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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.

This study aims to examine the impact of corporate governance on firm performance for a large representative sample.

This empirical analysis focuses on a large number of companies covering 20 important industries of the Indian manufacturing sector for the period 2001-2010. Several alternative specifications and estimation techniques are used for analysis purposes, including system generalized methods of moments, which effectively overcomes the problem of endogeneity and simultaneity bias.

On one side, the findings indicate that larger boards are associated with a greater depth of intellectual knowledge, which in turn helps in improving decision-making and enhancing the performance. On the other side, the results indicate that return on equity and profitability is not related to corporate governance indicators. The results also suggest that CEO duality is not related to any firm performance measures for the sample firms.

The outcomes of the analyses advocated that companies that comply with good corporate governance practices can expect to achieve higher accounting and market performance. It implies that good corporate governance practices lead to reduced agency costs. Hence, it is concluded that firms of the developing world can possibly enhance their performance by implementing good corporate governance practices.

Departing from the conventional system of the prior studies and instead of focusing on a single measure framework, a range of measures of corporate governance and firm's performance variables are used. Also, several alternative specifications and estimation techniques are used for analysis purposes. Furthermore, the sample also covers a large sample of manufacturing firms.

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.

This paper provides new evidence on Indian tourism firms by investigating the role of a firm's financial conditions typified by its leverage, earnings, size, cash holdings, and excess cash in moderating the pandemic-led idiosyncratic volatility in its stock prices.

The authors employ a firm-level panel comprising 82 publicly-listed tourism firms from India. Firm risk is estimated for the period beginning January 2020 to December 2020.

This paper finds non-linear effects of the pandemic on the idiosyncratic risk of the sample firms. Precisely, stock price volatility rises, but as the market absorbs this information, volatility subsides even as the disease spreads further. Further, lower levels of past debt and earnings and higher cash holdings ameliorate the pandemic's effects on tourism firms' risk. Contrasting the view that “excess” cash reflects poor operational performance, we show that “excess” cash firms are better prepared to face the adverse effects of the pandemic.

This study’s sample period fully encompasses the first wave of the pandemic (January–December 2020) of the novel coronavirus infection spread.

To the best of the authors’ knowledge, this is the first study to assess the moderating effects of company fundamentals on the risk of Indian tourism firms. In doing so, the authors account for non-linear effects of the pandemic on firms' idiosyncratic volatility over time.

This research has two primary goals: first, to develop a composite index that evaluates the degree to which Asian–Pacific economies are prepared to engage in public–private partnerships (PPPs), and second, to investigate the factors that have been most influential in the formation of PPP arrangements in the nations' infrastructure over the course of the period 1995–2016.

The study constructs sectoral and overall index of possible determinants of PPP. Subsequently, it examines each constructed index's role in PPP investment. The author also conducted a panel data analysis to understand the role of each of the potential determinants on PPP projects and investments. This paper analyzes the author’s empirical models using a range of cross-section and panel estimators, including Poisson, zero-inflated Poisson and fixed effect.

The study’s results based on cross-section analysis suggest that regulatory and institution quality, institutional arrangement and regulatory frameworks, financial market development and macroeconomic stability positively impact investment in PPP. Moreover, the results depict that financial market development has the most substantial impact on PPP investment, followed by macroeconomic stability and prior experience with PPPs. The panel data analysis shows that per-capita income, financial development, inflation, debt, resource import and fuel export are crucial determinants of PPP in Asian–Pacific economies.

Governments of the countries should promptly amend the important policies outlined in this study and adopt a more robust strategy to foster a competitive PPP environment. This will aid in maintaining transparency and gaining the confidence of investors. The study’s findings may assist policymakers in focusing on specific areas in need of improvement. Social welfare and industrialization are ultimately enhanced by the formulation of such policies and by attracting additional infrastructure investment.

This is the first attempt to rank countries on the basis of PPP enablers. Unlike previous studies, this study examines the role of a large number of indicators in determining PPP investment and projects in cross-section as well as panel data framework. The study also investigates the effects of PPP specific provisions and rules. Furthermore, the focus is specifically on Asian–Pacific countries, which are a mix of third-world, emerging, developing and developed countries. Focusing on Asia–Pacific is also crucial because the region is home to most of the world's population, and the region's infrastructure outcomes significantly impact their lives.

This paper aims to examine the informational value of credit rating changes for investors. The article analyses whether credit rating changes indicate the future financial performance of a firm.

The study employs pooled time-series cross-section regression technique and two-sample t-test for analysis. The paper utilizes a firm's operating profit as a proxy of its future financial performance to understand what inference can be drawn about future financial performance from a change in a firm's credit rating.

The paper finds that a firm operating profit declines in the year after a credit rating downgrade. However, no such significant relationship is evident in the case of a rating upgrade. The results are consistent across rating categories and individual years of the sample period.

The study uses non-financial corporate rating data; hence, the findings may not apply to credit rating changes in financial corporates and structured finance.

Investors and analysts can incorporate credit rating downgrade by CRAs as a key input in a firm's future financial forecast. Analysts and investment managers can also look at credit rating changes of firms in the same industry and draw a definite conclusion about which firm is likely to see a higher deterioration in performance.

The author has not come across any literature that directly investigates credit rating changes from the perspective of information content about future financial performance.

