The Trump administration’s conflict with China has little to do with US external imbalances, closed Chinese markets, or even China’s alleged theft of intellectual property. It has everything to do with containing China by limiting its access to foreign markets, advanced technologies, global banking services, and perhaps even US universities.
This year’s Nobel Peace Prize has been awarded to two activists for their efforts to end the use of sexual violence in armed conflict. But if their efforts are to lead to real change, heightened awareness must be turned into concerted action, and recent research can help.
Macroprudential policies have been the focus of increased attention in the post-crisis regulatory reform agenda. These policies are intended to limit systemic risk – the risk of disruptions to the provision of financial services caused by impairment to parts or all of the financial system (IMF 2013).
While an expanding literature has explored the effect of macroprudential policies on the aggregate economy and bank-level credit (Akinci and Olmstead-Rumsey 2017, Cerutti et al. 2015, Claessens et al. 2013), there has been little empirical evidence on the relationship between macroprudential policies and firm outcomes. Importantly, macroprudential policies might have differentiated effects across different types of firms. In particular, evidence that the ex-ante most financially constrained firms tend to be most affected by macroprudential policies could be suggestive of a trade-off between financial stability and inclusion.
Our recent research (Ayyagari et al. 2018) assesses the effectiveness of macroprudential policies and their impact on firms Specifically, using data across 900,000 firms from 49 countries for the period 2003-2011, we gauge the differential impact of macroprudential policies on small and young firms that tend to be more financially constrained to begin with and that the literature has found are more responsive to policy shocks. Our findings are important for policy formulation both in terms of the effectiveness of macroprudential policies and in terms of their unintended consequences.
Why macroprudential tools?
The case for macroprudential policies rests on the notion that a high correlation in the behaviour across financial institutions can result in contagion effects which can cause idiosyncratic distress to become systemic. In addition, strong credit cycles may have the potential not only to exacerbate business cycles, but also to lead to systemic banking distress. In the broadest sense, one can distinguish between a cross-sectional and a time-series dimension of macroprudential tools. In the cross-sectional dimension, macroprudential policies (e.g. higher capital requirements or regulatory restrictions on institutions whose failure would have a strong negative impact on the system) seek to limit the build-up of vulnerabilities that arise through linkages across financial institutions and from the critical role played by some institutions. In the time-series dimension, macroprudential policies intend to reduce systemic vulnerabilities arising from procyclical feedback between asset prices and credit and limiting unsustainable increase in leverage and volatile funding (IMF 2013). Our empirical analysis focuses on on the use of macroprudential tools to smooth credit cycles over time.
We distinguish between: (i) tools targeted at borrowers’ leverage and financial positions, and (ii) tools targeted at financial institutions using data from the Global Macroprudential Policy Instruments, as described inCerutti et al. (2015). The former includes loan-to-value and debt-service-to-income ratios, while the latter includes the following ten instruments: dynamic loan-loss provisioning, countercyclical capital buffers, bank leverage ratios, capital surcharge for systemically important financial institutions, limits on interbank exposures, concentration limits, limits on foreign currency loans, limits on domestic currency loans, reserve requirement ratios, and taxes or levies on financial institutions. We further use recent data on the intensity of macroprudential tools (Cerutti et al. 2017), as discussed below.
Data and methodology
To explore the relationship between macroprudential policies and firms’ financing, investment and sales growth, we combine the macroprudential indicators with data from Orbis, a commercial database distributed by Bureau van Dijk which contains basic firm-level information including data on external financing for over 900,000 companies across 49 countries over the period of 2003 to 2011. Compared with other databases, the unique advantage of using Orbis is that it includes data on large, small, listed and unlisted firms. We explore both short-term (with residual maturity of less than one year) and long-term (with residual maturity of one year of more) financing, as well as investment and sales growth.
There are several advantages to using micro data to examine the impact of macroprudential policies. First, using firm-level data and focusing on the differential effects of macroprudential policies across firm groups helps to mitigate endogeneity concerns regarding the adoption of macroprudential policies, as it is harder to argue that credit developments in individual firms or specific firm groups will drive the adoption of aggregate macroprudential policies. Second, by conducting the analysis at the firm level we can include country and time fixed effects to control for the impact of other macroeconomic developments (e.g. monetary policy) that might also affect firm credit growth.
We run firm-level regressions of financing, investment and sales growth on country-year indicators of macroprudential policies and control for other macroeconomic factors. We also focus on the differential relationship between macroprudential policies, financing, investment and sales growth of small and young firms relative to other firms. This allows us to control for any confounding time-varying country factors that might affect financing growth for any average firm in the country.
