Table 3

Correlates of structural quality (hospital assets and staffing), general-acute hospitals

Model 1

Model 2

Model 3

Model 4

Dependent variable:

Assets (ln (value of assets in 10,000 RMB))

Number of machines valued over 10,000 RMB

(ln (#machines valued over 10,000 RMB))

Total employees

(ln (#employees))

Physicians

(ln (#physicians))


Explanatory variables:

Non-government non-profit (indicator variable)

-0.974

(6.69)**

-0.662

(3.87)**

-0.392

(4.20)**

-0.629

(5.70)**

Private for-profit (indicator variable)

-0.433

(2.62)**

0.024

(0.12)

-0.147

(1.37)

-0.329

(2.60)**

ln (beds)

1.073

0.886

0.729

0.657

(19.19)**

(13.57)**

(20.09)**

(15.34)**

Contract with social insurance ("appointed" indicator variable)

0.513

(4.02)**

0.401

(2.67)**

0.245

(2.96)**

0.296

(3.03)**

University hospital (indicator variable)

0.1

(0.37)

0.409

(1.33)

0.148

(0.85)

0.259

(1.26)

Rural hospital (indicator variable)

0.002

0.189

0.016

-0.054

(0.01)

(0.84)

(0.13)

(0.37)

Constant

3.022

-0.042

1.61

0.825

(10.25)**

(0.12)

(8.42)**

(3.66)**

Observations

286

275

288

288

R-squared

0.74

0.58

0.73

0.66


Note: Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

Each column presents a separate ordinary least squares (OLS) regression, with the dependent variable as listed in the column heading and the explanatory variables as listed in the first column; each cell of the table reports the estimated regression coefficient, with the absolute value of the associated t statistic in parentheses below the estimated coefficient. We take the natural logarithm of each dependent variable e.g., ln (value of assets in 10,000 RMB) and use that converted form for the econometric analysis to avoid an artificial bias in the statistical results from the skewed distribution of the continuous dependent variables.

Eggleston et al. BMC Health Services Research 2010 10:76   doi:10.1186/1472-6963-10-76

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