Tag: international trade

Russian Exporters in the Face of the COVID-19 Pandemic Crisis

Posted on November 8, 2021 | Policy Brief

20211108 Russian Exporters in the Face of the COVID-19 Image 01

This brief summarizes the results of recent work on the effects of the COVID-19 pandemic on Russian exporting companies (Volchkova, 2021).  We use data from the CEFIR NES survey of exporters conducted in 2020. 72% of respondents reported that they were affected by the crisis. We scrutinize this impact. Contrary to popular wisdom, we observe little difference in delays of inputs by domestic and foreign suppliers. On the other hand, exporters experienced more disruptions in their sales in foreign destinations than in the domestic market. Possible reasons for this may be due to restrictions on international travel.

Introduction

According to experts at the Gaidar Institute (Knobel, Firanchuk, 2021), in 2020, Russia’s non-resource non-energy exports, decreased by 4.3%, while export prices fell by 4.1 % on average. The export of high-tech goods decreased by 14% due to a reduction in the physical volume of export. These changes in export intensity are mainly associated with the COVID-19 pandemic crisis. But are exporting firms more affected by the crisis than firms only active in the domestic market? What are the main channels through which the crisis influenced exporters?  And how do exporters adjust to the COVID-19 related shocks?

The analysis in this brief is based on forthcoming publication in the Journal of New Economic Association (Volchkova, 2021). We use data from a survey of Russian non-resource exporters conducted in 2020. We show that involvement in international trade did not affect the company’s vulnerability to the crisis on the production side: supply delays were equally likely to occur from domestic and foreign suppliers. These findings are consistent with Bonadio et al. (2021) who consider a numerical multi-sectoral model for 64 countries around the world linked by supply chains. They show that, in the face of the employment shocks associated with quarantine measures and switching to a remote work format, the contribution of global chains to the decline of real GDP is about one quarter. Importantly, the authors show that the “re-nationalization” of supply chains does not make countries more resilient to shocks associated with quarantine measures on the labor market because these shocks are also bad for domestic industries.

At the same time, our results indicate that exporting companies are exposed to additional risks associated with the need to adjust to shocks in the sales markets. According to the data, exporters find it more difficult to adjust their sales in foreign markets than in the domestic one. This is consistent with the fact that, during the pandemic, all countries introduced a strict ban on international travel, reducing the possibility of establishing new business ties through personal contacts. Similarly, Benzi et al. (2020) show a significant negative effect of international travel restrictions on the export of services.

Survey of Non-resource Exporters

The survey of exporters was carried out in June – November 2020 by CEFIR NES. The primary purpose of the survey was to identify and estimate barriers to the export of non-primary non-energy products. In the context of the developing economic crisis caused by the COVID-19 pandemic, we have added several questions to identify how the crisis influenced companies’ operations and how the respondent firms adjusted to the new conditions.

The survey was conducted using a representative sample of Russian exporting firms. As a control group, we interviewed non-exporting firms with (observable) characteristics (region, industry, labor productivity) similar to those of the surveyed exporters. Altogether, 928 exporting companies and 344 non-exporting companies were interviewed during the field stage of the study.

Most exporting companies that took part in the survey produce food products, chemicals, machinery and equipment, electrical equipment, metal products, and timber. On average, a surveyed exporter had 827 full-time employees; 25% of the firms had fewer than 26 employees. More than half of the surveyed exporting firms (53%) are also importers: 81% import raw materials and other inputs, 66% import equipment, and 22% import technology. Most interviewed exporters sell their products both abroad and on the domestic market. On average, an enterprise supplies 67% of its output to the domestic market and 32% abroad.

Impact of the COVID-19 Crisis on Firms’ Performance

Among exporters that participated in the survey, 25% reported that their business was not affected by the COVID-19 crisis, while 72% of respondents stated that the crisis did have an impact. Like any crisis, the COVID-19 pandemic created problems for some enterprises and provided new beneficial opportunities for others. According to the data, exporting businesses were significantly more likely to be negatively affected by the crisis than their non-exporting counterparts, and the impact of the crisis was not correlated with the size of the enterprise. Figure 1 presents the exporters’ answers to the question of how their sales in the domestic and foreign markets have changed with the COVID-19 pandemic.

