Morphological study of the ancient dogs from three neolithic sites in China

Ancient Chinese dogs excavated at three archaeological sites and stored in the Institute of Vertebrate Paleontology and Paleoanthropology, Academia Sinica, were the object of this study (Xiawanggang site, 5000–6000 years BP; Keshengzhuang site, 3000–4000 years BP; Shangsunjia site, Bronze age). During these periods, there were large morphological variations in Chinese dogs beyond a single dog class, as with Jomon dogs. Dogs with a large ‘stop’ were already present in China but not in Japan. At the Keshengzhuang site, middle to large class dogs were also excavated. At least three types were confirmed within these dogs. In the aged dog in Xiawanggang, antemortem loss of many teeth was observed suggesting active use of the teeth and it is possible that this dog was used for hunting as were the Japanese Jomon dogs. © 1998 John Wiley & Sons, Ltd.     

Racisms compared: Fascist Italy and ultra-nationalist Japan

This article tries to compare the racisms of Fascist Italy and ultra-nationalist Japan as evidenced by foreign policy, attempting to categorize them into three dimensions: the inner logic of the nation state; the justification of expansionist foreign policies; and as an accelerator of cruelty in war. It also surveys Italo-Japanese relations by choosing some points of contact to scrutinize how different racisms reacted to each other.     

DISPERSION FORMS: AN INTERACTION OF MARKET ACCESS,COMPETITION, AND URBAN COSTS*

ABSTRACT This paper analyzes a two‐region model including multiple industries with different transport costs. Two results are derived. First, dispersion occurs for small transport costs, but the specific dispersion patterns depend on the level of urban costs. This results from an interaction of the market‐access effect on consumers, the market‐access effect on firms, the competition effect, and the urban‐cost effect. Second, decreasing transport cost tends to let industries with lower transport costs disperse, although the shares of industries locating in the larger region are not in order of their transport costs. We further provide some empirical data concerning the second result.     

Abolition of Leprosy Isolation Policy in Japan

The termination of a policy is often considered an important step for correcting existing policies and allocating resources efficiently. Scientific advances, for example, may make the existing policy obsolete and unjustifiable. Delay in terminating a flawed policy may result in increasing its harm. In many cases, however, it is suggested that inertia and other obstacles must be overcome before a termination can be accomplished. Health policy is no exception to these findings.
The isolation of leprosy patients, a practice introduced early in this century, was maintained in Japan even after it proved scientifically unnecessary. It was only after a few decades of inertia and political struggles that the policy was abolished. As can be seen in previous studies in the other domains, a set of obstacles, such as intellectual reluctance, opposition of vested interests, and bias in expert opinions, existed, and hampered the timely termination of the isolation policy. Over a long time, policy adaptation was gradually made in practice through the loose administration of policy. It was only by the leadership of a skillful terminator that the issue was finally brought to the forefront, consensus achieved among key actors, and the policy abolished.     

Application of LASSO to the Eigenvector Selection Problem in Eigenvector‐based Spatial Filtering

Eigenvector‐based spatial filtering is one of the often used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, a subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the selection procedure of the forward stepwise model, but it is disappointingly slow when the observations n take a large number. Hence, as a complement or alternative, the present article proposes the use of the least absolute shrinkage and selection operator (LASSO) to select the eigenvectors. The LASSO model selection procedure was applied to the well‐known Boston housing data set and simulation data set, and its performance was compared with the stepwise procedure. The obtained results suggest that the LASSO procedure is fairly fast compared with the stepwise procedure, and can select eigenvectors effectively even if the data set is relatively large (n = 10⁴), to which the forward stepwise procedure is not easy to apply.     

ECONOMIC GEOGRAPHY OF FIRMS AND SKILLED LABOR*

ABSTRACT This paper investigates the relationship between firm location and skilled‐labor location. While existing new economic geography (NEG) models could not explicitly analyze the relationship due to their assumptions, I construct a new NEG‐type model allowing for different location dynamics of firms and skilled labor for this objective. The main results are as follows. First, a relatively large pool of skilled labor attracts firms when trade costs are small, while it might repel firms when trade costs are sufficiently large. Second, assuming that skilled workers are mobile between regions, the model shows that skilled workers agglomerate faster than firms with decreasing trade costs. Third, the model supports the hypothesis that firms follow skilled labor rather than the reverse. These results are consistent to Indian and Chinese experiences, and some “creative‐class” or “skilled‐city” stories.     

Weighted‐Average Least Squares Applied to Spatial Econometric Models: A Monte Carlo Investigation

