Background
In the current competitive economic landscape, policymakers and industry leaders are focused on strategies to stimulate economic growth and development Central to this effort is the identification of key factors that significantly influence economic advancement.
Economic development entails a continuous enhancement of the well-being of a state's citizens, achieved through the growth of physical, human, environmental, and social capital.
Traditional measures of economic development primarily focused on employment and wage growth However, there is a growing recognition of the importance of quality of life factors, including education, health, environmental quality, social amenities, and the overall business climate, in assessing economic progress.
Employment and wage growth are essential indicators of economic development, while emerging indicators can create favorable conditions for this growth A successful economic development strategy for a region or state relies on recognizing these enabling conditions and incorporating them into relevant policies and programs This study aims to pinpoint the factors that significantly enhance economic development, particularly within the framework of the New Economy.
Many states traditionally pursue economic development by attracting investment and job creation through appealing tax incentives for corporations, leading to a competitive "race to the bottom" that undermines their fiscal capacity and public services This competition often results in lower tax revenues, as seen in Washington, where tax breaks from 2003-2005 decreased state revenue by $214 million, adversely affecting essential services like healthcare and education Moreover, the trend of outsourcing jobs to lower-wage countries has diminished traditional investment and put states with higher wages at a competitive disadvantage Therefore, it is crucial to explore alternative strategies that leverage untapped state attributes and underutilized resources for sustainable economic growth.
The New Economy (NE) is gaining recognition as a significant economic transformation, drawing parallels to historical shifts It is believed to have played a crucial role in alleviating the recession that nearly impacted the U.S economy a decade ago (Economy 2002).
The Public Policy Institute (PPI), which publishes frequent state rankings on NE readiness, describes the NE with distinct and clearly differentiating features from the
“Old Economy.” The NE differs substantially from the Old Economy (OE) in several important ways Figure 1.1 tabulates some of these important structural differences
Figure 1.1 Differences between Old and New Economy
Dynamic Global Networked Flexible Production Innovative Ideas Digitization Innovation Quality Collaborative Broad and Changing
Source: Adapted from http://www.kauffman.org.
The Original Economy (OE) originated during the Industrial Revolution, emphasizing the production and consumption of tangible goods and services It heavily depends on natural resources like minerals and essential physical infrastructure, including ports and roadways In this economic model, the value attributed to non-physical elements, such as labor, is significantly lower than that of physical components.
The NE, on the other hand, involves a range of qualitative and quantitative changes which have radically altered the shape, conduct and institutions of the economy
The New Economy (NE) represents a significant shift characterized by a complex and integrated economic base, heavily influenced by information and communication technologies and strong connections to global markets This paradigm emphasizes the importance of cultivating a highly skilled workforce, investing in research and development, and adopting scientific innovations Additionally, it focuses on enhancing environmental quality and overall quality of life Viewed from this angle, the NE can be seen as an organic and somewhat autonomous evolution stemming from the foundations of the Old Economy (OE).
The NE is widely recognized as a key strategy for future growth in the U.S and globally, emphasizing the importance of a state's capacity to adapt and innovate for sustainable development.
1 The New Economy is sometimes compared with the factory economy in the 1890s and the mass production corporate economy of the 1940s and 1950s.
Stable National Hierarchical Mass Production Capital/Labor Mechanization Economies of Scale
Go it AloneJob SpecificOrganization ManSecure attract and/or retain population and generate employment, which will provide an attractive wage for its citizens
From the New Economy Index reports completed by organizations, such as the
The Progressive Policy Institute indicates that the economic transformation to the New Economy (NE) varies significantly among states, leading to differing economic prospects The prevailing view is that a state's economic prosperity hinges on its ability to adapt to NE measures and integrate them into its strategic direction Conversely, states that successfully combine the strengths of the Old Economy (OE) with NE policies are likely to experience more favorable growth opportunities in the future.
In today's competitive landscape, grasping the New Economy (NE) concept and the essential policies for economic transformation is crucial States that evaluate their position on the "Old Economy" to "New Economy" spectrum and inventory their available resources for NE development are better equipped to identify the necessary steps for a successful transition.
The concept of the NE is widely recognized as an emerging phenomenon; however, it has not yet been the focus of extensive and sustained research efforts.
The Kauffman Foundation has effectively identified key variables of the New Economy (NE) and has tracked the performance and rankings of states over time However, there is a critical need to comprehend how the NE operates and the nuances that set it apart from previous economic systems Understanding how various NE variables influence population, job, and wage growth is essential for policymakers and community leaders Insights gained from this research could foster confidence in implementing NE strategies within a comprehensive state growth model This study aims to deepen the understanding of NE dynamics and inform the ongoing transformation process.
Research Objectives
This study presents a quantitative model which helps explain the patterns of growth within the context of the NE across all 50 states The aims of this study are to:
Analysis growth dynamics in population, employment and wages between the years 2000 and 2005;
Decompose population, employment and wages dynamics into NE and OE factors; and
Recommend general policy strategies, which can be employed to move a state towards a NE growth path.
Analyzing a longer timeframe and specific geographic regions can lead to more effective models Given that the NE is a developing phenomenon, accessing all relevant data can be challenging Therefore, this study aims to explore the connection between NE factors and growth at the state level.
Organization of the Study
This study is structured to provide a thorough examination of various factors influencing population, employment, and wage growth in Section Two In Section Three, it establishes the theoretical framework for understanding the drivers of both the Old Economy and New Economy Section Four details the conceptual models employed to assess growth determinants, including Ordinary Least Squares Regression (OLS) analysis, data transformation methods, and the composite indices used Section Five presents the quantitative analysis results, accompanied by concise interpretations and discussions Finally, Section Six explores policy implications, offers recommendations, and concludes the study with final remarks.
Literature Review
Population, employment and wage are determined by many factors In modeling the dynamics of population, employment and wage changes, it is important first to review the drivers of each.
Population Growth
Population growth in a country, state, or city is influenced by birth rates, death rates, and net migration In the U.S., demographic changes have led to fertility rates dropping below replacement levels and life expectancy increasing due to healthcare improvements Consequently, the growth of the U.S population is now largely driven by immigration rather than natural births This trend highlights disparities among states, as their appeal to new and internal migrants varies, creating competition for attracting residents.
Since World War II, there has been a pattern of demographic shifts within the U.S.
