across percentile groups of hospitals
It should be noted that the hypotheses concerning differential effects also rely on the fact that there is a certain population of hospitals to which each of the step-rates apply (Monrad Aas, 1995). Hence, the threshold and/or benchmark value in the national schedule may be worse than the value in a given hospital. Therefore, reimbursement with benchmarking becomes an additional cause of undesired effects.
Figure 2. Mean change of scores owing to value-based purchasing across percentile groups of hospitals
Our analysis confirms the presence of adverse effects of quality performance pay in health care. A remedy may be found in establishing benchmark at the value of the best performing hospital or employing ‘episode-based’ payment, which rewards a hospital for treating each patient case with corresponding criteria satisfied (Werner and Dudley, 2012; Rosenthal, 2008).
While the above results are based on the US data, they suggest that cautiousness is required in applying the pay-for-performance schemes to healthcare financing also in transition countries, and much attention should be paid to the potential adverse effects.
- Besstremyannaya, Galina, 2015. “The adverse effects of incentives regulation in health care: a comparative analysis with the U.S. and Japanese hospital data” (2015) CEFIR/NES Working Papers, No.218, www.cefir.ru/papers/WP218.pdf
- Damberg, Cheryl L, Raube, Kristiana, Teleki, Stephanie S and dela Cruz, Erin, 2009. ”Taking stock of pay-for-performance: a candid assessment from the front lines”, Health Affairs, Volume 28, pages 517-525.
- Kahn, Charles N, Ault, Thomas, Isenstein, Howard, Potetz, Lisa and Van Gelder, Susan, 2006. “Snapshot of hospital quality reporting and pay-for-performance under Medicare”, Health Affairs, Volume 25, pages 148-162.
- Lindenauer, Peter K, Remus, Denise, Roman, Sheila, Rothberg, Michael B, Benjamin, Evan M, Ma, Allen and Bratzler, Dale W, 2007. “Public reporting and pay for performance in hospital quality improvement”, New England Journal of Medicine, Volume 356, pages 486-496.
- Monrad Aas, I., 1995. Incentives and financing methods, Health policy, Volume 34, pages 205-220.
- Pearson, Steven D, Schneider, Eric C, Kleinman, Ken P, Coltin, Kathryn L and Singer, Janice A, 2008. “The impact of pay-for-performance on health care quality in Massachusetts, 2001-2003”, Health Affairs, Volume 27, pages 1167-1176.
- Rosenthal, Meredith B, Fernandopulle, Rushika, Song, HyunSook Ryu and Landon, Bruce, 2004. “Paying for quality: providers’ incentives for quality improvement”, Health Affairs, Volume 23, pages 127-141.
- Ryan, Andrew M and Blustein, Jan, 2011. “The effect of the MassHealth hospital pay-for-performance program on quality”, Health Services Research, Volume 46, pages 712-72.
- Werner, Rachel M and Dudley, R Adams, 2012. “Medicare’s new hospital value-based purchasing program is likely to have only a small impact on hospital payments”, Health Affairs, Volume 31, Number 9, pages 1932-1940.
Leniency policies have become an important antitrust tool but it is not clear whether they have effectively prevented recidivism or whether firms have learned to collude under, and even make strategic use of them. If “recidivism” is really an industry-level phenomenon, the appropriate policy measures are very different from what is necessary if individual firms, having been detected and punished for colluding, engage in the behavior again. Following Levenstein et al. (2015), this brief discusses the recidivism question as one about post-cartel behavior, i.e. the set of policies required to assure that effective competition emerges post-cartel breakup.
Cartels are one of the main concerns of the European Commission (EC) and the US Department of Justice (DOJ) and so, the US and EU Leniency Programmes (LPs) were designed, in 1978 and 1996 respectively, as a device for the deterrence and dissolution of collusive agreements (see Marvão and Spagnolo (2015a) for an in-depth review on the available evidence of the effects of LPs).
In the analysis of cartel formation, recidivism is an important issue. In the set of 510 cartel members fined by the EC in 1998-2014, Marvão (2015) identifies 89 “multiple offenders” (firms fined for collusion more than once), 10 “repeat offenders” (firms which initiate a cartel after being investigated for another cartel), and 5 recidivists following the definition from Werden et al. (2011): firms which initiate a cartel after being fined for another cartel.
The DOJ dataset compiled by Levenstein and Suslow (2015), spanning 1961-2013, preliminarily finds 113 “multiple offenders” but only 14 “repeat offenders”. Of these 14 firms, 5 that had been previously indicted were caught in the 1990s, but none was indicted again by the DOJ in the 2000s.
Although the number of (discovered) “true recidivists” is not zero, it is less than 1% in these two samples (EU, US). Recidivism seems to arise when there are lapses in enforcement; not surprisingly, some firms take advantage of these lapses to return to old behaviors. Designing policies that are able to prevent recidivism requires understanding whether this is an industry or firm-level phenomenon.
Levenstein et al. (2015) use the above-mentioned EU and US datasets to show that collusion occurs in virtually all sectors of the economy, but with discernable patterns.
In the US, construction and chemicals are frequently cartelized (pre and post leniency). There are a large number of cartels in local markets in some industries, such as retail gasoline stations and dealers and ready-mix concrete. While collusion in these local markets is frequently uncovered, it is not necessarily amongst the same firms.
In the EU, chemicals and transport cartels are also frequent areas of collusive activity (although cartels that are strictly within national boundaries and prosecuted by national competition authorities are not included in the sample).
The authors show that there is a large share of repeat and multiple offenders in chemicals and a surprisingly high proportion of repeat offenders in the manufacture of transport and electrical equipment. The highest proportion of multiple offenders is found in pharmaceuticals and refined petroleum products. The transportation and storage market is a sector with a high incidence of collusion (83 convicted cartel members), but no repeat offenders.
While the determinants of cartel activity are varied and endogenous, some correlations with industry-driven recidivism can be discussed:
- Industry concentration. It increases the ease of tacit collusion and it should increase the likelihood of explicit collusion, but there are many cartel examples in unconcentrated industries. In some industries, it has been argued that high fixed costs make competition unstable, so that, absent collusion, firms price below long-run marginal cost and are unable to cover fixed costs (Pirrong, 1992).
- Culture and history. Spar (1994) argues that the cooperative culture necessary for survival for diamond miners facilitated collusion as the industry matured. Policy fluctuations can also contribute to this problem, as was the case in the US during the Great Depression.
- Inelastic demand. This is empirically challenging to capture if the observed prices have been affected by monopoly power, thus potentially raised to a level at which demand is elastic. In many cases, the direct consumer is a producer, so the downstream cost function and competitive intensity also influence elasticity of demand for the cartelized product. Grout and Sonderegger (2005) estimate the likelihood of collusion in the US and EU and rank industries accordingly. This could be used to target competition authority resources to select industries.
Once a cartel breaks-up, cartel members may decide to compete in the market, merge, tacitly collude, or explicitly collude again. The latter does not mean that the cartel re-forms: a firm may collude in a new industry or product line or with a new set of co-conspirators.
U.S. Steel was involved in 6 different US cartels between 1948 and 1969, with different cartel partners and in different steel products. VSL construction was similarly involved (including as a leader) in multiple US cartels across several decades with distinct, but overlapping partners.
In the EU, Akzo Nobel N.V. has been convicted for 9 cartels, which lasted between 1987 and 2007, and in which its co-conspirators were mostly overlapping – e.g. collusion with Arkema in 6 instances (although the latter changed its name during the period). Many of the other co-conspirators were also multiple offenders. While Akzo only received one fine increase for recidivism, it received 7 leniency reductions, of which 3 were full immunity.
Other EC repeat offenders are ABB and Degussa Evonik – both convicted 4 times and received full immunity twice – as well as Brugg and Sumitomo. The latter was convicted for 7 cartels, of which 5, in the automotive wire harness, were self-reported.
What may influence repeated cartel participation, at the firm level?
- Firm’s corporate culture. In such a case, the leadership of the organization expects managers to collude, and collusion occurs in many markets in which the firm operates. Firm norms and expectations of managerial behavior can repeatedly encourage collusion and “disregard” previous fines, as illustrated in the ADM case (Eichenwald, 2000).
- Firm structure. Multi-market collusion literature focuses on the ability of firms to target punishments in particular markets. Multi-market firms may also encourage the spread of collusion if they have learned to collude in one market and share their “best practices” in another. This seems to have been the case, for example, in the spread of the vitamin cartel from vitamins A and E to other vitamins (Connor, 2008). Multi-market collusion is encouraged not only by multi-product multinationals, but also multi-market relationships between what appear to be smaller firms in local markets. For example, if gas stations are owned by multi-market firms such as large oil firms or chains of stations, that may facilitate repeated collusion over time and/or across geographic locations.
In complementarity with LPs, Levenstein et al. (2015) discuss additional (possibly) effective post-cartel policies, aimed at preventing firm-driven recidivism.
- Company Fines and Leniency. Theoretical research has emphasized the aptitude of well-designed and well-run LPs to improve cartel detection and deterrence (for a survey, see Spagnolo, 2008). However, Marvão and Spagnolo (2015b) note the generosity of the current EU LP: the average LP reduction is 45% and leniency is granted to 52% of convicted cartel members. In addition, Marvão (2015) shows that repeat offenders appear to receive larger EC leniency reductions, which suggests that firms can learn the “rules of the game”, colluding repeatedly and reporting the cartel to reduce their penalties. As such, fines need to be tougher and recidivism needs to be dealt with differently.
- Individual Accountability. Senior management in EU cartels does not seem to suffer from their participation in cartels. For example, Robert Koehler became CEO of SGL Carbon in 2012, after being convicted in 1999 of price-fixing in the graphite electrodes cartel. Imposing tougher sanctions, such as individual prison sentences or disqualification of senior executives from employment in their sector or role, may prevent repeated collusive behaviors (in new firms) and thus, increase deterrence levels.
