Hasil untuk "Revenue. Taxation. Internal revenue"

Chun Pang Chow, Hiroyuki Kasahara, Yoichi Sugita

We establish nonparametric identification of production functions, total factor productivity (TFP), price markups, and firms' output prices and quantities, as well as consumer demand, using firm-level revenue data, without observing output quantity, in a monopolistically competitive environment with a fully nonparametric demand system. This result overturns the widely held view -- formalized by Bond, Hashemi, Kaplan, and Zoch (2021) -- that output elasticities and markups are not nonparametrically identifiable from revenue data without quantity information. Under the additional restriction that demand satisfies the homothetic single-aggregator (HSA) structure of Matsuyama and Ushchev (2017), we further nonparametrically identify the representative consumer's utility function from firm-level revenue data. This new identification result enables counterfactual welfare analysis without parametric assumptions on preferences. We propose a semiparametric estimator that is feasible for standard firm-level datasets under a Cobb--Douglas production specification. Monte Carlo simulations show that the estimator performs well, while treating revenue as output induces substantial bias. Applying the estimator to Chilean manufacturing data, we reject the CES specification in favor of HSA, and find that market power reduces welfare by approximately 3%--6% of industry revenue in the three largest manufacturing industries in 1996.

Wei Tang, Yifan Wang, Mengxiao Zhang

We study the pricing query complexity of revenue maximization for a single buyer whose private valuation is drawn from an unknown distribution. In this setting, the seller must learn the optimal monopoly price by posting prices and observing only binary purchase decisions, rather than the realized valuations. Prior work has established tight query complexity bounds for learning a near-optimal price with additive error $\varepsilon$ when the valuation distribution is supported on $[0,1]$. However, our understanding of how to learn a near-optimal price that achieves at least a $(1-\varepsilon)$ fraction of the optimal revenue remains limited. In this paper, we study the pricing query complexity of the single-buyer revenue maximization problem under such multiplicative error guarantees in several settings. Observe that when pricing queries are the only source of information about the buyer's distribution, no algorithm can achieve a non-trivial approximation, since the scale of the distribution cannot be learned from pricing queries alone. Motivated by this fundamental impossibility, we consider two natural and well-motivated models that provide "scale hints": (i) a one-sample hint, in which the algorithm observes a single realized valuation before making pricing queries; and (ii) a value-range hint, in which the valuation support is known to lie within $[1, H]$. For each type of hint, we establish pricing query complexity guarantees that are tight up to polylogarithmic factors for several classes of distributions, including monotone hazard rate (MHR) distributions, regular distributions, and general distributions.

Zhiyuan Fan, Tianyi Lin, Bolun Xu

This paper develops a multi-period optimization framework to design a voluntary renewable program (VRP) for an electric utility company, aiming to maximize total renewable energy deployments. In the business model of VRP, the utility must ensure it generates renewable energy up to the total amount of contract during each market episode (i.e., a year), while all the revenue collected from the VRP must either be used to invest in procuring renewable capacities or to maintain the current renewable fleet and infrastructure. We thus formulate the problem as an optimal pricing problem coupled with revenue allocation and renewable deployment decisions. We model the demand function of voluntary renewable contracts as an exponential decay function based on survey data. We analytically derive the optimal pricing policy of the VRP as a function of the current grid carbon intensity. We prove that a myopic policy is conditionally optimal, which maximizes renewable capacity in each period, attains the long-run optimum due to the utility's revenue-neutral constraint. We show different binding conditions and marginal values of decision variables correspond to different phases of the energy transition, and that the utility should strategically design its revenue-sharing decisions, balancing investments in renewable expansion and subsidizing existing renewable fleets. Finally, we show that voluntary renewable programs can only extend renewable penetration but cannot achieve net-zero emissions or a fully renewable grid. This pricing-allocation-expansion framework highlights both the potential and limitations of voluntary renewable demand, providing analytical insight into optimal policy design and the qualitative shifts occurring during the energy transition process.

