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Fertility and population policy
Mehmet S. Tosun* 
Mehmet S. Tosun
Affiliation: University of Nevada, Reno, Department of Economics, College of Business, University of Nevada-Reno, USA
 0000-0002-7034-6838
Correspondence
tosun@unr.edu
Jingjing Yang*
Jingjing Yang
Affiliation: University of Nevada, Reno, Department of Economics, College of Business, University of Nevada-Reno, USA
0000-0001-7566-8089
Article | Year: 2018 | Pages: 21 - 43 | Volume: 42 | Issue: 1 Received: February 14, 2017 | Accepted: December 28, 2017 | Published online: March 8, 2018
|
FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
Dependent variable: | (1) | (2) | (3) | (4) | (5) | Change in total fertility rate | SAR | SAR | SAR | SEM | OLS | Variables: | Direct | Indirect | Total | | | Anti-fertility policy | -0.0205*** | -0.116* | -0.136* | -0.0183*** | -0.0317*** | (0.0067) | (0.0670) | (0.0709) | (0.0066) | (0.0081) | GDP per capita
($ thousand) | 0.00352*** | 0.0195** | 0.0230** | 0.00355** | 0.00803*** | (0.001) | (0.01) | (0.01) | (0.002) | (0.002) | Health spending
per capita ($ thousand) | -0.0128*** | -0.0733* | -0.0861** | -0.0120*** | -0.00817** | (0.004) | (0.04) | (0.042) | (0.004) | (0.004) | Trade to GDP | 0.000259** | 0.00146 | 0.00172 | 0.000265** | 0.000255** | (0.0001) | (0.0010) | (0.0011) | (0.0001) | (0.0001) | Share of urban
population | 0.00323*** | 0.0176** | 0.0209*** | 0.00324*** | 0.00725*** | (0.0009) | (0.0076) | (0.0080) | (0.0010) | (0.0009) | Spatial parameter
(rho) | 25.00*** | | | | | (1.1740) | | | | | Spatial parameter
(lambda) | | | | 25.78*** | | | | | | | Constant | | | | | -0.514*** | | | | | (0.0529) | Observations | 798 | 798 | 798 | 798 | 798 | Number of countries | 133 | 133 | 133 | 133 | 133 | Econometric model | SAR | SAR | SAR | SEM | FE | Country and time fixed effects | Yes | Yes | Yes | Yes | Yes |
|
Dependent variable
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
|
Change in total fertility rate
|
SAR
|
SAR
|
SAR
|
SEM
|
OLS
|
|
Variables
|
Direct
|
Indirect
|
Total
|
|
|
|
Pro-fertility policy
|
-0.00077
|
-0.00598
|
-0.00675
|
-0.00384
|
0.00917
|
|
(0.0060)
|
(0.0383)
|
(0.0438)
|
(0.0057)
|
(0.0072)
|
|
GDP per capita
($ thousand)
|
0.00383***
|
0.0234**
|
0.0272**
|
0.00366**
|
0.00825***
|
|
(0.001)
|
(0.012)
|
(0.013)
|
(0.002)
|
(0.002)
|
|
Health spending per capita
($ thousand)
|
-0.0130***
|
-0.0825*
|
-0.0955*
|
-0.0119***
|
-0.00918**
|
|
(0.004)
|
(0.048)
|
(0.05)
|
(0.004)
|
(0.004)
|
|
Trade to GDP
|
0.000249**
|
0.00154
|
0.00179
|
0.000251**
|
0.000242*
|
|
(0.0001)
|
(0.0011)
|
(0.0012)
|
(0.0001)
|
(0.0001)
|
|
Share of urban
population
|
0.00329***
|
0.0199**
|
0.0231**
|
0.00329***
|
0.00732***
|
|
(0.0009)
|
(0.0097)
|
(0.0101)
|
(0.0010)
|
(0.0010)
|
|
Spatial parameter (rho)
|
25.30***
|
|
|
|
|
|
(1.1290)
|
|
|
|
|
|
Spatial parameter
(lambda)
|
|
|
|
25.92***
|
|
|
|
|
|
(0.9920)
|
|
|
Constant
|
|
|
|
|
-0.535***
|
|
|
|
|
|
(0.0532)
|
|
Observations
|
798
|
798
|
798
|
798
|
798
|
|
Number of countries
|
133
|
133
|
133
|
133
|
133
|
|
Econometric model
|
SAR
|
SAR
|
SAR
|
SEM
|
FE
|
|
Country and time fixed effects
|
YES
|
YES
|
YES
|
YES
|
YES
|
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
|
Dependent variable
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
|
Change in total fertility rate
|
SAR
|
SAR
|
SAR
|
SEM
|
OLS
|
|
Variables
|
Direct
|
Indirect
|
Total
|
|
|
|
Family planning policy
|
0.00583
|
0.0437
|
0.0496
|
0.00523
|
0.00903
|
|
(0.0255)
|
(0.1960)
|
(0.2180)
|
(0.0241)
|
(0.0266)
|
|
GDP per capita
($ thousand)
|
0.00369*
|
0.0236
|
0.0273
|
0.00359
|
0.00849***
|
|
(0.002)
|
(0.019)
|
(0.021)
|
(0.003)
|
(0.003)
|
|
Health spending
per capita ($ thousand)
|
-0.0127**
|
-0.0822
|
-0.0949
|
-0.0123**
|
-0.00830
|
|
(0.006)
|
(0.063)
|
(0.066)
|
(0.006)
|
(0.005)
|
|
Trade to GDP
|
0.000254*
|
0.00179
|
0.00204
|
0.000258**
|
0.000246*
|
|
(0.0001)
|
(0.0021)
|
(0.0022)
|
(0.0001)
|
(0.0001)
|
|
Share of urban
population
|
0.00321**
|
0.0215
|
0.0247
|
0.00324*
|
0.00735***
|
|
(0.0015)
|
(0.0237)
|
(0.0245)
|
(0.0017)
|
(0.0014)
|
|
Spatial parameter
(rho)
|
25.28***
|
|
|
|
|
|
(1.3270)
|
|
|
|
|
|
Spatial parameter
(lambda)
|
|
|
|
25.88***
|
|
|
|
|
|
(1.2090)
|
|
|
Constant
|
|
|
|
|
-0.546***
|
|
|
|
|
|
(0.0808)
|
|
Observations
|
798
|
798
|
798
|
798
|
798
|
|
Number of countries
|
133
|
133
|
133
|
133
|
133
|
|
Econometric model
|
SAR
|
SAR
|
SAR
|
SEM
|
FE
|
|
Country and time fixed effects
|
YES
|
YES
|
YES
|
YES
|
YES
|
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
|
Variables
|
(1)
SEM
|
(2)
SAR
|
(3)
OLS
|
|
Pro-fertility policy
|
0.0176
|
-0.0338
|
0.0288
|
|
(0.0877)
|
(0.0777)
|
(0.0936)
|
|
GDP per capita
($ thousand)
|
0.0201***
|
0.0166***
|
0.0193***
|
|
(0.005)
|
(0.004)
|
(0.007)
|
|
Trade to GDP
|
0.000128
|
-0.000449
|
0.00022
|
|
(0.0007)
|
(0.0006)
|
(0.0006)
|
|
Share of urban
population
|
-0.00135
|
0.000492
|
-0.00144
|
|
(0.0016)
|
(0.0016)
|
(0.0019)
|
|
Constant
|
-0.630***
|
-0.779***
|
-0.563***
|
|
(0.1350)
|
(0.1070)
|
(0.0708)
|
|
Spatial parameter
(lambda)
|
-0.245
|
|
|
