{"title":"Dynamic labor demand and informality","authors":"Armela Mancellari","doi":"10.1016/j.jedc.2025.105082","DOIUrl":null,"url":null,"abstract":"<div><div>Formal firms across the size distribution face static and dynamic incentives to employ informal labor. In this paper, I explore the implications of these incentives for resource allocation within and across firms and for policies that address informality. I build and estimate a structural model in which firms employ informal labor to evade payroll taxes (a static incentive) and to avoid the adjustment costs incurred when hiring or firing formal workers (a dynamic incentive). Formal firms do not report informal labor in official data. I overcome this obstacle with a novel strategy that exploits a 2015 shock to the enforcement of Albanian tax laws to extract information about firms' use of informal labor which I use to estimate the model. I reach three conclusions. First, I show that the gains in allocative efficiency that accrue to better enforcement of labor laws are far more modest after accounting for firms' dynamic incentives to use informal labor to adjust to shocks. Second, failing to account for informal labor results in an overstatement of formal labor adjustment costs by a factor of two. Intuitively, firms use informal labor to avoid the cost of varying output, and thus the reported data understates variation in their actual use of labor. Third, I show that reducing the costs of rigidities in formal labor markets is as effective as enhanced enforcement in reducing the aggregate informal share of employment.</div></div>","PeriodicalId":48314,"journal":{"name":"Journal of Economic Dynamics & Control","volume":"174 ","pages":"Article 105082"},"PeriodicalIF":1.9000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic Dynamics & Control","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016518892500048X","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Formal firms across the size distribution face static and dynamic incentives to employ informal labor. In this paper, I explore the implications of these incentives for resource allocation within and across firms and for policies that address informality. I build and estimate a structural model in which firms employ informal labor to evade payroll taxes (a static incentive) and to avoid the adjustment costs incurred when hiring or firing formal workers (a dynamic incentive). Formal firms do not report informal labor in official data. I overcome this obstacle with a novel strategy that exploits a 2015 shock to the enforcement of Albanian tax laws to extract information about firms' use of informal labor which I use to estimate the model. I reach three conclusions. First, I show that the gains in allocative efficiency that accrue to better enforcement of labor laws are far more modest after accounting for firms' dynamic incentives to use informal labor to adjust to shocks. Second, failing to account for informal labor results in an overstatement of formal labor adjustment costs by a factor of two. Intuitively, firms use informal labor to avoid the cost of varying output, and thus the reported data understates variation in their actual use of labor. Third, I show that reducing the costs of rigidities in formal labor markets is as effective as enhanced enforcement in reducing the aggregate informal share of employment.
期刊介绍:
The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.