Policy Frameworks Supporting Africa–Europe STI Cooperation: Past Achievements and Future Responsibilities

This chapter provides an overview of the chronological developments of the Africa–Europe cooperation on science, technology and innovation (STI). The authors first remind readers that African–European cooperation bears the mark of the prior colonisation of the African continent and the deep establishment of unequal and (geographically) unbalanced cooperation patterns. They then reflect on the renewed interest in STI in Africa–Europe cooperation. The chapter explains how more balanced cooperation structures and more efficient financing systems can be achieved and how science can become an integrated part of global development strategies.

IntroductIon
Science, technology and innovation (STI) cooperation between Europe and Africa has undergone significant change in the past decade. For example, by September 2013, towards the close of the EU's Seventh Framework Programme (FP7), there were 1315 participants from 45 African countries in 565 projects, with a total grant funding of 178 million euros from the European Commission (EC). About 82% of these projects received financing mainly through the FP7 "Cooperation Specific Programme" and were largely centred on health, agri-food and the environment.
The adoption in 2007 of the Joint Africa-EU Strategy (JAES) prepared the ground for partnerships based on egalitarian relationships and mutual benefits while acknowledging the critical role STI can play in boosting both human and socio-economic development (African Union & European Union 2007a). Nevertheless, cooperative efforts must be strengthened. More particularly, both European and African countries must continue to join forces to further align STI cooperation politics, harmonise their monitoring instruments and better coordinate their programmes.
This chapter first describes the prominent regional and bi-regional policy frameworks that underpin Africa-Europe cooperation on STI. Our chronology of Africa-Europe cooperation begins in 1957 (with the Treaty of Rome) and continues to more recent developments, with the adoption, in 2007, of the JAES and the establishment of a bi-regional partnership on food, nutrition security and sustainable agriculture (FNSSA) in 2016. It then focuses on two key issues that Africa-Europe STI cooperation is currently facing: first, the necessity to find new models for more equitable and sustainable funding of STI in Europe and Africa alike; and second, the need for continuous efforts to prioritise science and technology in the global development agenda (Agenda 2030). This chapter argues that if policymakers do not give these issues earnest consideration, cooperation risks not only forgoing the important gains it has made but also faces the risk of being weakened.

The Cotonou Agreement
In 1957, in the context of both African decolonisation and the early stages of building the modern-day European Union (EU), the Treaty of Rome made a provision for the creation of a European Development Fund (EDF) with a view to granting technical and financial assistance to African countries, some of which at that time were still colonies of European powers. The Cotonou Agreement paved the way for greater Africa-Europe cooperation in broad terms and, until today, serves as an umbrella under which many funding instruments fall. Preceded by the four successive Lomé Conventions (Lomé I-IV), the Cotonou Agreement was concluded in 2000 for a 20-year period as a partnership between the EU and 79 countries of the African (excluding South Africa), Caribbean and the Pacific (ACP) regions. Its fundamental principles include the equality of the partners, as well as inclusive participation, dialogue and regionalisation.
The Cotonou Agreement was revised in 2005 and 2010 and now rests on three pillars, namely, development cooperation, political cooperation, and economic and trade cooperation. The 2010 revision was particularly significant because it recognised climate change as a global challenge, committing the parties to include it in development cooperation and to support ACP efforts in mitigating and adapting to the effects of climate change. This revision was also instrumental in putting into practice the internationally agreed aid effectiveness principles laid down in the Paris Declaration, especially the principle of donor coordination.
Although the EDF falls outside the EU's Multiannual Financial Framework (MFF), it is the main source of funding under the Cotonou Agreement, and mainly directed to development cooperation with African governments, either through national programmes or through Africa's Regional Economic Communities (RECs). Financed by direct contributions from EU member states according to contribution shares (or "keys"), it is governed by its own rules. The current 11 th EDF has 30.5 billion euros at its disposal for the period 2014-2020. The EDF operates thematic development cooperation programmes in areas such as health and the environment as well as programmes with a pan-African focus. While the EDF covers all ACP countries, the large number of African states forms a majority. A few national or regional activities funded by the EDF have a specific science and technology capacity building orientation.

The European Consensus on Development
The European Consensus on Development (ECD), adopted in 2006, formed the common basis of EU development policies and actions. The ECD was adopted in March 2005, aiming at eradicating poverty and promoting sustainable development. In-depth political dialogue was set out as an essential ingredient for good governance, the respect of human rights and the rule of law, the fight against corruption and spread of democratic rule. Furthermore, to address the problems of fragile states, the EU pledged to engage in institution building and to foster linkages between emergency aid, rehabilitation and long-term development.
The ECD reiterated its support for global initiatives such as the Millennium Development Goals (MDGs)-today replaced by the Sustainable Development Goals (SDGs)-covering key development issues such as poverty, health, food security, access to education, gender equality and environmental sustainability, and which emphasised the need of a global partnership for development. Just like the MDGs, the SDGs involve various sectoral and thematic domains of STI, whose advancement and application are implicated in their realisation. At the same time, it reaffirmed the need to coordinate with the Bretton Woods institutions' policies and programmes and to enhance cooperation with the United Nations (UN) system and other relevant institutions such as the Development Assistance Committee of the Organisation for Economic Co-operation and Development (OECD), especially the Good Practice Guidelines of the latter.
In terms of the volume of resources put at the disposal of development partners, the EU adopted a timetable for its member states to reach the 0.7% of gross national income (GNI) goal by 2015, with an intermediate collective target of 0.56% by 2010. If these targets had been achieved, it would have doubled EU development assistance to 66 billion euros by 2010. Although failing to achieve these targets, the EU has remained a major development partner for Africa. From 2007 until 2013, official development assistance in the form of national budget and project support disbursed to Africa by the EU and its member states was estimated at 144 billion euros, or about 20.6 billion euros on average per year (European Commission 2016c). The EU is committed not only to providing aid (with efficiency and quality) but also to entering into economic and trading partnerships with developing countries. It is worth mentioning that although the ECD did not prioritise STI cooperation, it has remained open, in principle, to such a collaboration.

Consolidated Plan of Action (2006-2014)
Africa's Science and Technology Consolidated Plan of Action (CPA) was adopted in 2005 to promote and use science and technology to foster socio-economic development and ensure Africa's integration into the world economy (NEPAD 2006). At the time of its formulation, the African continent had made only modest progress in the area of STI, owing to numerous systemic constraints: weak inter-institutional linkages and collaboration; disjointed STI and development policies; weak policy implementation capacity; modest national allocations for science and technology (below 1% of gross domestic product (GDP)); as well as a limited capacity to translate research results into industrial products and services (Barugahara and Tostensen 2009a).
To address these challenges, the CPA established three priority areas: capacity building, knowledge production (as well as scientific research) and technological innovation. The following goals were set: (1) implementing the African Science, Technology and Innovation Indicators Initiative; (2) improving regional cooperation in STI (through capacity building, exchange of good practices, the formation of a common African framework for cooperation in science and technology, and a more active participation in EU facilities); (3) building a public understanding of STI; (4) building a common African strategy for biotechnology; (5) building science and technology policy capacity; (6) creating technology parks.
The CPA was implemented through networks of centres of excellence, which were dedicated to specific STI and capacity building programmes, while complementing other African Union (AU) and New Partnership for Africa's Development (NEPAD) specific programmes for agriculture, environment, health infrastructures, industrialisation and education. The AU Commission (AUC) provided overall political and policy leadership for the implementation of the CPA, while the NEPAD office for science and technology and the African Ministerial Council on Science and Technology (AMCOST) provided overall technical and intellectual leadership.
The CPA successfully managed to raise awareness among many African governments about the usefulness of STI in societal transformation and development processes. It also unified the so-far fragmented national systems of innovation (NSI) of the countries that had jointly formulated STI priorities. While the CPA was elaborated to address STI challenges in Africa, it was acknowledged, very early on, that a more thorough engagement with the EU would be advantageous to achieve its ambitious goals. Such engagement encompassed professional collaboration in research and innovation (R&I) activities, but also funding and capacity building in prioritised fields (Barugahara and Tostensen 2009a, p. 44).

Science, Technology and Innovation Strategy for Africa (STISA-2024) (2014-Present)
In 2014, the AU further committed itself to support STI policies and, in so doing, set up the Science, Technology and Innovation Strategy for Africa (STISA-2024), which was to replace the CPA. STISA-2024 was adopted during the 23 rd Ordinary Session of the AU Executive Council during the AU Summit held in Malabo, Equatorial Guinea, and served as the continental framework for accelerating Africa's transition to an innovation-led, knowledge-based economy (EX.CL/839[XXV]). As an integral part of the Agenda 2063 set by the AUC, which recognised STI as a major driving force for socio-economic development, STISA-2024 was designed as a ten-year incremental strategy and emphasised the necessity to integrate STI into critical sectors such as agriculture, health, infrastructure development, mining, water, energy, and environment. Its six key priority areas included (1) eradication of hunger and achieving food security, (2) prevention and control of diseases, (3) communication, (4) protection of our space, (5) Living Together-Building the Society and (6) wealth creation. The meeting specifically called upon African states and the RECs to integrate STISA-2024 into their STI development agendas for implementation.

