The demographic and educational drivers of international emigration.
Date: 2018-06-01
Institutions: Joint Research Centre
Subjects: demographic analysis, demography, economic development, economic growth, education, emigration, research, research report

The demographic and educational 
drivers of international 
emigration

Grapsa, E. (Centre of Expertise on 
Population and Migration) 

2018 

EUR 29201 EN 

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information  
Name: Erofili Grapsa 
Email: erofili.grapsa@ec.europa.eu 
Tel.: 0039 0332 786329  

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC110388 

EUR 29201 EN 

PDF 

ISBN 978-92-79-81879-0 

ISSN 1831-9424 

doi: 10.2760/836439 

Luxembourg: Publications Office of the European Union, 2018 

© European Union, 2018 

Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents 
is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

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How to cite this report: Grapsa, E., The demographic and educational drivers of international emigration, 
EUR 29201 EN, Publications Office of the European Union, Luxembourg, 2018, ISBN 978-92-79-81879-0, 
doi:10:2760/836439, JRC110388. 

All images © European Union 2018 

Contents 

Acknowledgements .............................................................................................. 2 

Abstract ............................................................................................................. 3 

1  Introduction ................................................................................................... 4 

2  Demographic and education trends in a global context .......................................... 6 

2.1  Demographic indicators .............................................................................. 6 

2.2  Education trends ....................................................................................... 9 

2.3  Emigration trends ..................................................................................... 11 

2.4  Additional variables ................................................................................... 14 

3  Results ......................................................................................................... 18 

4  Conclusions ................................................................................................... 22 

References ........................................................................................................ 24 

Annexes ............................................................................................................ 26 

Annex 1. Regressions by income group of the country ......................................... 26 

i 

Acknowledgements 

The  author  would  like  to  acknowledge  the  contribution  and  constructive  comments  of 

Michaela Potančoková, Fabrizio Natale, Silvia Migali and Nikos Stilianakis in finalising this 

report.  This  work  has  been  executed as part  of  the  Joint  JRC-IIASA  Centre of  Expertise 

on Population and Migration (CEPAM) and in collaboration with the Knowledge Centre on 

Migration and Demography (KCMD). 

Authors 

Erofili Grapsa 

2 

Abstract 

The  focus  of  this  report  is  to  explore  the  link  between  demographic  and  education 

indicators  and  emigration  at  a  global  level.  The  relationship  between  demographic 

pressure  and  migration  has  been  previously  explored  and  findings  have  shown  that 

population bulges may lead to increased emigration. Nevertheless, this study approaches 

the  research  question  by  challenging  previous  findings  and  discussing  the  interactions 

between  economic  growth  and  demographic  pressure.  In  particular,  findings  show  that 

demographic transition may or may not affect emigration patterns depending not only on 

the level of economic development of the country but also the rate of economic growth 

and the various other aspects of human development.  

3 

 
1  Introduction 

This  report  investigates  the  relationship  between  demographic  as  well  as  education 

variables and emigration. It has been previously argued that demographic factors, such 

as  population  growth  and  dependency  ratio,  are  linked  to  international  migration  when 

certain  economic  conditions  are  met.  In  particular,  high  fertility  rates  in  developing 

countries  lead  to  an  increase  of  the  share  of  young  and  working  age  population  which 

then might lead to a rise in emigration rates of young people looking for jobs (De Haas 

2010, 2011, Hatton & Williamson 2003, 2005, Kim & Cohen 2010). However, education is 

also  an  important  factor  that  only  lately  has  been  included  in  demographic  projections 

and  is  often  ignored  when  exploring  migration  drivers.  As  education  is  linked  to  many 

demographic indicators, in particular fertility rates, and is crucial in changing population 

composition  and  dynamics,  it  may  be  important  in  explaining  emigration  patterns.  In 

addition, demographic and education variables (KC & Lutz 2014) are used in population 

projections  and  their  future  uncertainty  is  low  compared  to  other  explanatory  variables 

for  migration  such  as  GDP,  labour  market  indicators  or  the  presence  of  conflicts  and 

natural  disasters.  Therefore,  the  goal  of  this  report  is  firstly  to  provide  a  description  of 

demographic and education trends globally, and then to explore the associations between 

emigration  rates  and  demographic  as  well  as  education  factors  while  controlling  for 

several  other  variables  such  as  GDP  per  capita  and  urbanisation.  This  work  mainly 

explores  associations  employing  regression  techniques  and  does  not  intend  to  prove 

causality.  

The  association  between  demographic  conditions  and  migration  has  been  previously 

researched  by  including  the  share  of  young  people  or  the  dependency  ratios  as 

explanatory variables in gravity models. Most often the demographic indicators are used 

as controls in regressions where the variables of interest are economic, environmental or 

conflict  indicators  (Backhaus  et  al.  2015,  Beine  and  Parsons  2015,  Mayda  2010),  while 

other studies focus more on the demographic pressure (Hatton & Williamson 2003, Kim & 

Cohen 2010). Beine and Parsons (2015) find a negative relationship between dependency 

ratio  and  migration.  Hatton  &  Williamson  (2003)  investigate  the  effect  of  demographic 

and economic factors on African out-migration by including the share of 15-29 years old 

in the regression of African net out-migration and finding a positive effect. Kim & Cohen 

(2010)  particularly  explore  the  effect  of  demographic  factors  such  as  life  expectancy, 

infant mortality and potential support ratio as proxies for economic and living conditions 

in  a  country.  The  authors  argue  that  demographic  indicators  such  as  life  expectancy  at 

birth  and  infant  mortality  ratio  often  might  be  the  only  available  measures  of  health  or 

quality of life and that a country with higher mortality should be expected to have higher 

4 

 
 
emigration.  On the other hand, the direct relationship between education and migration 

has been little explored with the exception of the study of Hatton and Williamson (2005). 

In  addition,  Grogger  &  Hanson  (2011)  look  at  migrant  selectivity  and  income 

maximization  by  education,  while  Brabo  &  Mbaye  (2015)  examine  the  differences  in 

migration determinants by education level in country of origin. 

This  analysis  uses  available  data  on  demographic  and  educational  indicators  from  two 

sources:  World  Bank  Development  Indicators  and  Wittgenstein  centre.  The  migration 

stock data are obtained through the United Nations  Department of Economic and Social 

Affairs,  Population  Division.  The  descriptive  as  well  as  the  regression  analysis  in  this 

report are performed and presented using R and the following R packages: countrycode, 

dplyr, tidyr, ggplot2 and stargazer (all data and software sources are included in the list 

of references). 

