4.A Data Appendix
The present appendix provides further details regarding the construction and cleaning of the database.
4.A.1 Deflation
All nominal monetary values are converted into real 2005 euros by applying deflators from the OECD STAN database. Values in the manufacturing sector are kept as they are, while values in the services sector are adjusted using country-level PPPs for 2005, from the Eurostat-OECD PPP Programme.10
4.A.2 Capital stock
The main measure of capital stock used in the analysis is built according to the PIM methodology. The initial value of capital stock is set to the capital stock reported by the firm in the initial year. The depreciation rate is also obtained from the STAN database, as consumption of fixed capital divided by the sum of consumption and net capital stock (CCFC/(CCFC+CAPN)). We use the reported value of capital for the robustness checks, and the correlation between these two measures is 0.78.
4.B Additional results
10For more information, see www.oecd.org/std/ppp/manual
Table 4.B.1: Results of Probit estimations of survival
Dependent Variable (1) (2) (3) (4) (5) (7)
Survival Baseline Exp. Ind. Ori Qualif. HC ω Quart.
Man. Hire 0.196***
(0.0341)
Exp Man. Hire 0.173***
(0.0593)
Ext. Promotion 0.253***
(0.0466)
Same det. Industry 0.197***
(0.0575)
Same broad industry 0.118*
(0.0678)
Diff broad industry 0.242***
(0.0538)
HE Man. Hire 0.237*** 0.155** 0.0900***
(0.0570) (0.0611) (0.0347)
Adv HE Man. Hire 0.253*** 0.143 0.0394
(0.0862) (0.0916) (0.0396)
Share HE Sender 0.247***
(0.0942)
Share advHE Sender 0.110
(0.152)
Q1ω Sender 0.0136
(0.0422)
Q2ω Sender 0.0472
(0.0493)
Q3ω Sender 0.0430
(0.0475)
Q4ω Sender 0.109***
(0.0390) Man Departure -0.493*** -0.495*** -0.493*** -0.490*** -0.495*** -0.513***
(0.0157) (0.0157) (0.0157) (0.0156) (0.0157) (0.0158) Non-Man.Departure -0.494*** -0.493*** -0.494*** -0.494*** -0.492*** -0.480***
(0.0173) (0.0173) (0.0173) (0.0173) (0.0173) (0.0182) Int. Prom 0.151*** 0.150*** 0.151*** 0.150*** 0.144*** 0.120***
(0.0189) (0.0189) (0.0189) (0.0189) (0.0190) (0.0190) Non-Man. Hire 0.505*** 0.505*** 0.505*** 0.506*** 0.505*** 0.503***
(0.0135) (0.0135) (0.0135) (0.0135) (0.0135) (0.0142) Observations 253,744 253,744 253,744 253,744 253,744 227,135 Standard errors obtained from block bootstrap in parentheses. All models include the following controls:
Log Labour, Log Capital, Log wages, industry share of startups, mean age, share of males, share of high school, higher education and advanced higher education graduates, and industry and time fixed effects.
* p <0.1, **p <0.05, *** p <0.01.
Dependent (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) variable: Agriculture Manufacturing Utilities Construction Trade Logistics Hospitality IT & Telcos Admin. Non-Mkt Prof.
