Bridging the Gap

[Full title: Bridging the Gap: Integrated Occupational and Industrial Approach to Understand the Regional Economic Advantage]

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sustainability Article Bridging the Gap: Integrated Occupational and Industrial Approach to Understand the Regional Economic Advantage Tuo Lin 1,2,3 , Kevin Stolarick 4 and Rong Sheng 2,5, * 1 School of Urban & Regional Science, East China Normal University, Shanghai 200062, China 2 Institute of Eco-Chongming, East China Normal University, Shanghai 200062, China 3 Institute of China Administrative Division, East China Normal University, Shanghai 200062, China 4 Martin Prosperity Institute, University of Toronto, Toronto, ON M 5 S 3 E 6, Canada 5 Institute of Urban Development, East China Normal University, Shanghai 200062, China * Correspondence: shengrong 5@126.com Received: 10 June 2019; Accepted: 30 July 2019; Published: 6 August 2019 Abstract: In the debates on regional economic analysis, scholars generally reach the consensus that the industrial frame and the occupational mix are not very accurate substitutes for each other. While industry concentration and mix are widely accepted as significant, the independent consideration of occupation has been shown to be important, especially for creativity-concentrated regions. However, neither the industrial nor the occupational mix is separately su ffi cient to be solely applied to understand the entire regional situation. This paper develops an integrated occupational and industrial structure (IOIS) at the state and also the national level in order to bridge the gap between separate industrial and occupational analytic results. The case of California is used to demonstrate that the integrated approach is a more e ff ective way than either the single occupational or industrial analysis. The further application of this approach to data for the fifty states provides a general view of joint occupational and industrial development across the nation. This approach further links the occupational approach and the industrial development together by providing a new way to measure and identify the regional comparative di ff erence to be able to implement more fruitful policy-making decisions Keywords: occupation; industrial structure; integrated approach; regional economics 1. Introduction The industrial analytical framework has long been a top focus in research related to cities and regions. Ever since the 1950 s, when trade was regarded as the major driving factor of regional productivity, regional industrial structure has always been the dominant model because the industrial output, or more specifically the products of regions and countries, has long driven the key questions guiding research in this field. As the role of human capital in economic development is increasingly gaining traction in the literature and policy realms, people’s knowledge and problem-solving abilities provide a new perspective in which urban and regional competitiveness can be explored Since the pioneering work of Thompson and Thompson [ 1 , 2 ], the occupational mix has become an important factor in the regional economic analysis. From educational attainment to broader knowledge and skills, the occupational research frame has gained great interest among scholars. Of course, this debate has experienced several shifts in focus, as is described in the next section. In regional development analysis, scholars have generally reached the consensus that these two approaches o ff er di ff erences in measuring the situations for a specific region. The industrial analysis is not enough to function as a replacement for occupational analysis. However, neither the industrial nor the occupational mix is enough to be solely applied to acquire the whole view of the regional economic Sustainability 2019 , 11 , 4240; doi:10.3390 / su 11154240 www.mdpi.com / journal / sustainability

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Sustainability 2019 , 11 , 4240 2 of 17 image. The challenge is that the occupational perspective now plays an increasingly important part in regional development analysis while the industrial framework is still of significant influence. As a result, an integrated approach to view the regional industrial and occupational mix as a whole is needed 2. Focus Shifts in Regional Economic Analysis Regional economic analysis has experienced four major research approaches with their own focuses during the past several decades. The first is the focus on trade. Since the 1950 s exports have been the focus of research on regional output, with scholars arguing that trade is the major element and contributor to regional productivity. In this period, the research approach is characteristic of firms gathering as industrial clusters for the shared benefit of convenient labor and resource supplies [ 3 – 11 ]. The second is the focus on human capital and division of labor as scholars became increasingly more interested in the role of human capital in regional development [ 12 – 24 ]. Ideas and innovation become more visible in the regional development agenda. Firms and labor cluster for more e ff ective communication and idea exchanges, rather than just labor availability and natural resources. Scholars concerned themselves more with the clustering behavior of talent and human resource, rather than just the companies. The third is the focus on human capital in its own as separated from industrial frame and as measured by education. The indicator of education attainment is used by researchers to identify and group the human capital resources [ 22 , 25 – 27 ]. The fourth is the focus on occupational analysis. In the past ten years, the human capital through the measurement of skills and knowledge in practical work rather than just as educational attainment has been gaining momentum. The occupation-based regional analysis uses this approach and is the most recent approach to understanding regional prosperity. It was identified to target both occupational and industrial aspects of regional economic development [ 1 , 2 , 28 – 33 ]. Some researches further take more specifically skill measures other than education or skills to examine the human capital structure [ 24 , 34 , 35 ]. For example, Scott [ 24 ] proposes the dimensions of analytical, socially interactive, and practical capabilities—as recognized from the database of DOT (Dictionary of Occupational Titles published by the US Department of Labor in 1991) Among the research focused on occupational development, Thompson and Thompson’s [ 1 , 2 ] pioneering work suggests the turn from industrial to occupational analysis. Other researchers provided methodologies to aggregate occupational clusters [ 28 , 29 ] to serve as the practical tools for decision-makers and planners. Balfe and McDonald [ 28 ] grouped the occupations into clusters based on their education and vocational skills. Feser [ 29 ] aggregates the occupations from the perspective of broad knowledge to provide a way to explore the general value of occupational groups in regional economies. Markusen [ 30 ] comes up with occupational targeting and shows planners and decision-makers the advisable steps to identify the key occupations as networks of workers Some researchers identify how occupation analysis links with industrial development [ 31 – 33 , 36 ]. Barbour and Markusen [ 31 ] examine whether a region’s occupational structure can be paralleled with the industrial structure and found that the approximation does not hold for specifically researched industries such as high-tech and information technology fields and suggest that the industries are not enough to determine the concentration and clustering of occupations. Mellander [ 37 ] distinguishes the knowledge industries and creative industry based on education and skills, respectively. Currid and Stolarick [ 32 ] contribute to the empirical work and specifically present the case of I.T. in Los Angeles to demonstrate the mismatch between the industrial and occupational analytical results Nolan et al. [ 33 ] make e ff orts to reveal di ff erent occupational contents through the construction of an occupation-industry index even though the industrial mix is the same among metro regions. Gabe and Abel [ 36 ] find that knowledge occupations with unique characteristics are more likely to cluster than the general knowledge occupation across the US metropolitan regions In the debate on regional economic analysis, scholars think that the industrial frame and the occupational mix are not substitutes for each other. The independent consideration of occupation

[Find the meaning and references behind the names: Files, Acs, Code, Start, File, Standard, Soc, North, Plus, Bureau, Cases, Noise, Counts, Areas, Under, Micro, Year, Ers, Lot, Rico, Place, Area, Sample, Given, Table, Full, See, Puerto, Common]

