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Description of Sectors Code in VSIC 2007 Sector In I/O 2012 Description of Sectors Classification by Pavitt (1984)2 1010 S_35 Processing and preserving of meat 2 1020 S_36 Fishery and processing and preserving of fishery product 2 1030 S_37 Processing and preserving of fruit and vegetables 2 1040 to 1050 S_38 Manufacture of vegetable and animal oils and fats 2 1061 to 1071 S_40 Manufacture of grain mill products, starches and starch products and bakery products 2 1072 to 1073 S_41 Manufacture of sugar 2 1074 to 1075 S_43 Manufacture of coffee and tea 2 1079 S_45 Manufacture of macaroni, noodles, couscous and similar farinaceous products; prepared meals and dishes and other food products 2 1080 S_46 Manufacture of prepared animal, fish, poultry feeds 2 1101 to 1102 S_47 Manufacture of wines 2 2 The classification by Pavitt (1984) was based on sources of technology, requirements of users, and possibilities for appropriation in firms. Supplier dominated firms (group 1) are mainly in traditional sectors of manufacturing like agriculture, housebuilding, informal household production. Scale intensive producers (group 2) have principal activities in food products, metal manufacturing, shipbuilding, motor vehicles, glass and cement. Science based firms (group 3) are to be found in the chemical and in the electronic/electrical sectors. xxi 1103 to 1104 S_48 Manufacture of beers 2 1311 to 1313 S_51 Spinning, weaving and finishing of textiles 1 1321 S_52 Manufacture of other textiles 1 1322 S_53 Manufacture of wearing apparel 1 1420 S_54 Manufacture of leather and related products 1 1511 to 1512 S_55 Manufacture of footwear 1 1610 to 1629 S_56 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 1 1701 to 1709 S_57 Manufacture of paper and paper products 1 1811 to 1820 S_58 Printing and reproduction of recorded media 1 2011 to 2013 S_62 Manufacture of basic chemicals, fertilizer and nitrogen compounds, plastics and synthetic rubber in primary forms 3 2021 to 2030 S_65 Manufacture of other chemical products 3 2100 S_67 Manufacture of pharmaceuticals, medicinal chemical and botanical products 3 2211 to 2212 S_68 Manufacture of rubber products 2 2220 S_69 Manufacture of plastics products 2 2310 S_70 Manufacture of glass and glass products 2 2391 to 2393 S_71 Manufacture of non-metallic mineral products 2 xxii 2394 to 2399 S_72 Manufacture of cement 2 2410 S_74 Manufacture of basic iron and steel 2 2420 to 2432 S_75 Manufacture of basic precious and other non- ferrous metals and Casting of metals 2 2511 to 2599 S_76 Manufacture of fabricated metal products, except machinery and equipment 2 2610 to 2680 S_77 Manufacture of computer, electronic and optical products 3 2710 to 2732 S_81 Manufacture of electric motor, generators, transformers and electricity distribution and control apparatus; batteries and accumulators; wiring and wiring devices 2 2740 to 2790 S_84 Manufacture of electric lighting equipment; domestic appliances and other electrical equipment 3 2811 to 2829 S_87 Manufacture of general-purpose machinery and special-purpose machinery 4 2910 to 3099 S_89 Manufacture of motor vehicles; trailers and semi- trailers and other transport equipment 2 3100 S_94 Manufacture of furniture 1 3211 to 3290 S_95 Manufacture of jewelry, bijouterie and related articles; musical instruments; sports goods and games and toys 4 3311 to 3320 S_98 Repair and installation of machinery and equipment 4 xxiii Table A2. The distribution of provinces by regions Region Province Region Province Region Province NorthWest Lào Cai North Central Thanh Hóa South East Bình Phước Lai Châu Nghệ An Tây Ninh Sơn La Hà Tĩnh Bình Dương Điện Biên Quảng Bình Đồng Nai Yên Bái Quảng Trị BRVT Hòa Bình TT-Huế Tp.HCM NorthEast Thái Nguyên South Central Đà Nẵng South West Long An Hà Giang Quảng Nam Tiền Giang Cao Bằng Quảng Ngãi Bến Tre Bắc Kạn Bình Định Vĩnh Long Tuyên Quang Phú Yên Đồng Tháp Thái Nguyên Khánh Hòa An Giang Phú Thọ Ninh Thuận Kiên Giang Lạng Sơn Bình Thuận Cần Thơ Quảng Ninh Hậu Giang Bắc Giang Sóc Trăng Red River Delta Highlands