|
(0.4170)
|
|
|
|
Spatial parameter
(rho)
|
|
1.335***
|
|
|
|
(0.1090)
|
|
|
Observations
|
102
|
102
|
102
|
|
Wald chi2(4)
|
27.6384
|
24.6198
|
|
|
Prob > chi2
|
0
|
0.0001
|
|
|
Econometric Model
|
SEM
|
SAR
|
OLS
|
|
Variables
|
(1)
SEM
|
(2)
SAR
|
(3)
OLS
|
|
Anti-fertility policy
|
-0.208***
|
-0.144**
|
-0.210***
|
|
(0.0666)
|
(0.0603)
|
(0.0609)
|
|
GDP per capita
($ thousand)
|
0.0180***
|
0.0149***
|
0.0179**
|
|
(0.00439)
|
(0.00431)
|
(0.00697)
|
|
Trade to GDP
|
0.0000679
|
-0.00049
|
0.0000846
|
|
(0.000661)
|
(0.000612)
|
(0.000536)
|
|
Share of urban
population
|
-0.00300*
|
-0.000645
|
-0.00303*
|
|
(0.00163)
|
(0.0016)
|
(0.00181)
|
|
Constant
|
-0.406***
|
-0.625***
|
-0.394***
|
|
(0.146)
|
(0.12)
|
(0.0805)
|
|
Spatial parameter
(lambda)
|
-0.0404
|
|
|
|
(0.394)
|
|
|
|
Spatial parameter
(rho)
|
|
1.314***
|
|
|
|
(0.129)
|
|
|
Observations
|
102
|
102
|
102
|
|
Wald chi2(4)
|
40.0463
|
31.5678
|
|
|
Prob > chi2
|
0
|
0
|
|
|
Econometric Model
|
SEM
|
SAR
|
OLS
|
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
| (1) | (2) | (3) | Variables | SEM | SAR | OLS | Family planning policy | 0.118 | 0.139* | 0.116 | (0.0927) | (0.0798) | (0.1240) | GDP per capita
($ thousand) | 0.0207*** | 0.0161*** | 0.020*** | (0.005) | (0.005) | (0.006) | Trade to GDP | 0.000247 | -0.0000804 | 0.000366 | (0.0007) | (0.0006) | (0.0005) | Share of urban
population | -0.00136 | 0.000492 | -0.00146 | (0.0016) | (0.0016) | (0.0019) | Constant | -0.754*** | -0.696*** | -0.676*** | (0.1610) | (0.1120) | (0.1500) | Spatial parameter
(lambda) | -0.277 | | | (0.4050) | | | Spatial parameter
(rho) | | 2.453*** | | | (0.3360) | | Observations | 102 | 102 | 102 | Wald chi2(4) | 29.6418 | 19.8782 | | Prob > chi2 | 0 | 0.0005 | | Econometric Model | SEM | SAR | OLS |
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
|
List of countries and codes in the Moran’s I graph
|
|
Africa
|
Code
|
Africa
|
Code
|
Oceania
|
Code
|
Northern America
|
Code
|
|
Algeria
|
2
|
Mauritania
|
75
|
Australia
|
6
|
Canada
|
17
|
|
Angola
|
4
|
Mauritius
|
74
|
Fiji
|
45
|
United States
|
124
|
|
Benin
|
13
|
Morocco
|
73
|
Kiribati
|
64
|
|
|
|
Botswana
|
105
|
Mozambique
|
80
|
New Zealand
|
91
|
|
|
|
Burkina
Faso
|
125
|
Niger
|
82
|
Samoa
|
130
|
|
|
|
Burundi
|
22
|
Nigeria
|
86
|
Tonga
|
116
|
|
|
|
Cabo Verde
|
33
|
Rwanda
|
101
|
Vanuatu
|
85
|
|
|
|
Cameroon
|
26
|
Senegal
|
106
|
|
|
|
|
|
Central
African Republic
|
31
|
Sierra Leone
|
107
|
|
|
|
|
|
Chad
|
27
|
Somalia
|
109
|
|
|
|
|
|
Comoros
|
28
|
South Africa
|
103
|
|
|
|
|
|
Congo, Dem.