5 

 
 
 
 
2  Demographic and education trends in a global context 

2.1  Demographic indicators 

In  this  section,  changes  in  the  demographic  indicators  across  the  globe  since  1960  are 

explored  and  discussed.  The  indicators  are  obtained  from  the  World  Bank  World 

Development  Indicators  website.  The  main  demographic  indicators  of  interest  in  this 

analysis  are:  crude  death  rate  (CRD),  total  fertility  rate  (TFR),  population  growth  rate, 

and dependency ratio (see below for the definitions of these indicators). Most developed 

countries  have  been  experiencing  a  common  pattern  in  their  demographic  changes,  i.e. 

undergoing demographic transition, with a decrease in mortality, fertility and population 

growth and an increase in old dependency ratio since 1960s. However, trends in fertility 

are  no  longer  as  strongly  differentiated  by  developing  status  (developed  versus 

developing)  as  before  (Lutz  et  al.  2014).  Persistent  declines  in  fertility  rates  have  also 

appeared  in  less  developed  countries  although  a  couple  decades  later.  In  2010,  only 

certain countries in sub-Saharan Africa and Asia still had fertility rates above four births 

per woman, while some other like China have lower TFR than Sweden or France. The TFR 

graph in Figure 1 shows that the TFR has been decreasing more rapidly in low middle to 

high income countries rather in low income countries1, for which the TFR in 2010 is still 

well  above  the  TFR  in  1960  for  high  income  countries.  In  general,  countries  undergo 

demographic  transition  at  different  times  and  with  different  rates  but  the  theory  claims 

that  the  fertility  decline  should  carry  on  to  the  replacement  level  and  possibly  below  it 

worldwide in the future. 

Low  mortality  is observed  mostly  in  high  and upper  middle  income  countries  while high 

mortality  is  prevalent  in  most  sub-Saharan  Africa  and  South  Asia  (India,  Pakistan  and 

Bangladesh).  As  presented  in  Figure  1,  CDR  tends  to  drop  over  time  for  all  income 

groups. The rate of reduction is higher for low income countries and the opposite for high 

income countries. This is mostly because death rate starts from really low values already 

in the 1960s for developed countries and perhaps has reached its lowest values. 

1 Income groups are used as defined by World Bank in the World Development Indicators and are invariant in 

time. 

6 

 
 
 
 
 
 
                                          
Table 1. Definitions of demographic indicators. 

Crude  death  rate  (CDR).  The  number  of  deaths  occurring  during  the  year,  per  1000 
population estimated at midyear. 

Infant  mortality  rate  (IMR).  Infant  mortality  rate  is  the  number  of  infants  dying 
before reaching one year of age, per 1000 live births in a given year. 

Total fertility rate (TFR). It represents the number of children that would be born to a 
woman  if  she  were  to  live  to  the  end  of  her  childbearing  years  and  bear  children  in 
accordance with age-specific fertility rates of the specified year. 

Population  growth  (PG).  The  exponential  rate  of  growth  of  midyear  population  from 
year t-1 to t, expressed as a percentage. Population is based on the de facto definition of 
population, which counts all residents regardless of legal status or citizenship2. 

Dependency  ratio  (DR).  The  ratio  of  people  younger  than  15  and  older  than  64  to 
those  ages  15-64  (working-age  population).  Data  are  shown  as  the  proportion  of 
dependents per 100 working-age population. 

Crude birth rate (CBR). Crude birth rate indicates the number of live births occurring 
during the year, per 1000 population estimated at midyear. 

The combination of low fertility and low mortality leads to population ageing worldwide, 

with steady increases in the old age dependency ratio and the opposite for the young age 

dependency  ratio  (Figure  1).  In  high  income  countries  the  steep  increase  in  the 

percentage of people above 65 years old and decline in the percentage of children below 

the  age  of  15  means  that  the  former  is  almost  equal  or  surpasses  the  latter  in  the  last 

decades.  In  addition,  the  percentage  of  children  under  15  years  old  in  upper  middle 

income countries has been declining very fast following the decline in fertility rates below 

replacement level. This means that the percentage of future 15-64 years old individuals 

will also decline substantially since the influx of young people shrinks. The changes in the 

age distribution in lower income countries have been slower but are expected to intensify 

in  the  next  decades  according  to  the  demographic  transition  theory.  Future  population 

2 Note that PG here is not natural growth but migration contributes to this rate. 

7 

 
 
 
 
 
 
 
                                          
projections  show  that  population  ageing  will  be  prevalent  in  every  country  but  its  rate 

and  extent  will  differ  by  groups  of  countries  depending  on  cultural  and  socio-economic 

factors. 

The patterns in crude death and birth rate show evidence of the demographic transition 

theory  (Willekens,  2014)  for all income  groups.  The  differences  lie  in  the  timing  as well 

as  the  speed  of  the  transition.  High  income  countries  have  been  transitioning  from  the 

late expanding phase to low stationary and even declining population phase with very low 

death and birth rates in the last couple decades. Although perhaps the rate of transition 

might  be  faster  nowadays  for  upper  middle  and  lower  middle  income  countries,  on 

average both groups are in the late expanding phase in 2015. The death rate has been 

declining  rapidly  in  the  low  income  countries  to  reach  levels  of  around  10  deaths  per 

1000 population while the birth rate is still high, which suggests that these countries are 

in  the  last  phase  of  the  early  expanding  phase,  or  perhaps  the  beginning  of  the  late 

expanding phase. Infant mortality rate has also been decreasing which accounts for the 

increase in the world population according to the newest UN population projections.  

Population growth appears to be dropping for all income groups but the low income, for 

which  there  is  a  more  or  less  constant  growth  rate.  Similarly,  the  trend  in  young 

dependency  ratio  shows  that  young  population  is  on  decline  for  all  income  groups 

reflecting  the  trend  in  birth  rates.  Even  if  it  is  still  high  for  the  low  income  countries,  it 

appears it has reached its peak and it is also on decline. The opposite is observed for the 

old  dependency  ratio  which  is  increasing  rapidly  for  the  high  income  countries  showing 

the rapid population ageing.  

8 

 
 
 
 
 
 
 
 
 
 
 
Figure 1. Trends in demographic indicators by income group. 

