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![Page 1: European productivity, lagged adjustments and industry competition Mary OMahony, Fei Peng and Nikolai Zubanov University of Birmingham.](https://reader030.fdocuments.in/reader030/viewer/2022032516/56649b4b550346318e8c307d/html5/thumbnails/1.jpg)
European productivity, lagged adjustments and industry
competition
Mary O’Mahony, Fei Peng and Nikolai Zubanov
University of Birmingham
![Page 2: European productivity, lagged adjustments and industry competition Mary OMahony, Fei Peng and Nikolai Zubanov University of Birmingham.](https://reader030.fdocuments.in/reader030/viewer/2022032516/56649b4b550346318e8c307d/html5/thumbnails/2.jpg)
Presentation Overview
• Context• First Regression Results• Econometric Issues• Industry competition measures – company
accounts• Herfindahl indexes, France and UK• Industry dynamics• Linking the macro and micro
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Context• Consider impact of technology shock on output growth
– specific lagged impact of ICT
• What factors can explain differences in speeds of adjustment and long run impacts
• Preliminary Regressions– production function ln(Q) on labour, non-ICT capital and ICT
capital, lagged up to five years – useful for stress testing – Dependent variable, MFP and ICT sole explanatory variable –
difficult to justify
• Panel regressions, fixed effects and time dummies included
• Panel is by country not conventional industry panel• But include some groups of like industries
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Regression results (1) AtBindustry: AGRICULTURE, HUNTING, FORESTRY AND FISHINGvariable lag coeff. std. dev.ln(labour input) 0 0.319 0.059 ***ln(non-ICT capital) 0 0.093 0.049 *ln(ICT capital) 0 0.111 0.031 ***
1 -0.005 0.029 2 -0.017 0.028 3 0.012 0.028 4 -0.020 0.030 5 -0.039 0.021 *
other controls: time, industry and country dummiesobs. 301
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Regression results (2) Cdependent variable: ln(value added, volume index)industry: MINING AND QUARRYINGvariable lag coeff. std. dev.ln(labour input) 0 0.491 0.135 ***ln(non-ICT capital) 0 1.527 0.202 ***ln(ICT capital) 0 0.008 0.096
1 -0.018 0.119 2 0.004 0.122 3 -0.016 0.122 4 0.035 0.122 5 0.111 0.083
other controls: time, industry and country dummiesobs. 301
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Regression results (3) D1Consumer goods
dependent variable: ln(value added, volume index)industry: FOOD, BEVERAGES, TOBACCO, TEXTILES, LEATHER, FOOTWEAR, WOOD AND CORK, MANNUFACTURING NEC, RECYCLINGvariable lag coeff. std. dev.ln(labour input) 0 0.481 0.027 ***ln(non-ICT capital) 0 0.285 0.032 ***ln(ICT capital) 0 -0.001 0.022
1 -0.007 0.028 2 -0.003 0.024 3 -0.004 0.019 4 -0.027 0.017 5 0.037 0.011 ***
other controls: time, industry and country dummiesobs. 1204
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Regression results (4) D2Intermediate goods
dependent variable: ln(value added, volume index)industry: PULP, PAPER, PAPER, PRINTING AND PUBLISHING, CHEMICAL, RUBBER, PLASTICS AND FUEL, OTHER NON-METALLIC MINERALvariable lag coeff. std. dev.ln(labour input) 0 0.846 0.078 ***ln(non-ICT capital) 0 -0.016 0.073 ln(ICT capital) 0 0.021 0.089
1 -0.025 0.138 2 0.027 0.099 3 0.019 0.050 4 0.013 0.045 5 0.008 0.029
other controls: time, industry and country dummiesobs. 1489
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Regression results (5) D3Investment goods
dependent variable: ln(value added, volume index)industry: BASIC METALS AND FABRICATED METAL, MACHINERY NEC, ELECTRICAL AND OPTICAL EQUIPMENT, TRANSPORT EQvariable lag coeff. std. dev.ln(labour input) 0 0.775 0.049 ***ln(non-ICT capital) 0 0.621 0.043 ***ln(ICT capital) 0 0.154 0.068 **