According to Rappaport (2003), population changes in the U.S reveal three key patterns: a migration from large cities to suburban areas, a resurgence in some cities after initial population declines, and around 25 major cities consistently gaining residents over a 50-year span This period was marked by a notable demographic shift from Northeastern cities to those in the South and West, driven by factors such as the desire for warmer climates, the increased availability of air conditioning in the 1950s, rising incomes, and a growing population of financially secure retirees due to improved life expectancy.
According to the U.S Census Bureau's 2006 net migration report, net migration to the Southern region of the United States increased significantly from 1990 to 2000, with the Midwest experiencing a rise in out-migration from 73,000 to 161,000 people annually The South Atlantic region saw an increase in migration from 254,000 per year in the 1990s to 313,000 per year post-2000 This demographic shift can be attributed in part to the decline of the manufacturing sector, primarily located in the Northeast and Midwest, as it became a smaller share of the U.S economy In contrast, the more adaptable service sector has emerged, allowing businesses to relocate closer to population centers.
A Washington Post article highlighted cities that are increasingly attracting skilled college graduates, resulting in greater wealth and density (Hardin 2003) These vibrant "hot spots" boast a high concentration of artists, writers, and musicians, alongside walkable communities, diverse dining options, and live entertainment Notably, these cities also have a significant percentage of foreign-born residents (Hardin 2003) Additionally, research indicates that empty nesters are moving from suburbs to urban centers, drawn by enhanced amenities like restaurants and bars (Glaeser, Kolko, and Siez 2000) Furthermore, a positive correlation exists between population growth in cities and the availability of restaurants and live performance venues.
Richard Florida's "Rise of the Creative Class" (2002) highlights the importance of tolerance towards immigrants and minority groups, introducing a diversity index that correlates high tolerance levels with economic development Florida's findings suggest that a welcoming environment is essential for attracting talent, which in turn drives economic and productivity growth He emphasizes that tolerance and diversity significantly enhance the quality of a location, serving as key pull factors for urban areas Additionally, Florida identifies other crucial factors for attracting talent, including robust labor markets, a critical mass of skilled individuals, vibrant town centers, engaging entertainment options, environmental quality, and unique neighborhoods.
Employment Growth
Wheeler (2005) differentiates between "good jobs," which are high-paying, and "bad jobs," characterized by lower wages, highlighting their interconnectedness He notes that an increase in "good jobs" can positively influence the hourly wages of "bad jobs." Five key factors determine the creation of "good jobs," with large urban centers generating 90 percent of these positions while hosting only 83 percent of total employment This concentration of higher-paying jobs in cities may be attributed to the larger labor market, urban amenities, and a significant population of educated individuals Furthermore, the report suggests that a one percent rise in the college-educated demographic leads to an increase in "good job" creation.
The creation of "good jobs" increased by 1.2 percent; however, cities with a significant manufacturing legacy, where over 30 percent of the economy relies on manufacturing, experienced a decline in the generation of quality employment opportunities.
Between 1990 and 2000, cities with less than 15 percent of their economy based on manufacturing experienced job growth, while higher levels of unionization and wages negatively impacted the creation of "good jobs." Specifically, a five percent rise in unionization rates correlated with a 3.5 percent decline in "good job" creation, and a one dollar increase in wages led to a 1.6 percent reduction in "good job" growth.
Wheeler's 2005 study highlights that the availability of personal amenities significantly impacts employee housing choices, which in turn affects business locations Key amenities include educational institutions, healthcare facilities, and entertainment options, along with considerations of environmental quality and aesthetics.
In the context of the New Economy (NE), the demand for technical skills is a crucial factor influencing employment growth Workers possessing high-tech expertise are highly sought after and tend to receive superior compensation for their services.
Technology (IT) sector highlights this fact, as demonstrated by the sustained demand for
Despite a slowdown in more traditional sectors of the economy, IT-related services have continued to thrive In the 1990s, the IT industry experienced growth at double the national average, leading to significant job churning across various economic sectors.
The expansion of the IT sector has led to a significant rise in part-time workers, self-employed individuals, and independent contractors, with an annual growth rate of 12 percent from 1992 to 2000 (Cooke 2002).
The IT sector has initiated extensive training and retraining programs to address the evolving employment demands, leading to increased public and private educational initiatives aimed at enhancing the supply of skilled IT workers and improving overall IT literacy Employment opportunities tend to cluster in areas with a significant concentration of educated and skilled professionals To combat the shortage of IT workers, the U.S government has temporarily raised the cap on H-1B visas for foreign workers, aiming to meet labor market requirements.
Wage Growth
The remuneration levels for the workforce are shaped by various economic, institutional, behavioral, and equity factors Key considerations include the employer’s ability and willingness to pay, as well as the employee’s willingness to accept the offered wages Traditionally, wage changes correlate with business cycles, although variations exist among different worker groups Ultimately, wage levels are established through a collaborative negotiation process involving business owners, employees, and management.
A survey by the American Compensation Association revealed that a firm's ability to pay is the primary factor influencing wages (DCL 2007) This insight highlights why more profitable companies tend to offer higher salaries and often reduce wages during economic downturns Unions may adjust their wage demands in line with a firm's profitability Additionally, a company's capacity to provide higher wages is linked to productivity improvements from wage increases and the effective utilization of labor and capital.
The impact of immigrants (or even in some cases internal migrants) on wage dynamics has long been a source of contention among workers and employers in the U.S (Wilmer
New immigrants often earn lower wages than native-born workers during their initial two years in the country Research indicates that immigrant wages are more sensitive to unemployment rates, suggesting that a high concentration of new immigrants may negatively impact wage levels However, as immigrants gain experience, this effect diminishes Furthermore, more educated immigrants exhibit a smaller wage elasticity in relation to unemployment, indicating that their earnings are less affected by labor market fluctuations.
The correlation between education and skill level with wage rates is evident in the IT sector, where, in 2000, IT workers earned an average wage that was double that of workers in other industries (Cooke 2002) From 1992 to 2000, wages in the IT sector increased by an average of 7.4% annually, surpassing the 4.1% growth rate of private industry wages Furthermore, highly skilled professionals, such as software developers and programmers, experienced even greater wage growth compared to their lower-skilled counterparts within the IT field.
Figure 2.1 Annual Wages per Worker: IT Producing and All Other Private Industries
Source: Adapted from “Jobs in the New Economy” ( page 43 ).
Theoretical Background
Economic growth at the macro level traditionally relies on key factors in the aggregate production function, including capital, labor, management, and land The efficiency with which an economy utilizes these resources has historically driven growth in Old Economy models Economies endowed with a substantial stock of these resources enjoyed a comparative advantage in fostering economic expansion Additionally, advancements in technology that facilitated efficient mass production using existing inputs have played a crucial role in sustaining economic growth and improving living standards over the decades.