The distribution of changes in sales volume in domestic and foreign markets significantly differ from each other. Estimates of the mean values of changes in sales volumes also differ significantly: the average drop in sales in the domestic market was 5%, while for the external market, it reached 17%. Hence, in times of the COVID-19 crisis, opportunities for growth were less prominent in foreign markets than in the domestic one, while significant market losses were more frequent.

Figure 1. Change in sales of export companies associated with the COVID-19 pandemic

Source: Survey of non-resource exporters, CEFIR NES, 2020.

Adjustment to the Crisis

The most frequently used crisis adjustment measure was employees transition to remote work – it was reported by 70% of the surveyed companies. 25% of exporters were forced to suspend their work during the crisis, while 72% were not. 14% of respondents stated they had to cut their payroll expenditures and other non-monetary benefits for employees (food, insurance, etc.), 12% of companies sent workers on unpaid leave. Only 6.5% of export firms had to lay off workers, while 91% handled the crisis without layoffs.

Comparing exporters’ answers with those of non-exporters while controlling for enterprise size, we conclude that exporting firms were more rigid in their adjustment  to the crisis. They were significantly more likely to suspend enterprise activities, dismiss of employees, send workers on unpaid leave, and reduce of wages. Also, these events were more likely to occur for smaller companies than for larger ones.

At the same time, flexible adjustment measures such as remote work were equally likely to be used by exporters and non-exporters, as well as by firms of different sizes. In general, Russian exporters of non-primary goods maintained their efficiency mainly by adjusting the labor relations to the new epidemiological conditions rather than by reducing employee-related expenses.

Dealing with Counterparties

Delays in the supply of components and raw materials were reported by 36% of the surveyed companies, and such delays were equally likely for shipments from abroad and domestic shipments. There is a perception that international supply chains in the context of the pandemic crisis are an additional risk factor. Our results indicate that domestic and international supply chains were equally challenged in 2020. Nevertheless, non-exporting companies faced the problem of delayed deliveries significantly less often than exporters did, and about 60% of companies experienced no problems at all on the input supply side.

27% of surveyed exporters stated that they delayed payments to counterparties. Non-exporting companies reported these reactions much less frequently regardless of firm size.

On the sales side, half of the surveyed exporters experienced delays in payments from their customers during the pandemic crisis. Non-exporting enterprises encountered the problems with the same frequency, and companies of all sizes were affected by this obstacle equally.

The cases of planned purchases cancellation on behalf of buyers were reported by 34% of exporting companies. Exporters experienced these problems significantly more often than non-exporters, and smaller companies experienced them much more often than larger ones.

Crossing international borders presented a certain problem for Russian exporters when it concerns product delivery. Just over half of the respondents indicated that they had to delay deliveries due to difficulties with border crossing. However, about the same share of companies (48%) reported that they delayed products delivery due to the introduction of lockdowns. Thus, during the COVID-19 pandemic, exporters’ operations were complicated to the same extent by problems related to border crossings as by those associated with lockdown regimes.

Conclusion

It is widely believed that international exposure of companies in the context of the COVID-19 pandemic crisis creates additional risks. Our study shows that, regarding existing inputs supply, international relations pose problems for Russian companies just as often as relations with domestic partners. As far as sales are concerned, adjustment to the crisis was better on the domestic market than on foreign markets. A possible explanation of this phenomenon is that, in addition to the shocks associated with quarantine measures in the labor market, access to foreign markets was hampered by restrictions on international travel, which is essential for readjusting contractual relations to explore new opportunities brought by crises (Cristea, 2011). Without personal interaction, new contracts were more difficult to launch. Thus firms’ opportunities to adjust foreign sales were more restricted than the ones in the domestic market.

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.

Media mentions: Key takeaways from this policy brief have been published by one of the most influential media outlets in Russia Kommersant – Коммерсант: «Ковид сильнее ударил по экспортерам». Исследование ЦЭФИР РЭШ. 

