Louisville/Jefferson County is the principal city of America’s 42nd largest metropolitan area, a 13-county, bi-state region with a 2006 population estimated at 1.2 million. It is the largest city by far in Kentucky, but it is neither Kentucky’s capital nor its center of political power.

The consolidated city, authorized by voter referendum in 2000 and implemented in 2003, is home to 701,500 residents within its 399 square miles, with a population density of 4,124.8 per square mile.² It is either the nation’s 16th or its 26th largest incorporated place, depending on whether the residents of smaller municipalities within its borders, who are eligible to vote in its elections, are counted (as local officials desire and U.S. Census Bureau officials resist). The remainder of the metropolitan statistical area (MSA) population is split between four Indiana counties (241,193) and eight Kentucky counties (279,523). Although several of those counties are growing rapidly, the new Louisville metro area remains the MSA's central hub, with 57 percent of the population and almost 70 percent of the job base.

Centrally located on the southern banks of the Ohio River, amid an agriculturally productive, mineral rich, and energy producing region, Louisville is commonly described as the northernmost city of the American South. Closer to Toronto than to New Orleans, and even slightly closer to Chicago than to Atlanta, it remains within a day’s drive of two-thirds of the American population living east of the Rocky Mountains.

This location has been the dominant influence on Louisville’s history as a regional center of trade, commerce and manufacture. The city, now the all-points international hub of United Parcel Service (UPS), consistently ranks among the nation’s top logistics centers. Its manufacturing sector, though much diminished, still ranks among the strongest in the Southeast. The many cultural assets developed during the city’s reign as a regional economic center rank it highly in various measures of quality of life and “best places.”

Despite these strengths, Louisville’s competitiveness and regional prominence declined during much of the last half of the 20th Century, and precipitously so during the economic upheavals of the 1970s and ‘80s. Not only did it lose tens of thousands of manufacturing jobs and many of its historic businesses to deindustrialization and corporate consolidation, it also confronted significant barriers to entry into the growing knowledge-based economy because of its poorly-educated workforce, lack of R&D capacity, and risk-averse business culture.

In response, Louisville began a turbulent, two-decade process of civic and economic renewal, during which it succeeded both in restoring growth in its traditional areas of strength, most notably from the large impact of the UPS hub, and in laying groundwork for 21st century competitiveness, most notably by substantially ramping up university-based research and entrepreneurship supports. Doing so required it to overhaul nearly every aspect of its outmoded economic development strategies, civic relationships, and habits of mind, creating a new culture of collaboration.

Each of the three major partners in economic development radically transformed themselves and their relationships with one another. The often-paralyzing city-suburban divide of local governance yielded to consolidation. The business community reconstituted itself as a credible champion of broad-based regional progress, and it joined with the public sector to create a new chamber of commerce that is the region’s full-service, public-private economic development agency recognized as among the best in the nation. The Commonwealth of Kentucky embraced sweeping education reforms, including major support for expanded research at the University of Louisville, and a “New Economy” agenda emphasizing the commercialization of research-generated knowledge. Creative public-private partnerships have become the norm, propelling, for instance, the dramatic resurgence of downtown.

The initial successes of all these efforts have been encouraging, but not yet sufficient for the transformation to innovation-based prosperity that is the goal. This report details those successes, and the leadership, partnerships, and strategies that helped create them. It begins by describing Louisville’s history and development and the factors that made its economy grow and thrive. It then explains why the city faltered during the latter part of the 20th century and how it has begun to reverse course. In doing so, the study offers important lessons for other cities that are striving to compete in a very new economic era. 

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Chattanooga a few years ago faced what many smaller cities are struggling with today—a sudden decline after years of prosperity in the "old" economy. This case study offers a roadmap for these cities by chronicling Chattanooga's demise and rebirth.

Chattanooga is located in the southern end of the Tennessee Valley where the Tennessee River cuts through the Smoky Mountains and the Cumberland Plateau. The city’s location, particularly its proximity to the Tennessee River, has been one of its greatest assets. Today, several major interstates (I-24, I-59, and I-75) run through Chattanooga, making it a hub of transportation business. The city borders North Georgia and is less than an hour away from both Alabama and North Carolina. Atlanta, Nashville, and Birmingham are all within two hours travel time by car.

Chattanooga is Tennessee’s fourth largest city, with a population in 2000 of 155,554, and it covers an area of 143.2 square miles. Among the 200 most populous cities in the United States, Chattanooga—with 1,086.5 persons per square mile—ranks 190th in population density.2 It is the most populous of 10 municipalities in Hamilton County, which has a population of 307,896, covers an area of 575.7 square miles, and has a population density of 534.8 persons per square mile.