In these macroeconomic growth models, a similar growth model can be specified as:
The aggregate output or income at time t, denoted as Yt, is influenced by several key factors: the economy-wide stock of capital (Kt), the labor force (Lt), the amount of land dedicated to production (Ldt), the available managerial skill (Mt), and a specific level of technological advancement (T) To analyze the contribution of each of these factors to output or income growth, one can differentiate the equation, yielding insights into the dynamics of economic growth.
Further decomposing equation (2) by introducing growth rates (through log transformation) and elasticities, equation (2) can be further expressed as: t t M d L t L t
The equation expresses the relationship between output and its influencing factors, represented by elasticities of output concerning capital, labor, land, and managerial skills Specifically, the elasticities are denoted as εK, εL, εLd, and εM, while the log transformations of these factors are indicated as dlnKt, dlnLt, dlnLd, and dlnMt This framework allows for a comprehensive analysis of how variations in capital, labor, land, and managerial skills impact overall output.
The share of capital in total economic output is crucial for understanding economic growth, which can be driven by increasing the stock of capital, labor, land, and managerial skills, or by enhancing the productivity of these factors Effective capital market policies, including monetary policies that influence interest rates and investment, alongside labor market strategies such as minimum wage regulations and immigration policies, play a vital role in fostering economic growth Additionally, land policies that manage land use and productivity, as well as initiatives to improve managerial skills through training and compensation, are essential for promoting prosperity in the Old Economy.
As the global economy transitions towards new economic growth (NE), the significance of talent, venture capital, entrepreneurship, and placemaking, particularly through green infrastructure, becomes increasingly important Key tradeoffs among labor and talent, managerial skills and entrepreneurship, land and green infrastructure, as well as capital and venture capital, must be carefully considered We contend that sustainable economic growth and prosperity in the NE can only be achieved by embracing and transforming these critical drivers.
Using this NE framework, the drivers of aggregate economic output and income can be re-specified by expanding equation (1) to:
The equation Y_t = t t d t t t t t t (4) defines the relationship between various economic factors, where VK_t represents the stock of venture capital at time t, T_t indicates the supply of talent-endowed labor, P_t reflects the degree of placemaking influenced by factors such as green infrastructure, and E_t signifies the supply of entrepreneurial skills By applying total differentiation to this equation, we can analyze the contributions of both New Economy (NE) and Old Economy (OE) elements to overall output or income growth.
T dT dVK f VK dM f M dL f L dL f L dK f
To determine the contribution of each factor to total output, we can further decompose equation (2) by incorporating growth rates and elasticities This approach leads to a clearer understanding of how each factor influences output dynamics over time.
Y d t t t t t t d t t ln ln ln ln ln ln ln ln ln
(6) where all variables remain as defined, VK t , T t , P t and E t are elasticities of output with respect to venture capital, talent, placemaking and entrepreneurial abilities, respectively; t t t T P
The log-transformed variables VK, ln, ln ln, and ln E t represent venture capital, talent, placemaking, and entrepreneurial ability, respectively Each factor's contribution to overall economic output or income is determined by its elasticity multiplied by the log-transformed change in that factor It is important to note that within this framework, at least three components are fundamentally and structurally distinct.
The NE growth model highlights that economic growth and prosperity are influenced by both OE and NE factors, including talent, placemaking, venture capital, and entrepreneurial ability These critical elements were previously overlooked in the OE framework and have not been effectively integrated into policy strategies aimed at enhancing prosperity and ensuring a sustained quality of life.
The new growth and prosperity framework emphasizes innovative policy options that are often neglected in traditional strategies Key drivers of long-term competitiveness include enhancing education, training, and high-quality labor, alongside targeted talent development Additionally, promoting venture capital and fostering entrepreneurial development are vital for economic growth Placemaking plays a crucial role in this strategy, as creating an economy that utilizes green infrastructure and enhances quality of life through cultural assets and outdoor activities is essential for sustained prosperity.
The interdependence among the factors in the NE framework fuels prosperity by creating a cycle that attracts talent, venture capital, and entrepreneurial spirit High quality of life in certain areas draws skilled individuals, which in turn invites investment seeking innovative ideas This cycle not only promotes sustained economic growth but also encourages the responsible and sustainable use of green assets.
The developed theoretical framework for analyzing Organizational Effectiveness (OE) and New Effectiveness (NE) enables the measurement of the contribution of OE and NE factors to new growth in any state.
Following equation (6), one can easily note that the share of NE factors in new economic growth can be measured as:
NE d t t t t t t d t t t t t ln ln ln ln ln ln ln ln ln ln ln ln
NE dln is the NE share of economic growth, the numerator in the bracket, i.e., VK d VK t T d T t E d E t t t t ln ln ln
is the total NE factors share in economic growth, and the denominator K d K t L d L t L d L d t M d M t VK d VK t t t t d t t ln ln ln ln ln
Equation (7) quantifies the contribution of new economic (NE) factors to overall economic growth by measuring their share as a percentage of the combined total of new economic and old economic (OE) factors This statistical approach allows for a clear understanding of the role NE factors play in driving economic development.
Alternatively, for time-series observation of economic transformation from the OE to the
In analyzing an economy over time, one can observe the ratio of Non-Employment (NE) to Occupied Employment (OE) This relationship can be quantified using a straightforward statistic that compares the proportions of NE and OE.
NE t t d t t t t t ln ln ln ln ln ln ln
The NE ratio represents the relationship between NE and OE factors in driving new economic growth The numerator quantifies the total NE contribution to this growth, while the denominator reflects the total OE contribution This relationship, as derived from equation (8), highlights the interplay between these factors in shaping economic progress.
N E p erfectly is eco n om y t he t hen
N E ad van ced i s econ o m y th e th en
O E f rom tra nsit io n i n is eco n om y t he then
O E perfectl y is econ o m y th e th en
Methodology and Data
A healthy economy is characterized by population growth, low unemployment, and high-income opportunities To develop a conceptual model for growth determinants, it is essential to identify the New Economy (NE) variables that influence population, employment, and wages This involves comparing the impacts of NE indicators with those of Old Economy (OE) indicators to understand how both contribute to new economic growth.
The literature review identified various explanatory variables relevant to both the "Old Economy" and the emerging economy A significant challenge encountered was the lack of high-quality NE variables over a substantial time frame suitable for quantitative analysis The subsequent sections detail the selected variables that influence changes in population, employment, and wages, explain the rationale behind their selection, and outline the econometric specifications applied in the analysis.