Ukraine’s Integration into the EU’s Digital Single Market

Posted on February 15, 2021 | Policy Brief

Blue EU flags in front of European Commission representing Ukraine’s integration into the single market

This brief is based on a study that investigates how Ukraine’s integration into the EU Digital Single Market (DSM) could affect EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.  The major benefits of integration are expected to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine digital trade 2) acceleration of the development of Ukraine’s digital economy in line with EU standards. According to the results, enhanced regulatory and digital connectivity between Ukraine and the EU is expected to increase Ukraine’s exports of goods and services to the EU by 11.8-17% and 7.6-12.2% respectively. At the same time, the acceleration of the digital transformation of the Ukrainian economy and society will produce a positive effect on its productivity and economic growth – a 1%-increase in the digitalization of the Ukrainian economy and society may lead to an increase in its GDP by 0.42%.

Background

Integration into the EU has been one of the key topics on Ukraine’s political agenda for a number of years. Recently, more emphasis has been put on an essential component of issue – integration into the EU’s Digital Single Market (DSM). The DSM is a strategy aimed at uniting and enhancing digital markets and applying common approaches and standards in the digital sphere across the EU. The Ukraine-EU Summit, held on October 6, 2020, stressed the paramount importance of the digital sector in boosting its economic integration and regulatory approximation under the EU-Ukraine Association Agreement. Implementation of the provisions of this agreement, in particular the updated Annex XVII-3, would introduce the latest EU standards in the field of electronic communications in Ukraine. The country is also gradually approximating its regulations with regard to other components of the EU DSM – electronic identification, electronic payments and e-payment systems, e-commerce, protection of intellectual property rights on the Internet, cybersecurity, protection of personal data, e-government, postal services, etc. These steps will, in turn, ensure Ukraine’s gradual integration into the EU’s Digital Single Market, which will facilitate digital transformations within the country and open a new window of opportunity for individuals and businesses.

This brief summarizes the results of our recent work (Iavorskyi, P., et al., 2020), in which we estimate the effect that Ukraine’s integration into DSM could have on EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.

Benefits of Integration into the EU DSM

The EU DSM strategy comprises three pillars: (1) better access for consumers and businesses to digital goods and services across Europe; (2) creating the right conditions and a level playing field for digital networks and innovative services to flourish; (3) maximizing the growth potential of the digital economy (EC, 2021).

These goals suggest that the major benefits of Ukraine’s integration into the DSM are likely to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine trade, 2) acceleration of the development of Ukraine’s digital economy in line with EU standards.

Indeed, the trade of goods and services is increasingly becoming “digital” – i.e., involving “digitally enabled transactions in goods and services that can be either digitally or physically delivered” (OECD, 2019). Trade digitalization (e.g., electronic contracts, electronic payments, e-customs, etc.) simplifies export and import procedures, reduces trade costs for exporters, and creates new opportunities for trade with the EU, in particular for SMEs. Therefore, the reduction of regulatory restrictions on cross-border digital trade reduces the overall level of restrictiveness of trade in goods and services.

Thus, digitalization is expected to facilitate and intensify the total EU-Ukraine trade in goods and services. It is also anticipated to increase the productivity of Ukraine’s economy which will have a positive impact on the country’s economic growth.

Major benefits include lower prices and greater access to EU online markets for Ukrainian consumers and business, digital innovative products and services, greater online consumer protection, lower transaction costs for businesses, improved quality and transparency of public digital services and e-government as well as an intensification of innovation development in Ukraine.

At the same time, Ukraine’s integration into the DSM entails several obligations: to align national legislation and standards with EU legislation and standards; to ensure institutional and technical capacity as well as interoperability of digital systems. For businesses in Ukraine, this means facing new EU requirements aimed at improving consumer and personal data protection, as well as increased competition from European companies in digital markets. However, these changes are necessary if the country wants to build a common economic space with the EU, especially given the growing impact of digital technologies on international trade and economy.

Ukraine in International Digital Rankings

Many international digital development rankings show that Ukraine lags behind EU countries, including its neighbors that recently joined the EU.