Rep.
|
21
|
Swaziland
|
131
|
|
|
|
|
|
Congo, Rep.
|
20
|
Tanzania
|
120
|
|
|
|
|
|
Cote
d'Ivoire
|
60
|
Togo
|
117
|
|
|
|
|
|
Egypt, Arab
Rep.
|
38
|
Tunisia
|
118
|
|
|
|
|
|
Equatorial
Guinea
|
40
|
Uganda
|
121
|
|
|
|
|
|
Ethiopia
|
42
|
Zambia
|
132
|
|
|
|
|
|
Gabon
|
48
|
|
|
|
|
|
|
|
Gambia, The
|
47
|
|
|
|
|
|
|
|
Ghana
|
49
|
|
|
|
|
|
|
|
Guinea
|
53
|
|
|
|
|
|
|
|
Guinea-Bissau
|
98
|
|
|
|
|
|
|
|
Kenya
|
63
|
|
|
|
|
|
|
|
Lesotho
|
104
|
|
|
|
|
|
|
|
Madagascar
|
70
|
|
|
|
|
|
|
|
Malawi
|
81
|
|
|
|
|
|
|
|
Mali
|
72
|
|
|
|
|
|
|
|
List of countries and codes in the Moran’s I graph (Continued)
|
|
Europe
|
Code
|
Asia
|
Code
|
Latin America & Caribbean
|
Code
|
|
Albania
|
3
|
Bahrain
|
7
|
Antigua and Barbuda
|
1
|
|
Austria
|
43
|
Bangladesh
|
10
|
Argentina
|
5
|
|
Belarus
|
69
|
Bhutan
|
24
|
Bahamas, The
|
9
|
|
Belgium
|
83
|
Brunei Darussalam
|
16
|
Barbados
|
8
|
|
Bulgaria
|
15
|
Cambodia
|
18
|
Belize
|
11
|
|
Denmark
|
35
|
China
|
23
|
Bolivia
|
12
|
|
Finland
|
44
|
Cyprus
|
34
|
Brazil
|
14
|
|
France
|
46
|
India
|
57
|
Chile
|
25
|
|
Greece
|
51
|
Indonesia
|
133
|
Colombia
|
29
|
|
Hungary
|
55
|
Israel
|
58
|
Costa Rica
|
30
|
|
Iceland
|
56
|
Japan
|
61
|
Cuba
|
32
|
|
Ireland
|
39
|
Jordan
|
62
|
Dominican Republic
|
36
|
|
Italy
|
59
|
Korea, Rep.
|
65
|
Ecuador
|
37
|
|
Luxembourg
|
84
|
Kuwait
|
66
|
El Salvador
|
41
|
|
Malta
|
76
|
Lao PDR
|
67
|
Grenada
|
50
|
|
Netherlands
|
87
|
Lebanon
|
68
|
Guatemala
|
52
|
|
Norway
|
88
|
Malaysia
|
79
|
Honduras
|
54
|
|
Poland
|
95
|
Mongolia
|
71
|
Mexico
|
78
|
|
Portugal
|
97
|
Nepal
|
89
|
Nicaragua
|
90
|
|
Romania
|
99
|
Oman
|
77
|
Panama
|
96
|
|
Spain
|
110
|
Pakistan
|
94
|
Paraguay
|
92
|
|
Sweden
|
112
|
Philippines
|
100
|
Peru
|
93
|
|
Switzerland
|
113
|
Saudi Arabia
|
102
|
St. Lucia
|
111
|
|
Ukraine
|
123
|
Singapore
|
108
|
St. Vincent and the Grenadines
|
127
|
|
United
Kingdom
|
122
|
Sri Lanka
|
19
|
Trinidad and Tobago
|
114
|
|
|
|
Thailand
|
115
|
Uruguay
|
126
|
|
|
|
Turkey
|
119
|
Venezuela, RB
|
128
|
|
|
|
Vietnam
|
129
|
|
|
Africa1 | Africa2 | Europe | Asia | Latin Amer.
