Fiscal Policy and Its Role in Reducing Income Inequality: A CGE Analysis for Pakistan

Income inequality is one of the critical barriers to growth and development in most of the developing countries including Pakistan. Every third man in Pakistan falls below the poverty line1. Moreover, the budget deficit has also been a serious issue throughout the history of Pakistan‟s economy. The persistent budget deficit is the constant source of increasing poverty and deterioration of income distribution. Since deficit is financed by increasing indirect taxes and money supply, it causes the reduction in purchasing power and leads the masses towards poverty [Arif and Farooq (2011)]. Therefore, it is a dire need of the economy to have a good public policy such that it could reduce budget deficit, alleviate poverty and redistribute income. Malik and Saqib (1985) suggest that the resources of the economy can be distributed equally only through appropriate changes in the tax system. Fiscal policy can have a significant influence on removing the gap between haves and havenots both directly and indirectly. It directly affects the disposable income of individuals, whereas affecting their future earning capacities indirectl

Macroeconomic Policies and Social Inclusion in the Developing World

Many in the developing world face social exclusion and discrimination, preventing them from actively participating in society itself. Sound macroeconomic policies with a focus on stabilizing the price level and social outcomes can help to achieve social justice for marginalized people. This study empirically examines the impact of macroeconomic policies on social inclusion, considering specifically the coordination among them in promoting that social inclusion. It deals primarily with pure non-income dimensions of social inclusion such as education, and health, etc. Using annual panel data of 51 developing countries for the period 1995-2017 this study employs state-of-the-art panel data estimation methods – pooled estimation, fixed-effect, and random-effect models. To check for robustness and to handle the problem of endogeneity, the 2SLS technique has also been used. This study argues that a well-designed macroeconomic policy framework can do much more than just achieve economic goals. Results suggest that fiscal and monetary policy, through resource mobilization, can play a significant and positive role in promoting social inclusion. However, these fiscal and monetary policy actions are not independent; thus, a policy mix is required to achieve the target of an inclusive society

Environmental Impact of ICT on Disaggregated Energy Consumption in China: A Threshold Regression Analysis

Due to resource scarcity, high energy demand, and environmental degradation, China’s rapid economic growth over the past three decades has been accompanied by certain serious issues that require quick attention. The excessive use of fossil fuels worsens the ecosystem and raises the level of carbon in the atmosphere. However, the use of ICT has affected the behavior of energy use in various sectors differently. Although ICT-induced activities, on one hand, may affect the environment positively by reducing energy consumption, on the other hand, they may affect the environment adversely by causing an energy rebound effect. Therefore, this study aims to investigate the nonlinear impact of ICT on the environmental effects of energy consumption in the residential, transport, and industrial sectors in China. The study used threshold regression for empirical analysis by employing data for the period from 1990 to 2021. ICT is used as a threshold variable, while energy consumption in the residential, industrial, and transport sectors is used as a regime-dependent variable. Based on the findings, we deduce that the use of ICT asymmetrically affects sectoral energy consumption and the empirical result varies across sectors. Based on the results, we recommend that the possibility of rebound effects should be given more attention in the development of policies regarding the digitalization of the sectors

Climate change and food security in South Asia: the importance of renewable energy and agricultural credit

Abstract Weather, trade restrictions, rising oil prices, a lack of financial support for farmers, and other factors have contributed to the destabilization of South Asian food security. The purpose of this study is to determine the long-run and short-run relationships between climate change, agricultural credit, renewable energy, and food security for a sample of South Asian countries between 1990 and 2021. The Dynamic Common Correlated technique is utilized for empirical analysis since it directly addresses the issue of cross-sectional dependency while delivering accurate cointegration findings. The study’s empirical findings show that climate change reduces food availability and increases the incidence of food insecurity in South Asia. In contrast, the use of renewable energy sources has a positive effect on food security in the short-run but not in the long-run, while the availability of credit to farmers has a positive effect on food security. Findings suggest that South Asian countries may reduce climate change’s negative effect on food security by investing in climate services, climate-resilient infrastructure, growing drought-resistant crops, using supplemental reinforced agricultural practices, and improving their weather forecasting capabilities

An ensembling approach to predict hepatitis in patients with liver disease using machine learning

With a 3.5% mortality rate, liver disease is one of the worst diseases in existence. Pakistan is targeting this major health issue from several perspectives, to improve prevention, diagnosis, and treatment due to having the highest incidence of liver disorders in the world. For liver problem disease, also known as HEP C, Pakistan is now the second most prevalent country in the world. This is due to the rapid progression of HEP C, which can only be stopped by early diagnosis. If not, it progresses to the last stage of HEP C cirrhosis, which has no other treatment options besides liver transplantation. One and only machine learning algorithms like logistic regression, random forest, KNN, K-Means, and XGBoost can be used to predict liver illness utilizing modern methods like artificial intelligence. Data is gathered from Kaggle and subjected to several machine learning algorithms after pre-processing in order to quickly diagnose liver disease. Additionally, to improve accuracy, all of these algorithms are ensemble, and accuracy is 78.96%, along with precision, recall, and F1 score. In this work, liver disease is predicted early on using pre-processing, feature extraction, and classification techniques. Recall, precision, and f1score metrics are used to compare the accuracy of the six algorithms, and these algorithms are then combined to provide the most accurate diagnosis of liver disease

ECONOMY-WIDE EFFECTS OF MONETARY POLICY SHOCKS: EVIDENCE FROM PAKISTAN

Monetary policy plays an effective role in affecting output, employment, prices, interest rate and exchange rate. The goal of a sustainable economic growth and employment is attainable only if the prices in an economy are stable because stable prices lead to an efficient allocation of resources and also encourage households to save more and investors to invest more, thus contributes in capital formation by minimizing the risk of erosion of assets value.There isa wide range of transmission channels through whichmonetary policy affects the macroeconomic indicators. Keeping in view the financial crisis of 2008 and considering the exchange rate channel, this paper is an attempt to assessthe effects of monetary policy shocks on major macroeconomic variables.Vector Autoregressive model is used in this study to trace out the effects of monetary policy shocks on output, prices and exchange rate. Our findings show that monetary policy shockstransmit into inflation and exchange rate thus affects output in the long run

Analyzing the Asymmetric Effect of Renewable Energy Consumption on Environment in STIRPAT-Kaya-EKC Framework: A NARDL Approach for China

This study aims to analyze the asymmetric relation between renewable energy consumption and CO2 emissions in China using the STIRPAT-Kaya-EKC framework. To delve into the asymmetric effect of renewable energy consumption on the environment, the non-linear ARDL model is used. The results of this study confirm the asymmetric impact of renewable energy on the environment in the long run as well as in the short run. However, the negative shocks to renewable energy have a greater detrimental influence on the environment than the benign effect due to the positive shock to renewable energy. Population growth affects the environment in the short run, whereas technology only affects environment quality in the long run. Moreover, the study supports the EKC theory in China. This research emphasizes that the administration can improve the economy’s lifespan by allocating substantial funds to establish legislation to maintain a clean environment by subsidizing renewable energy infrastructure and research and innovations for low-carbon projects

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Abstract Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety