This study investigates the determinants of gender disparities in financial inclusion in Pakistan using Global Findex 2021 survey data. We aim to quantify gender gaps in financial access and use, and to analyze the socio-economic factors influencing these disparities. Grounded in Sen’s capability approach and behavioral economics, we employ logistic regression to examine how gender influences the ownership and usage of financial products. Our results reveal significant gender gaps: only 13% of Pakistani women have financial accounts compared to 34% of men, with similar disparities in digital finance. Socio-economic variables like education, income, and employment are found to influence financial inclusion differently for men and women. While generally supportive of financial inclusion, these factors have a weaker effect for women, suggesting deeper societal barriers. This study adds to the global financial inclusion discourse by providing a comprehensive analysis of gender disparities in Pakistan. Our findings highlight the need for gender-sensitive policies that address these disparities to achieve Sustainable Development Goals related to gender equality and economic empowerment.

Groundwater irrigation has a critical role in the sustainability of arable farming in many developing countries including Pakistan. Groundwater irrigation is generally practiced to supplement surface water supplies in Pakistan. Nevertheless, uninterrupted and extensive use of groundwater irrigation has raised several concerns about its sustainability and resultant environmental implications. Due to the scarcity of groundwater and heterogeneity in farmers’ resources, informal groundwater markets have emerged in Pakistan, where farmers trade water using a contractual system. Yet, the effects of these markets on agricultural productivity and equity remain largely unknown. This paper aims to analyze the impact of participation in the groundwater market on farmland utilization, cropping patterns, water access, and income. We analyze these impacts using primary data collected from 360 farmers in three different zones of the country’s largest province. The farmers were categorized as buyers, sellers, and self-users of water. Results indicate that participation in water markets increased agricultural land utilization, evinced by a higher cropping intensity among participants. A horizontal and vertical equity analysis of water markets shows that although large farmers have better access to groundwater irrigation, water market participation improves equity to water access. Based on income inequality measures such as the Gini coefficient and the Lorenz curve, water market participation also improves farmer incomes regardless of farm size. Propensity score matching revealed that wheat yield and income among water-market participants went up by approximately 150 kg and PKR 4503 per acre compared with non-participants. Groundwater market participants’ higher crop productivity and income level suggest that water markets need a thorough revisit in terms of policy focus and institutional support to ensure sustainable rural development.

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.

Informal groundwater markets have spontaneously developed in Pakistan due to the heterogeneity of farmers’ resource endowments, irrigation water shortages, and productivity gains of groundwater. Evidence shows that water allocation through formal or informal water markets can result in significant benefits for buyers and sellers and improve sustainable water use. Existing literature on water markets generally takes only buyers’ perspectives into account when studying the factors influencing the purchase of groundwater for irrigation. In contrast, we look at the perspectives of both buyers and sellers to investigate farmers’ participation in informal water markets. We conducted this study in the three agroecological zones of Punjab. The data was collected from a sample size of 360 farmers, with an equal proportion of water buyers, sellers, and self-users. Cragg’s double hurdle model was used to investigate the factors influencing the extent of water buying or selling. Results of the demand side analysis show that educated has negative correlation with water market participation and level of water purchasing. In addition, large farmers are less likely to buy water, indicating that the majority of farmers in water markets are smallholders. However, the degree of land fragmentation, soil fertility, and adoption of improved seeds each have may increase water market participation. Water cost and farmers’ family size has negative impact on level of water purchasing. The results of supply side analysis show that tubewell capacity, degree of land fragmentation, and the number of operational tubewells at a given farm have a positive relationship with the likelihood of selling water. When it comes to the amount of water sold, two factors that positively influence the extent of water selling are the income from water sales and the degree of land fragmentation. Finally, we discuss the implications of these results for sustainable water extraction in the water markets.

The Global Value Chain (GVC) is an essential aspect of sustainable economic growth and environmental quality in countries that participate in it. Therefore, comprehending the relationship between GVC and economic growth and carbon emissions is critical to achieving global climate neutrality targets. However, there is a paucity of knowledge regarding the impacts of disaggregated levels of GVC on economic growth and carbon emissions in countries with different income levels. In this study, we utilized the SYS-GMM model to explore the relationships between backward and forward GVCs and the economic growth and carbon emissions of 42 lower-middle, 36 middle-upper, and 48 high-income countries using data from 1995 to 2018. Our key findings suggest that forward GVC significantly increases economic growth and reduces CO₂ emissions. Conversely, backward GVC reduces the economic growth and CO₂ emissions of high-income countries. We also found that upper-middle-income economies can enhance their economic growth and reduce CO₂ emissions by increasing their backward GVC. In contrast, lower-middle-income economies can increase their economic growth by participating in both forward and backward GVCs. However, higher levels of participation in both GVC components result in increased CO₂ emissions. Our findings emphasize the importance of considering income levels when analysing the impact of GVC participation on economic growth and environmental sustainability. Overall, the relationship between economic growth and CO₂ emissions with backward and forward GVCs varies significantly across country categories.