The World Wide Network‘s (WWN) international database from Dun & Bradstreet (D&B) is one of the company’s claims to fame in the credit information industry. Unparalleled in its breadth, the database contains information about hundreds of millions of global firms and other “entities.”
At least 70% of the recorded economy in 16 non-US countries is included in the WWN database, which enables D&B’s macroeconomic team to analyze the economic geography of the globe — and determine which parts of the world are vulnerable to different types of economic shock. The WWN data are also available to certain customers — a subset classified as “power users” — for finance, marketing, sales, services, and operations applications via the D&B Direct 2.0 application protocol interface (API).
Some of the insights available are unsurprising. For example, you don’t need to pore through the Panama Papers to know that there are many millions of holding and investment companies located in the Americas in Belize, Bermuda, the British Virgin Islands, the Cayman Islands, Curaçao, and Panama, as well as in Liechtenstein in Europe. Nor is it a great surprise that there are thousands of textile and apparel manufacturers in Bangladesh’s capital of Dhaka and port of Chittagong.
But D&B Analytics can also use the data to look at industry concentration ratios. Antitrust regulators use concentration ratios to gauge market cartelization. The ratios can also be helpful in assessing any (dangerous) concentration of risks in a credit portfolio. The Herfindahl Index is the sum of the squares of the ratios of each constituent component and the most common concentration ratio. This measure can map the economic diversification of the world at a country, state, or county level, using Standard Industry Classification (SIC) codes for each business record.
Measuring Economic Diversity
Weighting each firm equally, given that sales and employee data vary in availability by market, the information provides insight into the degree of a region’s economic diversification. A region with a low concentration index has a diverse economy, and vice versa. Highly diversified regions are less likely to be hit by sector-specific shocks such as those sweeping the world’s energy, mining, and steelmaking industries since 2015.
By ranking over 400 subnational provinces and states in the 16 selected economies, some interesting and useful data artifacts can be gleaned. For example, England’s city of Manchester and Australia’s New South Wales are among the most economically diverse regions of the world. On the other hand, Sharjah, long the poorest of the United Arab Emirates’ constituent kingdoms, is one of the least diverse, along with Piauí and Maranhão, two states in Brazil’s rural northeast. Other areas lacking diversity include Ningxia, Gansu, and Qinghai provinces, home to some of China’s poorest rural backwaters. Several of France’s quieter départements also lack diversity.
Labor Market Diversity — China Case Study
Moving on to look at the diversity of employment — a key measure of how complex and well developed a local area has become in terms of its worker base — the pattern for China shows a clear gradation between the developed coastal provinces and municipalities and the peripheral inland provinces. Shanghai, Tianjin, and Shandong — two municipalities and a province — are the most diversified, reflecting a history of globalization that extends back to at least the 1920s and was renewed in the reform era from the 1980s onwards. By contrast, far western and interior provinces have the least diverse job markets, including mountainous Guizhou, and the part-ethnically Turkic Xinjiang (the “New Frontier”) region.
The diversity of employment in itself hasn’t been a perfect predictor of the distribution of the economic shock sweeping China’s upstream-dominated regions since 2014. Liaoning province has one of the most diverse jobs markets in China, ranking #7, but it has suffered one of the worst slowdowns, with its economy contracting in real terms in Q1 2016. Its high employment diversity is due to the presence of high-tech Dalian, a port first developed by the Russians and Japanese before 1945. Take Dalian out and it is clear why Liaoning and its job base of large steel mills is in recession.
The heat map of China’s employment diversity is thus a fair guide to which provinces will be more resilient and witness more consumer-spending growth in the next few years, as well as which ones have more single-industry towns and thus will be more dependent on government spending. This is just one example of the global trends that D&B WWN data can help illuminate.
Isaac Leung is a senior economist on D&B’s Global Data, Insight & Analytics team. Based in Marlow/United Kingdom, he covers China, India, and other parts of the Asia/Pacific region as a contributor to D&B Macro Market/Country Insight Products. His areas of interest include maritime economics. He has degrees from Cambridge University and the London School of Economics.