<!-- Start of picture text --> Figure 1: The Division of Income in the US<br>(A) Labor’s Income Share (B) Capital’s Income Share<br>ICT Share (left axis)<br>Non-ICT Share (right axis)<br>Capital Share (right axis)<br>Routine Occupations<br>Non-Routine Occupations<br>Labor Share<br>1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year<br>70<br>4 42<br>60<br>40<br>3<br>50<br>38<br>2<br>40 36<br>Income Share (% of GDP)<br>30 ICT Income Share (% of GDP) 1 34<br>Non-ICT & Capital Income Share (% of GDP)<br>20 0 32<br><!-- End of picture text --> _Notes:_ Occupation specific income shares are based on CPS earnings data from the annual march supplement (1968 and after) and rescaled to match the aggregate income share in the Non-Farm Business Sector (BLS). The underlying earnings data are topcode adjusted using Piketty and Saez’s (2003) updated estimates of the US income distribution (PS). Non-routine workers are those employed in “management, business, and financial operations occupations”, “professional and related occupations”, and “service occupations”. Routine workers are those in “sales and related occupations”, “office and administrative support occupations”, “production occupations”, “transportation and material moving occupations”, “construction and extraction occupations”, and “installation, maintenance, and repair occupations” (Acemoglu and Autor, 2011). For details see Section 2. The construction of capital-type specific income shares is described in Section 3. The underlying data are nominal gross capital stocks and depreciation rates, drawn from the BEA’s detailed fixed asset accounts. The vertical dashed line in panel B marks the year 1968.

estimate represents an upper bound for the overall welfare gains from the “ICT revolution”.<sup>4</sup> We begin with a decomposition of U.S. income shares over the period 1968-2013 (Figure 1 and Table 1). Our analysis builds on the organizing framework of Autor, Levy and Murnane (2003) who highlight the distinction between “routine” and “non-routine” occupations, where routine occupations are jobs which are relatively more prone to automation.<sup>5</sup> This decomposition confirms that the relatively steady but mild decline in the aggregate labor income share—recently documented on a global scale by Karabarbounis and Neiman (2014)—masks a much steeper decline in the income share of routine labor. However, the declining share of routine labor was

> 4It is also worth noting that our welfare gains do not take into account the costs associated with organizational change (Brynjolfsson and Hitt, 2003; Gaggl and Wright, 2015) and the accumulation of intangible capital (Bloom et al., 2012), shown to significantly affect on the efficiency of using ICT, and should thus be taken as an upper bound.

> 5Autor et al.’s (2003) seminal work has spurred a substantial body of literature documenting that ICT complements “non-routine” tasks—involving hard to automate, often inter-personal skills—and it replaces “routine” tasks—ones that follow exact, pre-specified procedures (Acemoglu and Autor, 2011). While the majority of this literature documents conditional correlations, two recent studies by Akerman et al. (2013) and Gaggl and Wright (2015) provide direct, causal evidence for this view.

3