The EU's International Scientific Cooperation Strategy
International cooperation is expected to foster new knowledge production, increase scientific quality and improve the competiveness of R&I systems. At the same time, internationalisation boosts the productivity of investments in research and development by enabling companies to gain more knowledge from international markets, to participate in new value chains and reap greater benefits from growing markets outside the EU (European Commission 2016c). Premised on the above expectations, in 2012, the EU adopted a strategy for international cooperation in R&I. The main objectives of the strategy are to strengthen the EU's R&I excellence, attractiveness and economic and industrial competitiveness, tackling global societal challenges, and supporting the EU's external policies. The research programmes carried out by the EU are open to participation by research institutions and researchers worldwide and cooperation is fostered through FPs for R&I (currently Horizon 2020). The EU is also developing targeted strategies with specific countries in order to achieve specific objectives: in 2015 the European Commission published 11 multiannual roadmaps for scientific cooperation with industrialised countries (Canada, South Korea, USA, Japan), emerging scientific powers (Brazil, Russia, India, China, South Africa) and the European Neighbourhood Policy countries in two groups (Eastern Partnership and Southern Mediterranean). Each roadmap presents the state of cooperation with the EU and defines thematic priorities for future cooperation in R&I (European Union 2015). Several other roadmaps with third countries, especially countries in Africa, have since been published.
As a result, R&I cooperation has been prioritised and intensified with the aim of making the EU a stronger global actor in solving global challenges in the areas of health, food, energy, water and climate change.
Notably, the results of the EU's R&I efforts have contributed to the development and implementation of a number of international commitments such as the United Nations Framework Conditions on Climate Change, the Convention on Biological Diversity, the 2030 Agenda for Sustainable Development and various resolutions of the World Health Organization. More than 1000 publications from FP7 projects contributed to the fifth Assessment Report of the Intergovernmental Panel on Climate Change that provided the evidence base for negotiations at the UN Climate Change Conference held in Paris in 2015 (European Commission 2016c).

The Joint Africa-EU Strategy (2007-Present)
From a starting point of largely separate continental approaches to STI strategies, the JAES specified the terms of engagement between the two continents, aiming to strengthen political partnership and cooperation. The JAES' Eighth Partnership on Science, Information Society and Space (also discussed in Chap. 1) specifically recognised STI as necessary to fostering knowledge-based societies, competitive economies and sustainable development. The JAES policy framework came with a first action plan (2008)(2009)(2010), later succeeded by a second one (2011-2013) reinforcing the commitments on STI, and reiterating the strategic importance of modern technologies to achieve the MDGs, and the SDGs subsequently, set up by the UN 2030 Agenda (African Union & European Union 2007b, 2010).
In 2014, the Fourth EU-Africa Summit reaffirmed that the JAES continues to frame continent-to-continent cooperation. On the same occasion, a roadmap for 2014-2017 was adopted for the implementation of the joint strategy, and redefined the priority areas as (1) peace and security; (2) democracy, good governance and human rights; (3) human development; (4) sustainable and inclusive development and growth and continental integration; (5) global and emerging issues. The third priority area concerned STI more specifically, while the roadmap made an unequivocal case for its role in shaping the relations between the two continents: "Investments in science, technology and innovation (STI) are vital to promote growth and employment, improve competitiveness and tackle pressing global challenges" (EU-Africa Summit 2014). The Summit also recognised the EU-Africa High Level Policy Dialogue (HLPD) on STI as an instrumental actor in the implementation of the STI part of the programme. Most recently, in April 2016, the College-to-College meeting between the European Commission (EC) and the AUC reaffirmed its commitment to continued collaboration to maximise the mutual benefits of STI towards addressing multiple challenges, including poverty (European Commission 2016a, b, c).
The inclusion in the JAES of STI cooperation reinforces the wide range of collaborative relationships that exist outside of the bi-regional policy framework. These include the relations between the EU and/or its member states with sub-continental groupings such as the RECs. Several European states have maintained long-standing relationships of significant magnitude with African counterparts, under the Commonwealth or similar groups. The rapidly changing global scientific, technological, socioeconomic and political landscape has motivated current efforts towards the strengthening of the EU-Africa partnership.
All of these efforts have shaped the agenda for the Fifth Africa-EU Summit, which is scheduled for November 2017 with the aim to review and deepen the Africa-EU partnership. In the Joint Communication to the European Parliament and the Council for a renewed impetus of the Africa-EU Partnership, the EU proposes a revitalised framework for joint action. It sets out policy priorities and concrete initiatives for 2018-2020 and beyond, to be developed jointly with African partners, in response to Africa's Agenda 2063 and building on the Global Strategy for the EU's foreign policy (European Commission 2017).
The quest for a strengthened Africa-EU partnership is based on shared values and interests enshrined in the JAES. It is also based on a thriving long-term partnership where the EU remains Africa's significant development partner as illustrated by the following indicators: (1) the EU is collectively Africa's main foreign investor (32 billion euros of EU Foreign Direct Investments (FDI) flowed to Africa in 2015 (33% of total FDI flows to Africa); the EU accounted for 33.5% of Africa's imports and 42% of Africa's exports in 2016-the European Investment Bank (EIB) also provides over 2 billion euros of annual financing in Africa; (2) the EU is Africa's main trading partner, offering free access to the EU market for all products via Economic Partnership Agreements, the Free Trade Agreements and the Union's Generalised Scheme of Preferences; (3) the EU is the main source of remittances (21 billion euros of remittances from Europe to Africa in 2015 [36% of global flow to Africa]); (4) the EU is the first partner in development and humanitarian assistance (21 billion euros of collective Official Development Assistance (ODA) [EU and its member states]) to Africa in 2015 (50% of total ODA to Africa) (European Commission 2017).
Building on these concrete achievements, the EU suggested the JAES (2018-2020) should focus more strongly on sustainable and inclusive economic development in Africa by creating jobs and highlighting the opportunities it offers to Europe. The proposed flagship actions on R&I cooperation include: (1) launching a new EU-Africa R&I partnership on climate change and sustainable energy focusing on deployment as well as capacity building in renewable energy and energy efficiency and in climate services; (2) generating EU and African investments to support R&I in agriculture via the EU-Africa Research and Innovation Partnership on FNSSA as well as increase the uptake of new technologies by local communities for increased agricultural income and nutrition; (3) intensification of Africa-EU collaboration on research by (i) facilitating collaboration between researchers and innovators from Africa and Europe, including through increasing professional development opportunities for researchers through the Marie Sklodowska-Curie Actions and other types of Horizon 2020 projects, (ii) supporting research capacity building in Africa through programmes such as the AURG, and (iii) supporting an open digital research environment for universities and research organisations. These are plausible actions that when concretised and implemented will strengthen Africa-EU R&I cooperation and put both continents on a higher trajectory of development.

Re-balancing Cooperation
There is robust evidence worldwide that greater use of technology is a major factor in enhancing productivity-driven growth and industrial competitiveness (Solow 1956;Temple 1999;Barro and Sala-i-Martin 2004;Romer 2007). What does this evidence mean for Africa-Europe STI cooperation, in the face of global challenges such as climate change, food security and human health? Apart from considering STI as an engine of growth, the EU has an interest in the creation of markets for its products in Africa, where competition from China, Japan and India is increasingly being felt. Economic growth and employment creation in Africa are also likely to ease the migration pressures EU countries are presently facing. In the event of higher growth, job creation and economic stability in Africa, as well as better adaptation to climate change, the anticipated need for recurring emergency relief operations will be reduced, as will be the threat of tropical diseases through the development of new medicines and vaccines, clinical trials and laboratory experiments. For the EU, these are among some clear and unequivocal reasons to cooperate.
Many African countries have recorded impressive economic growth rates in recent years, but this growth is likely to taper off unless buttressed by genuine economic transformation and technological advances (Booth and Therkildsen 2012). Cooperation between Africa and Europe can help both sides to achieve their objectives more effectively and efficiently than if they work in isolation from each other. However, more attention is required to address several systemic and structural challenges that tend to limit the continents' ability to effectively participate in R&I cooperation with the EU and the rest of the world (Barugahara and Tostensen 2009a). To ameliorate this situation, resources need to be leveraged in order to strengthen the capacity of African institutions and researchers to become effective and genuine partners with their EU counterparts in building robust STI systems across Africa, and in the EU. More effort is required to augment the existing Africa-EU collaborations, including CAAST-Net Plus which designed and implemented a series of research capacity building events to complement the work of related initiatives such as AfricaLics, whose secretariat is based at the African Centre for Technology Studies (ACTS) in Kenya, and the DfiD-funded Climate Impacts Research Capacity and Leadership Enhancement (CIRCLE) programme, implemented by the African Academy of Sciences. Policymakers therefore need to place greater emphasis on creating incentives and building adequate network infrastructure and human resource capacities that are required for effective participation in international cooperation activities.

Financing Cooperation
The appropriate modality of financing for various EU and African STI initiatives should involve a mix at multiple levels. The current practice with direct funding of national activities alongside funding through the RECs as well as through continental bodies may facilitate budgetary control and cost-effectiveness. Mechanisms should be put in place to ensure harmonisation of interventions at various levels for greater development impact and uptake of research findings with more efficient use of resources.
Specific project support measures for short-term research and/or development activities in the mutual EU-Africa STI priorities as given in the JAES should be emphasised. Longer-term and sustainable funding arrangements might be established through, for example, regional financial institutions such as the African Development Bank for regional and continent-wide STI initiatives, complemented by funding sources at the national level. Proposals towards that end have been mooted in several fora and need to be reinforced and made operational.

