Bayesian Estimation of Spatial Externalities Using Regional Production Function: The Case of China and Japan

This paper used regional panel data for Chinese provinces from 1979 to 2003, and for Japanese prefectures from 1955 to 1998, to estimate the spatial externalities (or spatial multiplier effects) using a production function and Bayesian methodology, and to investigate the long-run behavior of the spatial externalities of each country. According to the estimation results, China's spatial externalities increased its domestic production significantly after 1994, which tended to increase until 2003. Before 1993, however, its spatial externalities were not significant. Japan's spatial externalities showed fluctuating values throughout the sample period. Furthermore, the movement of the spatial externalities was correlated with Japan's business conditions: the externalities showed a high value in the economic boom, and a low value in the economic depression. This could mean that spatial externalities depend mainly on business conditions.Spatial Externalities; Bayesian Estimation; Production Function

Bayesian estimation of spatial externalities using regional production function: the case of China and Japan

This paper used regional panel data for Chinese provinces from 1979 to 2003, and for Japanese prefectures from 1955 to 1998, to estimate the spatial externalities (or spatial multiplier effects) using a production function and Bayesian methodology, and to investigate the long-run behavior of the spatial externalities of each country. According to the estimation results, China's spatial externalities increased its domestic production significantly after 1994, which tended to increase until 2003. Before 1993, however, its spatial externalities were not significant. Japan's spatial externalities showed fluctuating values throughout the sample period. Furthermore, the movement of the spatial externalities was correlated with Japan's business conditions: the externalities showed a high value in the economic boom, and a low value in the economic depression. This could mean that spatial externalities correlate mainly with business conditions.Spatial externalities, Bayesian estimation, Production function

The sustainability of trade balances in Sub-Saharan Africa: panel cointegration tests with cross-section dependence

This paper investigates the sustainability of trade balances in the Sub-Saharan African regions, using both the panel unit root (IPS) test proposed by Im et al. (2003) and the cross-sectionally augmented version of the IPS (CIPS test) suggested by Pesaran (2007), where the former test is based on the assumption of cross-section independence and the latter allows for it. On the one hand, the empirical results based on the IPS test indicate that the balance of trade in Sub-Saharan African regions is sustainable. On the other hand, the empirical results of the CIPS test reveal that it is not. Since cross-section dependence was recognized using the CD test developed by Pesaran (2004), the empirical results based on the IPS test could be spurious.sustainability; trade balances; Sub-Saharan Africa; CIPS test

Small sample properties of CIPS panel unit root test under conditional and unconditional heteroscedasticity

This paper used Monte Carlo simulations to analyze the small sample properties of cross-sectionally augmented panel unit root test (CIPS test). We considered situations involving two types of time-series heteroskedasticity (unconditional and ARCH) in the unobserved common factor and idiosyncratic error term. We found that the CIPS test could be extremely robust versus the two types of heteroskedasticity in the unobserved common factor. However, we found under-size distortion in the case of unconditional heteroskedasticity in the idiosyncratic error term, and conversely, over-size distortion in the case of ARCH. Furthermore, we observed a tendency for its over-size distortion to moderate with low volatility persistence in the ARCH process and exaggerate with high volatility persistence.panel unit root test; CIPS test; heteroskedasticity; cross-section dependence

Market power of China\u27s state-owned firms : evidence from manufacturing firm-level data

There has been a great discussion about a phenomenon: Guojin Mintui (i.e., the state advances, the private sector retreats) since the latter half of the 2000s. Has the stateowned sector been expanding and undermining private enterprises? To address this issue, this paper estimates changes in markups of China\u27s state-owned firms from 2003 to 2007, using manufacturing firm-level data. It is found that the relative markup of the state sector is smaller than those of the private and foreign sectors, while it tends to steadily increase and be catching up with the private and foreign sector during 2004--2007; However, the catching up process is not observed in surviving firms. This implies that the exit of the state-owned firms with lower markups causes the increase in the average markups of the state sector. In terms of the relative markups in the manufacturing sector for 2003 to 2007, this study does not support the argument of Guojin Mintui

Allocation efficiency in China : an extension of the dynamic Olley-Pakes productivity decomposition

This paper develops a quantitative measure of allocation efficiency, which is an extension of the dynamic Olley-Pakes productivity decomposition proposed by Melitz and Polanec (2015). The extended measure enables the simultaneous capture of the degree of misallocation within a group and between groups and parallel to capturing the contribution of entering and exiting firms to aggregate productivity growth. This measure empirically assesses the degree of misallocation in China using manufacturing firm-level data from 2004 to 2007. Misallocation among industrial sectors has been found to increase over time, and allocation efficiency within an industry has been found to worsen in industries that use more capital and have firms with relatively higher state-owned market shares. Allocation efficiency among three ownership sectors (state-owned, domestic private, and foreign sectors) tends to improve in industries wherein the market share moves from a less-productive state-owned sector to a more productive private sector

Allocation efficiency in China\u27s state-owned, private, and foreign sector firms

Despite the fact many scholars have shown an interest in China\u27s allocation efficiency, few studies have examined quantitative analysis of allocation efficiency within and between the state-owned and private sectors. To address this issue, this paper develops a quantitative measure of allocation efficiency, which is an extension of the dynamic Olley-Pakes productivity decomposition proposed by Melitz and Polanec (2015). The extended measure enables the simultaneous capture of the degree of misallocation within a group and between groups and parallel to capturing the contribution of entering and exiting firms to aggregate productivity growth. Using China\u27s manufacturing firm-level data from 2003 to 2007, the author examine the efficiency of resource allocation within and between three ownership sectors (state-owned, domestic private, and foreign sectors). It is found that the between allocation efficiency tends to improve in industries wherein market shares move from the less-productive state sector to the more-productive private sector

Has China's Interregional Capital Mobility Been Low? A Spatial Econometric Estimation of the Feldstein-Horioka Equation

We conducted a Feldstein-Horioka test for the degree of China's inter-provincial capital mobility each year from 1978 to 2007 using the spatial error model (SEM), a model of spatial econometrics considering spatial dependence, and a data set reflecting revision of historical national and provincial accounts after China's first economic census in 2004. We found that the likelihood ratio test rejected the null of no spatial error correlation, or the appropriateness of the standard OLS model (OLSM), for 17 out of 30 years and that the Akaike information criterion selected the SEM over the OLSM for 20 years. Our estimations demonstrate that the mobility was high until the late 80's, fell to a bottom in the mid-90's, recovered, peaked in the early 2000's, and has weakened recently, even though it has been argued that mobility has been low since 1978 reform, leaving the impression that it has consistently been low.fiscal and financial reform; Feldstein-Horioka paradox; spatial econometrics

Firm agglomeration and aggregate fluctuations

This study uses Indonesian plant-level manufacturing data from 2000 to 2014 to examine the role of individual firms located in agglomeration areas in generating aggregate fluctuations. While previous studies have used a method to decompose aggregate fluctuations into macroeconomic (sectoral) and firm-specific components, this paper illustrates an approach to further decompose the firm-specific component into higher and lower agglomeration groups. Our results suggest that plant-specific fluctuations in higher agglomeration areas have a greater impact on aggregate fluctuations than those in lower agglomeration areas, and the interaction or co-movements of plants in higher agglomerations areas have a significant role to drive aggregate fluctuations

Agglomeration and firm-level productivity : a Bayesian spatial approach

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures