Women employed in informal sectors are integral to a nation’s overall economic growth, and to better development outcomes. This study aims to examine the capabilities of these women not only in terms of personal empowerment, but in the context of sustainable development via social inclusion. Previous studies have only focused on microcredit as a “magic wand” for increasing women's empowerment and their standard of living, without the inclusion of feminist theory. The inclusion of feminist theory and social inclusion in the discussion concerning the provision of financial support to women in Pakistan, allows us to properly examine the role of sustainable development and women's empowerment in Pakistan. This study is quantitative and descriptive. Using a survey questionnaire on the cross-sectional data source, the 118 women borrowers of Lahore, Pakistan, have been investigated by stratified random sampling technique. The Multinomial logit and Probit regression models have been used for data analysis. The study's results supported the actuality that women need to be financially and personally empowered, in order to benefit from greater social inclusion.
KEYWORDS: Social inclusion, sustainable development, microfinance, women's personal empowerment, informal sector.
JEL: O40, O17.

This study investigates the socioeconomic, administrative, and service quality determinants of electricity theft in Pakistan. The effect of service quality standards on electricity theft is estimated using panel data from eight electricity distribution utilities from 2006 to 2018. The results of utilizing the One-step system Generalized Method of Moments estimator confirm the statistically significant impact of social, administrative, and service quality variables on the illegal use of electricity. Based on the findings, Pakistan's National Electric Power Regulatory Authority is advised to take measures to improve the quality of services. In contrast, the nation’s Central Power Purchasing Agency is advised to improve administrative structure by separating retail function from distribution by electric distribution.
KEYWORDS: Pakistan, Electricity theft, Distribution utilities;, Service quality.
JEL: C33, O14.

This study examines the uncertainty of consumer inflation expectations in Pakistan using the data collected by the State Bank of Pakistan (SBP) through the Consumer Confidence Survey (CCS). CCS has been conducted every second month since January 2012. The research employs round numbers to calculate inflation expectation uncertainty and finds it countercyclical and positively correlated with inflation. Further, it also displays a weakly positive correlation with inflation disagreement, inflation volatility, and the Economic Policy Uncertainty (EPU) index. The study also reveals that inflation expectation uncertainty is higher for female, less educated, and young respondents compared to businessmen, males, older people, and educated. The study suggests asymmetric behavior of inflation expectations uncertainty for high and low inflation levels, where uncertainty is high when inflation is high. The study also suggests that inflation uncertainty is significantly related to food inflation. Lastly, the study establishes that inflation expectation uncertainty affects the consumption of durable goods by influencing consumer spending attitudes.
KEYWORDS: uncertainty, environmental resources, Inflation Expectations, Consumer.
JEL: E31, D18.

This paper introduces the monthly State Bank of Pakistan’s EasyData, for conducting empirical macroeconomic analysis and forecasting for Pakistan's economy. For this purpose. We perform a forecasting exercise using the conventional econometric models and the most recent machine-learning algorithms. We find that the machine- learning models outperform the benchmark and regression models based on observed factors. Furthermore, the dataset has a higher ability to predict the external variables, a possible outcome of Pakistan's economy and its persistent balance of payment problem. The focus of policy has been to address this issue.
KEYWORDS: Pakistan, forecasting, EasyData, factors, machine learning, machine-learning.
JEL: F47, C55.

This study examines the supply and demand shocks in Pakistan that affected occupations and industries during the COVID-19 pandemic. We use the remote labor index and essential scores for undertaking work activities from home across occupations proposed by del Rio-Chanona et al. (2020). To estimate demand shocks, we follow del RioChanona et al. (2020), who employed estimates from the US Congressional Budget Office (2006) that attempted to forecast how the US economy would be affected at the industry level if a severe influenza epidemic occurred. We document that demand shocks most significantly affect the transport and food services industries. In contrast, the manufacturing, mining and quarrying, and handicraft and printing industries are likely to be impacted by supply shocks. Food services and restaurants experience a bigger combined shock. Relative to the pre-pandemic period, aggregate shocks suggest a decrease in the output of Pakistan’s economy by one-fifth if the pandemic were to seriously affect the economy, threatening 21 percent of jobs and lowering total wage income by 18 percent. Considering a second wave and a new variant of coronavirus, we estimate that aggregate shocks may continue, and the economy's output could deteriorate by one-fourth if the region experiences a significant outbreak. Finally, we compare our findings with the US economy and find differences between supply and demand shocks in both economies.
KEYWORDS: wages, financing shocks, value-added, COVID-19, employment.
JEL: J21, O49, I15.