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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 authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

Given the serious question raised by the subprime of the 2008 global financial crisis over the rising practices of excessive rewarding of executives in the USA and European firms, the executive pay-performance nexus has emerged as a popular topic of debate in the contemporary corporate finance research. Conducted mostly on the Anglo-Saxon contexts, research outcomes have been inconclusive and dichotomous. Considering this backdrop, this study aims to investigate the endogenous relationship between executive compensation and risk taking in the context of the USA.

Using a large sample of non-financial firms from 2010 to 2020 based on panel data and two-stage least square regression. In this study, the riskier corporate decision is measured as book leverage and ratio of R&D expense to total assets. Chief executive officers’ (CEO) experience and age are used as instrumental variables, and these are expected to influence compensation incentives and, hence, affect firm riskiness indirectly. Firm size, return on assets and CEO turnover are reported to affect compensation and corporate decisions, therefore, included as control variables. Given that higher executive compensation is related to riskier corporate decision in firms, this study incorporates total wealth (i.e. accumulated equity related compensation) as an additional proxy of compensation, and this selection is justifiable by the perfect contracting notion of the agency theory.

The results of this study show a significant positive and increasing nexus among compensation and riskier corporate decisions. Besides, the compensation level proxied through the percentage of each form of compensation in total compensation is very important as greater equity and greater salary diminishes risk taking.

The outcomes of this study have useful implications for firm stakeholders and policymakers.

The level of pay measured by the percentage of each type of compensation in total compensation is of utmost importance as it can increase or decrease risk taking in corporate decisions.

The Indian pharmaceutical industry currently faces multiple challenges, including rising costs and slowing export growth, which in turn have limited its ability to expand presence in global canvas. Given the nature of sectoral dynamics, a pharmaceutical firm must undertake huge investments in R&D to introduce product innovation, in turn enhancing market share and sustaining profit streams. The development of novel medicines, confirmed by the granting of patent rights, provides a pharma company edge over its competitors. In addition, presence of innovator firms within the industry invigorates the sectoral value chain and raises efficiency. Hence, it is important to analyze whether granting patent rights enhances the exports of pharmaceutical products in the Indian context.

The current study explored this question using a simultaneous-equation framework. Specifically, the authors use the methods developed by Davidson and MacKinnon (1993) and Greene (2003) to obtain heteroscedasticity-consistent estimates. The time-series properties of the data were further probed, and robust estimates were used to test the theory. Methods developed by Baltagi (1981) have been used further to refine the authors’ estimations.

After controlling for relevant variables, it is observed that granting of patents caused a significantly positive impact on pharmaceutical exports. Furthermore, the change in the patent administration regime had a significant impact on patent fillings, which further impacted their exportability. Compared to patents granted patents filed had a higher impact on pharmaceutical exports.

This study attempts to apply the framework developed by Goldstein and Khan (1978) with necessary modifications to suit the context of a developing country. The application of the 3SLS method to estimate the export supply equation for pharmaceutical products is a novel approach to the research question in general and to the Indian context in particular. System autocorrelation and heteroscedasticity tests were performed to refine the results further.

Although the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view.

From the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al., 2020).

While the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view.

There are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

This chapter examines the influence of external public borrowing resources on economic progress in Tunisia. The study focuses on two stages: First, the influence is studied in a direct sense and then in an indirect sense, i.e., through a transmission channel of this influence. By applying the autoregressive distributed technique with staggered lags (ARDL), over a period ranging from 1986 to 2019, the results showed that the influence of external borrowing resources on growth seems to be unfavorable in the short term but positive in the long term, hence the importance of the empirical technique chosen. Second, three interaction variables were tested, namely total government expenditure, government investment expenditure, and the real effective exchange rate. The results obtained call for better attention to the channels identified to maximize the positive influence of external public debt on the country's economic progress.