Đắk Nông Bạc Liêu Vĩnh Phúc Kon Tum Cà Mau Bắc Ninh Gia Lai Hải Dương Đắk Lắk Hưng Yên Lâm Đồng Thái Bình Hà Nam Nam Định Hà Nội Hải Phòng Ninh Bình Notes: This list includes the provinces in the data. The number of provinces were reduced from 63 provinces to 62 provinces (no presence of Tra Vinh Province by merging the data in the period. xxiv Table A3. Test on SDM versus SEM and SAR Model Test [Wx] S_RD_mean=[Wx]S_FDI_Supplier= [Wx]S_FDI_Customer = [Wx]S_InputImport= [Wx] S_export =0 testnl ([Wx]S_RD_mean = - [Spatial]rho*[Main]S_RD_mean) ([Wx]S_FDI_Supplier = -[Spatial]rho*[Main]S_FDI_Supplier) ([Wx]S_FDI_Customer = - [Spatial]rho*[Main]S_FDI_Customer)([Wx]S_InputImport = -[Spatial]rho*[Main]S_InputImport) ([Wx]S_export = - [Spatial]rho*[Main]S_export) Chi2 (5) Prob>chi2 Chi2 (5) Prob>chi2 1 31.63 0.0000 23.95 0.0002 2 25.42 0.0001 25.29 0.0001 3 19.76 0.0014 11.95 0.0355 4 16.31 0.006 9.81 0.08 5 23.68 0.0002 23.78 0.0002 6 25.42 0.0001 25.29 0.0001 Table A4. Test on SDM versus SAC Spatial Durbin Model Model Obs ll(model) df AIC BIC 1 190 -509.731 12 1043.462 1082.426 2 190 -257.86 15 545.7 594.43 3 190 -560.9 14 1149.8 1195.3 4 190 -560.21 17 1154.44 1209.6 5 190 -259.1 12 542.21 581.18 6 190 -257.86 15 545.72 594.43 Spatial Autocorrelation Model Model Obs ll(model) df AIC BIC 1 190 -514.12 8 1044.2 1070.2 2 190 -513.34 11 1048.7 1084.4 5 190 -265.23 8 546.46 572.44 6 190 -262.53 11 547 582.77 Table A5. LR test to compare the model 1 and the model 3 in the Table 4.7 Likelihood-ratio test LR chi2(3) = 1240.89 (Assumption: model1 nested in model3) Prob > chi2 = 0.0000 Table A6. LR test to compare the model 2 and the model 3 in the Table 4.7 Likelihood-ratio test LR chi2(3) = 1293.79 (Assumption: model2 nested in model3) Prob > chi2 = 0.0000 xxv Table A7. Collinearity Diagnostics in the model with Innovation activities as the Dependent variable Vaiable VIF SQRT VIF Tolerance R- Squared Innovation Activities 1.38 1.17 0.73 0.27 R&D activities 1.56 1.25 0.64 0.36 Input from FDI suppliers 1.27 1.12 0.8 0.2 Output to FDI customers 1.9 1.4 0.53 0.47 Imported Input 2.44 1.56 0.41 0.59 Exported Output 1.92 1.38 0.52 0.48 Capital per worker 1.4 1.2 0.7 0.29 Monopoly 1.21 1.1 0.83 0.17 Concentration level 1.3 1.14 0.77 0.22 Mean VIF 1.59 Table A8. Collinearity Diagnostics in the model with Modification activities as the Dependent variable Vaiable VIF SQRT VIF Tolerance R- Squared Modification Activities 1.39 1.18 0.72 0.28 R&D activities 1.59 1.26 0.63 0.37 Input from FDI suppliers 1.26 1.12 0.79 0.2 Output to FDI customers 1.85 1.36 0.54 0.46 Imported Input 2.44 1.56 0.41 0.59 Exported Output 1.97 1.4 0.51 0.49 Capital per worker 1.37 1.17 0.7 0.27 Monopoly 1.22 1.1 0.82 0.18 Concentration level 1.3 1.14 0.77 0.22 Mean VIF 1.6 xxvi Table A9. Wooldridge test for autocorrelation in panel data with Innovation activities as the Dependent variable H0: no first-order autocorrelation F (1, 37) = 3.561 Prob > F = 0.0670 Table A10. Wooldridge test for autocorrelation in panel data with Modification activities as the Dependent variable H0: no first-order autocorrelation F( 1, 37) = 1.739 Prob > F = 0.1953 Figure A1. Number of firms by sectors 71 857 568 130 1327 144 625 87 545 59 462 645 97 3086 321 559 2202 1601 743 455 833 346422 2204 104 1577 1470 598 266 3309 410419 315 785880 2175 686 196 0 1, 00 0 2, 00 0 3, 00 0 4, 00 0 N u m be r of fir m s 35 36 37 38 40 41 43 45 46 47 48 51 52 53 54 55 56 57 58 62 65 67 68 69 70 71 72 74 75 76 77 81 84 87 89 94 95 98 xxvii Figure A2. The correlation of Innovation activities and R&D activities in each year in the period In particular, the relationship between innovation activities and R&D activities is described in each year (Figure 3.19). There seems to be a slightly positive relationship between innovation activities and R&D activities. This positive relationship may be more revealed in the year of 2010, 2011 and 2013. In comparison to the average level of innovation activities and R&D activities in the period, there may be more sectors having R&D activities in 2010, but more sector having innovation activities in 2014. 