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The purpose of this paper is to compare and analyze the modern productivity estimation techniques, namely, Levinsohn and Petrin (LP, 2003), Ackerberg Caves and Frazer (ACF, 2006), Wooldridge (2009) and Mollisi and Rovigatti (MR, 2017) on unit-level data of 32 Indian industries for the period 2009-2015.

The paper first analyzes different issues encountered in total factor productivity (TFP) measurement. It then categorizes the productivity estimation techniques into three logical generations, namely, traditional, new and advanced. Next, it selects four contemporary estimation techniques, computes the industrial TFP for Indian states by using them and investigates their empirical outcomes. The paper also performs the robustness check to ascertain, which estimation technique is more robust.

The result indicates that the TFP growth of Indian industries have differed greatly over this seven-years of period, but the estimates are sensitive to the techniques used. Further results suggest that ACF and Wooldridge yield the consistent outcomes as compared to LP and MR. The robustness test confirms Wooldridge to be the most robust contemporary technique for productivity estimation followed by ACF and LP.

To the authors’ knowledge, this is the first study that compares the contemporary productivity estimation techniques. In this backdrop, this paper offers two novelties. First, it uses advanced production estimation techniques to compute TFP of 32 diverse industries of an emerging economy: India. Second, it addresses the fitment of estimation techniques by drawing a comparison and by conducting a robustness test, hence, contributing to the limited literature on comparing contemporary productivity estimation techniques.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

This study aims to determine the association of Transparency and Disclosure (TD) with financial distress (FID) while the competition (as Lerner Index) moderates the association between the two.

The panel data analysis (static model) is performed to examine the effect of disclosures on the bank's FID. A TD index is built to assess the level of TD. All three versions of Altman's Zscore are employed to measure a bank's FID (High Zscore is opposite of FID). The data of 34 banks running in India for the timeframe 2015–16 to 2018–19 is utilized. Lerner index (LI) is taken as the moderator. The bank-size, valuation and financial leverage are control variables.

There exists no linear connection between TD and FID. However, TD is positively associated with financial stability (opposite FID). It means TD initially reduces financial stability and improves it after TD crosses a threshold level. Competition (as LI, where the higher value of LI means reduced competition) negatively moderates the association of TD with financial stability. Hence, the findings of this study support the competition-fragility premise. Surprisingly, the negatively significant interaction term of LI and TD implies either high competition and high TD or low competition with low TD, which helps in the bank's financial stability.

The findings provide input to a long-term policy of disclosures and competition in the banking sector, keeping in view the financial stability of the banks. Therefore, findings are novel and carry immense value to the existing knowledge on the topic.

This study aims to explore the involvement of middle management in forming strategies to manage variable acute patient flows at a hospital.

Empirical evidence from a university hospital was gathered via interviews, internal documents, observation and participation in meetings. The role of middle management in the development of strategies was analyzed using literature on middle management involvement.

In managing variable acute patient flows, middle management adopts a number of roles and behavioral characteristics that have been previously described in research. The role of facilitator is the most prominent, with middle managers prioritizing individual goals and strategies for the clinical departments that they manage before their collective responsibility for hospital performance. Unclear responsibilities and mandates within the organization, together with a lack of hospital-wide strategies concerning how the acute patient flow should be managed, are contributing factors to this behavior.

The research is based on an explorative, single case study methodology. Future research assessing the extent of different middle management roles in health care, in which more empirical data and quantitative analysis is conducted, is encouraged.

There is a need for top management to establish long-term goals to enhance middle management roles when developing strategies for managing variable patient flows.

Middle management involvement in developing strategies for managing variable patient flows is a novel topic for research. The interface and division of tasks between top and middle management is crucial for successful strategies in managing variable patient flows.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

The author proposes analyzing the dynamics of income positions using dynamic panel ordered probit models. The author disentangles, simultaneously, the roles of state dependence and heterogeneity (observed and non-observed) in explaining income position persistence, such as poverty persistence and affluence persistence. The author applies the approach to Chile exploiting longitudinal data from the P-CASEN 2006–2009. First, the author finds that income position mobility at the bottom and the top of the income distribution is much higher than expected, showing signs that income mobility in the case of Chile might be connected to economic insecurity. Second, the observable individual characteristics have a much stronger impact than true state dependence to explain individuals’ current income position in the income distribution extremes.

The goal of this study is to investigate the nexus among TFP (total factor productivity), IT (information technology) capital accumulation, skills and key plant variables of 34 Indian industries for the period of 2009–2015.