Abheek Ghosh, Tzeh Yuan Neoh, Nicholas Teh et al.

We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.

Ryosuke Sato

This study explores the monotonicity of adaptive clinching auctions -- a key mechanism in budget-constrained auctions -- with respect to fluctuations in the number of bidders. Specifically, we investigate how the addition of new bidders affect efficiency and revenue. In a symmetric setting, where all bidders have equal budgets, we show that while the allocated goods and payments for many bidders decrease, overall both liquid welfare and revenue weakly increase. Our analysis also extends to scenarios where bidders arrive online during the auction. In contrast, for asymmetric budgets, we provide counterexamples showing that these monotonicity properties no longer hold. These findings contribute to a better theoretical understanding of budget-constrained auctions and offer insights into the behavior of adaptive clinching auctions in social networks, where new bidders emerge through information diffusion.

Yijing Zhang, Ferdous Pervej, Andreas F. Molisch

Performance of video streaming, which accounts for most of the traffic in wireless communication, can be significantly improved by caching popular videos at the wireless edge. Determining the cache content that optimizes performance (defined via a revenue function) is thus an important task, and prediction of the future demands based on past history can make this process much more efficient. However, since practical video caching networks involve various parties (e.g., users, isp, and csp) that do not wish to reveal information such as past history to each other, privacy-preserving solutions are required. Motivated by this, we propose a proactive caching method based on users' privacy-preserving multi-slot future demand predictions -- obtained from a trained Transformer -- to optimize revenue. Specifically, we first use a privacy-preserving fl algorithm to train a Transformer to predict multi-slot future demands of the users. However, prediction accuracy is not perfect and decreases the farther into the future the prediction is done. We model the impact of prediction errors invoking the file popularities, based on which we formulate a long-term system revenue optimization to make the cache placement decisions. As the formulated problem is NP-hard, we use a greedy algorithm to efficiently obtain an approximate solution. Simulation results validate that (i) the fl solution achieves results close to the centralized (non-privacy-preserving) solution and (ii) optimization of revenue may provide different solutions than the classical chr criterion.

Ojo Johnson Adelakun, Thapelo Lekena, M. E. Motampane et al.

Inflation remains a critical macroeconomic variable shaping South Africa’s international trade competitiveness. This study investigates the long- and short-run effects of inflation on trade balance dynamics and export-import performance. It hypothesizes that inflation significantly influences international trade by altering exchange rates, foreign direct investment, and competitiveness. Data were analyzed using Augmented Dickey-Fuller unit root tests, Johansen cointegration tests, and an Autoregressive Distributed Lag (ARDL) model to capture both short- and long-term relationships. The analysis utilized 1990–2021 secondary data from the World Bank and the South African Revenue Service, covering variables such as inflation, exports, imports, exchange rates, GDP growth, manufacturing output, and FDI. Results reveal that a 1% increase in inflation is associated with a 0.45 correlation with trade balance and a 5.53% short-run decline in trade balance deficit, though these effects were statistically insignificant. ARDL estimates show imports exert a strong negative influence (coefficient = –1.104, p < 0.01), while exports positively contribute to the balance (coefficient = 1.033, p < 0.001), explaining 83.07% of the trade balance variance. Johansen tests indicate two cointegrating relationships among variables, underscoring persistent long-term linkages between inflation and trade metrics. These findings highlight the need for stable inflation targeting and exchange rate management to strengthen South Africa’s trade performance and open prospects for future research integrating structural reforms and sectoral shocks.