With its extensive railroads and river access, Chattanooga was at one time the “Dynamo of Dixie”—a bustling, midsized, industrial city in the heart of the South. By 1940, Chattanooga’s population was centered around a vibrant downtown and it was one of the largest cities in the United States. Just 50 years later, however, it was in deep decline. Manufacturing jobs continued to leave. The city’s white population had fled to the suburbs and downtown was a place to be avoided, rather than the economic center of the region. The city lost almost 10 percent of its population during the 1960s, and another 10 percent between 1980 and 1990. It would have lost more residents had it not been for annexation of outlying suburban areas.

The tide began to turn in the 1990s, with strategic investments by developing public-private partnerships—dubbed the “Chattanooga way.” These investments spurred a dramatic turnaround. The city’s population has since stabilized and begun to grow, downtown has been transformed, and it is once again poised to prosper in the new economy as it had in the old.

This report describes how Chattanooga has turned its economy around. It begins with a summary of how the city grew and developed during its first 150 years before describing the factors driving its decline. The report concludes by examining the partnerships and planning that helped spur Chattanooga’s current revitalization and providing valuable lessons to other older industrial cities trying to ignite their own economic recovery. 

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Part of the larger Northeast Ohio regional economy, the Akron metropolitan area is composed of two counties (Summit and Portage) with a population of just over 700,000, and is surrounded by three other metropolitan areas. Akron is located approximately 40 miles south of Cleveland, 50 miles west of Youngstown, and 23 miles north of Canton. The Cleveland metro area is a five-county region with a population of 2.1 million. The Youngstown metro area includes three counties, extending into Pennsylvania, and has a population of 587,000. Canton is part of a two-county metropolitan area with a population of 410,000.

The adjacency of the Akron and Cleveland Metropolitan Statistical Areas (MSAs) is an important factor in the economic performance of the Akron region. The interdependence of economies of the two MSAs is evidenced by the strong economic growth of the northern part of Summit County adjacent to the core county of the Cleveland metropolitan area. This part of Summit County beyond the city of Akron provides available land, access to the labor pools of the two metropolitan areas, and proximity to the region’s extensive transportation network.

Although affected by economic activity in the larger region, the fate and future of Akron and its wider region are not solely determined by events in these adjacent areas. While sharing broad economic trends with its neighbors, the Akron metro area has been impacted by a different set of events and has shown different patterns of growth from other areas in Northeast Ohio.

This study provides an in-depth look at Akron’s economy over the past century. It begins by tracing the industrial history of the Akron region, describing the growth of the rubber industry from the late 1800s through much of following century, to its precipitous decline beginning in the 1970s. It then discusses how the “bottoming out” of this dominant industry gave rise to the industrial restructuring of the area. The paper explores the nature of this restructuring, and the steps and activities the city’s business, civic, and government leaders have undertaken to help spur its recovery and redevelopment. In doing so, it provides a series of lessons to other older industrial regions working to find their own economic niche in a changing global economy. 

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It is estimated that there are 95 mobile phone subscriptions per 100 inhabitants worldwide, and this boom has not been lost on the developing world, where the number of mobile users has also grown at rocket speed. In fact, in recent years the information communication technology (ICT) revolution has provided opportunities leading to “death of distance,” allowing many obstacles to better livelihoods, especially for those in remote regions, to disappear. Remarkably, though, the huge proportion of poverty-stricken populations in so many of those same regions persists.

How might, then, we think differently on the relationship between these two ideas? Can and how might ICTs act as an engine for eradicating poverty and improving the quality of life in terms of better livelihoods, strong education outcomes, and quality health? Do today's communication technologies hold such potential?

In particular, the mobile phone’s accessibility and use creates and provides us with an unprecedented volume of data on social interactions, mobility, and more. So, we ask: Can this data help us better understand, characterize, and alleviate poverty?

Mapping call data records, mobility, and economic activity

The first step towards alleviating poverty is to generate poverty maps. Currently, poverty maps are created using nationally representative household surveys, which require manpower and time. Such maps are generated at a coarse regional resolution and continue to lag for countries in sub-Saharan Africa compared to the rest of the world.

As call data records (CDRs) allow a view of the communication and mobility patterns of people at an unprecedented scale, we show how this data can be used to create much more detailed poverty maps efficiently and at a finer spatial resolution. Such maps will facilitate improved diagnosis of poverty and will assist public policy planners in initiating appropriate interventions, specifically at the decentralized level, to eradicate human poverty and ensure a higher quality of life.

In order to create these detailed poverty maps, we first define the virtual network of a country as a “who-calls-whom” network. This signifies the macro-level view of connections or social ties between people, dissemination of information or knowledge, or dispersal of services. As calls are placed for a variety of reasons, including request for resources, information dissemination, personal etc., CDRs provide an interesting way to construct a virtual network for Senegal.

We start by quantifying the accessibility of mobile connectivity in Senegal, both spatially and across the population, using the CDR data. This quantification measures the amount of communication across various regions in Senegal. The result is a virtual network for Senegal, which is depicted in Figure 1. The circles in the map correspond to regional capitals, and the edges correspond to volume of mobile communication between them. Thicker edges mean higher volume of communication. Bigger circles mean heavier incoming and outgoing communication for that region.

