Journal of Quantitative Methods

Evaluating Nonresponse Bias for a Hypernetwork Sample Generated from a Probability-Based Household Panel

2022-12-08T10:19:00+00:00

Hypernetwork sampling aims to generate representative samples of populations for which a sample frame does not exist or is too costly to construct. This multi-level sampling method relies on nominations from one sample source (Stage 1 sample) to construct another sample (Stage 2 sample). However, nonresponse from the Stage 1 sample has the potential to produce bias in Stage 2 of the hypernetwork sample if Stage 1 respondents differ from nonrespondents. This paper examines nonresponse in a hypernetwork sample of religious congregations in the U.S. generated from a probability-based household panel that includes background information for all panelists including Stage 1 nonrespondents. This study also illustrates the benefits of constructing a hypernetwork sample by using a sample of already recruited panelists for whom information has already been collected. We find Stage 1 nonrespondents tend to be from rural areas and not from the Midwest, compared to Stage 1 respondents. Results also suggest that the impact of subsequent survey reminders on key Stage 1 estimates decreased after the third reminder during Stage 1 fielding. Additionally, we find that Stage 1 nonresponse impacts the Stage 2 estimates for congregational characteristics. Specifically, the congregations nominated by Stage 1 late respondents tend to have the following characteristics: located in the South, predominantly African American, more likely to be conservative/evangelical Protestant or black Protestant, younger, urban or suburban, helped people register to vote, less likely to have a school, and have fewer child participants. Post-survey weighting adjustment of the Stage 1 sample decreased the risk for nonresponse bias in the Stage 1 hypernetwork sample and in the Stage 2 sample of congregations.

Estimating Gender Wage Gap and Its Decomposition in Pakistan

2022-11-04T12:28:30+00:00

This study estimates the gender wage gap in Pakistan by applying Nopo’s decomposition procedure. This technique is a non-parametric alternative to the Blinder-Oaxaca (B-O) technique of decomposition. It addresses the issue of gender wage gap in the common support and decomposes the total wage gap into four components. One of these components focuses on gender discrimination, while the other three explain individual characteristic differences. For this purpose, the study used Pakistan Social and Living Standard Measurement Survey data for the years 2013-14 and 2018-19. The results of the non-parametric procedure revealed that there was an 18.91% gender wage gap in Pakistan in 2013-14. This figure increased to 29.48% in 2018-19. Furthermore, the results of the decomposition technique showed that about 17.7% and 27.54% of the total wage gap was due to gender discrimination in the labor market in 2013-14 and 2018-19, respectively. In the same time periods, almost 2% was due to the differences in individual characteristics. The policy implication is that the government should chalk out and implement such policies that can decrease gender discrimination in the labor market.

Effects of COVID-19 Pandemic on Household Savings: Evidence from the CESEE Region

2023-01-04T12:52:11+00:00

Covid 19 has changed the life style of individuals and their propensity to save money for the future in order to manage unexpected expenses and the higher uncertainty that may characterize the after pandemic scenario. Savings dynamics are expected to be different country-by-country due to the unique features that may characterize citizens on the basis of the historical trend and the financial habits.

The paper analyses the CESEE countries and evaluated the change in the saving rate and the differences with respect to the long term average and the expectations for the after crisis period. Results show some interesting differences among countries that may matter for the policy maker in order to identify the best solution for reducing the negative effects of the pandemic.

Social Spending and Economic Growth: The Case of Pakistan

2022-11-30T04:04:41+00:00

Using the annual data of Pakistan's economy from 1975 to 2018, this paper explores the channels linking social spending and economic growth. With separate modeling for education and health capital, the paper finds that health spending has a statistically significant and negative impact on the mortality rate which infers that health improvement in Pakistan is dependent on the volume of health expenditures. Also, educational spending has a statistically significant and positive impact on the educational level. Last model report that health and education capitals have a positive association with the economic growth of the country. Overall findings have made a great advance in the existing literature on how health and education capitals can play a vital role in boosting the economic growth of the country.

Choices of Deficit Financing, Saving-Investment Gap and Inflation Nexus: Evidence from Pakistan

2022-09-26T13:37:52+00:00

The study is the analysis of possible consequences of various financing options at a disaggregated level on macroeconomic variables like the savings-investment gap and the general price level to choose a less-lethal financing option. Financing from domestic sources causes an increase in the general price level except for National Saving Schemes (NSSs) and crowding out of private sector credit and investment but external financing and deficit-financed by bonds appeared free from such costs. The government of Pakistan restricts printing of money to control the rate of inflation and may improve bonds and floating debt management to avoid crowding out effect. The study suggests bonds financing or external financing as compared to any other source because domestic financing is more distortionary as compared to external financing. Therefore, the study favors external financing because it appeared non-inflationary, satisfy the savings-investment gap, overcome the issue of crowding out, and bonds financing.