According to the UN e-Government Development Index (EGDI) for 2020, Ukraine ranks 69th among 193 countries and is included in the group of countries with high levels of e-government development. It received the lowest scores for Telecommunications Infrastructure and Online Services, and the highest for Human Capital. Nevertheless, Ukraine is lagging behind its neighboring EU members, – Poland, Hungary, Slovakia, Romania, Bulgaria, Lithuania, etc., – which belong to the group of countries with very high levels of e-government development (UN, 2020).

In the Network Readiness Index (NRI) ranking for 2019, Ukraine ranked 67th among 121 countries. As for the components of the index, Ukraine ranks worst in the following indicators: Future technologies (82nd out of 121), ICT Use by Government and Online Government Services (87th), and Regulatory Environment (72nd). Neighboring EU countries have higher rankings (Poland – 37, Latvia – 39, Czech Republic – 30, Croatia – 44). Other neighboring countries do somewhat better than Ukraine (Turkey is ranked 51st, Russia – 48th) or occupy positions close to Ukraine (Belarus – 61, Moldova – 66, Georgia – 68) (Portulans Institute, 2019).

In 2019, the country ranked 60th among 63 countries included in the World Digital Competitiveness Ranking (WDCR) rating. Just as in the other rankings, Ukraine scored well in the Knowledge component (40th among 63 countries), while in terms of Technology and Future Readiness it was at the bottom (61st and 62nd position respectively) (IMD, 2019).

Hence, it is primarily the technological and regulatory issues, that need to be addressed in order to improve Ukraine’s digital position in the region and the world.

Methodology

Measuring Ukraine’s Digitalization level

In order to estimate the impact of digitalization, a Composite Digitalization Index is calculated for Ukraine, the EU, and other countries included in the model. This index is based on 11 digital indicators, combined into five components that characterize different areas of the digital economy and society – Connectivity, Use of the Internet by citizens, Human capital, Integration of digital technology by businesses, and Digital public services.

Our results confirm that the level of digital development in Ukraine is far below the EU average. It also lags behind the new EU Member States, which have a lower level of digital development compared to the other EU countries. As of 2018, the widest gaps between Ukraine and the EU average are found in Digital Public Services, Connectivity and Use of Internet by citizens. At the same time, Ukraine performed better in Human Capital and Integration of digital technology by businesses.

Measuring Digital Services Trade Restrictiveness in Ukraine

To assess the impact of digital regulatory barriers on trade, we use the Digital Services Trade Restrictiveness Index (Digital STRI) (OECD, 2020). It quantifies the regulatory barriers in five different policy areas (communication infrastructure, electronic transactions, electronic payments, intellectual property, other restrictions) that affect trade in digital services (Ferencz, J., 2019). OECD calculates Digital STRI for OECD countries and some non-OECD countries. As Ukraine is not included in this index, we estimate it for 2016-2018 using the OECD methodology.

Our estimations show that the level of digital services trade restrictiveness in Ukraine is much higher than the EU average. The regulatory differences in the digital sphere between Ukraine and the EU increase the cost of cross-border digital transactions between countries.

For Ukraine, most barriers are related to cross-border electronic payments and settlements, protection of intellectual property rights on the internet, cross-border electronic transactions (for example, the divergence of the national requirements for foreign trade agreements, including electronic ones, from international practices and standards, lack of practical mechanisms for the application of the electronic digital signature in foreign trade contracts, lack of mutual recognition of electronic identification and electronic trust services between Ukraine and major trading partners, etc.), other barriers (requirements for the use of local software and cryptography, etc.). These regulatory restrictions significantly hinder the development of cross-border cooperation and Ukraine’s integration into the European and global digital space.

Ukraine’s integration scenarios

In the event of Ukraine’s integration into the EU DSM, the country’s regulatory environment and digital development are expected to gradually approach the EU averages. We model it through assuming that the regulatory differences between Ukraine and the EU (captured by the Digital STRI Heterogeneity Indices – see OECD, 2020) will be decreasing, and level of digitalization in the country (captured by the Digitalization Index – OECD, 2020) will converge towards that of EU-DSM members.

We considered three integration scenarios that imply high, medium, and low levels of Ukraine’s approximation to the regulatory environment and digital development of the EU. For instance, the high scenario implies the highest level of Ukraine’s digital development and the lowest level of regulatory differences between Ukraine and the EU.

