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The Social Mobility Report 2020

<Previous Next>
  • Preface
  • Key Findings
  • Introduction
  • Benchmarking Social Mobility: The Global Social Mobility Index
  • Global Findings
  • Using Big Data to Track Inequalities
  • Conclusions: Implications for a New Economic Agenda
  • Appendices & References
  • Contributors and Acknowledgements
  • Social Mobility Rankings
  • Economy Profiles
  • Shareable Infographics
  • Press Release
  • Download the GSMI data
Home Previous Next
Home Previous Next
Home Previous Next
  • Report Home
  • Preface
  • Key Findings
  • Introduction
  • Benchmarking Social Mobility: The Global Social Mobility Index
  • Global Findings
  • Using Big Data to Track Inequalities
  • Conclusions: Implications for a New Economic Agenda
  • Appendices & References
  • Contributors and Acknowledgements
  • Social Mobility Rankings
  • Economy Profiles
  • Shareable Infographics
  • Press Release
  • Download the GSMI data

Using Big Data to Track Inequalities

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New data sources and innovative metrics can offer fresh opportunities to measure and track inequalities. This section presents new metrics collaborations with three private sector companies. ADP LLC, LinkedIn and Burning Glass Technologies offer fresh insights into new and under-measured inequalities. The critical opportunity presented by such new measurement efforts is that of more granular and timely insights that can dynamically inform the direction of action by public and private sector actors. 

Addressing Unequal Footing in Social Capital 

Professional networks are a key aspect of social capital—the non-financial, relational assets that individuals have at their disposal. They describe the collection of individuals with whom an individual collaborates in their working lives and those who individuals seek out for professional advice or guidance. Networks are measured according to their strength, assessing not only who one knows but also who their connections know.

Strong networks are key to professional success. Individuals with stronger networks report that their careers progress at a better pace than their peers. They experience smoother job transitions and steeper increases in personal income. Networks help establish trust, spread relevant information and consolidate individual status. Iterative inter-personal connections increase trust and confidence in people’s abilities and decrease the perception of risk in future partnerships.51 Connections act as social proof—a cue for estimating individual capabilities.52

While strong networks are critical for success, individuals who come from lower-income households or were raised in distinctive geographic locales may start with a disadvantage. Data gathered by IPSOS on behalf of LinkedIn shows that individuals in the United States labour market who grew up in a high-income household are three times more likely to report having a strong network than those who grew up in a low-income household.53 This means those individuals experience a double advantage in both social and financial capital. 

Individuals with stronger networks have access to greater sets of information, making them aware of more opportunities that, in turn, make them more productive and innovative.54 LinkedIn data scientists tracked the network strength and the seniority of individuals employed in the United States who were active on their platform between 2011 and 2019. They identified a trend: individuals with the strongest networks were 9.6% more likely to be employed in management positions than those with weaker networks.55

Complementary measures reveal that individuals living in more urban states in the United States tend to acquire an advantage in the strength of their networks. The locations where individuals have the strongest social networks in the United States are urbanized states such as the District of Columbia, which is the country’s capital, as well as Massachusetts, New York, Connecticut, New Jersey and California. At the opposite end of the scale are a set of rural states—namely, Kansas, West Virginia, Mississippi and Arkansas in ascending order.  The findings call for a renewed focus on the geography of equality and opportunity.

Fair Work Across Industries and Occupations 

The nature, availability and renumeration of work opportunities can be obscured by aggregate averages which fail to reflect the dynamic circumstances that shape the livelihoods and social mobility of individuals. 

Fully understanding the nature of opportunities across labour markets requires more granular insights into the labour market available to each worker.56 It is commonplace to assume that individuals are likely to stay within the boundaries of their profession or their current geographic location. Yet workers’ true labour market encompasses their current profession as well as the range of job transitions within reach upon reskilling, upskilling or relocation. The size of those additional options determines the bargaining power of employees in wage negotiations and, ultimately, the ability to prosper on the basis one’s skills.  