Bridging the Gap Between Science and Global Development
The world is facing formidable development challenges, above all reducing poverty and food insecurity. Meeting those challenges are the overarching objectives of development cooperation, and STI is a critical means to reach them. Although this book deals with bi-regional cooperation in STI, such cooperation is not an end in itself. It is indeed a means to an end. Hence, STI endeavours must be considered in conjunction with development efforts. So the uptake and application of research results is pivotal.
There is a plethora of policies and instruments related to STI, on the one hand, and development issues, on the other-at both EU and AU ends. Those policies and instruments need to be integrated into a coherent whole with a view to informing and underpinning development efforts with technologies stemming from research projects. This is not an easy task. Bridging the gap between STI and development efforts has been the subject of a long-standing and continuing discussion (e.g., Court et al. 2005;Leach et al. 2008).
A permanent forum for Africa-Europe dialogue was established in 2014 with the HLPD on STI and the adoption of a roadmap for cooperation. The regular College-to-College meetings serve the same purpose of ensuring political commitment and technical follow-up on the implementation of the strategy. These fora will provide the needed follow-up of the policy aspirations of the continental authorities and provide an arena for professional interaction among policymakers, technocrats and development practitioners across Africa and the EU as an essential first step towards bridging the gap between science and global development (Barugahara and Tostensen 2009b;Diyamett 2008).
The relative importance and priority (in terms of resource envelopes) accorded to research as distinct from development activities requires conceptual attention. While debate is rife between proponents of both aspects, it should be noted that the road from research to products on the market shelf is not as straightforward (or "linear") as it may seem in the case of development interventions. Taking this into consideration, a greater appreciation of the uncertainties of research endeavours is required when balancing the funding priorities between STI and development interventions. Sustainable and adequate funding mechanisms could be explored within the development cooperation instruments with assurance for higher returns from investments in R&I in the longer-term perspective.
The interface between STI and development efforts requires the building of operational models for bridging the existing gap. The models of collaboration between policymakers, scientists and practitioners with a view to achieving a greater uptake of research findings for development ends are essential components of development cooperation. This is only possible if the models are workable; that is, involving all relevant stakeholders operating on a common understanding and within a policy environment conducive to such collaboration. It would probably not be possible to arrive at a generic collaborative model that would fit all circumstances and sectors. Hence, most models, while replicable in some respects, will need to be "customised" to the specific conditions at hand, be they institutional or otherwise. ERAfrica is a new platform for research collaboration between Africa and Europe, co-funded by the EU and the collaborating partners, at a ratio of 20:80. The ERAfrica and the associated LEAP-Agri project model might herald the establishment of such innovative and viable models of development cooperation. Greater emphasis should be placed on the application aspects of Horizon 2020 projects in order to enhance the uptake of research outputs.

conclusIon
The socio-political, humanitarian, economic, and technological drivers of Africa-Europe cooperation are well articulated in the ECD (2006), the CPA (NEPAD 2006), the JAES (African Union & European Union 2007a) and the STISA-2024. Encompassing the aspirations of the two continents in development and STI, these policy documents constitute key elements of Africa-Europe cooperation. All of these frameworks note the challenges ahead, especially relating to the digital and economic divides between the two continents that characterise the cooperation landscape. Moreover, closing the existing digital and economic divides between developed and developing economies lends itself to joint bi-regional efforts. Given the potential dividends of STI, dialogue and negotiation across many themes and sectors between the two continents have given rise to a number of policy initiatives and funding schemes to facilitate biregional collaboration in joint endeavours. Apart from basic research, emphasis is increasingly put on the application of findings towards meeting major global challenges such as food and nutrition security, health and climate change. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.


Empirical Estimates for how Changes in China’s Foreign Reserves Are Hurting Chinese Exports and Helping US Exports

This paper estimates the change in China's exports and the change in US exports due to a one dollar increase in China's foreign reserves. The statistical technique used produces reduced form estimates that capture the influence of omitted variables without having to construct and estimate complex structural models. I find that in August 2000 China's accumulation of 621 million dollars of foreign reserves is correlated with China's exports increasing by 151 million and the US's exports falling by 628 million dollars. In contrast, in November 2016, China spending 69 billion dollars of its foreign reserves supporting the value of the yuan is correlated with China's exports falling by 4.77 billion and the US's exports rising by 2.42 billion. Donald Trump's accusation that China is suppressing the yuan exchange rate to help Chinese exports at the expense of US exports did not fit the facts between August 11, 2015 and December 31, 2016.

14, China's holdings of foreign reserves climbed from one trillion US dollars to 3.99 trillion dollars. During this time frame, the Chinese government was artificially suppressing the value of the Chinese yuan which would help Chinese exports and hurt US exports, ceteris paribus. However, as Figure 1 shows, starting in July 2014, China's holdings of foreign reserves have been on a downward trend. Indeed, by December 2016, China's holdings of foreign reserves had fallen to 3.01 trillion US dollars. In other words, between July 2014 and December 2016, China spent (Note 1) slightly less than a trillion dollars, or one fourth of her total, reserves.
A significant portion of the fall in China's foreign reserves between July 2014 and August 2015 could have been due to the Chinese going on a buying spree for foreign assets (Leightner, 2017). However, after August 11, 2015 the Chinese government was actively supporting the value of the Chinese yuan in an effort to keep its value from plummeting; hurting Chinese exports and helping US exports; contrary to Donald Trump's accusations.
This paper provides estimates of the change in China's exports and the change in US exports due to a one dollar increase in China's foreign reserves. The analysis is then repeated using net exports (exports minus imports). I find that in August 2000 China's accumulation of 621 million dollars of foreign reserves is correlated with China's exports increasing by 151 million and the US's exports falling by 628 million dollars. In contrast, in November 2016, China spending 69 billion dollars of its foreign reserves supporting the value of the yuan is correlated with China's exports falling by 4.77 billion and the US's exports rising by 2.42 billion. The remainder of the paper is structured as follows. Section II of the paper explains what happened on August 11, 2015 and why China shifted from accumulating to spending foreign reserves. Section III discusses data, modelling, and estimation issues. Section IV presents the empirical results, and Section V concludes.