0 10 20 30 40 0 10 20 30 40 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 2010 2011 2012 2013 2014 35 36 37 38 40 41 43 45 46 47 48 51 52 53 54 55 56 57 58 62 65 67 68 69 70 71 72 74 75 76 77 81 84 87 89 94 95 98 xxviii Figure A3. The correlation of Innovation activities and R&D activities by each sector in the period The positive correlation between innovation activities and R&D activities seems to be clearer in consideration of each sector (Figure 3.20). Some sectors such as sector number 41 and number 67 had both R&D activities and innovation activities to be higher than the average level of all sectors in all years in the period. The innovation activities in 2013 seem to be below the average level in all sectors, except the sector number 41 and number 45. 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 35 36 37 38 40 41 43 45 46 47 48 51 52 53 54 55 56 57 58 62 65 67 68 69 70 71 72 74 75 76 77 81 84 87 89 94 95 98 2010 2011 2012 2013 2014 xxix B. Spatial Regression Model in analysis on Knowledge Spillover among Sectors B1. General idea of Spatial Regression Model Spatial Regression Model has become a prominent tool for measuring spatial spillover. It is widely acknowledged that what occurs in one region may be related to what happens in neighboring regions. Several economic and socio-demographic variables may be referred to spatial clustering or geographic-based correlation such as unemployment, crime rates, house prices, per capital health expenditures and the alike (Solle Olle, 2003; Moscone and Knapp,2005; Reveilli,2005; Kostov,2009; Elhorst and Freret,2009; Elhorst et al.,2010). It is obvious that unemployment, crime rates or house prices in this region may have some effects on that in other regions. These regions could be countries, states, census tracts or zip codes. As stipulated by Tobler (1979), everything is related to everything else, but closer things more so. These relations may be observed and analyzed by spatial regression models. In spatial relations, the variable change for a specific unit may have effect on the change of other units that is regressed by different typical spatial models. The change in dependent variable of this unit may be correlated with that variable’s change of other units. This case is appropriate with the application of Spatial Autoregressive Model (SAR). In other case, the change in unobserved factors of this unit may be affected by the change in these factors of other units that is solved by Spatial Error Model (SEM). Besides, when there exists the effect of an explanatory variable’s change for a specific unit on the unit itself and, potentially, all other unit indirectly, Spatial Durbin Model (SDM) is prominent method to explore these relations. In general, spatial regression models determines the relations among units basing on the correlation matrix that indicates which regions are spatially related with a given region. This is usually a square symmetric RxR matrix xxx with (i,j) element equal to 1 if regions i and j are neighbors of one another, and zero otherwise. The diagonal elements of this “spatial neighbors” matrix are conventionally set to zero. Depending on the border between regions, LeSage and Pace (2009) pointed out four ways to construct such a matrix including linear contiguity, rook contiguity, bishop contiguity and queen contiguity Besides, distance-based criteria is also used to determine neighboring relations among regions. This approach can be expanded in a lot of ways by different distances or weights. The usage of different methods in determining the matrix depends on the context. In practice, the spatial neighbors’ matrices are usually slightly transformed into spatial weights matrices. The most common transformation method is to make the sum of each row in the neighbor matrix to be unity. In this method, called “row-standardization”, each element in a row is divided by the sum of the elements in the row. Therefore, a spatial weights matrix W, with element wij is defined by !H = ›zŒ∑ ›zŒ (b1) Depending on the method, the value of !zH is determined differently. Under