Evaluating the number of contracts awarded to companies from each nation within the dataset results a ranking based on the total number of contracts awarded and one based on the total value in U.S. dollars. Interestingly, Italy ranks 51st in terms of the number of contracts won but ranks in the top ten (4th position) in terms of the value of contracts obtained.
Focusing the analysis on four European Union countries (Italy, France, Germany and Spain) shows (Fig. 6) that Italy and Spain have the same structure, i.e., a low number of contracts won but a high economic value. In contrast, France and Germany follow the opposite trend, with more than double the number of contracts won but a slightly lower total economic value. The sum of contracts awarded to Spain and Italy does not reach the total of those awarded to Germany and is less than half that of France.
Analyzing the two distributions in more detail, the number of contracts won in the twenty years studied is almost always the same. It changes in some years for Italy and Spain, which alternate outperforming each other a few times from 2011 onward.
On the contrary, the trend in value (Fig. 7) is very different; in fact, for Italy and Spain, the most important growth, which significantly increases the average value, occurs after 2011, precisely in the years that make the economic value of contracts awarded increase exponentially.
Continuing with a more detailed analysis of contracting companies with a comparison between Italy and France (Table 5), the stark difference in the number of suppliers awarded contracts between the two nations is evident. In fact, Italian organizations for the same period (2001–2022) turn out to be only 30 percent of the total number of French organizations.
Moreover, Italian companies secure only 1/3 of the total number of contracts awarded to their French counterparts. This striking difference highlights the challenges faced by Italian companies in participating in World Bank contracts, with an average of only 26 contracts per year throughout the 20-year analysis period. However, when comparing the average number of contracts per company, the results are more aligned. Firms from both countries manage to secure an average of approximately 2.5 contracts. This suggests that the lower number of contracts won by Italian firms is due to a lower number of firms which succeed in procurement tenders. The final metric evaluated in this analysis is the total contract value. Interestingly, despite the significant disparity in the number of contracts obtained by Italian firms, the overall dollar value of their contracts remains about 20 percent higher than that of French companies.
Table 5. Comparison between Italy and France.
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)
Upon examining the companies based on the number of contracts awarded and comparing the top ten firms in each of the two nations, it becomes evident that French companies secure a substantially higher number of contracts than their Italian counterparts.
When examining persistence, which refers to the consistency with which companies win contracts over a 21-year period, it is expected that a firm capable of winning numerous tenders would maintain this success over time, securing at least one contract per year. French companies, particularly those at the top of the rankings, demonstrate high persistence with a value of 20 out of 21 years, while Italian companies exhibit lower persistence, with a value of 15 out of 21 continuous years. This shows that French companies may have developed skills that allow them to consistently win contracts or the fact that they participate in many more tenders in total also increases the award rate, which is three times higher than in Italy. Conversely, Italian companies display lower persistence but still maintain a respectable level, seemingly adopting a quality-focused strategy in which they participate in fewer contracts but manage to win all of them.
The final descriptive statistic highlights the maximum number of contracts secured by a single company in one year. A French company managed to win as many as 19 contracts within a single year, while the maximum number for Italian companies was 11.
These descriptive statistics suggest that the Italian market is characterized by a high concentration of a selected few companies that dominate in terms of both the number and value of contracts won. To explore this hypothesis further, we conducted several concentration analyses.
The initial indicator employed in our analysis is the Herfindahl-Hirschman Index (HHI), which measures market or economic sector concentration by calculating the sum of the squared market shares of individual. An HHI value of 1 represents high level of concentration, while a value of 0 represents low level. It was calculated by measuring the market share, which tells us the percentage of the value of all contracts awarded by a company, out of the total value of contracts that year. We then calculated the square of the market share for each individual company and added up all the squares to obtain the HHI. As depicted in Fig. 8, Italy exhibits a higher concentration level compared to France.
Our second approach utilizes the entropy index. Entropy is a measure used to quantify the level of diversity or variety within a distribution of categories. In the context of the presented data, entropy is calculated based on the frequency of appearance of different companies. It involves assessing the normalized probability of each company's appearance and combining it with the logarithm of that probability. This resultant entropy value indicates the contribution of each company to the overall diversity. A higher entropy value suggests a more diverse distribution, while a lower value indicates a more concentrated distribution. In our data, the entropy values for both Italian and French companies across the years show interesting trends. In the case of Italian companies, the entropy values fluctuate, ranging from 10–23%, with a notable peak in 2004. This suggests varying levels of diversity in company appearances within the dataset over time. On the other hand, French companies exhibit a comparatively more stable pattern, with entropy values ranging from 5–16%. Both Italian and French companies seem to have experienced increased diversity in their appearances around the mid-2010s, as indicated by the higher entropy values during those years.