Rabia Shaheen, He Shuquan

The impact of sectoral trade channels (agriculture, manufacturing, and services) on economic growth in four South Asian economies (Bangladesh, India, Pakistan, and Sri Lanka) over the period 1993‒2023 is examined in this study. Trade is disaggregated into sectoral components, unlike in conventional analyses where it is treated as a single aggregated measure, to capture heterogeneous effects on growth. Panel data estimation techniques, including Ordinary Least Squares (OLS), Fixed Effects (FE), and Random Effects (RE), are employed, and potential endogeneity is addressed through two-stage least squares (2SLS). Key macroeconomic determinants, such as exchange rates, gross capital formation, and labor force participation, are also incorporated. It is found that agricultural and manufacturing trade exert positive and statistically significant effects on economic growth, whereas services trade is negatively associated with growth, a result attributed to structural inefficiencies, weak export orientation, and limited value addition in much of the services sector. Diverse growth drivers are revealed at the country level: manufacturing-led growth in Bangladesh and India, agriculture-driven growth in Sri Lanka, and capital formation-led growth in Pakistan. Economic expansion is consistently supported by exchange rate stability and gross capital formation, while labor force participation is shown to have mixed effects across countries. The results highlight the importance of country-specific, sector-focused trade and industrial policies through which high-performing sectors can be reinforced and lagging ones modernized, thereby promoting sustainable, inclusive, and resilient growth. By disaggregating trade channels, addressing endogeneity, and integrating macroeconomic determinants, robust empirical evidence is provided for policymakers, development agencies, and trade strategists seeking to enhance sectoral productivity, competitiveness, and long-term economic development in South Asia.

Eric Baudry

The tax revenue gap—the difference between how much the IRS collects in tax revenue and how much it should collect based on the text of the Internal Revenue Code—is both well-defined and well-studied. But raising revenue is just one purpose of taxation; the tax code also operates to redistribute wealth. Drawing from the tax revenue gap and redistribution literatures, this article coins a parallel concept, the tax redistribution gap, to map the extent to which the tax system falls short of its redistributive goals. Introducing a tax redistribution gap measure challenges background assumptions in current tax discourse: first, it would call out a reliance on pre-market income as a distributive baseline, which serves to overstate the redistributive impact of the tax code; second, it would increase the profile of redistribution among policymakers and the public—understandably, measures like the tax revenue gap and tax expenditure budgets focus dialogue on tax evasion and over-spending by the government. Ultimately, the tax redistribution gap would provide a single measure that displays how we are falling short of a key task of the state (redistribution). And by understanding and comparing the component ways our current tax systems falls short of it intended outcomes, we can better tailor redistributive policy solutions.

A. Adamczyk, D. Dawidowicz

Purpose - The aim of the article is to answer the question of which factors contribute to the decline in the fiscal significance of the real estate tax. Research method - A literature review was conducted to analyze the research results on the tax system, tax strategies and real estate tax design. The study used a panel data model with fixed effects and a nonparametric Mann‑Whitney test for independent samples. Results - The study shows that internal factors related to the tax structure as well as external factors constituting the taxation environment may affect the amount of revenue from real estate tax. However, only external factors are responsible for the decrease in the fiscal significance of real estate taxation. Originality / value / implications / recommendations - The literature lacks quantitative research on the real estate tax, in particular, on the factors affecting the amount of tax revenue from this levy. The article therefore fills the existing research gap. The analyses conducted lead to the conclusion that changes in the tax base may affect the amount of revenue from this tax, but they will not stop the decline in its fiscal significance. The increase in revenue from real estate tax requires a thorough reconstruction of its structure.

Xinyu Wanga, Qianwei Zhanga, Binwei Guib et al.

How to design a fair and reasonable allocation plan for the common revenue of the alliance is considered in this paper. We regard the common revenue to be allocated as an exogenous variable which will not participate in the subsequent production process. The production organizations can cooperate with each other and form alliances. As the DEA cross-efficiency combines self- and peer-evaluation mechanisms, and the cooperative game allows fair negotiation among participants, we combine the cross-efficiency with the cooperative game theory and construct the modified Shapley value to reflect the contribution of the evaluated participant to the alliance. In addition, for each participant, both the optimistic and the pessimistic modified Shapley values are considered, and thus the upper and lower bounds of the allocation revenue are obtained, correspondingly. A numerical example is presented to illustrate the operation procedure. Finally, we apply the approach to an empirical application concerning a city commercial bank with 18 branches in China.

Djouadi Issam, Abdellaoui Okba, Madani AbdelRahim et al.

The global outbreak of COVID-19 in 2020 became unprecedented and sent shockwaves through financial markets worldwide. This study investigates the impact of the pandemic on the performance of the U.S. financial market from March 1, 2020, to April 14, 2022. Utilizing Bai and Perron’s (1998) least squares with breaks during this period. The study’s test findings validate the existence of seven structural changes, signifying the occurrence of eight effects of independent factors on the S&P 500 index. The empirical findings demonstrate a substantial influence of COVID-19 pandemic on the performance of financial markets. Specifically, the impact of the number of COVID-19 cases and new fatalities on financial market performance, exhibits variations in terms of direction, nature, depth, and level. Based on an analysis of the structural changes, it can be inferred that the initial period exhibits the most pronounced negative impact on the number of new COVID-19 cases. Subsequently, the direction and nature of this impact undergo a transformation from the second to the eighth periods. Specifically, the impact of the number of new COVID-19 cases becomes positive in the second, third, sixth, seventh, and eighth periods, gradually diminishing until it reaches its lowest levels of impact in the eighth period. The research further identifies a detrimental impact on the occurrence of new fatalities. However, the periods spanning from the third to the fifth period exhibit very modest levels of influence, which then transition into a beneficial effect during the fifth period. Moreover, the research reveals that the impact of mortality rates on the performance of the United States stock market was greater than that of COVID-19 cases across all periods linked to structural changes. Additionally, the exchange rate of the dollar has a consistent and favorable impact, and the real interest rate has a pronounced negative impact, which gradually reduces over time and eventually transitions into a positive value by the eighth period.

Gyan Mani Adhikari, Amrita Sapkota, Devendra Parajuli et al.

Microfinance institutions have been used as a catalyst tool for increasing the welfare of poor people and fostering economic growth in many regions globally, including Nepal, since the decade of the 70s. However, the issue of financial sustainability in their operations is still emerging. The study aimed to analyze the factors influencing the financial sustainability of microfinance institutions in Nepal. The research employed the two-step system General Method of Moments estimator to analyze the financial sustainability of twenty-five sampled microfinance institutions, out of fifty-seven D-class financial institutions categorized as microfinance institutions and regulated by Nepal Rastra Bank, using cross-sectional data obtained from their comprehensive financial statements spanning from 2016 to 2023. The study used three key indicators - operational self-sufficiency, return on equity, and return on assets - to assess the financial sustainability of the selected microfinance institutions in Nepal. The findings of this study suggested that savings and the number of borrowers significantly and positively affect the financial stability of microfinance. In contrast, member-per-staff and non-performing loans negatively and significantly affect financial sustainability. However, loan portfolios do not significantly affect the financial sustainability of microfinance in Nepal. Further researchers can broaden the scope of the study by including variables such as geographic location, developmental stages, ownership structures, age, product delivery strategies of microfinance institutions, microfinance institution size, and government policy and regulations.

Martin Bichler, Alok Gupta, Matthias Oberlechner

The transition of display ad exchanges from second-price auctions (SPA) to first-price auctions (FPA) has raised questions about its impact on revenue. Auction theory predicts the revenue equivalence between these two auction formats. However, display ad auctions are different from standard models in auction theory. First, automated bidding agents cannot easily derive equilibrium strategies in FPA because information regarding competitors is not readily available. Second, due to principal-agent problems, bidding agents typically maximize return-on-investment (ROI), not payoff. The literature on learning agents for real-time bidding is growing because of the practical relevance of this area; most research has found that learning agents do not converge to an equilibrium. Specifically, research on algorithmic collusion in display ad auctions has argued that FPA can induce symmetric Q-learning agents to tacitly collude, resulting in bids below equilibrium, leading to lower revenue compared to the SPA. Whether bids are in equilibrium cannot easily be determined from field data since the underlying values of bidders are unknown. In this paper, we draw on analytical modeling and numerical experiments and explore the convergence behavior of widespread online learning algorithms in both complete and incomplete information models. Contrary to prior results, we show that there are no systematic deviations from equilibrium behavior. We also explore the differences in revenue of the FPA and SPA, which have not been done for utility functions relevant to this domain, such as ROI. We show that learning algorithms also converge to equilibrium. Still, revenue equivalence does not hold, indicating that collusion may not be the explanation for lower revenue with FPA, and the change in auction format might have had substantial and non-obvious consequences for ad exchanges and advertisers.

Simon Finster, Paul W. Goldberg, Edwin Lock

In markets with budget-constrained buyers, competitive equilibria need not be efficient in the utilitarian sense, or maximise the seller's revenue. We consider a setting with multiple divisible goods. Competitive equilibrium outcomes, and only those, are constrained utilitarian efficient, a notion of utilitarian efficiency that respects buyers' demands and budgets. Our main contribution establishes that, when buyers have linear valuations, competitive equilibrium prices are unique and revenue-optimal for a zero-cost seller.

Dekkiche Djamal, Chrayett Fairou, Laila Oulad Brahim

This study tests the influence of human capital expressed by spending on education on South African GDP during the period 2021-2000, depending on the self-regression model of the distributed time gaps (ARDL). This work aimed to study the effect of Human Capital (H) on GDP per capita in South Africa during the period 2000-2021, based on the autoregressive distributed lag (ARDL) model. The study results concluded that there is a long-run equilibrium relationship between GDP and the independent variables (physical capital K, labor force L, and Human Capital H). The results revealed that there is a positive effect of Human Capital on GDP (moral for K) in the short run, and an adverse effect of Human Capital on GDP in the long run due to the interest in employing internationally qualified professionals, contributing to an increase in unemployment and indigenous workers' health and well-being rates. These findings are consistent with the H-related literature. Likewise, from the results of the short-run test, L is the largest among the independent variables by (0.65), K by (0,086), the least of which is H with a coefficient of (0,029). This indicates that H (skilled workforce) in South Africa does not play an important role in the individual GDP in South Africa compared to the regular labor force due to its large size compared to the qualified labor. With regard to the long-run results, there is a negative impact of H on the local product due to the fact that the interest in supporting the qualified workforce coming to South Africa contributed to increasing unemployment rates and influencing the luxury of indigenous workers in South Africa. The study recommended the necessity of supporting local skills. The support must include enhancing skills for all three categories of workers (skilled, semi-skilled, and unskilled).

Hanna Yarovenko, Aleksandra Kuzior, Alona Raputa

Insider cyber fraud in the banking sector is a serious and complex issue for financial institutions. This form of cyber fraud is particularly insidious due to insiders’ inherent access and knowledge, necessitating banks to implement comprehensive strategies for detecting, preventing, and responding to these internal threats. The aim of this study is to develop a scientific and methodological approach to model the probable behaviour of insider cyber fraudsters in banks based on a complex combination of principal component analysis, k-means clustering, and associative analysis. During the analysis of current challenges in the financial sector regarding the evolution of cyber fraud and its implications, the systematization of existing theoretical approaches concerning the examination of cyber fraud in banks was performed. Its result revealed a positive trend in the dynamics of the number of published materials in conferences and articles using keywords “cyber” and “frauds” in the Scopus database from 2000 to 2023. Additionally, utilizing the VOSviewer software facilitated the systematization of keyword combinations used in scholarly publications on the chosen topic, forming clusters to visualize and organize vectors of scientific research. Analytical data from Google Trends on critical issues related to cyber fraud were chosen as input data. Twenty variables were formed, which are the results of search queries, characterizing cyberattacks and decreased trust in financial institutions. The principal components method was used to reduce the dimensionality of the input data array, making it possible to select the nine most significant for the study. Conducting a cluster analysis using the k-means method made it possible to form 3 main groups of search queries, which included 12 of the selected variables. The results of the performed procedures contributed to the implementation of associative analysis for three sets of variables. It has been found that what intrigues potential insider cybercriminals in banks the most is the personal financial information of the client, access to the client’s profile in online banking and gaining access to his phone data. The obtained results can be utilized by commercial banks for identifying potential insider cyber fraudsters and ensuring a higher level of client protection against the actions of insider cyber fraudsters, by bank clients for analysing and mitigating potential threats from insider cyber fraudsters, and by law enforcement agencies for prompt responses to potential threats posed by insider cyber fraudsters in banks.