Old and New Economy Variables
Table 4.1 summarizes key drivers of growth and development, emphasizing previously identified factors in the literature related to Old and New Economy variables It outlines econometric models that analyze changes in state population, employment, and wages The table distinguishes between Old Economy (OE) variables, which are traditionally recognized as company attractors, and New Economy (NE) variables, which are emerging as significant economic drivers.
Table 4.1 Old and New Economy Variables
New Old Variable Rationale for Selection
Per capita income (PCI) serves as a significant positive factor attracting immigrants, as it reflects the economic opportunities available in a region Additionally, a higher PCI enables states to offer better services to their citizens, enhancing overall quality of life.
O Average Annual Wage (2000) A higher wage is expected to be a pull factor for population
O Unemployment Rate (2001) A low unemployment rate is expected to be related to population growth.
N O Median Housing Value (2000) The presence of affordable housing could be a pull factor for population, as well as jobs.
O GSP per Capita (2001) A high Gross State Product (GSP) per capita could imply high productivity, which is expected to be positively related with wage
O Energy Costs (2000) High energy cost is expected to be disincentive for new businesses.
The presence of minority populations may signal a growing demographic trend, driven by both immigration and higher natural birth rates According to U.S Census data, certain minority groups exhibit fertility rates that surpass those of the general population, highlighting their significant contribution to population growth.
The foreign-born population serves as a key indicator of population growth, reflecting the influx of new immigrants who often possess specialized skills needed by vital industries These newcomers tend to settle in urban areas, helping to counteract the population decline and economic downturn typically linked to suburban sprawl.
N Percentage Population in Area with PAQ 2 (2000) The disutility associated with Poor Air
Quality (PAQ) and/or other environmental conditions may be a push (negative) factor for population, as well as new businesses.
O Population Density (2000) A higher population density may be indicative of scale effects and efficiency gains across different levels of the economy.
N Percent Urban Population More urbanized states may enjoy greater economies of scale in some areas of the economy, which may impact productivity.
May translate into environmental performance, which makes states attractive for residents and businesses.
N Acres of Forests (2000) Population may be attracted by amenity value ascribed to forests and other natural areas
N Acres of State Park (2002) Population may be attracted by amenity value ascribed to state parks.
N Miles of Coastline (2000) Population may be attracted by amenity value ascribed to coastlines.
Expenditure on environmental protection may translate into tangible outcomes that may make a state attractive for residents, as well as new businesses.
O Violent Crimes (2000) Violent crimes are expected to be a push factor for population, as well as a disincentive for the creation of businesses.
N Milken State Technology Index States with a high ranking on this index
Score (2002) are expected to be attractive for businesses.
N Number of Patents (2000) This may be indicative of the levels of creativity and the potential for future innovation businesses.
Connections (2005) Broadband technology may be indicative of the available infrastructure a location has to accommodate IT type jobs, and the ability of firms to adopt information technology in business processes.
N Number of Women and Minority
Owned Firms (2000) Previous studies have shown that women and minorities are more likely to start new businesses
An educated/skilled workforce endowment for supporting “Knowledge Based Economy.”
An educated/skilled workforce endowment for supporting “Knowledge Based Economy.”
This may be indicative of the levels of creativity and the potential for future innovation and new businesses.
N Urban Mass Transit per Capita
It is anticipated that more densely populated and urbanized states will provide more public transport
N Number of IT Jobs (2002) This may be indicative of the ability of a state to innovate and to provide a conducive environment for attracting
O Value of Exports (2000) This may be indicative of a state’s ability to be competitive.
Population Growth
The study identifies key variables influencing population growth, including per capita income, average annual wage, unemployment rate, and the percentage of minorities in the population Additional factors such as air quality, violent crime rates, forested areas, coastal mileage, mass transit availability, median housing values, foreign-born population percentage, NE index score, urban population percentage, and Green Plan Capacity Index are also considered It emphasizes that wage rates, unemployment levels, housing affordability, and crime rates significantly impact company locators and, consequently, population and wage dynamics in the region.
The population is anticipated to react favorably to various OE drivers, including PCI, wages, and high employment rates Additionally, positive responses are expected towards NE drivers such as good air quality, diversity, urban environments, open spaces, water amenities, mass transit, immigrant populations, and green infrastructure.
The population dynamics were modeled with the change in population between 2000-
2005 as the dependent variable regressed against the earlier mentioned explanatory variables (lagged by one period of five years) The equation specification is indicated in equation (10) below.
X 5t-1 Percentage of population in an area with poor air quality in 2000,
X 6t-1 Violent crimes per 100,000 state residents in 2000,
X 8t-1 Miles of ocean coastlines and lake coastlines in 2002,
X 9t-1 Urban mass transit in miles per capita in 2000,
X 12t-1 Milken State Technology Index Score in 2002,
X 13t-1 Percentage of urban population in 2000, and
X 14t-1 Green Plan Capacity Index (Score).
To evaluate regional variations in population dynamics, dummy variables were incorporated into the population equation, utilizing the regional classification established by the U.S Census Bureau, as illustrated in Figure 4.1.
The dummy variables and represented regions are listed below:
Dum_WNC West North Central = 1, Zero otherwise,
Dum_Mt Mountains =1, Zero otherwise,
Dum_Pac Pacific = 1, Zero otherwise,
Dum_WSC West South Central = 1, Zero otherwise,
Dum_ESC East South Central = 1, Zero otherwise,
Dum_SA South Atlantic = 1, Zero otherwise,
Dum_MA Middle Atlantic = 1, Zero otherwise, and
Dum_NE New England = 1, Zero otherwise.
Employment Growth
The job growth model analyzed various key variables, including the proportion of the population aged 18-24, median housing values, GSP per capita, annual wages, the number of patents issued, and the number of broadband connections It also considered the presence of women and minority-owned firms, the percentage of the population with a college education, research and development expenditures, and the incidence of violent crimes Additionally, factors such as urban mass transit, energy costs, acres of forests, acres of state parks, the Green Plan Capacity Index, and the percentage of the urban population were integral to understanding job growth dynamics.
The relationship between median household value, violent crime rates, and energy costs is inversely related to job growth, as high housing costs can deter new residents and hinder employment opportunities Additionally, high violent crime rates can stifle business development, further limiting job growth Rising energy costs contribute to increased business expenses, prompting companies to consider relocating or reducing output, which negatively impacts employment Conversely, wealthier states tend to attract more jobs, and innovation, as indicated by patents issued, can lead to new employment opportunities A highly educated workforce enhances a state's appeal to businesses, while the presence of natural amenities, such as parks and forests, serves as an attractive factor for both individuals and companies seeking to relocate.
