Tag: China

Trade Induced Technological Change: Did Chinese Competition Increase Innovation in Europe?

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The last 30 years has witnessed a shift of the world’s manufacturing core from Europe and North America to China. A key question is what impact this has had on manufacturing workers in other developed economies, and also on innovation, patenting, IT adoption, and productivity growth. While a rigorous data analysis on these variables for developing economies, particularly in Eastern Europe, is not yet available, this brief examines the impact of the rise of China on innovation in Western Europe, and also reviews the evidence on the impact of the rise of China generally. Recent research by Bloom, Draca, and Van Reenen (2016) found that Chinese competition induced a rise in patenting, IT adoption, and TFP by 30% of the total increase in Europe in the early 2000s. Yet, we find numerous problems with the Bloom et al. analysis, and, overall, we do not find convincing evidence that Chinese competition increased innovation in Europe.

Few events have inspired the ire of economists as much as Brexit and the rise of Donald Trump, two events seen as related as both were a seeming reaction to both globalization and slowing economic growth, particularly as some (such as Trump himself) saw the former as a key cause of the latter. Both Brexit and the trade war spawned by Trump do seem to have had negative economic effects – US equities have suffered every time the trade war has escalated, while anecdotal reports and more sophisticated economic analyses seem to suggest that Brexit has cost the UK jobs.

And yet, there is a need for policy makers and economists to hold two ideas in our heads simultaneously: Trump’s trade war and Brexit may be policy disasters, and yet globalization can create both winners and losers, even if it is clear that, generally speaking, the overall gains are likely positive and large. This is likely also true of the rise of China – one of the most dramatic events in international economics in the past 50 years. Figure 1 shows the increase in trade with China from the early 1980s to 2017, a period in which US imports from China grew from 7 to 476 billion dollars.

Figure 1. Chinese Imports (in logs, deflated)

Source: World Bank WITS

The academic literature tends to show that this impact, the rise of China, may have cost the US as much as 2.2 million jobs directly (Autor et al.), and as much as 3 million jobs once all input-output and local labor market effects are included. While approximate, these numbers are large enough for the China shock to have played a role in the initial onset of “secular stagnation” – the growth slowdown which began around 2000 for many advanced nations, including the US and Europe. In addition, Autor et al. (forthcoming) found that Chinese competition also resulted in a decline in patent growth. In the European context, however, other authors have found that although China did do some damage to certain sectors, overall, it does not appear to have been quite as damaging, particularly in Germany, which also benefitted from exporting increased machine tools to the Chinese manufacturing sector. And, in a seminal paper, Bloom, Draca, and Van Reenen (2016) find that Chinese competition actually led to an increase in patents, IT adoption, and productivity in Europe from 1996 to 2005, along accounting for nearly 30% of the increase. This is important, as it implies that without the rise of competition with China, the slowdown in European growth would have been even more pronounced than it was. It also implies that, far from being a source of stagnation, Chinese competition has been a source of strength. It also makes it more likely that the slowdown in growth since 2000 was caused by supply-side factors, such as new inventions becoming more difficult over time, as is perhaps the leading explanation among economists, notably Northwestern University business professor, Robert Gordon (2017), and also supported by others (see this VoxEU Ebook featuring a “who’s who?” among economists). It would also be evidence that contradicts the “Bernanke Hypothesis” that the former US Fed Chair first laid out in a 2005 speech at Jackson Hole, in which he suggested that international factors – particularly the savings glut and US trade deficit – were behind falling interest rates in the US. Since then, Ben Bernanke has followed up with a series of blog posts suggesting that these international factors were the cause of the initial onset of secular stagnation.