Figure 1: Virtual network for Senegal with MPI as an overlay

Source: Author’s rendering of the virtual network of Senegal based on the dataset of CDRs provided as a part of D4D Senegal Challenge 2015

Figure 1 also shows the regional poverty index[1] as an overlay. A high poverty index corresponds to very poor regions, which are shown lighter green on the map. It is evident that regions with plenty of strong edges have lower poverty, while most poor regions appear isolated. 

Now, how can we give a more detailed look at the distribution of poverty? Using the virtual network, we extract quantitative metrics indicating the centrality of each region in Senegal. We then calculate centrality measures of all the arrondissements[2] within a region. We then correlate these regional centrality measures with the poverty index to build a regression model. Using the regression model, we predict the poverty index for each arrondissement.

Figure 2 shows the poverty map generated by our model for Senegal at an arrondissement level. It is interesting to see finer disaggregation of poverty to identify pockets of arrondissement, which are most in need of sustained growth. The poorer arrondissements are shown lighter green in color with high values for the poverty index.

Figure 2: Predicted poverty map at the arrondissement level for Senegal with MPI as an overlay

Source: Author’s rendering of the virtual network of Senegal based on the dataset of CDRs provided as a part of D4D Senegal Challenge 2015.

What is next for call data records and other Big Data in relation to eradicating poverty and improving the human development?

This investigation is only the beginning. Since poverty is a complex phenomenon, poverty maps showcasing multiple perspectives, such as ours, provide policymakers with better insights for effective responses for poverty eradication. As noted above, these maps can be used for decomposing information on deprivation of health, education, and living standards—the main indicators of human development index.

Even more particularly, we believe that this Big Data and our models can generate disaggregated poverty maps for Senegal based on gender, the urban/rural gap, or ethnic/social divisions. Such poverty maps will assist in policy planning for inclusive and sustained growth of all sections of society. Our methodology is generic and can be used to study other socio-economic indicators of the society.

Like many uses of Big Data, our model is in its nascent stages. Currently, we are working towards testing our methodology at the ground level in Senegal, so that it can be further updated based on the needs of the people and developmental interventions can be planned. The pilot project will help to "replicate" our methodology in other underdeveloped countries.

In the forthcoming post-2015 development agenda intergovernmental negotiations, the United Nations would like to ensure the “measurability, achievability of the targets” along with identification of 'technically rigorous indicators' for development. It is in this context that Big Data can be extremely helpful in tackling extreme poverty.

Note: This examination was part of the "Data for Development Senegal" Challenge, which focused on how to use Big Data for grass-root development. We took part in the Data Challenge, which was held in conjunction with NetMob 2015 at MIT from April 7-10, 2015. Our team received the National Statistics prize for our project titled, "Virtual Network and Poverty Analysis in Senegal.” This blog reflects the views of the authors only and does not reflect the views of the Africa Growth Initiative.

[1] As a measure of poverty, we have used the Multidimensional Poverty Index (MPI), which is a composite of 10 indicators across the three areas: education (years of schooling, school enrollment), health (malnutrition, child mortality), and living conditions.

[2] Senegal is divided into 14 administrative regions, which are further divided into 123 arrondissements.

Baltimore provides a prime example of how metropolitan areas around the nation are turning to clean, green industries as a source of vibrant, sustainable growth. Expert Jennifer Vey outlines how such communities can identify their assets and capitalize on them to revitalize their economies.

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• Baltimore as a Case Study in Metro Economic Recovery

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• @ Brookings Podcast: Baltimore as a Case Study in Metro Economic Recovery

Editor’s note: This post is adapted from a forthcoming full-length case study; the second in a series from the Engelberg Center’s Merkin Initiative on Physician Payment Reform and Clinical Leadership designed to support clinician leadership of health care delivery, payment, and financing reform. The case study will be presented during the Merkin Initiative’s “MEDTalk” event on July 9 from 10:30 AM to 12:30 PM EDT, featuring live story-telling and knowledge-sharing from patients, providers, and policymakers.

Oncology practices and hospitals across the nation struggle with providing sustainable, comprehensive, and coordinated cancer care. Clinical leaders with strategies and models to improve the quality and value of health care often don’t know how to navigate the landscape of payment and delivery reform options to sustain their innovations.

We use a case study approach to investigate and tell the story of the New Mexico Cancer Center (NMCC), an independent cancer center that is experimenting with innovative ways to improve patient-centered oncology care. We identify challenges for creating sustainable and supportive payments models, and we share the broader strategic and policy lessons for adopting alternative payment models.

The Clinical Scenario: Living With Cancer

Vicky Bolton, a 58-year-old full-time medical legal coordinator from Albuquerque, has stage 4 adenocarcinoma lung cancer. She started chemotherapy in 2003 and has consistently received treatments over the last 11 years. Vicky is one of 13 million Americans currently living with cancer, with more than 1.6 million new diagnoses added each year.