Models

We study the effect of reduced regulatory differences in the digital sphere on Ukraine-EU trade using a gravity model – one of the traditional approaches in the international trade literature. A gravity model predicts bilateral trade flows based on the size of the economy and trade costs between countries (affected by distance, cultural differences, FTAs, tariffs, etc.)

The study uses the following specification of the model for exports of goods and services in 2016-2018:

• Dependent variable – the total export flow of goods and services from country into country j (all possible pairs of countries).

• Independent variables – distance between countries and common characteristics (borders, language, law), existence of a free trade agreement, level of tariff protection (for goods), level of regulatory heterogeneity in the digital sphere between the two countries, and a set of fixed effects for each country.

We also estimate how digital development affects technical modernization, productivity, and economic growth. Technically, we use a Cobb-Douglas production function to describe each country’s output and model its total factor productivity component as a function of digital development (captured by the Digitalization index).

Results

The results suggest that Ukraine’s integration into the EU DSM will be beneficial for both Ukraine and the EU. Under all integration scenarios, bilateral trade between Ukraine and the EU is expected to intensify considerably due to enhanced regulatory and digital connectivity between the two.

Ukraine’s total exports of goods and services to the EU are estimated to grow by 11.8-17% ($2.4-3.4 billion) and 7.6-12.2% ($302.5-485.5 million), respectively – a cumulative increase throughout the period of implementation of reforms aimed at regulatory and digital approximation of Ukraine to the EU.

 Figure 1. The impact of Ukraine’s integration into the EU’s DSM on the exports of services from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of Ukraine’s exports of services to the EU – as of 2018

Figure 2. The impact of Ukraine’s integration into the EU’s DSM on exports of goods from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of exports of Ukrainian goods to the EU as of 2018

The EU would increase its exports of goods and services to Ukraine by 17.7-21.7% ($4.1-5 billion) and 5.7-9.1% ($191-305 million), respectively.

The acceleration of Ukraine’s digital development will bring productivity gains that would transform into higher GDP growth. It is estimated that a 1% increase in Ukraine’s digitalization level is expected to raise its GDP by 0.42%. As a result, the country’s gradual approximation to EU levels of digitalization would result in additional Ukraines GDP growth of 2.4-12.1% ($3.1-15.8 billion), depending on the scenario.  

Figure 3. Impact of digitalization on Ukraine’s GDP growth: three digitalization increase scenarios

Source: own calculations. The left axis – GDP growth (%), the right axis – the level of digitalization. The current level of digitalization of Ukraine as of 2018.

Conclusion

According to our estimations, improved digitalization and reduction of regulatory barriers in the digital sphere between Ukraine and the EU will have a positive effect on trade for both Ukraine and the EU. There is also a significant potential for economic growth to be attained in Ukraine by increasing digitalization and productivity of various spheres of the economy and society.

Realization of this potential would, however, require a substantial regulatory approximation on the Ukrainian side to achieve alignment with the EU DSM. The main emphasis needs to be put on electronic identification and transactions, payment systems and electronic payments, protection of intellectual property rights on the internet, cybersecurity, and personal data protection.

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.

US-China Trade War of 2018 and Its Consequences

Posted on December 17, 2018 | Policy Brief

20181216 US-China Trade War Image 01

The trade war between the United States and China has become one of the main events in the global economy this year. What could be its consequences for the US and China, and how might it affect other countries – for example, Russia? 

Chronology of the trade war

Donald Trump started the war, raising import tariffs on solar panels in January 2018, of which the main supplier is China. In response, on April 2nd, China raised import duties on 128 commodities originating from the United States. On July 6th, the US increased tariffs on Chinese goods by 25 pp., imports worth $34 billion. China responded symmetrically. In August, the United States increased the tariffs on another $16 billion of imported goods from China, to which a symmetrical response again followed. In September, the United States again applied higher tariffs for $200 billion of Chinese exports, and China for $60 billion of US exports. At each stage of the conflict escalation, China appealed to the WTO with complaints about the actions of the United States, pointing to the inconsistency of their actions with the obligations and principles of the WTO. There were several meetings of official representatives from the United States and China – without any significant results.