The purpose of this paper is to study the impact of infrastructure and governance quality on the state-level productivity of Indian manufacturing for the period 2008–2011.

The authors first rank Indian states on their quality of governance using benefit-of-the-doubt approach. Next, to explain state-level differences in total factor productivity (TFP), the authors assess the impact of a composite index of governance on industrial TFP of Indian states using alternate techniques and controlling for endogeneity. The authors also decompose the composite effect of governance in terms of economic, social and financial infrastructure and other key governance dimensions, which serves as another robustness check for the findings.

The authors find that TFP varies significantly across states, so does governance quality. Further, results suggest that TFP of Indian industries is sensitive toward public service deliveries of economic, social and financial infrastructure. However, the authors fail to find any impact of law and order indicators, for instance, rate of violent crimes, police strength and judicial service quality on the manufacturing productivity. The estimated coefficient of governance index is robust across alternate methodologies.

To the authors’ knowledge, this is the first study to assess the impact of regional governance factors on the manufacturing sector of India. The study has identified governance factors that impact manufacturing productivity in the Indian states. Findings suggest that an effective way to eliminate regional growth inequality in India is to ensure that the lagging states initiate reforms to improve the quality of institutions, regulation and governance. Findings of the study contribute to the limited literature on governance at the regional/sub-national level.

The issue of black economy has long been debated in India and it has been one of the key targets of policy action from last four decades. The debate is further fueled by demonetization of higher currency notes in the country. In this context, the purpose of this paper is to estimate the size of black economy in India for the period 1970–2017.

A currency demand approach is adopted for this purpose. The test of structure break indicates for a break in the system; therefore, the authors employ Johansen et al. (2000) cointegration test. For estimating the empirical model, the authors utilize fully modified ordinary least squares in a cointegration framework for taking care the endogeneity problem.

The estimates indicate that the Indian economy has a sizable black economy. In early 1970s, when the tax rate in India was significantly higher, the estimated black economy was above 30 percent of the official GDP. A variety of economic reforms including taxation, regulation and industrial licensing have drastically reduced the size to below 15 percent of official GDP in the last two decades. In the last estimated year (2017), the black economy was 23,849bn Indian rupees at current market price (around $400bn), which was 14 percent of the official GDP.

On the basis of the findings, the authors suggest some important fiscal, administrative and regulatory reforms to curb the generation of black economy in India.

The structural breaks can induce stochastic behavior similar to an integrated process, which makes it difficult to differentiate between the lack of cointegration and a structural shift. Thus, in the present study, the authors attempt to address this issue by incorporating the issue of structural break in the analysis. Furthermore, India is a cash-based economy; therefore, it is likely that currency-based models are more suitable. The application of advanced time-series techniques is likely to yield better and robust results.

This study aims to examine the impact of the stringency of COVID-19 protocols on the volatility of sectoral indices during the period 03:2020–05:2021. Specifically, this study investigates the role of economic disturbances on sectoral volatility by applying a range of conditional volatility techniques.

For this analysis, two approaches were adopted. The first approach considers COVID stringency as a factor in the conditional variance equation of sectoral indices. In contrast, the second approach considers the stringency indicator as a possible determinant of their estimated conditional volatility.

Results show that the stringency of the protocols throughout the pandemic phase led to an instantaneous spike followed by a gradual decrease in estimated volatility of all the sectoral indices except pharma and health care. Specific sectors such as bank, FMCG, consumer durables, financial services, IT, media and private banks respond to protocols expeditiously compared to other sectors.

The key contribution of this study to the existing literature is the innovative approach. The inclusion of the COVID stringency index as a regressor in the variance equation of the conditional volatility techniques was a distinctive approach for assessing the volatility dynamics with the stringency of COVID protocols. Furthermore, this study also adopts an alternative approach that estimates the conditional volatility of the indices and then tests the effect of the stringencies on estimated volatility in a regression framework.

This study aims to examine the relationship between exchange rate risk and export at commodity level for the Indian case.

The monthly panel data used for analysis are at a disaggregated level, which cover around 100 products, encompassing all merchandize sectors for the period spanning from 2012:12 to 2017:11. To measure the exchange rate volatility, the authors use real as well as nominal exchange rate concepts and predict the volatility of exchange rate using the autoregressive conditional heteroscedastic-based model. They use pooled mean group, mean group and common correlated effects mean group estimator that is suitable for the objectives and data frequency.

The empirical analysis indicates both short- and long-term negative effects of exchange rate variations on exporting. Specifically, in the long run, real exchange rate as well as nominal exchange rate volatility has significant effects on export performance, yet, the effects of uncertainty of nominal exchange rate is much severe and intense. In the short run, it is the nominal exchange rate uncertainty that hurts exports from India. Nevertheless, the short-run effect is much lesser than the long-run, supporting the argument that the short-term exchange rate risk can be hedged, at least partially, through financial instruments; however, uncertainty of the long-term horizon cannot be hedged easily and cost-effectively.

Reducing uncertainty and attaining stability in exchange rate and price level should be an important policy objective in developing countries such as India to achieve higher export growth, both in the short and long run.

Unlike previous studies, this paper tests the relationship using micro-level data and uses advanced econometric techniques that are likely to provide more precise information regarding the association between exchange rate volatility and trade flows.