ω Activities Services Services
ω -1.467 1.070** -0.222 -0.0609 -0.860* -0.0725 -0.180 2.307*** -0.705 1.540* 0.239
(1.267) (0.431) (0.721) (0.601) (0.520) (0.642) (0.312) (0.607) (0.489) (0.806) (0.605)
ω2 -0.181** 0.187*** -0.0111 0.0388 0.0263 -0.114*** 0.113*** 0.167*** 0.153*** 0.112*** 0.0829
(0.0895) (0.0240) (0.00741) (0.0586) (0.0472) (0.0364) (0.0302) (0.0364) (0.0338) (0.0235) (0.0567)
Prob.Surv -2.387*** 0.164*** 0.377* -0.223*** 0.0608 -0.115 -0.0985*** 0.272** -0.250** 0.351*** 0.0441
(0.887) (0.0504) (0.208) (0.0657) (0.0399) (0.104) (0.0330) (0.127) (0.117) (0.102) (0.0856)
Prob.Surv 2 2.144** -0.184*** -0.387* 0.206*** -0.0768* 0.102 0.0908*** -0.277** 0.213* -0.384*** -0.0497
(0.895) (0.0516) (0.212) (0.0669) (0.0412) (0.106) (0.0345) (0.131) (0.120) (0.104) (0.0877)
ω*Prob.Surv 1.953 -0.388 1.162 0.670 1.735*** 0.873 0.873*** -1.679*** 1.471*** -0.753 0.537
(1.249) (0.446) (0.727) (0.618) (0.536) (0.654) (0.323) (0.627) (0.496) (0.823) (0.620)
Int. Promotion -0.00491 0.0125*** 0.0279*** -0.000567 0.00935*** -0.0115** 0.00321 0.0146** 0.0239*** -0.00517 0.0210***
(0.0117) (0.00234) (0.00939) (0.00286) (0.00179) (0.00511) (0.00298) (0.00703) (0.00578) (0.00516) (0.00404) Man. Departure -0.0817*** -0.108*** -0.0432*** -0.0506*** -0.0528*** -0.0344*** -0.0290*** -0.207*** -0.0486*** -0.0357*** -0.125***
(0.0276) (0.00351) (0.0168) (0.00603) (0.00350) (0.00658) (0.00442) (0.00905) (0.00825) (0.00673) (0.00609) Non-Man. Hire -0.00581 0.0141*** -0.00351 0.00844* 0.00778** 0.0120** -0.00897* -0.0754*** -0.0117 -0.00205 -0.0226**
(0.0132) (0.00407) (0.0188) (0.00445) (0.00357) (0.00578) (0.00536) (0.0198) (0.00794) (0.00695) (0.00920)
Man. Hire 0.0291* 0.0335*** 0.00639 -0.00388 0.0294*** 0.00357 0.0176*** 0.0901*** 0.0174*** 0.0277*** 0.0850***
(0.0167) (0.00269) (0.0221) (0.00523) (0.00277) (0.00656) (0.00401) (0.00853) (0.00669) (0.00768) (0.00624)
Observations 4,400 40,505 2,896 33,043 47,170 18,859 11,240 9,698 11,563 14,683 17,622
R-squared 0.957 0.610 0.957 0.389 0.752 0.755 0.567 0.527 0.656 0.671 0.649
All independent variables are lagged one period. All models include a polynomial of order 2 in lagged productivity and survival probability, unreported. Standard errors obtained from block bootstrap in parentheses. *p <0.1, **p <0.05, ***p <0.01
Dependent (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) variable: Agriculture Manufacturing Utilities Construction Trade Logistics Hospitality IT & Telcos Admin. Non-Mkt Prof.
ω Activities Services Services
ω -1.908 1.035** 0.412 -0.0387 -0.833 -0.201 -0.209 2.226*** -0.701 1.689* 0.236
(1.225) (0.483) (0.787) (0.624) (0.532) (0.708) (0.333) (0.553) (0.492) (0.868) (0.533)
ω2 -0.155* 0.186*** -0.0134* 0.0410 0.0262 -0.122*** 0.112*** 0.158*** 0.159*** 0.114*** 0.0912**
(0.0871) (0.0251) (0.00760) (0.0598) (0.0398) (0.0369) (0.0307) (0.0230) (0.0364) (0.0248) (0.0415)
Prob.Surv -2.523*** 0.164*** 0.339 -0.220*** 0.0653* -0.111 -0.105*** 0.255** -0.264** 0.353*** 0.0286
(0.809) (0.0583) (0.231) (0.0663) (0.0352) (0.118) (0.0317) (0.130) (0.113) (0.103) (0.0915)
Prob.Surv 2 2.329*** -0.183*** -0.348 0.203*** -0.0812** 0.0996 0.0974*** -0.260* 0.227** -0.386*** -0.0334
(0.821) (0.0597) (0.235) (0.0674) (0.0363) (0.120) (0.0333) (0.134) (0.115) (0.105) (0.0939)
ω*Prob.Surv 2.490** -0.355 0.522 0.647 1.704*** 1.012 0.907*** -1.595*** 1.466*** -0.907 0.530
(1.215) (0.498) (0.793) (0.642) (0.550) (0.721) (0.345) (0.568) (0.500) (0.886) (0.547)
Int. Promotion -0.00478 0.0122*** 0.0294*** -0.00110 0.00918*** -0.0113** 0.00252 0.0148* 0.0217*** -0.00565 0.0213***