Source:  Author's  own  calculations  using  the  World  Development  Indicators,  World  Bank.  Averages  and  their 
95% confidence intervals are shown in solid lines and similarly shaded bands. 

2.2  Education trends 

3.2 Education trends 

The impact of demographic factors should not be explored separately from education as 

it  has  been  shown  that  education  greatly  affects  mortality  and  fertility  rates  as  well  as 

desired  family size  (Lutz  et  al.  2014).  In  particular,  better educated women  have  fewer 

children,  and  the  children  of  better  educated  individuals  have  higher  survival  rates. 

However, the impact of education on demographic composition takes a long time before 

it  can  be  observed:  more  girls  at  school  now  will  have  an  effect  on  fertility  in  15-20 

years. Moreover, a drop in fertility rate will not immediately cause lower absolute number 

of  births  because  of  the  large  age-structural  momentum  of  population  growth  (more 

young  women  moving  into  reproductive  ages  as  consequence  of  past  high  fertility). 

9 

 
 
 
 
Education influences not only every aspect of an individual's life but also the structure of 

societies.    Lutz  et  al.  (2014)  discuss  in  detail  how  alternative  scenarios  of  future 

education  expansion  may  affect  population  growth.  Several  measures  of  the  education 

level at the country level exist, such as educational attainment by gender and age group, 

average  years  of  schooling  and  gender  gap  in  educational  attainment  and  years  of 

schooling. Data are obtained through the Wittgenstein Centre3 data explorer and certain 

indicators have been projected backward to account for missing values in some countries 

and years.  

Figure  2  presents  trends  in  three  education  indicators:  the  share  of  population  20-39 

years old with secondary or higher education, the mean years of schooling for individuals 

20-39 years old, and the gender gap in mean years of schooling for people 25 years old 

and  above.  Nevertheless,  as  the  trends  for  the  first  two  indicators  are  very  similar  it  is 

only  the  first  one  that  is  included  in  the  regression  analysis.  The  gender  gap  in  mean 

years  of  schooling  may  be  thought  to  be  a  proxy  for  gender  equality  and  female 

empowerment  in  a  specific  country.  Nevertheless,  this  theory  is  not  valid  for  every 

country  as  there  are  exceptions  where  women  of  young  cohorts  have  higher  education 

than their male peers such as Saudi Arabia. 

A  general  upwards  trend  is  observed  in  educational  attainment  and  mean  years  of 

schooling  for  males  and  females.  The  rate  of  increase  in  education  is  faster  for  high 

income  countries  and  much  slower  for  low  income  countries.  The  trend  in  schooling 

gender  gap  shows  a  steady  decline  for  high  income  countries,  while  it  firstly  increases 

and  then  decreases  for  the  other  income  groups4.  This  pattern  is  more  obvious  for  the 

low  and  lower  middle  groups,  perhaps  due  to  the  fact  that  men  have  better  access  to 

education than women in developing countries. It is worth noting that the gender gap in 

schooling  was  less  different  among  income  groups  in  the  70s  and  80s,  while  after  then 

the differences are more prominent in particular for low and lower middle income group 

countries.  

3 The link for this and other data websites are provided in the list of references. 
4 Defined as the difference between mean years of schooling for male 25 years old or older and mean years of 

schooling for female 25 years old or older. 

10 

 
 
 
 
 
 
                                          
Figure 2. Trends in education indicators by income group. 

Source: Author's own calculations using data from Wittgenstein Centre data explorer. Averages and their 95% 
confidence intervals are shown in solid lines and similarly shaded bands. 

2.3  Emigration trends 

The  following  graphs  show  the  average  emigration  trends  together  with  their  95% 

confidence  intervals  by  5-year  period  and  continent.  The  data  is  available  through 

UNDESA  website  that  provides  estimates  of  the  international  migrant  stock  by  age,  sex 

and  origin  for  the  mid-point  (1  July)  of  each  year:  1990,  1995,  2000,  2005,  2010  and 

2015.  The  migrant  stocks  are  then  aggregated  over  country  of  birth  to  obtain  the  total 

number of emigrants by country of birth. The highest emigration is observed for Asia in 

all  time  points  and  it  appears  to  be  steadily  increasing.  Emigration  in  Europe  is  the 

second highest and also shows an increase since 2000. Finally although emigration in the 

Americas and Africa were about the same in 1990, emigration in the Americas has been 

rising faster than emigration in Africa.  Table 1 presents how the emigrant stock evolves 

over  time  for  the  top  three  emigrating  countries  by  continent  and  by  income5.  The 

highest  stock  of  emigrants  for  each  continent  is  observed  for  Egypt  in  2015,  Mexico  in 

2015,  India  in  2015  and  Russia  in  1990.  The  top  three  emigrating  countries  are:  Great 

Britain,  Germany  and  Italy  (high  income),  Russia,  Mexico  and  China  (upper  middle 

income),  India,  Bangladesh  and  Ukraine  (lower  middle  income),  Afghanistan,  Burkina 

Faso and Somalia (low income).  

5 Emigration for countries in Oceania is not shown in this table as it is considerably lower than emigration in the 

other continents. 

11 

 
 
 
 
 
 
                                          
Figure 3. Emigration by continent 

Source:  Author's  own  calculations  using  data  from  UNDESA.  Averages  and  their  95%  confidence  intervals  are 
shown in solid lines and similarly shaded bands. 

Table 2. Emigrant stocks (1000s) for top three emigrating countries by continent. 

1990 

1995 

2000 

2005 

2010 

2015 

Africa 

Morocco 

1587 

1735 

1947 

2383 

2779 

2834 

Egypt 

1320 

1488 

1704 

1898 

2607 

3268 

Algeria 

905 

963 

1024 

1590 

1633 

1763 

Asia 

India 

6717 

7209 

7952 

9617 

13285 

15574 

China 

4227 

4901 

5753 

7192 

8597 

9545 

Bangladesh 

5449 

5417 

5432 

5761 

6748 

7205 

Americas 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Mexico 

4393 

6722 

9329 

10515 

11828 

12339 

USA 

1735 

1836 

1988 

2303 

2721 

3023 

Colombia 

1007 

1199 

1421 

1865 

2492 

2638 

Europe 

Russia 

12696 

11657 

10812 

10537 

10430 

10576 

Ukraine 

5549 

5613 

5607 

5592 

5497 

5825 

Great Britain 

2684 

2602 

3849 

4121 

4555 

4917 

Source: Author's own calculations using data from UNDESA. 

