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The evolution of firm level practices over time has always been a keen area of interest for management scholars. However, in comparison to other social scientists, particularly economists, the relative dearth of firm level panel data sets has restricted the methodological options for exploring inter-temporal changes.

This paper applies a pseudo panel methodology to investigate the evolution of training spend at the firm level over time.

The analysis is framed within a varieties of capitalism lens and by adopting a more meaningful approach to examining changes over time it leads us to question some of the “truisms” linked to firms expected behaviours within different national institutional frameworks.

As with any large-scale quantitative analysis, it would always benefits from a larger number of observations and/or a longer time period, in this instance access to annual data rather than 4 or 5 year intervals would have been helpful.

By adopting a different, and more appropriate, approach to analysing existing cross-sectional data over time this empirical research helps to achieve a deeper understanding of the complex issues that influence decision making at the firm level.

At the firm level, in line with the practical implications above, this will enable decision makers to achieve a deeper understanding of the evolution of the external context in which they operate and the likely influence of that evolution within their own organisation.

This approach enables a more meaningful exploration of inter-temporal changes in situations where longitudinal data does not exist.

The purpose of this paper is to discuss opportunities to obtain more information from the Agricultural Resource Management Survey (ARMS). Specifically, the paper will explore the issue of survey nonresponse, the development of pseudo panels, and more frequent updating of cost of production data on an enterprise basis.\

Researchers from the Land Grant University System and the Economic Research Service have relied on ARMS to evaluate the effect of agricultural, macroeconomic, and other factors on the US farm sector, farm businesses, and the households that manage them. This paper will identify gaps in understanding and proposes approaches to extract additional information from ARMS.

The relevance of ARMS in the future will depend on the ability to continue to understand potential pitfalls and areas of additional research that can develop new procedures to extract additional information. Three issues which are in need of further study include continuing to examine the issue of non‐response, refining methods to develop pseudo panel data, and examining methods to develop commodity specific financial information between the commodity specific surveys.

The National Research Council completed a review of ARMS to address challenges in keeping the survey relevant into the future. However, research that examines the construction of financial statements and other information had not been conducted since the early 1990s. This study fills part of that gap.

The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013.

A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation.

Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment.

These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs.

This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

The purpose of this study is to evaluate the impact of research and development (R&D) subsidies and tax credits on the innovative processes of Chilean firms.

Probit and tobit models for pseudo-panel with instrumental variables are estimated using data from different versions of the Innovation Survey covering the period 2007–2016.

The results show that R&D subsidies and tax credits have a statistically significant and positive effect on the probability of performing internal and external R&D, but do not affect the intensity of R&D spending, reflecting a crowding-out effect on private funds of both instruments. On the other hand, firms that simultaneously receive R&D subsidies and tax credits have a lower percentage of innovative sales. Furthermore, there are not effects statistically significant of the R&D subsidies and/or tax credits on the number of intellectual property rights applications.

It is concluded that both instruments have not been effective to encourage innovative outputs in Chilean firms.

Este estudio evalúa el impacto de los subsidios e incentivos tributarios para investigación y desarrollo (I&D) sobre los procesos innovativos de las firmas chilenas.

Se estiman modelos Probit y Tobit con variables instrumentales para pseudo-panel a partir de datos provenientes de diversas versiones de la Encuesta de Innovación que cubren el periodo 2007–2016.

Los resultados muestran que los subsidios e incentivos tributarios para I&D tienen un efecto positivo y estadísticamente significativo sobre la probabilidad de realizar I&D interna y externa, pero no afectan la intensidad del gasto en I&D lo que refleja un efecto expulsión sobre los fondos privados de ambos instrumentos. Por otro lado, las firmas que reciben simultáneamente subsidios e incentivos tributarios para I&D tienen menor porcentaje de ventas innovativas. Además, no se detecta un impacto significativo de los subsidios y/o incentivos tributarios sobre los derechos de propiedad intelectual solicitados por las firmas.

Así, se concluye que ambos instrumentos no han sido efectivos para incentivar los outputs innovativos en las firmas chilenas.

Purpose: The paper analyzes the variability of labor incomes in Argentina from mid-1980s to 2005. The magnitude of income instability and its determinants are evaluated under different macroeconomic contexts. It also analyzes how income fluctuations have influenced income distribution. Finally, the income convergence hypothesis is explored.