border approach, !zH is equal to 1 when two regions satisfy the criteria of the same border. Another typical criterion to define !zH is distance-based contiguity with dij to be the distance between (centroids of) regions i and j. !zH is defined to be 1 if dij <d and zero otherwise for a pre-specified d. B2. Knowledge Spillover among Sectors under Spatial Regression Model approach Spatial Regression Model is every applicable in analyzing on knowledge spillover among sectors. It is widely known that knowledge is a public good that facilitates innovation and creates externalities due to knowledge spillover (Aghion and Jaravel, 2015). This spillover is emphasized by Cohen and xxxi Levithal (1989) to verify the role of R&D to enhance a firm’s absorptive capacity in assimilating knowledge from its environment. Basically, firms could not only learn the knowledge from other firms in the same sector but also gain the knowledge from other sectors due to the transaction of input and output. It means that R&D, an investment in knowledge creating activities, by this sector may have impact not only on innovation performance itself but also innovation capacity of other sectors. Besides, knowledge from other countries can be transferred and adopted by local firms through the impact of trade and foreign direct investment (FDI). These confirms on the existence of knowledge spillover of R&D, FDI and trade on sector innovation capacity. Spatial Regression Model idea is the modern approach to investigate the impact of knowledge spillover of R&D, FDI and trade on sector innovation capacity. By exploiting the complicated dependence structure between units, spatial regression models could estimate the direct, indirect and total marginal effects of this spillover (Belotti et al., 2016). In general, spatial regression model separate the direct and indirect effects by weighting matrix that covers the relationship between units. By traditional models, several studies investigated intra-sectoral effect by summing the knowledge spillover factor of all other firms in the sector. Meanwhile, the inter-sectoral effect is estimated by the sum of the knowledge spillover from all other firms in other sectors under the weight matrix of correlation between two sectors. Goya et al. (2016) investigated the inter-industry R&D spillover by the weighting matrix wsm. This matrix is defined as the quotient between the intermediate purchase by sector s of goods and services supplied by sector m and the total sum of intermediate purchase of sector s. However, spatial models allow a dynamic specification by implementing the bias corrected maximum likelihood approach described in Yu and Lee (2008). Moreover, the command in Stata could automatically distinguish the short-run and long-run marginal effects when a dynamic spatial model is estimated. xxxii B3. Robust Hausman Test In general, the choice between fixed and random-effects variants could be confirmed by the Hausman test with the Hausman (1978) statistic: D5 = V5œ ‰V5 (b5) Where V5 = (5žŸ - 5 Ÿ) is the difference between the fixed and random effects estimates and ‰ is an estimate of the variance-covariance matrix of V5. In spatial panel data, especially in small samples, ‰ = ‰žŸ - ‰ Ÿ is not ensured to be positive definite under alternative hypothesis. This may lead to the failure of Hausman specification test in meeting its asymptotic assumptions. In order to overcome this issue, xsmle in Stata directly takes into account the a‘ ¡ (5žŸ , 5 Ÿ ) so ‰ = ‰žŸ + ‰ Ÿ - 2a‘ ¡ (5žŸ , 5 Ÿ ). In particular, this command estimates ‰ through Dx¢`œ where D = (Ic, -Ic) and Ic denotes the identity matrix of size c. The joint variance-covariance matrix x¢ is consistently estimated by using the following sandwich formula. x¢ = (£žŸ ¤¤ £ Ÿ)(jžŸ,žŸ jžŸ, Ÿj Ÿ,žŸ j Ÿ, Ÿ) (£žŸ ¤¤ £ Ÿ), (3.7) With £¢š = -1/n ∑ Š¥ ¦§Œ *¢§Š*Š*¨K with p = FE, RE, j5š1 = 1/n ∑ Š ¦§Œ *¢§Š©K Š ¦ªŒ *¢ªŠ*¨ , p,q=FE,RE !ℎ88 £¢žŸ(j5žŸ,žŸ) £¢žŸ and £¢ Ÿ(j5 Ÿ, Ÿ) £¢ Ÿ are the cluster-robust variance- covariance matrices of 5žŸ , 5 Ÿ where the cluster is represented by the panel unit.