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US-China Trade War of 2018 and Its Consequences

Posted on December 17, 2018 | Policy Brief

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The trade war between the United States and China became one of the most significant events in the global economy in 2018. This policy brief explores the main drivers of the US-China Trade War, including trade imbalances and intellectual property concerns, and examines the potential consequences for both countries as well as the broader impact on other economies, such as 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.

Populism

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.

Deterrence

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.

References

  • 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.

Note

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.

Money Laundering: Regulatory or Political Capture?

Posted on December 10, 2018 | Policy Brief

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Danske Bank has recently been accused of having laundered more than 200 billion Euros through its Estonian branch. The size of the scandal has reinvigorated the discussion over lax enforcement by regulators and poor bank compliance with anti-money laundering laws. In this brief, we concisely review some recent cases of poor regulatory and political behaviour with respect to these matters, focusing in particular on the UK, whose financial system seems to have become a main hub for this type of financial misconduct.

A widespread phenomenon

The size of the recent money laundering scandal at Danske Bank, involving more than 200 billion Euros, has surprised many. Money laundering is a widespread issue in an increasingly complex world where financial transactions are many and instantaneous, while oversight slow and limited (Radu 2016). According to the United Nations Office on Drugs and Crime, an estimated $800 – $2 trillion is laundered every year (United Nations Office on Drugs and Crime). The source of laundered money is often from corruption, crime and drug cartels (as with the HSBC scandal, see below). Attempts to blow the whistle on these illegal transactions have gotten several people killed, especially in Russia (The Daily Beast, October 2018).

Malta’s Pilatus bank recently had its license revoked by the European Central Bank after its chairman was charged with money laundering (Reuters, October 2018). The investigative reporter Daphne Caruana Galizia was killed in a car bomb in October of 2017 in Malta (The Guardian, October 2017). She was leading the Panama Papers investigation into corruption in the country and had accused Pilatus bank of processing corrupt payments (The Guardian, November 2018). In Sweden, some banks have recently been criticized for insufficient actions against money laundering. Experts at the regulator recommended extensive sanctions, but upper management stopped them (Svenska Dagbladet, December 2018). In November, Deutsche Bank’s headquarters in Frankfurt were raided by prosecutors in a money laundering investigation (BBC, November 2018).

Back to Danske Bank. Its Estonian branch was recently accused of having laundered money, amounting to over 200 billion Euros of suspicious transfers (Financial Times, November 2018). In 2011 the Estonian branch accounted for 0.5% of Danske Bank’s assets, while generating 12% of its total profits before taxes. In 2013, 99% of the profits in the branch came from non-residents. Many of the non-resident customers are believed to be from Russia and other ex-soviet states (Forbes, September 2018). The alleged money laundering came to light due to the whistleblower Howard Wilkinson, who headed Danske Bank’s market trading unit in the Baltics from 2007 to 2014. Surprisingly, his anger over these transactions was not primarily aimed at top management in Copenhagen, or failure of rank and file employees to follow protocol in customer acquisition, but against the UK, who he claimed is “the worst of all” when it comes to combating money laundering (Financial Times, November 2018). In fact, the UK institutions seem to have been at the very heart of the scandal (ibid):

“Mr Wilkinson’s emails to Danske executives in 2013 and 2014 highlighted how UK entities were “the preferred vehicle for non-resident clients” at the heart of the scandal.”

In an address to European Union Lawmakers, he said (Reuters, November 2018):

“The role of the United Kingdom is an absolute disgrace. Limited liability partnerships and Scottish liability partnerships have been abused for absolutely years”.

Regulatory or political capture?

The increasingly central role that the UK appears to be playing as a hub for financial crime is perhaps not new or surprising. The UK has indeed come to be widely recognized as one – though certainly not the only – main hub for these illegal transactions (see e.g. Radu 2016, p.15). The UK’s National Crime Agency estimates 93 billion GBP of tainted money is flowing into Britain annually (Financial Times, September 2018).

And according to the classic theory of regulatory capture (Stigler, 1970), it is to be expected that a large, wealthy and highly concentrated sector such as the UK financial industry, will be able to capture regulatory institutions and lead them to act more in its favour than in that of the (national or international) community. However, besides being a concentrated source of special interests, the financial sector also represents a large share of the UK economy. It could be the case, therefore, that the capture goes all the way up to the political system and the government (as in Becker 1983, and Laffont, 1996). So, is it the alleged crime-friendly environment in the UK financial system linked more to problems of regulatory capture, or to deeper political capture?

Already in 2004 there were worrying signs of possibly deep political capture.  At the time, Paul Moore, a senior risk manager at Halifax Bank of Scotland (HBOS), raised concerns about the bank’s risk taking and was subsequently fired by the executive James Crosby. Crosby then proceeded to become Deputy Chairman at the Financial Services Authority (FSA). HBOS then collapsed during the financial crisis of 2008 and merged with Lloyds bank, leading to one of the most concentrated banking systems in the world (the top 5 banks have 85% of the UK banking market). Many took this to substantiate Moore’s claim that the bank had been taking excessive risks. During Prime Minister’s question time in the House of Commons, David Cameron commented on then Prime Minister Gordon Brown’s decision to appoint Crosby to the FSA:

“Sir James Crosby, the man who ran HBOS and whom the Prime Minister singled out to regulate our banks and to advise our Government, has resigned over allegations that he sacked the whistleblower who knew that his bank was taking unacceptable risks.” (cited in Dewing and Russell 2016, p.165)

A suggestive episode directly involving politicians and money laundering is the case of HSBC, with headquarters in London. HSBC avoided criminal prosecution in the US and entered into a deferred prosecution agreement with the DOJ in 2012 (Department of Justice, December 2012). HSBC was found to have violated U.S. Anti-Money Laundering and Sanctions Laws by laundering billions of dollars linked to Mexican drug cartels, groups in Iran and Syria, and groups linked to terrorism. While HSBC apparently had systems to flag suspicious transactions, employees were told to disregard red flags (Garrett 2014, p.201). The case led to a 2016 House Committee report entitled “too big to jail” that was extensively used against the Democrats by the Trump presidential campaign (Committee on Financial Services, 2016).

The report states that on the 10th of September 2012 UK Chancellor George Osborne (the UK’s chief financial minister) wrote a letter to Federal Reserve Chairman Ben Bernanke (with a copy transmitted to then Treasury Secretary Timothy Geithner). In the letter, Chancellor Osborne insinuated that the U.S. was unfairly targeting UK banks by seeking settlements that were higher than comparable settlements with U.S. banks. He also worried about what criminal sanctions against HSBC would imply for financial stability. Criminal charges could also lead to a revoked license, making the bank unable to do business in the US (Financial Times, July 2016). HSBC was eventually ordered to pay a 1.9 billion dollar fine, while another whistleblower claims that the money laundering still went on (Huffington Post, August 2013).

The FSA also appeared much more concerned about criminal sanctions against HSBC than with money laundering for the bloodiest drug cartel in history (estimated to be responsible for several tenths of thousands of murders). In fact, the house committee report states that “The FSA’s Involvement in the U.S. Government’s HSBC Investigations and Enforcement Actions Appears to Have Hampered the U.S. Government’s Investigations and Influenced DOJ’s Decision Not to Prosecute HSBC” (p.24).

Things have not improved more recently. In 2013 the FSA was split up into the Financial Conduct Authority and the Prudential Regulation Authority (FCA & PRA). In 2014 the FCA & PRA came out with a note requested by the British parliament on whether financial incentives for whistleblowers should be introduced in the UK. These financial incentives, or reward programs, are used extensively in the US in tax, procurement, and securities. The FCA & PRA came out strongly against rewards in their seven-page note, yet do not cite a single piece of evidence (PRA and FCA, 2014). Most importantly, the note contains important factual misstatements about available evidence on their effectiveness that were easy to check at the time of the report (Nyreröd & Spagnolo 2017, National Whistleblower Center 2018). Nor was the note amended when one of us repeatedly communicated the mistakes to the agencies. This suggests persistent and deep regulatory capture. Consistent with this interpretation is the sanctioning behavior of UK regulators.

A blatant recent example is the ridiculous fine against CEO of Barclays Bank Jes Staley. He ordered his security team to unveil the identity of an uncomfortable whistleblower, going so far as to request video footage of the person who bought the postage for the letter. Yet, the FCA & PRA decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, May 2018). When Moore was asked about the fine he replied that “it is a very clear sign to whistleblowers not to bother” (Reuters, April 2018).

Conclusion

Is this regulatory capture, or political capture? The impressive list of consistent cases of regulatory slack and of political complacency suggests both, at least in the case of the UK. But the problem of regulatory capture in the case of financial crimes goes way beyond the somewhat extreme case of the UK. In all jurisdictions financial misbehavior has recently only led to settlements between regulators and the infringing financial institution, with settlement payments way too low to generate (financial stability concerns, and) deterrence effects. Banking regulators appear mainly concerned about banks’ health and profitability, so that large financial institutions have not only become too big to fail, but also too big to jail, and now even too big to fine, at least to the appropriate extent (Spagnolo 2015). All this even though the financial crime has been that actively supporting through money laundering criminal organizations that killed tenths of thousands of innocent people.