Recently, model averaging techniques have been employed widely in empirical investigations as an alternative to the conventional model selection procedure, a procedure criticized because it disregards a major component of uncertainty, namely, uncertainty regarding the model itself, and, thus, it leads to the underestimation of uncertainty regarding the quantities of interest. Bayesian model averaging (BMA) is one of the most popular model averaging techniques. Some studies indicate that BMA has cumbersome aspects. One of the major practical issues of using BMA is its substantial computational burden, which obstructs the process of obtaining exact estimates. A simulation method, such as Markov chain Monte Carlo (MCMC), is required to resolve this problem. Weighted‐average least squares (WALS) estimation has been proposed as an alternative to BMA. The computational burden of WALS estimation is negligible; therefore, it does not require the MCMC method. Furthermore, WALS estimation has theoretical advantages over BMA estimation. This article presents two contributions to the WALS literature. First, it applies WALS to spatial lag/error models in order to consider spatial dependence. Second, it extends WALS in order to consider explicitly the problem of multicollinearity by employing the technique of principal component regression. The small sample properties of the estimators of the proposed models are examined using Monte Carlo experiments; the results of these experiments suggest that the standard WALS may produce biased estimates when the underlying data‐generating process is a spatial lag process. Results also indicate that when the correlation among the regressors is high, the standard WALS estimators may suffer from large variances and root mean squared errors. Both of these problems are significantly mitigated by using the proposed models. Las técnicas de promediado de modelos (model averaging) vienen siendo empleadas con creciente frecuencia en las investigaciones empíricas como una alternativa a los procedimientos convencionales de selección de modelos estadísticos. Dichos procedimientos convencionales han sido criticados por no tomar en cuenta un componente clave de la incertidumbre: la incertidumbre del modelo en sí, y por lo tanto, conducen a la subestimación de la incertidumbre en la cuantificación de las valores estimados. El promediado bayesiano de modelos (Bayesian Model Averaging‐BMA) es una de las técnicas de promediado más usadas. Algunos estudios indican que BMA tiene aspectos engorrosos: uno de los principales aspectos prácticos a considerar en su uso es su pesada carga computacional, la cual obstruye el proceso de obtención de estimaciones exactas. Esta limitación hace necesario el uso de métodos de simulación, como el de la cadena de Markov de Monte Carlo (Markov Chain Monte Carlo‐MCMC). La estimación de mínimos cuadrados usando un promediado ponderado (Weighted‐Average Least Squares‐WALS) ha sido propuesta como alternativa a BMA. La carga computacional de la estimación WALS es mínima y por lo tanto no requiere del uso de MCMC. Más aun, la estimación WALS posee ventajas teóricas sobre BMA. Este artículo presenta dos contribuciones a la literatura especializada de WALS. En primer lugar, aplica WALS a modelos espaciales tipo lag/error con el fin de incorporar la dependencia espacial. En segundo lugar, modifica el método WALS, a fin de considerar explícitamente el problema de la multicolinealidad entre variables mediante el empleo de la técnica de regresión de componentes principales (Principal Component Regression‐PCR). Luego los autores utilizan experimentos Monte Carlo para examinar las propiedades de tipo “muestra pequeña” (small simple) de los estimadores de los modelos propuestos. Los resultados de los experimentos sugieren que el método WALS estándar puede producir estimaciones sesgadas cuando el proceso generador de datos subyacente (Data Generating Process‐DGP) es un proceso de retardo espacial (Spatial Lag Process‐SLP). Los resultados también indican que cuando la correlación entre las variables es alta, los estimadores estándar de WALS pueden padecer de varianzas y errores cuadráticos medios (root mean squared errors‐RMSEs) atípicamente grandes. Ambos problemas son mitigados significativamente mediante el uso de los modelos propuestos en el presente artículo. 近来,模型平均技术作为与传统模型选择流程可替换的方法,在经验调查中得到广泛应用。传统的模型选择流程忽视了模型本身的不确定性,进而低估了感兴趣样本数量的不确定性而受到批评。贝叶斯模型平均技术(BMA)是最为流行的模型平均技术之一。但已有研究表明,BMA在某些方面较为繁琐复杂,一个最主要的问题是其巨大的计算负荷阻碍模型了精确估计的过程,因此需要利用马尔可夫‐蒙特卡洛(MCMC) 之类的模拟方法进行解决。加权平均最小二乘(WALS)估计可作为BMA的可替换方法,其优点在于计算负荷可以忽略不计,因此不需要采用MCMC方法解决计算负荷问题。此外,WALS估计相比于BMA估计在理论上有一定的优势。本文针对WALS的贡献有两点：将WALS应用于空间滞后/空间误差模型以考虑空间依赖性,并利用主成分回归(PCR)拓展WALS以明确考虑多重共线性问题。本文利用蒙特卡洛实验对所提模型估计的小样本特征进行测试,结果显示当潜在数据生成过程(DGP)是一个空间滞后过程时,标准WALS可能产生有偏估计;此外,当回归量的相关性较高时,标准WALS估计量可能有较大的方差和根均方差(RMSEs).而本文提出的加权平均最小二乘估计模型能很好地缓解这两个问题。     

Dental calculus and other dental disease in a human skeleton of the Okhotsk Culture unearthed at Hamanaka-2 site,Rebun-Island,Hokkaido, Japan

A human skeleton of the Okhotsk Culture from the Hamanaka-2 site of Rebun Island, northern part of Hokkaido, was found with abnormally large deposits of dental calculus, especially on the right upper 2nd and 3rd molars. This may relate to the early loss of the corresponding right lower molars. Root apex cementum hyperplasia and resorption of alveolar bone due to periodontal disease were also observed; these may have been associated with the calculus. The severe dental calculus and other diseases observed appear to have nothing to do with the subsistence pattern and ethnological background of the Okhotsk Culture, but rather with the individual's poor oral hygiene and digestive dysfunction.