Pakistan Institute of Development Economics

The comparative advantage theory perspective has been extended to the public innovation and trade openness of OECD and Non-OECD countries by focusing on the role of tariff and asymmetric effect of trade openness by comparing imports and exports growth with the causal relationship among the respective factors. The study’s findings indicate that trade openness, innovation, and tariffs are linked in developing countries more than in developed countries due to the difference in economic perspectives and policies, which disprove the idea of the tariff being ineffective for developed nations. The causality effectiveness in developed and underdeveloped countries has resulted among public innovation toward openness of trade and tariff but not in case of tariff to trade openness. Additionally, the results hold up to sub-indicators and measures of public innovation, evaluating that input sub-index is more impactful than output sub-index, and both are even stronger in developing countries than developed countries. Finally, this study implied the usefulness of monetary policy to encourage the governments to lower import tariffs, paved the way to increased investment in research and development and collaboration between foundations, businesses, and academia. JEL Classification: H7, H8, O31, C26

The paper attempts to contextualize the significance of the work of unpaid female family helpers’ (UPFFH) for women empowerment. Additionally, it brings into light the subjective feelings and perceptions of unpaid family helpers. The current research is based on primary data collected through a self-structured questionnaire from 200 females aged 15+ years who were helping in the household income generation without getting paid. A cross-sectional survey was conducted among UPFFHs residing in district Lahore, Pakistan. The participants were selected using a simple random sampling technique. The analysis of UPFFHs empowerment was performed by employing a percentage distribution approach based on four levels of participation in decision making. The research puts emphasis on the impact of excessive, unequal, and unpaid work on women and further argues for social rational of recognizing the work through empowering women in decision-making process at their personal, household, and community level. The findings of the study indicates that the young UPFFHs are less empowered as they face the greatest familial and community pressure when it comes to making their life decisions. However, the paper further documents their aspiration to attain economic independence which would then bring change in gender perceptions and their overall social standing. The paper concludes by highlighting the problems associated with “unpaid work” and why it must be addressed, especially if the country has to fully realize the valued goal of women empowerment and gender equality.

The depositional period of the early Miaolingian Maozhuang Formation in the North China Platform (NCP) is characterized by sediments of a restricted tidal-flat facies zone, i.e., transgressive red bed (sabkha facies) and highstand dolostone, and by sediments of an open tidal-flat facies zone, i.e., transgressive red bed and highstand limestone. The Maozhuang Formation at the Jinzhouwan section is composed of sediments of an open tidal-flat facies in which highstand limestone is marked by a set of bioherms that represent rare examples of microbial carbonates in the NCP. They consist of stromatolites and leiolites with intercalations of oolitic grainstones that can be categorized into four units: Unit [1] encompasses large-scale columnar stromatolites, small quartz grains and trilobite fragments; Unit [2] consists of structureless leiolites that contain trilobite fragments, quartz grains, and few dark and dense clumps of micrite dominated by calcified sheaths of filamentous cyanobacteria which probably grew in relatively thick cyanobacterial mats; Unit [3] comprises radial ooids that contain abundant fossils of calcified sheaths of filamentous cyanobacteria that might be genetically involved in the formation of ooids; Unit [4] contains small columnar stromatolites and small-scale micritic clumps dominated by filamentous cyanobacteria. The filamentous fossils in units [3] and [4] provide useful information regarding the origin of radial ooids and stromatolites in normal marine environment. Further, the large and small columnar stromatolites of early Miaolingian age in units [1] and [4] represent a unique example because they belong to the first episode of cyanbacterial calcification at the base of Phanerozoic.

In many developing countries, poor households face multiple shocks that disrupt their consumption patterns and lead to an increased welfare loss. Consequently, households adopt coping mechanisms that negatively impact their overall well-being. While social safety nets have been implemented to protect vulnerable households, it is essential to assess their overall effectiveness, both in terms of their impact and their potential to replace the harmful coping strategies adopted by households. Therefore, the primary objective of this study is to examine the impact of cash transfers provided by the Benazir Income Support Program (BISP) on the consumption patterns of households in Pakistan, with a particular emphasis on how these transfers provide consumption insurance to ultra-poor families encountered with idiosyncratic and covariate shocks. The study analyzes both food and non-food expenditure to gain insights into the effectiveness of the program in providing economic security to vulnerable households. Using three rounds of the BISP survey (2011–2013–2016), the empirical analysis is done by employing regression discontinuity design (RDD), difference-in-difference (DID) technique, and ordinary least squares method. Using the DID approach, we find that from 2011 to 2016 the overall food and non-food consumption of BISP beneficiaries have increased considerably and these results are highly significant as well. However, the estimates for RDD are not significant for the years 2013 and 2016. We also observed households’ consumption behavior in the presence of shocks and found that in the wake of idiosyncratic shocks, poor households can protect their consumption through informal transfer mechanisms by securing loans from family, friends, and landlords. However, this mechanism collapses when households encounter covariate shocks. Furthermore, BISP cash transfers are primarily effective in mitigating the impact of employment loss, loss of livestock, and the rise in food prices and remain inadequate in providing insurance against major other shocks encountered by the targeted poor households. The results indicate that BISP has the potential to substitute informal coping strategies used by the poor, it requires substantial transfers to offer comprehensive consumption insurance to the poorest of the poor in Pakistan.

