A SPATIAL ECONOMETRIC STAR MODEL WITH AN APPLICATION TO U.S. COUNTY ECONOMIC GROWTH, 1969-2003

Spatial regression models incorporating non-stationarity in the regression coefficients are popular. We propose a spatial variant of the Smooth Transition AutoRegressive (STAR) model that is more parsimonious than commonly used approaches and endogenously determines the extent of spatial parameter variation. Uncomplicated estimation and inference procedures are demonstrated using a neoclassical convergence model for United States counties.spatial autoregression, smooth transition, spatial econometrics, STAR, GWR

REGIONAL HOUSING PRICE CYCLES: A SPATIO-TEMPORAL ANALYSIS USING US STATE LEVEL

We present a study of the effects of macroeconomic shocks on housing prices in the Western United States using quarterly state level data from 1988:1 – 2007:4. The study contributes to the existing literature by explicitly incorporating locational spillovers through a spatial econometric adaptation of vector autoregression (SpVAR). The results suggest these spillovers may Granger cause housing price movements in a large number of cases. SpVAR provides additional insights through impulse response functions that demonstrate the effects of macroeconomic events in different neighboring locations. In addition, we demonstrate that including spatial information leads to significantly lower mean square forecast errors.Housing prices, VAR, spatial econometrics

Technological Leadership, Human Capital and Economic Growth: A Spatial Econometric Analysis for U.S. Counties, 1969-2003

The traditional view of cities as monocentric conglomerates of people clustered around an employment center, driving economic growth in cities that subsequently trickles down to the hinterland, is increasingly being challenged. In particular, the role of space, technological leadership, human capital, increasing returns to scale and industrial clustering as well as hierarchical organization principles have been emphasized in the more recent literature. This paper utilizes exploratory and spatial econometric data analysis techniques to investigate these issues for U.S. counties using data from 1969 through 2003. Ultimately, contiguous and hierarchical organization and interaction patterns are captured using an endogenous growth model allowing for spatial effects, inspired by earlier work on human capital and technology gaps. We investigate a neoclassical growth model and compare it to a spatial version of an endogenous growth model allowing for “domestic” investment in human capital and catch-up to the technology leader, and find that human capital strongly contributes to growth in a neoclassical setting, but much less so in an endogenous setting. In the endogenous model the catch-up term dominates in comparison to “domestic” human capital effects.economic growth, human capital, technological leadership

The Role of Knowledge Externalities in the Spatial Distribution of Economic Growth: A Spatial Econometric Analysis for US Counties, 19692003

The traditional view of cities as monocentric conglomerates of people clustered around an employment center, driving economic growth in cities that subsequently trickles down to the hinterland, is increasingly being challenged. In particular, the role of space, technological leadership, human capital, increasing returns to scale and industrial clustering as well as hierarchical organization principles have been emphasized in the more recent literature. This paper utilizes exploratory and spatial econometric data analysis techniques to investigate these issues for US counties using data from 1969 through 2003. Ultimately, contiguous and hierarchical organization and interaction patterns are captured using an endogenous growth model allowing for spatial effects, inspired by earlier work on human capital and technology gaps. We investigate a neoclassical growth model and compare it to a spatial version of an endogenous growth model allowing for "domestic" investment in human capital and catch-up to the technology leader, and find that human capital strongly contributes to growth in a neoclassical setting, but much less so in an endogenous setting. In the endogenous model the catch-up term dominates in comparison to "domestic" human capital effects.Labor and Human Capital,

Modeling Non-Linear Spatial Dynamics: A Family of Spatial STAR Models and an Application to U.S. Economic Growth

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.spatial econometrics, non-linearity, utoregressive smooth transition, Research Methods/ Statistical Methods, C12, C21, C51, O18, R11,

Employment Growth and Income Inequality: Accounting for Spatial and Sectoral Differences

This paper revisits the inequality-growth relationship accounting for sectoral differences and focusing on US counties. For 8 two-digit industries of the NAICS classification, we estimated a conditional growth model where employment growth depends on regional income inequality and a number of control variables. Spatial econometrics techniques are used to account for spatial dependence. Results indicate that there is no association between employment growth and family income inequality for the Agriculture, Forestry, Fishing and Hunting sector and the Real Estate, Rental and Leasing sector. However, income inequality consistently shows a negative impact on employment growth in the construction sector, and results are mixed for other sectors such as: Manufacturing; Retail Trade; Professional Scientific and Technical Services; Accommodation and Food Services; Educational Services. In several sectors, mixed results were obtained when differentiation is made between urban and rural samples.employment growth, inequality, spatial dependence, Community/Rural/Urban Development, R0, R11, O15, D30,

