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There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

The purpose of this paper is to examine the effect of biased technology on employment distribution and labor status in income distribution of China. It also testifies a threshold effect of the capital per labor and employment distribution on labor status from biased technology.

This paper presents a normalized supply-side system of three equations to measure the bias of technology in China. Linear and threshold regressions approaches are applied over cross-province panel data to investigate the influence which biased technology has on labor status under different capital per labor and employment distribution regimes.

This paper empirically shows that technology has been mostly capital-biased in China. The regression results indicate that capital-biased technology impairs labor income status and tend to modify employment distribution and labor income between industries. Furthermore, it reveals the threshold effect of capital per labor and employment distribution on the relationship between biased technology and labor status.

This paper extends the literature by explaining labor status from the perspective of biased technology and the effect of inter-industry employment distribution in China. It further explores the asymmetric effect of biased technology on labor productivity and income, which promotes inter-industry labor mobility and modifies employment distribution. This paper highlights the implications of this explanation for labor relations and human resource management.

The authors analyse a growth model to explain how economic fluctuations are primarily driven by productive capacities (i.e. capacity utilization driven by innovations and know-how) and productive inefficiencies.

This study’s methodology consists of the combination of the economic growth model, à la Solow–Swan, with a sigmoidal production function (in capital), which may explain growth, poverty traps or fluctuations depending on the relative levels of inefficiencies, productive capacities or lack of know-how.

The authors show that economies may experience economic growth, poverty traps and/or fluctuations (i.e. cycles). Economic growth is reached when an economy experiences both a low level of inefficiencies and a high level of productive capacities while an economy falls into a poverty trap when there is a high level of inefficiencies in production. Instead, the economy gets in cycles when there is a large level of the lack of know-how and low levels of productive capacity.

The authors conclude that more capital per capita (greater savings and investment) and greater productive capacity (with less lack of know-how) are the economic policy keys for an economy being on the path of sustained economic growth.

The purpose of this paper is to investigate convergence or divergence trends at global scale.

The paper questions the methodology and findings of the conventional convergence literature using linear OLS models. It introduces polynomial (quadratic) weighted least square (WLS) regression analysis to explore whether a number of economic performance indicators follow a non‐linear pattern of change.

The results indicate the formation of two groups in the world: a convergence one, including countries with low to medium‐high development levels, and a divergence one including countries with medium‐high to very high development levels.

Data availability after 1990 (for the composite indicators).

The findings shed light on important issues, such as the decrease of economic disparities between countries, the prospects for global economic convergence, and the development of a more equal world. Apart from obvious policy implication such findings are also of theoretical significance, providing a basis to check (indirectly) the validity of alternative growth theories.

This is the first paper (to the authors' knowledge) that explores world convergence/divergence employing quadratic WLS regression analysis with a number of economic indicators. WLS regressions enable the removal of the impact of country size on results, whereas non‐linear modelling allows the possibility of multiple equilibria and different development trajectories to be taken into account. Finally, the employment of various economic‐performance indicators (simple and composite) works as a cross‐check of validity for the results provided.

The authors provide a novel interpretation of the relationship between skill demand and labour market concentration based on the training rationale.

The authors use a novel data set on Italian online job vacancies during 2013–2018 to analyse the relationship between labour market concentration and employers' skill demand. The authors construct measures of market concentration and skill intensity in the local labour market. The authors regress the measures of skill demand on market concentration, controlling for sector, occupations and other features of the labour market. The authors also use the Hausman–Nevo instrument for market concentration.

The authors show that employers in a highly concentrated labour market demand competencies associated with the ability of workers to learn faster (e.g. social skills) rather than actual knowledge. They also require less experience but higher education. These results are consistent with the hypothesis that employers in more concentrated labour markets are more prone to train their employees. Instead of looking for workers who already have job-specific skills, they look for workers who can acquire them faster and efficiently. The authors provide a theoretical framework within which to analyse these aspects as well as providing a test for the relevant hypotheses.

In addition to cross-countries differences in labour market regulations, the authors' findings suggest that policy authorities should consider the local labour market structure when studying workforce development programmes aimed at bridging the skill gap of displaced workers. Moreover, the authors show that market concentration can have relevant implications for human resource (HR) managers by affecting their recruitment behaviour through the demand for skills. In fact, concentrated markets tend to favour firms' collusion and anti-competitive behaviour that could strongly affect HR management practices.

The authors' paper innovates on the literature in a number of ways. First, the authors provide evidence of local labour market concentration in Italy. Second, the authors provide evidence of skill demand at the local level using a detailed skill taxonomy that goes beyond the classical distinction between high and low skills. Third, and most importantly, the authors provide evidence of the relationship between skill demand and labour market concentration. By analysing detailed skills and competencies, the authors take one step beyond understanding the features of labour demand in monopsonistic markets.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

We examine the two-level nested constant elasticity of substitution production function where both capital and labor are disaggregated in two classes. We propose a normalized system estimation method to retrieve estimates of the inter- and intra-class elasticities of substitution and factor-augmenting technical progress coefficients. The system is estimated for US data for the 1963–2006 period. Our findings reveal that skilled and unskilled labor classes are gross substitutes, capital structures and equipment are gross complements, and aggregate capital and aggregate labor are gross complements with an elasticity of substitution close to 0.5. We discuss the implications of our findings and methodology for the analysis of the causes of the increase in the skill premium and, by implication, inequality in a growing economy.