Metal-organic frameworks (MOFs) with different functional groups have wide applications, while the understanding of functionalization influences remains insufficient. Previous researches focused on the static changes in electronic structure or chemical environment, while it is unclear in the aspect of dynamic influence, especially in the direct imaging of dynamic changes after functionalization. Here we use integrated differential phase contrast scanning transmission electron microscopy (iDPC-STEM) to directly ‘see’ the rotation properties of benzene rings in the linkers of UiO-66, and observe the high correlation between local rigidity and the functional groups on the organic linkers. The rigidity is then correlated to the macroscopic properties of CO2 uptake, indicating that functionalization can change the capability through not only static electronic effects, but also dynamic rotation properties. To the best of our knowledge this is the first example of a technique to directly image the rotation properties of linkers in MOFs, which provides an approach to study the local flexibility and paves the way for potential applications in capturing, separation and molecular machine.

Proposing new families of probability models for data modeling in applied sectors is a prominent research topic. This paper also proposes a new method based on the trigonometric function to derive the updated form of the existing probability models. The proposed family is called the cotangent trigonometric-G family of distributions. Based on the cotangent trigonometric-G method, a new version of the Weibull model, namely, the cotangent trigonometric Weibull distribution, is studied. Certain mathematical properties of the cotangent trigonometric-G family are derived. The estimators of the cotangent trigonometric-G distributions are obtained via the maximum likelihood method. The Monte Carlo simulation study is conducted to assess the performances of the estimators. Finally, two applications from the health sector are considered to illustrate the cotangent trigonometric-G method. Based on seven evaluating criteria, it is observed that the cotangent trigonometric-G significantly improves the fitting power of the existing models.

The crude oils from the reservoirs of Mela-01 and Mela-04 wells located in the Kohat Basin, Pakistan, were geochemically analyzed to evaluate the origin, depositional conditions, and thermal maturity of the rock units and possible facies from which these oils were sourced. Gas chromatography-mass spectrometry (GC-MS) was performed on the samples to obtain biomarker and non-biomarker parameters. Analyzed non-biomarker parameters, including carbon preference index (CPI), terrigenous to aquatic ratio (TAR), isoprenoids pristane to phytane (Pr/Ph), and biomarker parameters, including steranes and dibenzothiophene/phenanthrene (DBT/P) of aromatic compounds, were utilized in the present study to achieve the objectives. Most of these parameters suggest a mixed source of organic matter (marine/terrestrial) with sub-oxic conditions in the source rocks for the analyzed oil samples in the studied wells from Mela oilfield, Kohat Basin. Furthermore, the CPI and different biomarker parameters such as steranes C 29 S/S + R, ββ/αα + ββ), moretane to hopane (M 29 /C 30 H), pentacyclic terpanes C 27 (Ts/Ts + Tm), H 32 (S/S + R) hopanes, and aromatic methylphenanthrene index (MPI) indicate that the analyzed oils have originated from thermally mature rocks falling in the oil window. As the studied Kohat Basin has multiple source rocks and contributes to the major petroleum production of the country, the present investigations reveal that its okthe Mela oils were generated by the strata of mixed organic matter that were deposited in marine sub-oxic conditions. Furthermore, this study suggests that this stratum would also have been produced in unexplored surrounding areas such as Tirah, Orakzai, and the Bannu Depression.