Lastly, we employ the C4 index to measure the concentration of values in a distribution. In several years, such as 2004, 2005, 2007, 2008, 2009, 2010, 2013, 2014, 2015, 2020, and 2021, Italy's C4 index appears to be relatively high. This indicates that, in these years, there was a high concentration of values within the distribution, with a small number of values representing a large portion of the total data. In contrast, France's C4 index fluctuates over time but generally remains lower than Italy's. This implies that the value distribution for procurement in France is less concentrated compared to Italy.
The analysis reveals significant differences in their performance. While Italian and Spanish companies secure a lower number of contracts but with a high economic value, French and German companies follow the opposite trend, winning more contracts with a slightly lower total economic value. The Italian market is characterized by a high concentration of a few companies dominating both the number and value of contracts won. This concentration is supported by the Herfindahl-Hirschman Index and C4 index, which all indicate that Italy's market is more concentrated than France's. Overall, these findings suggest that Italian companies may need to explore strategies to increase their participation and success in World Bank contracts, while also addressing the high concentration of a selected few dominating companies within the market. The next section addresses this issue by analyzing the characteristics of companies that are awarded contracts.
Then, we compared the Italian and French company datasets to uncover variations in key variables, including Employees, Sales, Publications, Intangibles/Turnover, and Return on Capital Employed (ROCE). The statistical analysis employed a two-sample t-test with a two-tailed distribution, considering unequal variance heteroscedasticity assumptions. The resulting p-values for the examined variables were as follows: Employees (p = 0.007), Sales (p = 0.099), Publications (p = 0.069), Intensity (Intangibles/Turnover) (p = 0.060), ROCE (p = 0.017), Turnover (p = 0,059) and Intangibles (p = 0,040). These p-values represent the likelihood of observing the obtained sample results or more extreme outcomes under the assumption of no substantial disparities between the Italian and French datasets. Proper consideration of the chosen significance level (alpha) is crucial when interpreting the findings. The analysis suggests compelling evidence of significant differences in the variables of Employees, Turnover, Intangibles and ROCE, whereas no statistically significant disparities were detected in the variables of Sales, Publications, and Intangibles/Turnover.
The results are presented in Table 6 together with the descriptive statistics of several variables for Italian and French companies that have won a World Bank procurement in the last two decades. Overall, there are some similarities and differences in the characteristics of these companies from the two countries.
Table 6
Key statistics of selected variables of Italian and French companies.
| ITA | FRA | ITA | FRA | ITA | FRA | ITA | FRA | ITA | FRA |
| Employees | Sales | Publications | Intangible Intensity | ROCE |
Mean | 1158 | 225 | 326 | 44,4 | 0,2 | 0,5 | 0,2 | 0,5 | 8 | 30 |
T-Test | 0,007*** | 0,099* | 0,069* | 0,060* | 0,017* |
Median | 34 | 95 | 9,9 | 9,7 | 0 | 0 | 0 | 0 | 8 | 14 |
Standard Dev | 6762 | 345 | 1565,6 | 85,1 | 0,3 | 0,4 | 0,3 | 0,4 | 37 | 128 |
Min | 1 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | -286 | -137 |
Max | 81365 | 2132 | 15692,8 | 507,4 | 0,81 | 1,53 | 0,81 | 1,53 | 137 | 15 |
Note: sales values are expressed in million USD
In terms of the number of employees, the mean for Italian companies is higher than that for French companies. This suggests that Italian companies may be larger than French companies. However, the standard deviation for both countries is relatively high, indicating a wide variation in the size of the companies. Regarding sales, the mean and median for Italian companies are higher than those for French companies. This indicates that Italian companies may have a higher revenue generation capability than French companies. However, the standard deviation for French companies is much larger than that for Italian companies, suggesting that there is a wider variation in the sales of French companies.