Evidence supports that emigration is highest in countries in upper middle income group, 

following by lower middle group (Figure 4) and thatemigration in high income countries is 

higher  than  in  low  income  countries.  Two  theories  regarding  the  relationship  between 

economic indicators and migration exist in the literature and evidence appears to support 

both.  The  first  claims  that  economic  growth  and  development  in  sending  countries  will 

reduce  international  migration  and  it  assumes  an  inversely  proportional  linear 

relationship  between  income  differentials  and  migration.  The  second  theory  claims  that 

economic  and  human  development  lead  to  higher  levels  of  migration  and  that  the 

relationship  between  them  has  an  inverted  U  shape  (De  Haas,  2010).  Hence,  evidence 

from  the  literature  finds  both  a negative  relationship  between  GDP  per  capita  at  source 

country  and  emigration  (Backhaus  et  al.  2015,  Drabo  &  Mbaye  2015,  Hatton  & 

Williamson 2003), and a positive relationship (Docquier et al. 2014, De Haas 2010).  

The  findings  in  this  report  are  in  line  with  the  theory  that  there  is  an  inverted  U 

relationship  between  a  country's  economic  conditions  and  emigration  (Hatton  & 

Williamson 2003, Clemens 2014). Although low income can incentivise people to migrate, 

it  also  imposes  certain  financial  constraints  to  those  who  might  want  to  migrate. 

Therefore  an  increase  in  income  (upper  and  lower  middle  income  countries)  will  reduce 

the  poverty  trap  and  allow  some  potential  emigrants  to  leave,  but  once  they  become 

richer  a  further  increase  in  income  might  affect  emigration  negatively  (high  income 

countries).  

13 

 
 
 
 
 
 
 
 
 
 
Figure 4. Emigration by income group. 

Source:  Author's  own  calculations  using  data  from  UNDESA.  Averages  and  their  95%  confidence  intervals  are 
shown in solid lines and similarly shaded bands. 

2.4  Additional variables 

In  addition  to  the  main  explanatory  variables  (demographic  and  education  indicators), 

several other variables describing conditions and emigration pressure at country of origin 

are included in the regression analysis as controls6. Some of these were previously found 

to be associated with migration such as urbanisation (percentage of the population living 

in  urban  areas and  urban  population  growth)  and  GDP  per capita  (constant  2010  US$). 

Urbanisation  is  rather  high  in  high  income  countries  with  more  than  70%  of  the 

population living in cities versus an average of 35% in low income countries for 2015. It 

has  also  been  increasing  globally,  however  the  rate  of  urbanisation  has  been  declining 

and more or less levelling out in the last three decades7. Regarding the GDP per capita, a 

large  gap between  the  average  GDP  per capita  (35552$  in  2015)  is present  in the  high 

income group and in the immediate after upper middle income group (6894$ in 2015). It 

6 All predictors are averaged over the 5-year interval (t, t+4). 

7 Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated 
using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. 

14 

 
 
 
 
 
 
 
                                          
 
appears  to  have  stopped  rising  for  all  income  groups  reflecting  the  stall  in  economic 

growth  which  has  an  important  impact  on  emigration  particularly  for  low  income 

countries.  

Moreover, I use the percentage of population with cell phone subscriptions as a proxy for 

access  to  mass  media  and  technology  in  the  country,  a  factor  that  has  been  known  to 

facilitate  emigration  (Piotrowski  2013,  Winkler  2017).  The  share  of  population  with  cell 

phone has increased dramatically since the 90s showing the rapid technological advances 

that  occurred  in  the  last  two  decades.  Finally,  I  control  for  immigration  rate  defined  as 

number  of  immigrants  per  1000  persons  as  it  appears  that  a  country  in  a  certain 

demographic transition phase may switch from being an emigration country to being an 

immigration  country  (Zelinsky  1971).  High  income  countries  have  the  highest 

immigration  rate  that  has  been  steadily  increasing  and  is more  than  three  times  higher 

than the immigration rate in upper middle income countries.  Although immigration rate 

has been increasing for high and upper middle income groups, the opposite is observed 

for lower middle and low income groups. 

Table 3. Descriptive statistics for control variables. 

1990 

1995 

2000 

2005 

2010 

2015 

High income 

Pop. with 
cellphone 
subscriptions 

2   (0.29) 

15 (1.38) 

61 (2.91) 

105 (2.87)  128 (4.47)  133 (5.21) 

GDP per capita 

16095 
(1299.4) 

18271 
(1482.3) 

21203 
(1587.8) 

33215 
(2490.6) 

38404 
(2970.2) 

35552 
(2736.8) 

Immigration rate 

157 
(22.25) 

166 
(22.93) 

171 
(22.34) 

183 (23) 

197 
(24.68) 

207 
(25.44) 

Urban population 

74 (2.25) 

75 (2.26) 

76 (2.28) 

76 (2.32) 

77 (2.36) 

78 (2.39) 

Urban population 
growth 

Upper middle 
income 

Pop. with 
cellphone 
subscriptions 

1.4  (0.2) 

1.2 (0.17) 

1.2  (0.2) 

1.6 (0.37) 

1.1 (0.24) 

1   (0.17) 

0   (0.03) 

2   (0.34) 

20 (1.93) 

69 (3.87) 

111 (4.47)  118 (5.02) 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
GDP per capita 

2330 
(258.3) 

2593 
(219.8) 

2909 
(200.9) 

5444 
(342.8) 

7622 
(435.3) 

6894 
(346.1) 

Immigration rate 

47 (9.09) 

48 (9.09) 

51 (9.83) 

52 (10.27)  58 (10.37)  63 (11.55) 

Urban population 

54   (2.3) 

56 (2.32) 

57 (2.33) 

59 (2.34) 

61 (2.35) 

62 (2.35) 

Urban population 
growth 

Lower middle 
income 

Pop. with 
cellphone 
subscriptions 

2.4 (0.27) 

1.9 (0.24) 

1.8 (0.24) 

1.7 (0.25) 

1.8 (0.23) 

1.7 (0.19) 

0        (0) 

0   (0.08) 

6   (0.73) 

37 (2.95) 

82   (4.1) 

94 (4.07) 

GDP per capita 

755 (59.8)  873 (78.3)  922 (83.4) 

1544 
(118.9) 

2284 
(146.2) 

2323 
(144.1) 

Immigration rate 

39      (7) 

32 (6.26) 

29 (5.8) 

25 (4.75) 

23 (4.17) 

21 (3.87) 

Urban population 

36 (2.32) 

38 (2.34) 

39 (2.37) 

41 (2.41) 

43 (2.46) 

44   (2.5) 

Urban population 
growth 

Low income 

Pop. with 
cellphone 
subscriptions 

3.2 (0.27) 

2.7 (0.27) 

2.7 (0.24) 

2.6 (0.22) 

2.5  (0.2) 

2.5 (0.18) 

0        (0) 

0   (0.02) 

2   (0.29) 

15 (1.86) 

48 (4.26) 

66 (5.46) 

GDP per capita 

309   (32) 

294 (29.6)  296 (21.9)  451 (32.9)  608 (36.4)  602 (35.6) 

Immigration rate 

31 (5.67) 

29 (5.58) 

23 (4.98) 

21 (4.15) 

18   (3.7) 

17 (3.28) 

Urban population 

25 (2.32) 

27 (2.19) 

29 (2.22) 

31 (2.29) 

33 (2.37) 

35 (2.48) 

Urban population 
growth 

4.2 (0.38) 

4.2 (0.42) 

4.2 (0.27) 

4.1 (0.26) 

4   (0.24) 

4   (0.22) 

Source:  Author's  own  calculations  using  data  from  WDI,  Wold  Bank  and  UNDESA.  Standard  errors  in 
parentheses. 

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3  Results 

To  model  the  emigration  rate,  I  employ  a  quasi-Poisson  or  pseudo-likelihood  Poisson 

model  that  accounts  for  over-dispersion  by  estimating  the  dispersion  parameter  (Silva 

and Tenreyro, 2006). This specification does not assume that the variance is equal to the 

mean  as  the  classic  Poisson  model  does.  The  Poisson  distribution  is  suitable  for  count 

data  such  as  the  emigrant  stock  used  in  this  analysis.  In  this  setting  the  outcome  Y 

(emigrant  stock)  has  a  Poisson  distribution  and  the  logarithm  of  its  expected  value  is 

linearly linked to the explanatory variables: 

log(𝐸(𝒚|𝒙)) = 𝒃′𝑿 

which means that the predicted mean of this model is 

𝐸(𝒚|𝒙) = 𝑒𝒃′𝑿 

This  is  the  main  difference  with  the  typical  log-normal  gravity  equation  used  by 

economists to model the migrant stock or flows, in particular that it estimates 𝐸(log⁡(𝑌)|𝑥) 

instead of 𝐸(𝑌|𝑥) and therefore no bias from the logarithmic transformation is introduced 

in  the  model  (see  Burger  et  al.  2009  for  further  elaboration).  The  quasi-Poisson 

regression  produces  the  same  coefficient  estimates  as  the  standard  Poisson  model  but 

inference is adjusted for over-dispersion. 

In  order  to  identify  the  most  relevant  factors  capturing  demographic  pressure  I  have 

used  the  following  indicators:  share  of  15-29  years  old  in  the  population  (model  1),  5-

year lagged population growth (model 2) and 20-year lagged birth rate (model 3)8. The 

regressions  are  run  for the  pooled  data  set  and  by  country  income  group  (tables  in  the 

appendix).  

Table  4  presents  the  estimated  regression  coefficients,  CI  and  p-values  for  the  pooled 

data  regressions.  In  general  it  is  evident  that  the  coefficients  of  the  common  variables 

across models have the same sign although the magnitude of the effect or its significance 

may  be  slightly  different.  Population  size  is  positively  associated  with  emigration  and 

countries with high infant mortality rate are more likely to have more people emigrating, 

8  Note  that  caution  has  been  exercised  in  order  to  avoid  including  variables  with  high  correlation  in  the  same 
regression, therefore not all discussed demographic variables are selected as covariates in the regressions. 

17 

 
 
 
 
 
                                          
which is the expected effect direction according to Kim & Cohen (2010). Urbanisation as 

well  as  the  rate  of  urbanisation  is  positively  associated  with  emigration.  The  results  for 

GDP  per  capita  show  that  better  economic  conditions  are  associated  with  higher 

emigration in the source country; however this effect is lessened as the GDP increases as 

we  see  from  the  squared  term  of  GDP  per  capita.  The  majority  of  previous  findings 

suggest  a  negative  relation  between  GDP  and  migration,  mostly  in  line  with  the  theory 

that economic growth reduces out-migration.  

In  all  three  regressions  higher  share  of  well-educated  20-39  years  old  individuals 

(secondary or higher education) is associated with higher emigration showing that better 

capabilities  and  aspirations  increase  emigration.  The  coefficient  of  gender  gap  in 

schooling  as  proxy  for  gender  equality  implies  that  countries  with  higher  gender 

inequality  in  education  tend  to have higher  emigration  but  a gender  analysis to  identify 

whether men or women emigrated more is not performed in this report. In addition, the 

coefficient of population share with cell phone subscriptions does not agree  with previous 

hypotheses in the literature that discuss how access to mass media and technology may 

facilitate  emigration  (Piotrowski  2013).  Finally,  countries  with  high  immigration  tend  to 

have lower emigration than those with low immigration.  

As  far  as  the  variables  describing  demographic  pressure,  model  1  shows  that  the 

coefficient  of  share  of  youth  in  the  population  is  not  statistically  significant,  however, 

both population growth and birth rate are negatively associated with emigration (model 2 

and 3). These results suggest that demographic pressure is linked to less emigration but 

they need to be interpreted with caution. Although it may be true that population growth 

does  not  lead  to  higher  emigration,  it  is  also  not  wise  to  assume  that  it  leads  to  lower 

emigration.  When  high  population  growth  occurs  in  countries  with  very  low  income  and 

stalled  economic  growth,  then  the  emigration  will  be  indeed  lower  and  this  is  perhaps 

what  drives  these  coefficients.  To  further  disentangle  these  effects,  regressions  by 

income group are run and presented in the appendix. 

Table 4. Quasi-Poisson regression of emigration (pooled model). 

Emigrant stock 

(1) 

(2) 

(3) 

Log(population) 

0.693*** 

0.930*** 

0.772*** 

(0.486, 0.900) 

(0.736, 1.124) 

(0.565, 0.980) 

18 

 
 
 
 
 
IMR 

0.012*** 

0.011*** 

0.014*** 

(0.010, 0.015) 

(0.008, 0.013) 

(0.011, 0.017) 

Share of 15-29 y.o. 