Good and bad news plays a crucial role in the stock market, significantly influencing investor sentiment, market expectations, and trading decisions. Positive news can boost market confidence and upward price movements. On the contrary, negative news can erode investor confidence and cause downward price movements. This study examines the impact of good and bad news on the effect of day-of-week in the Pakistan stock market, which has been largely overlooked in previous research focusing mainly on macro factors. The study applies different ARCH and GARCH models to investigate the influence of news and day-of-week patterns on stock market outcomes. The findings reveal a significant day-of-week effect, with the highest returns on Friday and the lowest returns on Monday. The negative shock has a more substantial impact than the positive shock, contributing to high future volatility, and bad news has a more significant influence than good news. The study highlights the role of news and day-of-week patterns in shaping stock market outcomes and fills the gap in previous research by emphasizing the importance of these factors.

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life, including the financial sector, education, transportation, health care, and so on. Among these sectors, the financial and health sectors are the most affected areas by COVID-19. Modeling and predicting the impact of the COVID-19 epidemic on the financial and health care sectors is particularly important these days. Therefore, this paper has two aims, (i) to introduce a new probability distribution for modeling the financial data set (oil prices data), and (ii) to implement a machine learning approach to predict the oil prices. First, we introduce a new approach for developing new probability distributions for the univariate analysis of the oil price data. The proposed approach is called a new reduced exponential-$ X $ (NRE-$ X $) family. Based on this approach, two new statistical distributions are introduced for modeling the oil price data and its log returns. Based on certain statistical tools, we observe that the proposed probability distributions are the best competitors for modeling the prices' data sets. Second, we carry out a multivariate analysis while considering some covariates of oil price data. Dual well-known machine learning algorithms, namely, the least absolute shrinkage and absolute deviation (Lasso) and Elastic net (Enet) are utilized to achieve the important features for oil prices based on the best model. The best model is established through forecasting performance.

Statistical modeling is a crucial phase for decision-making and predicting future events. Data arising from engineering-related fields have most often complex structures whose failure rate possesses mixed state behaviors (i.e., non-monotonic shapes). For the data sets whose failure rates are in the mixed state, the utilization of the traditional probability models is not a suitable choice. Therefore, searching for more flexible probability models that are capable of adequately describing the mixed state failure data sets is an interesting research topic for researchers. In this paper, we propose and study a new statistical model to achieve the above goal. The proposed model is called a new beta power very flexible Weibull distribution and is capable of capturing five different patterns of the failure rate such as uni-modal, decreasing-increasing-decreasing, bathtub, decreasing, increasing-decreasing-increasing shapes. The estimators of the new beta power very flexible Weibull distribution are obtained using the maximum likelihood method. The evaluation of the estimators is assessed by conducting a simulation study. Finally, the usefulness and applicability of the new beta power very flexible Weibull distribution are shown by analyzing two engineering data sets. Using four information criteria, it is observed that the new beta power very flexible Weibull distribution is the best-suited model for dealing with failure times data sets.

Probability models are frequently used in numerous healthcare, sports, and policy studies. These probability models use datasets to identify patterns, analyze lifetime scenarios, predict outcomes of interest, etc. Therefore, numerous probability models have been studied, introduced, and implemented. In this paper, we also propose a novel probability model for analyzing data in different sectors, particularly in biomedical and sports sciences. The probability model is called a new modified exponential-Weibull distribution. The heavy-tailed characteristics along with some other mathematical properties are derived. Furthermore, the estimators of the new modified exponential-Weibull are derived. A simulation study of the new modified exponential-Weibull model is also provided. To illustrate the new modified exponential-Weibull model, a practical dataset is analyzed. The dataset consists of seventy-eight observations and represents the recovery time after the injuries in different basketball matches.

It is undeniable fact that financial development and technological capital are fundamental determinants that help in the achievement of green growth. This is important to explore the nexus between financial development, technological capital, and green growth in China. This study utilizes the quantile autoregressive distributed lag (QARDL) approach for exploring the effect of financial development and technological capital on green growth. The study measures financial development through financial market development and financial institutions development. The study concludes that technological progress and both measures of financial development produce a positive impact on green growth in China in most quantities in long run. The study provides various important policy suggestions that help in upgrading sustainable green growth in China.

The environmental goods and services industry consists of the activities that generate products and services to monitor, avoid, restrict, reduce, or repair environmental risk and decrease non-renewable energy resource usage. Although the environmental goods industry does not exist in many countries, mainly developing countries, through international trade, its impacts are reaching developing countries. This study examines the impact of environmental and non-environmental goods trade on emissions in high and middle–income countries. For empirical estimation, the panel ARDL model is applied using the data from 2007 to 2020. The results indicate that importing environmental goods decreases emissions while imports of non-environmental goods increase the emissions in high-income countries in the long run. It is found that imports of environmental goods in developing countries decrease emissions in both the short and long run. However, in the short run, the imports of non-environmental goods in developing countries have an insignificant impact on emissions.

