WORKSHOP Linkages of Agriculture, Nutrition and Economic Development Co-Organized by IFPRI, UPNG, INA, Australian National University, and Department of Foreign Affairs and Trade JUN 12, 2019 - 09:00 AM TO 12:30 PM +10

1. Macro Determinants of Mortality
in Papua New Guinea
Manoj K. Pandey
ANU Crawford School Development Policy Centre
&
UPNG School of Business and Public Policy Division of Economics
1

2. Presentation Scheme
➢Trends in Mortality Indicators
➢Cause specific Mortalities
➢Data and Model
➢Econometric Results
➢Conclusions
➢Limitations
2

3. Neonatal Mortality Rates : 1990-2016
3
13.4
15.7
31.1
16.4
14.5
10.2
15.1
8.8
10.8
23.5
9.2
10.4
6.8
11.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Fiji Pacific island small
states
Papua New Guinea Samoa Solomon Islands Tonga Vanuatu
Rate
Neonatal Mortality Rates (per 1,000 live births)
1990 2000 2011 2016
PNG worst performer in
the Pacific

4. 4
Infant Mortality Rates: 1990-2016
23.8
29.4
64.4
25.8
31.1
18.6
29.2
18.7
21.5
42.4
14.8
21.8
14.1
23.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Fiji Pacific island small
states
Papua New Guinea Samoa Solomon Islands Tonga Vanuatu
Rates
Infant mortalityrates (per 1000 live births)
1990 2000 2011 2016

5. 5
Under-5 Child Mortality Rates : 1990-2016
28.4
36.2
88.0
31.0
38.2
21.8
35.5
22.0
25.7
54.3
17.3
25.8
16.4
27.6
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Fiji Pacific island small
states
Papua New Guinea Samoa Solomon Islands Tonga Vanuatu
Rates
Under-5 mortalityrates (per 1000 live births)
1990 2000 2011 2016

6. 6
Maternal Mortality Ratios : 1990-2015
63
176
470
156
364
75
225
30
75
215
51
114
124
78
0
50
100
150
200
250
300
350
400
450
500
Fiji Pacific island small
states
Papua New Guinea Samoa Solomon Islands Tonga Vanuatu
Ratio
Maternal MortalityRatio (per 100,000 live births)
1990 2000 2011 2015

7. 7
Crude death Rates: 1950-210005
1015202530
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Source: United Nations Department of Economic and Social Affairs
1950-2100
PNG's crude death rates
Close to 7

8. 8
Life Expectancy at birth:1990-2016
66
64
59
65
57
70
63
70 71
66
75
71
73 72
0
10
20
30
40
50
60
70
80
Fiji Pacific island small
states
Papua New Guinea Samoa Solomon Islands Tonga Vanuatu
Lifeexpectancy(inyear)
Life expectancy at birth
1990 2000 2011 2016

9. 9
Population is young with lower LE0
1020304050607080
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Life expectancy Median age
Source: United Nations Department of Economic and Social Affairs
1950-2100
PNG's Life expectancy and Median age

10. 10
CVD, Cancer, Diabetes or CRD caused
Mortality in PNG: 2000-2016

11. 11
Suicide Mortality in PNG: 2000-2016
43
34
32
34 33
98
81
76
85 86
71
58
54
60 60
0
20
40
60
80
100
120
2000 2005 2010 2015 2016
Suicidemortalityrates
Year
Suicide mortality rate
(per million gender-specific population)
Female Male All Poly. (Male)

12. Preston Curve for PNG
12

13. Data
➢Data Sources: WDI, IHME and ANU election
database.
➢Period: 1990-2016 (but varied)
➢All the variables stationary at level, except log of
GDP per capita (1st difference) [Dickey-fuller and
Phillip-Perron unit root tests]
➢Structural break tests: tried, inconclusive. We
use use dummy for years ruled by political
parties/coalition as control variable
13

