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The purpose of this study is to understand the impact mechanism of grassland transfer on herders' production behaviour in pastoral areas. The impact of grassland transfer on herders' livestock production and grazing intensity is quantified.

Using the survey data collected for 356 herder households from Inner Mongolia and Gansu, China, quantile regression is employed to assess the heterogeneous effects of grassland transfer on livestock production and grazing intensity. To correct the potential self-selection bias of grassland transfer, the propensity score matching technique is used.

Results show that labour, percentage of livestock income and livestock stock are the main factors affecting herders' choice to transfer grassland. The positive effect of grassland transfer on livestock numbers on behalf of those who rented additional grassland is statistically significant but declines with livestock numbers. The sustainability-enhancing effect of grassland transfer on grazing intensity is significant, and the effect becomes larger amongst herder households with higher grazing intensity. The analysis on the impact mechanism shows that grassland transfer significantly promotes the adoption of sustainable grazing modes, such as rotational and seasonal rest grazing, which in turn increases herders' livestock numbers and decreases grazing intensity.

Few studies have empirically analysed the influence of grassland transfer on livestock numbers and grazing intensity. This study fills this gap by employing a quantile regression to assess the heterogeneous effects of grassland transfer on livestock numbers and grazing intensity, while accounting for self-selection bias. In addition, the authors have examined the influencing mechanisms under which grassland transfer impacts on livestock numbers and grazing intensity.

This paper aims to contribute to knowledge by investigating the return behaviour of seven global real estate investment trusts (REITs) with respect to the appropriate distributional fit that captures tail and shape characteristics. The study adds to the knowledge of distributional properties of seven global REITs by using the generalised lambda distribution (GLD), which captures fairly well the higher moments of the returns.

This is an empirical study with GLD through three rival methods of fitting tail and shape properties of seven REIT return data from January 2008 to November 2017. A post-Global Financial Crisis (GFC) (from July 2009) period fits from the same methods are juxtaposed for comparison.

The maximum likelihood estimates outperform the methods of moment matching and quantile matching in terms of goodness-of-fit in line with extant literature; for the post-GFC period as against the full-sample period. All three methods fit better in full-sample period than post-GFC period for all seven countries for the Region 4 support dynamics. Further, USA and Singapore possess the strongest and stronger infinite supports for both time regimes.

The REITs markets, however, developed, are of wide varied sizes. This makes comparison less than ideal. This is mitigated by a univariate analysis rather than multivariate one.

This paper is a reminder of the inadequacy of the normal distribution, as well as the mean, variance, skewness and kurtosis measures, in describing distributions of asset returns. Investors and policymakers may look at the location and scale of GLD for decision-making about REITs.

The novelty of this work lies with the data used and the detailed analysis and for the post-GFC sample.

The purpose of this study is to introduce a matching function approach to analyze matching in financial reporting.

The matching function is first analyzed analytically. It is specified as a multiplicative Cobb-Douglas-type function of three categories of expenses (labor expense, material expense and depreciation). The specified matching function is solved by the generalized reduced gradient method (GRG) for 10-year time series from 8,226 Finnish firms. The coefficient of determination of the logarithmic model (CODL) is compared with the linear revenue-expense correlation coefficient (REC) that is generally used in previous studies.

Empirical evidence showed that REC is outperformed by CODL. CODL was found independent of or weakly negatively dependent on the matching elasticity of labor expense, positively dependent on the material expense elasticity and negatively dependent on depreciation elasticity. Therefore, the differences in matching accuracy between industries emphasizing different expense categories are significant.

The matching function is a general approach to assess the matching accuracy but it is in this study specified multiplicatively for three categories of expenses. Moreover, only one algorithm is tested in the empirical estimation of the function. The analysis is concentrated on ten-year time-series of a limited sample of Finnish firms.

The matching function approach provides a large set of important information for considering the matching process in practice. It can prove a useful method also to accounting standard-setters and other specialists such as managers, consultants and auditors.

This study is the first study to apply the new matching function approach.

The purpose of this paper is to better understand the possible reasons behind gender pay disparities, focussing on the unique features of male and female human capital and their wage returns. Despite increasing convergence of male and female human capital attainments, substantial differences remain. Extraction of human capital components non-overlapping across genders provides more profound explanation of the unexplained wage gap of men and women.

Starting with the non-parametric matching-based decomposition technique, the authors extend the pay gap estimation framework and focus on males and females having no counterpart in a set of characteristics within the opposite gender. The authors identify gender-unique human capital in terms of differences in distribution of individual characteristics across men and women and gender-specific combination of human capital characteristics. Wage returns to gender-specific profiles are evaluated applying wage regression on both full distribution of earnings and wage quantiles. The research relies on the Survey of Adult Skills (PIAAC) database for Estonia, which incorporates both formal education and cognitive skill records.