& Caribbean | Oceania | Algeria | Mauritius | Albania | Bahrain | Argentina | Fiji | Angola | Morocco | Austria | Bangladesh | Bahamas | Kiribati | Benin | Mozambique | Belarus | Bhutan | Barbados | New Zealand | Botswana | Niger | Belgium | Brunei Darussalam | Belize | Samoa | Burkina Faso | Nigeria | Bulgaria | Cambodia | Bolivia | Tonga | Burundi | Rwanda | Denmark | China | Brazil | Vanuatu | Cameroon | Senegal | Finland | Cyprus | Chile | Antigua and Barbuda | Cape Verde | Sierra Leone | France | India | Colombia | | Central African Republic | Somalia | Greece | Indonesia | Costa Rica | | Chad | South Africa | Hungary | Israel | Cuba | | Comoros | Swaziland | Iceland | Japan | Dominican Republic | | Congo | Togo | Ireland | Jordan | Ecuador | | Cote d'Ivoire | Tunisia | Italy | Korea, Republic of | El Salvador | | Congo Democratic Republic | Uganda | Luxembourg | Kuwait | Grenada | | Egypt | Tanzania | Malta | Lao | Guatemala | | Equatorial Guinea | Zambia | Netherlands | Lebanon | Honduras | | Ethiopia | | Norway | Malaysia | Mexico | | Gabon | | Poland | Mongolia | Nicaragua | | Gambia | | Portugal | Nepal | Panama | | Ghana | | Romania | Oman | Paraguay | | Guinea | | Spain | Pakistan | Peru | | Guinea-Bissau | | Sweden | Philippines | Saint Lucia | | Kenya | | Switzerland | Saudi Arabia | Saint Vincent and the Grenadines | | Lesotho | | Ukraine | Singapore | Trinidad and Tobago | | Madagascar | | United Kingdom | Sri Lanka | Uruguay | | Malawi | | | Thailand | Venezuela | | Mali | | | Turkey | | | Mauritania | | Viet Nam | | |
|
Africa1
|
Africa2
|
Europe
|
Asia
|
Latin America & Caribbean
|
Oceania
|
Northern America
|
|
Algeria
|
Mali
|
Austria
|
Bangladesh
|
Argentina
|
Australia
|
Canada
|
|
Benin
|
Mauritania
|
Belgium
|
Brunei Darussalam
|
Barbados
|
Fiji
|
United States
|
|
Botswana
|
Mauritius
|
Denmark
|
China
|
Bolivia
|
Kiribati
|
|
|
Burkina Faso
|
Morocco
|
Finland
|
Cyprus
|
Brazil
|
Papua New Guinea
|
|
|
Burundi
|
Niger
|
France
|
India
|
Chile
|
|
|
|
Cameroon
|
Nigeria
|
Greece
|
Indonesia
|
Colombia
|
|
|
|
Central African Republic
|
Rwanda
|
Iceland
|
Iran, Islamic Rep.
|
Costa Rica
|
|
|
|
Chad
|
Seychelles
|
Ireland
|
Israel
|
Cuba
|
|
|
|
Congo, Rep.
|
Sierra Leone
|
Italy
|
Japan
|
Dominican Republic
|
|
|
|
Cote d'Ivoire
|
Somalia
|
Luxembourg
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Jordan
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Ecuador
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Congo,
Dem. Rep.
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South Africa
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Malta
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Korea, Rep.
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El Salvador
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Egypt,
Arab Rep.
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Swaziland
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Netherlands
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Malaysia
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Guatemala
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Gabon
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Togo
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Norway
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Nepal
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Guyana
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Gambia, The
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Tunisia
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Portugal
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Oman
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Honduras
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Ghana
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Zambia
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Spain
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Pakistan
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Mexico
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Guinea-Bissau
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Zimbabwe
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Sweden
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Philippines
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Nicaragua
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Kenya
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United Kingdom
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Saudi Arabia
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Peru
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Lesotho
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Sri Lanka
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Suriname
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Liberia
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Syrian Arab Republic
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Trinidad and Tobago
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Madagascar
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Thailand
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Uruguay
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Malawi
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Turkey
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Venezuela, RB
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Missing Countries from the Table 4-6 Regressions Africa | Europe | Asia | Latin Amer.
