Hospital treatment rates and spillover effects: Does ownership matter?

Baltagi, Badi H.; Yen, Yin Fang

doi:10.1016/j.regsciurbeco.2014.01.005

“…As predicted, the network effects in Subsample (1) are more than twice those in Subsample (2) regardless of the estimation methods. Recall that the percentage of contract-free jobs in Subsample (1) is also higher than that in Subsample (2). The estimation outcome is consistent with the literature that the quality of jobs introduced by contacts is usually low especially for low-educated workers.…”

Section: Migrants Who Found Jobs Through Networksupporting

confidence: 86%

“…There are around 271 million internal migrants in China in 2011, which accounts for one-third of total urban population. 2 Therefore, the welfare and return of migrant workers are important for China to move from an economic pattern heavily dependent on exports to one in which the demand of the country's own internal market plays a larger role in economic growth (Becker and Elfstrom, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Network effects on labor contracts of internal migrants in China: a spatial autoregressive model

Baltagi

¹

,

Deng

²

,

Ma

³

2017

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This paper studies the fact that 37 percent of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant's decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the covillager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants' contract decisions: job referral mechanism, limited information on contract benefits, and the "mini labor union" formed among co-villagers, which substitutes for a formal contract. In the sub-sample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications. JEL No. O15, R12, J14

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“…The main causes of spatial spillovers in hospital expenditures have been identified in supply characteristics, such as competition and knowledge transfers, and in heterogeneity of patients' health profiles and preferences on the demand side (Baltagi & Yen, ; Gravelle, Santos, & Siciliani, ). A relatively unexplored issue is to what extent spillovers differ across types of treatments (e.g., complex vs. basic ones).…”

Section: Introductionmentioning

confidence: 99%

Spatial effects in hospital expenditures: A district level analysis

Bruni

¹

,

Mammi

²

2017

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Geographical clusters in health expenditures are well documented and accounting for spatial interactions may contribute to properly identify the factors affecting the use of health services the most. As for hospital care, spillovers may derive from strategic behaviour of hospitals and from patients' preferences that may induce mobility across jurisdictions, as well as from geographically-concentrated risk factors, knowledge transfer and interactions between different layers of care. Our paper focuses on a largely overlooked potential source of spillovers in hospital expenditure: the heterogeneity of primary care providers' behaviour. To do so, we analyse expenditures associated to avoidable hospitalisations separately from expenditures for highly complex treatments, as the former are most likely affected by General Practitioners, while the latter are not. We use administrative data for Italy's Region Emilia Romagna between 2007 and 2010. Since neighbouring districts may belong to different Local Health Authorities (LHAs), we employ a spatial contiguity matrix that allows to investigate the effects of geographical and institutional proximity and use it to estimate Spatial Autoregressive and Spatial Durbin Models.

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“…As for hospital expenditures, the main causes of spatial spillovers have been identified, on the supply side, in the competition across hospital providers and in the knowledge transfer influencing local practice styles, while, on the demand side, in the heterogeneity in patients' health profiles and preferences (Gravelle et al 2014;Baltagi and Yen 2014). More recently, a different stream of works has focused on primary care as a key channel that affects hospital utilisation (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial Effects in Hospital Expenditures: A District Level Analysis

Bruni

¹

,

Mammi

²

2015

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Geographical clusters in health expenditures are well documented and accounting for spatial interactions may contribute to properly identify the factors affecting the use of health services the most. As for hospital care, spillovers may derive from strategic behaviour of hospitals and from patients' preferences that may induce mobility across jurisdictions, as well as from geographically-concentrated risk factors, knowledge transfer and interactions between different layers of care. Our paper focuses on a largely overlooked potential source of spillovers in hospital expenditure: the heterogeneity of primary care providers' behaviour. To do so, we analyse expenditures associated to avoidable hospitalisations separately from expenditures for highly complex treatments, as the former are most likely affected by General Practitioners, while the latter are not. We use administrative data for Italy's Region Emilia Romagna between 2007 and 2010. Since neighbouring districts may belong to different Local Health Authorities (LHAs), we employ a spatial contiguity matrix that allows to investigate the effects of geographical and institutional proximity and use it to estimate Spatial Autoregressive and Spatial Durbin Models.

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Hospital treatment rates and spillover effects: Does ownership matter?

Cited by 19 publications

References 43 publications

Network effects on labor contracts of internal migrants in China: a spatial autoregressive model

Network effects on labor contracts of internal migrants in China: a spatial autoregressive model

Spatial effects in hospital expenditures: A district level analysis

Spatial Effects in Hospital Expenditures: A District Level Analysis

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