What Happened on August 11, 2015 and Its Aftermath
On May 26, 2015 the International Monetary Fund (IMF) publically announced that China's yuan was finally at fair market value. On that day, 6.2075 yuan traded for one US dollar. The IMF also strongly encouraged China to, as quickly as possible, eliminate its exchange rate controls (Magnier and Kazer, 2015). Prior to August 11, 2015 the People's Bank of China (PBOC) collected yuan per dollar quotes from entities involved in the foreign exchange market, like state owned banks. The PBOC then set a "central parity" or "midpoint" exchange rate based on the average of these quotes. If the actual exchange rate rose more than two percent over the set midpoint, the PBOC would sell yuan in order to bring its price down, accumulating foreign currencies (reserves) in the process. If the actual exchange rate fell more than two percent under the midpoint, the PBOC would buy yuan to increase its value, spending foreign reserves in the process.
In theory, it is the market makers, not the central bank, which set the midpoint and thus the trading band. In practice, the PBOC gets market makers to submit rates that will yield its preferred midpoint, irrespective of market sentiment (state-owned banks are pliant, after all) ("Battle of Midpoint," 2015).
China changed this process on August 11, 2015; however, before that date, China's stock market plummeted.
In 2014 the Shanghai stock market gained 37 percent, making it the world's best performing major stock market (Hong, 2014). By June 14, 2015, the Shanghai Stock Index was up 136 percent for the past 12 months and 51 percent since the beginning of the year. The Shanghai stock market started falling on June 15, 2015. By July 9, 2015 the Shanghai Index had lost 32 percent of its value or US$ 3.5 trillion of value (Hong and Wei, 2015). By the end of July 2015, US$ 5 trillion of value had been erased from China's stock market (Wei, 2015). No longer trusting the Shanghai stock exchange, many Chinese wanted to move their assets abroad to safer locations, like the USA, which put downward pressure on the yuan.
On August 11, 2015, the People's Bank of China (PBOC) informed the public that it was changing how the yuan's central parity rate would be set; specifically, the new procedure would set each day's central parity rate as the previous day's closing value. The PBOC continued to use this new procedure for the remainder of 2015 (Trivedi, 2016). If one views exchange rates as lying along a continuum between fixed and flexible, this new procedure was an extremely important move towards a more flexible exchange rate. However, "the new market-determined midpoint immediately fell by 1.9%, the biggest single-day drop in the yuan's modern history." The next day, August 12, 2015, the yuan fell again. On that day the PBOC ordered China's state owned banks to use their dollar reserves to purchase yuan, in order to keep the yuan from falling further ("Battle of Midpoint," 2015).
The Economist ("Battle of Midpoint," 2015) believes that China adopted the new central parity method because China wanted the International Monetary Fund (IMF) to include the yuan in the IMF's listing of global reserve currencies (a status China received in November 2015) and the week before the IMF had "hinted" that the yuan continued to be too heavily controlled to be included. The Economist also believes that China spent foreign reserves to keep the yuan from falling further because China was scared that a sustained devaluation would lead to a surge of capital flight out of China causing a vicious downward spiral in the value of the yuan ("Battle of Midpoint," 2015), a downward spiral similar to the June -July, 2015 collapse of China's stock market (Wei and Trivedi, 2015).
Indeed, the international currency market did bet that China would devalue the yuan even more as evidenced by the cost of insuring (hedging) against an additional fall in the value of the yuan surging "from US$ 30,500 per US$ 100 million to US$ 1.7 million" in August 2015 (Cui, Trivedi, and Albanese, 2015). Furthermore, between August 11, 2015 and January 2017, the Chinese government imposed progressively stronger capital controls in an effort to arrest the fall in the value of the yuan. These measures included (1) reducing the amount of yuan available to Hong Kong banks and driving up the interest cost of accessing yuan in Hong Kong, (2) obstructing the repatriation of earnings by foreign companies located in China by making repatriation documentation onerous, (3) banning overseas investments made in yuan, and (4) "imposing reserve requirements on Hong Kong-based yuan deposits parked by offshore banks at the Bank of China Ltd. Unit" which decreased the amount of yuan available to Hong Kong banks by an amount equal to US$ 23 billion (Wei, January 28, 2016). Moreover, Xi's government imposed limits on the amount of money that can be withdrawn from foreign ATMs by Chinese travelling abroad. Xi's government also sternly warned banks to check all foreign currency transactions to make sure that they are legitimate and justifiable. Bank leaders were told that, if foreign currency transactions increased, they would be called before the government where they would have to give a thorough accounting. The banks understood that the government wanted the amount of foreign currency transactions to significantly decline. China's banks responded by delaying the process of supplying foreign currency via asking for additional documentation many times (Wei Gu and Yap, 2016). Multinational companies used to be able to "'sweep' US$ 50 million worth of yuan or dollars in or out of China with minimal documentation. Now the cap is the equivalent of US$ 5 million" (Areddy and Wei, 2016). China has also started to limit foreign purchases by Chinese companies (Wei, November 26-27, 2016).
Chinese state banks purchasing yuan on January 8, 2016 (a Friday) and on January 11, 2016 (the next Monday) "sent the cost of borrowing yuan overnight soaring past 66% by Tuesday [Monday had set a new record at 13.4%], making it prohibitively expensive for investors to finance bets against the currency, known as a short sale" (Trivedi and Law, 2016). "Yuan short sellers would borrow yuan, sell it for dollars, and then when the yuan fell in value, trade in the dollars for yuan to pay off the debt while pocketing the difference in the original and final exchange rates" (Leightner, 2017). China's state banks purchasing yuan increased the demand for the yuan which drove up the value of the yuan by 1.8% causing short sellers to lose money (Trivedi and Law, 2016). On September 12, 2016, the PBOC again drove up the cost of borrowing yuan to 5.52 percent (Vaishampayan and Wei, 2016). At one point in January 2017, the overnight interest rate to borrow yuan offshore "reached an incredible 105%" (Mauldin, 2017). The PBOC wants the yuan to be unpredictable because that discourages speculation (Wei, January 15, 2016).
Contrary to Trump's allegations, China is now supporting, not suppressing, the value of the yuan. Although Trump might point to the fact that the yuan has fallen in value by more than ten percent against the US dollar and by seven percent against a basket of China's major trading partners' currencies since August 2015 (Wei, 2017), the litmus test for an undervalued currency is an increase in foreign reserves (not a fall in the exchange rate), and China's foreign reserves fell (the opposite of rising) by almost 547 billion US dollars between August 2015 and December 2016 and by almost a trillion US dollars between June 2014 and December 2016. If China had not been buying up surplus yuan since August 12, 2015 and if China had not imposed strong controls limiting the movement of capital out of China, then the Chinese yuan would have fallen in value even more than it did.

Data, Modelling, and Estimation Issues
Data on China's foreign reserves was downloaded from China's State Administration of Foreign Exchange Website, and data on China exports and imports was obtained from China's National Bureau of Statistics website. Data on US exports and imports was downloaded from the US Census website. All data series are monthly, starting in December 1999 and ending in November 2016. China publishes the total dollar amount of its foreign reserves, but does not tell what percent of those reserves are held in dollars, euros, yen, pounds, etc. Historically China has held more than half of its foreign reserves in US dollars. Given limitations to the data available, there is no good way to adjust this paper's analysis for changes in the allocation of China's foreign reserves between different currencies.
Currency speculators watch the foreign reserves of countries, and when those foreign reserves fall noticeably, speculators tend to expect the value of the currency to fall causing the speculators to sell the currency which puts downward pressure on its value, making the government's defense of the currency even harder. Expectations are self-fulfilling, and they can lead to a vicious downward spiral. Knowing this, some governments defend the value of their currencies in ways that do not immediately cause a depletion of foreign reserves. For example, some governments defend by purchasing forward contracts which do not immediately affect official foreign reserves. When China's foreign reserves fell less in September 2015 than in August 2015, J.P. Morgan suspected that the People's Bank of China was "window-dressing" its foreign reserves by using forward interventions to hide actual declines (Cui, 2015). The International Monetary Fund (IMF) became suspicious that China was taking similar actions in February 2016 (Wei, March 22, 2016). Likewise, when China's foreign reserves increased by US$ 10.26 billion in March 2016, some currency experts wondered if China was including yuan-denominated assets (which were outside of China) in its "foreign" reserves so that China could report a rise in foreign reserves instead of the fall that was expected. Supposedly evidence backing that concern exists (Chang, 2016). Since there is no good way to adjust China's official foreign reserves for these types of "window-dressings," this paper accepts the Chinese data at face value.
To use traditional regression procedures to estimate the change in the US's exports due to a one dollar increase in China's holdings of foreign reserves, d(US exports)/d(China's reserves), a researcher would have to build a structural model that included all the forces that affect the supply and demand for the US dollar, the Chinese yuan, and US exports. Such a model would need to explain foreign investment, remittances, tourism, interest rates, expectations, etc. for both the US and China. Furthermore, such a model would have to include everything that affects comparative advantage including resource endowments, technology, and input prices. Similar models would have to be built to estimate d (US net exports)/d(China's reserves), d(China's exports)/d(China's reserves), and d(China's net exports)/d(China's reserves). Once the model was constructed and all the equations estimated, it could then be solved for the relationships listed above, which are "total derivatives." The construction and estimation of such models that accurately include every possible force that could affect exports, imports and foreign reserves are impossible.
Fortunately there are ways to estimate total derivatives that capture the effects of omitted variables without having to construct and estimate entire structural models (Inoue, Lafaye de Micheaux, and Leightner, 2016). If a researcher estimates equation (1) while ignoring equation (2), the resulting estimate of β 1 is a constant when in truth β 1 varies with q t . (Note 2) This constitutes an "omitted variable" problem where "q t " represents the combined influence of all omitted variables plus any random variation in β 1 itself. Y t = α 0 + β 1 X t + u (1) β 1 = α 1 + α 2 q t (2) One convenient way to model the omitted variable problem is to combine equations (1) and (2) to produce equation (3).
(dY t /dX t ) True = α 1 + α 2 q t Derivative of (3) Y t /X t = α 0 /X t + α 1 + α 2 q t + u t /X t (3) divided by X (5) α 1 + α 2 q t = Y t /X t -α 0 /X tu t /X t (5) rearranged (6) (dY t /dX t ) True = Y t /X t -α 0 /X tu t /X t From (4) and (6) If an estimate for α 0 could be found, then it could be used to calculate a separate slope estimate for each observation using equation (8). The error due to such a procedure is shown in equation (9). The u t /X t term in equation (9) should be extremely small because random error, u t , is usually tiny relative to the size of X t , making u t /X t even smaller. This implies that the accuracy of calculating a separate slope estimate for each observation using equation (8) depends primarily upon the accuracy of the α 0 estimate.
(dY t /dX t )^ = Y t /X t -α 0^ /X t (8) (dY t /dX t ) True -(dY t /dX t )^ = (α 0^ -α 0 )/X tu t /X t From (7) and (8) Inoue et. al. (2016) explore three ways to obtain an estimate for α 0 : they are (I) using Ordinary Least Squares to estimate equation (1), (II) using Generalized Least Squares to estimate equation (1), and (III) using Bi-Directional Reiterative Truncated Projected Least Squares (BD-RTPLS) which produces separate slope estimates for layers of the data by peeling the data down layer by layer and then peeling the data up after which equation (10) is used with the resulting layer slopes to estimate α 0 . Leightner and Inoue (2012b) explain the math that underlies BD-RTPLS.
(dY t /dX t )^ -Y t /X t = -α 0^ /X t (8) rearranged (10) Inoue et. al. (2016) show that when the omitted variable problem is ignored by estimating equation (1) using OLS, the resulting estimate for β 1 is approximately α 1 + α 2 E [q t ] which leaves an "error" for the t = i th observation of approximately α 2 X i (q i -E[q i ]) + u i . The three methods of correcting for the omitted variables problem explored by Inoue et. al. (2016) would be better than ignoring the omitted variables problem if | (α 0^ -α 0 )/X iu i /X i | is less than |α 2 {q i -E[q i ]}|. Aitken (1935) implies that the GLS estimate of α 0 will be the Best Linear Unbiased Estimate (BLUE) if the q i s are i.i.d. N (μ q , σ q 2 ) because GLS is BLUE for heteroscedastic models and Inoue et.al (2016) show that equation (3) is a heteroscedastic model if q i is unknown. Inoue et.al (2016) test the three methods using simulations (Leightner 2015 and Leightner and Inoue 2012b also provide simulation tests but solely for BD-RTPLS). Inoue et.al. (2016) run sets of 5,000 simulations each for the 27 combinations of 100 observations, 250 observations, and 500 observations with the omitted variable making a 1,000 percent difference to the slope, a 100 percent difference to the slope, and a 10 percent difference to the slope, and with random error being zero, one percent, and ten percent. Inoue et.al. (2016) gives the name "Variable Slope Ordinary Least Squares" (VSOLS) to the process of using OLS to estimate α 0 which is then plugged into equation (8) to generate a separate slope estimate for each observation and the name "Variable Slope Generalized Least Squares" (VSGLS) to the process using GLS to estimate α 0 which is then plugged into equation (8).
VSGLS and BD-RTPLS noticeably out performed VSOLS in all simulations. VSGLS and BD-RTPLS out performed using OLS while ignoring the omitted variables problem except for the case where the omitted variable makes only a ten percent difference to the slope and random error is ten percent. When the importance of the omitted variable was 100 times as big as random error, using OLS while ignoring omitted variables produced approximately 35 times the error of both VSGLS and BD-RTPLS. When the importance of the omitted variable was 10 times as big as random error, then using OLS while ignoring omitted variables produced approximately 3.8 times the error of both VSGLS and BD-RTPLS. When there was no random error, then BD-RTPLS produced less than half the error of VSGLS. (Note 3) This last result implies that, since VSGLS is BLUE, BD-RTPLS must be better than BLUE when there is no random error which is reasonable if BD-RTPLS is better at capturing non-linear aspects of the data. Published applications of BD-RTPLS include Leightner, 2015, 2013, 2011a, 2011b, 2010a, 2010b, 2008, 2007, 2005a, 2005b, 2002and Leightner and Inoue, 2012a, 2012b, 2009, 2008a, 2008b. The next section of the paper uses BD-RTPLS to estimate the effects of China's holdings of foreign reserves on China's exports, China's net exports, US exports, and US net exports.   Table 1 lists only the empirical results for April 2014 through November 2016. Each estimate was based on 203 observations. Ninety nine percent confidence intervals were constructed by using the Central Limit Theorem (Equation 11) applied to a given estimate and the two estimates before and the two estimates after it where "s" is the standard deviation, "n" is the number of observations and "t" is from the Student's T Table. This confidence interval implies that a researcher can be 99 percent confident that the next estimate will lie in that interval if omitted variables fluctuate no more than they recently have fluctuated (Leightner 2015).