What are the main reasons for this unprecedented escalation?

Imbalance and intellectual property

The economies of the US and China today are by far the largest in the world, and the trade turnover between the two countries is one of the most important. A remarkable feature of these trade flows over last decades is their imbalance. In 2017, the United States imported $526 billion worth of goods from China, while China’s imports from the United States amounted to $154 billion. Part of this imbalance is offset by trade in services, but it is not enough to even it out: in the same the year the United States delivered $57 billion worth of services to China while importing services of $17 billion from China.

Experts have different views on this imbalance. On the one hand, there is a perception that it is a source of world economy vulnerability, a source of potential crisis. Therefore, it is necessary to reduce the trade deficit. Another point of view is that this imbalance merely reflects the fact that the US economy and its assets are very attractive to investors from all over the world, including Chinese – and that, in turn, requires that the surplus of capital flows biased to US side, was compensated by the corresponding deficit of trade in goods and services. One such investor is the Chinese state itself, which for many years has been pursuing a policy of exchange rate undervaluation in order to promote foreign trade. It led to an enormous accumulation of foreign exchange reserves and as of January 2018, China held $1.17 trillion of US bonds and was the largest creditor of US government.

US President Donald Trump referred to this trade imbalance as one of the reasons for the outbreak of this trade war against China. Trump aims at reducing the deficit by $100 billion from the current $375 billion. The unilateral increase in import tariffs applied to Chinese goods was the first action of the US administration in this direction.

The second, no less important, formal reason for the trade war is the inadequate protection of intellectual property rights in China. China’s production of counterfeit products, the lack of adequate practices and laws to protect foreign technologies from illegal dissemination in the country, is not news to anyone. And although the almost two decades since China’s WTO accession have meant a largely modernized legal framework in this regard, a number of important provisions are still inconsistent with international practices, and the implementation of existing intellectual property rights leaves much to be desired. Established in 2012, The Commission on the Theft of American Intellectual Property identifies China as the most malicious violator of US rights. The exact damage is not known, but the commission assessment of the losses to the American economy due to the forced transfer of technology to Chinese partners – which is an unspoken condition of foreign manufacturers access to the Chinese market – industrial espionage, contradictions in legislation, requirements for the storage of sensitive data in China are in the range from $225 to $600 billion per year (Office of US Trade Representative, 2018).

While both the trade deficit and the intellectual property rights issue were recognized for many years, it was in 2018 that Trump started acting on them. Therefore, in order to discuss the potential impact of the conflict between the world’s largest economies on themselves and other economies, such as Russia, it is important to understand what drives the actions undertaken by Trump’s administration.

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.

Losers and Winners of Russian Countersanctions: A welfare analysis

Posted on October 1, 2018 | Policy Brief

20181001 Losers and Winners of Russian Countersanctions Image 01

In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables.  By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.

In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).

Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.

The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.

We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.

Partial equilibrium welfare analysis

We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.

In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.

Figure 1. Visualization of deadweight losses, consumer and producer surplus changes

Welfare analysis

Data

We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.

Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.

Table 1. Summary table of the welfare effects of countersanctions

Group Price (RUR per kg, 2013) Production (thous. tons) Consumption (thous. tons) Elasticity Consumer losses, RUR mn Producer surplus, RUR mn Deadweight loss, RUR mn Importer gains, RUR mn
2016 2013 2016 2013 2016 2013 demand supply
Beef 376 357 238 240 600 897 -0.78 0.1 11311 4388 234 6690
Poultry 109 108 4468 3610 4577 4084 -0.78 0.45 3263 3173 13 77
Pork 286 289 2042 1299 2282 1919 -0.78 0.2 -7167 -6447 38 -757
Milk 55 47 5540 5386 5704 5595 -0.93 0.3 48234 42507 4443 1284
Butter 343 271 251 225 340 340 -0.93 0.18 27468 17680 3370 6419
Cheese 358 283 605 435 748 764 -0.93 0.28 63493 44259 8437 10797
Fromage frais 233 190 407 371 456 457 -0.93 0.3 21803 17104 2600 2099
Apples 84 70 324 313 986 1665 -0.85 0.1 15225 4562 1238 9425
Bananas 61 47 0 0 1141 1165 -0.9 0.1 18967 0 2315 16652
Oranges 65 59 0 0 932 1059 -0.9 0.1 6054 0 272 5782
Grapes 175 131 174 101 366 459 -0.85 0.1 18312 7527 2351 8435
Tomatoes 82 65 1130 863 1583 1718 -0.97 0.1 28824 18177 3290 7357

Data sources: Rosstat, Euromonitor, UN COMTRADE

Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.

