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"ECONOMETRIC ANALYSIS"
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The Determinants of Foreign Direct Investment in Pakistan: An Econometric Analysis
This study contributes to an understanding of locational determinants of FDI in Pakistan. Although there exists a great deal of literature in this area, there is hardly any evidence of such a study in the case of Pakistan. Economy level analyses are carried out to explore the determinants of FDI through multivariate regression analysis. The results of the multivariate regression analyses reveal that market size, relative interest rates and exchange rates are the major determinants of FDI in Pakistan. The variables such as market growth and political instability were consistently insignificant in the analyses. However, mixed findings were revealed by the variables such as consumer goods imports and the political regime in Pakistan.
Journal Article
The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics
The emergence of big data has stimulated enormous investments into business analytics solutions, but large-scale and reliable empirical evidence about the business value of big data and analytics (BDA) remains scarce. This article presents the results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets. Using a unique panel data set that contains detailed information about BDA solutions owned by 814 companies during the time frame from 2008 to 2014, on the one hand, and their financial performance, on the other hand, we estimate the relationship between BDA assets and firm productivity and find that live BDA assets are associated with an average of 3-7 percent improvement in firm productivity. Yet we also find substantial differences in returns from BDA when we consider the industry in which a firm operates. While firms in information technology-intensive or highly competitive industries are clearly able to extract value from BDA assets, we did not detect measurable productivity improvement for firms outside these industry groups. Taken together, our findings provide robust empirical evidence for the business value of BDA, but also highlight important boundary conditions.
Journal Article
Relative age effect and second-tiers: No second chance for later-born players
The main objective of this research was to determine the existence of relative age effect (RAE) in five European soccer leagues and their second-tier competitions. Even though RAE is a well-known phenomenon in professional sports environments it seems that the effect does not decline over the years. Moreover, additional information is required, especially when taking into account second-tier leagues. Birthdates from 1,332 first-tier domestic players from France, England, Spain, Germany and Italy and birthdates from 1,992 second-tier domestic players for the 2014/2015 season were taken for statistical analysis. In addition to standard statistical tests, the data were analyzed using econometric techniques for count data using Poisson and negative binomial regressions. The results obtained confirmed a biased distribution of birthdates in favor of players born earlier in the calendar year. For all of the five first-tier soccer leagues there was an unequal distribution of birthdates (France χ2 = 40.976, P<0.001; England χ2 = 21.892, P = 0.025; Spain χ2 = 24.690, P = 0.010; Germany χ2 = 22.889, P = 0.018; Italy χ2 = 28.583, P = 0.003). The results for second-tier leagues were similar (France χ2 = 46.741, P<0.001; England χ2 = 27.301, P = 0.004; Spain χ2 = 49.745, P<0.001; Germany χ2 = 30.633, P = 0.001; Italy χ2 = 36.973, P<0.001). Econometric techniques achieved similar results: estimated effect of month of birth, i.e., long-term RAE on players' representativeness, is negative (statistically significant at the 1% level). On average, one month closer to the end of the year reduces the logs of expected counts of players by 6.9%. Assuming this effect as linear, being born in the month immediately before the cut-off date (i.e., December/August), reduces the logs of expected counts of players by approximately 75.9%. Further, ID (index of discrimination, that is, the ratio between the expected counts of players born in the middle of the first and the twelfth month of the selection year) is 2.13 and 2.22 for the first- and second-tier, respectively. In other words, in the top five European first-tier and second-tier leagues, one should expect the number of players born in the first month of the calendar year to be twice the number of those born in the last month. The RAE in the second-tiers is the same as in the first-tiers, so it appears that there is no second chance for later born players. This reduces the chances to recover talented players discarded in youth simply because of lower maturity.
Journal Article
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures
Managers and researchers alike have long recognized the importance of corporate textual risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from unstructured text. In this paper, we develop a variation of the latent Dirichlet allocation topic model and its learning algorithm for simultaneously discovering and quantifying risk types from textual risk disclosures. We conduct comprehensive evaluations in terms of both conventional statistical fit and substantive fit with respect to the quality of discovered information. Experimental results show that our proposed method outperforms all competing methods, and could find more meaningful topics (risk types). By taking advantage of our proposed method for measuring risk types from textual data, we study how risk disclosures in 10-K forms affect the risk perceptions of investors. Different from prior studies, our results provide support for all three competing arguments regarding whether and how risk disclosures affect the risk perceptions of investors, depending on the specific risk types disclosed. We find that around two-thirds of risk types lack informativeness and have no significant influence. Moreover, we find that the informative risk types do not necessarily increase the risk perceptions of investors-the disclosure of three types of systematic and liquidity risks will increase the risk perceptions of investors, whereas the other five types of unsystematic risks will decrease them.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.1930
.
This paper was accepted by Alok Gupta, special issue on business analytics
.
Journal Article
Natural resources, neither curse nor destiny
This volume studies the role of natural resources in development and economic diversification. It brings together a variety of analytical perspectives, ranging from econometric analyses of economic growth to historical studies of successful development experiences in countries with abundant natural resources.
eBook
The Effects of Absenteeism on Academic and Social-Emotional Outcomes: Lessons for COVID-19
In March 2020, most schools in the United States transitioned to distance learning in an effort to contain COVID-19. A significant number of students did not fully engage in remote learning opportunities due to resource or other constraints. An urgent question for schools around the nation is how much did the pandemic impact student academic and sociale-motional development. This paper uses administrative panel data from California to approximate the impact of the pandemic by analyzing how absenteeism affects student outcomes. Our results suggest student outcomes generally suffer more from absenteeism in mathematics than in ELA. Negative effects are larger in middle school. Absences negatively affect social-emotional development, particularly in middle school. Our results suggest districts will face imminent needs for student academic and social-emotional support to make-up for losses due to the COVID-19 pandemic.
Journal Article
Unemployment in Croatia: Trends and Challenges in the Digital Age (2010-2024)
This paper investigates the trend of unemployment in the Republic of Croatia during the period from 2010 to 2024, using econometric methods of time series analysis. By applying linear trend models and second-degree trend-polynomials, the research quantifies the tendency of decreasing the number of unemployed, while simultaneously identifying oscillatory movements within the observed period. Data analysis shows that the peak of unemployment was recorded in 2013, followed by a significant decline, which is reflected both in absolute values and in structural changes in the shares of unemployed by gender and age. In addition to quantitative analysis, the paper looks at the challenges of the digital era and the transition to Industry 5.0, emphasizing the need for managing organizational changes and adapting the education system. This transformation places new demands on the workforce, which is reflected in the need for lifelong learning, retraining and the development of digital competencies, especially in the context of the application of artificial intelligence. This indicates a two-way impact - on the one hand, technological progress can result in the loss of traditional jobs, while on the other hand, it opens up opportunities for the creation of new, better jobs.
Journal Article