(0.0107) (0.00230) (0.0105) (0.00291) (0.00178) (0.00568) (0.00332) (0.00760) (0.00601) (0.00598) (0.00369) Man. Departure -0.0662** -0.108*** -0.0434** -0.0502*** -0.0521*** -0.0333*** -0.0287*** -0.207*** -0.0453*** -0.0328*** -0.126***
(0.0259) (0.00331) (0.0187) (0.00614) (0.00351) (0.00604) (0.00409) (0.00879) (0.00852) (0.00709) (0.00600)
Non-Man. Hire 0.00149 0.0149*** -0.00894 0.00889** 0.00685* 0.0128** -0.00854 -0.0671*** -0.00938 -0.00249 -0.0237***
(0.0116) (0.00455) (0.0223) (0.00380) (0.00355) (0.00595) (0.00563) (0.0187) (0.00796) (0.00791) (0.00904)
Exp. Man. Hire 0.0432*** 0.0367* -0.0244** 0.0456*** 0.0173 0.0242*** 0.148*** 0.000115 0.0292* 0.143***
(0.00600) (0.0218) (0.0122) (0.00529) (0.0148) (0.00785) (0.0127) (0.0100) (0.0151) (0.00971)
Ext. Promoition 0.00360 0.0215*** 0.00624 -0.00531 0.0232*** -0.00667 0.0145*** 0.0540*** 0.00400 0.0175** 0.0588***
(0.0189) (0.00385) (0.0304) (0.00595) (0.00314) (0.00804) (0.00486) (0.0120) (0.00901) (0.00812) (0.00782)
Observations 4,355 40,505 2,896 33,043 47,170 18,859 11,240 9,698 11,563 14,683 17,622
R-squared 0.960 0.607 0.954 0.387 0.747 0.742 0.570 0.523 0.652 0.669 0.653
All independent variables are lagged one period. All models include a polynomial of order 2 in lagged productivity and survival probability, unreported. Standard errors obtained from block bootstrap in parentheses. *p <0.1, **p <0.05, ***p <0.01
Dependent (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) variable: Agriculture Manufacturing Utilities Construction Trade Logistics Hospitality IT & Telcos Admin. Non-Mkt Prof.
ω Activities Services Services
ω -1.665 1.035** 0.0616 -0.0731 -0.858 0.908* -0.185 2.268*** -0.676 1.637* 0.298
(1.337) (0.433) (0.719) (0.673) (0.545) (0.551) (0.342) (0.617) (0.503) (0.870) (0.646)
ω2 -0.182** 0.188*** -0.0115 0.0382 0.0250 -0.0107*** 0.120*** 0.164*** 0.154*** 0.113*** 0.0852
(0.0895) (0.0260) (0.00749) (0.0627) (0.0406) (0.00210) (0.0327) (0.0351) (0.0333) (0.0254) (0.0585)
Prob.Surv -2.575*** 0.161*** 0.423* -0.224*** 0.0648* -0.0517 -0.0991*** 0.311** -0.255** 0.357*** 0.0313
(0.937) (0.0524) (0.227) (0.0638) (0.0342) (0.142) (0.0319) (0.128) (0.113) (0.111) (0.101)
Prob.Surv 2 2.333** -0.181*** -0.433* 0.207*** -0.0810** 0.0337 0.0912*** -0.318** 0.217* -0.390*** -0.0376
(0.943) (0.0535) (0.231) (0.0652) (0.0355) (0.144) (0.0332) (0.132) (0.116) (0.113) (0.103)
ω*Prob.Surv 2.152 -0.355 0.874 0.683 1.733*** -0.0398 0.875** -1.639** 1.438*** -0.853 0.471
(1.314) (0.449) (0.724) (0.694) (0.565) (0.557) (0.353) (0.638) (0.514) (0.888) (0.659)
Int. Promotion -0.00443 0.0123*** 0.0276** -0.000623 0.00913*** -0.0185*** 0.00307 0.0143* 0.0238*** -0.00546 0.0208***
(0.0111) (0.00234) (0.0109) (0.00304) (0.00192) (0.00485) (0.00320) (0.00760) (0.00573) (0.00634) (0.00413) Man. Departure -0.0815*** -0.108*** -0.0452** -0.0507*** -0.0527*** -0.0571*** -0.0290*** -0.207*** -0.0503*** -0.0359*** -0.126***
(0.0266) (0.00356) (0.0178) (0.00543) (0.00343) (0.00819) (0.00403) (0.0102) (0.00766) (0.00658) (0.00598) Non-Man. Hire -0.00668 0.0144*** -0.00477 0.00841** 0.00837** 0.0251*** -0.00909* -0.0750*** -0.0131* -0.00301 -0.0240***
(0.0129) (0.00442) (0.0201) (0.00392) (0.00329) (0.00629) (0.00495) (0.0192) (0.00771) (0.00748) (0.00911)
Same 2D industry 0.0406 0.0139** 0.0289 -0.00600 0.0245*** -0.00967 0.0219*** 0.108*** 0.00727 0.0331 0.0635***
(0.0343) (0.00609) (0.0345) (0.00975) (0.00342) (0.0101) (0.00529) (0.0137) (0.0138) (0.0204) (0.0108)