Figure 2. European Growth Relative to Trend

Source: World Bank WDI

In this brief, I present new research in which my coauthor and I test the robustness of the research finding that China had a positive impact on innovation in Europe (Campbell and Mau, 2019). We find that these findings are very sensitive to controls for time trends and other slight changes in specification. We also find that the number of patents matched to firms in the sample shrinks over the sample period (from 1996 to 2005). Overall, we conclude that, unfortunately, it is unlikely that the rise led to a significant increase in innovation in Europe, although more research is needed. Our research also sheds light on the so-called “replication crisis” currently gripping the social sciences, as researchers begin to realize that many published findings are not robust.

Trade-Induced Technical Change?

Bloom, Draca, and Van Reenen (2016) – hereafter BDV – tried to isolate the impact of the rise of China on Europe using several methods, using firm-level data for Europe. They placed each firm in a 4-digit sector, where they measured imports from China over time. First, they just looked at changes in patents, IT, and total factor productivity (TFP) at the firm level for sectors in which Chinese imports increased a lot vs. other sectors. But, because economists are always weary of the difficulty of isolating a causal relationship from non-experimental data, the authors, worrying that the sectors which saw increases in Chinese imports might differ systematically from the others, the authors also used what is called an instrumental variable. That is, they used the fact that when China joined the WTO in 2001, they also negotiated a reduction in textile quotas. Thus, BDV reason that textile sectors which had tightly binding quotas prior to removal were likely to have had fast growth in Chinese imports after China’s accession to the WTO. Thus, they end up comparing textile sectors in which the quotas were binding to sectors in which they were not binding. We went back and compared the evolution of patents in these same groups (sectors with binding textile quotas vs. not binding) below in Figure 3.

Figure 3. Patent Growth in China-Competing Sectors (Quota Group) vs. Other Sectors

Notes: The vertical red lines are dates when textile quotas were removed. The blue line shows the evolution of patents in the sectors without binding quotas (non-competing sectors), and the red line is the evolution of patents in the China-competing sectors. The dotted lines are 2 standard deviation error bounds.

What is immediately obvious in Figure 3 is that patents are declining rapidly over the whole period in both groups. The overall level of patents was falling in both groups for the full period. There is a 95.8% decline in patenting for the China-competing group, vs. a 96.2% decline for firms in the non-competing (“No quota”) group. By 2005, average patents per firm are close to zero in both groups (.04 in the China-competing sectors vs. .11 in the others). However, in the “No quota” group, the initial level of patents – close to three per firm per year – was much larger than in the quota group. Since patents are falling rapidly in both groups but bounded by zero, the level of the fall in patents in the non-quota group is larger, but one can easily see that much of this decline happens before quotas are removed. If we control for simple time trends, the effect goes away. Also, given the tendency of patents to decline, we can also remove the correlation between Chinese competition and patent growth in some specifications by simply controlling for the lagged level of patents. The overall declining share of patents in the BDV data also raises questions about data selection issues, as patents granted in the BDV data in the later years were a smaller share of the total patents actually granted in reality.

BDV also look at the impact of the rise of China on IT adoption. However, here they proxied IT adoption by computers per worker, but they did not collect enough data to control for pre-trends properly in the data, so we cannot be sure whether this correlation is causal or not. (For what it is worth, on the data we do have, from 2000 to 2007, including trends in the data renders the apparent correlation between Chinese import growth and computers-per-worker insignificant.)

Lastly, BDV look at the impact of the rise of China on TFP growth. Here, unlike before, we find that their measure is robust across various estimation methodologies. However, when we look at changes in a commonly used alternative measure of productivity, value-added per worker, instead of TFP (as TFP needs to be calculated using strong assumptions about the functional form of technology), we find no impact (see Figure 4 below).

Figure 4. Value-Added per worker Growth: China-competing sectors vs. others

Figure 4 above compares the evolution of value-added per worker in the most China-competing sectors vs. the others. Trends look similar for firms in either group of sectors (China-competing or otherwise), and we do not find a correlation. We also do not find that Chinese competition led to an increase in profits, nor an increase in sales per worker (in fact, we found a significant decrease in most specifications).