1 -0.096 0.110 2 0.044 0.113 3 -0.001 0.110 4 -0.027 0.104 5 0.010 0.063
other controls: time, industry and country dummiesobs. 1204
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Regression results (6) E
industry: ELECTRICITY, GAS AND WATER SUPPLYvariable lag coeff. std. dev.ln(labour input) 0 -0.018 0.060 ln(non-ICT capital) 0 0.020 0.068 ln(ICT capital) 0 -0.072 0.062
1 0.117 0.102 2 -0.043 0.103 3 -0.004 0.102 4 -0.039 0.102 5 0.028 0.061
other controls: time, industry and country dummiesobs. 301
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Regression results (7) F
dependent variable: ln(value added, volume index)industry: CONSTRUCTIONvariable lag coeff. std. dev.ln(labour input) 0 0.505 0.052 ***ln(non-ICT capital) 0 0.202 0.056 ***ln(ICT capital) 0 -0.023 0.057
1 -0.008 0.094 2 -0.002 0.092 3 0.025 0.066 4 -0.011 0.021 5 0.005 0.014
other controls: time, industry and country dummiesobs. 301
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Regression results (8) Gdependent variable: ln(value added, volume index)industry: WHOLESALE AND RETAIL TRADEvariable lag coeff. std. dev.ln(labour input) 0 0.546 0.048 ***ln(non-ICT capital) 0 -0.059 0.032 *ln(ICT capital) 0 0.203 0.058 ***
1 -0.121 0.103 2 -0.040 0.103 3 0.015 0.099 4 0.025 0.089 5 -0.021 0.048
other controls: time, industry and country dummiesobs. 903
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Regression results (9) H
dependent variable: ln(value added, volume index)industry: HOTELS AND RESTAURANTSvariable lag coeff. std. dev.ln(labour input) 0 0.518 0.064 ***ln(non-ICT capital) 0 -0.133 0.032 ***ln(ICT capital) 0 0.070 0.043
1 -0.019 0.069 2 0.000 0.066 3 -0.059 0.041 4 0.004 0.013 5 0.018 0.010 *
other controls: time, industry and country dummiesobs. 301
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Regression results (9) Idependent variable: ln(value added, volume index)industry: TRANSPORT AND STORAGE, POST AND TELECOMvariable lag coeff. std. dev.ln(labour input) 0 -0.030 0.054 ln(non-ICT capital) 0 0.487 0.050 ***ln(ICT capital) 0 0.106 0.098
1 -0.116 0.172 2 0.047 0.178 3 -0.039 0.171 4 -0.066 0.159 5 0.125 0.086
other controls: time, industry and country dummiesobs. 602
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Regression results (10) Jdependent variable: ln(value added, volume index)industry: FINANCIAL INTERMEDIATIONvariable lag coeff. std. dev.ln(labour input) 0 0.709 0.119 ***ln(non-ICT capital) 0 -0.250 0.052 ***ln(ICT capital) 0 0.029 0.104
1 0.007 0.198 2 0.044 0.204 3 -0.017 0.195 4 -0.237 0.184 5 0.345 0.094 ***
other controls: time, industry and country dummiesobs. 301
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Regression results (11) Kdependent variable: ln(value added, volume index)industry: RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIESvariable lag coeff. std. dev.ln(labour input) 0 0.623 0.038 ***ln(non-ICT capital) 0 -0.003 0.013 ln(ICT capital) 0 0.152 0.077 **
1 -0.090 0.144 2 -0.024 0.144 3 0.030 0.143 4 -0.168 0.135 5 0.192 0.071 ***
other controls: time, industry and country dummiesobs. 301
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Regression results (12) O
dependent variable: ln(value added, volume index)industry: OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICESvariable lag coeff. std. dev.ln(labour input) 0 0.831 0.090 ***ln(non-ICT capital) 0 -0.088 0.035 **ln(ICT capital) 0 0.007 0.080
1 -0.001 0.134 2 -0.035 0.136 3 -0.003 0.130 4 0.004 0.124 5 0.045 0.072
other controls: time, industry and country dummiesobs. 301
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Summary – long run impact ICTMostly positive, some very large e.g. J &KBut warning – not well specified production