Although Vicky’s condition is currently stable, she is at high risk for venous thrombosis (blood clots), life-threatening infections, and other complications, which put her at high risk for repeated hospitalizations. In the past six months, she has taken advantage of “after hours” care on three occasions as an outpatient at NMCC. Fortunately, each of her providers and services — oncology, radiation therapy, labs, x-rays, and internal medicine — are centralized in a single location at NMCC, reducing the need for emergency room (ER) visits or hospitalizations for these episodes.

The Challenge: Controlling Spending While Improving Patient-Centered Care

Cancer is the second leading cause of death in the U.S. Forty-one percent of Americans will be diagnosed with cancer during their lives. Cancer care is also expensive, accounting for $125 billion of total health care spending annually. In 2011, Medicare alone spent nearly $35 billion in fee-for-service (FFS) payments for cancer care, representing 9 percent of all Medicare FFS payments.

The high costs of cancer care are driven by issues that plague the entire health system: uncoordinated care delivery, duplication of services, fragmentation, and volume-based payments. A common impact of these drivers in oncology is the use of the ER to relieve symptoms associated with adverse effects of chemotherapy or other treatments that can also result in hospitalization.

For example, research shows that the most common reasons for cancer patient ER admissions are pain, respiratory distress, nausea, and vomiting. More than half of the ER visits occurred on weekends or in the evening, and over 60 percent resulted in hospital admission. This suggests that if a patient’s symptoms could be managed at home or in the community, costly hospital admissions could be avoided. ER visits, where patients are exposed to germs and infections as they wait — often hours — to be admitted, can have catastrophic outcomes for patients that are actively in treatment since they have weakened immune systems and are more prone to infections.

In addition to the inherent issues with fee-for-service (FFS) payments — with payments incentivizing volume of procedures rather than the value of care delivered — the current payment system further exacerbates problems: If a practice provides higher-value care to patients at a lower cost to the overall system (that is, they perform fewer services and have lower revenue), the financial winner is the payer who reimburses fewer services, not the practice (which merely has less revenue). This combination of the misaligned incentives of FFS and the lack of financial benefit for improving care while reducing costs means that many practices simply cannot afford to make the transformations needed without other funding mechanisms.

The Real World: How Has An Independent Cancer Center Responded To These Challenges?

NMCC delivers care to roughly 2,700 patients and provides care to one in three New Mexicans with cancer. The changes that the center has made have focused on reducing the impact of fragmentation of care on their patients (Table 1).

A key innovation was enhancing comprehensive after-hours and weekend care on site and creating a telephone and urgent care triage program to avoid expensive emergency room and inpatient care, which NMCC termed the COME HOME model.

As part of its redesign process in 2012, NMCC – along with six community oncology practices — secured a $20 million Center for Medicare and Medicaid Innovation (CMMI) Health Care Innovation Award (HCIA), for a three-year period. The award has an explicit aim of reducing ER visits by 50 percent and hospitalizations by 20 percent to justify the program costs.

Table 1: Care Redesign Elements Undertaken by NMCC

The Key Levers: How Can COME HOME Be Sustained?

On the heels of the Affordable Care Act (ACA) and numerous quality and payment focused initiatives in the private sector, health care organizations need to enhance the competitiveness and efficiency of their systems in the marketplace.

Alternative payment models (APMs) such as Accountable Care Organizations (ACOs), bundled payments, and patient-centered oncology medical homes (PCOMH) are just a few of the initiatives supported by public and private payers to align care redesign and payment reform and encourage continuous improvement. (Clinical pathways, a strategy recently embraced by WellPoint, offer PCOMH-like incentives to encourage adherence to practice guidelines, a strategy primarily geared to encourage higher-value chemotherapy practice.)

Broader or larger case-based payments may also provide stronger incentives to limit costs, to help assure that promising delivery reforms actually lead to cost reduction, but this exposes oncologists to greater levels of financial risk, as shown in Table 2. Consequently, implementing payment reforms that are viewed as feasible and desirable by both providers and payers is difficult.

Table 2: Comparison of Alternative Payment Models for Oncology

The Path Ahead: How Can These Models Assist NMCC?

NMCC currently receives approximately $70,000 per month from the CMMI grant and has not yet identified a clear strategy to sustain the delivery reforms in the COME HOME care model past the end of the grant (July 2015). As for payment reform options, NMCC has been unable to contract as part of a comprehensive ACO due to local health care market conditions.

Clinical pathways are geared primarily to guidelines and chemotherapy adherence, and are not designed to provide funding for after-hours care or triage programs that are intended to achieve offsetting savings through avoiding costly complications. Possible remaining options include:

• PCOMH: Using the data it gathers, NMCC intends to quantify the additional costs the COME HOME model requires, and the savings that it achieves. Based on that estimate, NMCC could suggest a per-member per-month (PMPM) payment from a private insurer to cover the costs of providing higher quality care. To encourage participation, NMCC could also enter into a risk-sharing agreement, in which overall costs of inpatient care and ER visits would be compared against a target. The PMPM payment could be at-risk if the targets are not achieved after a certain period of time.