All in all, we find that the BDV findings suggesting that the rise of China had a large impact on innovation in Europe is not robust. However, in most specifications, we also don’t find a negative impact as did Autor et al. (forthcoming) for the US. This might have to do with data quality, although it does seem to be closer to other work, such as Dauth et al. (2014), which suggests that the rise of China had a smaller impact in Germany than in the US.

We also felt it was a bit alarming that a simple plot of  the trends in patents for China-competing and not-competing sectors was enough to seriously question the conclusions of BDV, as their paper was published in the Review of Economic Studies, a top 5 journal in academic economics. If influential articles published in the most fancy journals can exhibit such mistakes, this underscores the extent which the profession of economics may suffer from many published “false-positive” results. The reasons why this could be the case are obvious: researchers are under pressure to find significant results, as top journals don’t often publish null results, and replication is exceedingly rare in a field in which one needs to make friends to publish. However, there are signs that replication is becoming more mainstream, and as it does, we can certainly hope that voters around the world will turn back to science.


  • Autor, D., D. Dorn, G. H. Hanson, G. Pisano, and P. Shu. Forthcoming. Foreign Competition and Domestic Innovation: Evidence from US Patents. Forthcoming: AEJ:Insights.
  • Bloom, N., M. Draca, and J. Van Reenen. 2016. “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity.” The Review of Economic Studies 83 (1): 87–117.
  • Campbell, Douglas and Mau, Karsten. 2019.. Trade Induced Technological Change: Did Chinese Competition Increase Innovation in Europe?”, mimeo
  • Dauth, W., S. Findeisen, and J. Suedekum. 2014. “The Rise of the East and the Far East: German Labor Markets and Trade Integration.” Journal of the European Economic Association 12 (6): 1643–1675.
  • Gordon, R.J., 2017. The rise and fall of American growth: The US standard of living since the civil war (Vol. 70). Princeton University Press.

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.

US-China Trade War of 2018 and Its Consequences

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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.

Obsolete weapon

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.

Leniency, Asymmetric Punishment and Corruption: Evidence from China

20150928 FREE Policy Brief Leniency, Asymmetric Punishment Image 01

Since coming into office two years ago, Chinese President Xi Jinping has carried out a sweeping, highly publicized anticorruption campaign. Skeptics are debating whether the campaign is biased towards Mr. Xi’s rivals, and even possibly related to the current economic slowdown. What is less debated is the next stage of Mr. Xi’s anti-corruption strategy, which is going to alter the legal statutes. Amendment IX, proposed in October 2014, includes heavier penalties, but two important tools in the fight of corruption – one-sided leniency and asymmetric punishment – became more limited and discretional. We argue that studying a 1997 reform and its effects can shed some light onto why the Chinese leadership seems dissatisfied with the current legislation and the likely effects of the proposed changes.

What We Know about Leniency

In our context, leniency can be defined as the concession of reduced sanctions (or full immunity) to wrongdoers that cooperate by self-reporting and providing information against former partners in crime. Formal and informal exchanges of leniency against information and collaboration are normal features of law enforcement in most countries. Policies of this kind have been extensively and quite successfully used to fight the Italian and American mafias, drug dealing and other organized crimes, and have become the main instrument to fight collusion in antitrust since the US reform in 1993 (see Spagnolo, 2008).

For crimes in which multiple offenders cooperate, one-sided leniency conditional on being the first to self-report can be a very powerful tool of law enforcement: by playing the partners in crime against each other, it may elicit information, greatly facilitate prosecution and generate deterrence at a very low cost. A conspicuous scientific literature with theoretical, experimental and empirical contributions shows the great potential of these policies, when properly designed and administered, for deterring collusive crimes (Miller 2009; Spagnolo 2008; Bigoni et al. 2012, 2015). On the other hand, Buccirossi and Spagnolo (2006) show specifically for the case of corruption that, when poorly designed or administered, these same policies may become ineffective or even counterproductive.