Our results suggest a significant association between the implementation of some macroprudential tools and firms’ financing and real sector growth, but we also find evidence of differential effects across different firm groups.
- There is a significant and negative association between borrower-targeted macroprudential policies and firms’ long-term financing growth. Specifically, applying one additional borrower-related macroprudential tool is associated with 4.8% lower long-term financing growth, while the correlation between financial-institution targeted macroprudential tools and firms’ financing growth is not significant.
- Micro, small and medium-sized firms’ as well young firms’ growth in short- and long-term financing decreases with the additional implementation of borrower-related macroprudential tools, while the association between financial institution-targeted macroprudential tools and financing growth is relatively stronger and more negative for micro firms.
- Both the implementation and intensity of macroprudential tools are significantly associated with firms’ financing growth. Both the level and the change in the loan-to-value ratio are associated with a relatively lower long-term financing growth of micro, small and medium sized enterprises, while neither short-term financing growth nor overall financing growth seem to be impacted. These findings hold both for microenterprises and small and medium enterprises, but not for young firms.
- Less financially healthy firms are more affected by the implementation of macroprudential policies. Specifically, highly levered and less profitable firms and firms with low interest coverage experience stronger reductions in financing growth than other firms after the implementation of macroprudential policies.
- Macroprudential policies are also associated with real sector outcomes. Specifically, applying one additional borrower-targeted macroprudential tool is associated with a 4.4% lower investment growth and 3.5% lower sales growth. As before, there is no significant association with financial institution-targeted macroprudential tools. The link between macroprudential tools and investment and sales growth is stronger for micro, small and medium-sized enterprises and for younger firms.
Our results show that macroprudential tools affect firms’ financing, investment and sales growth, speaking for their effectiveness. However, it is borrower-targeted policies rather than measures targeted at banks that are most effective, consistent with previous findings that macroprudential measures targeted at banks are subject to leakage (Aiyar et al. 2014).
The effectiveness of macroprudential tools works primarily through reducing financing growth for micro, small and medium-sized enterprises and young firms that have fewer alternative financing sources. However, it is the financially less healthy firms among these groups that experience a higher reduction in financing growth, thus providing limited evidence for a trade-off between financial stability and inclusion.
This finding is consistent with the literature that has found that small firms are more affected by policy changes (Forbes 2007, Gertler and Gilchrist 1994). Furthermoreit points to a trade-off between financial stability and financial inclusion. Reassuringly, however, banks seem to be reducing financing growth primarily to riskier (more financially fragile) firms.
Authors’ note: The views expressed here are those of the authors and do not necessarily represent those of the institutions with which they are affiliated.
Aiyar, S, C W Calomiris and T Wieladek (2014), “Does macroprudential regulation leak? Evidence from a UK policy experiment”, Journal of Money, Credit, and Banking 46(1): 181-214.
Akinci, O and J Olmstead-Rumsey (2017), “How effective are macroprudential policies? An empirical investigation”, Journal of Financial Intermediation 33: 33-57.
Ayyagari, M, T Beck and M S Martinez Peria (2018), “The micro impact of macroprudential policies: Firm-level evidence”, IMF working paper 18/267.
Cerutti, E, S Claessens and L Laeven (2015), “The use and effectiveness of macroprudential policies: New evidence”, IMF working paper 15/61.
Cerutti, E, R Correa, E Fiorentino and E Segalla (2017), “Changes in prudential policy instruments: A new cross-country database”, International Journal of Central Banking 13(1): 477-503.
Claessens, S, S Ghosh and R. Mihet (2013), “Macroprudential policies to mitigate financial system vulnerabilities”, Journal of International Money and Finance 39: 153-185.
Forbes, K (2007). “One cost of the Chilean capital controls: Increased financial constraints for smaller traded firms”, Journal of International Economics 71(2): 294-323.
Gertler, M and S Gilchrist (1994), “Monetary policy, business cycles, and the behaviorof small manufacturing firms”, Quarterly Journal of Economics 109(2): 309-340.
IMF (2013), “Key aspects of macroprudential policy”, IMF policy paper.
Meghana Ayyagari, Thorsten Beck, Maria Soledad Martinez Peria
Lending-based crowdfunding platforms represent a new mode of financial intermediation by connecting lenders and borrowers directly using internet platforms. While their share of the loan market is still very small, in some niches these platforms are a real alternative to bank credit. For example, in 2017, business lending by UK crowdfunding platforms amounted to almost 30% of new loans to small businesses (Zhang et al. 2018) (see Figure 1).
Figure 1 Business lending-based crowdfunding in the UK
Source: Zhang et al. (2018).
Technically, lending-based crowdfunding platforms do not perform risk and maturity transformation, but they are experimenting with business models that could allow them to perform bank-like functions in the future (Havrylchyk and Verdier 2018).