• Bundled Payments: NMCC could potentially use the medical home approach with risk sharing (described above) as a first, interim step toward a bundled payment system, NMCC’s long-term preferred model. Computing actuarially sound expected costs for the bundled payments would require merging claims data with clinical data (for example, ICD-9 codes fail to distinguish between subtypes of breast cancer that have radically different treatments). A bundled payment pilot might be performed for high volume cancers, such as breast and lung.

Lessons Learned

The experience of innovative pioneers like NMCC can shed some light on potential barriers to conceptualizing and implementing sustainable clinical redesign. The lessons learned have been sorted into three main categories: relationships with payers and networks, payment model selection, and data collection and quality improvement considerations.

Relationships with payers and networks. Though counterintuitive, merely demonstrating significant value from care design, perhaps from lower utilization of inpatient and emergency department utilization, does not automatically create a financial pathway for sustainable delivery reform. To do so, innovative providers should consider involving lead payer partners early on to help identify end-points of interest to payers and potential payment strategies that may emerge later.

Providing support for health care delivery reforms requires new activities by payers towards aligning their payments with value, rather than volume and intensity of services. However, fragmented health care markets face the challenge of the “free rider” problem: payers may be unwilling to shoulder delivery transformation costs that may benefit other payers’ clients while they wait for CMS or others to make the financial investment, pay for the program evaluation, and enact policy change). Other challenges include payer inertia and long lag times between care redesign and subsequent data demonstrating results.

Large ACOs and other integrated payer-provider plans, including those large enough to form Medicare Advantage plans, are moving forward on negotiating payment and delivery reforms. This may be more difficult for innovative, smaller practices, even if they can provide higher-value clinical services. In turn, this may have anti-competitive consequences, such as discouraging delivery innovation that leads to “demand destruction” of high-cost hospital-based services. Private and public payers should be particularly interested in developing models that enable smaller, specialized providers like oncology practices to undertake key delivery reforms.

Sustainable Payment Model Selection. While substantial attention has been paid to primary care focused APMs, specialty-focused APMs are needed for practices like NMCC. Their development should be a high priority for public and private payers. Clinical transformation grants, such as those offered by CMMI, should include clear pathways for transitioning to APMs if initial cost savings targets or projections are met. Otherwise, delivery system innovations are at high risk of failure despite evidence of improved value.

Data Collection and Quality Improvement Considerations. Timely sharing of actionable information from claims and other administrative data remains a major challenge, with complex and varied procedures for obtaining claims from payers; smaller practices are particularly challenged in interpreting the claims data. Some states, such as Maryland, Massachusetts, Vermont, and Colorado (among others) are proceeding with creating all-payer claims databases. (Maryland, for example, offers almost instantaneous provider feedback from claims through their CRISP database.)

Others, such as Minnesota, are using “distributed” approaches in which multiple payers and systems produce measures in consistent ways. As NMCC’s early efforts illustrate, practices can produce more clinically sophisticated performance measures. Strategies to achieve consistent methods for sharing key data on cost and quality need to be expanded to encourage quality improvement and payment reform.

Louisville/Jefferson County is the principal city of America’s 42nd largest metropolitan area, a 13-county, bi-state region with a 2006 population estimated at 1.2 million. It is the largest city by far in Kentucky, but it is neither Kentucky’s capital nor its center of political power.

The consolidated city, authorized by voter referendum in 2000 and implemented in 2003, is home to 701,500 residents within its 399 square miles, with a population density of 4,124.8 per square mile.² It is either the nation’s 16th or its 26th largest incorporated place, depending on whether the residents of smaller municipalities within its borders, who are eligible to vote in its elections, are counted (as local officials desire and U.S. Census Bureau officials resist). The remainder of the metropolitan statistical area (MSA) population is split between four Indiana counties (241,193) and eight Kentucky counties (279,523). Although several of those counties are growing rapidly, the new Louisville metro area remains the MSA's central hub, with 57 percent of the population and almost 70 percent of the job base.

Centrally located on the southern banks of the Ohio River, amid an agriculturally productive, mineral rich, and energy producing region, Louisville is commonly described as the northernmost city of the American South. Closer to Toronto than to New Orleans, and even slightly closer to Chicago than to Atlanta, it remains within a day’s drive of two-thirds of the American population living east of the Rocky Mountains.

This location has been the dominant influence on Louisville’s history as a regional center of trade, commerce and manufacture. The city, now the all-points international hub of United Parcel Service (UPS), consistently ranks among the nation’s top logistics centers. Its manufacturing sector, though much diminished, still ranks among the strongest in the Southeast. The many cultural assets developed during the city’s reign as a regional economic center rank it highly in various measures of quality of life and “best places.”

Despite these strengths, Louisville’s competitiveness and regional prominence declined during much of the last half of the 20th Century, and precipitously so during the economic upheavals of the 1970s and ‘80s. Not only did it lose tens of thousands of manufacturing jobs and many of its historic businesses to deindustrialization and corporate consolidation, it also confronted significant barriers to entry into the growing knowledge-based economy because of its poorly-educated workforce, lack of R&D capacity, and risk-averse business culture.

In response, Louisville began a turbulent, two-decade process of civic and economic renewal, during which it succeeded both in restoring growth in its traditional areas of strength, most notably from the large impact of the UPS hub, and in laying groundwork for 21st century competitiveness, most notably by substantially ramping up university-based research and entrepreneurship supports. Doing so required it to overhaul nearly every aspect of its outmoded economic development strategies, civic relationships, and habits of mind, creating a new culture of collaboration.

Each of the three major partners in economic development radically transformed themselves and their relationships with one another. The often-paralyzing city-suburban divide of local governance yielded to consolidation. The business community reconstituted itself as a credible champion of broad-based regional progress, and it joined with the public sector to create a new chamber of commerce that is the region’s full-service, public-private economic development agency recognized as among the best in the nation. The Commonwealth of Kentucky embraced sweeping education reforms, including major support for expanded research at the University of Louisville, and a “New Economy” agenda emphasizing the commercialization of research-generated knowledge. Creative public-private partnerships have become the norm, propelling, for instance, the dramatic resurgence of downtown.

The initial successes of all these efforts have been encouraging, but not yet sufficient for the transformation to innovation-based prosperity that is the goal. This report details those successes, and the leadership, partnerships, and strategies that helped create them. It begins by describing Louisville’s history and development and the factors that made its economy grow and thrive. It then explains why the city faltered during the latter part of the 20th century and how it has begun to reverse course. In doing so, the study offers important lessons for other cities that are striving to compete in a very new economic era. 

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Chattanooga a few years ago faced what many smaller cities are struggling with today—a sudden decline after years of prosperity in the "old" economy. This case study offers a roadmap for these cities by chronicling Chattanooga's demise and rebirth.

Chattanooga is located in the southern end of the Tennessee Valley where the Tennessee River cuts through the Smoky Mountains and the Cumberland Plateau. The city’s location, particularly its proximity to the Tennessee River, has been one of its greatest assets. Today, several major interstates (I-24, I-59, and I-75) run through Chattanooga, making it a hub of transportation business. The city borders North Georgia and is less than an hour away from both Alabama and North Carolina. Atlanta, Nashville, and Birmingham are all within two hours travel time by car.

Chattanooga is Tennessee’s fourth largest city, with a population in 2000 of 155,554, and it covers an area of 143.2 square miles. Among the 200 most populous cities in the United States, Chattanooga—with 1,086.5 persons per square mile—ranks 190th in population density.2 It is the most populous of 10 municipalities in Hamilton County, which has a population of 307,896, covers an area of 575.7 square miles, and has a population density of 534.8 persons per square mile.

With its extensive railroads and river access, Chattanooga was at one time the “Dynamo of Dixie”—a bustling, midsized, industrial city in the heart of the South. By 1940, Chattanooga’s population was centered around a vibrant downtown and it was one of the largest cities in the United States. Just 50 years later, however, it was in deep decline. Manufacturing jobs continued to leave. The city’s white population had fled to the suburbs and downtown was a place to be avoided, rather than the economic center of the region. The city lost almost 10 percent of its population during the 1960s, and another 10 percent between 1980 and 1990. It would have lost more residents had it not been for annexation of outlying suburban areas.

The tide began to turn in the 1990s, with strategic investments by developing public-private partnerships—dubbed the “Chattanooga way.” These investments spurred a dramatic turnaround. The city’s population has since stabilized and begun to grow, downtown has been transformed, and it is once again poised to prosper in the new economy as it had in the old.

This report describes how Chattanooga has turned its economy around. It begins with a summary of how the city grew and developed during its first 150 years before describing the factors driving its decline. The report concludes by examining the partnerships and planning that helped spur Chattanooga’s current revitalization and providing valuable lessons to other older industrial cities trying to ignite their own economic recovery. 

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Part of the larger Northeast Ohio regional economy, the Akron metropolitan area is composed of two counties (Summit and Portage) with a population of just over 700,000, and is surrounded by three other metropolitan areas. Akron is located approximately 40 miles south of Cleveland, 50 miles west of Youngstown, and 23 miles north of Canton. The Cleveland metro area is a five-county region with a population of 2.1 million. The Youngstown metro area includes three counties, extending into Pennsylvania, and has a population of 587,000. Canton is part of a two-county metropolitan area with a population of 410,000.