functions
AGRICULTURE, HUNTING, FORESTRY AND FISHING 0.040 MINING AND QUARRYING 0.124CONSUMER GOODS -0.006INTERMEDIATE GOODS 0.063INVESTMENT GOODS 0.082 ELECTRICITY, GAS AND WATER SUPPLY -0.013 CONSTRUCTION -0.014 WHOLESALE AND RETAIL TRADE 0.063 HOTELS AND RESTAURANTS 0.014 TRANSPORT AND STORAGE, POST AND TELECOM 0.057 FINANCIAL INTERMEDIATION 0.170RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES 0.092 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICES 0.017
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Summary – long run impact ICTDependent variable = MFP
Not highly correlated with previous estimatesSome wild swings in lag structure
AGRICULTURE, HUNTING, FORESTRY AND FISHING -0.015 MINING AND QUARRYING -0.684CONSUMER GOODS 0.113INTERMEDIATE GOODS 0.240INVESTMENT GOODS 0.010 ELECTRICITY, GAS AND WATER SUPPLY 0.066 CONSTRUCTION 0.038 WHOLESALE AND RETAIL TRADE 0.028 HOTELS AND RESTAURANTS -0.011 TRANSPORT AND STORAGE, POST AND TELECOM 0.080 FINANCIAL INTERMEDIATION 0.066RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES -0.061 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICES 0.000
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Econometric estimation issues• The impact of ICT capital on output is distributed across
many years• We need to allow for dynamic adjustment of output to
inputs• Estimation options available: • Short-run vs. long-run coefficient estimation (like Engle-
Granger two-stage procedure)• One-stage autoregressive distributed lag estimation• estimators available: OLS, fixed-effects, pooled or
weighted mean group• other options: instrumental variables, GMM or system
estimators
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Industry competition and dynamics• need to integrate country and industry context into the
model, in particular, market concentration and firm dynamics
• Using AMADEUS database, 250,000 large to medium size companies – very small unincorporated companies not available
• Data from 1996-2005, all EU countries included • estimate concentration rates by country and industry• Cannot use for entry/exit but know when company was
incorporated• So can derive variables such as average age of
companies (sales weighted)• Shares of ‘new’ companies in turnover • First results 133 three digit industries, all manufacturing,
50-52,55,63, 71-74
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2.4.2 Market structure – firm Competitiveness and
concentration: Amadeus
UK
1%
2-5%
6-10%
11-20%
21-30%
31%+
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2.4.2 Market structure – firm data Competitiveness and concentration: Amadeuspercentage of industries by concentration range
1%
2-5%
6-10%
11-20%
21-30%
31%+
FRANCE
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23 18.04.23
Market structure – firm data Competitiveness and concentration:
Amadeus Three digit and two digit industry Herfindahl
indices generated for 2004, calculated as:
The closer this is to 1, the more concentrated the industry
Also can calculate a normalised Herfindahl – useful if comparing companies across countries where there is a possibly of different reporting rates
H* = (H-1/N) / (1- 1/N)
Shown in charts for UK and France
i iSH 2)(
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24 18.04.23
Market structure – firm data Competitiveness and concentration:
Amadeus
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80 100 120 140
UK
France
Normalised Herfindahl Index, Company accounts, 2004
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25 18.04.23
Market structure – firm data Competitiveness and concentration:
Amadeus
Shows wide variance in index in both countries but again suggests UK more concentrated.
Correlation H (UK, France) = 0.31
Examples - UK highest 283 (manufacture of boilers), only ranked 44 in France
France highest 323 (TV and radio transmitters) – ranked 45 in UK
Both countries show greater concentration in manufacturing
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26 18.04.23
Market structure – firm data Competitiveness and concentration:
Amadeus
NACE UK France Diff NACE UK France Diff151 132 137 -5 201 126 77 49152 52 84 -32 202 5 80 -75153 97 88 9 203 103 29 74154 24 5 19 204 4 124 -120155 80 87 -7 205 105 69 36156 34 70 -36 211 39 96 -57157 19 115 -96 212 106 114 -8158 76 104 -28 221 116 128 -12159 66 107 -41 222 70 111 -41171 43 64 -21 223 49 93 -44172 60 63 -3 232 3 2 1173 71 82 -11 241 65 52 13174 58 133 -75 242 41 59 -18175 98 134 -36 243 9 119 -110176 123 141 -18 244 30 101 -71177 31 22 9 245 7 18 -11182 113 132 -19 246 101 99 2191 111 129 -18 247 95 78 17192 32 10 22 251 82 21 61193 16 83 -67 252 138 56 82
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27 18.04.23
2.4.2 Market structure – firm data Competitiveness and concentration:
Amadeus
NACE UK France Diff NACE UK France Diff261 23 76 -53 293 21 38 -17262 48 73 -25 294 78 86 -8263 90 39 51 295 91 116 -25264 12 14 -2 296 28 7 21265 6 20 -14 297 47 71 -24266 94 110 -16 300 55 49 6268 100 31 69 311 85 79 6271 20 53 -33 312 26 40 -14272 15 36 -21 313 29 35 -6273 93 41 52 314 141 66 75274 74 51 23 315 109 48 61275 50 103 -53 316 133 65 68281 86 138 -52 321 40 54 -14282 2 68 -66 322 8 30 -22283 1 33 -32 323 45 1 44284 89 112 -23 331 121 45 76285 75 109 -34 332 99 11 88286 115 81 34 333 11 60 -49291 62 120 -58 334 35 3 32292 124 131 -7 335 140 61 79
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28 18.04.23
2.4.2 Market structure – firm data Competitiveness and concentration:
Amadeus
NACE UK France Diff NACE UK France Diff341 61 25 36 505 54 9 45342 127 126 1 511 42 106 -64343 53 121 -68 512 87 122 -35351 81 13 68 513 136 136 0352 18 6 12 514 112 118 -6353 72 15 57 515 38 139 -101354 10 26 -16 518 119 127 -8355 107 37 70 519 134 46 88361 69 91 -22 521 59 67 -8362 73 44 29 522 22 98 -76363 63 140 -77 523 51 42 9364 46 17 29 524 120 125 -5365 68 27 41 525 117 23 94366 64 12 52 526 77 75 2371 33 92 -59 527 17 24 -7372 25 135 -110 551 27 4 23501 137 113 24 552 56 8 48502 118 43 75 553 92 95 -3503 108 94 14 555 13 58 -45504 14 16 -2
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29 18.04.23
2.4.2 Market structure – firm data Competitiveness and concentration:
Amadeus
NACE UK France Diff NACE UK France Diff631 122 57 65 724 84 108 -24632 102 32 70 725 125 85 40633 110 100 10 731 57 28 29634 129 130 -1 741 139 105 34711 44 62 -18 742 131 123 8712 67 72 -5 743 130 19 111713 104 55 49 744 114 89 25714 37 50 -13 745 135 34 101721 36 47 -11 746 83 90 -7722 128 117 11 747 96 102 -6723 79 97 -18 748 88 74 14
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Industry competition and dynamics• Reporting rates may vary across country – can
cross check with industry data, e.g. UK Amadeus covers over 90% employment
• Econometric difficulty is that market concentration data are available for a short period of time
• can regress the industry fixed effects on market concentration or/and
• introduce cross-products of production factors and market concentration