Sustainability 2019 , 11 , 4240 3 of 17 is especially important in regions with a high concentration of creativity-oriented industries and occupations. An integrated approach is needed to view the regional industrial and occupational structure as a whole. Industrial structure and occupational mix di ff er a lot—especially in certain industries and geographic areas. A comprehensive understanding of both is more important than just finding the gap. The integrated approach presented o ff ers a potential solution This paper tries to develop such an approach of integrated occupational and industrial structure (IOIS). The detailed comparison in the case of California demonstrates that this approach does provide something new. It is also applied to data for the fifty states. The research is limited to the United States given data availability. The two-tier integrated approach using both occupational and industrial frameworks provides a new way to facilitate policy-making and refuel the prosperity of regions and cities 3. Data and Methodology 3.1. Data Source The research in this paper is based on three data sources. The first two are the Standard Occupational Classification system (SOC) in 2000 and 2010 by the Bureau of Labor Statistics and North American Industry Classification System (NAICS) in 2002 and 2007 from the Census Bureau. They provide the coding standards under which the industrial and occupational structures are organized. The third data source is the Public Use Micro-data (PUMS) files from the American Community Survey (ACS) released by the US Census Bureau. This single file contains data from 2006 to 2010. ACS PUMS has a single year of data, 3-years of combined data, and 5-years of combined data. The 5-year dataset covers the data from 2006–2010 in a single file, providing a larger sample which covers 5% of the total population compared with 3% in the 3-year dataset and 1% in the single year dataset. This 5-year database rather than a single-year or three-year file is selected to provide a larger sample. This data jointly provides both industry code and occupation code for working individuals. The industrial and occupational data from the PUMS includes the variables of NAICS code, SOC code, INDP (Census industrial code) and OCCP (Census occupational code). Each individual is also identified to specific geography. For this initial analysis, the geographic dimension is limited to the fifty states (plus Puerto Rico and the District of Columbia). The ACS PUMS file would also permit analysis at the metropolitan area or even county (for some counties). However, counting the individual crossing metropolitan units results in many instances where because of ACS sampling frames, the amount of “noise” in the counts is significant. To eliminate that as much as possible, only states will be used for this initial analysis. As the overall intention is to present, discuss, and validate the approach presented, and, since state level analysis is common practice, that level of analysis is a reasonable place to start. Eventually, this work can be supplemented with limited metropolitan level analysis The ACS PUMS files are especially useful for studying population and household groups for a specific use where published tables may be limited. In this five-year data file, the data previously available in OCCP and SOCP are now presented in 4 separate fields. OCCP 10 and SOCP 10 contain data for 2010 cases only, using the 2010 occupational classification system. OCCP 02 and SOCP 00 contain data for 2006, 2007, 2008 and 2009 cases only, using the 2002 occupational classification system As for the data related to industries, the INDP and NAICS are also divided into four separate fields INDP 07 and NAICS 07 contain data for 2008 to 2010 cases, using the 2007 industrial classification system. INDP 02 and NAICS 02 contain data for 2006 to 2007 cases only, using the 2002 industrial classification system. Therefore, the data for 2006 and 2007 are based on NAICS 2002 and SOC 2000, the data of 2008–2009 based on NAICS 2007 and SOC 2000, the data of 2010 based on NAICS 2007 and SOC 2010. (See Table 1 ). Based on the above situation there is one problem. In the five-year data file from 2006 to 2010 the NAICS codes include both NAICS 2002 to NAICS 2007 and the SOC codes from SOC 2000 and SOC 2010. Based on the full concordance of NAICS 2007 matched to NAICS 2002 the changes are all in the same

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Sustainability 2019 , 11 , 4240 4 of 17 NAICS two-digit codes after combining the same categories. Even at the three-digit-level, the changes in the codes and their contents are not big enough to influence the results seriously. Therefore, while at a finely-grained detailed level, the variation in coding could present a challenge, our summarization of the data (to twoand three-digit levels) eliminates the potential for any impact. Meanwhile the SOC codes had no substantial changes Table 1. Applied variables and codes of di ff erent years Years Applied Variables Applied Codes 2006–2007 INDP 02 OCCP 02 NAICS 2002 SOC 2000 2008–2009 INDP 07 OCCP 02 NAICS 2007 SOC 2000 2010 INDP 07 OCCP 10 NAICS 2007 SOC 2010 3.2. Constructing IOIS Matrix Using two-digit NAICS codes, the cases are aggregated into the groups as the columns. The SOC codes are grouped to create the rows based on two-digit codes. Combined, they constitute a matrix with each cell representing the employment corresponding to both a two-digit NAICS code and two-digit SOC code. Given that PUMS is a weighted sample, the weights are applied to each individual before the individual cell totals are calculated to approximate the whole population, and the standard errors of estimation are shown in next section. The reason why the NAICS and SOC in the PUMS are used rather than the INDP and OCCP is that the industrial categories in INDP are too specific. One industrial category in the same major group often includes several di ff erent codes, even in a two-digit level. But the NAICS and SOC codes create meaningful group numbers and meet the intended requirements based on their links to the INDPs and OCCPs. This is the Integrated Occupational and Industrial Structure (IOIS) approach this paper constructs to represent the general occupational and industrial situations in states and the nation. In order to examine the industrial and occupational dimension in more details the matrix constructed as the IOIS is also completed for more digit levels (representing di ff erent levels of summarization) such as three-digit industrial NAICS code by two-digit occupational SOC code, two-digit industrial NAICS code by three-digit occupational SOC code, three-digit industrial NAICS code by three-digit occupational SOC code. The di ff erence in the results is discussed in the next section The matrix shows the linkages between industries and occupations. It is also a manifestation of the integrated occupation / industry structure. The state of California’s IOIS matrix for two-digit level NAICS and two-digit level SOC is in Appendix A . The national level numbers are in Appendix B . Due to the space limitation only the matrices of two-digit NAICS by two-digit SOC codes of California and the US are shown in the appendices. The two-digit NAICS code of 99 and the two-digit SOC codes of 55 and 99 are deleted because they do not represent actual employment. The state IOIS can be compared with the national IOIS to examine the variance and determine how large it is With the above matrices, we first make a quick and simple test of comparison in two-digit NAICS by two-digit SOC to determine the di ff erence in employment percentage in every cell rather than discuss a specific comparison of every one of the actual numbers. California and D.C. are selected to compare with the whole US situation. These two are of a really di ff erent character. California is diversified with abundant resources and multiple economic functions while D.C.’s role is much simpler When we compare the IOIS matrix of California with that of the US by means of simple subtraction (the US minus California), we find the range in the share di ff erence is from − 0.0065 to 0.0075. When it comes to D.C., it is from − 0.0271 to 0.0297. Not even in the same order of magnitude, the di ff erent range of California suggests it has a similar IOIS with the whole nation while that D.C o ff ers a sharply di ff erent framework compared with the US. Given the economic characters of California and D.C., it is expected that most of the other states should fall in the middle between these two This method provides for the di ff erence, but does so by returning a whole collection of di ff erences that then can be investigated. But the above method is somewhat problematic in matrix comparison

