Building an Annual Retrospective for French Labor Market (1959–1975) As a Complement of the INSEE’s Time Series (1975–2021)

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-07-03 DOI:10.1007/s10614-024-10661-x
Rodolphe Buda
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Abstract

This paper presents the steps of the building of PAC (Active available population), PEMP (Population in employment) and TCHO (Unemployment rate) time series along the period 1959–2021 in order to complete those produced by INSEE along the period 1975–2021. Most of the annual macroeconomic INSEE’s data describe the period 1959–2020. So it seems relevant to complete the labor market INSEE’s time series (1975–2020). Our work was based on INSEE’s data which had various degrees of revision. In a first step, we used some rare overseas department data (1954 to 1974) and some data of France metropolitan (1987 and 1994) that we combined with those published in 2020. In a second step, we updated them thanks an other econometric adjustement with the last INSEE’s data published in 2022. During the discussion, we recalled the dilemma that INSEE systematically encounters, namely the dilemma Data quality/quick delivery. Finally, we proposed some assessement’s criteria of our results, based on econometric adjustement and a “confidential interval” that we built.

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建立法国劳动力市场年度回顾(1959-1975 年),作为国家统计和经济研究所时间序列(1975-2021 年)的补充
本文介绍了 1959-2021 年期间 PAC(在业人口)、PEMP(就业人口)和 TCHO(失业率)时间序列的构建步骤,以完善国家统计和经济研究所 1975-2021 年期间的数据。国家统计和经济研究所的大部分年度宏观经济数据描述的是 1959-2020 年这一时期。因此,完成 INSEE 的劳动力市场时间序列(1975-2020 年)似乎很有意义。我们的工作以 INSEE 的数据为基础,这些数据经过了不同程度的修订。第一步,我们使用了一些罕见的海外省数据(1954 年至 1974 年)和法国本土的一些数据(1987 年和 1994 年),并将其与 2020 年公布的数据进行了合并。第二步,我们利用 2022 年公布的国家统计和经济研究所的最新数据,通过其他计量经济学调整对数据进行了更新。在讨论过程中,我们回顾了 INSEE 经常遇到的两难问题,即数据质量/快速交付的两难问题。最后,我们根据计量经济学调整和我们建立的 "保密区间",对我们的结果提出了一些评估标准。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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