The study identifies sets of characteristics and competencies exclusive for both genders, proving that male and female profiles cannot be directly compared. The results suggest that men possess high individual and combined abilities in numeracy and problem solving in technology-rich environment, not always reached by females. This potentiates men’s higher earnings in spite of their generally lower formal educational attainments. Wage gap analysis over the full distribution of earnings shows even larger “glass ceiling” effect for females, possessing woman-specific human capital.

The authors raise a research from a novel perspective towards a role of human capital in gender wage inequality. Instead of usual reference to observable gaps in male and female characteristics, the authors identify the gender-specific human capital profiles, to a large extent non-reached by the opposite gender. Analysed associations between gender-specific characteristics and earnings provide an insight to possible effects of gender-unique human capital on a male-female wage disparity.

The purpose of this paper is to quantify the wage gap between native and immigrant women in Spain, taking into account differences in their characteristics and the need to control for common support. If immigrant women are segregated in occupations with few native women, it is important to take this into account to analyse wage differentials between both collectives.

Microdata from the Continuous Sample of Working Histories (Muestra Continua de Vidas Laborales) on wages and other personal characteristics such as gender, country of origin, and age were used to apply the matching procedure and the decomposition of the wage gap, along the lines of Ñopo, for the analysis of wage differentials between native and immigrant women. The advantage of this procedure is that one can simultaneously estimate the common support and the mean counterfactual wage for the women on the common support (i.e. comparing native and immigrant women with similar observable characteristics). In addition, differences not only at the mean but also along the entire wage distribution can be described.

The results obtained indicate that, on average, immigrant women earn less than native women in the Spanish labour market. This wage gap is bigger when immigrant women from developing countries are considered, but the authors’ main finding is that an important part of this wage gap is related to differences in common support (i.e. immigrant women are segregated in certain jobs with low wages different from those occupied by native women). If the need to control for common support is neglected, estimates of the wage gap will be biased.

Studying the case of Spain is particularly interesting because it is a country with abundant and recent immigration. Immigrant women account for more than half of the total immigrants in Spain, and unlike other host countries, they come from a highly varied range of countries, with origins as diverse as Latin America, the Maghreb and Eastern Europe. To the authors’ knowledge, no other study has explicitly focused on the analysis of the wage differential of immigrant women in the Spanish labour market by taking into account the need to control for common support. Moreover, published papers illustrating the potentiality of Ñopo's methodology are also very scarce.

The purpose of this paper is to examine the effect of coffee production on poverty among smallholder farmers.

National Household Survey data for Uganda were triangulated with qualitative field data. A mix of propensity score matching (PSM) and quantile treatment effect techniques was employed.

The results reveal a significant effect of coffee production on poverty reduction, through incremental household consumption expenditure. Households engaged in coffee production are associated with a lower incidence of poverty. The interesting evidence suggests that coffee production is a pro-poor intervention. These findings are confirmed by qualitative assessment that reveals farmers’ welfare improved to greater extent to satisfactory levels from coffee income.

Econometrically robust strategies were employed to ensure minimal estimation bias; however, the authors are mindful of PSM limitation of selection on observables.

This paper is part of a limited body of literature that combines quantitative and qualitative assessment, a growing issue in contemporary research. In addition to employing one of the conventional impact evaluation techniques, the paper accounts for heterogeneity in the effects of coffee production.

In this study, the authors argue that the private information obtained and transmitted by institutions during the corporate visits can alleviate the degree of information asymmetry between firms and investors, so institutional visits may influence investors' heterogeneous beliefs. Therefore, the authors investigated whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs.

This study examines whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs using the data of A-share companies from the Shenzhen Stock Exchange (SZSE) during 2013–2019. Using empirical research method, this study designs and conducts an empirical research according to empirical research's basic norms.

The authors find that institutional visits effectively decrease investors' heterogeneous beliefs, especially institutional investors. Meanwhile, institutional site visits and sell-side institutional visits have a more significant negative effect on investors' heterogeneous beliefs. The findings remain after robustness tests with the alternative variable, instrumental variable, propensity score matching and quantile regression methods.

The development of China's capital market is imperfect, resulting in a strong speculative atmosphere. So, investors' irrational investment behaviors occur from time to time, leading to sizeable heterogeneous beliefs in China's capital market, which increases the risk of investment and is not conducive to the discovery of corporate value and the efficient allocation of resources. Therefore, exploring the factors influencing heterogeneous beliefs and finding ways to alleviate heterogeneous beliefs can reduce the proportion of speculative investors and promote the healthy development of China's capital market.

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.

Overnight risk is of particular interest for many market participants including traders who provide liquidity to the market, but also to market participants with longer investment horizons who want to determine whether a given risk–return tradeoff can justify possible intermediate portfolio hedging transactions. Overnight risk may in particular play a highly significant role in emerging markets, given that information is incorporated into prices at a slower rate and liquidity may hinder a quick unwinding of portfolio positions.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.