-0.300 

(-1.280, 0.680) 

Lagged pop. growth 

Lagged birth rate 

-0.144*** 

(-0.169, -0.118) 

-0.011*** 

(-0.017, -0.004) 

Urban population 

0.673** 

0.222 

0.532** 

Urban population growth 

0.024* 

0.017 

0.023* 

(0.130, 1.215) 

(-0.276, 0.719) 

(0.005, 1.058) 

(0.000, 0.048) 

(-0.004, 0.039) 

(-0.0002, 0.047) 

Log(GDP per capita) 

0.510*** 

0.270** 

0.499*** 

Log(GDP per capita sq.) 

-0.020** 

-0.004 

-0.019** 

(0.266, 0.754) 

(0.042, 0.498) 

(0.259, 0.740) 

(-0.036, -0.004) 

(-0.019, 0.010) 

(-0.035, -0.004) 

Share of 20-39 with 
secondary+ 

1.452*** 

1.266*** 

1.263*** 

(1.003, 1.901) 

(0.858, 1.675) 

(0.796, 1.729) 

Gender gap in schooling 

0.081** 

0.075** 

0.059 

Share with cellphone subs. 

-0.107** 

-0.074* 

-0.149*** 

(0.003, 0.159) 

(0.001, 0.149) 

(-0.024, 0.142) 

Immigration rate 

-2.329*** 

-1.350*** 

-2.488*** 

(-0.194, -0.021) 

(-0.153, 0.005) 

(-0.235, -0.063) 

Year: 1995 

0.049* 

-0.006 

0.047* 

(-3.342, -1.316) 

(-2.310, -0.390) 

(-3.518, -1.457) 

(-0.004, 0.101) 

(-0.055, 0.043) 

(-0.004, 0.098) 

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Year: 2000 

0.129*** 

0.028 

0.129*** 

(0.061, 0.196) 

(-0.036, 0.093) 

(0.064, 0.195) 

Year: 2005 

0.168*** 

0.018 

0.183*** 

Year: 2010 

0.256*** 

0.084 

0.273*** 

(0.067, 0.268) 

(-0.078, 0.114) 

(0.085, 0.281) 

Year: 2015 

0.302*** 

0.133* 

0.308*** 

(0.121, 0.391) 

(-0.043, 0.211) 

(0.142, 0.404) 

(0.149, 0.456) 

(-0.009, 0.275) 

(0.160, 0.456) 

Observations 

966 

965 

961 

Country  fixed  effects  included  in  the  regression  but  not  shown  here.  Significance  thresholds  are:  <0.01  ***, 
<0.05 **, <0.1 *. 95% confidence intervals are in parentheses. 

The results by income group show how the associations differ depending on the level of 

economic  development  of  the  country  (tables  A1-4).  Moreover,  certain  coefficients 

change  sign  which  means  there  is  an  interaction  between  the  particular  covariate  and 

income  group.  For  high  income  countries,  population  size  appears  to  be  positively  and 

statistically  significantly  associated with  emigration.  A  positive relationship  is also found 

between IMR, share of young people, lagged birth rate, GDP per capita and emigration. A 

large  share  of  highly  educated  individuals  is  linked  to  lower  emigration  for  high  income 

countries as opposed to the rest of income groups. The relationship between urbanisation 

and  emigration  is  negative  and  statistically  significant  suggesting  that  perhaps  some 

countries  in  this  group  have  transitioned  to  the  phase  with  very  low  international 

emigration  but  high  immigration  of  low  skilled  workers  and  more  urban-to-urban 

mobility.  

Results  for  upper  middle  income  countries  show  few  statistically  significant  associations 

apart from the demographic pressure variables which all show that demographic pressure 

is  associated  with  lower  emigration.  On  the  other  hand,  a  high  share  of  well-educated 

individuals in the population is linked to greater emigration. This is in agreement with De 

Haas  (2010),  who  poses  that  youth  or  general  population  bulge  does  not  necessarily 

mean  an  increase  in  out-migration  unless  these  people  have  the  means,  as  well  as  the 

potential to migrate which is often strengthened by education. None of the demographic 

pressure  variables  are  statistically  significant  for  the  lower  middle  income  group 

countries,  while  urbanisation  is  negatively  associated  with  emigration.  One  can 

20 

 
 
 
 
 
 
hypothesize  that  this  also  links  migration  to  demographic  transition,  as  high  urban-to-

urban  migration  and  low  international  migration  is  typically  observed  during  early 

transition stages in societies (De Haas 2010).  

The regression analysis for low income countries further reveals that the share of young 

people  in  the  population  is  strongly  related  to  lower  emigration,  as  well  as  the  lagged 

population  growth  but  not  the  20-year  lagged  birth  rate.  This  result  again  confirms  the 

hypothesis  that  population  growth  itself  does  not  make  people  move  but  it  has  to  be 

accompanied  by  economic  growth  and  human  development.  Finally,  it  is  evident  that 

although  the  income  group  of  the  country  is  linked  to  its  demographic  transition,  there 

are countries in  grey  areas with high income but also high fertility (such as Oman) and 

the opposite (such as Sri Lanka), which also have non-conforming emigration patterns. 

4  Conclusions 

Although  demographic  transition  has  been  linked  to  migration  transition  in  previous 

studies  (Zelinsky  1971),  it  has  since  been  argued  that  this  theory  is  not  adequate  to 

explain  migration  patterns  (De  Haas  2010).  According  to  migration  transition  theory, 

societies  in  the  high  stationary  phase  exhibit  only  circular  migration,  all  types  of 

migration increase for societies in the early expanding phase while the opposite starts to 

happen for societies in the late expanding phase. Finally, migration becomes very specific 

(urban-to-urban, immigration of unskilled and semi-skilled workers) in the low stationary 

and  declining  population  phase.  The  theory  is  appealing  as  it  appears  to  hold  for  many 

countries,  but  other  factors  need  to  be  considered  in  order  to  account  for  global 

migration  patterns.  More  specifically,  economic  conditions  and  in  general  the  level  of 

human development is as important as ever in order to explain migration.  