References

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.

Unemployment in Transition and Its Long-Term Consequences

Posted on December 3, 2018 | Policy Brief

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We examine the relationship between the experience of unemployment in the early years of the socio-economic transition in Poland and a number of wellbeing measures about two decades later. The analysis takes advantage of the rich content of data from the Survey of Health, Ageing and Retirement in Europe (SHARE) by matching retrospective information on labour market experiences with outcomes observed in the survey after year 2006. While there is a strong correlation between unemployment and general wellbeing measures such as life satisfaction, depression and subjective assessment of material conditions, the relationship cannot be interpreted as causal. On the other hand, we find that unemployment in the early years of the transition has strong, negative, long-term consequences for income and house ownership. The analysis sheds light on the implications of unemployment and on the nature of job losses in the follow-up of the Polish ‘shock-therapy’.

Introduction

Next year, the countries of Central and Eastern Europe will celebrate the 30th anniversary of the political breakthrough and the beginning of a major socio-economic transformation which followed. In the Polish case, the ‘shock therapy’ approach to the reform process implemented by the Mazowiecki government, though not without faults, has generally been viewed as the origin of the country’s economic success story. Afterwards, Poland experienced nearly three decades of uninterrupted economic growth and the Polish GDP returned to its pre-reform level already in 1995.

However, discussions of negative implications of the reform package still fuel the academic discourse as well as the political debate. While the majority of the population managed to avoid significant economic difficulties, many families experienced the painful hardship of the transition period in the form of job losses, poverty and exclusion. Given the scale of the socio-economic change, surprisingly little is known about the long-term consequences of individual experiences at that time. In particular, it is unclear if the negative outcomes observed many years after the reforms started can be causally linked to individual experiences in the early 1990s.

This lack of evidence is not unique for Poland and is largely due to unavailability of good individual-level data spanning the time before and after the collapse of communism. Since the transition cannot be lived through again, we shall never know how socio-economic conditions would have looked like under numerous alternative reform scenarios. However, as we show in a recent paper (Myck & Oczkowska, 2018), much can be learnt from the combination of contemporary and retrospective information on the nature of labour market histories during the transition and their relationship to outcomes recorded many years later.

The analysis presented in Myck and Oczkowska (2018) relies on the treatment of the systemic changes in the early 1990s as a major exogenous shock and on differentiating between reasons behind individual experiences of unemployment. We demonstrate that the observed strong correlation between unemployment in the initial years of the transition and a number of subjective wellbeing measures in later life is endogenous, and may reflect unobservable individual characteristics. It seems plausible to argue that these characteristics were the reasons behind the recorded job losses once the economy was liberalised and firms could fire their least productive employees.

Work histories in the SHARE dataset

The analysis is based on individual-level data from the Polish part of the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary biennial panel survey focusing on individuals aged 50 years and over. Since the start of the project in year 2004 seven waves of data have been collected, and the survey was conducted in Poland in waves 2, 3, 4, 6 and 7. While the standard waves of the survey focus on contemporary conditions of respondents such as health, economic conditions, labour market activity and social networks, in wave 3 (the so-called SHARE-Life), participants were asked about their life histories including their family history, mobility and labour market experiences. The detailed labour market histories recorded in SHARE-Life allow us to identify transition-related job losses, which can be matched with current information on several measures of material conditions and wellbeing for the same individuals.

In Figure 1 we present labour market profiles since 1988 of those in the sample who were working prior to the start of the reform process.

Figure 1. Labour market status 1988 – 2008 conditional on working in 1988 in Poland

Source: Myck and Oczkowska, 2018.

The figure shows that along with rapidly increasing unemployment rates, the degree of inactivity of the Polish population grew substantially in the two decades following the transition. This data confirms that in the follow-up of the ‘shock-therapy’ reforms many individuals faced unemployment, while others, especially among older groups of employees, used several other labour market exit options, such as retirement or disability.

Analysing long-term consequences of economic shocks

To examine the role of unemployment experiences in the initial years of the transition for outcomes observed a few decades later, we use data from waves 2, 3 and 4 of the SHARE study. The analysis focuses on two groups of later-life outcomes – objective measures of material conditions such as household income, real assets and house ownership, and subjective indicators of wellbeing such as life satisfaction, depression or reporting difficulties in making ends meet.

We are able to control for an extensive set of individual characteristics which are usually unobservable to the researcher, through a complex set of background variables available in SHARE. These include respondents’ childhood conditions, parental background as well as health and labour market experience prior to 1988. With regard to the experience of unemployment we differentiate the instances of unemployment between the initial (1989-1991) and later (1992-1995) period of the transition to examine the potential differential implications of the rapid pace of the reforms in the early 1990s. Most importantly though, the data allows us to distinguish between different reasons behind job losses and we can separately examine the relationship with plant/office closures and other reasons for unemployment. Following other examples in the literature (Farber, 2011; Jacobson et al., 1993), we argue that plant closures can be treated as reasons for exogenous job separations. This in turn allows us on the one hand, to give a causal interpretation to the estimated coefficients, and on the other, to interpret those on other reasons for unemployment in the light of the causal relations.

Effects of unemployment experience on later-life outcomes

We find that experiencing unemployment due to plant/office closure between 1989 and 1991 is associated with almost a 30 percent lower level of household income and a lower probability of house ownership of about 10 percentage points (pp) some two decades afterwards. There is also a strong relationship between unemployment in the early years of the transition and wellbeing measures two decades later – individuals who experienced unemployment in the first three years of the transition have a 14 pp. higher likelihood of reporting great difficulties in making ends meet, a 10 pp. lower probability of  high life satisfaction and a 11 pp. higher likelihood of depression. However, since these relations do not hold for unemployment due to plant closures, they cannot be treated as causal. The results are therefore most likely driven by unobserved factors which simultaneously determine the lower level of outcomes two decades after the ‘shock-therapy’ reforms, and the likelihood of experiencing unemployment in the early 1990s.

Conclusion

In this policy brief we outline recent results on long-term implications of labour market developments in the early years of the economic transition in Poland. The analysis is based on a combination of contemporary and retrospective data from the SHARE survey, and focuses on the associations between the experience of unemployment in the initial years of the transition in Poland and a number of outcomes measured about two decades later. Using plant/office closures as exogenous sources of job separations during the early 1990s, we find a strong and statistically significant, negative, long-term effect on income and home ownership, which can be treated as causal.

We also find strong negative associations between unemployment for other reasons than plant / office closures and a number of subjective measures of wellbeing. This relationship however, does not hold for the exogenous reasons for job losses, which suggests an important role of unobservable factors that lead to unemployment and at the same time are responsible for the lower level of outcomes in later life. This is consistent with the labour market reality of central planning characterised by labour hoarding and maintaining employment regardless of workers’ productivity. When the economic reality changed in 1989, the least productive individuals were the first to be fired, and as our analysis shows, these are also the individuals with lower subjective levels of wellbeing two decades later. We confirm thus that those who lost their jobs in the early 1990s have lower measures of the subjective wellbeing outcomes, although the latter cannot be identified as specific consequences of unemployment in the first years of transition.

References

Acknowledgement

The authors gratefully acknowledge the support of the Polish National Science Centre through project no. 2015/17/B/HS4/01018. For the full list of acknowledgements see Myck and Oczkowska (2018).

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.

Managing Relational Contracts

Posted on November 26, 2018 | Policy Brief

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A wide range of important economic activities depend on self-enforcing informal “relational” contracts. For instance, a firm may buy a good knowing that it cannot sue the other firm if the quality is low – instead high quality is maintained through threat of the firm not making any future purchases. Relational contracts are typically modeled as being between a principal and an agent, such as a firm owner and a supplier. Yet in a variety of organizations, relationships are overseen by an intermediary such as a manager. Such arrangements open the door for collusion between the manager and the agent. We develop a theory of such managed relational contracts. We show that managed relational contracts can be both more and less efficient than the principal agent ones. In particular, kickbacks from the agent can help solve the manager’s commitment problem. When commitment is difficult, this can result in higher quality than the principal could incentivize directly. However, making relationships more valuable enables more collusion and hence can reduce quality.

Introduction

In 2006, the American retailer Aéropostale accused its chief merchandising manager Christopher Finazzo of receiving more than $25 million in kickbacks from a supplier, South Bay. Aéropostale argued that Finazzo had paid inflated prices to South Bay in exchange. Finazzo responded that he had favoured South Bay since they provided higher quality and a willingness to adapt to Aéropostale’s procurement needs. He argued that Aéropostale often remained “loyal” and “committed” to long-time “vendors even when those vendors charged higher prices” (Droney, 2017). In 2013, a jury found Finazzo and South Bay guilty of fraud. They appealed the restitution amount and in 2017 the Court of Appeals for the Second Circuit demanded a recalculation. Judge Droney argued that it was possible that Aéropostale did not lose money as a result of the kickback scheme. He argued that instead Finazzo’s “conduct may have reduced transactions costs for South Bay” and the relationship may have made it profitable for South Bay to pay kickbacks even at non-inflated prices (Droney, 2017).