14. Econometric Model
The Model:
𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝑡 = 𝛽0 + 𝛽1∆𝐿𝑛𝐺𝐷𝑃𝑃𝐶𝑡−1
+𝛽2 𝐿𝑛𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑡−1 +𝛽3 𝐿𝑛𝑃𝑈𝑟𝑏𝑎𝑛 𝑡 +
+𝛽4 𝐿𝑛𝑃𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦𝑡 +𝛽5 𝐿𝑛𝐷𝐴𝐻𝑡−1 +
𝛾𝐷𝑃𝑜𝑙𝑖𝑡𝑃𝑎𝑟𝑡𝑦 + 𝜀𝑡 … … … … … … … . . 1
➢No presence of serial correlation (D-W &
Breusch-Godfrey LM tests)
➢Robust regression estimates are
obtained
14

15. 15
Variables Neonatal Infant Under-5 CDR MMR LE
∆ Log of per capita GDP (t-1) -3.24* -5.80 -8.42 0.09 0.17 0.05
(1.71) (4.32) (6.32) (0.34 (0.27) (0.83)
Log of inflation (t-1) -0.03 -0.10 -0.15 0.07** 0.08*** -0.15**
(0.11) (0.28) (0.42) (0.02) (0.02) (0.06
Log of % urban population (t) -35.83*** -85.54*** -134.36*** -1.76 -4.08*** 9.53**
(7.24) (18.28) (26.74) (1.42) (1.18) (3.51)
Log of % population with
electricity access (t)
-8.05*** -21.84*** -34.00*** -1.91*** -2.07*** 5.77***
(0.64) (1.63) (2.38) (0.13) (0.11) (0.31)
Log of per capita DAH (t-1) -0.38* -0.96* -1.50** -0.11*** -0.15*** 0.31***
(0.19) (0.47) (0.68) (0.04) (0.03) (0.09)
People’s progressive party 0.72 1.13 1.67 0.13 -0.11 -0.42
(vs Pangu) (0.53) (1.33) (1.94) (0.10) (0.08) (0.26)
People’s democratic movement 0.48 0.71 0.94 0.05 -0.06 -0.19
(0.51) (1.27) (1.86) (0.10) (0.08) (0.25)
People’s National Congress 0.11 -0.22 -0.48 0.09 -0.05 -0.23
(0.56) (1.42) (2.08) (0.11) (0.09) (0.27)
National Alliance Party 0.90 1.73 2.46 -0.08 -0.19* 0.03
(0.61) (1.55) (2.27) (0.12) (0.10) (0.30)
Constant 143.13*** 335.67*** 513.57*** 17.67*** 19.36*** 22.63**
(20.59) (52.00) (76.06) (4.03) (3.37) (9.99)
Observations 26 26 26 26 25 26
F-statistics 104.2*** 133.8*** 150.8*** 355.0*** 490.7*** 438.9***
Robust Regression Mortality Results

16. 16

17. 17
Variables Neonatal Infant Under-5 MMR
∆ Log of per capita GDP (t-1) -0.06 -0.50 -0.52 -0.03
(0.83) (2.38) (3.34) (0.19)
Log of inflation (t-1) -0.02 -0.12 -0.20 0.04***
(0.06) (0.18) (0.25) (0.01)
Log of % urban population (t) -54.65*** -117.13*** -179.98*** -5.37***
(3.98) (11.35) (15.97) (1.11)
Log of % population with electricity access (t) -10.32*** -25.82*** -39.94*** -2.32***
(0.38) (1.07) (1.51) (0.12)
People’s progressive party 0.92*** 1.82*** 2.69*** 0.01
(vs Pangu) (0.21) (0.59) (0.84) (0.04)
People’s democratic movement 0.61** 1.40* 2.04* -0.02
(0.26) (0.74) (1.05) (0.05)
People’s National Congress 0.61* 1.04 1.46 0.07
(0.32) (0.88) (1.24) (0.05)
NationalAlliance Party 1.16*** 2.58** 3.81*** -0.11*
(0.31) (0.87) (1.23) (0.05)
Log of PCDAH targeted towards child and new born (t-1) -0.07*** -0.18** -0.23**
(0.02) (0.07) (0.09)
Log of PCDAH targeted towards maternal health (t-1) -0.011
(0.011)
Constant 196.55*** 424.61*** 642.19*** 22.95***
(11.27) (32.13) (45.21) (3.22)
Observations 24 24 24 24
F-statistics 403.4*** 385.6*** 466.9*** 1057***
Overall health aid vs child and maternal health specific aid
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