Between 2000 and 2005, the change in jobs served as the dependent variable in the regression analysis, while the explanatory variables were lagged by one period, specifically five years The wage change equation was formulated to reflect these relationships.
E t Change in jobs (employment) from 2001-2005,
X 15t-1 Percentage of population in the 18-24 age cohort in 2000,
X 16t-1 Gross state product per capita in 2001,
X 18t-1 Number of patents issued per 100,000 persons,
X 19t-1 Number of Broadband connections per capita,
X 20t-1 Number of women/minority firms per 100 residents, 100 residen
X 21t-1 Percent of population with first degree or higher, higher
X 21t-1 Research and development expenditure per capita,capita
X 22t-1 Urban mass transit in miles per capita in 2000,
X 23t-1 Energy costs (cents per KWH),
X 25t-1 Acres of state parks in 2002,
X 26t-1 Green Plan Capacity Index (Score), and
X 27t-1 Percentage of urban population in 2000.
The regional dummy variables mentioned in the previous section were also applied to this model.
Wage Growth
The wage growth model incorporates various key variables, including population density, net migration rates, and the percentage of minority populations It also considers the proportion of individuals aged 18-24, the number of IT sector jobs, and broadband connections Additionally, the model examines the prevalence of minority and women-owned firms, the share of creative jobs, and the percentage of the population with a college education High school completion rates, violent crime statistics, expenditures on green infrastructure, park acreage, and export levels are also critical factors in understanding wage growth dynamics.
The anticipated growth in wages is positively correlated with several factors, including the number of IT jobs, broadband connections, patents issued, the percentage of the college-educated population, and investments in green infrastructure Research shows that employees in the IT sector typically earn higher wages compared to other industries Additionally, the availability of broadband connections not only reflects a state's capability to support IT firms but also enhances remote work opportunities, boosting productivity and consequently wage growth Furthermore, the issuance of patents is linked to job creation, which can further elevate wage levels Conversely, a higher incidence of violent crime is expected to negatively impact wages by hindering investment.
Between 2000 and 2005, changes in wages were analyzed by regressing them against explanatory variables, which were lagged by five years, while incorporating regional dummy variables into the model.
X 15t-1 Percentage of population in the 18-24 age cohort in 2000,cohort 2000
X 18t-1 Number of patents issued per 100,000 persons,persons
X 19t-1 Number of Broadband connections per capita, capita
X 20t-1 Number of women/minority firms per 100 residents, firms per
X 21t-1 Percent of population with first degree or higher, or higher 20
X 31t-1 Number of IT Jobs per 10,000 in 2000,
X 32t-1 Creative industry jobs as a percentage of employed, of empl
X 34t-1 Per capita expenditure on green infrastructure, andinfrastructure
Data
Data Transformation
Data transformation involved converting absolute values into per capita values or ratios, enabling more effective comparisons between states while considering variations in size and population This process entailed dividing the absolute data values at a given time by the respective state populations The transformed variables are presented in Table 4.2.
Gross State Product Gross State Product per Capita
Number of IT Jobs IT Jobs per 100,000 Residents
Number of Patents Number of Patents per Capita
Number of Minority Owned Firms Minority Owned Firms per 100 Residents
Expenditure on Green Infrastructure Per Capita Expenditure on Green
Infrastructure Number of Violent Crimes Violent Crimes per 10,000 State Residents
Number of Creative Industry Jobs Number of Creative Industry Jobs as a
The indicators chosen were ranked based on their relative scores across all 50 states
Appendix D, specifically Tables D1 to D3, presents a comprehensive analysis of state performance across twenty-eight selected indicators, detailing each state's score and rank relative to others Scores are derived by subtracting the average indicator value from individual scores and dividing by the standard deviation, resulting in both positive and negative values that illustrate how states compare to the mean For instance, Michigan's per-capita expenditure score on green infrastructure, found in Table D2, is -0.82, indicating it falls 0.82 standard deviations below the average expenditure among all states Actual values can be calculated using the mean and standard deviations provided in the tables.
Table 4.3 illustrates Michigan's ranking compared to the average ranks of the top ten and bottom ten performing states, categorized into economic, demographic, environmental, and economic variables Key indicators within these categories include OE, commerce, demographics, green infrastructure, and various other drivers For a comprehensive ranking of all states, please refer to Appendix D.
Table 4.3 Relative Ranking of States
1 Connecticut California Massachusetts New Jersey New Jersey
2 Massachusetts New Jersey Connecticut Massachusetts Massachusetts
3 New Jersey New York New Jersey New York New York
4 New York Maryland California Connecticut Connecticut
5 Delaware Florida New Hampshire California California
6 Colorado Hawaii Minnesota Maryland Maryland
7 Virginia Texas Vermont Delaware Delaware
8 Minnesota Illinois Colorado Virginia Virginia
9 Maryland Rhode Island New York Illinois Illinois
10 New Hampshire Georgia Washington Rhode Island Rhode Island
23 Michigan 24 Michigan 20 Michigan 23 Michigan 23 Michigan
29 Indiana 25 Indiana 29 Indiana 31 Indiana 28 Indiana
25 Ohio 23 Ohio 28 Ohio 26 Ohio 25 Ohio
41 Idaho Iowa Oklahoma Idaho Idaho
42 New Mexico New Hampshire Nevada South Dakota South Dakota
43 South Carolina West Virginia South Carolina Oklahoma Oklahoma
44 Kentucky Idaho Wyoming Louisiana Louisiana
45 Alabama South Dakota Alabama Alabama Alabama
46 Montana Vermont Louisiana Montana Montana
47 Louisiana North Dakota Kentucky Kentucky Kentucky
48 Arkansas Maine Arkansas Arkansas Arkansas
49 West Virginia Wyoming West Virginia Mississippi Mississippi
50 Mississippi Montana Mississippi West Virginia West Virginia
The table indicates that certain states, including Connecticut, Massachusetts, California, New York, and New Jersey, consistently rank high in the selected indicators, whereas states like Kentucky and Mississippi maintain consistently low rankings.
Louisiana and West Virginia are falling behind in performance compared to other states, while Michigan, Indiana, and Ohio are positioned in the middle A scatter plot in Figure 4.2 illustrates the relationship between the mean rank of various indicators and per capita income, revealing that states with higher rankings tend to have greater per capita income The focus of this analysis is to assess the correlation between NE factors and performance indicators in contrast to OE factors.