Asymmetric Punishment

A related way of using leniency towards one party (to play it against the other) in the fight against corruption has been at the center of a recent intense policy debate after the popular note “Why, for a Class of Bribes, the Act of Giving a Bribe Should Be Treated as Legal”, by Kaushik Basu (2011). Then chief economist of the Indian government and now of the World Bank, Basu advocated asymmetric depenalization of bribe giving, which can be thought of as a form of unconditional, one-sided leniency. More precisely, the note proposed to legalize bribe giving in the form of harassment bribes (also called extortionary, or discharge-of-duty bribes) paid to obtain something one is entitled to, while strengthening sanctions against bribe taking. As with other forms of leniency, the idea is to create a conflict of interests between the partners in crime by increasing the temptation for one party to betray and report the illegal act, leading to a severe punishment of the other.

In the debate sparked by this note many different arguments have been put forward, both against it and in favor of it. Dufwenberg and Spagnolo (2015) discuss formally some of the issues raised by critics of the proposal, while Abbink et al. (2014) provide (mixed) experimental evidence on its effectiveness. Later, a blogpost by a Chinese law scholar, Li (2012), attracted our attention to the case of China, where asymmetric punishment (bribe-giver impunity) has been in place since 1997. She argued, probably reflecting the political debate in the country rather than based on factual evidence, that the system had not been successful. We felt this claim granted a deeper investigation into the details of the Chinese legal reform and the changes it introduced, and of course a careful inspection of the data to back it.

A Study in Red

In a new working paper, Perrotta Berlin and Spagnolo (2015), we set out to understand the evolution of the anti-corruption legislation in China over the last decades, and then to evaluate the effects of the policy changes occurring in 1997. Two new elements were given the strongest legal status in 1997: leniency for wrongdoers that self-reported and cooperated with investigators, and asymmetric punishment (no charge for bribe givers) for bribes paid to obtain something one was entitled to. Concurrently, penalties were decreased, in particular for bribe-takers.

To understand the likely effects of this policy change we would ideally look at correspondent changes in corrupt transactions. Data on the prevalence of bribery, however, are notoriously hard to come by because of the secretive nature of this activity. Instead, we use several data sources which capture on the one hand actual corruption cases tried in courts, and on the other hand surveys of corruption perceptions. In particular, we have collected the number of arrests and public prosecutions on the counts of corruption and bribery from the Procuratorates’ Yearly Reports for each Chinese province since 1986.

It is not straightforward to infer changes in total corruption, which is unobserved, from changes in discovered cases tried in court. The data on prosecutions mix together corruption and anticorruption activities, as they fail to distinguish occurrence of the criminal activity from detection. A policy that deters crimes but at the same time increases the fraction of those that are successfully prosecuted will have an ambiguous effect on the number of prosecutions. We adapt for this purpose the testable predictions developed by Miller (2009): he models the occurrence of criminal activity (cartel formation, in this case) and derives predictions for how changes in the rate of occurrence and the rate of detection affect the time series of detection.

The preliminary evidence we have so far points to a substantial and stable reduction in the number of major corruption cases around the 1997 reform, a result consistent with a positive deterrence effect of the 1997 reform. The evidence is suggestive, and some alternative interpretations of the patterns in the data, shown in the plot below, cannot be excluded at the moment. While a peak-and-slump pattern as in Miller (2009) would have been much stronger evidence supporting the success of the reform at deterring corruption, we cannot exclude that the drop in prosecutions is simply due to a general worsening in detection. Although we deem this unlikely in the light of the general political climate of the time, we need more and better data to support our interpretation. Still, claims that the reform did not have an effect appear not supported by the data.

Figure 1. Change in Corruption Prosecutions before and after law reform in 1997

MariaGiancaPicSource: Perrotta-Berlin and Spagnolo (2015).