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Sustainability 2019 , 11 , 4240 5 of 17 because it is too simple, and lots of information is lost in processing the matrix values. Therefore, this paper proposes a di ff erent and more reasonable approach to better understand the comparison of the state IOIS with the national one In the above two-dimensional Matrix M io , m io corresponds to the employment of industry i and occupation o at the same time in actual numbers. Any element in the state Matrix M r is expressed in r io while that in the national Matrix M n in n io . This paper applies commonly used methods to normalize the matrices and determine their similarity. The first step is the normalizations of the state and national matrices to make them comparable (seen the Formulas (1) and (2)). After the normalization the matrices are expressed with R io in state level and N io in national level. Actually the Matrix R io and Matrix N io are highly dimensional vectors of i × o respectively. So in the next step the inner product of the two vectors as shown as s in Formula (3) is used to represent how similar they are. The inner product is handled with exponentiation of cube to inflate the variance among the v values. Based on this specific research, the cubic exponentiation is tested to be an adequate degree to make the variance more visible. It helps us better identify the variance values for the following analysis. Then we make the s subtracted by 1 to represent the variance ( v ) of the two matrices seen in Equation (4). It can be expressed as follows, R io = r io q P i P o ( r io ) 2 , (1) N io = n io q P i P o ( n io ) 2 , (2) s = ( X i X o ( R io × N io )) 3 ( 0 ≤ s ≤ 1 ) , (3) v = 1 − s ( 0 ≤ v ≤ 1 ) (4) In which, r io represents the state employment and R io is the normalized state employment; n io is the national employment and N io is the normalized national employment; i refers to a certain industry and o represents an occupation; s is the similarity; Finally v is the variance between the state and the nation. The variances of di ff erent code digit levels are shown and discussed in the next section, including two-digit NAICS by two-digit SOC, two-digit NAICS by three-digit SOC, three-digit NAICS by two-digit SOC, three-digit NAICS by three-digit SOC. In the setting of the above approach v is ranging from 0 to 1. When it is going close to 1 the variance is suggested to be bigger while it is becoming smaller when it is increasingly near 0 Suppose we have the national and state matrices as n io = " 0 1 1 0 # , r io = " 1 0 0 1 # respectively Noticeably, they are totally di ff erent from each other Using the above formulas, we have N io =        0 1 √ 2 1 √ 2 0        , R io =        1 √ 2 0 0 1 √ 2        and finally v = 1. It is the biggest variance value, which in turn shows there is no similarity between the state and national occupational and industrial mix The same methods will also be applied in the single industry or occupation case as a one-dimensional matrix. The industrial and occupational mixes are both to be examined in twoand three-digit level, respectively. If the integrated industry and occupation structure matrix identifies a bigger variance between the state and national level than the single simpler matrix, we can say that the integrated approach has a more e ff ective distinguishing ability. Or, at least it provides a di ff erent view, greater information and shows the result of existing regional di ff erentiation The comparison of integrated occupational and industrial approach and the single approach is first conducted in the state of California. The case of California facilitates well to identify the di ff erence between the two approaches given its vibrant and diversified economic activities. The human capital situation in the whole state fits well with the analysis focusing more on the occupational aspect. In a later analysis of more states, counterparts can be identified. Then, the integrated approach is applied

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Sustainability 2019 , 11 , 4240 6 of 17 to the fifty-state data to achieve a generalized view of the occupation and industry circumstances in other states and across the nation In order to further reveal if the states are spatially correlated in the variance values, Moran’s I is added to conduct such an analysis. The three-digit industry by three-digit occupation results of the states are used to run Stata software. The geographical distance between the states are applied as the weights-matrix in the Stata software. The approaches of the global spatial autocorrelation and local spatial autocorrelation are both run. The former is to show if all the states have a spatial autocorrelation based on the variance of the regional IOIS feature from the national level. The latter is to tell us if in some areas there exists a certain spatial autocorrelation among some of the states. The scatterplot will help us better identify these states which gather spatially in the next section. After this, a more direct approach of the map with di ff erentiated variance values of all the states is taken to reveal some regional similarities 4. Results and Discussion 4.1. California Case The variance between the two matrices mentioned above has been calculated. It is conducted at four di ff erent levels of detail: Two-digit NAICS by two-digit SOC, two-digit NAICS by three-digit SOC, three-digit NAICS by two-digit SOC, three-digit SOC and three-digit NAICS. The variances between California and national IOIS in four levels are as follows. (See Table 2 ). Table 2. The variance between the state and national integrated occupational and industrial structure (IOIS) in four detailed level Two-Digit NAICS Three-Digit NAICS Two-Digit SOC 0.0297 0.0349 Three-Digit SOC 0.0461 0.0501 If the single industrial or occupational structure is applied, it can be regarded as a simpler matrix with just one column or row. The results of these calculations are shown as Tables 3 and 4 below Table 3. The variance between the state and national industrial structure by NAICS Two-Digit NAICS Three-Digit NAICS Industrial structure 0.0151 0.0310 Table 4. The variance between the state and national occupational mix by SOC Two-Digit SOC Three-Digit SOC Occupational mix 0.0106 0.0214 If the integrated occupational and industrial structure as matrix has a larger variance between the state and national level than the single simpler matrix, we can infer that the integrated approach more e ff ectively distinguishes the di ff erentiation between state and the national economic industry / occupational structure. From the table results above, it can be seen that the variances of a single industrial or occupational structure in two-digit level are 0.0151 and 0.0106 respectively. But our integrated approach in both two-digit level shows the result of 0.0297. The di ff erence grows even larger when it comes to more digit data analysis. The integrated approach reveals a bigger variance between the state and national level. It better recognizes the state variation when compared with the national situation The IOIS approach provides an integrated approach to explore the joint industrial and occupational distribution. The state integrated occupational and industrial structure is clearly di ff erent from that of

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Sustainability 2019 , 11 , 4240 7 of 17 the whole country, which reflects the state di ff erence in occupational and by industrial content based on respective codes. It also provides a better way to see the national industrial and occupational linkages From the above comparison with the single industrial or occupational mix, the IOIS framework indeed provides a di ff erent result of v value. It reveals the state development status di ff ers from the overall national economy in another way. Therefore, the state comparative di ff erence shall be identified in this “another” way. It is a more comprehensive and exact way to grasp the view of state development, whether in conceptual or practical aspect 4.2. From the Californian Case to 50 States in the US Presented next are results for the fifty US states with Puerto Rico and D.C. using PUMS data from 2006–2010. These results are presented in Table 5 . The appropriate sample weights are applied to calculate the values and are used to determine standard errors. (See the 2006–2010 Accuracy File for details on standard error calculations if needed). Given the use of state-level data, the standard errors are relatively low, and the details corresponding with the results in Table 5 are presented in Appendix C . Table 5. Variance results between the 50 states of the US I 2 O 2 I 2 O 3 I 3 O 2 I 3 O 3 Alabama 0.0370 0.0381 0.0393 0.0429 Alaska 0.1931 0.2017 0.1737 0.1948 Arizona 0.0410 0.0501 0.0393 0.0494 Arkansas 0.0598 0.0685 0.0778 0.0837 California 0.0297 0.0461 0.0349 0.0501 Colorado 0.0554 0.0623 0.0461 0.0557 Connecticut 0.0542 0.0579 0.0586 0.0619 Delaware 0.0456 0.0511 0.0474 0.0579 DC 0.4877 0.5627 0.4504 0.5714 Florida 0.0533 0.0497 0.0437 0.0426 Georgia 0.0276 0.0343 0.0221 0.0288 Hawaii 0.1661 0.1465 0.1507 0.1500 Idaho 0.0722 0.0994 0.0713 0.0980 Illinois 0.0184 0.0215 0.0217 0.0247 Indiana 0.1355 0.1032 0.0908 0.0753 Iowa 0.0911 0.1269 0.1066 0.1370 Kansas 0.0373 0.0584 0.0541 0.0687 Kentucky 0.0495 0.0471 0.0550 0.0556 Louisiana 0.0487 0.0544 0.0487 0.0528 Maine 0.0469 0.0623 0.0576 0.0777 Maryland 0.1104 0.1049 0.0749 0.0850 Massachusetts 0.0514 0.0611 0.0636 0.0740 Michigan 0.1274 0.1128 0.1509 0.1321 Minnesota 0.0380 0.0495 0.0431 0.0536 Mississippi 0.0565 0.0575 0.0723 0.0751 Missouri 0.0136 0.0197 0.0195 0.0255 Montana 0.1198 0.1527 0.0945 0.1329 Nebraska 0.0930 0.1759 0.1112 0.1671 Nevada 0.2163 0.2121 0.2564 0.2426 New Hampshire 0.0407 0.0496 0.0449 0.0524 New Jersey 0.0675 0.0763 0.0709 0.0863 New Mexico 0.0659 0.0650 0.0453 0.0529 New York 0.0605 0.0725 0.0737 0.0937 North Carolina 0.0237 0.0266 0.0264 0.0280 North Dakota 0.1867 0.3207 0.1998 0.3082 Ohio 0.0716 0.0693 0.0582 0.0600 Oklahoma 0.0291 0.0447 0.0419 0.0556 Oregon 0.0318 0.0490 0.0370 0.0607 Pennsylvania 0.0215 0.0312 0.0238 0.0320 Rhode Island 0.0395 0.0569 0.0635 0.0748 South Carolina 0.0346 0.0394 0.0380 0.0406 South Dakota 0.1866 0.2882 0.1852 0.2707