Annual Survey of Industries (ASI) data series are extracted and formulated using Microsoft SQL server. The authors employ Wooldridge (2009) technique to estimate productivity. To investigate the linkages among productivity, IT, skills and key plant variables, the authors estimate specifications using system generalized method of moments (sys-GMM). Advanced estimation techniques such as Heckman two-step process, probit equations, inverse Mills ratio and panel cointegration are applied to overcome problems of nonstationarity, omitted variables, endogeneity and reverse causality.

The results indicate that the level of IT capital influences the TFP of Indian industries, so does the level of skilled workers. The outcome suggests that intermediate capital goods, location and ownership type enable the strength of IT capital and that in turn boosts productivity. The authors fail to find any impact of regional factors and contractual labor on IT capital and productivity. While medium-level gender diversity is statistically significant to influence productivity, however, no complementarities exist between gender diversity and IT capital accumulation. The results also indicate that IT demand of Indian industries is sensitive to availability of skilled workforce, fuel and electricity and access to short-term funding.

To the authors' knowledge, this is the first study to investigate the nexus among TFP, IT capital accumulation, skills and organizational factors using ASI unit level data. Besides this, the paper offers two more novelties. First, it uses Wooldridge (2009) technique to estimate productivity, which is used by a handful of studies in the context of India. Second, the study identifies factors that impact productivity growth, IT demand and its adoption in Indian industries and thus contributes to growth and development literature.

While strategy was traditionally perceived as exclusive, and limited to small groups within organizations, recently a shift toward greater openness through inclusion of a larger number and variety of actors is emerging. The purpose of this paper is to adopt a social network perspective to develop a theoretical framework on how this increased openness has a varying impact in the different phases of the strategy process.

The author suggests that the strategy process is shaped through social interactions between individuals. Specifically the author conceptualizes how introducing openness affects individuals’ structural and relational characteristics, which impact generating new strategic ideas (variation), and selecting (selection), and integrating them into the existing set of routines (retention).

The framework shows that benefits and costs of increased openness balance differently. While substantial benefits may be realized in the idea generation phase, costs may outweigh the benefits in the selection and retention phase.

Based on the framework, implications can be drawn on how openness should be introduced in the different phases of the strategy process. Specifically the author discusses appropriate open strategy tools based on social technologies, which organizations can use to benefit from openness in the different stages.

Open strategy is a newly emerging phenomenon, which seems to fundamentally change the strategist’s work. More open, inclusive ways of strategizing offer new benefits but also create costs in the strategy process. This paper deepens the theoretical understanding of the consequences of openness in the strategy process.

The purpose of this paper is to test the existence of true persistence in the generation and adoption of product innovations in the context of a developing country.

A dynamic probit model with random effects is used to test true persistence relying on a panel data set constructed from three waves of the Colombian innovation survey (Encuesta de Desarrollo e Innvovación Tecnológica) covering the time span from 2003 to 2008.

This paper empirically shows the existence of true innovation persistence for two of the three types of product innovation studied: the adoption of product innovation that is new to the firm; and the adoption of product innovation that is new to the national market. However, the study could not confirm true persistence in the generation of product innovation.

To the best of our knowledge, this is the first study that systematically tests innovation persistence differentiating between the adoption of innovations that are new to the firm and innovation that is new to the national market. It is also the first study in this research area that uses a dynamic probit model with random effects according to the original specification by Wooldridge (2005).

En este trabajo se prueba la existencia de verdadera persistencia en la generación y adopción de innovaciones de productos en el contexto de un país en desarrollo.

Para probar la existencia de verdadera persistencia se estima un modelo probit dinámico con efectos aleatorios utilizando tres cohortes de la Encuesta de Desarrollo e Innvovación Tecnológica (EDIT) que cubren el periodo de tiempo 2003-2008.

Este trabajo muestra empíricamente la existencia de verdadera persistencia en dos de los tres tipos de innovación de productos estudiados: en i) la adopción de innovación de productos nuevo para la empresa, y ii) la adopción de innovación de productos nuevo para el mercado nacional. Sin embargo, el estudio no pudo confirmar la verdadera persistencia en la generación de innovación de productos.

Este es el primer estudio que evalúa sistemáticamente la persistencia en la innovación diferenciando entre la adopción de innovaciones que son nuevas para la empresa de las que lo son para el mercado nacional. También es el primer estudio en esta área de investigación que utiliza un modelo probit dinámico con efectos aleatorios de acuerdo con la especificación original de Wooldridge (2005).

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.