As of 2016, informal employment is estimated to be between 15.1 and 21.2 percent. The variation in estimates is largely determined by the different definitions used<sup>12</sup> (Figure 6). While these rates are not particularly high levels by international comparison, the continued increase over the past two decades warrants further attention.

For the rest of the paper, we maintain the definitions of informal employment described in this section. For the analysis on well-being, we rely on three surveys (RLMS, LiTS and ESS) as the LFS has very limited information on welfare outcomes. We focus on the latest year for which data are available, which for all surveys is 2016.

# **_Does de-industrialization explain the trends in informal employment?_**

Some have hypothesized that rising informal employment could be related to de-industrialization. As the sectoral structure of the economy shifts from manufacturing to services, and because the latter has been observed to have higher informality rates than the former in some countries (see, for instance (Vanek, Chen, Carré, Heintz, & Hausmanns, 2014) ), deindustrialization could bring higher incidence of informal employment. While a deeper analysis into the drivers of the increase in informal employment is beyond the scope of this paper, we first test whether changes in the shares of employment across different sectors is a plausible explanation of the increase in informality in Russia.

To investigate this, we modify the original Huppi-Ravallion decomposition to examine whether changes in the rate of informal employment are due to increases in the average informality in specific sectors (“intra-sectoral shift effect”) or whether employment in high-incidence sectors is increasing (“population shift effect”).<sup>13</sup> We would expect the latter effect to dominate if the increase in informal employment was associated with de-industrialization trends and the relative share of employment shifting away from manufacturing and towards services sectors that have a higher incidence of informality.

Specifically, this decomposition is an accounting exercise that breaks down sources of observed changes in informal employment rate into the intra-sectoral effect and population shift effect as follows: the population shift effect shows how much of the change in the outcome was induced by changes in employment shares of sectors between two years. The intra-sectoral effect shows the contribution that is due to changes in the incidence of informality rate within sectors. There is also

> 12 In order make comparisons across surveys, our analysis concentrates on main job only. The richness of the RLMS also allows for analysis of informal employment in workers’ second job, which some researchers have taken advantage of. See for example (Lehmann H. &., 2013).

> 13 The original Huppi-Ravallion decomposition was applied to changes in poverty rates (Huppi & Ravallion, 1991). This is an application of the shift-share approach which started in regional science around the 1940s (some report the following as a first example of that analysis: Creamer, D.: “Shifts of Manufacturing Industries, in Industrial Location and National Resources,” <u>Government Printing Office, Washington, D.C. 1943). We thank Maurizio Bussolo for this reference.</u>

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