Data and methodology

sources of informality. The relative priorities will depend on the economy-specific features of informality. The rest of the chapter is organized as follows. It first presents a range of fiscal policy options that may be used to help remove barriers to joining the formal sector. It then discusses a wide range of policies that can ease the transition from the informal to the formal sector. The chapter also illustrates the importance of having a comprehensive and complementary policy package to tackle the challenges posed by informality and how to implement it successfully. In addition, the chapter describes the implications of digital technologies for coping with informality. The final section summarizes the conclusions.

This chapter relies on the database detailed in chapter 2 for measures of output and employment informality. It applies several statistical tests to quantify the links between a wide range of policies and informality, without establishing or assuming causality. It then estimates a series of local projection models to help quantify the cumulative response of informality to various policy actions over the short and medium terms. Data. Both output and employment informality are considered here. Output informality is proxied by estimates based on the dynamic general equilibrium (DGE) model in percent of official gross domestic product (GDP), and employment informality is proxied by self-employment in percent of total employment. Both measures are available for up to 121 EMDEs over the period 1990-2018.5 For the local projection estimation, all data series on informality are detrended using the Hodrick-Prescott filter to mitigate concerns that the results are driven by the declining trend in informality (chapter 2). A wide range of policy measures is considered here, ranging from changes in corporate tax rates to actions to improve the ease of doing business (table 6B.2). Detailed data descriptions are provided in annex 6A. Empirical strategy. The chapter applies two empirical approaches to assess the links between informality and policies. First, differences between average policies in EMDEs with above-median and belowmedian informality are tested for statistical significance. The sample of EMDEs is grouped into those with an above-median share of informal output and those with a below-median share of informal output, on average during (up to) 1990-2018.6 For each subsample, simple averages of policy indicators are generated and the difference between these two group averages is tested for statistical significance. EMDEs with high

5 In the case of financial development, absolute levels of informal output and informal employment, rather than their relative share of official GDP or total employment, are used as robustness checks when a local projection model is estimated (figure 6A.1). Using absolute levels of informal output and informal employment avoids the possibility that the results are driven by movements in total official GDP or total employment (the denominator) rather than movements in output or employment in the informal sector (the numerator). 6 The results are the same when EMDEs are grouped according to employment informality (table 6B.3).