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.

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.