- Many platforms allow lenders to automate their lending process by setting lending criteria (risk, maturity, and so on). This lowers transaction costs and permits diversification.
- Platforms use credit scoring to assign a risk band to every borrower and effectively play the role of a delegated monitor insofar as lenders delegate to them due-diligence.
- Some platforms provide liquidity services by creating secondary markets in which lenders sell their loans to other investors.
Regulation of crowdfunding
To review existing regulatory practices, we sent a questionnaire about regulation of crowdfunding to all OECD countries, and received 17 replies (Havrylchyk 2018). While many OECD countries require lending-based crowdfunding platforms to adapt to existing regulation, nine of the responding countries had put in place legislation designed to regulate this mode of financial intermediation.
In March 2018, following consultations, the European Commission presented a proposal for an EU-wide passporting regime for both lending-based and investment-based crowdfunding (European Commission 2018).
Table 1 Summary of activities that are allowed by lending-based crowdfunding platforms
Notes: Yes/No – if activity is allowed/not allowed; NR – if activity is not regulated or not mentioned in the law. Information is provided separately for two legal statuses of lending-based platforms that facilitate loan agreements and investment-based platforms that facilitate debt securities.
Source: OECD Questionnaire in Havrylchyk (2018).
Table 1 summarises the results of the questionnaire for countries that have put in place specifically designed regulation for lending-based crowdfunding. These regulatory texts do not propose any instruments, but rather define the scope of activities of crowdfunding platforms. While some regulators restrict crowdfunding platforms to simple credit intermediation, others explicitly set high maximum amounts for originated loans, allow automated lending, provision funds, and secondary markets, and permit a platform to invest in loans that it facilitates.
Restricting crowdfunding platforms to credit intermediation limits short-term risks, but it also prevents platforms from experimenting with business models, and eventually competing with banks. In this context, the recent European Commission regulatory proposal appears to be conservative, since it does not mention automated lending, limits the maximum loan amount to €1 million, explicitly forbids platform from investing in loans, and limits the responsibility of platforms in the organisation of secondary markets (called ‘bulletin boards’).
There are three types of market failures in the financial system that need to be addressed by the regulators: coordination problems and runs, moral hazard and adverse selection, and market power or barriers to entry.
Coordination problems and runs
Large lending-based crowdfunding platforms offer secondary markets, which are a useful feature for providing liquidity to investors and could be essential if platforms are to scale up. Theoretically, platforms with secondary markets would not be subject to coordination problems and self-fulfilling runs in the same way as banks are. The direct link between lenders and borrowers ensures that lenders’ strategy (to run or not to run) does not depend on the strategy of other lenders, but only on the solvency of borrowers in their loan portfolio.
Nevertheless, secondary markets might become illiquid and misprice traded loans. Also, it would be misleading for platforms to promise lenders maturity transformation (that is, their investment can be always liquidated). Currently, secondary markets are forbidden in many countries – and in some cases the regulatory approach is not clear, as Table 1 shows. In particular, the EU proposal explicitly states that platforms cannot put in place a trading system but, instead, proposes a bulletin board that allows investors to interact directly with each other to buy and sell loan agreements or transferable securities. Importantly, this buying and selling activity on crowdfunding platforms is at the client’s own discretion and responsibility.
Regulatory challenges: Coordination problems and runs:
- How do we ensure well-functioning secondary markets?
- How can we ensure that platforms do not promise the transformation of maturity to lenders?
Adverse selection and moral hazard
Retail lenders face severe adverse selection problems when choosing to whom they lend. This problem is particularly acute because most regulators set a maximum loan size (Table 1). This allows lending only to small businesses, and this type of lending tends to be riskier and opaque. To mitigate this adverse selection, lenders delegate due diligence to crowdfunding platforms. Hence, it is important to ensure that platforms have good risk management systems. Platforms are likely to experiment with new methods of credit scoring that rely on big data and machine learning. While these techniques are promising, they are untested. Importantly, due diligence and scoring models are currently not supervised.
Since lenders bear all credit losses, aligning incentives between platforms and lenders should be at the heart of regulation. To signal that they retain some risk, several platforms invest in loans that they facilitate, while others use their own capital to create provision funds that absorb first losses. Indeed, if platforms do not retain the risk of the loans they facilitate, their business model could resemble the ‘originate and distribute’ model of the securitisation process. This was one of the major causes of the global crisis, as it loosened credit standards.
In the EU proposal, platforms are explicitly forbidden to invest in loans that they facilitate in several countries (Table 1). This rule is motivated by the potential conflict of interest, because platforms might cherry-pick the best loans. this is a legitimate concern. If platforms invest in any loans, they should invest in allloans. A well-designed minimum capital requirement could be used as a tool to provide incentives to platforms managers and shareholders not to expose lenders to excessive risks.
Finally, if platforms become systemically important in the future, it is important to ensure that an orderly resolution is possible – in other words, the continuation of loan repayments so that lenders do not lose money if the platform fails. This is important because if there are financial institutions that are too big or too interconnected to fail, this increases the likelihood of state bail-outs. This intensifies moral hazard problems. No large platform has yet failed, but the direct connection between lenders and borrowers means it would be easier to design an orderly resolution for a platform than for a bank. Since all lenders bear their own losses, it is comparable to a bail-in mechanism.
Regulatory challenges: Adverse selection and moral hazard
- How can we ensure that platforms have good risk management systems in place, without constraining innovation in scoring models?
- How do we regulate the alignment of incentives between platforms and lenders?
- How can we ensure a smooth resolution in case of failure?
Market power and barriers to entry
Lending-based crowdfunding platforms are entering a market dominated by large banks and face many barriers to entry. Due to high switching costs, platforms are forced to pursue a market-expansion strategy toward risky borrowers who are underserved by incumbent banks (De Roure et al. 2016, Morse 2015).
This worked during the post-crisis period, when banks needed to deleverage and cut their loan supply to creditworthy borrowers (Havrylchyk et al. 2018). But if entrants serve only borrowers who have been rejected by banks, they risk higher default rates and lose servicing fees. This means that the long-term viability of the crowdfunding business model is likely to require that banks and platforms directly compete for good borrowers.
This is complicated: incumbent banks have an informational advantage over lending-based crowdfunding platforms. Long-term relationships give banks granular data on their borrowers, that allow them to model the default risk. The scale and scope economies inherent in financial intermediation are also a significant barrier to entry. Finally, explicit and implicit government guarantees imply a funding-cost advantage. They allow large banks to provide credit at a lower interest rate than crowdfunding platforms, all other things being equal.
Regulatory challenges: Market power, and barriers to entry
- How should we level the playing field between platforms and banks?
- Should policymakers make it easier to switch to new technologies, and with what instruments?
- How can we ensure that policies towards incumbent banks (such as explicit and implicit guarantees that result in lower funding costs) do not distort the level playing field between banks and crowdfunding platforms?
Institutional lenders on crowdfunding platforms
The above discussion suggests that the crowdfunding business model could potentially be more stable than banking, because it is less leveraged, less prone to runs and easier to resolve. On the other hand, most platforms allow sophisticated institutional lenders to invest because this provides an important guarantee to borrowers that their loans will be funded. But if these institutional lenders are leveraged and prone to runs and moral hazard problems, all the advantages of the lending-based crowdfunding business model could be lost.
Regulatory challenges: Institutional lenders
- How can we ensure fair treatment of both retail and institutional investors?
- How can we limit the interconnectedness of lending-based crowdfunding platforms and leveraged, too-big-to-fail institutions?
To sum up, lending-based crowdfunding platforms are experimenting with different business models. Therefore it is important to ensure that this experimentation happens under the eye of strong regulators and supervisors, who are able to intervene to address the potential market failures.
De Roure, C, L Pelizzon, P Tasca (2016), “How does P2P lending fit into the consumer credit market?”, Deutsche Bundesbank discussion paper 30/2016.
European Commission (2018), “Proposal for a regulation of the European parliament and of the council on European crowdfunding service providers for business”.
Havrylchyk, O and M Verdier (2018), “The financial intermediation role of the P2P lending platforms”, Comparative Economic Systems 60(1): 115-130.
Havrylchyk, O, C Mariotto, T Rahim and M Verdier (2018), “What drives the expansion of the peer-to-peer lending?”, working paper.
Havrylchyk, O (2018), “Regulatory framework for the loan-based crowdfunding platforms”, OECD working paper 1513.
Morse, A (2015), “Peer-to-Peer Crowdfunding: Information and the Potential of disruption in consumer lending”, NBER working paper 20899.
Zhang, B, T Ziegler, L Mammadove, D Johanson, M Gray and N Yerolemou (2018), 5th UK Alternative Finance Industry Report, Cambridge Centre for Alternative Finance.
Many innovations come in the shape of machines that replace workers. We hear of cars that drive themselves, of robots that perform more and more tasks, and of how artificial intelligence can replace smart jobs. These technological developments cause alarm among many, and this has intensified since the last recession that began in 2008. The recovery from the recession has been slow, and especially in creating new jobs. That is why many have called it ‘a jobless recovery.’
Interestingly, this fear of ‘technological unemployment’ is not new and has surfaced many times since the Industrial Revolution, as workers feared that new machines might drive them out of jobs. This led to the rebellion of the Luddites in 1811-1817. They were artisans who viewed in awe how the mills of the textile industry threatened their existence. They started a mutiny and the British government had to send a large army to oppress it.
The issue came up again often, especially in periods of rapid technical change. On 26 February 1928, Evan Clark wrote an article in the New York Times, titled “March of the Machine Makes Idle Hands”. He claimed that “the onward march of machines into every corner of our industrial life had driven men out of the factory and into the ranks of the unemployed”. At the time, the US rate of unemployment was only 4.2%. Around then, Keynes (1930) coined the term ‘technological unemployment’ in his famous essay “Economic Possibilities for our Grandchildren”.
The experience of the past two centuries has shown that this fear did not materialise, but the issue keeps returning to public discussion. This is partly because our memory is short and partly because some claim that ‘this time is different’ and that the risk of automation is greater. A recent publication by the World Bank discusses this claim seriously and offers ways to reduce the risk of unemployment (Chuah et al. 2018). Throughout the years, many economists were more optimistic. Keynes (1930) suggested that future technologies will increase leisure and thus will increase employment. Zeira (1998) has shown that machines that replace labour in some tasks make labour in other tasks more productive, so the demand for labour increases. In a sequence of papers, Acemoglu and Restrepo (2018a, 2018b among others) show that if technology creates new tasks in addition to automation, it increases the demand for labour and keeps the rate of unemployment from rising.
Our recent paper takes this line of research further and makes a bold contribution to the debate (Nakamura and Zeira 2018). We argue that the rate of technological unemployment will decrease in the future and will converge to zero in the end. As we show below, the mechanism we describe is different from those suggested both by Keynes (1930) and by Acemoglu and Restrepo (2018a, 2018b).
Our analysis of automation and unemployment uses a theoretical framework of production by tasks, in which technical change involves two processes. One is automation, namely shifting existing tasks from production by labour to production by newly invented machines. The second process is adding new tasks. Unlike Acemoglu and Restrepo, we do not impose any assumption on the rate of creation of new tasks, and this rate can be zero as well. As shown below, the dynamics of automation are what drives the creation of new tasks.
To explain the main result of our paper, first note that the dynamics of automation can fall into two main cases. The first is that automation does cover all tasks and stops at some finite level. In this case, the rate of automation must decline as it gets close to the upper bound, and it converges to zero over time. Since the rate of technological unemployment is proportional to the number of new automated tasks relative to the total number of labour tasks, it follows that the rate of unemployment converges to zero as well. Clearly, this is a simple case, in which our main result holds.1 The more interesting case is when automation grows without bounds, which we discuss next.
Automation grows only if producers adopt it, i.e. if they prefer capital over labour for the next tasks. We assume that newly invented machines or robots are more expensive, as we first invent the easy machines and then over time invent increasingly more complicated ones. This is a reasonable assumption that is supported by empirical studies. Producers prefer a machine for a certain task to labour only if it costs less. This means that in order to continue to adopt the ever-costlier machines, wages must rise as well. This is required to keep automation going.
In our model, the wage level depends positively on the number of labour tasks and negatively on the share of labour in output. The reason for the first positive dependence is straightforward – the larger the number of labour tasks, the smaller the number of workers who perform each task. Hence, their marginal productivity in the task is higher and this raises wages. The negative effect of the share of labour on wages is harder to explain intuitively. If wages are higher, the tasks performed by labour are more expensive and producers will buy less of them. In order to keep output at the same level, producers purchase more of the automated tasks. This increases the share of capital in output and reduces the share of labour in output. Hence, the share of labour has a negative correlation with the wage rate.
As a result, if automation is unbounded wages rise continuously, and that can happen in one of two ways: either the number of labour tasks increases rapidly (actually faster than automation) or the share of labour goes down continuously all the way to zero. If we rule out the case that the share of labour converges to zero, then the number of labour tasks must increase continuously. This means that the rate of unemployment is decreasing and is converging to zero, since it is proportional to the number of new automated tasks divided by the number of existing labour tasks. If the denominator – the number of existing labour tasks – increases, the rate of unemployment falls and converges to zero.
Hence, our study shows that the dynamics of the rate of unemployment depend crucially on the dynamics of the share of labour. Many empirical studies, the most famous of which is Kaldor (1961), have actually found that the share of labour in output is very stable, at around two-thirds, both across countries and over time. In recent decades, the share of labour has experienced a slight decline, but it is still around 60% of GDP and in any case, it is far from going down to zero. Hence, the assumption that the share of labour does not converge to zero is fully in line with the well-known stylised facts of modern economic growth. This supports our result that the rate of automation unemployment should decline over time and converge to zero.
Our paper therefore presents a strong result. Help is on the way and automation unemployment will not rise – on the contrary, it will go down to zero at the end of times. Nevertheless, we should treat this prediction with some caution. In general, economic models are better at explaining processes and mechanisms than at predicting the future. Our paper should be treated accordingly. Its main message is that although automation causes unemployment by turning labour tasks into machine tasks, it might also ignite a mechanism that reduces unemployment. Automation requires rising wages, and that requires increasing the set of labour tasks. This increase reduces the rate of unemployment. Hence, automation leads to a continuing reduction in unemployment by its own adoption mechanism. This is an important point to bear in mind when we consider the effect of automation on unemployment and on the labour market in general.
Acemoglu, D, and P Restrepo (2018a), “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment”, American Economic Review 108, 1488-1542.
Acemoglu, D, and P Restrepo (2018b), “Artificial Intelligence, Automation and Work”, mimeo.
Chuah, L L, N V Loayza, and A D Schmillen (2018), “The Future of Work: Race with – not Against – the Machine”, World Bank Group, Research & Policy Briefs.
Kaldor, N (1961), “Capital Accumulation and Economic Growth”, in F A Lutz and D C Hague (eds.), The Theory of Capital, 177–222, New York: St. Martins Press.
Keynes, J M (1930), “Economic Possibilities for our Grandchildren”, in Essays in Persuasion, New York, W.W. Norton & Co., 1963, 358-373.
Nakamura, H, and J Zeira (2018), “Automation and Unemployment: Help is on the Way”, CEPR Discussion Paper no. 12974.
Zeira, J (1998), “Workers, Machines, and Economic Growth”, Quarterly Journal of Economics 113, 1091-1117.
 There is a sub-case of this case, in which the economy converges to an equilibrium with production by machines and robots only, with no labour. We rule out this sub-case below, as the share of labour in output goes to zero.
Hideki Nakamura, Joseph Zeira
The saving grace of a nasty divorce is durable insight into the true values of the parties involved. And so, with Brexit.
The Withdrawal Agreement – which has triggered rancorous opposition in parliament and a political crisis in the UK – lays bare the diplomatic cards. Whatever its eventual fate, the way that Agreement was reached and the reaction it has now provoked provide analysts with rich insight into some of the forces now at play in Europe.
First, the anguished yelps in London show where power has prevailed. As Australia’s ex-envoy pointed out, the UK was set to become a regulatory colony of the European Union for at least 20 months. The Agreement also required UK taxpayers to foot a £39 billion (A $68 billion) divorce fee, and parliament to surrender, indefinitely, the right to legally extract itself from EU institutions thereafter.
In contriving to impose such terms, the EU revealed some unanticipated qualities: unity, discipline, and exceptional statecraft. The laurels go to the chief orchestrator, the European Commission. That furtive chink of glassware in Brussels three weeks ago now looks premature, but if the Withdrawal Agreement survives in modified terms, then November 2018 will mark the date when the Commission stepped forth as the principal power-broker in Europe.
How was this putative victory gained? Last December, the EU demanded Northern Ireland as a hostage for the trade talks: if UK and EU cannot agree on seamless trade, then a “backstop” arrangement would see Northern Ireland excised from the UK’s regulatory and customs ambit, and retained within EU jurisdiction. It is to mitigate this legal-regulatory annexation that the UK offered itself in entirety into backstop bondage.
Such diplomacy is predatory, and former foreign secretary Boris Johnson called it out. Put simply, the EU demanded that UK cede regulatory and judicial sovereignty over part of its territory as the price of a deal. In asserting this right, the Good Friday Agreement (GFA) was, proverbially, honoured more in the breach than the observance. The Withdrawal Agreement obviated a physical border in Ireland, sure, but this consequential benefit was secured by subverting the GFA’s principal provisions: acknowledgement of UK sovereignty; and constitutional change only via popular consent.
Were no qualms expressed in Brussels? Respect for sovereignty and popular consent are precisely what differentiate post- from pre-war European diplomacy, yet the Agreement respects neither and the British parliament bridled. Perhaps for the EU, the ends justified the means. If so, it’s worth examining the inevitable result of the backstop if it isn’t removed from the Agreement: UK’s forced retention in a customs arrangement and adherence to single market regulation.
To appreciate the gravity of this outcome it is necessary to grasp one fundamental point: that, although the Customs Union‒Single Market looks like a free trade area it doesn’t behave like one, at least not for UK.