Relational contracts between organizations are ubiquitous and are crucial for enforcing promises. Indeed, “lack of trust and commitment” is behind most supplier collaboration failures (Webb, 2017). The task of maintaining these relationships is often delegated to a manager like Finazzo. As illustrated by Aéropostale’s case, the firm can never guarantee that the manager will exclusively act in the firm’s best interest. Managers can exploit the (otherwise very valuable) trust relationship with their suppliers to collude with them. Does collusion between the manager and agent crowd out quality? Is collusion always detrimental for the principal?

In a new paper (Troya-Martinez and Wren-Lewis, 2018), we develop a theory of managed self-enforcing relational contracts.

Our model features a manager and an agent who have a bilateral relational contract over time (Levin, 2003). To model that the relationship is managed on behalf of a third party, we assume that profits are shared between the manager and a principal. Every period, the agent privately exerts costly effort to produce a quality which cannot be formally contracted on. To motivate effort, the manager promises to reward high quality with a price premium. This price is paid in part by the principal and in part by the manager. The manager and agent can also make side payments (which represent kickbacks, bribes or other favours) after the quality has been realized. The payment of both the price and side payments needs to be self-enforced.

Kickbacks as an enforcing mechanism

We find that collusion resulting from a managed relational contract can disincentivize quality if the manager pays a discretionary price premium regardless of quality. In particular, she may do so when she trusts that the agent will respond by making a side payment. More surprisingly, side payments can enhance a manager’s ability to commit, and hence allow higher quality. This is because the supplier will renege on paying side payments if the manager reneges on the promised price. This is consistent with evidence that side payments can help contract enforcement. Cole and Tran (2011) analyse informal payments in an Asian country and find that when contract payments are dependent on non-contractible quality, “the kickback is paid only after all contract payments have been made”. In a similar case, Paine (2004) describes how “a purchasing official called about an overdue payment for items already received, [explaining] ‘we can get you a check by next week if you can give us a discount — in cash so we can distribute it to employees’”.

Side payments are thus not necessarily detrimental for the firm when commitment is scarce. This theory thus provides an instance of the “reduced transaction costs” mentioned by Judge Droney.

More trust is not always better

Another interesting implication of a managed relational contract is the non-monotonicity of the relation between trust and efficiency. In the standard principal-agent model of relational contracts, more trustworthy relationships produce higher quality. In managed relational contacts, we show that the opposite may happen.

Figure 1 depicts the effort (and hence quality) exerted by the agent when the manager is in charge (purple) and when the principal is in charge (green). It depicts the effort as a function of the time discount factor delta, which is a measure of how valuable the relationship is (i.e. a larger delta implies a more valuable future). More valuable relationships produce higher effort, and hence higher quality, only up to a point. Once the relationship is sufficiently valuable, extra value facilitates collusion, which reduces effort. In particular, it allows the manager to pay the agent a high price in exchange for a side payment even when quality is low. This non-monotonicity result is consistent with evidence on firms’ use of guanxi, a system of trust-based “informal social relationship” in China which is often used to ensure “that a contract is honored” (Chow, 1997). Vanhonacker (2004) observes that “it would be naive to think—as many Western executives do—that the more guanxi you have on the front lines in China, the better”. Instead, he argues too much guanxi can “divide the loyalties of the sales and procurement people”.

Figure 1. Effort (or quality) with and without delegation to a manage

Source: Troya-Martinez and Wren-Lewis (2018). This figure plots the effort incentivized by the manager (in purple) and by the principal (in green) as a function of the discount factor (delta), which is a measure of how valuable the future is.

This result has important implications for policies designed to reduce fraud or corruption in contexts where relational contracts are valuable. Many such policies involve disrupting relational contracts in order to reduce manager-agent collusion, for instance by encouraging competition or increasing personnel rotation. The results of the analysis suggest that, in some circumstances, weakening manager-agent relations may simultaneously cut corruption and improve output. In other circumstances, however, there will be a trade-off, and reducing corruption may come at the cost of holding back potentially productive relationships.

Conclusion

The paper summarized by this brief is the first paper that studies the impact of collusion on relational contracts. The main take away messages are the following: First, when trust is a scarce resource, managed relational contracts are more credible and can incentivize more quality than direct relational contracts.

Second, collusion can crowd out productive effort when the relationship between manager and agent is too strong. In this case, trust is used to overpay the agent when quality is low.

Before the most recent Aéropostale judgment, it was common to use “the value of the kickbacks” as “a reasonable measure of the pecuniary loss suffered” by the third party (Droney, 2017). Judge Droney, however, argued that this “negative correlation” between kickbacks and loss should not be taken for granted. Indeed, our model has shown when this negative correlation may not exist. Hence, our conclusions may help explain why politicians and firm owners frequently turn a blind eye to employees accepting side payments (Banfield, 1975). On the other hand, our model also identifies when side payments undermine effort. In other words, it emphasizes the complex relationship between kickbacks and productive relational contracts. This complexity needs to be accounted for in policymaking.

References

  • Banfield, Edward C. 1975. “Corruption as a Feature of Governmental Organization.” The Journal of Law & Economics, 18(3): 587-605.
  • Chow, Gregory C. 1997. “Challenges of China’s economic system for economic theory.” The American Economic Review, 87(2): 321-327.
  • Cole, Shawn; and Anh Tran. 2011. “Evidence from the Firm: A New Approach to Understanding Corruption.” In International Handbook on the Economics of Corruption Vol. II. , ed. Susan Rose-Ackerman and Tina Soriede, 408-427. Edward Elgar Publishing.
  • Droney, J. 2017. “United States v. Finazzo.” 14-3213-cr, 14-3330-cr.
  • Levin, Jonathan. 2003. “Relational Incentive Contracts.” American Economic Review, 93(3): 835-857.
  • Paine, Lynn S. 2004. “Becton Dickinson: Ethics and Business Practices (A).” Harvard Business School Case 399-055.
  • Troya-Martinez, Marta; and Liam Wren-Lewis, 2018. “Managing Relational Contracts”, CEPR Discussion Paper Series DP12645 (v. 2).
  • Vanhonacker, Wilfried R. 2004. “When Good Guanxi Turns Bad.” Harvard Business Review, 82(4): 18.
  • Webb, Jonathan, 2017. “Why Do Supplier Collaborations Go Wrong? What Can Be Done About It?”, Forbes, 28 September 2017.

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.

Towards a More Circular Economy: A Progress Assessment of Belarus

Posted on November 19, 2018 | Policy Brief

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This policy brief summarizes the results of our study, Shershunovich and Tochitskaya (2018),  on the circular economy development in Belarus. The aim of the work was to measure the circularity of the Belarusian economy using European Commission indicators. The analysis reveals that the circular economy in Belarus is still in the initial stage of its development. In 2016, the employment in circular economy sectors in Belarus accounted for 0.49% of total employment, and the investment amounted to only 0.27% of total gross investment. Belarus is also falling behind many European countries in waste recycling.

Introduction

The circular economy represents an economic system based on a business model of reduction, reuse, recirculation and extraction of materials in production, distribution and consumption of goods and services (Batova et al., 2018).

Transition to it offers great opportunities to transform the Belarusian economy and make it more sustainable and environmentally friendly, while preserving primary resources, creating new jobs and increasing competitiveness of enterprises.

In order to encourage the transition to a circular economy, it is important to have a proper monitoring system based on reliable and internationally comparable data. It helps to track progress towards a circular economy, conduct policy impact assessment, and analyze whether measures being taken are sufficient to promote an economy that reduces the generation of waste.

To assess the development of a circular economy in Belarus, a set of the European Commission (EC) indicators was used to capture the evolution of the main elements of closing the materials and products loop. The EC monitoring system comprises 10 indicators which are part of 4 pillars: production and consumption; waste management; secondary raw materials; competitiveness and innovation.

The reasons to use this system for Belarus are as follows: first, there is no set of indicators that provide a comprehensive overview of a circular economy in Belarus, while the EC monitoring framework allows us to capture its main elements, stages, and aspects; second, Eurostat calculates circular economy indicators for the European Union (EU) countries on a regular basis, which proves the high level of their practical application,     relevance and robustness; third, the EC is constantly working on their improvement. Thus, the EC set of indicators can be a tool to monitor trends in transition to a circular economy in Belarus.

Tight spots of waste statistics in Belarus

While calculating the circular economy indicators for Belarus the following problems with data affecting the quality of statistics have been identified:

  • methodological issues;
  • challenges with recording and coverage;
  • insufficient degree of international comparability of data, in particular woth the EU countries.

Such methodological problems as the blurred boundaries between the definitions of ‘waste’ and ‘raw materials’, and the lack of criteria for categorizing substances or objects as waste allow enterprises to classify certain substances or objects not as waste and therefore not to file information on them. As a result, less than half of the enterprises which might generate industrial waste, report it. Therefore, the question arises whether the statistical data reflect the real level of waste generation, recycling, and disposal in Belarus.

