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Foreign direct investment (FDI) plays an important role in facilitating the process of international technology diffusion. While FDI among industrialized countries primarily occurs via international mergers and acquisitions (M&As), investment headed to developing countries is more likely to be greenfield in nature; that is, it involves the establishment or expansion of new foreign affiliates by multinational firms. M&As have the potential to yield productivity improvements via changes in management and organization structure of target firms, whereas greenfield FDI leads to transfer of novel technical know-how by initiating the production of new products in host countries as well as by introducing improvements in existing production processes. Given the prominent role that multinational firms play in global research and development (R&D), there is much interest in whether and how technologies transferred by them to their foreign subsidiaries later diffuse more broadly in host economies, thereby potentially generating broad-based productivity gains. Empirical evidence shows that whereas spillovers from FDI to competing local firms are elusive, such is not the case for spillovers to local suppliers and other agents involved in vertical relationships with multinationals. Multinationals have substantially increased their investments in research facilities in various parts of the world and in R&D collaboration with local firms in developing countries, most notably China and India. Such international collaboration in R&D spearheaded by multinational firms has the potential to accelerate global productivity growth.

The potential for health information technology (HIT) to reshape the information-intensive healthcare industry has been recognized for decades. Nevertheless, the adoption and use of IT in healthcare has lagged behind other industries, motivating governments to take a role in supporting its use to achieve envisioned benefits. This dynamic has led to three major strands of research. Firstly, the relatively slow and uneven adoption of HIT, coupled with government programs intended to speed adoption, has raised the issue of who is adopting HIT, and the impact of public programs on rates of adoption and diffusion. Secondly, the realization of benefits from HIT appears to be occurring more slowly than its proponents had hoped, leading to an ongoing need to empirically measure the effect of its use on the quality and efficiency of healthcare as well as the contexts under which benefits are best realized. Thirdly, increases in the adoption and use of HIT have led to the potential for interoperable exchange of patient information and the dynamic use of that information to drive improvements in the healthcare delivery system; however, these applications require developing new approaches to overcoming barriers to collaboration between healthcare organizations and the HIT industry itself. Intertwined through each of these issues is the interaction between HIT as a tool for standardization and systemic change in the practice of healthcare, and healthcare professionals’ desire to preserve autonomy within the increasingly structured healthcare delivery system. Innovative approaches to improve the interactions between professionals, technology, and market forces are therefore necessary to capitalize on the promise of HIT and develop a continually learning health system.

New sanitation and health technology applied to treatments, procedures, and devices is constantly revolutionizing epidemiological patterns. Since the early 1900s it has been responsible for significant improvements in population health by turning once-deadly diseases into curable or preventable conditions, by expanding the existing cures to more patients and diseases, and by simplifying procedures for both medical and organizational practices. Notwithstanding the benefits of technological progress for the population health, the innovation process is also an important driver of health expenditure growth across all countries. The technological progress generates additional financial burden and expands the volume of services provided, which constitutes a concern from an economic point of view. Moreover, the evolution of technology costs and their impact on healthcare spending is difficult to predict due to the revolutionary nature of many innovations and their adoption. In this respect, the challenge for policymakers is to discourage overadoption of ineffective, unnecessary, and inappropriate technologies. This task has been long carried out through regulation, which according to standard economic theory is the only response to market failures and socially undesirable outcomes of healthcare markets left on their own. The potential welfare loss of a market failure must be confronted with the costs of regulatory activities. While health technology evolution delivers important value for patients and societies, it will continue to pose important challenges for already overextended public finances.

Science parks, also called research parks, technology parks, or technopolis infrastructures, have increased rapidly in number as many countries have adopted the approach of bringing research-based organizations together in a park. A science park’s cluster of research and technology-based organizations is often located on or near a university campus. The juxtaposition of ongoing research of both the university and the park tenants creates a two-way flow of knowledge; knowledge is transferred between the university and firms, and all parties develop knowledge more effectively because of their symbiotic relationship. Theory and evidence support the belief that the geographic proximity provided to the participating organizations by a science park creates a dynamic cluster that accelerates economic growth and international competitiveness through the innovation-enabling exchanges of knowledge and the transfer of technologies. The process of creating innovations is more efficient because of the agglomeration of research and technology-based firms on or near a university campus. The proximity of a park to multiple sources of knowledge provides greater opportunities for the creation and acquisition of knowledge, especially tacit knowledge, and the geographic proximity therefore reduces the search and acquisition costs for that knowledge. The clustering of multiple research and technology-based organizations within a park enables knowledge spillovers, and with greater productivity from research resources and lower costs, prices for new technologies can be lower, stimulating their use and regional development and growth. In addition to the clustering of the organizations within a park, the geographic proximity of universities affiliated with a park matters too. Evidence shows that a park’s employment growth is greater, other things being the same, when its affiliated university is geographically closer, although evidence suggests that effect has lessened in the 21st century because of the information and communications technology revolution. Further stimulating regional growth, university spin-off companies are more prevalent in a park when it is geographically closer to the affiliated university. The two-way flow of knowledge enabled by clusters of research and technology-based firms in science parks benefits firms located on the park and the affiliated universities. Understanding the mechanisms by which the innovative performance of research and technology-based organizations is increased by their geographic proximity in a science park is important for formulating public and private sector policies toward park formations because successful national innovation systems require the two-way knowledge flow, among firms in a park and between firms and universities, that is fostered by the science park infrastructure.

