Relying upon an original (country-sector-year) measure of robotic capital ($RK$), we investigate the degree of complementarity/substitutability between robots and workers at different skill levels. We employ nonparametric methods to estimate elasticity of substitution patterns between $RK$ and skilled/unskilled labor over the period 1995–2009. We show that: i) on average, $RK$ exhibits less substitutability with skilled workers compared to unskilled workers, indicating a phenomenon of “RK-Skill complementarity”. This pattern holds in a global context characterized by significant heterogeneity; ii) the dynamic of “RK-Skill complementarity” has increased since the early 2000s; iii) the observed strengthening is more prominent in OECD countries, as opposed to non-OECD countries, and in the Manufacturing sector, compared to non-Manufacturing industries.

Recently, there has been a surge in interest in exploring how common macroeconomic factors impact different economic results. We propose a semiparametric dynamic panel model to analyze the impact of common regressors on the conditional distribution of the dependent variable (global output growth distribution in our case). Our model allows conditional mean, variance, and skewness to be influenced by common regressors, whose effects can be nonlinear and time-varying driven by contextual variables. By incorporating dynamic structures and individual unobserved heterogeneity, we propose a consistent two-step estimator and showcase its attractive theoretical and numerical properties. We apply our model to investigate the impact of US financial uncertainty on the global output growth distribution. We find that an increase in US financial uncertainty significantly shifts the output growth distribution leftward during periods of market pessimism. In contrast, during periods of market optimism, the increased uncertainty in the US financial markets expands the spread of the output growth distribution without a significant location change, indicating increased future uncertainty.

Building upon recent developments in production function identification and decomposition methods, this paper investigates the sources of output and productivity growth among China’s listed manufacturing companies from 2000 to 2022. While previous studies on China’s manufacturing have predominantly focused on the period preceding 2007, our study extends the analysis to a broader timeframe and divide it into four sub-periods to accommodate diverse economic conditions and varying growth rates. We provide new insights into the Chinese economy during a period marked by gradual economic transformation. Specifically, we first decompose industry output growth into factor deepening and firm productivity progress within each sub-period. To account for heterogeneity across firms in terms of production technology and sources of growth, we employ a nonparametric production function and decompose firm output growth at both the mean and different quantiles of the output distribution. We find that increased materials usage and productivity growth are primary growth drivers. However, the contribution of productivity experiences a significant decline, particularly in recent years and among median-sized and large firms. Furthermore, we examine China’s industry aggregate productivity growth and its origins among state-invested, foreign-invested, and domestic private firms. Our findings suggest that reforms among state firms are the largest contributor to industry productivity growth before the 2008 financial crisis, whereas productivity progress of domestic private firms emerges as the sole significant driver in recent years. Additionally, there is no evidence of improvements in output reallocation efficiency within China’s manufacturing sector throughout our sample period.

The paper investigates the gender-driven disparities in total factor productivity (TFP) between women-owned enterprises (WOEs) and male-owned enterprises (MOEs) across 30 developing countries. Utilizing firm-level data from the World Bank Enterprise Surveys, the study addresses biases in previous gender literature by employing a semi-parametric technique to more accurately measure TFP. The results reveal a significant TFP gap, with WOEs being 5.5% to 6.7% less productive than MOEs, even after controlling for key firm characteristics like age, innovation, human capital, and ownership status. The study attributes this productivity disparity primarily to financial obstacles faced by WOEs, which hinder their ability to innovate and capitalize on opportunities. The lack of access to credit leads to a misallocation of capital, excluding equally efficient women entrepreneurs from financial resources that could stimulate productivity. Contrary to some assumptions, the study finds no evidence that WOEs underperform in sectors with high financial dependence, suggesting that WOEs are not inherently inefficient in their use of capital. Our findings provide causal evidence as we control for selectivity bias in the TFP-WOEs nexus by identifying exogenously the factors that affect financial constraints and innovation.

This paper builds on Hsieh and Klenow’s (2009) model to offer a refined analysis of how input misallocations impact aggregate total factor productivity (TFP). We enhance the original model by relaxing the assumption of uniform input prices and adopting an econometric approach to estimate parameters using firm-level data. Estimation of model parameters and allocation efficiency is based on the system of input demand and the production function. We use an indirect inference approach to estimate the system to avoid maximum likelihood estimation, which often faces convergence issues, when there are numerous constraints. We demonstrate our model using the US firm-level manufacturing panel data from 1975 to 2010. Our final sample contains 55,518 observations. We divide the manufacturing industry into seven major categories. Our findings indicate that between 1975 and 2010, the average productivity growth rate was 2.8% but could have reached 3.2% without misallocation, highlighting the substantial gains possible through better resource allocation.

Rigorous methods have recently been developed for statistical inference of Malmquist productivity indices (MPIs) in the context of nonparametric frontier estimation, including the new central limit theorems, estimation of the bias, standard errors and the corresponding confidence intervals. The goal of this study is to briefly overview these methods and consider a few possible improvements of their implementation in relatively small samples. Our Monte-Carlo simulations confirmed that the method from Simar et al. (2023) is useful for the simple mean and aggregate MPI in relatively small sample sizes (e.g., up to around 50) and especially for large dimensions. Interestingly, we also find that the “data sharpening” method from Nguyen et al. (2022), which helps in improving the approximation in the context of efficiency is not needed in the context of estimation of productivity indices. Finally, we provide an empirical illustration of the differences across the existing methods.