Same 1D industry 0.0437*** 0.0356*** 0.000307 0.0211** 0.0631*** 0.0216** -0.0818 0.0692***
(0.00661) (0.00667) (0.0117) (0.00930) (0.0144) (0.00905) (0.0706) (0.0115)
Outside 1D industry 0.0228 0.0365*** 0.00525 -0.00260 0.0328*** -0.00413 0.00597 0.106*** 0.0228** 0.0300*** 0.105***
(0.0211) (0.00398) (0.0210) (0.00589) (0.00382) (0.0166) (0.00727) (0.0153) (0.00963) (0.00731) (0.00814)
Observations 4,400 40,505 2,887 33,043 47,170 18,859 11,240 9,698 11,563 14,683 17,622
R-squared 0.956 0.605 0.957 0.389 0.753 0.938 0.566 0.526 0.652 0.669 0.640
All independent variables are lagged one period. All models include a polynomial of order 2 in lagged productivity and survival probability, unreported. Standard errors obtained from block bootstrap in parentheses. *p <0.1, **p <0.05, ***p <0.01
Dependent (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) variable: Agriculture Manufacturing Utilities Construction Trade Logistics Hospitality IT & Telcos Admin. Non-Mkt Prof.
ω Activities Services Services
ω -1.546 1.070** -1.282 -0.0501 -0.892* 0.0980 -0.0761 2.241*** -0.687 1.543* -0.0485
(1.404) (0.487) (0.911) (0.580) (0.511) (0.607) (0.329) (0.622) (0.489) (0.887) (0.603)
ω2 -0.181** 0.190*** -0.00736 0.0401 0.0261 -0.118*** 0.113*** 0.169*** 0.158*** 0.110*** 0.0827
(0.0908) (0.0288) (0.00524) (0.0539) (0.0457) (0.0329) (0.0350) (0.0349) (0.0335) (0.0228) (0.0556)
Prob.Surv -2.519** 0.152*** 0.0966 -0.225*** 0.0555 -0.0940 -0.0937*** 0.279** -0.278** 0.339*** 0.0536
(0.982) (0.0569) (0.323) (0.0618) (0.0363) (0.0970) (0.0343) (0.131) (0.115) (0.102) (0.0783)
Prob.Surv 2 2.276** -0.172*** -0.101 0.208*** -0.0717* 0.0819 0.0859** -0.284** 0.241** -0.371*** -0.0592
(0.995) (0.0581) (0.328) (0.0629) (0.0374) (0.0986) (0.0359) (0.135) (0.118) (0.104) (0.0803)
ω*Prob.Surv 2.031 -0.391 2.238** 0.658 1.764*** 0.697 0.769** -1.617** 1.449*** -0.759 0.825
(1.394) (0.503) (0.919) (0.598) (0.530) (0.620) (0.342) (0.642) (0.499) (0.907) (0.620)
Int. Promotion -0.0867*** -0.109*** -0.0454** -0.0506*** -0.0515*** -0.0349*** -0.0257*** -0.206*** -0.0435*** -0.0346*** -0.123***
(0.0253) (0.00354) (0.0187) (0.00512) (0.00359) (0.00615) (0.00362) (0.00982) (0.00771) (0.00706) (0.00588) Man. Departure -0.00438 0.0120*** 0.0247** -0.000674 0.00927*** -0.0110** 0.00176 0.0127* 0.0234*** -0.00474 0.0207***
(0.0114) (0.00213) (0.0122) (0.00290) (0.00178) (0.00467) (0.00305) (0.00729) (0.00602) (0.00519) (0.00337)
Non-Man. Hire -0.00515 0.0150*** 0.00102 0.00822** 0.00899** 0.00925* -0.00717 -0.0720*** -0.0129* -0.00458 -0.0201**
(0.0139) (0.00399) (0.0197) (0.00405) (0.00368) (0.00548) (0.00584) (0.0201) (0.00765) (0.00738) (0.00998)
HE Man Hire 0.0239 0.0297*** -0.0187 -0.00522 0.0415*** 0.00532 0.0206*** 0.0723*** 0.0214** 0.0541*** 0.0498***
(0.0231) (0.00387) (0.0322) (0.00780) (0.00458) (0.00934) (0.00671) (0.0143) (0.00870) (0.0109) (0.00790)
Adv HE Man Hire 0.177 0.0442*** -0.00923 -0.0386 0.0748*** 0.118* 0.0293 0.127*** 0.0527*** 0.0669*** 0.141***
(0.124) (0.00837) (0.0529) (0.0317) (0.0107) (0.0625) (0.0340) (0.0189) (0.0190) (0.0210) (0.0110)
Observations 4,400 40,505 2,896 33,043 47,170 18,859 11,240 9,698 11,563 14,683 17,622
R-squared 0.957 0.601 0.960 0.388 0.749 0.731 0.569 0.523 0.653 0.669 0.644
All independent variables are lagged one period. All models include a polynomial of order 2 in lagged productivity and survival probability, unreported. Standard errors obtained from block bootstrap in parentheses. *p <0.1, **p <0.05, ***p <0.01
Abowd, J., Kramarz, F., and Margolis, D. (1999). High wage workers and high wage firms. Econometrica.