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Sustainability 2019 , 11 , 4240 8 of 17 Table 5. Cont I 2 O 2 I 2 O 3 I 3 O 2 I 3 O 3 Tennessee 0.0391 0.0373 0.0359 0.0374 Texas 0.0206 0.0273 0.0201 0.0276 Utah 0.0296 0.0949 0.0417 0.0948 Vermont 0.0577 0.0819 0.0899 0.1112 Virginia 0.0744 0.0811 0.0768 0.0839 Washington 0.0313 0.0457 0.0368 0.0569 West Virginia 0.0902 0.1016 0.0945 0.1070 Wisconsin 0.0891 0.0838 0.0636 0.0732 Wyoming 0.1957 0.2603 0.1647 0.2409 Puerto Rico 0.2721 0.3787 0.3361 0.4321 Notes: I 2 O 2 equals two-digit NAICS by two-digit SOC; I 2 O 3 equals two-digit NAICS by three-digit SOC; I 3 O 2 equals three-digit NAICS by two-digit SOC; I 3 O 3 equals three-digit NAICS by three-digit SOC. The same applies to the following text Descriptive variables such as mean, standard deviation, and maximum and minimum values are presented in Table 6 . D.C. and Puerto Rico are excluded in the descriptive analysis, because they are not really of the state character Table 6. Descriptive Analysis of variance values across 50 state data files (D.C. and Puerto Rico excluded) I 2 O 2 I 2 O 3 I 3 O 2 I 3 O 3 Mean 0.0715 0.0866 0.0732 0.0889 Standard Deviation 0.0527 0.0670 0.0516 0.0647 Minimum 0.0136 (Missouri) 0.0197 (Missouri) 0.0195 (Missouri) 0.0247 (Illinois) Maximum 0.2163 (Nevada) 0.3207 (North Dakota) 0.2564 (Nevada) 0.3082 (North Dakota) The mean values of the variance at any data detail level are relatively high. Not surprisingly, the average state IOIS situation is very similar to the national IOIS. Based on the standard deviation of the variance values, the specific values for the various states are not so distant from the mean, and the industries and the occupations structures are distributed relatively evenly across the nation However, the results also show specific di ff erences, by state, in the 50 state data files. The state of Missouri has the least variation in all the data detail levels except for the state of Illinois in three NAICS by three SOC codes, while Nevada and North Dakota are least like the national distribution The analysis of data values above and below the mean is conducted in the three NAICS by three SOC detail level. Only 15 states have a greater variance from the national values than the mean. They are North Dakota, South Dakota, Nevada, Wyoming, Alaska, Nebraska, Hawaii, Iowa, Montana, Michigan, Vermont, West Virginia, Idaho, Utah and New York (ordered from most to least variance). These states have a larger di ff erence in occupational and industrial structure compared with the national level They have a more distinctive collection of industry and occupational pairing shares, due to either unique combinations of function or other specialized locational characteristics. The top three states with the least variance with the national level are Illinois, Missouri and Texas. These states look very similar to each other and to the whole nation From two-digit industrial by two-digit occupation to the three-digit industrial by three-digit occupation, the mean variance values experience changes with the adjustment of either industrial or occupation or both. As expected, the more detailed industrial or occupational codes used to construct the matrix and calculate the variance generate greater variation Now we move to the analysis of some specific states. When the occupational data level is becoming more detailed, there are some states with the variance value experiencing relatively large increase, for example, Iowa, Vermont, Maine, California, Rhode Islands, Kansas, Nebraska, Massachusetts, Oregon, Oklahoma, and Utah. The current occupational and industrial situations of these states are more outstanding and di ff erent from the national framework as the occupational aspect, and regional

[Find the meaning and references behind the names: Real, Harder, Matter, Trend, Might, Get, Lower, Positive, Location]