Data on municipal solid waste (MSW) have proved to be one of the areas of most serious concern. Absence of direct MSW weighing makes the data on it very sensitive to the conversion factor from volume to mass units. The differences between the Belarusian and European waste classifiers and definitions of key concepts (‘waste’, ‘recycling rate’) complicate the data analysis.

In addition, since Belarus is the 3rd world potash fertilizers producer, the share of potash waste in the total volume of waste generation is very high (63-68%). Only a small portion of this type of waste stream is recycled in Belarus (no more than 4%) due to lack of appropriate technologies of potash waste utilization used internationally.  As only Germany counting as one of the world’s largest producers of potash fertilizers within the EU, to increase the comparability of data between the EU countries and Belarus, potash waste hasn’t been considered when calculating the circular economy indicators. Given all the above mentioned problems, some of the EU indicators have been adapted to the existing Belarusian statistical data.

Illustration of waste statistics problems

Waste statistics problems result in overestimation or underestimation of some circular economy indicators. A good example is the recycling rate of all waste, excluding major mineral wastes. Belarus, which is a country without a proper legal framework for the circular economy or a well-established secondary raw materials market,  had one of the best performances in terms of the recycling rate (72-80%) among the EU countries in 2010-2016. This fact reflects the problems with waste statistics rather than success in waste recycling in Belarus.

Table 1. Recycling rate of all waste excluding major mineral wastes, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology».

Actual picture of the circular economy development in Belarus

The indicators with minimum distortions in waste statistics show that some elements of the circular economy in Belarus are still in the initial stage of their development (tables 2, 3, 4, 5). Our study reveals that the recycling rate of MSW amounted to 15.4 % in 2014-2016, which is much lower than the EU average in 2014 and 2016. Thus, Belarus has a considerable potential to increase the recycling rate of MSW. The experience of Czechia and Lithuania shows that the MSW recycling rate can be increased relatively fast if efforts are made and resources permit.

Table 2. Recycling rate of MSW, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the SE  “Operator of SMRs” and Belstat.

In 2016, the recovery rate of construction and demolition waste in Belarus reached 81%, though this indicator fluctuated between 59% and 79% in previous years. However, it can be further improved as in some European countries (Denmark, the Netherlands, Germany, Czechia, Poland and Lithuania) the recovery rate of this type of waste stream exceeds 90%.

Table 3. Recovery rate of construction and demolition waste, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based of the data from the RUE “Bel RC «Ecology».

Despite the fact that the decoupling of economic growth from an increase in waste volumes is an important issue on the international agenda, trends in waste generation in many countries follow a development of GDP. In 2010-2012, the generation of waste excluding major mineral wastes per GDP unit (42-46 kg/thsd of $, PPP) in Belarus (table 4) was comparable with countries such as Czechia, Lithuania, Germany, Denmark, Sweden. However, in 2014 due to waste generation growth, this indicator in Belarus exceeded above-mentioned EU countries and approached the level of Hungary and the Netherlands. It was far above Norway that was the best performer among the European countries and a good example of how a country could really decrease waste generation.

Table 4. Generation of waste excluding major mineral wastes per GDP unit (kg per thsd constant 2011 international $) in 2010-2016

Source: for the EU countries and Norway the data on generation of waste excl. major mineral wastes – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology». For the EU countries, Norway and Belarus the data on GDP, PPP in constant 2011 international $ – The World Bank.

In 2012, the share of gross investment in the circular economy sectors in Belarus (table 5) decreased in comparison with 2010, however, since 2014 it have shown an upward trend. For the EU countries and Norway this indicator also includes investment in the repair and reuse sector. For Belarus this sector has not been taken into account in calculation due to lack of data. In addition, the gross investment in tangible goods is a bit different from the gross investment in fixed assets used for Belarus as the latter doesn’t include non-produced tangible goods such as land.  Yet, even bearing in mind these differences in calculation, the circular economy appeared to be underinvested in Belarus compared to the EU countries and Norway.

Table 5. Gross investment in tangible goods (% of total gross investment) in circular economy sectors in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – Belstat.

The employment in the circular economy in Belarus accounted for only 0.49% of total employment in 2016, while in the EU countries and Norway this indicator was approaching 3%. This again proves the fact that Belarus has a long way to go towards the creation of a circular economy.

Conclusion

The analysis revealed contradictory results of the circular economy development in Belarus. While the country scores highly across some indicators compared to the EU countries and Norway, this to a large extent reflects the problems with waste statistics, rather than success in waste  management. The indicators with minimum distortions in waste statistics show that Belarus is falling behind leading countries in circular economy development. However, in the transition to a circular economy, the monitoring framework is an important component of this process, which permits to track a progress using the system of indicators. In order to ensure that these indicators accurately capture the key trends in the circular economy in Belarus it would seem useful to:

  • align the definition of ’waste’, ‘recycling rate’ with the international one, identify clear criteria for classifying substances or products as waste and secondary raw materials;
  • strengthen the accountability of entities for filing reports on waste;
  • improve the system of MSW and SMRs reporting and recording, and introduce MSW recording based on weighing wherever possible;
  • consider the option of improving the comparability of Belarus’ waste classifier with the European waste statistical nomenclature.¨

References

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.

Gender Gaps in Transition – What do we learn (and what do we not learn) from gender inequality indexes?

Posted on November 12, 2018 | Policy Brief

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We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. By extending the measure back to 1990, we show that even though gender inequality in transition countries for the most part has decreased since the fall of the iron curtain, once overall development is taken into account, transition countries did better in relation to other countries in terms of rank differences before transition. We, however, caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations across different overall indexes, as well as across different sub-indexes that make up each index, suggest that such an approach has limitations.

Indexes of gender inequality

In the public debate of socio-economic issues there is an understandable interest in single measures that summarize complex issues, describe historical developments and allow international comparisons. The use of GDP to measure economic development is the most immediate example of this way of proceeding. The same applies to gender inequality. Over the past decades a number of “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few. These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions.

In this brief, we study the development of the Gender Inequality Index (GII) in transition countries, contrasting these to Western European countries.  By transition countries, we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Whenever we have been able to find the underlying data, we extend the GII measure back to the early 1990s. This extension allows us to measure the development of gender inequality through the lens of a single index since the beginning of the transition. We then discuss what the GII tells us about gender inequality in transition, but also – perhaps more importantly – what it does not tell us. Our analysis is discussed as well as shown in some more detail in our forthcoming companion FREE Policy Paper.

The Gender Inequality Index

The GII was reported for the first time in the 2010 Human Development Report. It measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation.

GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of the female seat share in Parliament in 1990. Using the UNDP data, and data on the female seat share in Parliament in 1990 from additional sources (see the FREE Policy Paper for a list of sources), we obtain values for the GII from the beginning of the transition in 1990 until 2015.

What does the GII index tell us about gender equality in transition economies?

Figure 1 reports values for the GII index in box plots, which show the index 25th and 75th percentile (respectively bottom and top of the box), its median (horizontal line in the box), its maximum and minimum (whiskers), and outliers (dots) for two groups of countries: transition countries and Western-European countries. We have reconstructed the values of the GII index for a limited set of countries within these groups (see the note to Figure 1 for the list of countries). When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

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Source: Own calculations based mainly on UNDP data. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, and the Russian Federation. For Western Europe the countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions, the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. As we show in the Policy Paper, this decrease is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries.

The evidence from the GII is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, it is important to notice that transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups’ performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their Human Development Index ranking (HDI) among all the countries with non-missing GII values in the years considered. The HDI is an UNDP-developed measure of overall human development. See the policy paper for details about its measurement. The larger the difference between GII- and HDI-ranking, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Nov122018_Figure2

Source: Own calculations based mainly on UNDP data.

The trends between transition countries and Western Europe are now opposite. In 1990, the median standing in terms of gender inequality was better than that in human development for transition countries, and the relative level of gender inequality was lower than in Western Europe. The (negative) difference between GII and HDI ranking however appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality ranking in relation to their ranking in terms of human development over the period studied. Put differently, the ranking improvement in terms of human development in former socialist countries since the transition have not translated into comparable gains in gender equality ranking as measured by the GII index.

It is also important to emphasize that, according to several scholars, a dichotomy in terms of gender relations existed in transition countries during the socialist period. This is because on one hand the socialists put substantial into effort to empower women economically (see e.g. Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018), but on the other hand they failed to eliminate patriarchy (LaFont, 2001). This suggests that a composite index can mask important contrasting patterns among its components. In the Policy Paper we uncover such contrasting patterns. By looking separately at the different components of the GII index, we show that while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: Women maintained, but did not improve, their relative status in the labor force. They gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

Conclusion

In this policy brief we have studied the development of gender inequality in transition countries through the lens of the Gender Inequality Index, whose span we have extended to the beginning of the transition period. We have shown that, based on this index, gender inequality has decreased since 1990 in transition countries, a trend which is common to that in Western Europe. However, once the changes in overall development during this period are taken into account, it appears that transition countries fared better in 1990 than today. Our analysis thus shows that analyzing gender inequality indexes in absolute terms and in relation to levels of development can deliver different conclusions. The factors that account for these differences should be kept in mind in policy discussions and policy-making. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment, do not necessary go hand in hand with overall development, and might therefore require more targeted policy interventions.