The Empirical Results
confidence interval = mean + (s/√n)t n-1, α/2 To sustain an export driven growth model via the suppression of the exchange rate by accumulating foreign reserves, a country can never spend the foreign reserves it accumulates (Leightner, 2015). If it ever spends its accumulated foreign reserves, then its exchange rate will rise, ceteris paribus, causing its exports to fall.

Conclusion
The economist's mantra that free trade benefits both traders does not imply that it is helpful to attempt to augment those gains by adding market distortions, like suppressing exchange rates via the accumulation of foreign reserves. The empirical results of this paper show that China's export driven growth model of 1985 to 2007 via the perpetual accumulation of more and more foreign reserves was not worth the costin dollar terms China gained much less in exports and net exports than the foreign reserves that they were accumulating and could never spend under that model. Furthermore, while accumulating foreign reserves, China did hurt US exports and net exports.


Vacancy in shrinking downtowns: a comparative study of Québec, Ontario, and New England

In North America and around the globe, there has been emerging recognition of the size and scope of urban shrinkage, yet little is understood about how decline impacts commercial centers and downtowns. In order to facilitate geographically targeted policymaking, this paper examines the physical patterns of downtown decline in three distinct regions. We seek to test the hypothesis that differences in the process of urban decline in downtown districts vary due to national or historic context. Using statistical analysis and direct observations, we found that while the scale of population decline was greatest in New England, downtowns in both Ontario and Québec have seen substantial decline and have appeared to have better weathered the change with respect to physical signs of decline.

Introduction
Urban decline, defined as the process of population shrinkage and property abandonment, has become an increasingly pressing issue as policymakers seek to avoid the misapplication of critical tax revenues and address broader community stabilization goals (Davey 2013). This has not gone unrecognized by the academic community, as illustrated by the formation of the Shrinking Cities International Research Network (SCIRN) (Wiechmann 2008). Evidence emerging from this-and earlier-academic inquiry suggests that building abandonment is an indicator of contemporary economic decline as well as a harbinger of future decline (Brady 1983;Accordino and Johnson 2000;Runfola and Hankins 2010;Dewar and Thomas 2013;Pallagst et al. 2014). While earlier research has contributed much to understanding the phenomenon of urban decline, less is understood about how decline impacts commercial centers and downtowns. In order to facilitate geographically targeted policymaking, this paper employs a combination of quantitative research methodologies to examine overall patterns of decline and downtown change in Québec and Ontario, Canada, and New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont), USA. Comparing US and Canadian regions is an especially fruitful line of inquiry. The USA and Canada share many economic, political, and cultural traditions, but they differ as well. The neighboring regions of Québec, Ontario, and New England are major economic and trading partners, sharing liberal democratic governance frameworks, yet urban policy is much more aggressive in Canada (Haddow 2015). As a result, shrinking cities have substantially more vacancy and abandonment in US cities than Canadian (Hackworth 2016). With global attention to the resurgence of downtowns, key questions remain about how these shrinkage dynamics play out in downtowns, where poverty has tended to colocate in US cities (Silverman et al. 2016). For this paper, we seek to test the hypothesis that differences in the process of urban decline in downtown districts vary due to national or historic context. 2 Literature review 2.1 The phenomenon of shrinking cities and neighborhood change Decline or shrinkage in the urban planning literature has often been used to describe population loss, employment loss, or decrease in neighborhood quality (Bradbury et al. 1982;Berg et al. 1982;Clark 1989;Rusk 1995). In the USA, dozens of cities have succumbed to massive depopulation in recent years (Hartt 2017b). According to the US Census, the City of Detroit lost 25% of its population from 2000 to 2010. But this trend is no longer confined to post-industrial Rust Belt cities: Some of the most prominent population losses in this recent period can be found in Sun Belt locales that exploded in population in the 1990s and early 2000s, including cities such as Las Vegas, Atlanta, Modesto, and Phoenix (Hollander 2011). In Canada, one of the fastest growing OECD countries, growth is predominantly limited to the major cities. Between 2006 and 2011, 46% of cities grew slower than the national average and 19% of cities lost population. No single rationale or explanation exists as to why these and other cities have declined. Shrinkage has been explained by everything from natural disasters (Vale and Campanella 2005), deindustrialization (Bluestone and Harrison 1982), suburbanization (Jackson 1985;Clark 1989), globalization (Sassen 1991;Hall 1997), and the boom-and-bust economic cycles (Rust 1975). Many shrinking cities scholars have taken a more holistic view, positing that urban growth and decline are a global multifaceted phenomenon, deeply related to broader metropolitan and global processes that include demographic change, economic restructuring, deindustrialization, and suburbanization (Oswalt andRieniets 2006, Audirac et al. 2012;Martinez-Fernandez et al. 2012a, b;Wiechmann and Pallagst 2012;Haase et al. 2013). Although shrinking cities research has grown considerably in the past decade, the current literature lacks robust empirical cross-national studies comparing the processes of how places change and exploring the role of different institutional and regulatory frameworks.