The adjacency of the Akron and Cleveland Metropolitan Statistical Areas (MSAs) is an important factor in the economic performance of the Akron region. The interdependence of economies of the two MSAs is evidenced by the strong economic growth of the northern part of Summit County adjacent to the core county of the Cleveland metropolitan area. This part of Summit County beyond the city of Akron provides available land, access to the labor pools of the two metropolitan areas, and proximity to the region’s extensive transportation network.

Although affected by economic activity in the larger region, the fate and future of Akron and its wider region are not solely determined by events in these adjacent areas. While sharing broad economic trends with its neighbors, the Akron metro area has been impacted by a different set of events and has shown different patterns of growth from other areas in Northeast Ohio.

This study provides an in-depth look at Akron’s economy over the past century. It begins by tracing the industrial history of the Akron region, describing the growth of the rubber industry from the late 1800s through much of following century, to its precipitous decline beginning in the 1970s. It then discusses how the “bottoming out” of this dominant industry gave rise to the industrial restructuring of the area. The paper explores the nature of this restructuring, and the steps and activities the city’s business, civic, and government leaders have undertaken to help spur its recovery and redevelopment. In doing so, it provides a series of lessons to other older industrial regions working to find their own economic niche in a changing global economy. 

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In partnership with the ESADE Business School and the City of Barcelona, the Metropolitan Policy Program planned and participated in three intensive days of learning in Barcelona in June 2011.  The focus of the session was to look at examples of strategies Barcelona, Spain and its greater metropolitan region is embracing to rebuild and re-invent their economies.  The goal is to share innovative ideas with U.S. metros engaged in similar initiatives as they face the challenge of moving to a new economic growth model.

This paper features brief synopses of the tours and meetings held with the City of Barcelona and the Catalonia Region on their economic development strategies.

Specific strategies include:

Barcelona Activa »

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Editors Note: This post is an extended version of a previous post.

Cozzens, Susan and Dhanaraj Thakur (Eds). 2014. Innovation and Inequality: Emerging technologies in an unequal world. Northampton, Massachusetts: Edward Elgar.

Historically, the debate on innovation has focused on the determinants of the pace of innovation—on the premise that innovation is the driver of long-term economic growth. Analysts and policymakers have taken less interest on how innovation-based growth affects income distribution. Less attention even has received the question of how innovation affects other forms of inequality such as economic opportunity, social mobility, access to education, healthcare, and legal representation, or inequalities in exposure to insalubrious environments, be these physical (through exposure to polluted air, water, food or harmful work conditions) or social (neighborhoods ridden with violence and crime). The relation between innovation, equal political representation and the right for people to have a say in the collective decisions that affect their lives can also be added to the list of neglect.

But neglect has not been universal. A small but growing group of analysts have been working for at least three decades to produce a more careful picture of the relationship between innovation and the economy. A distinguished vanguard of this group has recently published a collection of case studies that illuminates our understanding of innovation and inequality—which is the title of the book. The book is edited by Susan Cozzens and Dhanaraj Thakur. Cozzens is a professor in the School of Public Policy and Vice Provost of Academic Affairs at Georgia Tech. She has studied innovation and inequality long before inequality was a hot topic and led the group that collaborated on this book. Thakur is a faculty member of the school College of Public Service and Urban Affairs at Tennessee State University (while writing the book he taught at the University of West Indies in Jamaica). He is an original and sensible voice in the study of social dimensions of communication technologies.

We’d like to highlight here three aspects of the book: the research design, the empirical focus, and the conceptual framework developed from the case studies in the book.

Edited volumes are all too often a collection of disparate papers, but not in this case. This book is patently the product of a research design that probes the evolution of a set of technologies across a wide variety of national settings and, at the same time, it examines the different reactions to new technologies within specific countries. The second part of the book devotes five chapters to study five emerging technologies—recombinant insulin, genetically modified corn, mobile phones, open-source software, and tissue culture—observing the contrasts and similarities of their evolution in different national environments. In turn, part three considers the experience of eight countries, four of high income—Canada, Germany, Malta, and the U.S.—and four of medium or low income—Argentina, Costa Rica, Jamaica, and Mozambique. The stories in part three tell how these countries assimilated these diverse technologies into to their economies and policy environments.

The second aspect to highlight is the deliberate choice of elements for empirical focus. First, the object of inquiry is not all of technology but a discreet set of emerging technologies gaining a specificity that would otherwise be negated if they were to handle the unwieldy concept of “technology” broadly construed. At the same time, this choice reveals the policy orientation of the book because these new entrants have just started to shape the socio-technical spaces they inhabit while the spaces of older technologies have likely ossified. Second, the study offers ample variance in terms of jurisdictions under study, i.e. countries of all income levels; a decision that makes at the same time theory construction more difficult and the test of general premises more robust.[i] We can add that the book avoids sweeping generalizations. Third, they focus on technological projects and their champions, a choice that increases the rigor of the empirical analysis. This choice, naturally, narrows the space of generality but the lessons are more precise and the conjectures are presented with according modesty. The combination of a solid design and clear empirical focus allow the reader to obtain a sense of general insight from the cases taken together that could not be derived from any individual case standing alone.