Beef

The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions.  In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that  consumer losses amount to  11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.

Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.

Cheese

Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1%  to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.

Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.

Apples

In 2013 constant prices, average real price for 1 kg of apples increased by 20.0%  up to  84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the  effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at  1.2  bn Rub or 1.1%; importers’ gains equal 9.4  bln Rub or 8.1%.

Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.

Overall effects for 12 commodity groups

We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.

Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).

Distribution of importers’ gains

Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).

Conclusion

There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year.  Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).

The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.

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.

Changes in Oil Price and Economic Impacts

Posted on December 7, 2015 | Policy Brief

Authors: Chloé Le Coq and Zorica Trkulja, SITE.

Oil has for decades been perceived as a necessary and highly addictive energy commodity, fueling the world economy. It is a crucial input good for most of the net-oil consumer countries, and it is an important source of revenue for the net-oil supplier countries. This means that any changes in the oil price will affect the entire world economy. However, the extent to which the oil-price fluctuations matter for the economy depends on the perspective (e.g. whether it is that of the macro economy, international trade, firm strategies, or the climate economy). In this policy brief, we outline the answers to this question that were provided at the 9th SITE Energy Day, held at the Stockholm School of Economics on November 5, 2015.                                                    

Non-Tariff Measures in the Context of Export Promotion Policies

Posted on May 11, 2015 | Policy Brief

20150511 Non-Tariff Measures FREE Network Policy Brief Image 01

This brief focuses on the role of non-tariff measures (NTMs) in international trade. While multilateral and bilateral trade negotiations have resulted in worldwide reductions in tariffs, we observe an increasing trend in the application of non-tariff measures. In this brief, we will discuss the evidence of the effect of such measures on exports. The brief also contributes to the discussion of export promotion policies: whether governments, especially in developing countries, should concentrate their efforts to remove only external barriers since there is empirical evidence that internal barriers are no less important for exports.

Economists, policy makers and international organizations are increasingly recognizing the importance of non-tariff measures (NTMs) as substantial impediments to international trade. A survey conducted by UNCTAD among exporters in several developing countries ranks SPS and TBT measures the top trade barriers with on average 73 percent of the respondents viewing them as the primary trade barrier (UNCTAD 2010). The World Bank published a book on NTBs where different authors contributed chapters addressing many aspects of the NTMs (World Bank, 2012). The World Trade Organization (WTO) itself devoted its entire 2012 World Trade Report to such measures with a particular focus on technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures. Availability of the new datasets on NTBs allowed researchers to study the effect of these measures on intensive (changes for existing exports) and extensive margins (changes due to entry and exit into exporting) of trade.

Even though trade theory does not specifically address the question of non-tariff barriers that include (but are not limited to) technical regulations, sanitary and phytosanitary measures, the logic of traditional models can easily be extended to these measures. In particular, they can be thought of as part of the fixed/additive costs for exporting firms as they impose compliance costs on exporters. These compliance costs are related to potential adjustments of production processes, and certification procedures needed to meet the requirements of countries imposing such regulations and standards (Schlueter et al., 2009). In a Melitz-type model, these costs are expected to have a negative impact on volumes of trade, number of exporters and number of goods exported. At the same time, average exports per firm may actually increase as the export market-shares are reallocated towards firms that are more efficient.

The existing empirical evidence of the impact of NTMs is mixed; researchers have found both positive and negative effects. The differences in results depend largely on the sector, country and type of NTM imposed. While the effect may overall be negative or null, for some sectors the effect is found to be positive (Moenius, 2004; Fontagné et al., 2005; Chen et al., 2006; Disdier et al., 2008; Medin and Melchior, 2015).