Sustainability 2019 , 11 , 4240 9 of 17 talent levels matter more, which might suggest that the human resources matter a lot for their current economic development. On the opposite side, there are states which are increasingly the same with the national IOIS and harder to di ff erentiate themselves from the overall national level if the codes are more occupationally detailed, such as Indiana, Nevada, Virginia, Ohio, New Mexico, Florida, Kentucky, Tennessee, Alabama, South Carolina etc An aspect of Moran’s I the results of the global spatial autocorrelation is shown in Table 7 . The value of -0.016 with the significance of 0.870 reveals no significant spatial correlation among all the states in the statistical meaning, indicating there is no emerging regional gathering pattern in the nation, based on the variance of regional IOIS from the nation examined in our paper Table 7. Moran’s I results (measures of the global spatial autocorrelation) Variables I E(I) Sd(I) z p -Value Variance − 0.016 − 0.020 0.024 0.164 0.870 However, the local spatial autocorrelation results, as shown in Table 8 , provide us with some clues to grasp some regional clustering features based on the variance values examined in our paper. Some states are spatially correlated with a p -value lower than 0.1, indicating a statistical significance. The Moran’s I value of South Dakoda and North Dakoda are positive, indicating these two states have a trend of clustering spatially based on the IOIS variance from the nation. Combining the illustration of the scatterplot in Figure 1 we can get a clearer view of the spatial clustering trend of the variance values. According to Moran’s I definition of the spatial autocorrelation, the points in the first quadrant present some states with relatively high Moran’s I values clustering together spatially. Specifically, around South Dakoda and North Dakoda there exist Wyoming, Montana and Nebraska. These points representing the five states are all in the first quadrant (as numbered in the figure), and they are indeed close and adjacent to each other in the real spatial layout. Other states with statistical significance involve DC and Puerto Rico. They are in the fourth quadrant, indicating there emerges di ff erentiated and dispersed spatial trend around these two states. Because no clear spatial correlations in real meaning are visible about these two states, we will not conduct detailed analysis in this particular paper Table 8. Moran’s I results (measures of the local spatial autocorrelation) No. Location Ii E(Ii) sd(Ii) z p -Value * 1 Alabama 0.130 − 0.020 0.121 1.231 0.218 2 Alaska − 0.010 − 0.020 0.077 0.127 0.899 3 Arizona 0.005 − 0.020 0.093 0.265 0.791 4 Arkansas 0.037 − 0.020 0.101 0.561 0.574 5 California − 0.135 − 0.020 0.201 − 0.573 0.567 6 Colorado − 0.092 − 0.020 0.151 − 0.479 0.632 7 Connecticut 0.043 − 0.020 0.187 0.334 0.738 8 Delaware − 0.098 − 0.020 0.190 − 0.415 0.678 9 DC − 1.251 − 0.020 0.245 − 5.015 0.000 ** 10 Florida 0.110 − 0.020 0.113 1.151 0.250 11 Georgia 0.144 − 0.020 0.118 1.389 0.165 12 Hawaii − 0.004 − 0.020 0.069 0.233 0.816 13 Idaho − 0.003 − 0.020 0.120 0.138 0.890 14 Illinois 0.096 − 0.020 0.109 1.056 0.291 15 Indiana 0.044 − 0.020 0.117 0.549 0.583 16 Iowa − 0.025 − 0.020 0.113 − 0.049 0.961 17 Kansas 0.014 − 0.020 0.119 0.281 0.779 18 Kentucky 0.071 − 0.020 0.119 0.764 0.445 19 Louisiana 0.088 − 0.020 0.121 0.890 0.374 20 Maine 0.029 − 0.020 0.163 0.296 0.767 21 Maryland − 0.165 − 0.020 0.249 − 0.583 0.560 22 Massachusetts 0.050 − 0.020 0.233 0.299 0.765

[Find the meaning and references behind the names: Peer, Tail]

Sustainability 2019 , 11 , 4240 10 of 17 Table 8. Cont No. Location Ii E(Ii) sd(Ii) z p -Value * 23 Michigan − 0.030 − 0.020 0.099 − 0.105 0.917 24 Minnesota − 0.017 − 0.020 0.100 0.028 0.977 25 Mississippi 0.061 − 0.020 0.120 0.666 0.506 26 Missouri 0.088 − 0.020 0.110 0.980 0.327 27 Montana 0.015 − 0.020 0.103 0.337 0.736 28 Nebraska − 0.031 − 0.020 0.121 − 0.093 0.926 29 Nevada − 0.198 − 0.020 0.192 − 0.928 0.353 30 New Hampshire 0.056 − 0.020 0.199 0.382 0.703 31 New Jersey − 0.000 − 0.020 0.157 0.123 0.902 32 New Mexico 0.009 − 0.020 0.097 0.299 0.765 33 New York 0.012 − 0.020 0.165 0.194 0.846 34 North Carolina 0.012 − 0.020 0.121 0.264 0.792 35 North Dakota 0.161 − 0.020 0.110 1.644 0.100 ** 36 Ohio 0.034 − 0.020 0.118 0.454 0.650 37 Oklahoma 0.046 − 0.020 0.095 0.692 0.489 38 Oregon 0.002 − 0.020 0.163 0.131 0.896 39 Pennsylvania − 0.151 − 0.020 0.157 − 0.834 0.404 40 Rhode Island 0.046 − 0.020 0.229 0.285 0.775 41 South Carolina 0.089 − 0.020 0.110 0.983 0.326 42 South Dakota 0.170 − 0.020 0.111 1.713 0.087 ** 43 Tennessee 0.117 − 0.020 0.105 1.300 0.194 44 Texas 0.081 − 0.020 0.089 1.131 0.258 45 Utah − 0.005 − 0.020 0.107 0.138 0.890 46 Vermont − 0.009 − 0.020 0.169 0.063 0.950 47 Virginia − 0.040 − 0.020 0.148 − 0.135 0.893 48 Washington 0.000 − 0.020 0.163 0.121 0.904 49 West Virginia − 0.002 − 0.020 0.115 0.152 0.879 50 Wisconsin 0.023 − 0.020 0.102 0.420 0.675 51 Wyoming − 0.066 − 0.020 0.150 − 0.305 0.760 52 Puerto Rico − 0.365 − 0.020 0.072 − 4.799 0.000 ** * 2-tail test, ** with an obvious statistical significance Sustainability 2019 , 11 , x FOR PEER REVIEW 10 of 18 20 Maine 0.029 − 0.020 0.163 0.296 0.767 21 Maryland − 0.165 − 0.020 0.249 − 0.583 0.560 22 Massachusetts 0.050 − 0.020 0.233 0.299 0.765 23 Michigan − 0.030 − 0.020 0.099 − 0.105 0.917 24 Minnesota − 0.017 − 0.020 0.100 0.028 0.977 25 Mississippi 0.061 − 0.020 0.120 0.666 0.506 26 Missouri 0.088 − 0.020 0.110 0.980 0.327 27 Montana 0.015 − 0.020 0.103 0.337 0.736 28 Nebraska − 0.031 − 0.020 0.121 − 0.093 0.926 29 Nevada − 0.198 − 0.020 0.192 − 0.928 0.353 30 New Hampshire 0.056 − 0.020 0.199 0.382 0.703 31 New Jersey − 0.000 − 0.020 0.157 0.123 0.902 32 New Mexico 0.009 − 0.020 0.097 0.299 0.765 33 New York 0.012 − 0.020 0.165 0.194 0.846 34 North Carolina 0.012 − 0.020 0.121 0.264 0.792 35 North Dakota 0.161 − 0.020 0.110 1.644 0.100 ** 36 Ohio 0.034 − 0.020 0.118 0.454 0.650 37 Oklahoma 0.046 − 0.020 0.095 0.692 0.489 38 Oregon 0.002 − 0.020 0.163 0.131 0.896 39 Pennsylvania − 0.151 − 0.020 0.157 − 0.834 0.404 40 Rhode Island 0.046 − 0.020 0.229 0.285 0.775 41 South Carolina 0.089 − 0.020 0.110 0.983 0.326 42 South Dakota 0.170 − 0.020 0.111 1.713 0.087 ** 43 Tennessee 0.117 − 0.020 0.105 1.300 0.194 44 Texas 0.081 − 0.020 0.089 1.131 0.258 45 Utah − 0.005 − 0.020 0.107 0.138 0.890 46 Vermont − 0.009 − 0.020 0.169 0.063 0.950 47 Virginia − 0.040 − 0.020 0.148 − 0.135 0.893 48 Washington 0.000 − 0.020 0.163 0.121 0.904 49 West Virginia − 0.002 − 0.020 0.115 0.152 0.879 50 Wisconsin 0.023 − 0.020 0.102 0.420 0.675 51 Wyoming − 0.066 − 0.020 0.150 − 0.305 0.760 52 Puerto Rico − 0.365 − 0.020 0.072 − 4.799 0.000 ** * 2-tail test, ** with an obvious statistical significance. Figure 1. Scatterplot based on the variance values. Figure 1. Scatterplot based on the variance values.