The landscape of shrinking cities
When individual people or groups leave an urban region, the physical form of the city does not naturally shrink-abandoned properties and infrastructure remain until it is either occupied by new tenants or maintained by a new group (i.e., see Runfola and Hankins 2010). This can have a wide variety of frequently negative feedback effects on local economies and future abandonment. Glaeser and Gyourko (2005) studied the durability of housing in their time-series sample of 321 US cities and towns with at least 30,000 residents in 1970, showing how housing prices declined at a faster rate in depopulating cities than prices grew in growing cities. Additional research has shown the role abandoned structures serve as havens for criminal activity, further deflating economic and social incentives for individuals to stay in (or move in) to an area (Wallace 1989;Wilson and Kelling, 1982).
Older cities also tend to have older buildings, with central, historic zones like downtowns tending to have some of the oldest building stock in a city (Silverman et al. 2016). Due to the advanced age of this downtown building stock, historic preservation costs may prohibit retrofits or updates allowing slum landlords to concentrate in these areas (Burchell and Listokin 1981).
Amplifying the challenges posed by urban decline, poorer economic conditions can result in lower demand for housing and, subsequently, a lower economic class of owners or renters (Hoyt 1933;Temkin and Rohe 1996). Ultimately, when demand sinks to a certain threshold level, owners tend to abandon their structures-resulting in even poorer economic conditions (Keenan et al. 1999). This process further reduces the tax base available to local municipal systems, potentially increasing crime rates and reducing funding for municipal activities such as park and road maintenance (Dewar and Thomas 2013). Rust's (1975) study of population and employment decline in 30 US metropolitan areas from the 1800s to the 1970s revealed much about how places decline. He found that these shrinking places experienced dramatic population loss and then ''a long period of profound resistance to demographic or economic change which continues until the people, artifacts, and institutions which were assembled in the truncated growth era gradually erode away'' (p. 169). The very physical fabric of neighborhoods-these artifacts-is expected to ''erode away'' in a period of decline. For many of the cases Rust studied, once the decline began, the effects are ''expected to be felt most strongly a generation after the cessation of growth and to persist for up to 50 years' ' (p. 187). For the booming cities of today, these results present a clarion call for a new way to do planning; a new model is needed to think about neighborhood change in the face of ongoing population and housing loss (Schilling and Logan 2008;Hollander et al. 2009).  (Garber and Imbroscio 1996;Reese 2006;Cullingworth 2015). In general, Canadian cities tend to be denser (Filion et al. 2004) and have more centrally focused employment markets (Garber and Imbroscio 1996;Filion et al. 2004;Reese and Rosenfeld 2004). Furthermore, government actors tend to play a more significant role in the real estate development process than their US counterparts (Garber and Imbroscio 1996). Reese (2006) found that Canadian and American cities employ similar economic development strategies and policies in response to global economic trends and associated local fiscal challenges. Although there are differences in governance and federal arrangements for government structure between the two nations, ''the organization of economic development (departmental structures and decision making processes) is essentially the same, economic stress is viewed in a similar fashion, and local officials define economic development goals in similar ways. And the same policies predominate'' (Reese 2006, p. 556).
Though Reese (2006) does concede that planning happens differently in the two countries: Planning is more accepted and more integral to policy in Canada. Perhaps the most fundamental difference is the strong American tradition of private property rights. The protection of these rights, dating from the foundation of the American nation in the 18th century, provides a significant check on the authority of government in general and planners in particular. In Canada the development of statutory and judicial protections of property rights came much later. As a result, Canadian planners enjoy greater latitude in pursuing the public good than do US planners (p. 556).
Regarding depopulation, research has established parallel patterns of depopulation in both the USA and Canada (Hall and Hall 2008;Warkentin 2012). The key difference, to some scholars, is the slow rate of decline in Canadian cities, in contrast to the more dramatic and faster decline more common in US cities (Hartt and Warkentin 2017;Warkentin 2012;Seasons 2007). Additionally, the tendency for Canadian cities to amalgamate with surrounding towns and suburbs has resulted in less population loss due to suburbanization. Nevertheless, research has explored depopulation in the Canadian context, as well as specific approaches to address the associated challenges (Hartt 2017a;Martinez-Fernandez et al. 2012a, b).
Turning to downtowns, research into retail business patterns has shown a discernable measure of sustained shrinkage in many Canadian Central Business Districts (CBDs). An examination of retail patterns in shrinking Canadian cities by the Center for the Study of Commercial Activity (CSCA) at Ryerson University found that ''Growing cities have more stores in downtowns…'' than declining cities (Simmons 2003, p. 6). The report went on to find that ''while the commercial structure of a city is formed over a long period of time, and is unlikely to respond to population decline in the last five years, these results confirm the patterns observed when growth differences over 25 years were observed…'' (p. 6).
Just as research in the USA has shown the impact of depopulation, Canadian research has confirmed that ''the loss of population by a city may signal more than a simple decline in market size. The loss of population becomes a signal that both leads to disinvestment and discourages new investment and enterprises.'' (p. 6). The cascade effect is the loss of even further commercial activity in downtowns, the focus of the next section.

Literature on downtowns
Prior to discussing our research on downtowns, a brief introduction to the concept is necessary. Traditionally, downtowns have been the ''regional hubs of commerce, culture, and shopping'' (Morcol et al. 2008, p. 140). Defined more formally as a Central Business District (CBD), this zone is a place where public and private investment efforts are concentrated. The CBD is often used as a proxy to measure the overall health of a city's broader real estate market. For example, several US Presidential Executive Orders have directed federal agencies to locate their major offices and courthouses in central business districts of cities (E.O. 12072 and E.O. 13006).
A lot of recent attention has been paid to what is described as a downtown renaissance in both growing and shrinking cities (Glaeser 2011). While many cities have been overwhelmed by millennials, new construction, and jobs, much more have been left behind. More common is a broader shift in technological, political, and economic forces that has pushed the center physically further and further outside these downtowns into regional shopping centers and suburban office parks (Lang 2003). This is a major challenge for a growing region as it sprawls far from its historic center, straining fiscal, transportation, and other infrastructure systems (Cullingworth 2015). The strain of sprawl presents unique, and arguably more onerous, challenges in a contemporary context of shrinking cities and regions. Blakely and Leigh's (2013) research found that during economic booms, downtowns benefit from an influx of residents and investments, spurning gentrification; but what about during downtimes? They, and others, have written about the role of government around the ''provision of water and sewerage lines, street lighting, access roads, and sidewalks'' (Blakely and Leigh 2013, p. 239). Overlay zoning allows developers to ''place facilities where demand exists rather than where planners think they ought to be'' (Blakely and Leigh 2013, p. 241). But it is Business Improvement Districts (BIDs), Tax Increment Financing, and public-private partnerships that have received the most attention as solutions to the problems of the shrinking downtown (Mallet 1994;Mitchell 2001;Mitchell 2008;Morcol et al. 2008). Blakely and Leigh (2013) conclude their research into reviving shrinking downtowns with this advice: Many small towns believe tourism is the antidote to the collapse of their town centers. Although tourist dollars are important, local trade is important as well. Tourism is both cyclical and fickle. Commercial centers must ultimately survive on local traffic…One of the best ways to make the downtown area attractive is to embark on programs that give it character or restore some character to it. (p. 241) What is missing in this literature is any substantial examination of ways that shrinking downtowns can adjust to a smaller business footprint, to fewer jobs, fewer businesses. A comparative view is especially useful here, because US downtowns have had a mixed history of efficacy in managing vacancy, while Canadian downtowns have been viewed as more successful: For Canadians, downtowns have traditionally been regarded as safe, healthy, and vibrant places, especially when compared to their U.S. counterparts. However, multiple pressures from the suburbanization of population and commercial structure Vacancy in shrinking downtowns: a comparative study of Québec… 595 (facilitated by car-based transport infrastructure expansion), the general aging of the downtown, and the demise of the downtown department stores have resulted in the signs of decline in many of Canada's downtowns (Morcol et al. 2008, p. 402).

Mapping and measuring urban decline
Bowman and Pagano (2004) conducted an exhaustive study on vacant land seeking to understand the extent of the vacancy problem in the USA. They administered written surveys to local officials and assembled a database of abandoned buildings and vacant lot counts across more than 100 cities in the USA. Unfortunately, this survey-based method has been shown to be unreliable when crosschecked against housing unit counts from the Decennial Census. Local officials use very different strategies from one another to account for vacancy and abandonment, making the use of locally distinct administrative data sources a suboptimal approach for making national generalizations (Bowman and Pagano 2000). Hillier et al. (2003) examined Philadelphia's housing databases to track vacancy and abandonment data, but their systems are not interoperable and study domain is of a limited scope, making broad-scope comparative analysis impossible. Wilson and Margulis (1994) developed a similarly localized analysis in Cleveland. Runfola and Hankins (2010) conducted field work in Atlanta to identify on-the-ground numbers of abandoned and derelict housing, but constructed information for only a limited subset of census block groups in the Atlanta region at a high time cost, making this approach infeasible for broad-scope analyses. Many remote-sensing and GIS-based studies have worked toward measuring urban population change, including Weber and Puissant (2003), Xiao et al. (2006), and Yang and Lo (2002). Ryznar and Wagner (2001) attempted to use remote-sensing to study the effects of population decline. However, they were only able measure net changes in forested and agricultural land, and had to extrapolate their findings to examine housing and commercial land use changes. Banzhaf et al. (2007) explored shrinkage in Leipzig, Germany, but found that the necessary data to validate their findings was lacking.
In order to overcome these issues, recent work has utilized housing occupancy counts produced by the United States Postal Service (USPS), a novel database only recently made available to interested researchers (for a nominal fee). Though changes in housing occupancy can happen for a variety of reasons, these data correlated strongly with segments of the US Sunbelt that experienced widespread vacancy and abandonment during the Great Recession in the USA. Building on this system, the Canadian Census has recently made occupancy information available at census tract level, extrapolated based on broad-scope decennial surveys. Both of these approaches allow for the systematic collection and analysis of occupancy data at fine temporal resolutions 1 and fill a gap in the literature to better understand the process of housing abandonment.
A bigger challenge is understanding abandonment in commercial centers and downtowns. USPS data are also available for commercial properties, but businesses do not constitute themselves in the same way that residential households do: there are minimum square foot requirements for domiciles, where a business can occupy an address with only a mailbox. Beyond government sources, real estate scholars have developed their own proprietary databases for measuring and studying commercial vacancy. The Centre for the Study of Commercial Activity at Ryerson University in Toronto regularly produces reports on the state of the commercial real estate industry in Canada. Hernandez et al. (2009) concluded that vacancy rates rose slightly in Greater Toronto between 2005 and 2009, based on data from shopping centers, power centers, and retail strips-1078 total retail venues. However, the data do not include the less formal commercial buildings typically found in historic centers and downtowns. In short, there is a lack of reliable, consistent data on the occupancy of downtown real estate in Canada and the USA. This deficiency of good data has limited our understanding of abandonment in commercial centers and downtowns.

