We perform the estimates with different specifications in order to check the robustness of our results.
Specifically, all estimates are replicated using the explanatory variables calculated for the same year of the independent variable (2001) and using different proxies for local level specialization.
Table 9 reports the results of the estimates for the overall population of business groups obtained by considering specialization and variety indicators at 2001. The results are similar to those obtained from the basic estimates. Moreover, the coefficients of the specialization and vertically related variety indicators show higher degrees of statistical significance.
Table 10 presents the result of the estimates obtained using different proxies for specialization at local level. We consider two new measures of absolute concentration: i) number of firms in an industry in a province (Industry firms); ii) number of employees in an industry in a province (Industry employment). The variables Industry firms and Industry employment are considered in logarithmic term and calculated for 1991 (Table 9), and 2001: the year of the dependent variable (Table 10).
The econometric results do not change using these measures of absolute concentration at the local level, which confirms the robustness of the relation between vertically related variety and vertical integration. Our results for manufacturing groups and disaggregation for geographical areas are also robust.
Table 9 – Determinants of vertical integration. Tobin estimates for all business groups in Italy
Log pseudolikelihood 27311.4 27319.6 27316.7 27320.3 27321.1 27323.3
McKelvey-Zavoina R2 0.090 0.091 0.091 0.091 0.091 0.091
BIC -303434.7 -303430.1 -303424.2 -303442.6 -303411.8 -303427.2
*** Significant at 1%; ** significant at 5%; * significant at 10%; regressions also include a constant term; standard errors are clustered at the province level (103 units) with t statistics in parentheses.
Table 10 – Determinants of vertical integration; all business groups ESTIMATION METHOD:TOBIT
[1] [2] [3] [4] [5] [6]
(
Industry employmenti,k,1991)
ln … … … -0.0001
Log pseudolikelihood 27308.1 27314.0 27315.0 27305.7 27305.5 27315.0
McKelvey-Zavoina R2 0.090 0.091 0.091 0.090 0.091 0.091
BIC -303417.1 -303418.8 -303420.9 -303423.3 -303418.8
-303420.9
*** Significant at 1%; ** significant at 5%; * significant at 10%; regressions also include a constant term; standard errors are clustered at the province level (103 units) with t statistics in parentheses.
Table 11 – Determinants of vertical integration; all business groups ESTIMATION METHOD:TOBIT
[1] [2] [3] [4] [5] [6]
(
Industry employmenti,k,2001)
ln
Log pseudolikelihood 27307.2 27315.6 27316.3 27305.5 27311.6 27314.6
McKelvey-Zavoina R2 0.090 0.091 0.091 0.090 0.090 0.090
BIC -303415.2 -303422.0 -303423.3
-303423.0
-303413.9 -303431.0
*** Significant at 1%; ** significant at 5%; * significant at 10%; regressions also include a constant term; standard errors are clustered at the province level (103 units) with t statistics in parentheses
6. CONCLUSIONS
This study used a large dataset of Italian business groups to investigate the relationship between agglomeration and inter-industry vertical integration. We define vertical integration as the ownership and control of activities along the production chain. Our unit of analysis to measure the degree of vertical integration is the business group – that is, the set of firms controlled by the same owner. The choice of business group as the unit of analysis is appropriate for this study because control over the different phases in the production chain is often achieved through the ownership of legally
independent companies rather than integration of activities within the same legal unit. To account for agglomeration forces and other characteristics of the local system, our geographical unit is the Italian province.
We investigated the influence of agglomeration forces on the vertical integration choices of firms by analyzing the effects of specialization and variety in the local system. We used two different
indicators of variety: the first is an indicator of unrelated variety and captures the presence of different industries within the same geographical area; the second is a more specific indicator of industry variety that captures the presence of vertically related industries.
Our results support the hypothesis that the firm’s choice to integrate activities along the production chain is influenced by the geographic concentration of activities.
For industry variety at the local level, our findings show that it is not variety per se that matters for vertical integration choices, but the degree of local level vertical relatedness of production activities.
For instance, a higher level of vertical related variety significantly reduces the need for firms to integrate activities along the production chain. This means that spatial proximity matters for vertical integration choices, because the distance over which firms are able to provide intermediate inputs significantly influences their choice to control of the production of intermediate inputs rather than acquire them through market transactions. In line with the literature, we found that the degree of specialization of the local system reduces the level of vertical integration; this confirms the effect of agglomeration forces in reducing opportunism at the local level and inducing firms to rely on market transactions. The concentration of specialized activities in a specific area is likely to attract firms engaged in complementary activities thus favoring the possibility of inter-industry outsourcing and reducing the need to control activities along the production chain. Therefore, specialization matters in the case of infra-industry vertical integration, as shown by previous studies, and also in the case of inter-industry integration.
Our results show also that the importance of agglomeration forces is not homogeneous across the country and depends on the characteristics of firms and local systems. In the North-West regions, which are characterized by the presence of large firms and established industrial urban areas, local system level specialization and variety do not significantly influence the degree of vertical
integration. However, in the North-Eastern and Center or Third Italy, which are characterized by the presence of small firms organized in specialized systems or industrial districts both specialization and vertically related variety are significant for firms’ decisions to control activities along the production chain.
