The employment - wage pattern conundrum: institutions, policies and labour market outcomes

就业-工资模式难题：制度、政策和劳动力市场结果

• 批准号: 2261510
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2261510
• 关键词: employment; wage; pattern; conundrum; institutions

Labour market reforms encompass a wide set of measures, which may have varying and non-linear effects on wage growth. Established datasets to evaluate reform effects such as the EU LABREF dataset, the OECD SPIDERdatabase or the ILO Inventory of Labour Market Policy Measures only include very broad categories of labour market reforms. The Eurostat labour market policy statistics and the OECD employment database provide high-leveldata on labour market institutions including EPL, UB replacement rates, ALMP spending and the number of enrolled participants. However, these datasets do not contain sufficient differentiation between different typesof reforms and measures of benefit conditionality.I intend to take a more comprehensive approach and plan to assemble country-level data of implemented labour market reforms and ALMP policies over the last two decades in a set of European economies. The ICTWSS dataset(Visser 2015) comprises a comprehensive collection of codified data on labour market institutions, mainly limited to wage setting institutions and social partners, which is relevant to use for CB data. The MISSOC dataset containsa relevant summary on unemployment benefits and the Comparative Unemployment Benefit Conditions & Sanctions dataset (Knotz & Nelson 2018) documents the increase in the strictness of unemployment benefitconditions and sanctions over the past decades. Text mining and data scrapping tools will help to collect and codify textual data from MISSOC and national sources (e.g. by using the R package tm). After the data collection, I planto follow three parallel approaches to answer the research question and its sub-questions. The first approach is intended to focus on all European Union countries, while the second and third approaches are planned to focuson a subset of European Union countries for which adequate data are available.1. On a country level, I plan to observe how major labour market flexibilizing reforms play out on aggregate wage growth. Using time-varying variables such as EPL strength, UB replacement rate, CB coverage rateand ALMP spending will allow applying a regression discontinuity design to the point in time when a major labour market reform was implemented. This will allow to observe any break in the effect of the variableson aggregate wage growth and its distribution before and after labour market reforms, and to detect heterogeneity between countries and different types of reforms2. Complementarily, I am envisaging to take a more granular approach for selected countries. I will estimate the effects of labour market flexibilizing reforms on wage growth of different income deciles using micropanel data for available countries (e.g. SOEP for Germany, BHPS/ASHE for the UK) and repeated cross-sectional micro data for others (e.g. EU-SILC weighted by EU LFS). The research strategy will follow adifference-in-difference design to observe the Phillips curve link and distributional differences across regions, sectors, occupations and income deciles.3. In the third approach, I plan to study the interactions of different policy changes over time. Established research methods often struggle to study different combinations of reforms and their time sequencing.Applying supervised machine learning (e.g. using the R package caret) allows studying nested data, which is particularly important as we can expect the effects of labour market policy changes to be path dependenton previous reforms and to have non-linear effects on wage growth and its distribution.

劳动力市场改革包括一系列广泛的措施，这些措施可能对工资增长产生不同的非线性影响。用于评价改革效果的既定数据集，如欧盟劳动力市场参考数据集、经合组织天基信息平台数据库或劳工组织劳动力市场政策措施清单，只包括非常广泛的劳动力市场改革类别。欧统局的劳动力市场政策统计数据和经合组织就业数据库提供了关于劳动力市场机构的高水平数据，包括EPL、UB替代率、ALMP支出和注册参与者人数。然而，这些数据集并没有包含不同类型的改革和福利条件的措施之间的足够区分。我打算采取更全面的方法，并计划收集在过去二十年中在一组欧洲经济体中实施的劳动力市场改革和ALMP政策的国家级数据。ICTWSS数据集（Visser 2015）包括关于劳动力市场机构的综合编纂数据，主要限于工资制定机构和社会合作伙伴，这与CB数据的使用有关。MISSOC数据集包含有关失业救济的相关摘要，而比较失业救济条件和制裁数据集（Knotz &纳尔逊，2018）记录了过去几十年来失业救济条件和制裁的严格程度。文本挖掘和数据报废工具将有助于收集和编纂来自MISSOC和国家来源的文本数据（例如通过使用R包tm）。在数据收集之后，我计划按照三个平行的方法来回答研究问题及其子问题。第一种方法旨在侧重于所有欧洲联盟国家，而第二种和第三种方法计划侧重于欧洲联盟国家的一个子集，这些国家有足够的数据。在国家层面，我计划观察主要的劳动力市场灵活化改革如何对总工资增长产生影响。使用时变变量，如EPL强度，UB替代率，CB覆盖率和ALMP支出将允许应用一个回归不连续设计的时间点时，一个重大的劳动力市场改革实施。这将有助于观察变量对总工资增长及其分布的影响在劳动力市场改革前后的任何中断，并检测各国和不同类型改革之间的异质性2。作为补充，我设想对选定的国家采取更细致的办法。我将使用现有国家的微观面板数据（例如，德国的SOEP，英国的BHPS/ASHE）和其他国家的重复横截面微观数据（例如，欧盟LFS加权的EU-SILC）来估计劳动力市场灵活化改革对不同收入阶层工资增长的影响。研究策略将遵循差异中的差异设计，观察菲利普斯曲线的链接和分布差异在地区、行业、职业和收入十分位数之间的差异.在第三种方法中，我计划研究不同政策变化随时间的相互作用。现有的研究方法往往难以研究改革的不同组合及其时间顺序。应用监督机器学习（例如使用R包插入符号）可以研究嵌套数据，这一点特别重要，因为我们可以预期劳动力市场政策变化的影响取决于先前的改革，并对工资增长及其分布产生非线性影响。