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Valuers should independently assess market value. The purpose of this article is to analyze whether the valuation behavior remains independent when commissioned by publicly listed companies in Taiwan.

This study used both quantitative and qualitative methods. Quantitative data analysis was used to examine the estimated premium ratio and estimated divergent ratio with the independent sample t test and Wilcoxon-Mann-Whitney test. To complement and validate the quantitative analysis, open-ended questionnaires were conducted, providing additional insights into the research findings.

The results showed that there is a significant difference in estimated valuations commissioned by representatives of buyers and sellers, and the estimated premium ratios commissioned by representatives of buyers were higher than those of sellers. Furthermore, the open-ended questionnaires results indicate that these findings may be influenced by clients for less experienced appraisers. However, for senior appraisers, this is seen as an action to gain a better understanding of the valuation purpose and always within a reasonable price range. In addition, client influence is not a static factor; it may transform into the valuer's behavior as the appraiser's experience grows and deepens.

It is difficult to obtain valuation reports commissioned by representatives of both buyers and sellers for the same property transactions. In this study, data were obtained from the Market Observation Post-System (MOPS) in Taiwan. As valuation reports could not be obtained, estimated valuations and transaction prices are used to calculate estimated premium ratio and estimated divergent ratios.

Previous investigations of the client effect have been conducted using qualitative methods including questionnaire surveys, in-depth interviews and experimental design. However, these studies are subject to moral hazard. This study may be the first study that has access to data on valuations for both buyers and sellers in such a formal setting.

While much of the literature testing for shirking by professional athletes have used performance metrics, some works have quantified shirking in dollar terms by comparing salary to estimated marginal revenue product (MRP). However, Ordinary Least Squares (OLS) approaches to measuring shirking by comparing salary to MRP have an endogeneity problem, as salary and contract length are determined simultaneously. We test for shirking in Major League Baseball (MLB) using an MRP approach, addressing this potential endogeneity.

This paper uses instrumental variables regression to address potential endogeneity using MLB season-level player and team data from 2010 to 2017.

Using OLS regression, the impact of an additional year of guaranteed contract on shirking is estimated at approximately $1m in 2010 US dollars, and the impact of having a long-term contract is estimated at $5m, estimates comparable to those in the literature. Using instrumental variables regression, these impacts increase to $1.6m and over $9m in 2010 dollars.

Given large, causal shirking estimates, profit maximizing sports organizations should take caution when negotiating long-term contracts. These findings also have important implications for other labor market settings where workers feel job security.

To our knowledge, this is the first work testing for shirking in sports using an MRP approach which uses instrumental variables regression to address potential endogeneity.

Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

The study attempts to estimate farm subsidies the governments can save by transitioning to a millet-based production system, replacing GHG emission-intensive crops.

It updates a 131 × 131 commodity input–output (IO) table of the year 2015–16 into 2021–22 using the RAS procedure and simulates the economy-wide impacts of replacing rice and wheat with pearl millet and sorghum using consumption and production approaches. It then quantifies fertilizer, electricity and credit subsidy expenses the government can save through this intervention. It also estimates the potential reduction in GHG emissions that the transition could bring about. India is taken as a case.

Results show pearl millet expansion brings greater benefits to the government. It is estimated that when households return to their pearl millet consumption rates that prevailed in the early-reform period, this could save the Indian government Rs. 622 crores (USD 75 m). The savings shall be reinvested in agriculture to finance climate adaptation/mitigation efforts, contributing to a sustainable food system. Net GHG emissions also decline by 3.3–3.6 MMT CO2e.

Indian government has been actively aiming to bring down paddy areas since 2013–14 through the Crop Diversification Program and promoting millets (and pulses and oilseeds) on these farms. The prime reason is to check rapidly declining groundwater irrigation in Green Revolution states. Regulations in the past in these states have not brought the intended results. Meanwhile, electricity and fertilizers are heavily subsidized for agriculture. A slight shift in the cropping system can help conserve these resources. Meanwhile, GHG emissions could also be brought down and subsidies could well be saved. The results of the study indicate the same.

A less warm society is what governments and nongovernment organizations across the world are aiming for at present. Financial implications affect actions against climate change to a greater extent, apart from technological innovations. The effects of policy strategies discussed in the study, taking a large country as a case, when implemented appropriately around the regions, could help move a step closer to action against climate change.

The paper addresses a key but rarely explored research issue – that how a climate-sensitive crop choice will help reduce the government’s fiscal burden to finance climate adaption/mitigation. It also offers a mechanism to estimate the benefits within an economy-wide framework.

Most research on tax evasion focused on microeconomic variables revolving around perceptions and decisions of individual taxpayers. However, a new wave of research is now investigating the role of macroeconomic variables in inducing tax evasion. This study adds to the limited studies in this new direction of research. Previous studies found that inflation, low gross domestic product (GDP) growth and gross fixed capital formation causes recession, increases unemployment, raise interest rates, hurts both domestic and foreign direct investments. This study examined the relationship between these variables and estimated tax evasion in Sub-Saharan Africa.

