Estimating economic incidence and labour supply elasticities of SSC - - PowerPoint PPT Presentation

Estimating economic incidence and labour supply elasticities of SSC based cross-country micro data

Stuart Adam, Nicole Bosch, Antoine Bozio, David Phillips, Michael Neumann March 1, 2016

Motivation Methodology Outlook

Outline

Motivation Methodology Outlook

Motivation Methodology Outlook

Motivation

◮ Challenges for country-specific micro studies

◮ Natural experiments: rare, availability of control groups,

external validity, short-term (legal=economic incidence)

◮ Panel studies: mainly exploit variation over earnings

distribution and/or time, exogenous variation?

◮ Individual vs. market-level outcomes

◮ Differences between micro and macro estimates

Motivation Methodology Outlook

This study

Estimate economic incidence of and behavioural responses to SSC based on administrative cross-country micro data

◮ Identification

◮ Use other countries as control groups (same location in the

earnings distribution)

◮ Exploit potentially larger variation across countries without

averaging on country-level

◮ Aggregate on different levels to examine differences in

individual- and market-level outcomes

Motivation Methodology Outlook

Literature

Jaentti, Pirttilae and Selin (2015)

◮ Estimation of labour supply elasticities (w.r.t. income taxes)

based on cross-country micro-data

◮ Micro, Macro, Micromacro ◮ Data: Repeated cross-sections, Luxembourg Income Study ◮ Tax measures from OECD tax database ◮ No evidence for systematic differences

Motivation Methodology Outlook

Our approach

◮ Focus on SSC ◮ Long panel of admin data (1975-2010)

◮ Accurate earnings data but (for most countries) no hours of

work

◮ Focus on our 4 countries (FRA, GER, NED, UK)

◮ Thorough calculation of marginal and average SSC rates

by micro-simulation

◮ Challenge: Comparable data/measures across time and

countries

Motivation Methodology Outlook

Aggregation

Aggregate data to cells defined by quantiles of the earnings distribution

◮ Data security prevents merging individual admin data across

countries

◮ From almost individual to country-level: More spillovers

but less precision

◮ Variation: Same quantiles of earnings distribution in different

countries

Motivation Methodology Outlook

Specification

Empirical specification mainly follows Lehmann, Marical and Rioux (2013) ∆jln(zqct) = α + βessc

τ

∆jln(1 − τ essc

qct ) + βessc t

∆jln(1 − tessc

qct )+

+ βrssc

τ

∆jln(1 − τ rssc

qct ) + βrssc t

∆jln(1 − trssc

qct )+

+ βinc

τ ∆jln(1 − τ inc qct) + βinc t

∆jln(1 − tinc

qct) + ǫqct

q: quantile, c: country, t: year essc: employee SSC, rssc: employer SSC, inc: income tax ∆j: change between t and t − j for j = (1; 3) τ: empirical marginal SSC rate: substitution effect t: empirical average SSC rate: incidence and income effect

Motivation Methodology Outlook

Instruments

◮ τ, t are functions of z: Need to be instrumented ◮ Gruber and Saez (2002)

◮ SSC rate in t based on earnings in t − j ◮ f (zt−j) controls for differential income trends and mean

reversion

◮ Kopczuk (2005): two separate controls for mean reversion

and differential income trends

◮ SSC rate in t based on zt−j ◮ Controls: f (zt−j−1) and f (zt−j − zt−j−1)

◮ Weber (2014): instruments don’t satisfy exogeneity

requirement

◮ SSC rate in t based on zt−j−k (we use k ∈ {1, 2}) ◮ Control for f (zt−j)

Motivation Methodology Outlook

Instruments and cells

Additional endogeneity issue: Cells are defined by outcome variable Two approaches

• 1. Make use of individual panel

◮ First calculate individual changes in labour costs and

(predicted) tax rates

◮ Then average within cells based on zt−j ◮ Mean reversion: Same as for instrument

Motivation Methodology Outlook

Instruments and cells II

• 2. Pseudo-panel

◮ First average labour costs and (predicted) tax rates within cells

based on zt

◮ Then calculate changes ◮ Mean reversion averaged out ◮ Selection into cells due to tax changes

Motivation Methodology Outlook

Outlook

◮ Output and merge data ◮ Estimation

Motivation Methodology Outlook

References Gruber, Jon and Emmanuel Saez, “The elasticity of taxable income: evidence and implications,” Journal of Public Economics, April 2002, 84 (1), 1–32. Jaentti, Markus, Jukka Pirttilae, and Hakan Selin, “Estimating labour supply elasticities based on cross-country micro data: A bridge between micro and macro estimates?,” Journal of Public Economics, 2015, 127, 87 – 99. The Nordic Model. Kopczuk, Wojciech, “Tax bases, tax rates and the elasticity of reported income,” Journal of Public Economics, 2005, 89 (1112), 2093 – 2119. Lehmann, Etienne, Franois Marical, and Laurence Rioux, “Labor income responds differently to income-tax and payroll-tax reforms,” Journal of Public Economics, 2013, 99 (C), 66–84.

Motivation Methodology Outlook

Weber, Caroline E., “Toward obtaining a consistent estimate of the elasticity of taxable income using difference-in-differences,” Journal of Public Economics, 2014, 117, 90 – 103.