Healthcare cost encompasses expenditures on the totality of scarce resources (implicit and explicit) given up (or allocated) to produce healthcare goods (e.g., drugs and medical devices) and services (e.g., hospital care and physician office services are major components). Healthcare cost accounting components (sources and uses of funds) tend to differ but can be similar enough across most of the world countries. The healthcare cost concept usually differs for consumers, politicians and health policy decision-makers, health insurers, employers, and the government. All else given, inefficient healthcare production implies higher economic cost and lower productivity of the resources deployed in the process. Healthcare productivity varies across health systems of the world countries, the production technologies used, regulatory instruments, and institutional settings. Healthcare production often involves some specific (e.g., drugs and medical devices, information and communication technologies) or general technology for diagnosing, treating, or curing diseases in order to improve or restore human health conditions. In the last half century, the different healthcare systems of the world countries have undergone fundamental transformations in the structural designs, institutional regulations, and socio-economic and demographic dimensions. The nations have allocated a rising share of total economic resources or incomes (i.e., Gross National Product, or GDP) to the healthcare sector and are consequently enjoying substantial increases in population health status and life expectancies. There are complex and interacting linkages among escalating healthcare costs, longer life expectancies, technological progress (or “the march of science”), and sectoral productivities in the health services sectors of the advanced economies. Healthcare policy debates often concentrate on cost-containment strategies and search for improved efficient resource allocation and equitable distribution of the sector’s outputs. Consequently, this contribution is a broad review of the body of literature on technological progress, productivity, and cost: three important dimensions of the evolving modern healthcare systems. It provides a logical integration of three strands of work linking healthcare cost to technology and research evidence on sectoral productivity measurements. Finally, some important aspects of the existing study limitations are noted to motivate new research directions for future investigations to explore in the growing health sector economies.

In the last several decades obesity rates have risen significantly. In 2014, 10.8% and 14.9% of the world’s men and women, respectively, were obese as compared with 3.2% and 6.4% in 1975. The obesity “epidemic” has spread from high-income countries to emerging and developing ones in every region of the world. The rising obesity rates are essentially explained by a rise in total calorie intake associated with long-term global changes in the food supply. Food has become more abundant, available, and cheaper, but food affluence is associated with profound changes in the nutritional quality of supply. While calories have become richer in fats, sugar, and sodium, they are now lower in fiber. The nutrition transition from starvation to abundance and high-fat/sugar/salt food is thus accompanied by an epidemiological transition from infectious diseases and premature death to chronic diseases and longer lives. Food-related chronic diseases have important economic consequences in terms of human capital and medical care costs borne by public and private insurances and health systems. Technological innovations, trade globalization, and retailing expansion are associated with these substantial changes in the quantity and quality of food supply and diet in developed as well as in emerging and rapidly growing economies. Food variety has significantly increased due to innovations in the food production process. Raw food is broken down to obtain elementary substances that are subsequently assembled for producing final food products. This new approach, as well as improvements in cold chain and packaging, has contributed to a globalization of food chains and spurred an increase of trade in food products, which, jointly with foreign direct investments, alters the domestic food supply. Finally, technological advancements have also favored the emergence of large supermarkets and retailers, which have transformed the industrial organization of consumer markets. How do these developments affect population diets and diet-related diseases? Identifying the contribution of supply factors to long-term changes in diet and obesity is important because it can help to design innovative, effective, and evidence-based policies, such as regulations on trade, retailing, and quality or incentives for product reformulation. Yet this requires a correct evaluation of the importance and causal effects of supply-side factors on the obesity pandemic. Among others, the economic literature analyzes the effect of changes in food prices, food availability, trade, and marketing on the nutrition and epidemiological transitions. There is a lack of causal robust evidence on their long-term effects. The empirical identification of causal effects is de facto challenging because the dynamics of food supply is partly driven by demand-side factors and dynamics, like a growing female labor force, habit formation, and the social dynamics of preferences. There are several important limitations to the literature from the early 21st century. Existing studies cover mostly well-developed countries, use static economic and econometric specifications, and employ data that cover short periods of time unmarked by profound shifts in food supply. In contrast, empirical research on the long-term dynamics of consumer behavior is much more limited, and comparative studies across diverse cultural and institutional backgrounds are almost nonexistent. Studies on consumers in emerging countries could exploit the rapid time changes and large spatial heterogeneity, both to identify the causal impacts of shocks on supply factors and to document how local culture and institutions shape diet and nutritional outcomes.

