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The purpose of this paper is to review policy or guidance on the implementation of Section 5(4) written by NHS mental health trusts in England and health boards in Wales.

A Freedom of Information request was submitted to all trusts in England (n=57) and health boards in Wales (n=7) asking them to provide a copy of any policy or guidance on the implementation of Section 5(4). Documents were analysed using content analysis. Specific attention was given to any deviations from the national Mental Health Act Codes of Practice.

In total, 41 (67.2 per cent) organisations had a policy on the implementation of Section 5(4). There was a high level of consistency between local guidance and the Mental Health Act Codes of Practice. There were however; different interpretations of the guidance and errors that could lead to misuse of the section. Some policies contained useful guidance that could be adopted by future versions of the national Codes of Practice.

The research has demonstrated the value of examining the relationship between national and local guidance. Further research should be undertaken on the frequency and reasons for any reuse of the section.

Greater attention should be given to considering the necessity of local policy, given the existence of national Codes of Practice.

This is the only research examining the policy framework for the implementation of Section 5(4).

The present study aims to construct and compare Composite Environmental Sustainability Index (CESI) for 20 emerging countries for the period 1990–2020.

The study constructs CESI using the principal component analysis (PCA). Furthermore, for the preparation of index weights, varimax rotation is used to get component loadings.

The study finds that the overall CESI values lies between 2 and 4.8 for the 20 emerging countries considered in the study. This study depicts a diverse picture of environmental sustainability among emerging countries. The study also shows the trend of CESI values from 1990 to 2020. The bottom three countries whose CESI is very low compared to others are Iran, South Africa and Saudi Arabia. However, Brazil, Columbia and Chile are top three highest scorers in 2020.

The study contributes to the literature by constructing a composite index comprising of three sub-indices to measure the environmental sustainability of an economy. These sub-indices include seven indicators that are more inclusive and comprehensive. To the authors' knowledge, this is a pioneering attempt in the construction of the index for emerging countries.

The present study aimed to examine the relationship between information and communication technologies (ICT) diffusion, financial development and economic growth in the panel of developing countries for 2005–2019.

The study employed the principal component analysis (PCA) to extract the index of ICT diffusion. First-generation panel unit root tests such as Levine Lin Chu (LLC), Im Pesaran Shin (IPS), Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) were employed to check the stationarity of the variables. Pedroni and Kao co-integration techniques were used to examine the existence of the long-run relationship, and co-integration coefficients were estimated using FMOLS and dynamic ordinary least squares (DOLS). The panel Granger causality approach examined the short-run and long-run causality.

The results confirmed that ICT diffusion, financial development and trade openness accelerate growth, whereas inflation dampens economic growth. Further, the causality test showed bidirectional causality between ICT growth and financial development growth but a unidirectional causality from financial development to ICT diffusion in developing countries.

The study recommends synchronizing public and private sector investment for a synergistic effect on ICT infrastructure and adequate investment in the financial sector to increase the growth rate in developing countries. Economic policies should be adopted toward incentives and subsidies to ensure affordable ICT services for disadvantaged communities. Also, training programs focussing on enhancing digital literacy to enable all segments of the population to use digital platforms for financial services are recommended.

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

This paper investigates the effect of abnormal increase in credit supply on economic growth in Nigeria after controlling for the quality of the legal system, size of central bank asset, banking sector cost efficiency and bank insolvency risk.

The authors employ the generalised method of moments (GMM) regression methodology to estimate the effect of abnormal increase in credit supply on two measures of economic growth in Nigeria.

The abnormal increase in credit supply has a significant effect on economic growth. Abnormal increase in credit supply increases real gross domestic product (GDP) growth. The abnormal increase in credit supply decreases real GDP per capita during the global financial crisis. The abnormal increase in domestic credit to the private sector has a significant positive effect on GDP per capita when there is strong legal system quality in Nigeria. In contrast, the abnormal increase in domestic credit to the private sector has a significant negative effect on real GDP growth when there is strong legal system quality in Nigeria.

The abnormal increase in credit supply is ineffective in increasing GDP per capita during crisis years. Policymakers should be cautious in pressuring financial institutions to release an abnormally large amount of credit into the economy particularly during financial crises. Rather, policymakers should encourage financial institutions to supply credit in a sustained manner – not in an abnormal manner –and in a way that supports growth.

The present study contributes to the literature by analysing the effect of abnormal increase in credit supply on economic growth in a developing country context.

Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.

The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.

The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.

This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.

This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.

This paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.

The authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.

This paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.

The study aims to investigate the nexus between total factor productivity and tourism growth in Latin American countries for time series data from 1995 to 2017.

Using the extension of the Granger noncausality test in the nonlinear time-varying of Ajmi et al. (2015), the study points out the interconnectedness between the variables during the period.

The study found nonlinear causality between the variables. Particularly, studying the conclusions for the time-varying Granger causality fashion, it can be noticed that the one-way causality from total factor productivity to tourism growth is obtained for Argentina, Bolivia, Brazil, Uruguay and Venezuela, while the vice versa is confirmed for Chile, Ecuador and Nicaragua. Lastly, the study dissected the plots of the curve causality.

In view of the results, some crucial policy implications could be suggested, such as, under certain circumstances and as an exceptional case, the use of policy instruments such as targeted investment, marketing and the support of tourism organizations focused on driving a tourism-led-based productivity and/or tourism programs and projects.

The current work is distinguished from the existing body of understanding in several substantial directions. This work explores, for the first time, the linkages between the total factor productivity index and tourism growth for Latin American countries. No single attempt has been known to investigate this interaction by using nonlinear causality, and this study determines the shape of the curve between the total factor productivity index and tourism growth for each country.

This study empirically aims to analyze the transmission of monetary policy in consideration of asymmetry based on the Bank of the Lao PDR (BOL)'s monetary policy tools and real and financial variables in the domestic market.

This study adopts two approaches, conventional vector autoregression (VAR) and asymmetric VAR, to investigate the impact of monetary policy on macroeconomic variables including inflation and real GDP growth in the Lao PDR.

Under a highly dollarized monetary regime, the policy rate change plays a weaker role compared with M0, which exerts significantly positive effects on real GDP growth and inflation. The results of the asymmetric VAR model further substantiate that the real economy responds to a positive M0 shock (easing monetary policy) rather than a negative shock (tightening monetary policy).

Overall estimation results suggest that the effectiveness of monetary policy is limited in Laos, which would take priority over efforts to strengthen the development of the short-term financial market and de-dollarization.

This study can fill the gap in the literature in which the discussions on the transmission mechanism of monetary policy in the BOL's monetary policy are still little known.

The purpose of this paper is to investigate the effects of income inequality on the economic growth of Brazilian states in the period from 1994 to 2014. The transmission mechanism of the effects of income inequality on economic growth is derived from the model proposed by Halter et al. (2014).

The empirical formulation adopted to achieve this goal is divided into two stages. The first stage is limited to short-term analysis, and panel data models with fixed effects, random effects, and instrumental variables are used. In the second stage, the discussion turns to the use of the error correction model for a cointegrated panel.

The findings of this study suggest that inequalities in income and educational level are the principal determinants of different growth rates among Brazilian states. More specifically, it is found that additional years of schooling positively influence growth. By contrast, income inequality negatively affects this indicator.

The present study differs from the others in that it adapts the discussion proposed by Halter et al. (2014) to the analysis of Brazilian states. Halter et al. (2014) derive the transmission channel between income inequality and economic growth, showing a non-monotonic adjustment trajectory of production that leads to a linear theoretical model of income inequality and economic growth that is similar to those used in this type of approach.