The novelty of our study lies in its consideration of inter-industry vertical integration and the
business group as a unit of analysis. We provide a new index of vertically related variety to measure the presence of vertically related industries at the local level.
Overall, our results confirm the results of previous studies on the role of agglomeration in
influencing the degree of firms’ vertical integration. Our study shows that in analyzing the role of variety at the local level it is necessary to take account of the type of variety relevant specifically to the phenomenon under investigation. In the case of vertical integration it is the presence of industries with potential input-output relations that matters rather than variety per se.
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Table A.1 – Distribution of business groups by geographic areas
AREAS
Number of
groups Employees
Average size (employees per
group)
N. % N. %
North_West 9,368 38.0 1,882,676 47.5 201
North-East-Centre 11,963 48.6 1,842,063 46.5 154
South and Islands 3,302 13.4 235,734 5.0 71
Total 24,633 100.0 3,960.473 100.0 161
Table A.2 – Distribution of business groups by macro-sector
ATECO Firms Employees
N. % N. %
Manufacturing 8,230 33.4 1,709.699 43.2 Services 16,403 66.6 2,250.774 56.8 Totale 24,633 100.0 3,960.473 100.0
Table A.3 – Correlation matrix – all business groups
Variables [1] [2] [3] [4] [5] [6]
[1] 1.000
[2] 0.123 1.000
[3] 0.004 0.057 1.000
[4] -0.001 0.023 -0.091 1.000
[5] 0.042 0.067 0.495 -0.200 1.000
[6] -0.035 -0.045 -0.334 0.343 -0.900 1.000
[1] VIf,2001
[2] ln
(
Groupsizef,2001)
[3] ln
(
LSsizek,1991)
[4] Speci,k,1991
[5] Uvarietyk,1991
[6] VRvarietyk,1991
Table A.4 – Correlation matrix –manufacturing groups
Variables [1] [2] [3] [4] [5] [6]
[1] 1.000
[2] 0.057 1.000
[3] 0.003 0.040 1.000
[4] -0.048 0.020 -0.183 1.000
[5] 0.030 0.055 0.553 -0.386 1.000
[6] -0.035 -0.032 -0.364 0.532 -0.911 1.000
[1] VIf,2001
[2] ln
(
Groupsizef,2001)
[3] ln
(
LSsizek,1991)
[4] Speci,k,1991
[5] Uvarietyk,1991
[6] VRvarietyk,1991
Table A.5 – Correlation matrix – all business groups
Variables [1] [2] [3] [4] [5] [6]
[1] 1.000
[2] 0.976 1.000
[3] -0.200 -0.222 1.000
[4] -0.167 -0.184 0.900 1.000
[5] 0.343 0.362 -0.900 -0.816 1.000
[6] 0.315 0.328 -0.783 -0.883 0.906 1.000
[1] Speci,k,1991
[2] Speci,k,2001
[3] Uvarietyk,1991
[4] Uvarietyk,2001
[5] VRvarietyk,1991
[6] VRvarietyk,2001
Table A.6 – Correspondence between Ateco_1991 classification and Pavitt’s sector
Description Ateco_1991 Pavitt's sector
Food products and beverages 15 SD
Tabacco 16 SD
Textile 17 SD
Clothing and furs 18 SD
Leather and footwear 19 SD
Wood and cork 20 SD
Paper 21 SD
Publishing, graphic arts, and reproduction 22 SI
Manufacture of coke oven products, petroleum
refining 23 SI
Chemistry 24
241+247 SI;
242+ 244 SB;
243 SS; 245+246 SD
Rubber and plastic 25 SD
Non-metallic mineral products 26 SD
Iron metallurgic products 27 SD
Non-iron metallurgic products 28
281+285 SI;
282+284 SS;
286+287 SS;
Mechanical machinery and equipment 29
291+292 SS;
293 SI;
296+297 SB
Office machines and computers 30 SB
Electrical material, machinery and components 31 SS
Radio apparatus, TV and communication 32 SS
Medical, precision and optical instruments 33 SB
Motor vehicles 34 SS
Other transport manufacturing 35 SS
Other manufacturing industries 36 SD
SB = Science based SD = Suppliers dominated SI = Scale intensive SS = Specialized suppliers
Table A.7 – Classification codes of economic activities Code Description
CB Other mining activity
DA Food products, beverages and tobacco
DB Textiles, wearing apparels, dressing and dyeing of fur DC Leather and leather products
DD Lumber and wood products
DE Pulp, paper and paper products, Printing and publishing DF Petroleum refining and related industries
DG Chemicals and allied products DH Rubber and plastic products
DI Non-metallic mineral products
DJ Primary metal industries, Fabricated metal products DK Industrial machinery and home appliances
DL Office machinery and computers, Electrical and electronic equipment, Measuring and controlling instruments
DM Automobiles and components, Other transportation vehicles DN Other manufacturing industries, Refuse systems, scrap and waste
materials
E Energy (electric power and gas), Water supply industry F Construction
G Wholesale and retail distribution H Hotels and restaurants
I Railways and road transport, Sea, air and other transport services, postal services and telecommunications
J Banking and financial intermediation, Insurance and pension funds K Business services
M Education
N Health and social work Source: ISTAT.