Conceptual framework
The aforementioned literature on shrinking cities and downtowns helps us consider a broader conceptual framework that can be applied in testing the following hypothesis: Differences in the process of urban decline in downtown districts vary due to national or historic context. While certainly the myriad of forces at work in cities and regions play a role in shaping what happens in a downtown, the scholarly literature cited above tells a story which we present in Fig. 1 below. Whatever broader economic, political, or social conditions might be at work, we begin at the top of the diagram with the notion that these forces will generate population and economic decline for some places (e.g., in a booming economy, there will be fewer of such declining places than in a weakening economy). But this paper seeks to isolate the direct and indirect impacts of that decline, rather than interrogating precisely why or how it is occurring. What follows such decline will most always be a concomitant decrease in demand for downtown real estate. Given that a downtown functions as a center for a city, in the face of larger decline, the number of firms, organizations, investors, and individuals seeking real estate in the downtown is expected to fall. The consequence of such a drop in demand for downtown real estate is a decrease in the occupancy rates of downtown buildings. With lower rents (and lower profits), building conditions are also expected to degrade due to decreased investment in maintenance, protection, and care.
Unfortunately, the story does not end there due to a well-documented trend of how vacancy tends to spread (explored in the literature above). Figure 1 indicates how the economic and demographic forces push down from the top of the diagram and forces of vacancy and abandonment impact downtown building occupancy and conditions from below. Figure 1 depicts a potentially devastating spiral effect of decline, vacancy and abandonment, poor conditions, and depressed occupancy, contributing to a spreading effect of worsened conditions and occupancy. In this research, we seek to draw on this conceptual framework to examine how this process unfolds across two sister regions, noting the ways in which national planning systems might generate different outcomes.

Data
The empirical analyses run for this study focus on data sets that indicate change in downtown building stock. Population change is frequently used as a proxy to measure urban decline, though we find it to be an imperfect measure-and one that could in fact confound our analysis. Rather, this study looks to identify prevalence of increasing vacant commercial and residential spaces and buildings.
This study offers a methodology for cross-national comparison of downtown real estate trends over the last decade, adding to the gaps in the literature around the need to study, empirically, the processes of urban decline through a comparative lens. The choice of New England, Québec, and Ontario was made because they act as sister regions across an international border, with many common and many unique economic, political, and social frameworks and institutions, and their histories of managing vacancy and depopulation in their downtowns are regarded as quite different.
For New England, housing occupancy data aggregated and disseminated at the postal code level by USPS was utilized in conjunction with US Census demographic, housing, and employment data. For Québec and Ontario, all of the data are obtained from Canadian quinquennial Census collections from 2006 to 2011 at the census tract scale. Older data have been widely seen as incongruent with the 2006 data and no more recent data are available publicly. Likewise, only population data were publicly available at the municipality scale, making comparisons with the New England cities difficult.
To begin, we examined some basic demographic and housing characteristics of all New England, Ontario and Québec cities. Next, in order to more clearly understand the phenomenon of downtown shrinkage, we sought to develop a method to identify CBDs. From our list of all New England cities with a population over 20,000, we began to search online for zoning maps or Chamber of Commerce diagrams to indicate the boundaries of CBDs. We found 41 cities with delineated CBDs, and then were able to roughly map out the corresponding census tracts in each city (in most cities it was just one, but in some cases it was two tracts). To identify CBDs in Ontario and Québec, we used a combination of Google maps and Streetview and interviews with local officials to delineate boundaries.
Based on the theory that CBDs tend to be predominantly business oriented, we returned to the full list of New England cities and selected all census tracts within those cities. Next, we calculated the ratio of total businesses to total residences for the 16,000 census tracts that fulfill this designation. Among those 41 New England cities where we acquired precise CBD boundaries, the CBD census tracts ranked in the top 20% of the business-to-residence ratio, thus confirming the validity of this approach.

Selection of cities and case study methodology
Case study cities were chosen from the results of the above analysis using a two-step process. First, cities with clear signs of population decline and downtown vacancy were identified. Of the remaining cities, selections were made with the aim of reaching a broad range of urban places, including differences in density, size, population levels, and economic conditions. Nine cities in total, three from New England, three from Ontario, and three from Québec, were sampled for the study.
New England is a diverse region, comprising six US states, Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut. Its largest city is Boston and today boasts a mix of industrial, technological, service, and extractive industries. Ontario's largest city is Toronto, which includes a substantial metropolitan area that dwarfs all other economic activity in the Ontario region, making Ontario that biggest contributor of all Canadian provinces toward GDP. Linguistically and culturally, Quebec is distinct from the rest of Canada and from New England, with its capital city of Montreal, and the region is a diverse mix of extractive, agricultural, high-tech, and industrial (Haddow 2015).
We conducted direct observation of the conditions of all buildings within the CBDs of the nine case study cities (Gaber and Gaber 2007). We developed a direct observation tool to record our observations. The unit of analysis we employed was the address. (So even if there are multiple units in a residential building or many different offices in a commercial building, we only looked at ''4 Main Street'' as one unit, for example.) The observation tool consists of three parts. First, identifying the ''occupancy status''-a categorical variable ranging from 0 to 4 where 1 is fully occupied; 2 is partially occupied with ''for rent'' signs; 3 is unoccupied; 4 is illegally occupied with evidence of squatting; and 0 is for a location experiencing renovations/construction. Second, identifying the ''structure condition''-again a categorical variable ranging from 0 to 4 where (1) is an empty lot where there is no structure or it is demolished; (2) is poor condition with evidence of fairly extensive disrepair, peeling paint or broken windows; (3) is fair condition with evidence of minor disrepair but is generally okay; (4) is excellent, in good condition, with exterior surfaces intact; and 0 is for a location experiencing renovations/construction. Lastly, we also took general notes to characterize the quality of the exterior of the building, or if the interior is notable.
We purposefully included key physical traits in the category descriptions (such as peeling paint for '2' in the structure condition variable) to help minimize disparities in the data. However, the categorization of direct observations by multiple individuals inherently introduces some subjectivity and potential inconsistencies to the data collection process. Vacancy in shrinking downtowns: a comparative study of Québec… 599 To further limit direct observation variance, if any team member was unsure of how to score a given address during a visit, other members of the research team were solicited for their opinions either based upon photographs taken or based on Google maps street view. Likewise, if there were buildings that did not have clear addresses during the visit, we sought to determine this with online mapping sites (or just by typing a business name into a search engine). This not only avoided incorrect categorizations, the discussion and analysis helped build understanding and cohesion within the research team. Due to limitations in our travel budget, we did not physically visit Baie-Comeau, instead relying on interviews with local officials and conducting a Google Streetview tour of the CBD there.

Analysis
In order to analyze the direct observation data, we first mapped the spatial patterns of occupancy and condition. The data were geocoded using tabular data and Google's geocoding engine. The resulting KML file was imported to ArcGIS and joined with the complete qualitative findings. Inverse distance weighted interpolation was used with a fixed search radius of 0.001 to complete the dataset. Lastly, we adjusted the symbology of the results from the inverse distance weighted interpolation to create the heat map visualizations.
We also conducted spatial analysis of the direct observation data using GeoDa software to compliment the visual analysis of the mapped data. In order to capture whether any of the cities exhibited statistically significant clustered patterns of occupancy or condition, the spatial autocorrelation of the direct observation data was tested by calculating global Moran's I as an overall measure of the phenomenon and Local Indicator of Spatial Association (LISA) to gauge the local effects of spatial autocorrelation. Moran's I (Moran 1950) tests the null hypothesis that the variable being examined is randomly distributed. If a significant p value is returned, the null hypothesis is rejected meaning that either: (1) the spatial distribution of high and/or low points are more spatially clustered than would be expected (indicated by a positive z-score), or (2) the spatial distribution of high and low points are more spatially dispersed than would be expected (indicated by a negative z-score). Although Moran's I can tell us whether spatial autocorrelation occurs, it does not provide detailed evidence of where statistically significant clusters occur. LISA provides a more localized analysis by assessing the similarity of each observation with its surroundings. LISA analysis detects five potential scenarios: (1) hot spots (locations with high values and high similarity with surroundings), (2) cold spots (locations with low values and high similarity with surroundings), (3) spatial outliers (locations with high values and low similarity with surroundings, (4) spatial outliers (locations with low values and high similarity with surroundings), and (5) locations without statistically significant autocorrelations.
The concept of spatial autocorrelation relies on notion that contiguous spatial elements can influence one another. Generally, a dichotomic contiguity matrix is defined based on whether one spatial area touches the boundary of another. However, with point data, a threshold distance is defined to gauge whether two points are contiguous. As this analysis concentrates on downtown CBDs, we defined the threshold distance to be 243 m (800 feet)-roughly two average North American city blocks. And since the observation data was categorical, dummy variables were created to test the spatial autocorrelation of each of the occupancy and condition variables (0 through 4) individually, as well as select combinations.