The study adopts a correlation research design with 2,300 data points collected from 23 countries in Sub-Saharan Africa. Specifically, tax to GDP ratio, gross fixed capital formation per GDP and the GDP annual growth report from each country for the period 2011–2020 was retrieved. Generalised least square regression technique was employed to analyse the data due to the presence of heteroskedasticity in the model and random effect was utilized based on the Hausman test. To avoid misspecification and biased result; therefore, all relevant test was conducted including the multicollinearity test.

The results indicate that GDP annual growth and gross fixed capital formation have a significant negative impact on estimated tax evasion in Sub-Saharan Africa. The findings further indicate a negative but insignificant relationship between inflation and estimated tax evasion in Sub-Saharan Africa. The study concludes that both GDP annual growth rate and gross fixed capital formation negatively influence estimated tax evasion and the policy implications in the African continent were discussed.

The new findings on the effects of GDP annual growth, growth fixed capital formation and inflation on estimated tax evasion provide novel knowledge that is currently lacking in the current literature, specifically Sub-Saharan African continent.

The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.

Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.

The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.

In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.

This study investigates the implementation challenges and economic consequences of the International Financial Reporting Standards 9 (IFRS 9) Financial Instruments.

Descriptive evidence on equity asset reclassifications and estimated impairment using the new expected credit loss (ECL) model are presented. Multivariate analyses on the disposal of available-for-sale (AFS) and fund investment post-announcement and the value relevance of impairments to financial assets post-implementation are performed.

Over 60% of sample firms report inconsistent equity asset reclassifications and do not change estimated impairment using the new expected credit loss model. Firms also switch from AFS to equity fund investments post-announcement. Lastly, impairments to financial assets increase in value relevance to investors’ post-implementation, but only in financial institutions and firms with Big 4 auditors.

This study's findings suggest that IFRS 9 presents implementation challenges and changes equity investment strategies. They also indicate cross-sectional differences in firms' ability to effectively apply the new standards. This study is valuable for policymakers, business leaders, investors and academics.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Education system stimulates the development of human capital and provides informative signaling allowing to differentiate productivity of individuals. If education system is efficient then higher levels of education usually associated with greater returns on labor market. To evaluate the efficiency of Russian education system we aim to estimate the effect of vocational education and different levels of higher education on wages.

We use data on 8,764 individuals in the years 2019–2021. Our statistical approach addresses two critical issues: nonrandom selection into employment and the endogeneity of education choice. To tackle these problems, we employed Heckman’s method and its extension that is a structural model which addresses the issue of self-selection into different levels of education.

The results of the analysis suggest that there is a significant heterogeneity in the returns to different levels of education. First, higher education, in general, offers substantial wage premiums when compared to vocational education. Specifically, individuals with specialist’s and bachelor’s degrees enjoy higher wage premiums of approximately 23.59–24.04% and 16.43–16.49%, respectively, compared to those with vocational education. Furthermore, we observe a significant dis-parity in returns among the various levels of higher education. Master’s degree provides a substantial wage premium in comparison to both bachelor’s (19.79–20.96%) and specialist’s (12.64–13.41%) degrees. Moreover, specialist degree offers a 7.16–7.55% higher wage premium than bachelor’s degree.

We identify a hierarchical pattern in the returns associated with different levels of higher education in Russia, specifically “bachelor-specialist-master.” These findings indicate that each level of education in Russia serves as a distinct signal in the labor market, facilitating employers' ability to differentiate between workers. From a policy perspective, our results suggest the potential benefits of offering opportunities to transition from specialist’s to master’s degrees on a tuition-free basis. Such a policy may enhance access to advanced education and potentially lead to higher returns for individuals in the labor market.

There are many studies on returns to higher education in Russia. However, just few of them estimate the returns to different levels of higher education. Also, these studies usually do not address the issue of the endogeneity arising because of self-selection into different levels of education. Our structural econometric model allows addressing for this issue and provides consistent estimates of returns to different levels of education under the assumption that individuals with higher propensity to education obtain higher levels of education.

Using Nigeria, as a point of reference, this study aims to explore the applicability of climatic variables as analytically valid factors for conceptual cost estimation. This is in view of the vastness and topographical alignment of Nigeria's landmass, which makes it a country of extreme climatic variability from north to south. As construction costs in Nigeria, similarly, tend to show a north-south alignment, the study's objective is to establish cost-estimating relationships (CERs) between the variability of climatic elements and the variance in construction cost, to arouse interest in the concept.

Deploying correlation analysis and multiple regression analysis, significant associations/relationships between meteorological variables and building cost for selected locations, following a North-South transect of the major climatic zones, are sought, to explain climate-induced construction cost variance. Validation of the regression model was carried out using variance analysis and the Mean Absolute Percentage Error of a different dataset.

Climatic indices of atmospheric moisture exhibited strong direct and partial correlations with construction costs, while sunshine hours and temperature were inversely correlated. The study further establishes statistically significant CERs between climatic variables and building cost in Nigeria, which accounted for 47.9% of the variance in construction cost across the climatic zones.

The study outcome provides a statistically valid platform for the development of more elaborate analytical costing models, for prototype buildings to be cited in disparate climatic settings.

This study establishes the statistical validity of climatic variables in constituting CERs for predicting construction costs in disparate climatic settings.