In a recent working paper, Besedina (2015) investigates the effect of introducing an NTM (either SPS or TBT) on export dynamics (in particular, exports concentration and entry and exit into exporting) using the World Bank Exporters database, with a special focus on trade in foodstuff. In particular, we examine how TBT and SPS measures affect export concentration and diversification (both at product and destination level) as well as entry and exit of firms into exporting. If introduction of an NTM increases costs of exporting, the ‘new’ trade theory started by Melitz (2003) predicts that some exporters will stop to export and thus the number of exported product varieties will fall as well (change in extensive margin).

The most important result from our analysis is that the introduction of a TBT or an SPS measure does not seem to affect sectoral export dynamics. Given the above discussion, this result may appear surprising at first. What can possibly explain this zero effect?

First, one may argue that the sector dynamic variables we use in our analysis may not capture changes in the behavior of economic agents (firms) well: while marginal firms may be affected by technical barriers and SPS, averaging across firms may actually conceal this. However, in our analysis we investigate exports at a relatively disaggregated level (4-digit product lines). So while averaging might be a concern, we believe it is not likely to be driving the zero effect.

Second, the concern is that the effect of introducing an NTM measure may not be felt immediately (within one year). In order to verify this, we include lagged trade-barrier variables two periods, but the results were unchanged. Third, it may be the case that it is the number of NTMs rather than the introduction of them that matters. In order to address this point, we performed the same type of analysis using the change in the number of measures introduced. The results were again not affected, and we still do not find any statistically significant relationship between NTMs and exports dynamics.

Despite the absence of an effect of NTMs, this paper reveals an important and policy-relevant finding: the home country’s business environment and institutional factors are important determinants of export performance. It is rather the monetary costs and more complicated exporting procedures imposed by the NTM measures that hamper product and market diversification of the country’s exporters. Hence, policy makers, especially in developing countries, should not only be concerned with removing external barriers to exports (like NTMs) but should also aim to reduce internal barriers and costs imposed on exporting firms by corrupt practices and burdensome regulatory procedures.

Another important dimension for domestic policies towards exporters stems from the work by Melchior (2015, forthcoming) who studies Norwegian exports to BRICS countries overtime and shows that export growth largely depends on the intensive margin (it explains 93 percent of the export growth). Using firm-level data for seafood exports, he finds that only 54% of “trades” – measured as firm/importing country/product combinations – survive from one year to the next. Hence, there is massive “churning” (entry and exit at the same time), and churning is relatively more important in small and in growing export markets. In other words, exporting companies constantly enter and exit foreign markets, add new products, or discontinue exporting some products. A policy implication from this finding is that export-promotion offices should help firms stay in export markets rather than focus on entering these markets. Hence, while it is important to enable domestic firms to enter foreign markets, it seems equally important to ensure their survival in foreign markets, which can be facilitated by a removal of both external and internal barriers.

▪

References

  • Disdier, A-S, L. Fontagné and M. Mimouni (2008), “The Impact of Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements”, American Journal of Agricultural Economics 90(2): 336-350.
  • Fontagné, L., F. von Kirchbach, and M. Mimouni (2005). “An Assessment of Environmentally-related Non-tariff Measures”, The World Economy 28(10): 1417-1439.
  • Medin H. and A. Melchior (2015) ”Trade barriers or trade facilitators? On the heterogeneous impact of food standards in international trade”, NUPI mimeo
  • Melchior (2015) ” Non-tariff barriers, firm heterogeneity and trade: A study of seafood exports, with a particular focus on BRICs”, NUPI mimeo
  • Melitz, M. J. (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71(6): 1695–1725.
  • Moenius, J. (2004), “Information versus Product Adaptation: The Role of Standards in Trade”, Working Paper, International Business & Markets Research Center, Northwestern University mimeo.
  • UNCTAD (2010), Non-Tariff Measures: Evidence from Selected Developing Countries and Future Research Agenda (UNCTAD/DITC/TAB/2009/3). New York and Geneva.
  • World Bank (2012), Non-Tariff Measures – A Fresh Look at Trade Policy’s New Frontier, ed. O. Cadot and M. Malouche, The World Bank, Washington D.C.