Methodology/approach: Different quantitative procedures are employed to measure mobility from dynamic information coming from the regular household survey. Four periods are distinguished that are relatively homogeneous. Dynamic pseudo-panels are also considered.

Findings: The growth in occupational instability registered since the mid-1990s led to a high variability of incomes despite the macroeconomic stability enjoyed throughout the nineties. Moreover, the panorama of growing inequality in the distribution of monthly income (the usual measure employed in Argentina) is also appropriate to describe what happened with the changes in the distribution of more permanent incomes. Finally, long-term income mobility in Argentina is scarce, indicating that the income path does not converge to the general mean.

Research limitations/implications (if applicable): Data refer only to Greater Buenos Aires since microdata are not available for the other areas covered by survey for the entire period under analysis. However, results are reasonably representative of the whole urban areas of the country.

Originality/value of paper: This research identifies the relative importance of labor market and macroeconomic factors in explaining income mobility. Moreover, it is for the first time in Argentina that dynamic information coming from panel data and pseudo-panels are analyzed together.

The purpose of this paper is to discuss sheepskin effects in Colombia based on repeated cross‐section or pseudo panel data using cohorts in seven larges cities in Colombia.

A Pseudo Panel Data methodology is used as the basis for determining and testing sheepskin effects using labor market microdata in Colombia in the time period from 1996 to 2000.

Empirical evidence suggests that there are additional salary increases of 14 percent for individuals who hold a secondary degree and approximately 26 percent for holders of university degrees in Colombia.

Testing sheepskin effects based on pseudo panel data using cohorts.

The purpose of this paper is to explore the determinants of life satisfaction in Turkey. Moreover, this study explores the effects of air pollution and crime problems on well-being.

The estimates are based on cross-sectional data from the health survey in Turkey during the years 2010 and 2012. Various econometric models are applied such as the ordered logit and the random-effects generalized latent class ordered logit. Moreover, using pseudo panel data created based on age and region cohorts adapted probit fixed effects and the “blow-up and cluster” estimators are applied. In addition, various estimates by sex, age group, urban and rural areas as well as between individuals with good and poor health status are followed.

The results show that the individuals who self-reported who are exposed to air pollution and crimes present on average 0.2-0.5 less satisfaction scores than those who are not exposed to air pollution and crimes. In terms of monetary values, they are willing to pay more than those who are not exposed to air pollution and crimes by 13-19 Turkish Liras per month. Moreover, the generalized latent class ordered logit shows that there is considerable heterogeneity among the most satisfied and least satisfied individuals.

The originality of the paper lies in the fact that this is the first study to provide an analysis of life satisfaction using micro-level data from Turkey. Moreover, various econometric approaches are applied to compare the results. In addition, examining the heterogeneous effects among individuals with different life satisfaction rankings, it is possible to examine the effects of various factors on well-being and how they differ among individuals. Finally, by examining exposure to air pollution and crimes in the neighbourhood and their effects on well-being, it is possible to control for characteristics of the deprived areas.

The main objective of this paper is to analyse the link between human capital depreciation and the educational level of Spanish salaried workers.

Wage equations are estimated by sector and occupation, following the empirical framework proposed by Neuman and Weiss. Data in this study refer to the Spanish labour market, using two cross‐sectional employee‐firm matched data.

The estimates provided in this paper suggest that human capital depreciation rates are not homogeneous for the whole sample; in contrast, they vary across educational levels, being greater as the workers' school attainment increases.

The main restriction of the paper is the limited availability of quality longitudinal data to estimate human capital depreciation.

Knowledge acquired by workers may quickly become obsolete in a context of technological change. Thus, the paper's main findings support the need for ongoing training programs to update workers' skills to changing market requirements.

The added value of this paper is two‐fold. On the one hand, returns to education and human capital depreciation for the Spanish labour force are estimated using a pseudo‐panel created from two cross‐sectional data bases. On the other hand, earnings equations are estimated by sector and occupation in order to calculate human capital depreciation rates; this procedure allows the authors to take into account the worker's occupation and their level of education as well as technological differences associated with their job.