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Table 4 Dynamic System GMM Regression Model (Low-Income countries)

From: Impact of macro-fiscal determinants on health financing: empirical evidence from low-and middle-income countries

VARIABLES (1) (2) (3) (4) (5) (6)
ln PHEit-1 0.649*** 0.706*** 0.658*** 0.644*** 0.695*** 0.732***
(0.030) (0.027) (0.029) (0.030) (0.028) (0.026)
ln TRit 0.030     0.035  
(0.028)     (0.027)  
ln DTit   0.017     −0.0004
  (0.018)     (0.017)
ln ITit   −0.041**     −0.051***
  (0.018)     (0.018)
FBit    −0.002***   −0.002*** − 0.003***
   (0.0009)   (0.0009) (0.0009)
ln DEBTit     0.015 0.0069 0.014
    (0.010) (0.010) (0.010)
ln PCGDPit −0.006 0.002 0.005 0.002 −0.006 − 0.002
(0.009) (0.010) (0.010) (0.009) (0.009) (0.009)
ln AGINGit −0.029 −0.009 − 0.008 −0.017 − 0.022 −0.006
(0.028) (0.027) (0.028) (0.027) (0.027) (0.027)
ln IMRit −0.008 0.001 0.038 −0.012 −0.0007 − 0.026
(0.031) (0.031) (0.030) (0.027) (0.027) (0.026)
Constant 0.321 0.316 0.098 0.326** 0.222 0.492***
(0.198) (0.200) (0.175) (0.162) (0.171) (0.175)
AB test AR(2) (p -level) 0.293 0.237 0.661 0.280 0.652 0.602
Sargan test (p -level) 1.000 1.000 1.000 1.000 1.000 1.000
Observations 488 488 488 488 488 488
No. of Country 48 48 48 48 48 48
  1. Note: Low-income includes the sample of both lower income and lower-middle income countries (Please see Table A1). Standard errors in parentheses; ***, **, * denotes the level of significance at 1, 5, and 10% respectively. ln = natural logarithm
  2. Source: Author’s estimation