What Are The Drivers of Labor Productivity in Italy?
用结构贝叶斯VAR模型分析意大利劳动生产率对供给和需求冲击的反应，发现自动化和技术冲击持续提升生产率，而劳动供给和需求冲击则持续降低生产率。
ABSTRACT This paper introduces a novel sign restriction identification within a structural Bayesian vector autoregression (VAR) to analyse how labour productivity responds to supply and demand shocks and to quantify the contribution of shocks to cyclical fluctuations. We apply our framework to the Italian labour market because it is characterised by several rigidities despite multiple labour reforms. Using data spanning 1984Q1–2023Q3, we show that the cyclical productivity‐output correlation has significantly declined since the late 1990s. Moreover, the relative volatility of employment to output has decreased, while that of hours per worker has increased. We estimate our specification on Italian data from 1996Q1 to 2023Q3 and disentangle three supply shocks from one demand shock. We find that automation and technology shocks generate persistent gains in productivity, while labour supply and aggregate demand shocks lower it persistently. The negative effect of demand shocks is due to the COVID‐19 pandemic. Labour supply, automation, and technology shocks are the dominant drivers of productivity both at short and long horizons. Labour supply shocks explain most of the productivity slowdown during the double‐dip recession, while all shocks contributed to its aftermath recovery during the period 2012–2019. We carry out a series of sensitivity analyses, which confirm our results.