Health care expenditure and GDP: panel data unit root test results—comment

Hansen, Paul; King, Alan

doi:10.1016/s0167-6296(98)00028-9

Cited by 51 publications

(25 citation statements)

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“…In the 1990s, Hansen and King and Blomqvist and Cater, among others, used the time-series model in their studies and stressed the stability of NHE and GDP to avoid spurious regression [9,10]. However, with the panel data model becoming more mature, especially with the proposal of a dynamic panel, many scholars have conducted studies based on the panel data model.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Models and Effectiveness Analysis of Government Education and Health Expenditures on Economic Growth

Huang¹,

Tang²,

Peng³

et al. 2015

TOCSJ

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This paper establishes a novel mathematical model to analyze the effects of government education and health expenditures on the economic growth. Based on the Cobb-Douglas production function, the analysis of education and health expenditures is conducted using the two-stage least square method and applying the Gaussian mixture model as the estimation method. The results provide not only the long-term relationship between education and economic growth, but also between health expenditure and economic growth. In addition, they show that both education and health expenditures have different contributions to economic growth, resulting in unbalanced development in various districts. We also compare the per capita public health and education expenditures and their economic effects in different regions. We conclude that the district with relatively less contribution may not necessarily increase their expenditure; hence, effective management may be more important. In accordance with the regional contribution divergence, the government should formulate corresponding policies to improve the allocation of education and health resources and ensure rational capital flow among regions.

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Section: Literature Reviewmentioning

confidence: 99%

Mathematical Models and Effectiveness Analysis of Government Education and Health Expenditures on Economic Growth

Huang¹,

Tang²,

Peng³

et al. 2015

TOCSJ

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“…As for the long-run relationship between healthcare spending and income, a number of papers have used time series approaches for various OECD countries (Blomqvist and Carter, 1997;Hansen and King, 1998;Gerdtham and Löthgren, 2000;MacDonald and Hopkins, 2002;Dreger and Reimers, 2005) or the US states (Wang and Rettenmaier, 2007;Tosetti and Moscone, 2010;Freeman, 2012). However, the results reported in such studies may not be robust if the underlying Data Generating Process (DGP) for the two series is characterised by structural change (Freeman, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

The Relationship between Healthcare Expenditure and Disposable Personal Income in the US States: A Fractional Integration and Cointegration Analysis

et al. 2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Juncal CunadoUniversity of Navarra, Pamplona, Spain Luis A. Gil -AlanaUniversity of Navarra, Pamplona, Spain Rangan GuptaUniversity of Pretoria, Pretoria, South Africa May 2015 AbstractThis study examines the relationship between healthcare expenditure and disposable income in the 50 US states over the period 1966-2009 using fractional integration and cointegration techniques. The degree of integration and nonlinearity of both series are found to vary considerably across states, whilst the fractional cointegration analysis suggests that a long-run relationship exists between them in only 11 out of the 50 US states. The estimated long-run income elasticity of healthcare expenditure suggests that health care is a luxury good in these states. By contrast, the short-run elasticity obtained from the regressions in first differences is in the range (0,1) for most US states, which suggests that health care is a necessity good instead. The implications of these results for health policy are also discussed. IntroductionAccording to the OECD Health Statistics (2014), in 2012the US spent 16.9 percent of its GDP on healthcare, which is far higher than the OECD average of 9.3%, while on a per capita basis it spent more than double the OECD average. Furthermore, from 1960, US healthcare expenditure has grown five times faster than GDP (from 7.1% in the late sixties to 16.9 percent in 2012), and faster than in other OECD countries, and is projected to grow at an average rate of 5.7 percent until 2023, 1.1 percentage points faster than the expected average annual growth rate of GDP. However, its level and growth rate have not been homogeneous across the US states, as pointed out in different papers analysing regional convergence in health spending (Wang, 2009; Pantelidis, 2012, 2013). For instance, in 2009 per capita personal healthcare spending in Massachusetts ($9,278) was almost twice than in Utah ($5,031).The existing literature (starting with Kleiman, 1974 andNewhouse, 1977) has suggested that disposable income, together with other demand and supply factors such as medical technological progress or demographic trends, is one of the key drivers of healthcare demand and therefore expenditure. However, the evidence on the existence of a long-run relationship between income and healthcare expenditure, as well as the income elasticity of healthcare expenditure and the relative importance of income as one of its drivers is mixed (...

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“…At the same time, these estimated trends can be spuriously correlated with the stochastic trends in the data, and, therefore, incorrectly lead to support for cointegration, in particular when the time dimension is relatively short. We have varied the leading case with the parameterization from (11). First, we allowed for more and stronger trends in the regressors,…”

Section: Regressions With a Linear Time Trendmentioning

confidence: 99%

“…Such a constellation is often met in practical applications, see for instance Coe and Helpman [8] and Westerlund [9] on R&D spillovers (total factor productivity and capital stock), Larsson et al [1] on log real consumption and income (per capita), or Hanck [10] on prices and exchange rate series testing the weak purchasing power parity (PPP). The relevance of a linear trend in panel data has been addressed in Hansen and King [11] when commenting on the link between health care expenditure and GDP, see McCoskey and Selden [12]; consequently, Blomqvist and Carter [13], Gerdtham and Löthgren [14] or Westerlund [15] worked (partly) with detrended series, i.e., they included time as an explanatory variable in their panel tregressions. Hansen ([16], p. 103), however, argue that "it seems reasonable that excess detrending will reduce the test's power".…”

Section: Introductionmentioning

confidence: 99%

Panel Cointegration Testing in the Presence of Linear Time Trends

Hassler

Hosseinkouchack

2014

SSRN Journal

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Abstract:We consider a class of panel tests of the null hypothesis of no cointegration and cointegration. All tests under investigation rely on single-equations estimated by least squares, and they may be residual-based or not. We focus on test statistics computed from regressions with intercept only (i.e., without detrending) and with at least one of the regressors (integrated of order 1) being dominated by a linear time trend. In such a setting, often encountered in practice, the limiting distributions and critical values provided for and applied with the situation "with intercept only" are not correct. It is demonstrated that their usage results in size distortions growing with the panel size N. Moreover, we show which are the appropriate distributions, and how correct critical values can be obtained from the literature.

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Health care expenditure and GDP: panel data unit root test results—comment

Cited by 51 publications

References 5 publications

Mathematical Models and Effectiveness Analysis of Government Education and Health Expenditures on Economic Growth

Mathematical Models and Effectiveness Analysis of Government Education and Health Expenditures on Economic Growth

The Relationship between Healthcare Expenditure and Disposable Personal Income in the US States: A Fractional Integration and Cointegration Analysis

Panel Cointegration Testing in the Presence of Linear Time Trends

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