In terms of the number of publications (obtained from Bureau Van Dijk's database Orbis Patent), the mean for Italian companies is higher than that for French companies. This may suggest that Italian companies are more active in publishing research or promoting their brand through marketing efforts. However, the standard deviation for both countries is relatively high, indicating that there is a wide variation in the number of publications across companies.
About intangible assets, the mean and median for Italian companies are much higher than those for French companies. This suggests that Italian companies may have a higher investment in intangible assets, such as intellectual property, brand recognition, or customer relationships, than French companies. The standard deviation for French companies is again much larger than that for Italian companies, indicating a wide variation in the level of investment in intangible assets. Regarding ROCE (Return on Capital Employed), the mean for Italian companies is lower than that for French companies. This indicates that Italian companies may have a lower efficiency in generating profits from their capital investments compared to French companies. The standard deviation for both countries is relatively high, indicating a wide variation in the ROCE across companies.
In conclusion, the results suggest that Italian companies may be larger in size, generate higher sales and have a higher investment in intangible assets than their French counterparts. However, French companies exhibit a wider variation in these variables, which may reflect a more diverse business landscape. Meanwhile, French companies seem to have a higher efficiency in generating profits from their capital investments than Italian companies. Looking at the geographical distribution and industries, the results indicate the concentration of industries in different NUTS2 regions, as measured by the Herfindahl-Hirschman Index (HHI).
Table 7
Concentration of industries in NUTS2 regions: a comparison between Italy and France.
HHI | NUTS2 | Industry |
ITA | 0,14 | 0,25 |
FRA | 0,30 | 0,22 |
The results show that the industry concentration in NUTS2 regions in the study is relatively low, with HHI values of 0.14 and 0.25, respectively. This suggests that there is a relatively diverse mix of industries across the regions, rather than a few dominant industries.
When we analyze the distribution of companies considering NACE code (Fig. 9) the results show us some insights. Among French companies, the predominant sectors include "M - Professional, scientific and technical activities," "G - Wholesale and retail trade," and "C - Manufacturing," which collectively make up a substantial portion of the contracts won. This indicates a strong presence of expertise in consulting, commerce, and manufacturing, showcasing their significant contributions to international development initiatives. Additionally, the noteworthy representation in sectors like "J - Information and communication" and "F - Construction" underscores the diverse skills and capabilities of French firms.
On the other hand, Italian companies have shown a concentrated strength in "C - Manufacturing," with 113 firms engaged in this sector. This highlights Italy's robust manufacturing capabilities and its pivotal role in contributing to development projects. The significant presence of Italian companies in the "M - Professional, scientific and technical activities" category further emphasizes their expertise in specialized services, while their involvement in "G - Wholesale and retail trade" and "F - Construction" sectors signifies a broad skill set.
Overall, the distribution patterns reveal the unique strengths and contributions of both French and Italian companies to the international development projects supported by the World Bank. These strengths are reflective of the diverse expertise and capabilities that these countries bring to the table, ensuring a well-rounded and comprehensive approach to global development initiatives.
Finally, we analyze the geographic distribution of companies (Fig. 10). The data reveals a significant disparity in the magnitude of participation between the two countries. Italy's representation is predominantly concentrated in several provinces, with "Lombardia" leading with 83 companies, followed by "Veneto" with 40 and "Lazio" with 39. This indicates a more centralized distribution, potentially reflecting the strong economic and industrial presence in these regions. In contrast, the French companies' distribution spans a broader range of provinces, with "Île-de-France" standing out remarkably with 200 companies. This underscores the pivotal role of the capital region, Paris, in housing a substantial number of companies engaged in international development projects. Other regions like "Auvergne-Rhône-Alpes," "Occitanie," and "Provence-Alpes-Côte d'Azur" contribute significantly as well, showcasing France's diverse economic landscape. The absence of representation in "Corse" among the French provinces could be due to various factors, including the nature of projects, economic activity, or the specific focus of the region. Similarly, Valle D’Aosta have no presence, suggesting potential opportunities for increased engagement in international development initiatives.
The contrast in geographic distribution highlights the differing strategies and strengths of the two countries in approaching international development projects. Italy's concentration in select provinces speaks to its specialization in certain sectors, while France's widespread presence across various regions emphasizes its diverse capabilities and contributions. These findings underline the need for strategic collaboration and knowledge sharing between regions to optimize their collective impact on global development endeavors.