This  analysis  suggests  not  only  that  most  of  the  emigration  drivers  have  a  non-linear 

relationship with emigration but also that there is a strong interaction with the stage of 

economic development and demographic transition of the country. In addition, economic 

growth is strongly connected to the demographic transition and often results have to be 

interpreted  by  taking  into  account  both  (interaction)9.  In  other  words,  the  effects  of 

migration drivers are not monotonic or even one way. Evidence suggests that migration 

is complex phenomenon which cannot be explained by simple pooled regression analysis, 

but  rather  more  sophisticated  methods  need  to  be  employed  to  reveal  and  interpret 

particularly nuanced relationships. Moreover, certain countries exhibit characteristics that 

9  Work  in  progress  focuses  on  exploring  the  interaction  between  demographic  transition  and  economic  growth 
and  its  effect  on  migration  to  EU  patterns  as  well  as  investigating  particular  groups  of  countries  with 
specific characteristics. 

21 

 
 
 
                                          
place them in contradictory phases of economic growth and demographic transitions and 

have to be examined separately as case studies or in groups by specific characteristics. 

In  addition,  as  Willekens  (2014)  discuss  demographic  transition  is  not  independent  of 

other  processes  such  as  science  and  technology  advances,  epidemics,  natural  disasters 

and social or political changes and factors such these need to be controlled for. 

22 

 
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24 

 
 
Annexes 

Annex 1. Regressions by income group of the country 

Table A 1. QP regression for high income countries. 

Emigrant stock 

(1) 

(2) 

(3) 

Log(population) 

0.665*** 

0.857*** 

0.891*** 

(0.324, 1.005) 

(0.517, 1.196) 

(0.563, 1.218) 

IMR 

0.033*** 

0.025** 

0.027** 

(0.009, 0.057) 

(0.001, 0.049) 

(0.003, 0.051) 

Share of 15-29 y.o. 

1.980*** 

(0.745, 3.215) 

Lagged pop. growth 

-0.008 

Lagged birth rate 

(-0.051, 0.036) 

0.016*** 

(0.005, 0.027) 

Urban population 

-1.341*** 

-1.368*** 

-1.235** 

(-2.313, -0.369) 

(-2.364, -0.372) 

(-2.216, -0.254) 

Urban population growth 

0.002 

0.017 

0.012 

(-0.029, 0.034) 

(-0.014, 0.048) 

(-0.019, 0.042) 

Log(GDP per capita) 

1.319*** 

1.552*** 

1.606*** 

(0.341, 2.298) 

(0.561, 2.543) 

(0.630, 2.581) 

Log(GDP per capita sq.) 

-0.055** 

-0.067** 

-0.068** 

(-0.108, -0.003) 

(-0.119, -0.014) 

(-0.120, -0.015) 

Share of 20-39 with 
secondary+ 

-0.766*** 

-1.120*** 

-0.820*** 

(-1.271, -0.260) 

(-1.587, -0.653) 

(-1.321, -0.320) 

Gender gap schooling 

0.131* 

0.200*** 

0.117* 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(0.000, 0.262) 

(0.072, 0.328) 

(-0.019, 0.254) 

Share with cellphone 
subs. 

0.054 

0.021 

0.069 

(-0.081, 0.189) 

(-0.116, 0.158) 

(-0.069, 0.207) 

Immigration rate 

-2.680*** 

-2.705*** 

-3.043*** 

(-3.871, -1.488) 

(-3.939, -1.470) 

(-4.259, -1.827) 

Year: 1995 

0.113*** 

0.088** 

0.086** 

(0.040, 0.186) 

(0.015, 0.161) 

(0.015, 0.158) 

Year: 2000 

0.265*** 

0.239*** 

0.205*** 

(0.142, 0.387) 

(0.112, 0.365) 

(0.080, 0.330) 

Year: 2005 

0.327*** 

0.303*** 

0.237** 

(0.136, 0.519) 

(0.104, 0.501) 

(0.038, 0.435) 

Year: 2010 

0.528*** 

0.505*** 

0.424*** 

(0.304, 0.752) 

(0.274, 0.737) 

(0.191, 0.657) 

Year: 2015 

0.701*** 

0.663*** 

0.591*** 

(0.471, 0.931) 

(0.424, 0.902) 

(0.354, 0.828) 

Observations 

273 

273 

273 

Country  fixed  effects  included  in  the  regression  but  not  shown  here.  Significance  thresholds  are:  <0.01  ***, 
<0.05 **, <0.1 *. 95% confidence intervals are in parentheses. 

Table A 2. QP regression for upper middle income countries. 

Log(population) 

(1) 

0.350* 

Emigrant stock 

(2) 

0.308 

(3) 

0.051 

(-0.029, 0.730) 

(-0.108, 0.724) 

(-0.345, 0.447) 

IMR 

0.004 

0.004 

0.007* 

(-0.003, 0.011) 

(-0.003, 0.012) 

(-0.001, 0.014) 

Share of 15-29 y.o. 

-4.123*** 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(-5.704, -2.543) 

-0.052* 

(-0.107, 0.004) 

Lagged pop. growth 

Lagged birth rate 

-0.019*** 

(-0.030, -0.008) 

Urban population 

-0.202 

0.080 

0.228 

(-1.167, 0.763) 

(-0.938, 1.098) 

(-0.748, 1.204) 

Urban population growth 

-0.023 

-0.006 

-0.021 

(-0.075, 0.029) 

(-0.061, 0.048) 

(-0.076, 0.034) 

Log(GDP per capita) 

0.220 

0.417 

0.489 

(-0.370, 0.810) 

(-0.198, 1.032) 

(-0.121, 1.100) 

Log(GDP per capita sq.) 

-0.003 

-0.015 

-0.020 

(-0.042, 0.035) 

(-0.055, 0.024) 

(-0.059, 0.019) 

Share of 20-39 with 
secondary+ 

3.329*** 

3.198*** 

2.925*** 

(2.419, 4.240) 

(2.231, 4.166) 

(1.952, 3.899) 

Gender gap schooling 

0.409*** 

0.455*** 

0.428*** 

(0.215, 0.603) 

(0.252, 0.658) 

(0.217, 0.639) 

Share with cellphone 
subs. 

