by Autor et al. (2003).<sup>9</sup> Within this organizing framework, routine and non-routine labor are effectively defined in terms of their substitutability with ICT. We therefore distinguish between ICT and non-ICT capital, rather than equipment and structures as in Krusell et al. (2000).<sup>10</sup> The rest of the paper is organized as follows: Sections 2 and 3 describe the details of the measurement of the disaggregated trends in income shares displayed in Figure 1. These sections further offer a decomposition of the income shares into price and quantity components as well as a discussion of the role of international trade, housing, and industrial composition. Section 4 illustrates our calibration exercise and Section 5 describes our main quantitative analysis. Finally, Section 6 offers some concluding remarks.

# 2. Decomposing the Labor Income Share

To measure routine and non-routine labor income shares, we consider two alternative measures of earnings at the occupation level in the U.S. Current Population Survey (CPS): annual earnings from the March supplements (MARCH) starting in 1968 (provided by IPUMS, Ruggles, Alexander, Genadek, Goeken, Schroeder and Sobek, 2010), and weekly earnings for the outgoing rotation groups (MORG) starting in 1979 (provided by the NBER). Based on these earnings data we decompose the U.S. aggregate labor share (BLS) into the portion going to routine and non-routine labor, respectively.

> 9They document that the set of tasks that are most readily prone to automation are primarily performed by middleskill jobs, and these jobs have seen a relative decline in both employment and wages. In contrast, both high skill occupations—that require creative thinking— _and_ low skill service jobs—that often require interpersonal skills—have gained along both dimensions. These facts are now well documented for many developed nations, starting with the work by Acemoglu (1999) for the US over the period 1983–1993, and in more recent periods by Goos and Manning (2007) for the UK, Goos, Manning and Salomons (2009) for 16 EU countries, and Autor, Katz and Kearney (2008) as well as Autor and Dorn (2013) for the US.

> 10While ICT capital is mostly included in equipment, the equipment category considered in Krusell et al. (2000) is much broader and includes vehicles, electrical machinery, and even furniture. Like Krusell et al. (2000), vom Lehn (2015) also studies the declining price of equipment. While his analysis of the labor market is also guided by the tasks framework (Autor et al., 2003), his approach differs from ours both in terms of the classification of occupations and the calibration approach. The most important distinction, however, is his focus on equipment, rather than ICT.

We note that this is not a trivial distinction for several reasons: first, the equipment share of output is roughly 25% (based on the calculations by Krusell et al., 2000), yet we estimate an ICT income share of at most 4% in recent years. Second, we further document that ICT accounts for essentially all of the decline in the relative price of equipment and that the price of non-ICT capital, a substantial part of which comprises equipment, was virtually constant relative to the GDP deflator throughout our entire sample (see panel A of Figure 8). Finally, we find that the income share of non-ICT is trend-less, yet that of ICT displays a strong upward trend (Figure 1).

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