Innovation and Top Income Inequality
GAICIL Insights
Not all billionaires are made the same way, and the difference matters for everyone else.1 Innovation and Top Income Inequality, by Philippe Aghion, Ufuk Akcigit, Antonin Bergeaud, Richard Blundell and David Hemous, examines the frequent charge that innovation drives rising top income inequality. Using cross-state panel and cross-US commuting-zone data, the authors find a positive and significant correlation between innovation and top income inequality. Innovation and broad measures of inequality are not significantly correlated, and top income inequality is not correlated with highly lagged innovation.
Instrumental variable analysis suggests the correlation at least partly reflects a causal effect from innovation to top income shares. Innovation creates rents for successful innovators that raise top incomes. Crucially, innovation, particularly by new entrants, is positively associated with social mobility, though less so in local areas with more intense lobbying activities. The rich list gets richer, and its membership turns over.
That signature sets innovation apart. It raises the top income share without increasing broad inequality, and it promotes social mobility. The authors conjecture that lobbying, unlike innovation, should be positively correlated with broad inequality and negatively correlated with social mobility. Two routes to the top, with opposite consequences for those below.
The approach examines aggregate effects as an essential first step in assessing innovation's quantitative importance for top income inequality, complementing micro studies using individual data on revenues and patenting. The model, enriched to account for firm heterogeneity, could become a building block for a full quantitative model of innovation, firm size distribution and top income inequality.
The application to AI is direct. AI sits at the center of this debate. AI is expected to generate large returns for successful innovators, raising inequality concerns. The paper suggests innovation-driven top incomes differ in important ways from other sources of inequality. They need not increase broad inequality, and innovation by new entrants is associated with greater social mobility. AI innovation that opens opportunities for newcomers could produce similar effects. The key variable is whether new AI fortunes come from new firms.
Lobbying is the crucial qualification. Innovation's association with social mobility weakens where lobbying is intense. When incumbents use political influence to protect positions, innovation's mobility-enhancing effects shrink. For AI, keeping markets open and limiting incumbents' ability to block entry through political influence matters for ensuring AI-driven innovation promotes mobility. An AI fortune defended in the capital looks different from one won in the market.
Policy should ask where top incomes come from. Policies aimed at reducing top income inequality should consider the source. Taxing innovation rents heavily could discourage the innovation that fosters mobility. Addressing rents from lobbying and entrenchment may reduce inequality at lower cost to innovation. Designing policy with these distinctions in mind balances growth and equity. A blunt instrument hits the inventor and the rent-seeker alike.
New entrants carry the mobility promise. Innovation by entrants is associated with social mobility, as new firms and new people displace incumbents. AI could be a powerful source of such entry, as new firms develop applications challenging established players. Supporting entrepreneurship and reducing entry barriers amplifies these effects.
The empirical care strengthens the case. Distinguishing top income shares from broad inequality, examining timing and using instruments, the authors provide credible evidence on a contentious question. Similar care will be needed to assess AI's distributional effects as they emerge.
Read as a whole, innovation raises top incomes while promoting social mobility, especially when driven by new entrants and when lobbying is limited. As AI drives innovation, its distributional effects will depend on whether markets remain open to newcomers and whether incumbents can entrench themselves politically.
Inequality debates about AI might start here: track the lobbying budgets of AI leaders alongside their fortunes. On this evidence, the money spent in the capital may say a great deal about future inequality.
Introduction
In this article, we use cross-state panel and cross-U.S. commuting-zone data to look at the relationship between innovation, top income inequality and social mobility. We find positive correlations between measures of innovation and top income inequality. We also show that the correlations between innovation and broad measures of inequality are not significant. Next, using instrumental variable analysis, we argue that these correlations at least partly reflect a causality from innovation to top income shares. Finally, we show that innovation, particularly by new entrants, is positively associated with social mobility, but less so in local areas with more intense lobbying activities.
Takeaway
In this article, we looked at the relationship between top income inequality and innovation. First, we found a positive and significant correlation between innovation and top income inequality. We also showed that innovation and broad measures of inequality are not significantly correlated, and that top income inequality is not correlated with highly lagged innovation. Second, we argued that this correlation at least partly reflects a causal effect from innovation to top income shares. Third, we showed that innovation is positively associated with social mobility. Our approach was to look at the aggregate effect of innovation on top income inequality. This is an essential first step to assess the overall quantitative importance of innovation in top income inequality. Thus, our analysis complements more microeconomics studies which explore the relationship between innovation, top income inequality and social mobility using individual data on revenues and patenting. Our findings also suggest interesting avenues for further research on innovation-led growth, inequality and social mobility. A first extension would be to contrast innovation with other sources of top income inequality, for example from financial and lobbying activities, and look at the effects of these other sources on other measures of inequality and social mobility. Our conjecture is that, unlike innovation, lobbying should be positively correlated with broad measures of inequality, and negatively correlated with social mobility.1
Second, our calibration results suggest that our simple model, once enriched to better account for firms’ heterogeneity, could be used as a building block towards a full quantitative model of innovation, firm size distribution, and top income inequality. Such a model would be useful to assess the contribution of innovation in the rise of market power (De Loecker and Eeckhout, 2017), and also to assess the impact of tax policy, innovation policy (R&D subsidies, patent policy) or competition and entry policy on innovation-led growth and top income inequality.
Notes
- Philippe Aghion et al., Innovation and Top Income Inequality, 86 Rev. Econ. Stud. 1 (2019), https://doi.org/10.1093/restud/rdy027. Takeaway source: 9. CONCLUSION, reproduced from the openly licensed version (CC BY 4.0). ↩
Cite as: Philippe Aghion et al., Innovation and Top Income Inequality, 86 Rev. Econ. Stud. 1 (2019), https://doi.org/10.1093/restud/rdy027
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