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The purpose of this paper is to present a theoretical model, which is aggregated across individuals to analyse the labour force participation rate, and empirical results to provide evidence of a U-shaped relationship between women’s labour force participation and economic development.

The U-shaped relationship is investigated by employing a panel data approach of 40 countries around the world over the period 1960–2005. It is investigated whether the labour force behaviour of women in different age groups can be lumped together by considering ten different age groups.

The paper finds evidence in favour of the U-shaped relationship. For every age group and explanatory variable in the model, a particular point is found where the regime of falling participation rates changes into a regime of rising participation rates.

To evaluate this relationship, microeconomic analysis with primary data can also provide significant insights.

Every country can narrow the gap between the labour participation rates of men and women in the long term. Fertility decline, shifts of employment to services, part-time work, increased opportunities in education, and the capital-to-labour ratio as a measure for economic development are the key determinants.

In addition to the U-shaped relationship, considerable research has been carried out on demographic transition. This paper brings these two strands of literature together, by econometrically investigating the impact of demographic transition on female labour force participation given its U-shaped relation with economic development, i.e., turning points for different explanatory variables are calculated and their implications for economic growth are discussed.

This paper tests the feasibility and empirical implications of a spatial econometric model with a full set of interaction effects and weight matrix defined as an equally weighted group interaction matrix applied to research productivity of individuals. We also elaborate two extensions of this model, namely with group fixed effects and with heteroskedasticity. In our setting, the model with a full set of interaction effects is overparameterised: only the SDM and SDEM specifications produce acceptable results. They imply comparable spillover effects, but by applying a Bayesian approach taken from LeSage (2014), we are able to show that the SDEM specification is more appropriate and thus that colleague interaction effects work through observed and unobserved exogenous characteristics common to researchers within a group.

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California.

A spatial Durbin error model is used with sales price data for 1,693 homes sold in Kern County in the third quarter of 2018. This paper compares 90 different spatial econometric models using Bayesian techniques to produce posterior model probabilities which guided model selection and the number of neighbors to use.

The results show that significant spatial dependence exists in home values in Kern County. Point estimates indicate that homes abutting golf courses are valued at less than those which are not. This study also finds that the farther away from golf courses the average home is, the higher its value.

This study contributes to the existing literature in three dimensions. First, this paper analyzes whether proximity to golf courses impacts home values in Kern County where a study of this nature has not been conducted. Second, the analysis uses transaction data for 2018 which was a period when the sport’s popularity was fading and golf courses closing. Third, Bayesian model comparison techniques are used to select the appropriate model.

There is a lack of understanding in the literature on the relation between parks and house price in relation to crime in Scandinavian context. This paper aims to investigate the effect of the amenity value of accessibility to parks on apartment prices with reference to crime rates in parks in Stockholm.

This paper analyses the effects of park proximity and crime in parks on apartment prices by using geographic information systems and hedonic modelling.

Findings show that the proximity of parks as an environmental amenity has an effect on apartment prices. The results also demonstrate that the impact of parks on apartment prices is different in the different segments of the apartment market in Stockholm. Moreover, various types of parks may differ in their impact, for instance, grass parks and park blocks are more desirable in Stockholm than landscape parks and neighbourhood parks. The effects of crimes in parks influence apartment prices negatively.

This paper provides a new methodology by using the shortest distance to a park as a main variable. The shortest distance to a park variable is considered a better choice than using park as an aggregate measure. To the best of the author’s knowledge, this is the first study that investigates the effect of specific park types, for instance, grass parks, neighbourhood parks, landscape parks and park blocks, in Stockholm housing market.

The study attempts to establish the relationship between demographic dividend and GDP growth rate by utilising panel data from 1990 to 2017 in Bangladesh, India, Pakistan, Nepal and Sri Lanka.

This study employs the pooled OLS model, using data from the World Bank's database for the period 1990–2017 for five selected South Asia Association for Regional Cooperation (SAARC) countries.

The results reveal that demographic dividend affects economic growth in Bangladesh, India, Nepal, Sri Lanka and Pakistan, thereby supporting the demographic dividend hypothesis. For the country-specific analysis, it was also observed that demographic dividend impacts the economic growth of the five SAARC countries. In addition, growth of gross capital formation is highly significant for both aggregated and country-specific analyses. However, economic growth is unaffected by trade openness and unemployment rates. Moreover, the rate of labour force participation is negatively related to the GDP growth rate in the aggregated model.

This paper bestows insight into the fact that the impact of demographic dividend on the economic growth of the SAARC regions cannot be fully actualised if the workforce are underutilised. This region needs to adopt appropriate policies to strengthen the considerable benefits of demographic dividend on the economic growth.

The purpose of this paper is to investigate the major determinants of China’s outward foreign direct investment (OFDI) in the economies along the “Belt & Road” Initiative (BRI afterward). China works on to advance the agenda of the BRI both at home and abroad. The BRI is set up to promote connectivity in five key areas: policy coordination, infrastructure connectivity, trade facilitation, financial cooperation and people-to-people contacts.

The existing literature is inconclusive with regards to the motives, patterns and determinants of the Chinese OFDI. The authors are, therefore, motivated to undertake this study to shed some new light on the influencing factors of the Chinese OFDI. The authors have made a unique data set that consists of China and its 64 partnering countries of the BRI over a time period of 12 years spanning from 2004 to 2015. This time period is chosen on the chief consideration of data availability. The authors have a balanced panel, and applied the gravity model in line with the theoretical arguments and econometric developments.

The paper assumes that China’s OFDI along the BRI was a function of gross domestic product (GDP), income per capita, distance and WTO. The findings showed that GDP, per capita income and distance were the key determinants of the OFDI. China’s entry into the WTO did not strongly affect the OFDI. China maintained a tradition of historical relationships along the BRI economies. After all, China is relocating its investment resources in line with the consideration of its partnering countries’ economic size, cross-border distance and per capita income.

This study is the first of its kinds to analyze the determinants of OFDI by means of gravity model. The authors have covered all the countries along the BRI. Hence, this paper aims to make a substantial contribution to the literature, both from a scientific and a policy perspective.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.