- Follow-On Damages. Private damage suits may increase deterrence. In the US, private litigation plays a major role in the enforcement of antitrust law. Conversely, access to private damages is relatively new in the EU. A recently adopted EU Directive on damages (11/2014) prevents the use of LP statements in subsequent damage actions. However, Buccirossi et al. (2015) show that the effectiveness of damage actions can be improved if the civil liability of the immunity recipient is minimized and claimants receive full access to all evidence collected by the competition authority. Access to previous cartel decisions, for a given firm, will increase the amount of available information and can increase the likelihood and/or amount of successful damage claims.
- Consent Decrees. These impose conditions on the behavior of convicted firms (e.g. maximum price, and transparency). If these are violated, the authorities intervene, thus lowering the cost of prosecuting recidivists. In the US, decrees were routinely used by the DOJ in the 1960s and 1970s, but the practice was abandoned due to concerns of effectiveness and large costs. More recently, in September 2007, the Brazilian Administrative Council for Economic Defense enacted a resolution that allows for the use of consent decrees with the aim to settle cartel investigations. Two have already been executed.
If recidivism is industry-driven, its prevention may require a different set of tools, including those below, to complement leniency.
- Structural Remedies. Competition authorities have repeatedly permitted mergers among former cartel members, often without review, let alone structural intervention. Davies et al. (2014) examine mergers among former cartel conspirators and conclude that only 29% of the mergers were investigated by the EC. Remedies such as disclosure, divestiture of assets, selling minority shares in competitors, or licensure of intellectual property to competitors may change the nature of competition in the market and make collusion more difficult (see Marx & Zhou, 2015 regarding post-cartel mergers). This is particularly relevant if recidivism is industry-driven.
- Monitoring and screening. Some antitrust authorities have implemented monitoring and screening techniques to identify anticompetitive behavior in a given industry. These initiatives involve the analysis or monitoring of the characteristics of products or market structures that are thought to be more prone to collusion (mostly due to repeated offenses). Some examples are watch lists (e.g. Australia, UK, Chile), price observatories (e.g. Belgium, Spain, France), statistical screens (e.g. US FTC, Korea FTC), gasoline retail in Brazil and public procurement in Sweden (see Abrantes-Metz (2013) for further details on screens).
While literal recidivism, i.e. the formation of a cartel after having been convicted of illegal collusion, appears to be rarely detected in the EU and US, there remain policy gaps closing which could improve competition post-cartel.
A variety of post-cartel policies should be explored for their ability to increase the likelihood that workable competition, rather than tacit collusion or single firm dominance, will emerge. These reduce the reliance of competition authorities on leniency-driven self-reports, which will in turn make leniency more effective and less amenable to strategic use by firms determined to collude.
- Abrantes-Metz, Rosa (2013). “Proactive vs Reactive Anti-Cartel Policy: The Role of Empirical Screens.” Available at SSRN: http://ssrn.com/abstract=2284740.
- Buccirossi, Paulo, Catarina Marvão, and Giancarlo Spagnolo (2015). “Leniency and Damages,” CEPR Working Paper DP 10682.
- Connor, John M. (2008). Global Price Fixing, 2nd ed. Berlin: Springer.
- Eichenwald, Kurt (2000). The Informant. New York: Random House.
- Grout, Paul and Silvia Sonderegger (2005) “Predicting Cartels,” Office of Fair Trading, Economic Discussion Paper.
- Levenstein, M., Marvão, C., Suslow, V., 2015. Serial Collusion in Context: Repeat Offenses by Firm or by Industry? OECD Global Forum on Competition. DAF/COMP/GF(10/2015)
- Levenstein, Margaret C., and Valerie Y. Suslow (2015). “Price Fixing Hits Home: An Empirical Study of U.S. Price-Fixing Conspiracies,” working paper.
- Marvão, C., 2015. The EU Leniency Programme and Recidivism. Review of Industrial Organization, 48(1), 1-27
- Marvão, Catarina and Giancarlo Spagnolo (2015a). “What do we know about the effectiveness of leniency policies? A survey of the empirical and experimental evidence,” in Beaton-Wells, C and C Tran (eds.), Anti-Cartel Enforcement in a Contemporary Age: The Leniency Religion, Hart Publishing.
- Marvão, Catarina and Giancarlo Spagnolo (2015b). “Pros and Cons of Leniency, Damages and Screens”. Competition Law and Policy Debate (forthcoming)
- Marx, Leslie M., and Jun Zhou (2015). “The Dynamics of Mergers among (Ex) Co-Conspirators in the Shadow of Cartel Enforcement,” working paper.
- Pirrong, Stephen Craig (1992). “An application of core theory to the analysis of ocean shipping markets” Journal of Law and Economics, 35(1): 89-131.
- Spar, Debora (1994). The Cooperative Edge: The Internal Politics of International Cartels, Ithaca: Cornell University Press.
- van Driel, Hugo (2000). “Collusion in Transport: Group Effects in a Historical Perspective.” Journal of Economic Behavior and Organization, 41(4): 385–404.
- Werden, Gregory, Scott Hammond, and Belinda Barnett (2011). “Recidivism Eliminated: Cartel Enforcement in the United States since 1999,” Georgetown Global Antitrust Enforcement Symposium, Washington DC, Sept. 22, 2011.