18. 18
Variables Neonatal Infant Under-5 CDR MMR LE
∆ Log of per capita GDP (t-1) 2.88 7.94 12.29 0.78 0.20 -2.789
(5.75) (13.83) (21.40) (0.99) (0.64) (3.833)
Log of inflation (t-1) 0.35 0.73 1.14 0.09* 0.07** -0.301
(0.33) (0.79) (1.23) (0.05) (0.03) (0.220)
Log of % urban population (t) -71.38 -157.61 -220.96 18.32** 27.38*** -18.929
(60.01) (144.48) (223.55) (6.78) (4.90) (40.036)
Log of % population with
internet access (t)
-1.59*** -4.20*** -6.23*** -0.20** -0.06 0.785***
(0.37) (0.89) (1.38) (0.07) (0.06) (0.247)
People’s democratic movement 0.11
(0.25)
People’s National Congress 2.04 5.10 7.62 0.10 0.08 -0.759
(1.36) (3.28) (5.07) (0.20) (0.17) (0.908)
National Alliance Party 0.07 -0.01 -0.069 -0.241** -0.12 0.538
(0.50) (1.21) (1.87) (0.10) (0.25) (0.334)
Fertility rate (detrended) 142.24** 357.30** 548.08** 15.72** 15.37*** -66.679*
(53.76) (129.44) (200.27) (5.55) (3.53) (35.867)
Constant 208.12 450.07 625.76 -39.80** -68.11*** 113.916
(152.80) (367.87) (569.19) (17.38) (12.73) (101.936)
Observations 19 19 19 20 20 19
F (7, 11) 26.42*** 31.40*** 30.93*** 39.75*** 118.7*** 24.68***
Is fertility rate a determinant of mortality?
Note: Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1. We did not include log of % population with electricity access as it is highly correlated with
fertlity (corr. Coeff=-0.93). Also, as fertility variable is non-stationary even after 1st difference, we used detrended fertility rates.

19. Conclusions
➢Mortality reduction progress: Slow, still worst
in the Pacific
➢Very high disease specific and suicide related
MRs
➢Population is young but slow pace of increase
in life expectancy with national income per
capita
19

20. Conclusions
➢Our preliminary econometric analysis find that
1. Per capita GDP growth: Not significantly
reducing all forms of MRs
2. Higher inflation: higher MMR and CDR .
3. Increased urbanisation significantly reduce
MRs: Not consistent across all models
4. % population with access to electricity (and
access to internet): Consistent and –vely
significant correlates of MR reduction
20

21. Conclusions
5. Per capita development aid for health:
significantly reduce MRs
6. While aid for health specific to newborn or child
is effective, aid for health aimed for maternal
health is not.
7. Higher fertility rate, higher MR and lower life
expectancy
21

22. Conclusions
7. While it requires further analysis to ascertain
performance of political parties in reducing MRs
by years they ruled, it is an important control
variable.
8. No robust association of per capita health
expenditure and MRs (results not presented)
22

23. Conclusions
9. A significant and +ve association of food imports
as % of merchandise imports with MRs, except
MMR (results not presented here)
To recapitulate: inflation control, inflow of health
aid, more access to infrastructure such as
electrification, internet coverage and rapid
urbanisation are some of the key macro-
determinants of mortality rates in PNG.
23

24. Limitations
1. Data is limited in terms of coverage of variables
and periods. As mortality and life expectancy are
reduced slowly, a long-term perspective is
important.
2. While extra care has been taken, it is difficult to
eliminate some of the issues such as
endogeneity, and high correlations among
covariates as some of the key variables are
modelled estimates.
3. Nonetheless, results are interesting and
consistent with the available literature
24

25. Thank you!
25