Figure 4.2 Income Mean Rank Relationships
Green Plan Capacity Index
The Green Plan Capacity (GPC) Index serves as a key focus in this study, examining whether green amenities contribute to economic growth Developed by the Resource Renewal Institute, the GPC Index underscores the growing significance of states in environmental protection and outlines a sustainable development pathway for the future (Siy, Koziol, and Rollins 2001).
Figure 4.3 Green Plan Capacity Index
Source: Adapted from the State of the State Report from the Resource Renewal Institute.
The composite index, illustrated in Figure 4.3, evaluates 63 indicators across four key sub-indices: the strength of the environmental management framework (35%), the level of environmental policy innovation (40%), fiscal and program commitment (10%), and the quality of governance (15%) Additionally, the GPC index is integrated into the model to emphasize the significance of environmental factors as essential drivers of growth.
Milken State Technology Index
The State Technology Index (STI), developed by the Milken Institute, serves as a comparative assessment tool to evaluate the advancements states have achieved in science and technology This index encompasses a broad array of technological and scientific assets that are vital for driving economic prosperity.
In compiling this composite index, 75 indicators grouped into four equally weighted sub- indices were considered These four groups are: (1) Research and Development Inputs;
The index integrates Risk Capital Infrastructure, Human Capital Investment, and Technology Concentration and Dynamism to emphasize the importance of innovation, creativity, and skilled workforce in driving the ongoing development of the New Economy.
The three econometric models outlined in equations (10), (11), and (12) are estimated using ordinary least squares, with a collinearity test confirming no significant multicollinearity issues among the variables The performance and significance of the models demonstrate a good fit to the data, reflecting the cross-sectional nature of the dataset.
Population Growth
Table 5.1 presents the results of the population change decomposition regression, with an R² value of 0.91 indicating a strong model fit for explaining population changes from 2000 to 2005 The table details the variables alongside their coefficients and t-values, highlighting statistically significant coefficients at the 10 percent level in bold.
Table 5.1 Results for Population Growth
Percentage Population Living in Areas with Poor Air Quality 357.32 0.30
Urban Mass Transit per Capita - 17435 -1.96
Milken Science and Technology Index Score 5176.46 1.25
The positive correlation between per capita income (PCI) and population growth indicates that individuals are drawn to states with higher PCI, with an increase of one dollar in PCI leading to an estimated rise of 32 residents over five years Conversely, higher wages appear to correlate negatively with population change, where a one dollar wage increase may result in a decrease of approximately 70 individuals within the same timeframe This finding challenges traditional economic assumptions that higher wage states attract more residents, suggesting a complex interplay between population dynamics, job availability, and wage levels, which will be explored further in the conclusion.
The study indicates a positive correlation between forest acreage and population change, though the results are not statistically significant Additionally, there is a notable positive relationship between population change and the GPC Index, highlighting a strong connection between environmental amenities and population growth This suggests that individuals are more likely to move to states that prioritize and actively work towards enhancing and preserving environmental quality.
A significant correlation exists between the percentage of foreign-born individuals and population change, indicating that a one percent increase in foreign immigrants can lead to an increase of over 5,000 residents in a state over five years Consequently, states with larger immigrant populations are likely to experience population growth Conversely, urban mass transit per capita shows a negative relationship with population change, suggesting a trend of migration from cities to suburban areas and less urban states This evidence supports the idea that immigration-based strategies can effectively drive economic development.
The introduction of a regional dummy variable revealed that the East South Central, West South Central, and South Atlantic regions experienced greater population changes than the East North Central region, which served as the base region Notably, the population shifts in other regions were statistically similar This significant finding highlights a pronounced population increase in the southern states, aligning with existing literature Such comparative growth may be attributed to an influx of new immigrants and ongoing migration from the Midwest and Northeast.
In regression analysis, the R² value indicates the model's explanatory power To analyze the contributions of both OE and NE factors to population change, R² decomposition is utilized Initially, an unrestricted complete model, which excludes dummy variables, is estimated to assess the explanatory power of NE variables The resulting R² reflects the percentage of variance explained by the model Subsequently, a restricted model that includes only NE variables is estimated, providing an R² that represents the explanatory strength of this subset.
The total population change in a state can be significantly influenced by various factors, including those previously discussed Understanding the specific contribution of these NE factors to the overall demographic shifts is crucial for a comprehensive analysis of population dynamics.
To address this question, R 2 from the overall model (unrestricted model) is compared to
The restricted model, which includes only NE factors, is nested within the unrestricted model The ratio of the restricted R² to the unrestricted R² serves as an indicator of the explanatory power of the NE variables This decomposition reveals the percentage of the explainable portion of the model that is accounted for by NE factors.
The population change decomposition model reveals that the unrestricted R², which includes both New and Old Economy factors, is 0.831, while the restricted R², focusing solely on New Economy factors, is 0.81 This indicates that New Economy factors account for 97.47 percent of the explainable portion of population change.
0 = 0.9747 This result suggests that approximately 97 percent of the change in population as estimated by the model could be explained by NE factors.
Employment Growth
The results of the model, detailed in Table 5.2, demonstrate a strong explanatory power for state employment growth decomposition, indicated by an R² value of 0.83 Additionally, the table presents the coefficients and t-values, with statistically significant variables highlighted in bold at the 10 percent level.
Table 5.2 Results for Employment Growth
Percent Population with B.Sc or Higher 25041.2 2.61
Urban Mass Transit per Capita - 1984.54 - 0.29
Research indicates a negative correlation between median housing value and employment opportunities, with a one-dollar increase in housing value potentially leading to a loss of four jobs within five years Similarly, an increase in average wages by one dollar could result in a reduction of approximately fifty job opportunities This aligns with traditional economic theory, suggesting that profit-maximizing firms prefer to establish operations in areas with lower housing costs and wages to minimize expenses and maximize job creation.
Education, state parks, green infrastructure, and urban population are positively correlated with employment growth Specifically, a one percent increase in college-educated individuals in a state can lead to an increase of over 25,000 employed persons within five years An educated workforce is essential for attracting investment and jobs, highlighting the importance of state spending on education to enhance competitiveness Additionally, the positive impact of green infrastructure on job creation underscores the value of locations with high-quality environmental amenities and strong regulatory frameworks The model indicates that a one-point improvement in the Green Plan Capacity Index could generate 4,154 new employment opportunities over five years Furthermore, more urbanized states benefit from a critical mass of human capital and robust infrastructure, facilitating job growth compared to less urbanized areas, aligning with New Economy principles.