We have also cautioned the reader about the limitation of using comprehensive indexes to describe developments in gender inequality. A comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions. This point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. It has been argued, for instance, that low levels of female representation in political institutions in transition countries can be the result of women’s large participation in the labor market while the division of roles in households remained traditional. In the words of anthropologist Suzanne LaFont (2001), “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden”. In such a context, average values of an index of gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.

References

  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labour Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.

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.

Resource Discoveries, FDI Bonanzas and Local Multipliers: Evidence from Mozambique

Posted on November 5, 2018 | Policy Brief

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Giant oil and gas discoveries in developing countries trigger FDI bonanzas. Across countries, it is shown that in the 2 years following a discovery, the creation of FDI jobs increases by 54% through the establishment of new projects in non-resource sectors such as manufacturing, retail, business services and construction. Using Mozambique’s gas driven FDI bonanza as a case study we show that the local job multiplier of FDI projects in Mozambique is large and results in 4.4 to 6.5 additional jobs, half of which are informal.

Natural Resources, FDI Job Multiplier and Economic Development

Large resource wealth has for several decades been associated with a curse, slowing economic growth in resource-rich developing countries (Venables, 2016). More recently, this wisdom has been questioned by several studies. Arezki et al. (2017) point out that giant discoveries trigger short-run economic booms before windfalls from resources start pouring in. And Smith (2017) provides evidence for a positive relationship between resource discoveries and GDP per capita across countries, which persists in the long term.

In a new paper (Toews and Vézina, 2018) we contribute to this research by showing that giant oil and gas discoveries in developing countries trigger foreign direct investment (FDI) bonanzas in non-extraction sectors. FDI has long been considered a key part of economic development since it is associated with transfers of technology, skills, higher wages, and with backward and forward linkages with local firms (Hirschman, 1957; Javorcik, 2015). Using Mozambique, where a giant offshore gas discovery has been made in 2009, as a case study,  we estimate the local multiplier of FDI projects. We find that the FDI job multiplier in Mozambique is large, highlighting the job creation potential of FDI in developing countries.

Resource Discoveries and FDI Bonanzas

In our study we focus on jobs created by FDI bonanzas triggered by resource discoveries. Multinationals might invest in countries being blessed by giant discoveries for a variety of reasons before production starts. First, they might expect to benefit from the decisions of oil and gas companies to increase investment in local infrastructure and to increase demand for local services provided by law firms and environmental consultancies. Second, multinationals may also expect governments and consumers to bring forward expenditure and investment by borrowing. Finally, multinationals might invest since particularly large discoveries have the potential to operate as a signal leading to a coordinated investment by a large number of multinationals from a variety of industries and countries.

Using data from fDi Markets we show that, indeed, FDI flows into non-extraction sectors following a discovery. FDI increases across sectors and by doing so creates jobs in industries such as manufacturing, retail, business services and construction. Using Mozambique as a case study we show that following the gas discovery, multinationals decided to invest in Mozambique triggering job creation in non-extraction FDI to skyrocket (see Figure 1).

Figure 1. FDI Bonanza in Mozambique

Source: Author’s calculations using fDiMarkets data.

FDI Job Multiplier

Using the FDI bonanza in Mozambique as a natural experiment, we proceed by estimating the FDI job multiplier for Mozambique. The concept of the local job multiplier boils down to the idea that every time a job is created by attracting a new business, additional jobs are created in the same locality. In our case, FDI jobs are expected to have a multiplier effect due to two distinct channels. Newly created and well paid FDI jobs are likely to increase local income and in turn the demand for local goods and services (Moretti, 2010). Additionally, backward and forward linkages between multinationals and local firms increase the demand for local goods and services (Javorcik, 2004).

Using concurrent waves of household surveys and firm censuses we estimate the local FDI multiplier for Mozambique to be large. In particular, we find that every additional FDI job results in 4.4 to 6.5 additional local jobs. Due to the combined use of household survey and the firm census we are also able to conclude that only half of these jobs are created in the formal sector, while the other half of the jobs are created informally.

Conclusion

Our results suggest that giant oil and gas discoveries in developing countries lead to simultaneous foreign direct investment in various sectors including manufacturing. Our results also highlight the job creation potential of FDI projects in developing countries. Jointly, our results imply that giant discoveries do have the potential to trigger extraordinary employment booms and, thus, provide a window of opportunity for a growth takeoff in developing countries.

References

  • Arezki, R., V. A. Ramey, and L. Sheng (2017): “News Shocks in Open Economies: Evidence from Giant Oil Discoveries,” The Quarterly Journal of Economics, 132, 103.
  • Hirschman, A. O. (1957): “Investment Policies and “Dualism” in Underdeveloped Countries,” The American Economic Review, 47, 550 – 570.
  • Javorcik, B. S. (2004): “Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages,” American Economic Review, 94, 605 – 627.
  • Javorcik, B. S. (2015): “Does FDI Bring Good Jobs to Host Countries?” World Bank Research Observer, 30, 74 – 94.
  • Moretti, E. (2010): “Local Multipliers,” American Economic Review, 100, 373 – 377.
  • Smith, Brock. “The resource curse exorcised: Evidence from a panel of countries.” Journal of Development Economics: 116 (2015): 57-73.
  • Toews and Vézina, (2018): “Resource discoveries, FDI bonanzas and local multipliers: An illustration from Mozambique” Working Paper.
  • Venables, A. J. (2016): “Using Natural Resources for Development: Why Has It Proven So Difficult?” Journal of Economic Perspectives, 30, 161 – 84.

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.

Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience

Posted on October 29, 2018 | Policy Brief

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This brief is based on research that investigates the probability of employment among displaced and non-displaced households in a region bordering territory with an ongoing military conflict in Eastern Ukraine.  According to the results, internally displaced persons (IDP) are more educated, younger and more active in their job search than locals. Nevertheless, displaced individuals, particularly males, have experienced heavy discrimination. After controlling for personal characteristics, the structure of the household, location, non-labour incomes and endogeneity of displacement, IDP males are 17% less likely to be formally employed two years after resettlement than locals.

Internally displaced persons in Ukraine

In 2014, 23 years after independence, Ukraine suddenly found itself among the top-ten of countries with the largest internally displaced population. During the period 2014–2016, 1.8 million persons registered as internally displaced. Potentially, about 1 million more reallocated to Russia and about 100,000 to other countries nearby, where they sought refugee or labour migrant status (Smal, 2016).

The Ministry of Social Policy of Ukraine (MSPU) has regularly published very general reports on displaced persons. According to these reports, at the end of February 2016, the internally displaced persons in Ukraine included 22,000 individuals from Crimea and over 1.7 million citizens from Eastern Ukraine. These are mostly individuals who registered as IDPs to qualify for financial assistance from the state and some non-monetary benefits. Among them, 60% are retired people, 23.1% are individuals of working age, 12.8% are children and 4.1% are people with disabilities (Smal and Poznyak, 2017). In fact, the MSPU registers not only displaced persons but also those who de facto live in the occupied territories and occasionally travel to territories controlled by the Ukrainian authorities to receive their pension or social benefits (so called ‘pension tourism’). On the other hand, some IDPs did not register either to avoid bureaucracy or because they were unable to prove their status due to lack of documents. Recent publications that are based on surveys portray a more balanced distribution: 15% are retired people, 58% are individuals of working age, 27% are children and 13% are people with disabilities (IOM and the Ukrainian Centre for Social Reforms, 2018).

Only limited information is available about IDPs’ labour market activity. According to the State Employment Service (SES), between March 2014 and January 2016, only 64,300 IDPs or 3.75% referred to the SES for assistance (Smal and Poznyak, 2017). On the one hand, this figure reflects the relatively low reliance of displaced Ukrainians on the SES services in their job search. On the other hand, the geographical variation in the share of SES applicants suggests that Ukraine’s IDPs who moved further from the war zone and their homes were more active in trying to find a job.

Data

Our primary data were collected in June–August 2016 by REACH and provided by the Ukraine Food Security Cluster (UFSC) as a part of the needs assessment in Luhansk and Donetsk oblasts of Ukraine – two regions that were directly affected by the conflict. These two regions have hosted roughly 53% of all IDPs in Ukraine (Smal and Poznyak, 2017). We argue that households that did not move far from the place of conflict are most likely to be driven by conflict only, while long-distance movers may combine economic and forced displacement motives.

The data set offers information on 2500 households interviewed in 233 locations and is statistically representative of the average household in each oblast. It includes respondents currently living in their pre-conflict settlements (non-displaced, NDs) and respondents who report a different place of residence before the conflict (IDPs). The IDP group comprises individuals with registered and unregistered status and from both sides of the current contact line. The non-IDP group includes only households living on the territory controlled by the Ukrainian Government that did not move after the conflict had started.

Our sample covers 1,135 displaced households that came from 131 settlements. Most of the reallocations took place in early summer 2014 with the military escalation of the conflict in Eastern Ukraine. Thus, the average duration of displacement up to the moment of the interview was 637 days (or 21 months). This is a sufficiently long period for adaptation and job search. However, there is enough variation in this indicator – some families left as early as March–April 2014, while others were displaced in June 2016, just a few days before the interviews started.