A metric provided by Burning Glass Technologies shares new insights into the wage differentials by location in the United States. By calculating the Theil’s Index of salaries by location and occupation, it is possible to identify the occupations in which workers can command a similar wage across different states of the United States in contrast to those where wages diverge.57 Figure 12 illustrates these differentials. Professionals such as Chief Executives, Dentists, Computer Research Scientists and Human Resources Managers are offered similarly (high) wages across different parts of the United States. On the other hand, Judges and Magistrates, Specialized Teachers, Transportation Workers, Gaming Managers and Agricultural Engineers face more unequal prospects around the United States. Such data indicates that some workers in the labour market are more bounded to one location and one occupation than others. The data also reveals that higher paid and higher skilled professions are more likely to retain their value across different locations. These findings call for more nuanced approaches to supporting workers through professional and geographic mobility transitions, particularly in the face of trends in the labour market driven by technological change.

Figure 12: Differences in salary by geography and average income in the United States

Source: Burning Glass Technologies.

Human Capital Management services provider ADP, LLC (ADP) also provides a metric that reveals the dynamics of changing income by industry. The metric examines the ratio of income earned by those at the top 10% of the industry contrasted with the ratio of the income earned by those at the bottom 40% of the industry. It also demonstrates the share of gross income that is deducted to arrive at approximate net pay.58 Figure 13 presents the scale of those inequalities. This metric reveals that the Media and Entertainment Industry (MEI) remains the most unequal despite gains between 2014 and 2018. On average in the United States, those at the top 10% of the income distribution can command more than two times the income of those at the bottom 40%. In fact, professions at the top 10% of the MEI industry earn more than four times the income of those at the bottom 40% of the industry. The Government and Public Sector and the Education sector are among the best-performing sectors in this measure of income equality, but median wages in the sector remain low. Among the industries reviewed, the Energy and Utilities sector demonstrates notably strong outcomes when it comes to equality of wages while retaining a large median wage.

Figure 13: Income inequality in the United States by industry and year

Source: ADP Research Institute.
Notes: The ratio of income earned represents the ratio of income earned by workers at the top 10% to bottom 40% of the income distribution. Net income is calculated by subtracting from gross pay federal tax, state tax, social security tax, Medicare tax and unemployment/disability insurance, etc. This provisional adjustment is subject to further refinement in annual tax returns.

The majority of industries in the United States that are measured using this method show narrowing inequalities in the years between 2014 and 2018. One of the most unequal industries, the Financial Services Industry, is one such industry. There are two exceptions. The Information and Communication industry and the Media and Communications Industries have seen a notable increase in wage inequality since 2014. Current taxation does not appear to have a consequential effect on those income inequalities. These findings also suggest more tailored approaches to workforce transitions that take into account wage structures across industries are needed.

51
51 Heider, 1946, Kramer, 1999, Parigi, et al., 2017, and Abrahao, et al, 2017.
52
52 Salganik, et al., 2006, Van de Rijt, et al., 2014, and Sauder, et al,. 2012.
53
53 The Network Trends Survey had an n=2,007 U.S. adults, age 18-65, conducted in April-May 2019.
54
54 Granovetter, 1977, Burt, 2004, Aral, 2016, and Bakshy, et al., 2012.
55
55 Comparison between the lowest and highest quartile by network strength. Results take into account the highest achieved degree, individuals industry, and their function.
56
56 Schubert, et al., 2019.
57
57 The Theil’s index is a measure of economic inequality that ranges from 0 to ln(N), where N is the number of regions used (in our case, counties). The theoretical maximum for this index in the current analysis is just over 8. The minimum is 0 which should be interpreted as no concentration of opportunity across regions.
58
58 The net pay is calculated by subtracting from gross pay federal tax, state tax, social security tax, Medicare tax and unemployment/disability insurance, etc. This provisional adjustment is subject to further refinement in annual tax returns.
Global Findings Conclusions: Implications for a New Economic Agenda
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