The analysis reveals that the Mountain, Pacific Coast, East South Central, and South Atlantic regions experienced more significant job growth compared to the East North Central Region, which serves as the baseline Notably, the southern states, particularly the East South Central and Mid-Atlantic regions, appear to attract both new jobs and population In contrast, job changes in other regions analyzed were statistically similar to those in the baseline region.
The analysis of employment change over time reveals that New Economy (NE) factors account for a significant portion of this change In the employment change decomposition model, the unrestricted model, which includes both New and Old Economy factors, has an R² value of 0.7461, while the restricted model, focusing solely on NE factors, shows an R² of 0.47 This indicates that NE factors explain approximately 62.9 percent of the explainable portion of employment change.
0 = 0.6299 Based on this decomposition of the model, approximately 63 percent of employment growth in our model is explained by NE variables.
Wage Growth
The results of the wage growth model, detailed in Table 5.3, indicate a strong explanatory power with an R² value of 0.88 The table also presents the statistical significance and coefficient signs of the variables, with those significant at the 10 percent level highlighted in bold.
Table 5.3 Results for Wage Growth
Percent Population with B.Sc or Higher 74.96 1.84
Per Capita Expenditure in Green Infrastructure 2.10 2.04
The correlation between IT jobs and wage growth is significant, with each new IT position contributing approximately $17 to average wages over five years Additionally, states with a more educated workforce see an increase of about $75 in average wages for every one percent rise in college-educated individuals, highlighting the crucial link between educational investment and wage growth Furthermore, public spending on environmental protection, as a measure of green infrastructure, positively influences wage changes, emphasizing the role of environmental factors in economic performance Conversely, an increase in violent crime by one per capita can lead to a decrease of around $2,500 in average annual wages, underscoring the relationship between crime reduction and economic prosperity.
The analysis revealed that the Pacific and Mid-Atlantic regions experienced smaller wage changes compared to the base region, while other regions in the model did not show statistically significant differences from the base region.
The analysis of wage change over time reveals that New Economy (NE) factors significantly contribute to this phenomenon In the wage change model, the unrestricted R², which includes both New and Old Economy factors, was 0.847, while the restricted R², focusing solely on NE factors, was 0.745 Consequently, NE factors account for 87.9 percent of the explainable variance in wage changes.
In the wage change model analyzed, the inclusion of a single OE variable yielded a statistically insignificant result, indicating its lack of impact Consequently, the findings suggest that wage changes are predominantly influenced by factors associated with the New Economy, highlighting the significance of these variables in understanding wage dynamics.
Conclusions and Policy Recommendations
Section Four reveals critical insights that significantly impact the development trajectory of the New Economy (NE) Achieving positive growth in population, employment, and wages is essential for economic progress Given that income is derived from wages and employment, a consistent increase in these three factors indicates the potential for sustained economic growth.
The completion of data analysis using state-level data highlights the significance of certain variables in economic development, as indicated by statistically significant coefficients It is possible that factors deemed not statistically significant at the state level could yield more conclusive results if analyzed at the metropolitan or regional level.
Per Capita Income and Wages
Increasing per capita income (PCI) is essential for a state's economic success, as it enhances fiscal capacity to offer services and amenities that attract new residents and businesses Productivity directly influences PCI, making it a critical factor in boosting a state's economy States that consistently achieve PCI growth also tend to attract a larger population.
Wages are closely linked to population and job growth, with evidence suggesting that wages are negatively affected by job availability rather than population growth itself The model indicates that states with job opportunities, even low-wage ones, will experience population growth However, in today's globalized economy, firms can outsource jobs to lower-wage markets, putting pressure on traditionally low-wage states Consequently, relying solely on low wages without transitioning to new economic strategies is no longer a sustainable path for economic growth.
Higher Education
Higher education plays a crucial role in driving economic development, as a higher percentage of college-educated individuals correlates with increased employment and wage growth A skilled workforce is a key factor for companies when choosing locations, and educated workers are more adaptable to the evolving demands of today’s labor markets Therefore, states must prioritize the provision of quality higher education for their citizens to thrive in the New Economy.
High Tech Jobs and Internet Infrastructure
To attract higher-paying jobs, states should focus on developing high-technology sectors as alternatives to traditional low-wage factory work Our model indicates a strong correlation between Information Technology (IT) jobs and wage growth, suggesting a similar potential for other knowledge-based fields like biotechnology and robotics Although research and development spending and patent issuance were not statistically significant in our findings, we advocate for the allocation of public resources towards applied research to stimulate new business development Additionally, fostering strategic partnerships among universities, private research labs, industries, and community organizations can enhance innovation and entrepreneurship.
Broadband Internet connections are significantly associated with wage growth, likely due to the rise of IT jobs As companies adopt information technologies to enhance efficiency, productivity increases, leading to higher wages By investing in this infrastructure, states can catalyze a new wave of productivity growth that was previously unattainable.
New Immigrants
In today's economy, the immigrant population plays a crucial role in enhancing the competitiveness of states, as foreign-born residents significantly contribute to population growth The adage "America is a country of immigrants" holds true when considering the economic impact of newcomers, who engage in various sectors from high-tech to essential services, create new businesses, and stimulate demand for goods and services To maximize this potential, proactive states can create a welcoming environment and provide support services, making themselves attractive destinations for highly skilled and educated immigrants who have benefited from U.S higher education opportunities.
Green Infrastructure and Other Environmental Considerations
Green infrastructure plays a crucial role in driving economic growth, as evidenced by its positive correlation with population and job growth, particularly through the Green Plan Capacity (GPC) Index Additionally, the presence of state parks and forests contributes to this growth, although the latter's impact is not statistically significant Furthermore, increased per capita spending on green infrastructure is linked to higher wage growth, highlighting the intrinsic value that both citizens and businesses place on environmental amenities.
The significance of R&D expenditure may be limited due to the short analysis period, as the real impact often takes time to manifest in the economy To maximize the benefits of their natural resources and enhance environmental quality, states should collaborate with local governments Investing in the refurbishment and establishment of parks and green spaces in urban areas can help mitigate the effects of suburbanization, which has contributed to various challenges faced by older cities.