This paper examines the impact of financially constrained intermediate inputs on within-industry total factor productivity loss. Utilizing exogenous tax reforms in China as a natural experiment, our difference-in-difference analysis reveals that reduced tax burdens lead to increased firm-level intermediate inputs, particularly among financially constrained firms. We incorporate financially constrained intermediate inputs into a partial equilibrium model of firm dynamics. Our calibration suggests that financially constrained intermediate inputs play a quantitatively more important role in accounting for misallocation than financially constrained capital. The presence of financially constrained intermediate inputs introduces a downward bias in the measurement of value-added productivity, especially for firms in the top decile of gross-output productivity. As a result, the average “efficient” levels of capital and labor for the top decile firms in the standard Hsieh and Klenow (2009) exercise are lower than what is truly efficient.

At the firm level, productivity is constantly evolving because of the introduction of new technology and innovations. Some of these productivity gains diffuse uniformly across firms, while others only spread out in the industry with time. The unequal evolution of productivity impacts the structure of the industry, the more the greater the degree of competition. We analyze the relationship between the distribution of firms’ productivity advantages and the distribution of market shares and show that this relationship is more intense with more competition. We briefly comment on two applications: we show that because productivity gains, market concentration and inflation can be negatively related, and we give an alternative interpretation to the case for a recent rise of US markups attributed to increased market power.

I use a new publicly available industry-year panel dataset capturing within-industry productivity dispersion to examine the relationship between various measures of industry-level leverage, a common measure of financial constraints, and industry-level productivity dispersion, a common measure of misallocation. Increases in short-term leverage are associated with increases in TFPR dispersion. Likewise, increased short-term leverage is associated with a persistent increase in labor productivity dispersion. Higher long-term leverage is generally associated with higher dispersion in TFPR. However, there is little correlation between long-term leverage and labor productivity dispersion. On the asset side of the balance sheet, the accumulation of inventories is associated with lower dispersion. I interpret these results in a model featuring sources of finance with different time horizons and nonuniform financial constraints across inputs.

Efficiency is a crucial factor in productivity growth and the optimal allocation of resources in the economy; therefore, measuring inefficiency is particularly important. This paper provides a comprehensive review of the latest developments in distance functions and the measurement of inefficiency within the stochastic frontier framework. Recent advances in several related areas are reviewed and evaluated, including various approaches to measuring inefficiency using distance functions, advancements in modeling inefficiency within the stochastic frontier framework, and the most common estimation techniques. A practical guide is provided on when these methods can be applied and how to implement them. The radial, hyperbolic, and directional measures of inefficiency are discussed and assessed. The development of modeling inefficiency concerning its temporal behavior, classification, and determinants is also examined. To ensure the use of appropriate estimation techniques, recent advancements in the most common estimation techniques are reviewed. This paper also addresses the importance of maintaining the theoretical regularity applied by neoclassical microeconomic theory when it is violated, as well as the econometric regularity when variables are non-stationary. Without regularity, inefficiency results can be extremely misleading. The paper discusses significant challenges related to estimation issues that must be managed in future applications. These challenges include the inaccurate choice of functional form, ignoring the possibility of heterogeneity and heteroskedasticity, and suffering from the endogeneity problem. The paper also examines various approaches to addressing these issues, as well as potentially productive areas for future research.

Our spatial general equilibrium model evaluates the impact of stamp duty reforms on social welfare through two channels: the direct positive impact on housing market outcomes and the indirect boost to national productivity due to better labor allocation. Analyzing detailed spatial data from Australia, we find that reducing stamp duties generates welfare gains of 3.57%, with the productivity channel accounting for 95% of these gains. This highlights the significant benefits of stamp duty reforms beyond the housing market.

We assess how an economy’s wealth distribution shapes its labor market dynamics. We do so in a quantitative job-ladder model featuring directed search, incomplete markets, aggregate shocks, and endogenous on-the-job human capital accumulation. Poorer workers apply for lower-wage jobs when unemployed and under-accumulate human capital when employed to self-insure against unemployment risk. In response to an aggregate downturn, poorer workers reduce their human capital accumulation, all else equal, while richer workers increase it. The wealth distribution therefore matters for the response of aggregate human capital. In the calibrated model, we show that a negative aggregate productivity shock leads to a persistent decline in aggregate human capital, and a more dispersed wealth distribution would amplify this decline.

This paper investigates both conditional and unconditional convergence in labor productivity within the manufacturing industries of the Eurozone over the period 1963 – 2018. We employ two innovative models: constant and varying-coefficient hierarchical panel data convergence regression models, each equipped with two sets of latent factor structures—one comprising global factors and the other industry-specific factors. These models offer distinct advantages, allowing for both global and industry-specific cross-sectional dependencies and permitting parameter heterogeneity across individual industries. Our findings reveal both conditional and unconditional convergence across the manufacturing industry as a whole, as well as among the majority of the 23 sub-manufacturing industries at the ISIC two-digit level. Moreover, we observe significant variation in convergence dynamics among these sub-manufacturing industries. Robustness checks, performed across different subperiods, confirm the reliability of our results. Furthermore, a comparison of our model’s outcomes with those of two alternative models provides additional support for our conclusions.