More to be done

A case study analysis is under way to corroborate and help the interpretation of these preliminary findings. We will analyze in depth a stratified random sample of prosecution case files between 1980 and 2010. Given that we sample a given number of cases, in this part of the analysis we cannot gain any insight about the incidence of bribery in general. We can instead observe the impact of the legislative reform on specific details of the corrupt behavior, and the mechanisms through which this behavior occurs or is deterred. In particular, we will be able to distinguish between cases of extortionary (harassment) bribes and bribes paid to obtain illegitimate benefits. Moreover, this will allow us to shed light on whether and how leniency and asymmetric punishment were applied in practice. The details of the case files might even allow us to gain insight into how the bribe-size and the value of corrupt deals evolved through the reform and even the selection into bureaucracy.


One-sided leniency, conditional on reporting an act first, or unconditional, as when bribe giving is depenalized, may be powerful corruption deterrence instruments if well designed and implemented in the right environment, but may also have negative effects. It has been argued that these instruments have been ineffective in China, after they were reformed in 1997, however, without data supporting the claim. Part of the reason lies in the difficulty to obtain good data on corruption. Another obstacle is the subtlety of interpreting them when they relate only to detected and convicted cases, rather than to the whole population of corruption cases.

We cannot solve completely the issue of data quality, as we also need to rely on official reports of counts of corruption cases. However limited, the exercise performed on aggregated data clearly shows that the 1997 Criminal Law reform did have an effect, consistent with increased corruption deterrence. To further support this finding we will collect and analyze micro-data from a randomized sample of these cases. This will allow us to isolate at a higher level of detail the changes in criminal behavior, reporting behavior and prosecution activity, and link them to the details of the legal reform to highlight the mechanisms at work.

China is home to a sixth of humanity, and currently undergoing a massive crackdown on corruption. Whatever we can learn about the effectiveness of their past and present anti-corruption policies is likely to have considerable welfare effects. Moreover, the 1997 reform was the object of a policy debate, and comments on its effectiveness came without data to support them. We believe our effort to use data to shed light on what this reform actually changed will be a valuable input to further research and policy discussion on this important topic.


  • Abbink, K., U. Dasgupta, L. Gangadharan, and T. Jain. “Let-ting the Briber Go Free: An Experiment on MitigatingHarassment Bribes.” Journal of Public Economics, 111,2014, 17–28.
  • Basu, K. “Why, for a Class of Bribes, the Act of Giv-ing a Bribe Should Be Treated as Legal.” WorkingPaper 172011 DEA, Ministry of Finance, Governmentof India, 2011
  • Bigoni, M., S.-O. Fridolfsson, C. LeCoq, and G. Spagnolo.“Fines, Leniency and Rewards in Antitrust.” RANDJournal of Economics, 43, 2012a, 368–90.
  • Bigoni, M., S.-O. Fridolfsson, C. LeCoq, and G. Spagnolo.. “Trust and Deterrence.”. Journal of Law, Economics, and Organization (2015)
  • Buccirossi, P., and G. Spagnolo. “Leniency Policies and Ille-gal Transactions.” Journal of Public Economics, 90,2006, 1281–97.
  • Buccirossi, P., Marvão, C. M. P., & Spagnolo, G. (2015). Leniency and Damages. Available at SSRN 2566774.
  • Dufwenberg, M. and Spagnolo, G., Legalizing Bribe Giving (April 2015). Economic Inquiry, Vol. 53, Issue 2, pp. 836-853, 2015.
  • Li, X. Guest post: bribery and the limits of game theory – the lessons from China. http://blogs.ft.com/beyond-brics/2012/05/01/guest-post-bribery-and-the-limits-of-game-theory-the-lessons-from-china/, 2012. Accessed: 2015-05-20.
  • Miller, N. H. Strategic leniency and cartel enforcement. The American Economic Review, pages 750–768, 2009.
  • Perrotta Berlin, M. and G. Spagnolo, Leniency, Asymmetric Punishment and Corruption: Evidence from China, SITE Working Paper, 2015 (forthcoming)