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Wojan - Subject Base innovation research 2014 ERS rural innovation survey

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Parallel session 4 - Wednesday 21 September 2016

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Wojan - Subject Base innovation research 2014 ERS rural innovation survey

  1. 1. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Putting the Subject Back into Subject- Based Innovation Research: Latent Class Analysis in the 2014 ERS Rural Establishment Innovation Survey Timothy R. Wojan Economic Research Service/USDA Paper presented at OECD Blue Sky III Ghent, Belgium 19-21 September, 2016
  2. 2. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Outline of Talk • Strong priors that rural innovation is rare and largely inconsequential • Challenge to conventional wisdom requires credible measure of substantive innovation • Assume experiences of substantive innovators unique and can be elicited with simple questions • Do identified substantive innovators satisfy tests of internal and external validity? • Feasibility and assessment of “rural innovation policy” requires credible measure of substantive rural innovators
  3. 3. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. CIS findings contradict but do not overturn conventional wisdom • NBER, Brookings, World Bank either wholly disregard or disqualify rural in regional studies of innovation • CIS findings on rural innovation based on response to single ambiguous question • North and Smallbone (2000): 49% of rural UK mftrs regarded selves as “innovative” based on CIS response but industry experts rated only 24% as “highly innovative”
  4. 4. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. 2014 ERS Rural Establishment Innovation Survey • First nationally representative self- reported innovation survey for Rural America • Oversampled rural establishments but allocated a quarter of the sample to urban establishments for comparison • Sample size 11,000 for all establishments with 5 or more employees in nonfarm, tradable sectors • Sought more efficient way of IDing substantive innovators
  5. 5. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Assume that struggling with innovation alters responses to key questions • EU CIS core questions in combination with other observable characteristics –New or significantly improved goods, services, processes, logistics, marketing methods. –Are innovation investments capital constrained? –Acknowledge failed innovation initiatives? –Possess intellectual property worth protecting? –Does data drive decision-making?
  6. 6. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. IDing Substantive and Nominal Innovators Using Latent Class Analysis (LCA) • Assumes that sample drawn from distinct but unobservable subpopulations inferred from the data • Latent class analysis resolves two main problems of classification in large datasets: – Classification is probabilistic – Can be estimated incorporating complex sample design with the MPlus statistical package
  7. 7. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Latent Class Analysis with Covariates Schematic Outcomes Outcome Explanatory Vars. Latent Classes Covariates Explaining Class Membership y1 y2 y3 y4 zi … … …. zk 33.09% 36.79%30.12% xis xks Core Innovation Covariates Data Driven Decision Making Covariates Substantive Innovators Nominal Innovators Non- Innovators Source: 2014 Rural Establishment Innovation Survey
  8. 8. Source: 2014 Rural Establishment Innovation Survey 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% Innovation Projects Abandoned and/or Incomplete Intellectual Property Protection Surplus Funds Used for Innovation- Definitely Track Employee Training Customer Satisfaction Analysis --Regularly Customer Satisfaction Analysis --Never Change Process Due to Complaint-- Regularly Enterprise Resource Planning Software Affirmative Responses to Variables Used to Determine Latent Class Membership Substantive Innovators Data-Drvien Nominal Innovators Non-Innovators Core Innovation Questions Data Driven Decision-Making % A n s w e r i n g Y E S
  9. 9. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Whether these subpopulations truly exist is an empirical question • Initial results will be in cross-section: – Do auxiliary questions provide a sufficient threshold? – Are establishments in more innovation intensive sectors more likely to respond affirmatively to auxiliary questions? • Linking REIS to the longitudinal business data at BLS or Census will provide dynamic performance data to compare substantive with nominal innovators • Broad but shallow survey research supplemented with narrow but deep case study research Source: 2014 Rural Establishment Innovation Survey 17.38% 16.63% 8.35% 30.97% 31.23% 6.02% 2.31% 1.10% 6.15% 8.31% 4.69% 3.02% 0.96% 5.66% 8.11% PURCHASE OR LICENSE PATENTS PARTICIPATED IN A PATENT APPLICATION REGISTERED AN INDUSTRIAL DESIGN REGISTERED A TRADEMARK PRODUCE MATERIAL ELIGIBLE FOR COPYRIGHT Validity wrt Survey Responses: Innovation Related Activities Substantive Innovators Data-Drvien Nominal Innovators Non-Innovators % Answering YES
  10. 10. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Source: Shackelford 2013 and 2014 Rural Establishment Innovation Survey 0 2 4 6 8 10 12 14 16 18 Rank Order Correlation Between NSF and REIS Innovation Intensive Industries Removing Likely Outlier (NAICS 3342 Communications Equip.) NSF Patent Apps REIS (21) (43) (24) (48) (9) (13) N = 13 but no Metro Substantive innovators (9) (135) (45) (75) (112) N in (2374) (751) (195) (431) (1932) (4206) Rank Order Correlation = 0.433** N S F a n d R E I S R a n k o f I n d u s t r y
  11. 11. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. The central question: Are rural substantive innovators common or rare? Substantive Innovators Data Driven Nominal Innovators Non-Innovators Nonmetro 22.56 38.52 38.92 Metro 31.27 32.26 36.47 Small Establishments Nonmetro 18.02 38.29 43.69 Metro 26.00 33.18 40.83 Medium Establishments Nonmetro 28.53 41.12 30.35 Metro 41.10 31.96 26.94 Large Establishments Nonmetro 52.14 29.99 17.87 Metro 48.36 22.97 28.67 Hi-tech Manufacturing Nonmetro 44.04 29.53 26.43 Metro 35.56 30.26 34.19 Hi-tech Services Nonmetro 32.71 26.75 40.54 Metro 40.41 24.21 35.38 Source: 2014 Rural Establishment Innovation Survey
  12. 12. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. How Reliable Measures of Rural Innovation Can Aid Rural Policy • Does rural policy need to address the problems that emerge from innovation-led growth? • Are market failures that plague sparsely populated areas impeding grassroots innovation? • How are rural areas best able to ameliorate the disadvantages of distance and kindle the creative spark?
  13. 13. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Thank you Comments? Questions? twojan@ers.usda.gov
  14. 14. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Is the innovation measure picking up things that citizens care about? • Associating substantive innovation with establishment performance such as productivity, exports, employment growth, survivability, etc. must wait for these data to become available • In the meantime, retrospective employment experience possible based on 2014 county- industry innovativeness estimate and county- industry employment growth in recovery 2009-2014.
  15. 15. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Table 9: Regressions of County- Industry Employment Growth, 2009- 2014Variable Parameter Estimate Standard Error t Value Pr > |t| Probability Substantive Innovator 82.69 43.02 1.92 0.0546 Share Introducing New Products or Processes (CIS Equivalent) -60.61 37.88 -1.60 0.1097 Probability Nominal Innovator -116.0698 54.081 -2.15 0.0319 Probability Non- Innovator -14.59 54.01 -0.27 0.7870 Source: 2014 ERS Rural Establishment Innovation Survey and BLS Quarterly Census of Employment and Wages Coefficient estimates for intercept, population, and industry controls not reported
  16. 16. The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA. Table 10: Regressions of County-Industry Employment Growth, 2009-2014, Selected Sectors Source: 2014 ERS Rural Establishment Innovation Survey and BLS Quarterly Census of Employment and Wages Coefficient estimates for intercept, population, and industry controls not reported Industrial Sector Variable Parameter Estimate Standard Error t Value Pr > |t| Fiber Probability Substantive Innovator 38.64 132.15 0.29 0.7709 Fiber Share Introducing New Products or Processes (CIS Eq.) 484.33 83.795 5.78 <.0001 Food Probability Substantive Innovator -146.081 52.49 -2.78 0.0057 Food Share Introducing New Products or Processes (CIS Eq.) -110.174 52.933 -2.08 0.0383 Information Probability Substantive Innovator 412.369 76.328 5.40 <.0001 Information Share Introducing New Products or Processes (CIS Eq.) 200.25 62.53 3.20 0.0015

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