Ackerberg, D., Benkard, C., Berry, S., and Pakes, A. (2007). Econometric tools for analyzing market outcomes. In Heckman, J. J. and Leamer, E. E., editors, Handbook of Econometrics, volume 6A, chapter 63, pages 4171–4276. Amsterdam: North-Holland.
Ackerberg, D., Caves, K., and Frazer, G. (2015). Identification properties of recent pro-duction function estimators. Econometrica.
Agterberg, M., van den Hood, B., Huysman, M., and Soekijad, M. (2010). Keeping the wheels turning: The dynamics of managing networks of practice. Journal of Manage-ment Studies.
Allen, D. (2006). Do organizational socialization tactics influence newcomer embeddedness and turnover? Journal of Management.
Allen, J., Levels, M., and can der Velden, R. (2013). Skill mismatch and use in developed countries: Evidence from the piaac study. Maastricht University Research Center for Education and the Labour Market RM/13/061.
Andrews, D., Criscuolo, C., and Gal, P. (2015). Frontier firms, technology diffusion and public policy: micro evidence from oecd countries. OECD Productivity working papers.
Andrews, D., Criscuolo, C., and Gal, P. (2016). The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy. OECD Productivity working papers.
Aral, S. and Weill, P. (2007). It assets, organisational capabilities and firm performance:
How resource allocations and organisational differences explain performance variation.
Organisation Science.
Arrighetti, A., Landini, F., and Lasagni, A. (2014). Intangible assets and firm hetero-geneity: Evidence from italy. Research Policy.
Atkeson and Kehoe (2005). Modeling and measuring organisational capital. Journal of Political Economy.
Aw, B. Y., Roberts, M., and Xu, D. Y. (2008). R&d investments, exporting, and the evolution of firm productivity. American Economic Review.
Aw, B. Y., Roberts, M. J., and Xu, D. Y. (2011). R&D investment, exporting, and productivity dynamics. American Economic Review, 101(4):1312–44.
Bailey, T., Berg, P., and Sandy, C. (2001). The effect of high-performance work prac-tices on employee earnings in the steel, apparel and medical electronics and imagery industries. Industrial and Labor Relations review.
Baumann, J. and Kritikos, A. (2016). The link between R&D, innovation and productiv-ity: Are micro firms different? Research Policy, pages 1263–1274.
Belitz, H., Eickelpasch, A., Le Mouel, M., and Schiersch, A. (2017). Wissensbasiertes kap-ital in deutschland: Analyse zu produktivitäts- und wachstumseffekten und erstellung eines indikatorsystems. Technical report, DIW Berlin.
Bertrand, M. and Schoar, A. (2003). Managing with style: The effect of managers on firm policies. The Quarterly Journal of Economics.
Birkinshaw, J. and Hagstrom, P. (2000). The flexible firm: Capability management in network organizations. Oxford University Press.
Black, S. and Lynch, L. (2001). How to compete: The impact of workplace practices and information technology on productivity. Review of Economics and Statistics.
Black, S. and Lynch, L. (2005). Measuring Capital in a New Economy, chapter Measuring Organizational Capital in the New Economy. University of Chicago Press.
Black, S., Lynch, L., and Krivelyova, A. (2004). How workers fare when employers inno-vate. Industrial Relations.
Bloom, N., Eifert, B., Mahakan, A., McKenzie, D., and Roberts, J. (2013). Does man-agement matter? evidence from india. Quarterly Journal of Economics, pages 1–51.
Bloom, N., Genakos, C., and Reenen, J. V. (2012). Management practices across firms and countries. National Bureau of Economic Research Working Paper No. 17850.