Violent crime significantly hampers employment and wage growth, making it clear that businesses struggle to thrive in areas plagued by such issues For economic growth to occur, reducing crime rates is essential States should prioritize resources to rehabilitate communities with persistent crime problems and implement strategies to shift public perception once these issues are addressed, as lingering negative perceptions can hinder future economic development opportunities.
In today's evolving global economy, recognizing and adopting new economic (NE) strategies for growth is crucial This study highlights essential NE strategies that can drive economic development and prosperity at local, regional, and state levels.
Investing in high-quality education is crucial for transitioning to a knowledge-based economy, as research indicates it significantly influences economic development Additionally, implementing effective retention strategies is essential for maintaining a skilled talent base, ensuring that the benefits of educational investment are fully realized.
Investing in green infrastructure is closely linked to population and income growth, indicating its significant role in fostering economic development This approach presents a viable alternative strategy for achieving growth through targeted investments in sustainable infrastructure.
States with high immigrant populations have shown strong growth performance, indicating a need to reevaluate current immigration policies A strategic approach that facilitates the influx of skilled labor and talent can foster small business development and overall economic growth By aligning immigration strategies with the demands of the New Economy, we can enhance the transition to sustainable economic growth.
Investing in high-tech infrastructure is essential for enhancing information exchange within the new economy (NE), lowering business costs, and boosting efficiency Prioritizing this investment will encourage the clustering of NE activities, as high-tech jobs are among the fastest-growing in both employment and wages Expanding high-tech infrastructure can unlock significant opportunities for economic growth.
A transformative policy environment is essential for achieving prosperity in the New Economy, necessitating a shift in how economic systems are structured and policies are aligned By adopting a broader policy framework rooted in New Economy principles, we can foster an environment that promotes widespread economic growth and prosperity.
As states such as Michigan address the challenges posed by global economic changes and their effects on local economies, a New Economy (NE) framework offers a viable policy alternative to enhance economic growth, create job opportunities, and elevate citizens' living standards This study seeks to spark a discussion aimed at developing targeted actions and strategies grounded in NE principles.
Appendix A: List of Data Sources
USGS National Land Cover Data
National Fish and Wildlife Service
Appendix B: State New Economy Index Scores and Ranks for
Source: The Progressive Policy Institute 2002 State Technology Index: http://www.neweconomyindex.org/states/index.html
2 New Jersey 6.4 New York 8.55 Connecticut 8.56 Oregon 8.00
3 Maryland 6.8 Massachusetts 8.64 New Jersey 10.00 Texas 14.64
4 New York 8.4 California 8.91 California 10.75 Utah 15.91
6 Delaware 13.6 New Jersey 9.73 Minnesota 13.00 Florida 17.55
8 Rhode Island 13.8 District of Colombia 15.18 Rhode Island 15.94 Illinois 18.91
9 Florida 14 Washington 16.55 Maryland 16.19 New York 19.00
10 Connecticut 14.2 New Hampshire 16.73 New York 16.50 Georgia 19.18
11 Georgia 14.2 Colorado 17.18 New Hampshire 17.00 Nevada 19.18
15 South Carolina 19.2 Hawaii 19.09 Oregon 18.31 Delaware 21.27
16 North Carolina 19.6 Minnesota 19.36 Virginia 19.00 New Mexico 21.45
17 Texas 19.6 Rhode Island 19.64 Vermont 19.63 South Carolina 22.09
20 District of Colombia 21.4 Oregon 21.64 Texas 23.75 North Carolina 22.82
21 Michigan 22.8 Virginia 21.64 Arizona 24.81 New Jersey 23.36
25 Arizona 24.4 North Carolina 24.82 Georgia 26.25 Virginia 26.00
28 Nevada 25.8 Arizona 25.45 District of Colombia 26.75 Michigan 26.45
29 New Mexico 26.2 Florida 26.64 Florida 26.75 Montana 27.45
30 Colorado 29.2 Kansas 30.27 North Dakota 28.31 Ohio 27.45
31 Oklahoma 29.2 Maine 30.73 Ohio 28.50 Rhode Island 27.91
32 Missouri 29.8 Wyoming 31.73 North Carolina 29.13 Connecticut 29.82
34 Arkansas 32 Indiana 32.36 New Mexico 29.50 Alabama 30.27
38 Kentucky 34.2 South Carolina 34.27 Indiana 31.38 Mississippi 32.27
39 Utah 34.2 New Mexico 34.82 Maine 32.44 West Virginia 32.36
41 New Hampshire 35 Iowa 35.27 Oklahoma 34.56 Kentucky 32.73
43 Iowa 39.6 Alabama 37.27 South Dakota 36.69 Maine 33.09
44 West Virginia 40.6 Idaho 37.82 Wyoming 36.94 Iowa 33.64
45 Idaho 42 South Dakota 38.36 South Carolina 39.31 Kansas 33.82
47 South Dakota 42.8 North Dakota 41.45 Kentucky 41.56 Alaska 34.82
48 Wyoming 45 Montana 42.18 Arkansas 41.94 North Dakota 37.00
49 North Dakota 45.2 Arkansas 42.64 Louisiana 42.63 New Hampshire 37.91
50 Montana 45.4 West Virginia 42.64 West Virginia 43.81 South Dakota 39.55
51 Maine 45.6 Oklahoma 42.73 Mississippi 47.31 District of Colombia 49.82
Appendix D: Economic, Demographic and Green Infrastructure Indicators and New Economy
GSI per Capita Score 2005 Rank
4 Lowest unemployment gets highest rank.
D 1 : Economic and Demographic Indicators (continued)
Foreign Born Population Score 2000 Rank
Percent Urban Population Score 2002 Rank
5 Lowest median hosing value gets highest rank
Mass Transit Per Capita Score 2000 Rank
Green Plan Capacity Score 2001 Rank
Acres of Forests Score 2002 Rank
Acres of State Parks Score 2002 Rank
Green Plan Capacity Score 2001 Rank
Per Capita Expenditure on Green Infrastructure Score 2002 Rank
6 Higher ranks are ascribed to states with lower crime rates.
State Broad Band Rank Minority Rank College Rank College Rank RD Rank RD Rank Energy Costs Rank
Milken State Technology Index Score
Appendix D 3 : New Economy Variables (continued)
Creative Industry Jobs Score 2005 Rank
Export as a Share of GSP Score 2003 Rank
Appendix E: Comparison of Michigan with Other Midwestern States
Gross State Income per capita 2001
Gross State Income per Capita 2005
7 Lower unemployment rate received higher rank.
Appendix E: Comparison of Michigan with other Midwestern states (continued)
Per Capita Expenditure on Green Infrastructure