Since 1998, UK’s goods exports to EU have stagnated while imports have grown at a brisk 3.2% per annum. The result: a series of chronic deficits that recur and augment like nightmares across UK trade: -£28 bn in autos; -£18 bn in food & agriculture; -£9 bn in pharmaceuticals; -£7 bn in machinery.
Scrutiny of UK’s historical trade data reveals a curious “captive market” effect. Take UK’s biggest traded sector: autos, worth £103 billion per year. Over 20 years, the EU has retained a vice-like market share of over 83% of UK imports, even as its own share of UK exports has plummeted. Meanwhile, UK’s growing dependence on EU agriculture force-feeds UK consumers on the most expensively produced food on earth.
Protected by external EU tariffs, EU producers are steadily cornering UK’s markets without commensurate reciprocity. From this, the backstop ensures no escape (without sacrificing Northern Ireland). Under the Agreement, UK’s financial services would get no special post-Brexit treatment, but even if they did, their surplus covers just one-quarter of UK’s £95 bn EU goods-trade deficit. For historians this is mercantilism; the practice of using power to capture markets. It, too, has nasty precedence in pre-war Europe.
As for the UK, enduring insights flow from reactions to the Agreement. Specifically, popular hostility to its terms proves there was always way more to Brexit than an anti-immigrant spasm. The Agreement would definitely end free movement, and yet no poll of leave voters comes close to showing a preference for the Agreement over simply walking away, with all its hazards.
The reaction amongst pro-Brexit Conservative MPs is more fascinating still. Almost all are implacably opposed. Tally up their antipathies to the Agreement – and its impediments to free trade, deregulation, cheap food, and an unfettered foreign policy – and analysts aren’t staring at swivel-eyed Tories; they are staring straight into the pupils of Gladstonian liberals who aren’t afraid of change.
So, this is a salutary moment. The rejection of the Agreement by pro-Brexit MPs reveals deep tides. To characterise Brexit, now, as populist xenophobia is as misplaced as it would be to characterise the Protestant Reformation as just an irate bout of iconoclastic statue-bashing. There’s more at stake than perceived public delinquency; specifically, a classic and compelling model of government.
As for the EU, the omens look ill. Following its rejection by Parliament, the EU has refused to renegotiate the Agreement. In its current form, the Agreement will fail. Playing for higher stakes, the EU may want it to fail. But nothing will elide the Agreement’s brutal logic or exploitative intent. Sadly, the EU’s diplomatic standards are slipping into unsavoury ways. What’s odd, though, is that none of its members object.
* Data source UK Office for National Statistics, September 2018 (November 2017 for Financial Services), with calculations made by the author, available here.
Philippines President Rodrigo Duterte is a very different leader than his predecessor, Benigno Aquino III.
Duterte has expressed his love for Xi Jinping. Aquino took China to court. Aquino significantly enhanced Philippine-US relations during the Obama administration. Duterte called Obama a “son of a whore” and “black and arrogant”. Aquino is the scion of the leading political family in the Philippines. Duterte is the first president from Mindanao.
Yet in two important ways, their presidencies are similar and these similarities challenge one of the most conventional of wisdoms about Philippine politics. Duterte and Aquino are the most popular presidents in the post-Marcos era, exhibiting much more durable levels of popular support than their predecessors. And both presidents, in their very different styles, have directly challenged the Catholic Church.
In 2012, four days before Christmas, Aquino signed the Reproductive Health Bill that had been successfully opposed by the Catholic Church for decades. A week earlier, the Catholic Bishop’s Conference of the Philippines released a pastoral letter to be read at all masses against the Reproductive Health Bill with the title “Contraception is Corruption!”
Duterte’s challenge to the Catholic Church has been much more profane, personal, and consistent. He supports the return of the death penalty and on 9 January 2017 signed an Executive Order calling for universal access to family planning methods and an accelerated implementation of the Reproductive Health Bill. Duterte has gone much further, repeatedly castigating Catholic priests and the Church for their criticisms of him and his bloody war on drugs, calling the Pope a son of a whore for aggravating Metro Manila’s traffic during his papal visit and the 2016 presidential campaign, and calling God “stupid”.
While opinion polls show that Filipinos are disappointed with these anti-Catholic comments, there is little sign that this has translated into any withdrawal of support for Duterte.
The Catholic Church in the Philippines is less politically important than the conventional wisdom based on the role of the church in the overthrow of the Marcos dictatorship suggests. Presidents can take the Church on directly at little cost and possibly even some political benefit.
In the view of Filipino Catholic voters, the Church is separate from the State, and this is upheld in political practice by the separation enshrined in the 1987 Constitution.