Halil D. Kaya, Julia S. Kwok, Joseph LaTurner

In this paper, using our actual experience as finance faculty, we go over the steps to build and run an Investment Lab at University. First, we explain how the curriculum may be changed in order to accommodate this experiential learning opportunity in a finance undergraduate program. We explain how a new course oriented towards portfolio management may be created. Next, we describe how the student body may benefit from this initiative. Why is an Investment Lab needed at a university? What are the disadvantages of not having an Investment Lab (i.e. losing market share to competition, not bridging between theory and practice, and so on)? What is the solution to these problems? We explain that the solution possibly requires a new “Applied Portfolio Management Concentration/Minor”. As an example, we show what courses may be included under this new minor. Then, we go over the operational plan, including the business plan. We propose the establishment of a four-course minor, the lab, and a student managed fund. We explain how the whole operation will be financed. Then, we present the timeline and explain what needs to be done throughout the whole process. Finally, we go over the costs in detail. We believe that this paper will help other universities that would like to start an Investment Lab.

Moussouni Habiba

The recent crisis has attracted much interest on the part of economists and prudential authorities. It resembles in some aspects: over-indebtedness widespread panic caused by the sharp devaluation of financial assets, poor management and prediction of crises by the prudential rules of Basel II. The Algerian banking authorities are engaged in a series of reforms of independence till the nowadays, to modernize their banking sectors. They include reforms to the restructuring of the liberalization and privatization of public banks, the establishment of prudential laws and systems of risk management and the strengthening of the powers of supervision. The aim of this article is to learn about the adequacy of the Basel agreements to achieve the basic ideas of reducing risks, stabilizing the financial sector, and maintaining its level of efficiency. Add to, the central idea is to show that our commercial banks are under the obstacle of the presence of the State in a very strong way in the shareholders of the banks, which weakens the regulatory governance. This obstacle more other political constraints preclude the application of the second Basel accord. This was done by first evaluating the different Basel agreements imposed internationally to banks by the Prudential Basel Committee authorities to avoid crises or at least prevent. Secondly the situation of Algerian bank compared to us neighbors and they are forced or the reasons for the slowness with the monetary authorities for the application of the agreement Basel 2, and finally analysis of some banks soundness indicators to measure the efficiency of the Algerian banking sector for the period from 2000 to 2021. The finding revealed that us to point out that they are improving for some and a slight decline for other. An analysis of the development of deposits by institutional sector appears at the end of December 2020 a decrease of -13.2% for deposits collected with public institutions and after government agencies, after the recorded height increased by 7.6% in 2018 and -2% in 2019 and 2020. These deposits moved from 3689.1billion dinars at the end of 2019 to 3202.5 billion dinars in 2020 and 3885.2 billion dinars in 2021.It also represents 33.9% of the total bank deposits collected compared to 39.3% at the end of 2019.This decline is due to the significant decrease in demand deposit for public institutions by (-18%) at the end of 2020, compared to a decrease of (-19.7%) at the end 2019. The Algerian economy’s indicators, it can be said temporarily because Algeria’s economy is rentier. In 2022, it has been expected that, the current account balance posts its first surplus since 2013 and international reserves have risen, stopping the constant downward trend of recent years. Similarly, A fiscal surplus is projected in 2022 reflecting windfall gains from hydrocarbon revenues and a significant under-implementation of budget spending, resulting from the global recovery and the war in Ukraine. The economic recovery strengthened, with non-hydrocarbon GDP growth projected to accelerate to 3.2% in from 2.1%.

Zishuo Zhao, Xi Chen, Xuefeng Zhang et al.

A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.