Results

Simple comparison shows that heads of displaced households are on average almost four years younger than those of non-displaced households (Table 1). In terms of education, displaced households are found to be more educated than non-displaced households, as there are significantly more IDP household heads with tertiary education and significantly fewer individuals with only primary, secondary or vocational degrees. In particular, 37% of IDP household heads hold a university degree compared with 22% of household heads among the local population. This seems to suggest positive displacement selection. IDPs are slightly more likely to be headed by females and unmarried persons, although these differences are statistically insignificant. Displaced households include more children aged under five (0.35 vs. 0.22 children per non-displaced household) and 6 to 17 years (0.42 vs. 0.34, respectively) and fewer members aged over 60 years (0.58 vs 0.66, respectively). There is no difference in the number of working-age adults or disabled individuals per household among IDPs and non-IDPs. The average household size is statistically similar for the groups (2.74 vs. 2.65 persons per IDP and non-IDP household, respectively).

Table 1. Selected descriptive statistics

Internally displaced households Non- displaced households
Household head employed 0.43*** 0.48***
Household head characteristics
Age (years) 48.10*** 52.85***
Male 0.49 0.52
Education
vocational 0.42*** 0.49***
university 0.37*** 0.22***
Household characteristics
Size (persons) 2.74 2.65
Number of children 0-5 0.35*** 0.21***
Number of children 6-17 0.42*** 0.34***
Number of members 60+ 0.58** 0.66**
IDP payments 0.50*** 0***
Humanitarian assistance 0.78*** 0.28***

There are further differences in the types of economic activity and occupations among IDPs and non-IDPs. Prior to the conflict, displaced respondents were more likely (than non-displaced persons) to be employed as managers or professionals and less likely to hold positions as factory or skilled agricultural workers. This result also speaks in favor of a positive displacement selection story.

As expected, the conflict has had a negative effect on human capital in the government controlled areas of Donetsk and Luhansk regions. We observe some deskilling at the time of the interviews, which is especially pronounced for IDPs. In particular, the share of managers among the IDPs had reduced from 12% to 5% and that of technicians from 15% to 12%, while the proportion of service and sales employees had increased from 10% to 13%, that of factory workers from 11% to 15% and that of skilled agricultural workers from 2% to 6%.

Considering the economic activity in the current location, we can note that on average the heads of displaced households are 5% less likely to be employed than those of non-displaced households (43% vs. 48%, respectively). In both groups, a large share of respondents report difficulties in their job search, but IDPs are 13% more likely to experience this problem. They report changing their pre-conflict occupation three times more often than non-IDPs (37% vs. 11%).

Government and non-government assistance may also drive the differences in employment. Economic theory states that individuals are less likely to work if they have some backup in the form of non-labour earnings. Financial support and humanitarian assistance are widely used to smooth a displacement shock. At the same time, improperly designed assistance schemes may reduce the stimulus to search for a job.

IDPs are 9% less likely to include earnings in their household’s top three main sources of income than the non-displaced population (46% vs. 55%, respectively), meaning that they rely more on various social payments and pensions. In addition, displaced households may be slightly more reluctant to search for a job due to displacement assistance from the government (received by 50% of IDPs compared with 0% for non-IDP households), although the amounts are quite modest. According to the existing legislation, IDPs can receive regular monthly state payments and one-time state payments. Regular monthly payments can be received by any IDP and cannot exceed UAH 3,000 (~$111) for an ordinary household, UAH 3,400 for a household with disabled people and UAH 5,000 (~$185) for a household with more than 2 children. Eligibility and the size of the one-time payment are determined by the local government. In the data set, 95% of IDPs receive less than UAH 3,000 while the 2016 average monthly wage was UAH 6,000 in Donetsk and UAH 4,600 in Luhansk regions.

In addition, IDPs are three times more likely to receive humanitarian assistance (78% vs. 28% among displaced and non-displaced persons, respectively). This support includes mostly food and winterisation items but also cash (26% among displaced vs. 12% among non-displaced assistance receivers). On the other hand, to cover reallocation and adaptation costs, some IDPs use their financial reserves, and as a result they are by 10 p.p. more likely to report no or already depleted savings. This may increase their stimulus to engage in a more active job search.

After taking into account the observed and unobserved differences between the groups as well as controlling for the location fixed effect, we find that the difference in the probability of employment between displaced and non-displaced persons increases from a casually observed slit of 5% to a chasm of 17.3%. This result suggests that IDPs are [negatively] discriminated despite being younger, more educated, skilled and more ‘able’ in the labour market. Specifically, 7 out of 17 p.p. (41% of the gap) are due to the variation in observed household head characteristics and family composition, while unobserved displacement-related features (such as attitude towards change, activism, mental and physical ability to reallocate) account for 5 p.p. (29%) of the gap. Controlling for particularities of a current location does not substantially affect the estimated differences.

Figure 1. Main results

We re-estimate these regressions using an employment indicator that includes both formal and informal employment (as defined by the respondents), accounting for occasional and irregular employment, including subsistence agricultural work. Since informal work is more common among IDPs, this definition of employment leads to a reduction in the average casually observed gap from 5% to 3%. However, after controlling for all the factors, we obtain the same result – a 17.8% difference between displaced and non-displaced households.

Conclusion

Policy makers and international donors should not be misled by the seemingly comparable probability of employment among IDPs and non-IDPs based on simple statistics. The average 0–5% difference in unconditional employment rates conceals the actual 17% gap in the likelihood of having a job. The contribution of unobserved displacement-related factors in hiding the true gap is large, especially for males seeking formal employment. Without adjusting for it, we would underestimate the real difference in employment probability by one-third to one-half.
Our study produces firm evidence that displaced individuals in Ukraine, particularly males, have been discriminated against in terms of employment. Our results further suggest that male heads of displaced households experience more discrimination in the formal labour market, while the situation is the opposite for females, who are more likely to face unequal treatment in the informal sector. Policy makers and volunteers should take this difference into account in the adaptation of male- and female-headed households.

Humanitarian assistance to displaced individuals was found to have no negative effect on their employment, which suggests that it is provided in an effective manner. Thus, this tool can be used to mitigate the discrimination.

References

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.

Energy Demand Management: Insights from Behavioral Economics

Posted on October 22, 2018 | Policy Brief

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It has long been recognized that consumers fail to choose the cheapest and most efficient energy-consuming investments due to a range of market and non-market failures. This has become known as the ‘Energy Efficiency Gap’.  However, there is currently a growing interest in terms of understanding on how consumers make decisions that involve an energy consumption component, and whether the efficiency of their decisions can be improved by changing the market incentives and governmental regulation. Meeting this interest, the most recent SITE Energy Talk was devoted to Demand Side Management.  SITE invited Eleanor Denny, Associate Professor of Economics at Trinity College Dublin, and Natalya Volchkova, Assistant Professor at the New Economic School (NES) in Moscwo and Policy Director at the Center for Economic and Financial Research  (CEFIR) to discuss the Demand Side Management process. The aim of this brief is to present the principles of Demand Side Management and discuss a few implemented programs in Europe, based on the discussions  during this  SITE Energy Talk.

For the last two decades, climate change policies have mostly been focused on the energy supply side, constantly encouraging new investments in renewables. But reducing energy demand may be as effective. Indeed, Denny and O’Malley (2010) found that investing 100MW in wind power is equivalent, in terms of emissions, to a decrease in demand of 50MW. Hence, there is a clear benefit of promoting energy saving. This has been the central point of different Demand Side Management (DSM) programs that may diversely focus on building management systems, demand response programs, dynamic pricing, energy storage systems, interruptible load programs and temporary use of renewable energy. The goal of these programs is to lower energy demand or, at least, smoothen the electricity demand over the day (i.e. remove peak-hour segments of demand to off-peak hours) as illustrated in Figure 1.

Figure 1 – Smoothing electricity demand during the day

A behavioral framework

DSM encompasses initiatives, technologies and installations that encourage energy users to optimize their consumption. However, the task does not seem easy, given the well-documented energy efficiency gap problem (e.g. Allcott & Greenstone, 2012 or Frederiks et al., 2015): consumers do not always choose the most energy efficient investments, despite potential monetary saving. One reason why might be that energy savings per se are not enough to trigger investment in energy efficient solutions or products. As Denny mentioned in her presentation, consumers will invest when the total  private benefits are higher than the costs of investment. This trade-off can be summarized by the following equation:

This equation illustrates that any DSM design should take into account both non-monetary benefits and consumers’ time preferences. The non-monetary benefits, such as improved comfort, construction and installation time, but also warm glow (i.e. positive feeling of doing something good) or social comparison, may play a major role. Moreover, the consumers’ time preferences (reflected here by the discount rate ) are also crucial in the adoption of energy efficient products. In particular, if consumers have present biased preferences, they would rather choose a product with a lower cost today and greater future cost than the reverse (i.e. higher cost today with lower future cost). Since energy-efficient products often require higher upfront investment, consumers that are impatient for immediate gains, may never choose energy efficient products.

Ultimately, it is an empirical (and context specific) question when and why DSM programs can reduce the energy efficiency gap. We describe below some DSM programs that have been implemented and discuss their impact.

Smart meters, a powerful DSM tool

A common DSM program is the installation of smart meters, which measure consumption and can automatically regulate it. The adoption of smart meters allows real-time consumption measures, unlike traditional meters that only permitted load profiling (i.e. periodic information of the customer’s electricity use).

Figure 2 – Energy Intensity in Europe

As illustrated in Figure 2, many European countries have implemented smart meter deployment programs. Interestingly, most of those countries have a relatively high level of energy efficiency (proxied by the energy intensity indicator of final energy consumption). On the contrary, in the Balkans and non-EU Eastern Europe countries, which fare poorly on the energy intensity performance scale, no smart meter rollout programs seem to be implemented.

Following the European Commission (EC) directive of 2009 (Directive 2009/72/EC), twenty-two EU members will have smart meter deployment programs for electricity and gas by 2020 (see Figure 2).  These programs are targeting end-users of energy, e.g. households that represent 29% of the current EU-28’s energy consumption, industries (36.9%) and services (29.8%) (EEA). With this rollout plan, a reduction of 9% in households’ annual energy consumption is expected.

The situation across the member states is however very different. Spain was one of the first EU countries to implement meters in 1988 for industries with demand over 5MW. All the meters will be changed at the end of 2018. 27 million euros for a 30-year investment in smart meter installations is forecasted (EC, 2013). Sweden started to implement smart meter rollout in 2003 and 5.2 million monthly-reading meters were installed by 2009. Vattenfall, one of the major utilities in Sweden, assessed their savings up to 12 euros per installed smart meter (Söderbom, 2012). Similarly in the United Kingdom, the Smart Metering Implementation Programme (SMIP) is estimated to bring an overall £7.2 billion (8.2 billion euros) net benefit over 20 years, mainly from energy saving (OFGEM, 2010). In general, smart metering has been effective, but its effectiveness may diminish over time (Carroll et al, 2014).

From smart-meter to real-time pricing

The idea of real-time pricing for electricity consumers is not new. Borenstein and Holland (2005) and Joskow and Tirole (2006) argue that this price scheme would lead to a more efficient allocation, with lower deadweight loss than under invariant pricing.

By providing detailed information about real-time consumption, smart meters enable energy producers to adopt dynamic pricing strategies. The increasing adoption of smart meters across Europe will likely increase the share of real-time-pricing consumers, as well as the efficiency gains. With the digitalization of the economy, it is likely that smart metering will grow. Indeed, Erdinc (2014) calculates that the economic impact of smart homes on in-home appliances could result in a 33% energy-bill reduction, due to differences in shift potential of appliances.

In 2004, the UK adopted a time-of-use programme called Economy 10, which provides lower tariffs during 10 hours of off-peak periods – split between night, afternoon and evening – for electrically charged and thermal storage heaters. The smart time-of-use tariffs involving daily variation in prices were only introduced in 2017.

Likewise, France’s main electricity provider EDF, implemented Tempo tariff for 350,000 residential customers and more than 100,000 small business customers. Based on a colour system to indicate whether or not the hour is a peak period, customers can automatically or manually monitor their consumption by controlling connection and disconnection of separate water and space-heating circuits. With this program, users reduced their electricity bills by 10% on average.

In Russia, the “consumptions threshold” program discussed by Natalya Volchkova, gave different prices for different consumption thresholds. But it seems that the consumers’ behaviour did not change. This might be due to the thresholds being too low, and an adjusted program should be launched in 2019.

Joskow and Tirole (2007), argue that an optimal electricity demand response program should include some rationing of price-insensitive consumers. Indeed, voluntary interruptible load programs have been launched, mainly targeting energy intensive industries that are consuming energy on a 24/7 basis. These programs consist of rewarding users financially to voluntarily be on standby. For instance, interruptible programmes in Italy apply a lump-sum compensation of 150,000 euros/MWh/year for 10 interruptions and 3000 euros/MW for each additional interruption (Torriti et al., 2010).

Nudging with energy labelling

Energy labelling has been also part of DSM. Since the EC Directives on Ecodesign and Energy Labelling (Directives 2009/125/EC and 2010/30/EU), energy-consuming products should be labelled according to their level of energy efficiency. For Ireland, Eleanor Denny has tested how labelling electrical in-home appliances may affect consumers’ decisions, like purchasing electrical appliances or buying a house. First, Denny and co-authors have nudged buyers of appliances, providing different information regarding future energy bills saving. They find that highly educated people, middle income and landlords are more likely to be concerned with energy-efficiency rates, rather than high-income people.

In another randomized control trial, Denny and co-authors manipulate information on the energy efficiency label for a housing purchase. In Ireland, landlords are charged for energy bills even when they rent out their property. The preliminary findings are that landlords informed about the annual energy cost of their houses are willing to pay 2,608 euros for a one step improvement in the letter rating – the EU label rating for buildings ranges from A to G – compared to the landlords that do not receive the information (see CONSEED project).

Similar to the European Directive, the 2009 Russian Energy efficiency law includes compulsory energy efficiency labels for some goods and improvements of the building standards (EBRD, 2011). Volchkova and co-authors run a randomized controlled experiment on the monetary incentives to buy energy efficient products. In 2016, people in the Moscow region received a voucher with randomly assigned discounts (-30%, -50% or -70%- for the purchase of LED bulbs. Vouchers were used very little, irrespective of the income. It seems that consumption habits and not so much monetary rewards were the main driver of LED bulb purchase.

How can DSM be improved?

Any demand response program requires some demand elasticity. For example, smart meters and dynamic pricing only improve electricity consumption efficiency if demand is price elastic. As Jessoe and Rapson (2014) show, one should provide detailed information (e.g. insights on non-price attributes, real-time feedback on in-home displays) to try to increase demand elasticity. Hence it seems that  the low adoption of energy efficient goods is partly due to a lack of information or biased information received by the consumers. First, it is difficult for many to translate energy savings in kWh in monetary terms. Second, many consumers focus on the short-term purchase cost and discount heavily the long run energy saving. These information inefficiencies can, in principle, be diminished by private actors and/or governmental regulation. Denny mentioned the possibility of displaying monetary benefits on labels in consumers’ decision-making in order to improve energy cost salience. For instance, in the US or Japan, the usage cost information is also displayed in monetary terms. Moreover, lifetime usage cost (i.e. cost of ownership) should also be given to the customers since it has been shown that displaying lifetime energy consumption information has significantly higher effect than presenting annual information  (Hutton & Wilkie 1980; Kaenzig 2010).

Summing up, DSM programs, including those with a behavioral framework, are an important tool for regulators, households and industries helping to meet emissions reduction targets, significantly decrease demand for energy and use energy more efficiently.

References

  • Allcott, Hunt ; Greenstone, Michael. 2012. “Is There an Energy Efficiency Gap?”, Journal of Economic Perspectives, 26 (1): 3-28.
  • Borenstein, Severin; Holland, Stephen. 2005. “On The Efficiency Of Competitive Electricity Markets With Time-Invariant Retail Prices”, Rand Journal of Economics, 36(3), 469-493.
  • Carroll, James; Lyons, Seán; Denny, Eleanor. 2014. “Reducing household electricity demand through smart metering: The role of improved information about energy saving,” Energy Economics, 45(C), 234-243.
  • Denny, Eleanor; O’Malley, Mark. 2010. “Base-load cycling on a system with significant wind penetration”, IEEE Transactions on Power Systems 2.25, 1088-1097.
  • Erdinc, Ozan. 2014. “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households”, Applied Energy, 126(C), 142-150.
  • European Bank for Reconstruction and Development. “The low carbon transition”. Chapter 3 Effective policies to induce mitigation (2011).
  • European Commission. Electricity Directive 2009/92. Annex I.
  • European Commission. Ecodesign and Energy Labelling Framework directives 2009/125/EC and 2010/30/EU.
  • European Commission. “From Smart Meters to Smart Consumers”, Promoting best practices in innovative smart metering services to the European regions (2013).
  • European Commission. “Benchmarking smart metering deployment in the EU-27 with a focus on electricity” (2014).
  • European Environment Agency. Data on Final energy consumption of electricity by sector and Energy intensity.
  • Frederiks, Elisha R.; Stenner, Karen; Hobman, Elizabeth V. 2015. “Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour”, Renewable and Sustainable Energy Reviews, 41(C), 1385-1394.
  • Hutton, Bruce R.; Wilkie, William L. 1980. “Life Cycle Cost: A New Form of Consumer Information.” Journal of Consumer Research, 6(4), 349-60.
  • Jessoe, Katrina; Rapson, David. 2014. “Knowledge is (less) power: experimental evidence from residential energy use”, American Economic Review, 104(4), 1417-1438.
  • Joskow, Paul; Tirole, Jean. 2006. “Retail Electricity Competition“, Rand Journal of Economics, 37(4), 799-815.
  • Joskow, Paul; Tirole, Jean. 2007. “Reliability and Competitive Electricity Markets”, Rand Journal of Economics, 38(1), 60-84.
  • Kaenzig, Josef; Wüstenhagen, Rolf. 2010. “The Effect of Life Cycle Cost Information on Consumer Investment Decisions Regarding Eco‐Innovation”, Journal of Industrial Ecology, 14(1), 121-136.
  • OFGEM. “Smart Metering Implementation Programme” (2010).
  • Söderbom, J. “Smart Meter roll out experiences”, Vattenfall (2012).
  • Torriti, Jacopo; Hassan, Mohamed G.; Leach, Matthew. 2010. “Demand response experience in Europe: Policies, programmes and implementation”, Energy, 35(4), 1575-1583.