In order to secure effective service access, coverage, and impact, it is increasingly recognized that the introduction of novel health technologies such as diagnostics, drugs, and vaccines may require additional investment to address the constraints under which many health systems operate. Health-system constraints include a shortage of health workers, ineffective supply chains, or inadequate information systems, or organizational constraints such as weak incentives and poor service integration. Decision makers may be faced with the question of whether to invest in a new technology, including the specific health system strengthening needed to ensure effective implementation; or they may be seeking to optimize resource allocation across a range of interventions including investment in broad health system functions or platforms. Investment in measures to address health-system constraints therefore increasingly need to undergo economic evaluation, but this poses several methodological challenges for health economists, particularly in the context of low- and middle-income countries. Designing the appropriate analysis to inform investment decisions concerning new technologies incorporating health systems investment can be broken down into several steps. First, the analysis needs to comprehensively outline the interface between the new intervention and the system through which it is to be delivered, in order to identify the relevant constraints and the measures needed to relax them. Second, the analysis needs to be rooted in a theoretical approach to appropriately characterize constraints and consider joint investment in the health system and technology. Third, the analysis needs to consider how the overarching priority- setting process influences the scope and output of the analysis informing the way in which complex evidence is used to support the decision, including how to represent and manage system wide trade-offs. Finally, there are several ways in which decision analytical models can be structured, and parameterized, in a context of data scarcity around constraints. This article draws together current approaches to health system thinking with the emerging literature on analytical approaches to integrating health-system constraints into economic evaluation to guide economists through these four issues. It aims to contribute to a more health-system-informed approach to both appraising the cost-effectiveness of new technologies and setting priorities across a range of program activities.

The evidence produced by healthcare economic evaluation studies is a key component of any Health Technology Assessment (HTA) process designed to inform resource allocation decisions in a budget-limited context. To improve the quality (and harmonize the generation process) of such evidence, many HTA agencies have established methodological guidelines describing the normative framework inspiring their decision-making process. The information requirements that economic evaluation analyses for HTA must satisfy typically involve the use of complex quantitative syntheses of multiple available datasets, handling mixtures of aggregate and patient-level information, and the use of sophisticated statistical models for the analysis of non-Normal data (e.g., time-to-event, quality of life and costs). Much of the recent methodological research in economic evaluation for healthcare has developed in response to these needs, in terms of sound statistical decision-theoretic foundations, and is increasingly being formulated within a Bayesian paradigm. The rationale for this preference lies in the fact that by taking a probabilistic approach, based on decision rules and available information, a Bayesian economic evaluation study can explicitly account for relevant sources of uncertainty in the decision process and produce information to identify an “optimal” course of actions. Moreover, the Bayesian approach naturally allows the incorporation of an element of judgment or evidence from different sources (e.g., expert opinion or multiple studies) into the analysis. This is particularly important when, as often occurs in economic evaluation for HTA, the evidence base is sparse and requires some inevitable mathematical modeling to bridge the gaps in the available data. The availability of free and open source software in the last two decades has greatly reduced the computational costs and facilitated the application of Bayesian methods and has the potential to improve the work of modelers and regulators alike, thus advancing the fields of economic evaluation of healthcare interventions. This chapter provides an overview of the areas where Bayesian methods have contributed to the address the methodological needs that stem from the normative framework adopted by a number of HTA agencies.

Drug companies are profit-maximizing entities, and profit is, by definition, revenue less cost. Here we review the impact of government policies that affect sales revenues earned on newly developed drugs and the impact of policies that affect the cost of drug development. The former policies include intellectual property rights, drug price controls, and the extension of public drug coverage to previously underinsured groups. The latter policies include regulations governing drug safety and efficacy, R&D tax credits, publicly funded basic research, and public funding for open drug discovery consortia. The latter policy, public funding of research consortia that seek to better understand the cellular pathways through which new drugs can ameliorate disease, appears very promising. In particular, a better understanding of human pathophysiology may be able to address the high failure rate of drugs undergoing clinical testing. Policies that expand market size by extending drug insurance to previously underinsured groups also appear to be effective at increasing drug R&D. Expansions of pharmaceutical intellectual property rights seem to be less effective, given the countervailing monopsony power of large public drug plans.