Bloom, N., Lemos, R., Sadun, R., Scur, D., and Reenen, J. V. (2014). The new empirical economics of management. NBER Working Paper 20102.
Bloom, N. and Reenen, J. V. (2010). Why do management practices differ across firms and countries. Journal of Economic Perspectives.
Bloom, N., Sadun, R., and Reenen, J. V. (2017). Management as a technology? NBER Working Paper No. 22327.
Bloom, N., Sadun, R., and Van Reenen, J. (2016). Management as a technology? NBER Working Paper 22327.
Bloom, N. and Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. Quarterly Journal of Economics, pages 1351–1408.
Bontempi, M. E. and Mairesse, J. (2015). Intangible capital and productivity at the firm level: a panel data assessment. Economics of Innovation and New Technology.
Borgatti, S. and Foster, P. (2003). The network paradigm in organizational research: A review and typology. Journal of Management.
Bounfour, A. (2010). Le capital organisationnel: Principes, enjeux, valeur. Springer-Verlag.
Bresnahan, T., Brynjolfsson, E., and Hitt, L. (2002). Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. The Quarterly Journal of Economics.
Brynjolfsson, E., Hitt, L., and Yang, S. (2002). Intangible assets: computers and organi-sational capital. Brookings Papers on Economic Activity.
Brynjolfsson, E. and MacAfee, A. (2011). Race Against The Machine: How the digital revolution is accelerating innovation, driving productivity and irreversibly transforming employment and the economy. Digital Frontier Press.
Byrne, D. M., Fernald, J. G., and Reinsdorf, M. B. (2016). Does the united states have a productivity slowdown or a measurement problem? Federal Reserve Bank of San Francisco, Working Paper 2016-03.
Cardona, M., Kretschmer, T., and Strobel, T. (2013). Ict and productivity: conclusions from the empirical literature. Information economics and policy.
Caroli, E. and Reenen, J. V. (2001). Skill-biased organisational change? evidence from a panel of british and french establishments. Quarterly Journal of Economics.
Chappell, N. and Jaffe, A. (2018). Intangible investment and firm performance. NBER Working paper series.
Che, N. (2009). Sectoral structural change in a knowledge economy. MPRA Paper.
Chen, W., Gouma, R., Los, B., and Timmer, M. (2017). Measuring the income to intan-gibles in goods production: a global value chain approach. World Intellectual Property Organisation Economic Research Working Paper N. 36.
Chen, W. and Inklaar, R. (2015). Productivity spillovers of organization capital. Journal of Productivity Analysis.
Chen, W., Niebel, T., and Saam, M. (2016). Are intangibles more productive in ICT-intensive industries? evidence from EU countries. Telecommunications policy.
Clinton, M., Knight, T., and Guest, D. (2012). Job embeddedness: a new attitudinal approach. International Journal of Selection and Assessment.
Cohen, L. (2013). Assembling jobs: A model of how tasks are bundled into and across jobs. Organization Science.
Corrado, C., Haskel, J., and Jona-Lasinio, C. (2014). Smart public intangibles: Spintan framework and measurement guidelines. SPINTAN Project: Smart Public Intangibles Publication.
Corrado, C., Haskel, J., Jona-Lasinio, C., and Iommi, M. (2016a). Investment and invest-ment finance in Europe: Financing productivity growth, chapter Growth, tangible and intangible investment in the EU and US before and since the Great Recession, pages 73–103. European Investment Bank.
Corrado, C., Hulten, C., and Sichel, D. (2005). Measuring Capital in a New Economy, chapter Measuring Capital and Technology: An Expanded Framework. National Bureau of Economic Research and University of Chicago Press.
Corrado, C., Hulten, C., and Sichel, D. (2009). Intangible capital and the us economy.
The Review of Income and Wealth.
Corrado, C., Jäger, K., and Jona-Lasinio, C. (2016b). Measuring intangible capital in the public sector: A manual. Technical report, Smart Public Intangibles project.
Crass, D. and Peters, B. (2014). Intangible assets and firm-level productivity. ZEW Discussion papers.
Crepon, B., Duguet, E., and Mairessec, J. (1998). Research, innovation and produc-tivity: An econometric analysis at the firm level. Economics of Innovation and New Technology, 7:115–158.
De Loecker, J. and Warzynski, F. (2012). Markups and firm-level exports. American Economics Review.
Doraszelski, U. and Jaumandreu, J. (2013). R&D and productivity: estimating endoge-nous productivity. Review of Economic Studies, 80:1338–1383.
Duda, R. and Hart, P. (1973). Pattern Classification and Scene Analysis. Wiley and Son, New York.