Economic and technology historians have tackled the effects of technological advancement, from the steam engine to the Internet, but those lessons are not easily applicable to the present because emerging technologies intimate at a different kind of reconfiguration of economic and social structures. It is still too early to know the long-term effects of new technologies like genetically modified crops or mobile phone cash-transfers, but this book does a good job providing useful concepts that begin to form an analytical framework. In addition, the mix of country case studies subverts the disciplinary separation between the economics of innovation (devoted mostly to high-income countries) and development studies (interested in middle and low income economies). As a consequence of these selections, the reader can draw lessons that are likely to apply to technologies and countries other than the ones discussed in this book.

The third aspect we would like to underscore in this review is the conceptual framework. Cozzens, Thakur and their colleagues have done a service to anyone interested in pursuing the empirical and theoretical analysis of innovation and inequality.

For these authors, income distribution is only one part of the puzzle. They observe that inequalities are also part of social, ethnic, and gender cleavages in society. Frances Stewart, from Oxford University, introduced the notion of horizontal inequalities or inequalities at the social group level (for instance, across ethnic groups or genders). She developed the concept to contrast vertical inequalities or inequalities operating at the individual level (such as household income or wealth). The authors of this book borrow Stewart’s concept and pay attention to horizontal inequalities in the technologies they examine and observe that new technologies enter marketplaces that are already configured under historical forms of exclusion. A dramatic example is the lack of access to recombinant insulin in the U.S., because it is expensive and minorities are less likely to have health insurance (see Table 3.1 in p. 80).[ii] Another example is how innovation opens opportunities for entrepreneurs but closes them for women in cultures that systematically exclude women from entrepreneurial activities.

Another key concept is that of complementary assets. A poignant example is the failure of recombinant insulin to reach poor patients in Mozambique who are sent home with old medicine even though insulin is subsidized by the government. The reason why doctors deny the poor the new treatment is that they don’t have the literacy and household resources (e.g. a refrigerator, a clock) necessary to preserve the shots, inject themselves periodically, and read sugar blood levels. Technologies aimed at fighting poverty require complementary assets to be already in place and in the absence of them, they fail to mitigate suffering and ultimately ameliorate inequality. Another illustration of the importance of complementary assets is given by the case of Open Source Software. This technology has a nominal price of zero; however, only individuals who have computers and the time, disposition, and resources to learn how to use open source operative systems benefit. Likewise, companies without the internal resources to adapt open software will not adopt it and remain economically tied to proprietary software.

These observations lead to two critical concepts elaborated in the book: distributional boundaries and the inequalities across technological transitions. Distributional boundaries refer to the reach of the benefits of new technologies, boundaries that could be geographic (as in urban/suburban or center/periphery) or across social cleavages or incomes levels. Standard models of technological diffusion assume the entire population will gradually adopt a new technology, but in reality the authors observe several factors intervene in limiting the scope of diffusion to certain groups. The most insidious factors are monopolies that exercise sufficient control over markets to levy high prices. In these markets, the price becomes an exclusionary barrier to diffusion. This is quite evident in the case of mobile phones (see table 5.1, p. 128) where monopolies (or oligopolies) have market power to create and maintain a distributional boundary between post-pay and high-quality for middle and high income clients and pre-pay and low-quality for poor customers. This boundary renders pre-pay plans doubly regressive because the per-minute rates are higher than post-pay and phone expenses represent a far larger percentage in poor people’s income. Another example of exclusion happens in GMOs because in some countries subsistence farmers cannot afford the prices for engineering seeds; a disadvantage that compounds to their cost and health problems as they have to use more and stronger pesticides.

A technological transition, as used here, is an inflection point in the adoption of a technology that re-shapes its distributional boundaries. When smart phones were introduced, a new market for second-hand or hand-down phones was created in Maputo; people who could not access the top technology get stuck with a sub-par system. By looking at tissue culture they find that “whether it provides benefits to small farmers as well as large ones depends crucially on public interventions in the lower-income countries in our study” (p. 190). In fact, farmers in Costa Rica enjoy much better protections compare to those in Jamaica and Mozambique because the governmental program created to support banana tissue culture was designed and implemented as an extension program aimed at disseminating know-how among small-farmers and not exclusively to large multinational-owned farms. When introducing the same technology, because of this different policy environment, the distributional boundaries were made much more extensive in Costa Rica.

This is a book devoted to present the complexity of the innovation-inequality link. The authors are generous in their descriptions, punctilious in the analysis of their case studies, and cautious and measured in their conclusions. Readers who seek an overarching theory of inequality, a simple story, or a test of causality, are bound to be disappointed. But those readers may find the highest reward from carefully reading all the case studies presented in this book, not only because of the edifying richness of the detail herein but also because they will be invited to rethink the proper way to understand and address the problem of inequality.[iii]