[Find the meaning and references behind the names: Northern, Rust, Bind, Belt, Sense, Last, Parts]

Sustainability 2019 , 11 , 4240 11 of 17 Besides the regional clustering feature with a statistical significance obtained from Moran’s I analysis, we can also get a more direct sense of the regional appearance of IOIS from the map. Figure 2 shows the integrated occupational and industrial structural variant results in three by three digits The map reveals some regional similarities in the amount of overall variance between each state and the national averages for the industry / occupation pairs. The southeastern US generally has smaller variance as do parts of the Midwest / rust belt and the southwest, including California; while the northern prairie states have industry / occupation distributions that are most di ff erent to the US averages. Some of the most populous US states (California, Texas, Illinois, Arizona, Florida, Georgia, Missouri) have a smaller variation with the US average. However, some of the more populated states (New York, New Jersey, Ohio, Virginia, Colorado, Michigan) show relatively higher variation with the US. Overall, the map shows that while there are some regional US patterns, some states still show individual variation Sustainability 2019 , 11 , x FOR PEER REVIEW 11 of 18 Besides the regional clustering feature with a statistical significance obtained from Moran’s I analysis, we can also get a more direct sense of the regional appearance of IOIS from the map. Figure 2 shows the integrated occupational and industrial structural variant results in three by three digits. The map reveals some regional similarities in the amount of overall variance between each state and the national averages for the industry/occupation pairs. The southeastern US generally has smaller variance as do parts of the Midwest/rust belt and the southwest, including California; while the northern prairie states have industry/occupation distributions that are most different to the US averages. Some of the most populous US states (California, Texas, Illinois, Arizona, Florida, Georgia, Missouri) have a smaller variation with the US average. However, some of the more populated states (New York, New Jersey, Ohio, Virginia, Colorado, Michigan) show relatively higher variation with the US. Overall, the map shows that while there are some regional US patterns, some states still show individual variation. Figure 2. US States Industry/Occupation Variance by State. 5. Conclusions with Policy Implications and Further Work The focus on the occupational mix in the regions and cities is nothing new. Researchers and planning practitioners have been making endeavors to integrate the occupational factor in economic development for the last several decades. The increasingly close linkages with the industrial analysis framework make occupations no longer just part of labor incentive research. The occupational analysis is supposed to be released out of the package and used broadly in the economic decisionmaking and agenda. The previous work in this field has done a lot to show the industrial and occupational analysis could not substitute each other. The gap and differences do, in fact, exist. Neither aspect will be neglected in effective planning practice. This paper constructs an integrated occupational and industrial structure (IOIS) to bind them together. It will lead to some policy implications in potential regional economic and human capital policy-making. Firstly, in regional perspective, the new integrated analytical approach will provide a more comprehensive and different view about the regional and national development situations. The state of California serves as the Figure 2. US States Industry / Occupation Variance by State 5. Conclusions with Policy Implications and Further Work The focus on the occupational mix in the regions and cities is nothing new. Researchers and planning practitioners have been making endeavors to integrate the occupational factor in economic development for the last several decades. The increasingly close linkages with the industrial analysis framework make occupations no longer just part of labor incentive research. The occupational analysis is supposed to be released out of the package and used broadly in the economic decision-making and agenda. The previous work in this field has done a lot to show the industrial and occupational analysis could not substitute each other. The gap and di ff erences do, in fact, exist. Neither aspect will be neglected in e ff ective planning practice. This paper constructs an integrated occupational and industrial structure (IOIS) to bind them together. It will lead to some policy implications in potential regional economic and human capital policy-making. Firstly, in regional perspective, the

[Find the meaning and references behind the names: Art, Change, Less, Gain, Alive, Goes, Original, Future, Pool, Fund, Few, Point, Fixed, Play, Grant, Grow, Author, Kind]

Sustainability 2019 , 11 , 4240 12 of 17 new integrated analytical approach will provide a more comprehensive and di ff erent view about the regional and national development situations. The state of California serves as the data source of the comparison between the integrated and single applied approach. It does demonstrate that the integrated approach reveals a bigger di ff erence between the state and national level because the identified variance values increase when using the integrated approach. As a new perspective and method, it will serve as protentional policy information tools for the regional policy-making. It will better recognize the regional advantage and competitiveness compared with the national situation as a whole. Regions can gain an advantageous position by targeting more precisely in the development fields. This integrated framework provides a di ff erent option either in theoretical research or in practical plans. The occupation included a framework of the regional economy is of greater importance to the states which have an abundant human resource and creative talents, such as California. The single industrial analysis only provides the relatively fixed structure of the regional economy. And the single occupational analysis gives the limited information of the human resource pool. But the integrated framework tells us which kind of human resource is “alive” in the practical use corresponding to a certain industry. It helps the policy-makers bridge the industry requirement with the occupational supply by giving deeper insight into the regional development Secondly, in a national perspective, it is applied in the 52 state data files in PUMS. The overview of the national industrial and occupational status is provided. The snapshot shows most of the states have a highly similar occupational and industrial development with the national level. The occupations and industries are distributed relatively evenly across the nation. It needs further and detailed analysis in specific industries and areas to identify more potential benefits. In some sense, the results reflect that occupations play an even more important role in the industrial structure. An adjustment, made by gradually more detailed data level in terms of either occupations or industries, reveals that focusing more on occupational mix helps the state IOIS become more di ff erentiated from the national level, while the industrial framework does less. The value change range is even bigger in the occupational aspect compared with the industrial one. It goes further to suggest the occupational aspect and human resources play an important role in achieving a unique regional advantage across the nation. From the variance results of the states, we learn that some states (such as Florida, Hawaii, Indiana, Michigan, Nevada) are not sensitive to the changes of more detailed occupational information. For example, their variance values even decrease when the I 3 O 2 change to I 3 O 3, indicating they grow more similar to the national average level. It might be an indication that under certain industrial frame, the human resource is in a disadvantageous position. In comparison, some states (such as California, Illinois, Maryland, to just name a few) have variance values increase from I 3 O 2 to I 3 O 3. These states have more available human capital potential. From the national point of view, it will help the policy makers mobilize the human resource and formulate labor incentive policies in a larger range more e ff ectively The integrated industrial and occupational approach provided in this paper is just a start Upcoming research shall be in more detailed and specific industrial and occupational groups to help with the policy-making of the urban and regional development. Because the occupational and human capital focus is mostly related to the industries containing the most knowledge and skill-intensified contents, the future research shall involve more e ff orts in the creative and knowledge industrial groups with the generalized ISIO approach. Of course, the creative industries refer to the broad sense, including design, software developing, art, performing, consulting and so on., all of which are of great significance to upgrade the industrial development and refuel the regional growth Author Contributions: Conceptualization, R.S. and K.S.; methodology, R.S.; formal analysis, R.S. and T.L.; data curation, R.S.; writing—original draft preparation, T.L. and R.S.; writing—review and editing, K.S. and T.L Funding: This research was funded by the major project of the National Social Science Fund of China, grant number 15 ZDA 032 Conflicts of Interest: The authors declare no conflict of interest.