& Caribbean | Oceania | Comoros | Belarus | Kuwait | Belize | Samoa | Cabo Verde | Hungary | Singapore | Antigua and Barbuda | Vanuatu | Senegal | Switzerland | Bahrain | St. Lucia | Tonga | Equatorial Guinea | Ukraine | Lao PDR | Panama | New Zealand | Uganda | Albania | Bhutan | Grenada | | Ethiopia | Romania | Lebanon | St. Vincent and the Grenadines | | Guinea | Poland | Mongolia | Bahamas, The | | Angola | Bulgaria | Cambodia | Paraguay | | Tanzania | | Vietnam | | | Mozambique | | | | |
Missing Countries from the Table 1-3 Regressions Africa | Europe | Asia | Latin Amer.
& Caribbean | Oceania | Seychelles | | Iran, Islamic Rep. | Guyana | Papua New Guinea | Liberia | | Syrian Arab Republic | Suriname | | Zimbabwe | | | | |
Figure 1Total fertility rate in 1976 DISPLAY Figure
Figure 2Total fertility rate in 2013 DISPLAY Figure
Figure 3Government policy on fertility in 1976 DISPLAY Figure
Figure 4Government policy on fertility in 2013 DISPLAY Figure
Figure 5Percent share of countries with anti-fertility and pro-fertility policies DISPLAY Figure
Figure 6Local indicators of spatial association (LISA Map), total fertility rate in 1976 DISPLAY Figure
Figure 7Local indicators of spatial association (LISA Map), total fertility rate in 2013 DISPLAY Figure
Figure 8Moran scatterplot for total fertility rate DISPLAY Figure
Table 1Change in total fertility rate and government’s anti-fertility policy DISPLAY Table
Table 2Change in total fertility rate and government’s pro-fertility policy DISPLAY Table
Table 3Change in total fertility rate and government’s family planning policy DISPLAY Table
Table 5Change in total fertility rate and government’s pro-fertility policy DISPLAY Table
Table 4Change in total fertility rate and government’s anti-fertility policy DISPLAY Table
Table 6Change in total fertility rate and government’s family planning policy DISPLAY Table
Figure A1Moran Scatterplot DISPLAY Figure
Table A1Regions and names of countries included in table 1-3 regressions DISPLAY Table
Table A2Regions and names of countries included in table 4-6 regressions DISPLAY Table
Table A3Missing countries from the table 4-6 and 1-3 regressions DISPLAY Table
* The authors would like to thank two anonymous referees for useful comments and suggestions.
1 The UN World Population Policies Database provides data for the years 1976, 1986, 1996, 2001, 2003, 2005, 2007, 2009, 2011, and 2013. Data for 2015 became available very recently but was excluded from our analysis due to lack of data for that year for other variables used in our regressions.
2 The term “pro-natal policy” is also used in many studies.
4 See United Nations (  2013) for more on these data sources. Box I.1 on page 43 in that publication has a chart that shows both the inputs to the database and major outputs or publications from the database.
5 Note that there are more countries added to the UN World Population Policies Database after 2000.
6 We coded “maintain fertility” response as 1 since a policy to maintain fertility or to prevent fertility from declining would still involve some pro-fertility intervention from the government. We have checked the robustness of our results by coding it as zero and found that our results did not change significantly and qualitatively.
7 See also Becker ( 1960) and Razin and Sadka ( 1995) for theoretical arguments on the relationship between income and fertility.
8 We included a different version of this graph (Figure A.1) with country codes and a list of countries used in the graph in the appendix section.
9 We also conducted more detailed spatial diagnostic tests where we find that spatial autocorrelation is a concern in our data.
10 The inverse distance matrix is generated using the latitude and longitude information for countries. Note that we also ran regressions with a contiguity matrix. Results are largely similar but inverse distance weighting allows more observations particularly from island nations, which would clearly be dropped from the regression analysis that uses contiguity weighting.
11 The OLS regression specification is very similar to the one shown in equation 2, with the exception that the error term is not subject to the spatially autoregressive process. That specification can be written as Change in fertility rateit=α0 + βX it +γ i + τ t + ε it.
12 Please see the list of countries used in different regression specifications and the countries left out in tables A.1-A.3.
13 Note that we had to drop health spending per capita due to lack of data for that variable in 1976.
14 Note that it was not possible to break down the SAR results into direct and indirect components as these regressions are run as spatial cross-sectional regressions.
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March, 2018
I/2018 |
