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Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

This study reexamines fiscal deficit sustainability in South Africa.

The study applies three cointegration testing approaches, namely testing for multiple structural changes in a cointegrated regression model, time-varying cointegration test and asymmetric cointegration test.

The results point to the existence of a level relationship between government revenue and spending. In addition, the long-run equilibrium relationship between government revenue and spending in South Africa is found to be characterized by breaks. As such, assuming a constant cointegrating slope may be misleading. Results from time-varying cointegration and an estimation of a cointegrated two-break model indicate that cointegrating coefficient has been time-varying but has remained less than 1 for the entire study period, indicating that fiscal deficits have been weakly sustainable. This finding is also confirmed by the results from an estimated asymmetric error correction model.

In view of the findings, authorities should put in place policies to improve the fiscal budgetary stance and reinforce the sustainability of the fiscal deficits in South Africa. Among other things, South Africa could undertake reforms to state-owned companies to reduce their reliance on public funds, slow down the pace of the public sector wage growth and devise effective economic measures to boost long-term growth. In addition, tax compliance and other revenue collection measures should be enhanced for additional tax revenue.

The contribution of this study is twofold; first, the study uses a long series of annual data spanning over a century, from 1913 to 2020. Indeed, cointegration is better modeled using long spans of time series data. Second, to examine the existence of a level relationship between spending and revenue, the study uses cointegration tests which allow capturing time-variation in the cointegrating slope coefficient, and accounting for asymmetries in the relationship between government spending and revenue. It is important to allow for time-variation in the cointegrating slope coefficient, especially when it has been hardly treated in the empirical literature on fiscal deficit sustainability. Allowing for time-variation in the cointegrating slope coefficient helps us to analyze fiscal deficit sustainability by periods of time. Indeed, the degree of fiscal sustainability can change from one time period to another.