Sustainability 2019 , 11 , 4240 13 of 17 Appendix A. IOIS Matrix of the State of California in Two-Digit NAICS by Two-Digit SOC Data Level in Actual Numbers, 2006–2010 Table A 1. IOIS Matrix of the State of California in Two-Digit NAICS by Two-Digit SOC Data Level in Actual Numbers, 2006–2010 SOC NAICS 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 11 53,650 3282 392 632 4009 55 118 214 589 129 10 5178 444 8126 5136 3418 15,737 311,783 1857 5958 7408 27,510 21 2974 1088 438 1691 1047 83 46 98 107 109 26 368 527 2283 12,075 3039 2805 36,56 22 17,581 8631 5354 13,546 3464 73 877 513 1220 550 22 1528 12 2635 24 R 55 28,383 399 13,473 15,259 24,728 5322 23 175,806 28,694 2182 22,013 1085 44 725 309 3925 88 15 1763 69 8263 246 14,326 89,603 555 1 × 10ˆ6 55,808 25,074 31,032 31 32,495 10,763 2537 2010 4270 18 225 362 7650 190 14 658 5288 7506 554 26,340 38,224 7273 1944 11,875 207,498 55,346 32 54,291 16,168 6806 11,273 18,667 135 854 653 8144 1700 85 1201 99 3976 111 23,347 49,502 354 7922 14,338 161,756 43,318 33 183,286 57,607 72,101 157,704 9974 41 3033 1977 22,705 1748 312 3149 890 8161 335 48,156 134,661 112 22,892 41,620 408,768 46,594 3 M 4891 1699 860 2164 178 140 633 66 32 207 139 926 2439 7477 32 584 1444 27,084 6861 42 56,048 36,473 10,129 3606 2647 81 763 997 6683 772 299 1127 2343 6882 602 233,093 133,498 16,633 4058 17,983 26,541 128,552 44 49,674 31,919 14,994 2190 1058 86 1042 1510 15,024 40,763 6802 5197 36,560 16,253 3922 842,501 246,454 1948 11,618 63,888 57,243 143,296 45 19,308 17,445 6339 644 602 242 228 1730 15,651 1023 218 5601 5304 9300 5906 412,216 148,076 251 1542 9977 15,927 37,591 48 35,006 9478 2769 2958 404 27 150 1075 978 199 317 4255 1184 5509 16,710 9978 76,811 191 3765 31,254 4716 326,106 49 17,483 2839 1715 1103 27 91 332 137 120 12 867 196 3961 104 4909 154,996 171 479 5032 5544 77,360 4 M 3685 3786 712 159 46 58 62 703 1846 107 16 369 568 886 225 102,612 21,706 21 117 2521 1791 3200 51 90,126 24,274 51,915 18,671 2321 40 3788 13,972 14,4287 172 81 1957 4465 4205 9843 64,885 104,115 5190 46,970 14,287 12,892 52 151,592 209,499 36,478 1437 2385 1739 11,513 2194 3808 3126 331 4448 162 1581 576 157,072 299,222 50 613 2125 2232 831 53 124,599 32,571 3701 930 683 824 4359 448 2345 378 585 2924 3082 28,883 2615 196,589 76,204 67 7990 15,961 3927 16,376 54 205,477 211,746 195,583 137,364 66,446 1805 162,224 6035 123,909 24,604 5622 3123 561 5003 4852 49,947 248,069 235 7914 13,239 20,575 8097 55 4086 2233 1235 347 187 49 544 90 360 143 58 77 250 108 589 5243 83 101 182 302 56 61,414 36,390 9767 5156 2875 1293 4677 1839 8811 13,501 5992 104,356 3402 390,164 6410 47,274 152,762 2269 15,276 32,496 33,835 85,960 61 130,048 32,709 26,470 7319 31,402 41,064 1461 100,1046 34,975 27,023 5555 15,298 49,595 66,046 49,146 10,553 193,282 462 8033 13,186 5674 21,612 62 142,624 40,081 20,791 2816 37,432 135,837 3457 83,835 7586 680,847 323,303 7882 41,080 59,219 335,115 9481 351,657 94 3569 7694 16,606 18,263 71 31,238 16,825 3508 1399 1758 1305 821 11,141 135,328 1180 3432 29,285 40,627 43,042 129,618 38,877 46,878 604 3658 9727 3851 12,270 72 159,381 9798 1324 616 292 431 312 2805 3908 674 1394 6977 914,915 80,205 19,003 160,899 74,948 202 1945 5635 14,478 29,150 81 56,708 21,609 5986 2454 1727 71,882 2088 8695 13,478 3813 19,760 3930 7381 144,325 281,554 58,156 97,184 518 4352 162,169 72,828 65,263 92 81,187 83,684 35,987 29,713 20,611 38,559 40,152 14,621 9766 28,341 13,481 229,157 5450 20,738 31,051 3377 208,909 2965 17,001 40,207 9292 21,517