Workers in informal employment suffered significant out-of-pocket healthcare expenditures (OOPHEs) due to their low earnings and a lack of a social safety net or health insurance. There is little or no evidence of impoverishment caused by OOPHEs in the context of labor market categorization. Therefore, this study examines the economic burden of OOPHEs and its associated consequences on households, whose members are in informal employment. This study estimates the incidence of catastrophic health expenditures (CHEs) and impoverishment across the households in formal and informal employment and their key determinants in Pakistan by employing the data from the two rounds of the Household Integrated Economic Survey (2015-16, 2018-19). For measuring CHEs and impoverishment, the budget share and capacity-to-pay approaches are applied. Various thresholds are used to demonstrate the sensitivity of catastrophic measures. We found a higher incidence of catastrophic healthcare payments among the informal workers, that is, 4.03% and 7.11% for 2015-16 and 2018-19, respectively, at a 10% threshold, while at a 40% threshold, the incidence of CHEs is found to be 0.40% and 2.34% for 2015-16 and 2018-19, respectively. These OOPHEs caused 1.53% and 3.66% of households who are in informal employment to become impoverished, compared with their formal counterparts. The study demonstrates that the probability of incurring CHEs and becoming impoverished is high among informal workers, compared with their formal counterparts. This result has clear policy implications, in which to protect the informal workers, it is necessary to expand the insurance coverage, particularly during the COVID-19 response and recovery efforts.

This report is based on the extrapolation to 2020 of data on the economic burden of mental illnesses in Pakistan in 2006. Given the resultant estimated high economic burden of mental illness in the country (£2.97 billion in 2020), we advocate a revised budget allocation to mental healthcare. As a resource-scarce nation that is entangled in natural disasters, Pakistan needs cost-effective psychological interventions such as culturally adapted manual-assisted problem-solving training (C-MAP) for the prevention of self-harm and suicide and to move towards attaining the United Nations’ Sustainable Development Goals (SDGs). Although government has taken initiatives to support healthcare services (such as the Sehat Sahulat Program for universal health coverage), there is still a need to implement a cost-effective national digital model for mental healthcare such as the Agha Khan Development Network Digital Health Programme.

The restaurant business is gaining popularity for its capacity to alleviate numerous adverse environmental influences to achieve a competitive edge. Green restaurants can employ a distinctive brand strategy. Nevertheless, additional research is necessary to better understand customer behavior in this subject. This study explores the relationship between brand awareness and brand image, and brand performance from the consumer’s perspective. However, it is unknown how this connection is affected by the attitude of green restaurant brands. This research aims to address the research gaps by determining the structure and function of brand attitudes. This study handles the quantitative data analysis to fit the study problem. The data was collected through a questionnaire form, and the questionnaire was collected from the customers from twelve restaurants in Karachi city of Pakistan by utilizing random sampling. In sum, 290 samples were obtained and interpreted with SPSS (Statistical Package for the Social Sciences) and PLS (Partial Least Squares) to come up with the results of the study. According to the findings, restaurant customers’ observed brand awareness and brand image positively impact brand attitude. The results of the structural equation analysis revealed that brand awareness and brand image have a substantial impact on brand performance, whereas brand attitude has a profound effect on meditation. The adaptation of brand attitude to brand management has sparked a lot of interest in the incredibly competitive restaurant business. There is a good likelihood that green restaurants will ultimately find value in using the measuring tools and suggestions offered in this research to analyze and lead their marketing efforts. In practice, it is recommended that green restaurant management cultivate familiar brand awareness and preserve the brand image to create brand attitude and performance.

In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan’s daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.

This study investigates the nexus between political instability, corruption, and environmental degradation for the selected South Asian countries over the time 1996 to 2019. After confirming cross-sectional dependency by using Breusch and Pagan (LM) test and Pesaran (CD) test, the second generational panel unit root test (CADF) of Pesaran is used to test the stationarity of variables and results reveal mixed order of integration. Furthermore, Westerlund (2007) test indicates that the variables are cointegrated and the panel ARDL approach is used to find the long-run and short-run relationship among the variables. Corruption and political instability have a positive and significant impact on carbon footprint and ecological footprint in the long run and short run except for corruption that has a negative effect on carbon footprint in the short run. Urbanization, foreign direct investment, and energy use have a significant positive impact on environmental degradation. The study also validates the existence of the Environment Kuznets curve (EKC) for South Asian countries. The findings suggest that to improve environmental quality, South Asian countries must enhance political stability and control of corruption.