Global and digitalised economy, new labour demand scenarios and optimal tax-transfer reforms

2021 
Processes like globalisation, automation and digitalisation might imply important changes on the size and structure of labour demand. An important policy issue is whether and how the tax-transfer rules should be reformed to cope with those changes. We present an extension of the numerical approach to empirical optimal taxation, allowed by a peculiar structure of a microeconometric model of labour supply that includes a representation of the demand side. This makes it possible to identify optimal tax-transfer rules while accounting for equilibrium constraints and to evaluate the effects of exogenous labour demand shocks, such as those that might be caused by globalisation, automation and digitalisation. We consider a flexible class of rules where household disposable income is a 4th polynomial in household taxable income. We perform three exercises. First, we identify optimal polynomial tax transfer rules accounting for labour market equilibrium under the observed labour demand scenario. Second, we investigate how the current tax-transfer rules are able to cope with an exogenous shift of labour demand. Third, we identify the optimal polynomial tax-transfer rule given the new labour demand scenario. We present results using the 2015 EU-SILC data sets for Italy and Luxembourg. The optimal rules, under both the current scenario and the shifted labour demand scenario, feature a universal and unconditional basic income (or, equivalently, a Negative Income Tax) and an almost flat marginal tax rate profile. The welfare gains from the optimal rules critically depend on how elastic the labour demand is, which ultimately depends on the degree of competitiveness of the markets. Therefore, optimal polynomial rules seem to represent a promising direction for reforming the tax-transfer systems, especially if complemented by other reforms aimed at improving the competitiveness of the economy.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    0
    Citations
    NaN
    KQI
    []