Sustainability 2019 , 11 , 4240 14 of 17 Appendix B. IOIS Matrix of the US in Two-Digit NAICS by Two-Digit SOC Data Level in Actual Numbers, 2006–2010 Table A 2. IOIS Matrix of the US in Two-Digit NAICS by Two-Digit SOC Data Level in Actual Numbers, 2006–2010 SOC NAICS 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 11 847,000 18,055 3949 4712 34,676 435 442 2275 3724 2095 657 21,480 4469 39,021 47,454 22,447 95,224 1 × 10ˆ6 13,208 28,848 40,623 120,867 21 79,327 31,311 11,083 43,188 24,305 70 4475 980 2338 1780 0 4355 1694 7026 235 11,378 69,985 228 314,951 66,260 54,470 103,242 22 144,307 67,749 43,347 106,632 26,584 535 5410 5140 9837 5059 79 13,024 443 26,769 214 21,001 262,060 1280 119,496 206,084 266,185 57,166 23 1,445,127 218,984 20,112 158,605 7435 740 5637 2756 29,819 2695 463 27,636 1971 61,963 3221 132,578 746,658 6133 9,204,169 598,165 243,444 363,352 31 233,423 81,484 25,248 29,138 39,134 208 2158 3366 30,793 3375 366 7179 35,000 63,521 2133 164,784 278,230 23,052 19,103 126,038 1,484,172 471,833 32 562,212 185,535 84,514 161,542 175,822 492 9454 7421 70,060 17,455 1740 14,411 2122 66,437 1621 220,950 562,505 9375 117,065 217,781 1,988,118 595,432 33 1,161,634 410,697 367,621 989,501 51,893 795 14,851 18,187 125,492 16,726 2411 25,660 3696 114,380 2204 361,454 1,065,870 989 251,485 505,573 4,666,107 700,516 3 M 33,150 12,344 4605 13,201 1411 161 430 673 2947 322 268 1286 733 9888 293 17,862 45,704 127 5873 12,110 215,945 63,298 42 400,040 260,418 79,554 32,715 17,393 441 6609 6885 41,615 10,075 2424 8274 16,089 45,176 2953 1,779,781 985,294 60,662 38,325 198,142 207,272 1,008,606 44 389,787 241,865 101,132 16,648 8253 919 17,032 11,426 99,953 490,423 46,919 34,397 397,083 133,630 19,596 7,245,578 2,072,594 17,671 95,777 601,368 467,394 1,292,112 45 168,501 149,979 47,002 5545 4964 1098 2620 15,766 135,863 21,862 3025 44,023 68,257 101,000 44,821 3,819,559 1,373,493 2886 17,367 114,096 158,147 393,581 48 307,536 85,815 35,192 31,181 3412 1100 3539 10,976 8660 2777 2560 42,908 11,885 48,638 179,729 96,544 668,648 4970 64,701 325,410 69,309 3,117,644 49 164,823 29,205 18,285 10,669 1023 172 1052 2322 1445 1021 106 7769 725 36,869 850 36,010 1,252,794 1060 4291 45,079 40,456 568,052 4 M 25,124 25,919 5644 860 617 145 354 4011 14,440 767 413 2897 5974 10,360 2153 779,271 162,775 174 1501 19,701 21,760 29,446 51 563,542 146,104 334,589 132,303 16,354 1636 16,204 139,077 664,700 1239 385 9904 38,212 31,816 49,231 531,421 855,145 141 17,556 384,565 109,960 78,119 52 1,340,730 2 × 10ˆ6 421,984 15,625 21,169 14,329 85,314 24,928 30,132 37,932 3214 38,669 3594 29,074 3787 1,389,574 2,896,675 168 5226 23,127 24,211 10,130 53 758,265 195,911 22,847 7366 3622 6968 27,335 3122 13,663 5290 6299 33,497 26,950 305,441 34,708 1,301,462 510,109 249 69,905 150,024 27,003 131,585 54 1,349,083 2 × 10ˆ6 1 × 10ˆ6 991,258 389,385 14,368 1,239,194 48,988 726,291 215,281 58,008 24,760 4516 41,594 46,576 360,111 1,875,090 3573 65,106 93,960 156,700 64,617 55 43,562 27,796 13,186 2560 1701 478 3923 773 2558 1368 303 1505 886 2131 523 5414 47,525 50 919 1953 2650 3227 56 485,123 280,008 90,341 37,161 20,950 13,695 33,305 20,125 48,648 125,002 79,758 665,961 35,585 3 × 10ˆ6 40,854 449,474 1,364,071 17,509 130,951 202,352 329,620 681,321 61 1,111,749 246,597 238,725 54,819 237,176 384,757 13,023 9,063,986 282,040 303,019 39,156 156,177 557,241 743,623 351,004 101,151 1,643,186 3386 79,632 132,996 60,635 346,493 62 1,289,298 338,811 167,811 23,727 228,796 1 × 10ˆ6 30,880 829,641 58,944 6,838,313 3,650,779 91,978 528,751 680,512 2,276,166 83,063 3,201,401 1770 47,861 96,250 213,860 184,382 71 210,014 81,029 18,853 8862 15,548 13,898 2733 97,624 773,426 8047 22,027 340,445 372,659 412,341 989,291 279,231 331,951 5953 26,964 82,999 28,603 99,720 72 1,402,381 80,965 11,322 4643 2827 7479 2724 27,986 35,137 5424 8310 69,938 8,605,685 769,346 215,606 1,252,807 635,670 1810 18,106 51,572 131,210 286,853 81 514,791 173,981 48,193 15,451 18,462 758,893 18,104 73,292 158,661 31,433 142,869 30,707 75,716 856,202 2,177,310 481,660 930,413 3112 36,446 1,273,193 571,557 450,793 92 841,951 738,358 304,787 204,626 193,890 387,304 332,932 124,825 83,087 246,013 68,632 2 × 10ˆ6 54,187 194,597 104,775 29,594 1,905,716 24,732 146,623 315,399 99,341 196,510

Sustainability 2019 , 11 , 4240 15 of 17 Appendix C. Details of Standard Errors Going with Variance results between the 50 states of the US Table A 3. Details of Standard Errors Going with Variance results between the 50 states of the US I 2 O 2 I 2 O 3 I 3 O 2 I 3 O 3 Alabama 0.0015 0.0014 0.0015 0.0017 Alaska 0.0083 0.0097 0.0091 0.0103 Arizona 0.0015 0.0019 0.0016 0.0021 Arkansas 0.0023 0.0026 0.0028 0.0029 California 0.0004 0.0006 0.0005 0.0007 Colorado 0.0016 0.0022 0.0017 0.0023 Connecticut 0.0021 0.0024 0.0026 0.0026 Delaware 0.0047 0.0063 0.0050 0.0081 D.C 0.0094 0.0114 0.0112 0.0138 Florida 0.0008 0.0008 0.0009 0.0009 Georgia 0.0008 0.0010 0.0007 0.0010 Hawaii 0.0049 0.0052 0.0055 0.0060 Idaho 0.0043 0.0055 0.0042 0.0054 Illinois 0.0008 0.0009 0.0008 0.0010 Indiana 0.0031 0.0023 0.0024 0.0021 Iowa 0.0030 0.0039 0.0030 0.0040 Kansas 0.0020 0.0032 0.0026 0.0037 Kentucky 0.0022 0.0019 0.0019 0.0021 Louisiana 0.0015 0.0017 0.0018 0.0018 Maine 0.0036 0.0040 0.0039 0.0051 Maryland 0.0021 0.0021 0.0021 0.0026 Massachusetts 0.0016 0.0020 0.0023 0.0026 Michigan 0.0022 0.0024 0.0028 0.0030 Minnesota 0.0016 0.0019 0.0016 0.0022 Mississippi 0.0022 0.0020 0.0024 0.0026 Missouri 0.0008 0.0011 0.0011 0.0014 Montana 0.0048 0.0072 0.0045 0.0071 Nebraska 0.0040 0.0063 0.0050 0.0062 Nevada 0.0051 0.0048 0.0058 0.0053 New Hampshire 0.0031 0.0041 0.0035 0.0049 New Jersey 0.0016 0.0018 0.0020 0.0024 New Mexico 0.0031 0.0039 0.0025 0.0037 New York 0.0011 0.0014 0.0014 0.0017 North Carolina 0.0007 0.0008 0.0007 0.0010 North Dakota 0.0082 0.0125 0.0081 0.0124 Ohio 0.0015 0.0016 0.0016 0.0018 Oklahoma 0.0012 0.0021 0.0017 0.0025 Oregon 0.0017 0.0028 0.0018 0.0032 Pennsylvania 0.0007 0.0010 0.0007 0.0010 Rhode Island 0.0036 0.0053 0.0050 0.0068 South Carolina 0.0014 0.0016 0.0017 0.0018 South Dakota 0.0090 0.0134 0.0084 0.0125 Tennessee 0.0016 0.0014 0.0015 0.0014 Texas 0.0006 0.0008 0.0005 0.0007 Utah 0.0021 0.0027 0.0024 0.0031 Vermont 0.0054 0.0074 0.0068 0.0086 Virginia 0.0017 0.0019 0.0021 0.0025 Washington 0.0010 0.0016 0.0012 0.0022 West Virginia 0.0031 0.0039 0.0035 0.0042 Wisconsin 0.0029 0.0026 0.0018 0.0022 Wyoming 0.0079 0.0097 0.0079 0.0112 Puerto Rico 0.0060 0.0077 0.0078 0.0088

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