Statistical Modeling and Forecasting of Gram Pulse Area and Production of Pakistan

2023-01-04T12:05:24+00:00

The main aim of this study is to forecast the area and production of gram pulse in Pakistan using best selected time series model based on time series data i.e. 1947-1948 to 2016-2017. The area and production of gram were forecasted over 2017-2018 to 2029-2030. A variety of time series models were applied to forecasts the gram area and production of Pakistan. The Akaike Information Criteria (AIC) and some others showed that the ARIMA (1, 1, 1) and ARIMA (2, 1, 2) are the more adequate models for gram area and production, respectively. Some diagnostic tests of the selected time series model are also considered to evaluate the quality of the selected model. Moreover, the quality, of the selected models is measured based on the minimum value of the mean error, root mean square error, mean percentage error, and mean percentage absolute error. From ARIMA (1, 1, 1) and ARIMA (2, 1, 2) model, we noticed that gram area and production of Pakistan are forecasted to be 2515.08 thousand acers and 518.325 thousand tons in 2030 under the condition that there is no unusual event occurred.

Exploring the Linkages between Gender-specific Human Capital, Foreign Direct Investment and Economic Growth: Evidence from Pakistan

2022-09-26T11:04:44+00:00

This study empirically examined the combined interaction of human capital and foreign direct investment on the economic growth of Pakistan as well as long and short term impact of foreign direct investment and human capital on the economic growth of Pakistan. For achieving the study objectives candidate variables annual time series data of thirty nine years (1976-2014) were collected from the various publications of economic surveys, state bank of Pakistan (SBP) and world development indicator (WDI). To examines the long-term impacts, autoregressive distributed lag (ARDL) econometric techniques were employed, and to estimate the short tun association among variables vector error correction model (VECM) were used to find the study results. The study findings concluded that the interactive term human capital and foreign direct investment has a direct and significant contribution in encouraging economic growth of Pakistan. Moreover, results reveal that in the short run, gender-neutral interactive human capital and foreign direct investment have a significant and direct linkage with economic growth. However, in gender-specific human capital, male human capital and female human capital have an inverse association with economic growth perhaps because of the fact that lack of opportunities and underutilization of labor force in the short run. The outcomes of the study suggests that government should give its attention to promote more human development strategies that provide educated and highly qualified people. The productive utilization of this qualified and skilled human capital in those sectors where most foreign direct investment, along with modern technologies also stimulates economic growth. This, in turn, attracts more quality FDI for Pakistan for sustainable development.

Analyzing the Firm and Country Level Determinants of Capital Structure in the Dynamic Political Regimes: A Case of Emerging Market Economy

2022-09-26T12:17:43+00:00

Capital structure acts as a backbone in the functioning of the firms. The disparities in the political system affect the whole economy of the country and this leads the firms to undergo financial stress and bankruptcy thus affecting their capital structure in several ways. This study analyzed and compared the firm level and country level variables of capital structure of firms during dynamic political regimes in the country. The sample comprised of secondary data of 50 companies from the 28 manufacturing sectors of Pakistan Stock Exchange for the time period 2005-2018. The data is divided into five regimes based on the tenure of the Prime Ministers of Pakistan. The results indicated that the manufacturing sector of Pakistan mostly relies on short term debt financing in all the political regimes. Firms which were highly profitable and had high liquidity did not rely much on the debt but in case of uncertain political conditions firms borrowed long term debt, when required, due to their high tangibility. The volatility risk of the firms was higher in regimes which comprised of global financial crises and the dismissal of the government. Due to high inflation and fluctuations in exchange rate the firms had to pay more interest on debt.

Elucidating the Impact of behavioral biases in Pakistan Stock Market: Moderating Impact of Financial Literacy

2022-08-02T08:46:58+00:00

The current study focuses on some of the most commonly relied upon biases in decision making. The study aimed at understanding the influence of herding, overconfidence, anchoring, and loss aversion on the decision-making style of investor besides it also investigates the role of financial literacy, since the traditional paradigm of finance is of the view that the knowledge of finance is directly associated with the degree of irrational outcomes. To explore this linkage data from investors trading at Lahore, Karachi, and Islamabad is gathered. Structural equation modeling is used for establishing the proposed associations. The results revealed that behavioral biases significantly impact the decision-making of investors. The results of moderation analysis presented that financial literacy plays a major role in de-biasing decision making. These findings can be extremely useful for investors, policymakers, and investment professionals. Not only to make optimal decision-making but also by providing a deeper understanding of the daily life stock market behavior.

Thinking of Going Canting Again: A Study of Revisit Intention to Chinese Restaurants

2022-08-02T08:23:50+00:00

This study measures the impact of the fairness of price, quality of food, and quality of service on revisit intention of Chinese restaurants' consumers, mediated by consumer satisfaction. Purposive sampling technique is used for data collection along with snowball sampling technique. Two hundred questionnaires were answered through online Google forms. The analysis of the collected data is executed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The descriptive statistics are analyzed by SPSS software, and the influential statistics are analyzed by Smart PLS software. The analyses' findings concluded that food quality has a highly noticeable impact on consumers' revisit intention. Quality of service and Fairness of price also has a prominent effect on the consumer's revisit intention. Limitations and recommendations are also mentioned at the end of the study.