Eisfeldt, A. and Papanikolaou, D. (2013). Organization capital and the cross-section of expected returns. Journal of Finance.
Evenson, R. and Westphal, L. (1995). Handbook of Development Economics, chapter Technological change and technology strategy. ELsevier.
Foster, L., Grim, C., Haltiwanger, J., and Wolf, Z. (2018). Innovation, productivity dispersion and productivity growth. NBER Working paper series.
Fritsch, M., Görzig, B., Hennchen, O., and Stephan, A. (2004). Cost structure surveys for Germany. Schmollers Jahrbuch: Journal of Applied Social Science Studies/Zeitschrift für Wirtschafts- und Sozialwissenschaften, 124(4):557–566.
Fukao, K., Miyagawa, T., Mukai, K., Shinoda, Y., and Tonogi, K. (2009). Intangible investment in japan: Measurement and contribution to economic growth. Review of Income and Wealth.
Gabaix, X. and Landier, A. (2008). Why has ceo pay increased so much? The Quarterly Journal of Economics, pages 49–100.
Gordon, R. J. (2012). Is u.s. economic growth over? flatering innovation confronts the six headwinds. NBER Working Paper No. 18315.
Gordon, R. J. (2013). U.s. productivity growth: The slowdown has returned after a temporary revival. International Productivity Monitor, 25:13–19.
Gordon, R. J. (2015). The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War. Princeton University Press.
Grant, R. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal.
Greenan, N. (2012). Measuring learning organisations. In OECD Expert meeting on the measurement of intangible assets.
Griffith, R., Huergo, E., Peters, B., and Mairesse, J. (2016). Innovation and productivity across four european countries. Oxford Review of Economic Policy, 22(4):483–498.
Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics.
Gulati, R. (1998). Alliances and networks. Strategic Management Journal.
Gulati, R. (2007). Managing network resources: Alliances, affiliations and other relational assets. New York: Oxford University Press.
Hales, C. (2002). Bureaucracy-lite and continuitities in managerial work. British Journal of Management.
Hall, B., Mairesse, J., and Mohnen, P. (2010). Handbook of the Economics of Innovation, chapter Measuring the returns to R&D, pages 1033–1082. Elsevier.
Hall, B. H. (2011). Innovation and productivity. Nordic Economic Policy Review, pages 167–204.
Hall, B. H., Lotti, F., and Mairesse, J. (2009). Innovation and productivity in SMEs:
Empirical evidence for Italy. Small Business Economics.
Hansen, G. and Wernerfelt, B. (1989). Determinants of firm performance: the relative importance of economic and organisational factors. Strategic Management Journal.
Hanushek, E., Schwerdt, G., Wiederhold, S., and Woessman, L. (2015). Returns to skills around the world: Evidence from piaac. European Economic Review.
Haskel, J. and Westlake, S. (2018). Capitalism without Capital: the rise of the intangible economy. Princeton University Press.
Hayton, J., Carbunaci, G., and Eisenberger, R. (2012). With a little help from my col-leagues: A social embeddedness approach to perceived organizational support. Journal of Organizational Behaviour.
Heining, J., Klosterhuber, W., and Seth, S. (2014). An overview on the linked employer-employee data of the institute for employment research (iab). Schmollers Jahrbuch, 134(1):141–148.
Johnson, W. (1999). An integrative taxonomy of intellectual capital: Measuring the stock and flow of intellectual capital components of the firm. International Journal of Technology and Management.
Jovanovic, B. (1979). Job matching and the theory of turnover. Journal of Political Economy.
Kancs, A. and Siliverstovs, B. (2016). R&D and non-linear productivity growth. Research Policy, 45:634–646.
Kaplan, S., Klebanoc, N., and Sorensen, M. (2012). Wich ceo characteristics and abilities matter? The Journal of Finance.
Kapoor, R. and Adner, R. (2011). What makes firms make vs. what they know: How firms’ production and knowledge boundaries affect competitive advantage in the face of technological change. Organization Science.
Koch, A. (2007). Neue Datenquelle Unternehmensregister: Mehr Informationen uber den Mittelstand ohne neue Buerokratie. Institut fuer Mittelstandsforschung. Bonn.
Le Mouel, M. and Squicciarini, M. (2015). Cross-country estimates of employment and in-vestment in organisational capital: A task-based methodology using piaac data. OECD Science, Technology and Industry Working Papers.
Lev, B. and Radhakrishnan, S. (2005). Measuring capital in the new economy, chapter The valuation of organization capital. National Bureau of Economic Research and University of Chicago PRess.