In 2016, I published a study of Xi Jinping, CEO China: The Rise of Xi Jinping (I B Tauris). This book has subsequently been reissued in paperback, and in 2018 I did a shorter overview, The World According to Xi with the same publisher. For one reason or the other, other the last few years I have had to think quite a bit about the current leader of China.
Two years on, we all now have seen a bit more and know a little more about a man The Economistmagazine stated was the most powerful in the world in late 2017.
The most puzzling aspect about his rule, however, remains the same. How has it been possible, over such a complex country, and with so many other contending figures who may have had the chance to compete with Xi, that he has become so seemingly dominant? Did we miss something about his innate political skills as he was emerging into view as a major leader? Or are we still misinterpreting this figure, and seeing the mirage of great power rather than the real thing? Is he really as all commanding as he seems?
Coming to the end of 2018, the atmosphere in the People’s Republic has become increasingly dominated by Xi’s presence and image.
During the Forum for China African Cooperation in September, an already infamous cover of the official People’s Daily had Xi’s name in headlines almost twenty times on the front page.
But this was symptomatic of a deeper penetration of Xi into the political life of the nation.
In October 2017, during the 19th Party Congress (a major event held every five years), “Xi Jinping Thought” was written into the Party Constitution. This was the first time such an accolade had been accorded to a named individual since 1945, when “Mao Zedong Thought” was promoted a similar way.
Even more remarkable was the removal of time limits for occupying the Presidency. Restricted to two periods of five years each from the early 1980s, at the National People’s Congress in March 2018, after hardly any discussion, the rules were changed. This was seen by commentators inside and outside China as a clear sign that Xi was intending in some shape or form to stay on long after the current unwritten retirement age of 68.
Are we now at the point where we can say we are looking at the face of a true contemporary Chinese autocrat? It is still hard to say with real certainty. For sure, Xi’s image hangs above China with a ubiquity that sometimes verges on the comically obsessive.
But the response China gave to the Trump administration implementation of tariffs on Chinese goods exported to the US revealed some the vulnerability of a leadership where everything seems to be decided by one man.
The Beijing leadership seemed dazed and slow in their response, with indications for the first time that there was only a small huddle of people around Xi running the country and that it was dependent on the quality of their advice over how his administration handled matters. That meant that ideas from elsewhere in the whole vast system that might have been worth listening to were simply ignored, or unimplemented, meaning the world’s most important bilateral relationship grew increasingly fractious and unstable.
Streamlined and centralised decision making may all be very well, as long as those making the final decisions happen to be broadly right. This episode, so far, has proved that the Xi style can be worryingly unsophisticated and inflexible.
There is little reason, yet, to change the central thesis of the CEO China book about Xi’s greatest source of authority and his good luck – the very unique situation that China has found itself in under his rule. With an economy still growing well, and with a national sentiment that continues to be forward-looking, and ready to embrace the mantle of “major power status”, finally winning the battle of modern history to be a strong, wealthy, powerful country, with a regional and global dominance, 2021, the year in which the first centennial goal arrives, still looms large on the horizon.
At the moment, China’s onward trajectory despite the turmoil with the US looks good. It has never before produced so much top-quality research, never before has it been so competitive against the US in key areas from IT to artificial intelligence and engineering. Its high-speed rail infrastructure remains the envy of the world. And with the Belt and Road Initiative, at least now the outside world is thinking, like it or not, about how they need to engage with this new behemoth.
Is the often pushy and assertive way that China now behaves towards, for instance, countries in its region over issues that matter to it like the South and East China Sea, or towards Hong Kong and Taiwan, symptomatic of the way Xi runs things within China – categorical, ruthless, and utterly unaccepting of any signs of dissent?
The current management of the Xinjiang region, with its massive, and almost unimaginably thorough social surveillance and use of vast “re-education camps” which look to be little more than prisons for sweeping thought reform, is the acme of this – a vision of a state so invasive and so gifted with new forms of intrusive technology that it can finally enter deep into the inner lives of the common people.
Or is it that these are in fact all the inevitable behaviour of a county that after a modern history of being the underdog is finally now in a position to assert itself and be respected and feared? In essence, this boils down to a simple question. Is Xi the servant of the inevitable processes or forces of history, or is he and the organisation he is in charge of shaping and decisively changing the direction of that history?
As in 2016, I remain sceptical about the kind of power that Xi has, and just how to interpret it as down to him and his political skills, and how to understand best the role of the Party he heads and the very special period he finds himself in power. Trump’s maverick presidency has posed the most significant challenges so far and shown vulnerabilities and lacunae.
As we enter 2019, this remains the core area to focus on. Can Xi produce a new kind of more reciprocal relationship with the world, and one where China is seen as less isolated and more of a true global leader? The opportunity is still there, but become harder because of the sharpness of China’s power mentioned above.