Project links

Eleanor Denny and co-authors’ European research projects:

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.

Revisiting Growth Patterns in Emerging Markets

Posted on October 15, 2018 | Policy Brief

20181014 Revisiting Growth Patterns Image 01

Recent studies document that emerging markets are rather similar in their growth patterns despite profound differences in starting conditions and productivity fundamentals. This challenges the common view on productivity as the main growth engine. The crucial role of the external environment for emerging markets emphasized by numerous studies adds to this doubt. I argue that productivity fundamentals still matter and remain the core driver of sustainable growth. However, external factors are crucial for understanding deviations from the trajectory of sustainable growth, i.e. episodes of growth accelerations/decelerations.

Challenges for Understanding Growth in Emerging Markets

As we enter the 4th decade of economic transition in Central and Eastern Europe (CEE), the causes and directions of causality of long-term growth in emerging markets might need to be reconsidered. Some recent studies emphasize that growth trajectories in emerging markets are pretty similar, i.e. average growth rates do not differ too much, while jumps and drops in growth rates are synchronous for the bulk of emerging economies (e.g. Fayad and Perelli, 2014). For instance, a decade ago the level of GDP per capita (in 2011 international $) in Macedonia was roughly 45% of that in the Slovak Republic, which likely reflected the productivity (measured through the Global Competitiveness Index) gap  between them. During the last decade, Macedonia has roughly closed this productivity gap. Growth theory would postulate that this should have transformed into faster output growth in Macedonia vs. Slovak Republic closing well-being gap. However, the two countries’ had throughout the decade roughly equal average output growth and the well-being gap today is still the same as it was ten years ago.

Such observations seem to conflict with existing theoretical views. First, this is a challenge to the well-being convergence concept that results from growth theory. Moreover, if we measure growth in terms of the speed of closing the well-being gap with respect to the frontier (the US economy), one may argue even for divergence. For instance, Figure 1 presents a scatter-plot for a sample of emerging markets relating the initial conditions – well-being level in 1995 (GDP per capita  relative to one of the US economy) – and the average speed of well-being gap (vs. the US economy) closing throughout 1996-2017  (measured in p.p. of corresponding gap ).

Second, the evidence that productivity gains do not automatically trigger output growth challenges a common view that productivity is the major driver for sustainable growth.

Figure 1.Starting Conditions and Well-Being Gains

Source: Own computations based on data from World Development Indicators database (World Bank).

What are possible explanations for the observed similarity in growth rates of emerging markets?

A study by the IMF (2017) suggests a response: growth in emerging markets is similar and synchronous due to the external environment. This study emphasizes the crucial dependence of medium-term growth in developing countries on the following factors: growth of external demand in trade partners, financial conditions, and trade conditions. Moreover, it states that these factors are dominant in explaining the episodes of growth strengthening/weakening.

Does this explanation change the growth nexus for emerging markets? Can one state, that while external factors are crucial for growth and growth in developing countries is rather homogenous, the productivity gains are not so important anymore?

I would say no. First, for better understanding of growth patterns we must clearly compare the relative importance of productivity gains vs. external factors in affecting the growth schedule. Second, we must separate relatively short-term fluctuations in GDP growth from sustainable growth.

Detecting Relative Importance of Growth Drivers

To answer the question about the relative importance of productivity fundamentals and growth factors, I study a panel of 34 emerging market economies (EBRD sample netted from 3 countries for which the data is not available) for 11 years (2007-2017).

To evaluate the relative importance of productivity and external factors, I use a standard approach of running panel growth regressions with fixed effects. At the same time, I make a number of novelties in the research design.

First, for measures of productivity, I engage a unique database – Global Competitiveness Indicators by World Economic Forum (WEF). Although this database provides an insightful perspective on productivity fundamentals at the country level, it is rather seldom a ‘guest’ in economic research. From this database, I extract a number of individual indicators in order to detect which ones among them that have the strongest growth-enhancing effect. For an alternative specification, I use principal components of 9 individual indicators from this database as proxies for productivity gains.

Second, for external factors, I use an approach similar to the IMF (2017) and calculate variables representing external demand growth, trade conditions, and financial conditions (such as a measure of capital inflows) for each country. Moreover, in respect to external demand growth, I use different competing measures (based on either imports of GDP growth of trade partners) and choose the best one in each individual equation. By doing so, I allow this dimension of the external environment to be represented in each model to the largest possible extent.

Third, I depart from using output growth as the only measure of economic growth and response variable in growth regressions. I argue that for international comparison purposes it is worthwhile to consider also the speed of closing the gap towards the frontier (the US economy). On the one hand, this measure is strongly correlated with the traditional output growth rate. On the other hand, this measure, in a sense, nets out the growth rate of a country from global growth, thus capturing something more unique and peculiar just to individual countries’ gains in well-being. Furthermore, I argue that in the discussion about the factors behind growth, one should distinguish between relatively short and long term growth. Annual growth rates, especially at relatively short time horizon, are too dependent on fluctuations, which may be interpreted in terms of growth rate strengthening/weakening. However, to emphasize the property of growth sustainability, we should get rid of ‘unnecessary noise’. For this purpose, I also introduce a trend growth rate measured in a most simple way as the 5 year moving average (following the discussion in Coibion et al. (2017), show that the bulk of measures of ‘potential’ growth are not good enough to get rid of demand shocks and these measures are pretty close to simple moving average measures).

I apply this definition of trend growth both to ‘standard’ GDP growth rate and to the speed of closing the gap towards frontier. So, finally I have 4 response variables: ‘standard’ growth rate, the speed of closing the gap to frontier, and two corresponding measures of trend growth.

Sustainable Growth Mainly Depends on Productivity

Having short-term (annual) growth rate as response variable (either ‘standard’ or the one in terms of closing the gap) provides results close to those in IMF (2017). It may be interpreted in a way that the external environment is more important than productivity factors. If dividing all regressors into two broad groups of factors – external and productivity – the former is responsible for up to 70% of the growth effect, while the latter for about 30%. Among external environment factors, the most important one is financial conditions. Its relative importance is roughly 50% of the group of external factors’ total.

Among productivity fundamentals, an important contributor to short-term growth is the quality of the macroeconomic environment. According to the methodology of WEF (2017), this indicator encompasses the fiscal stance, savings-investment balance, the external position, inflation path, debt issues, etc.

When refocusing from short-term growth to the growth trend as a response variable, the relative importance of the factors behind growth changes. Productivity fundamentals in this case drive up to 80% of growth effect, while external factors are responsible for the remaining 20%. It is worth noting here that the proportion in favor of productivity factors is higher for the concept of closing the gap to frontier rather than for ‘standard’ trend growth rate. This evidence may be interpreted as additional justification for treating this measure of growth as ‘good’ at reflecting individual properties of a country in a global landscape.

Furthermore, the role of individual variables also changes. Among external factors, the most important role in driving sustainable growth belongs to trade conditions and external demand growth, while the role of financial conditions is either miserable or insignificant at most. Among productivity factors as drivers of trend growth, the quality of the macroeconomic environment seems to play a special role, as well as the efficiency of the goods market and the financial system.

Conclusions

The evidence showing rather similar and synchronous growth in emerging markets and recent evidence on the crucial importance of external factors for emerging markets should not lead us to incorrectly believe that productivity fundamentals do not matter anymore. Productivity fundamentals are still the core driver of sustainable growth. At the same time, we should keep in mind the important role of the external environment for emerging markets. However, changes in the external environment are more likely to generate relatively short-term growth rate fluctuations, while having a modest impact on the sustainable growth trajectory. Hence, a country aiming to secure sustainable growth should still first of all think about productivity fundamentals.

References

  • Coibion, O., Gorodnichenko, Y, Ulate, M. (2017). The Cyclical Sensitivity in Estimates of Potential Output, National Bureau of Economic Research, Working Paper No. 23580.
  • EBRD (2017). Transition Report 2017-2018, European Bank for Reconstruction and Development, London, UK.
  • Fayad, G., and Perelli, R. (2014). Growth Surprises and Synchronized Slowdown in Emerging Markets—An Empirical Investigation, IMF Working Paper, WP/14/173.
  • IMF (2017). Roads Less Traveled: Growth in Emerging Markets and Developing Economies in a Complicated External Environment, in IMF World Economic Outlook, April, 2017, pp. 65-120.
  • World Economic Forum (2017). The Global Competitiveness Report 2017-2018, Geneva: World Economic Forum.

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.