Lev, B., Radhakrishnan, S., and Zhang, W. (2009). Organization capital. Abacus.
Levinsohn, J. and Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies, 70:317–341.
Lounsbury, M. and Ventresca, M. (2003). The new structuralism in organisational theory.
Organization.
Ludwig, O. and Sadowski, D. (2009). Measuring organisational capital. Schmalenback Business Review.
Maliranta, M., Mohnen, P., and Rouvinen, P. (2009). Is inter-firm labor mobility a channel of knowledge spillovers? evidence from a linked employer employee panel. Industrial and Corporate Change.
Marrano, M. G., Haskel, J., and Wallis, G. (2009). What happened to the knowledge economy? ict, intangible investment, and britain’s productivity record revisited.Review of Income and Wealth.
Marrocu, E., Paci, R., and Pontis, M. (2011). Intangible capital and firms’ productivity.
Industrial and Corporate Change, 21(2).
Marschak, J. and Andrews Jr., W. H. (1944). Random simultaneous equations and the theory of production. Econometrica, 12(3/4):143–205.
Meyer, K., Mudambi, R., and Narula, R. (2011). Multinational enterprises and local con-texts: The opportunities and challenges of multiple embeddedness. Journal of Man-agement Studies.
Mion, G. and Opromolla, L. (2014). Managers’ mobility, trade performance and wages.
Journal of International Economics.
Miyagawa, T. and Hisa, S. (2013). Measurement of intangible investment by industry and economic growth in japan. Public Policy Review, 9(2):405–432.
Miyagawa, T. and Kim, Y. (2008). Measuring organisation capital in japan: An empirical estimate using firm-level data. Seoul Journal of Economics.
Miyagawa, T., Lee, K., and Kabe, S. (2010). Management practices and firm performance in japanese and korean firms. an empirical study using interview surveys. InConference of Chaire europeenned de management de l’immateriel.
Moran, P. (2005). Structural vs. relational embeddedness: social capital and managerial performance. Strategy of Management Journal.
Ng, T. and Feldman, D. (2009). How broadly does education contribute to job perfor-mance? Personnel Psychology.
Niebel, T., O’Mahony, M., and Saam, M. (2017). The contribution of intangible assets to sectoral productivity growth in the eu. Review of Income and Wealth.
Nocke, V. and Yeaple, S. (2014). Globalisation and multiproduct firms. International Economic Review.
Nonaka, I. (1994). A dynamic theory of organisational knowledge creation. Organization Science.
OECD (2013). New Sources of Growth: Knowledge-Based Capital - Key Analyses and Policy Conclusions. OECD Publishing.
OECD (2015). The Future of Productivity. OECD Publishing. Paris.
Olley, S. and Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6):1263–1297.
Parrotta, P. and Pozzoli, D. (2012). The effect of learning by hiring on productivity.
RAND journal of economics.
Pellizzari, M. and Fichen, A. (2013). A new measure of skills mismatch: Theory and evi-dence from the survey of adult skills (piaac). OECD Social, Employment and Migration Working Papers.
Ployhart, R. and Moliterno, R. (2011). Emergence of human capital resource: A multilevel model. Academy of Management Review.
Poole, J. (2013). Knowledge transfers from multinational to domestic firms: Evidence from worker mobility. The Review of Economics and Statistics.
Prescott, E. and Visscher, M. (1980). Organizational capital. Journal of Political Econ-omy.
Richter, P. M. and Schiersch, A. (2017). CO2 emission intensity and exporting: Evidence from firm-level data. European Economic Review, 98.
Rosen, S. (1972). Learning by experience as joint production. Quarterly Journal of Economics.
Roth, F. and Thum, A.-E. (2013). Intangible capital and labor productivity growth: Panel evidence for the EU from 1998-2005. Review of Income and Wealth, 59(3):486–508.
Schiersch, A. and Schmidt-Ehmcke, J. (2011). Is the Boone-indicator applicable? Ev-idence from a combined data set of German manufacturing enterprises. Journal of Economics and Statistics (Jahrbücher für Nationalökonomie und Statistik), 231(3):336–
357.
Serafinelli, M. (2017). ’Good firms’, worker flows and local productivity. Journal of Labor Economics.
Squicciarini, M. and Le Mouel, M. (2012). Defining and measuring investment in organi-sational capital: Using us microdata to develop a task-based approach. OECD Science, Technology and Industry Working Papers.
Squicciarini, M., Marcolin, L., and Horvat, P. (2015). Estimating cross-country investment in training: an experimental methodology using piaac data. OECD Science, Technology and Industry Working Papers.