Growth and Technological Leadership in US Industries: A Spatial Econometric Analysis at the State Level, 1963-1997

For several decades, cross-country analyses have dominated the literature on economic growth. Recently, these analyses have been extended to include sectoral variation as well as spatial variation across sub-national regions. This paper investigates economic growth and potential determinants of the process of catch-up to technology leaders for several economic sectors, using data for the lower 48 US states from 1963 through 1997. We analyze the potential influence of factors such as human capital, and geographical distance to the technology leader. A spatially explicit growth model in which technological progress is endogenously determined is used to model productivity growth in nine US industries, ranging from mining to government, and including a combined sector of totals. The results indicate that none of the sectors exhibits ó-convergence, but they all show strong evidence of â-convergence with a convergence club pattern that is apparent for the wholesale/retail sector. The catch-up effect to the technology leader dominates the growth process in almost all sectors, and it works through the interaction with human capital.regional economic growth, convergence, industry level, technological leadership, spatial econometrics, Industrial Organization, C21, I23, O33, R12,

Guidance and Technology: An Assessment of Project Intervention and Promoted Technologies

This study used primary data, collected as part of theCereal Systems Initiative for South Asia (CSISA) project tocompare net returns and cost efficiency between farmers who arebeneficiaries of the project to farmers who are not beneficiaries.Additionally, non-beneficiary farmers who use the promotedtechnologies from the project are compared to other nonbeneficiaryfarmers who do not use the promoted technologies.Propensity score matching is used to account for selection biaswhen comparing the outcomes of beneficiary and control groups.Results indicate higher return for project recipients as well asfarmers who use the CSISA promoted resource-conservingtechnologies (RCTs)

What do we know about the future of rice in relation to food system transformation?

Food, land, and water systems face daunting challenges in the future, and the body of research exploring these challenges is growing rapidly. This note is part of a series developed by the CGIAR Foresight Initiative to summarize what we know today about the future of various aspects of food systems. The goal of these notes is to serve as a quick reference, point to further information, and help guide future research and decisions. Key messages Global rice production remains more stable than maize and wheat in recent years, while rice consumption continues to increase, albeit at a slower pace. Rice production and consumption is projected to increase worldwide, and Asia to continue as the world’s leading source of rice through 2050. Southeast Asia’s rice surplus will increase by 2040 by closing the exploitable yield gap by half. The global rice sector will experience an increasing economic surplus and declining number of undernourished children and population at risk of hunger with faster productivity growth. Demographic changes and rice trade policy reforms will be the main drivers of rice demand and prices in rice-producing and rice-importing countries

Economic Growth in the Philippines: A Spatial Econometrics Analysis at the Provincial Level, 1991 – 2000.

Investigating the determinants of economic growth remains a long research tradition in the economic growth literature. Most studies in this literature have tried to link economic growth and different economic factors using either neoclassical growth theories or endogenous growth approaches. These studies apply these growth theories to identify the factors responsible for the observed differences/disparities between regions or countries. While early studies focused on cross-country analyses, the recent most studies consider regions or sub-national entities as unit of analysis. This has raised the question of whether theories developed for cross-country analysis could be automatically applied for regional or sub-national analysis. Given the profound difference between nations and regions in terms of degree of openness, theories developed in cross-country analysis may not be automatically applied in regional analysis (see Mangrini, 2004). However, properly accounting for the spatial interaction effects may provide a way to use these theories in regional analysis. Regional analysis of economic growth has therefore spurned the development of specialized quantitative methods designed to account for the spatial dimensions of higher resolution, spatially referenced data. The goal of this research is to investigate the process of regional economic growth in the Philippines focusing on provincial data. Previous studies on regional growth within the Philippines have analyzed the regional growth process following neoclassical growth models or endogenous growth models without explicitly modeling spatial dependence between regions and the role of spillover effects. Traditional growth regressions with ordinary least squares may yield biased or inconsistent estimates if spatial autocorrelation is present but have been accounted for. This paper uses spatial econometrics techniques to estimate three theoretical growth models: the unconditional growth model, the Solow model and the Mankiw Romer and Weil model. Investment and human capital were found to be the main drivers of economic growth.Community/Rural/Urban Development, Research and Development/Tech Change/Emerging Technologies, Research Methods/ Statistical Methods,