Descriptions and analysis of demographic data
The US Census, combined with other US real estate and housing databases, allowed for a rich exploration of both historic trends and contemporary conditions in New England at a fairly fine-grained level. In contrast, the Canadian Census underwent a major transformation recently, making historical comparisons difficult and the limitations of publicly available data make the results quite coarse geographically.
For New England, we began with a broad picture of all 182 cities and towns with a population of 20,000 or higher in the 2010 US Decennial Census. Among those cities, their populations grew on average 17% between 1980 and 2010, and their number of occupied housing units grew by 31%. But growth was not a hallmark of all New England cities (see Table 1). During this same time period, 24 lost more than 2% of their population and six lost occupied housing units ( For Canada, we started by looking at all 72 cities in Ontario and Québec. However, due to data limitations, we only had access to data over a much shorter time period (the 2006 and 2011 censuses). Following the Canadian Census definitions, dwellings occupied by usual residents was used as a proxy for occupied housing units. The results show in Table 2, that like New England, many Ontario and Québec cities and towns are likewise experiencing depopulation. Among the 29 Québec cities, three lost more than 2% and six were roughly stable. Among the 43 Ontario cities, one lost more than 2% and 24 were stable. Given all of the municipal consolidation in both regions in recent years, it is quite remarkable that the Canadian Census would show any net population decline, let alone more than 2% in just 5 years.
Based on the results from our quantitative analysis, nine cities from the three regions were selected as individual case studies. Cities were selected in each region where sustained population, housing, and economic decline were present and they were, statistically speaking, representative of shrinking cities in their region across density, size, population  DOUR dwellings occupied by usual residents levels, and economic conditions. Additionally, in our selection, we sought some degree of geographic diversity. Bridgeport (CT), Springfield (MA), and Lewiston (ME) were selected from New England. Leamington, Windsor, and Chatham-Kent were selected from Ontario. And Shawinigan, Baie-Comeau, and Dolbeau-Mistassini were selected from Québec. Table 3 provides descriptive statistics for each of the case study cities.

Direct observation data
For each CBD we collected between 62 (Baie-Comeau) and 270 (Shawinigan) observations (see Appendix 1). While not the total universe of all addresses in each CBD, these observations accounted for most of the addresses in the most important, central business areas of each city. For the Ontario cities, Windsor was in the worst shape regarding occupancy, with 28% of all addresses showing either partial or complete vacancy. Leamington and Chatham-Kent had 15 and 20% partial or complete vacancy, respectively. Regarding condition, Windsor also did the worst, with five properties rated as poor, where neither Leamington nor Chatham-Kent registered any as poor.
These figures are significantly better than what emerged in the New England cities, where more than one-third of Springfield's addresses were either partially or fully vacant and Lewiston and Bridgeport also reporting more than one-quarter of such properties. Likewise, the New England cities have proportionally much lower quality building conditions in their downtowns, ranging from 3 to 4% getting a poor rating.
Turning to Québec, Shawinigan stands out as having 22% vacant addresses, but the much smaller Dolbeau-Mistassini and Baie-Comeau only showing 8 and 4% vacancy, respectively. The same trend held true for building conditions, where Shawinigan had eight properties rated as poor (3%) where the other two cities had none rated as poor.
To better understand these results and their spatial distribution, we mapped and analyzed these trends for all nine cities. The maps show where concentrations of vacancy  . 2) and poor conditions (Fig. 3) tend to exist, the intensity and strength of those patterns. A comparative look across these regions shows a discernably stronger scope and scale of vacancy in the New England and Ontario downtown, as compared to the Québec cities. Of particular interest is the tendency for spatial clustering in the New England cities, and to a lesser extent the Ontario cities. For both Bridgeport and Windsor, a clustering in the northern edge of the CBDs suggests a major source of disinvestment in that area, generating above average vacancy levels. This contrasts with the Shawinigan, which has comparable vacancy levels, but where there is no cluster or hot spot of vacancy. Examining the condition of the CBDs (Fig. 3), the New England and Ontario cities once again demonstrate the spatial polarization of their downtown built environment. Pockets of poor and excellent condition areas are clearly visible in the maps. In many of the cities (Bridgeport, Springfield, Lewiston), the spatial clustering of poor condition built environment closely matches the spatial clustering of vacancy. However, in Windsor and Chatham-Kent there are distinctly different patterns of condition and occupancy. In complete contrast, the three Québec cities show a relatively even distribution of poor condition real estate in the CBD.

Spatial analysis of direct observation data
The spatial autocorrelation of the direct observation data was first measured using global Moran's I. Results from the spatial analysis of the direct observation data echo the trends identified visually using the heat maps (Figs. 2, 3). Concentrating on trends of poor condition and low occupancy, we found that there was no statistically significant clustering of  The stronger scope and scale of vacancy in New England and Ontario inferred from the heat map analysis was confirmed as Bridgeport, Lewiston, Leamington, and Windsor all had statistically significant clustering patterns. In Lewiston, partially occupied buildings with for rent signs (occupancy variable 2) were clustered, whereas the other three cities had clusters of unoccupied buildings (occupancy variable 3). As global Moran's I only provides insight to the study area as a whole, the significant results in Bridgeport, Lewiston, Leamington, and Windsor encourage further local analysis.
Results from LISA (Fig. 4) depict the centers of ''hot'' and ''cold'' spots as well as spatial outliers (points that were not statistically significant are not included in the figures). As we saw in the occupancy heat map of Bridgeport, Fig. 4 demonstrates that clusters of low occupancy (red dots) in Bridgeport are all located at the northern edge of the CBD. In contrast, the lone statistically significant cluster of low occupancy in Lewiston is squarely in the middle of downtown. Like Bridgeport, clusters of low occupancy in Leamington are also at the edge of downtown; however, in this case they are to the south. Lastly, Windsor had no ''hot spots'' of low occupancy, only ''cold spots''-especially in the southern half of the downtown area (Fig. 4).

Discussion and conclusions
With measurable population decline and increases in vacancy across Ontario, Québec, and New England, what we sought to do in this research was to understand how that change manifested spatially in downtown areas. Drawing on a conceptual framework presented in  Fig. 1, we were able to track the forces of decline in both regions and pick up on evidence of a spreading effect in some cities, but ultimately discovered varying impacts of decline on occupancy and building conditions. We found that while the scale of population decline was certainly greatest in New England, both Ontario and Québec have seen substantial decline and have appeared to have better weathered the change. This may also be due, in part, to the slower pace of change in the Canadian provinces, relative to the New England cities. The Québec cities show less vacancy and less concentration of poor condition buildings. Baie-Comeau and Dolbeau-Mistassini, in particular, appeared to have managed depopulation and adjusted to a smaller population better than the New England cities. In Ontario, Windsor looked most similar to the New England cities, with high levels of vacancy and relatively poor building conditions, where the smaller Ontario cities looked more like their Québec counterparts. In general, the smaller cities faired better than large ones. (Baie-Comeau, Dolbeau-Mistassini, and Leamington all have populations under 50,000, while Windsor has 319,246 residents and Chatham-Kent has 104,075.) This could be the result of less relative activity and dependence on the downtown area due to a more rural economic base. The downtowns of these smaller cities simply may not play the same role as their bigger city equivalents and, therefore, have not experienced the same physical decline.
The research also raises key questions about the differences between residential and commercial land use in city centers. We focused here on commercial uses, but residential patterns may also play a role in occupancy and conditions of buildings. Future research should seek to further untangle the relationships between residential and commercial land uses in these shrinking downtowns. The spatial analysis revealed clusters of low occupancy in several of the cities. The direct observation data and resulting heat and LISA maps (Figs. 2, 3, and 4) show a distinctive pattern of hot spots of vacancy in the edges of the downtowns. Local officials can use this information to develop land use plans for those areas, considering market demand and the overall health of the local and regional economy. Specifically, these edge zones may be better suited for non-CBD uses, in order to direct and focus CBD uses (office, retail, housing, arts) into what could then be a smaller, denser CBD.
All three of the Québec cities examined had no statistically significant clusters of low occupancy. Cities, such as Shawinigan, with no identified vacancy hot spots demonstrate an opportunity to rethink the core of the CBD. Here, a new thinking around future uses can be valuable. While much of the shrinking cities literature has examined the programming of shrinking residential areas for non-residential uses, the shrinking downtown is harder to envision that way. In this research, we have found that many of the shrinking cities studied are surrounded by rich and economically vital agricultural regions. While urban agriculture has been widely seen as a prospective future use in residential areas of shrinking cities, CBDs would hardly seem to be compatible with agriculture. But with proper siting and considerations for preserving existing streetscapes, it might not be such a crazy idea.
We recommend downtown planners consider the emerging urban practice of hydroponic, indoor, (also known as vertical) farming. Using containers retrofitted to support hydroponic farms, planners can work with property owners to preserve the historic facades and exteriors of CBD buildings and then ''drop'' in these pre-built, pre-configured, and module hydroponic containers. Efforts to do this in relatively open areas of Boston have been met with much success, and we recommend exploration of this idea for the shrinking downtowns which have been the subject of this study (Abraham 2015;National Public Radio 2015).