0.024 

-0.092 

-0.108 

(-0.148, 0.196) 

(-0.265, 0.080) 

(-0.279, 0.063) 

Immigration rate 

-0.512 

0.859 

0.520 

(-2.930, 1.905) 

(-1.772, 3.489) 

(-1.951, 2.991) 

Year: 1995 

0.063 

0.072 

0.066 

(-0.027, 0.152) 

(-0.024, 0.169) 

(-0.028, 0.160) 

Year: 2000 

0.142** 

0.157** 

0.158** 

(0.017, 0.267) 

(0.021, 0.293) 

(0.028, 0.288) 

Year: 2005 

0.116 

0.188* 

0.205** 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(-0.079, 0.312) 

(-0.020, 0.396) 

(0.008, 0.402) 

Year: 2010 

0.063 

0.223 

0.205 

(-0.202, 0.327) 

(-0.048, 0.494) 

(-0.058, 0.468) 

Year: 2015 

-0.044 

0.213 

0.153 

(-0.355, 0.267) 

(-0.094, 0.520) 

(-0.151, 0.457) 

Observations 

279 

279 

277 

Country  fixed  effects  included  in  the  regression  but  not  shown  here.  Significance  thresholds  are:  <0.01  ***, 
<0.05 **, <0.1 *. 95% confidence intervals are in parentheses. 

Table A 3. QP regression for lower middle income countries. 

Emigrant stock 

(1) 

(2) 

(3) 

Log(population) 

0.710*** 

0.701*** 

0.796*** 

(0.442, 0.978) 

(0.431, 0.970) 

(0.520, 1.071) 

IMR 

0.0004 

0.001 

0.001 

(-0.003, 0.004) 

(-0.003, 0.004) 

(-0.002, 0.005) 

Share of 15-29 y.o. 

-0.337 

(-1.857, 1.183) 

Lagged pop. growth 

-0.034 

(-0.092, 0.023) 

Lagged birth rate 

0.007 

(-0.002, 0.016) 

Urban population 

-0.962** 

-0.869** 

-0.603 

(-1.742, -0.182) 

(-1.640, -0.098) 

(-1.392, 0.185) 

Urban population growth 

-0.076*** 

-0.072*** 

-0.067*** 

(-0.114, -0.039) 

(-0.111, -0.033) 

(-0.105, -0.029) 

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Log(GDP per capita) 

0.549** 

0.544** 

0.462* 

(0.067, 1.030) 

(0.066, 1.023) 

(-0.011, 0.934) 

Log(GDP per capita sq.) 

-0.028 

-0.028 

-0.024 

(-0.064, 0.008) 

(-0.064, 0.008) 

(-0.059, 0.012) 

Share of 20-39 with 
secondary+ 

0.878** 

0.950** 

1.213*** 

(0.151, 1.606) 

(0.230, 1.670) 

(0.485, 1.941) 

Gender gap schooling 

0.124*** 

0.138*** 

0.060 

(0.040, 0.208) 

(0.045, 0.231) 

(-0.036, 0.157) 

Share with cellphone 
subs. 

0.036 

0.039 

0.008 

(-0.096, 0.167) 

(-0.092, 0.171) 

(-0.124, 0.139) 

Immigration rate 

1.248 

1.163 

0.811 

(-0.249, 2.744) 

(-0.412, 2.738) 

(-0.782, 2.404) 

Year: 1995 

-0.052 

-0.062* 

-0.049 

(-0.117, 0.013) 

(-0.129, 0.005) 

(-0.113, 0.014) 

Year: 2000 

-0.045 

-0.065 

-0.051 

(-0.132, 0.042) 

(-0.159, 0.028) 

(-0.136, 0.035) 

Year: 2005 

-0.053 

-0.084 

-0.037 

(-0.194, 0.088) 

(-0.235, 0.066) 

(-0.178, 0.103) 

Year: 2010 

0.032 

-0.005 

0.074 

(-0.178, 0.242) 

(-0.224, 0.215) 

(-0.136, 0.284) 

Year: 2015 

0.098 

0.064 

0.141 

(-0.144, 0.340) 

(-0.185, 0.313) 

(-0.102, 0.384) 

Observations 

257 

256 

254 

Country  fixed  effects  included  in  the  regression  but  not  shown  here.  Significance  thresholds  are:  <0.01  ***, 
<0.05 **, <0.1 *. 95% confidence intervals are in parentheses. 

29 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table A 4. QP regression for low income countries. 

Emigrant stock 

(1) 

(2) 

(3) 

Log(population) 

-2.540*** 

-0.345 

-2.313*** 

(-3.990, -1.089) 

(-1.882, 1.192) 

(-3.839, -0.786) 

IMR 

0.021*** 

0.017*** 

0.019*** 

(0.015, 0.027) 

(0.010, 0.024) 

(0.013, 0.026) 

Share of 15-29 y.o. 

-11.283*** 

(-17.225, -5.341) 

Lagged pop. growth 

Lagged birth rate 

-0.128*** 

(-0.190, -0.065) 

0.046** 

(0.010, 0.082) 

Urban population 

-0.293 

0.089 

-0.604 

(-2.149, 1.563) 

(-1.800, 1.977) 

(-2.564, 1.355) 

Urban population growth 

0.115*** 

0.059** 

0.090*** 

(0.072, 0.158) 

(0.012, 0.107) 

(0.045, 0.135) 

Log(GDP per capita) 

0.802 

0.300 

-0.221 

(-1.752, 3.357) 

(-2.246, 2.846) 

(-2.846, 2.403) 

Log(GDP per capita sq.) 

-0.071 

-0.027 

0.018 

(-0.292, 0.150) 

(-0.248, 0.193) 

(-0.208, 0.245) 

Share of 20-39 with 
secondary+ 

0.706 

1.620 

1.539 

(-1.507, 2.918) 

(-0.648, 3.889) 

(-0.782, 3.859) 

Gender gap schooling 

-0.151* 

-0.266*** 

-0.276*** 

(-0.317, 0.015) 

(-0.437, -0.094) 

(-0.455, -0.097) 

Share with cellphone 
subs. 

-0.104 

0.188 

0.120 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(-0.545, 0.336) 

(-0.256, 0.632) 

(-0.334, 0.574) 

Immigration rate 

-4.507* 

-3.265 

-5.551** 

(-9.659, 0.646) 

(-8.686, 2.157) 

(-10.863, -0.239) 

Year: 1995 

0.679*** 

0.252 

0.553*** 

(0.390, 0.968) 

(-0.048, 0.553) 

(0.263, 0.843) 

Year: 2000 

1.328*** 

0.516* 

1.076*** 

(0.775, 1.881) 

(-0.043, 1.076) 

(0.526, 1.626) 

Year: 2005 

2.167*** 

0.878** 

1.817*** 

(1.345, 2.990) 

(0.039, 1.717) 

(0.997, 2.637) 

Year: 2010 

2.876*** 

1.115* 

2.438*** 

(1.777, 3.974) 

(-0.014, 2.244) 

(1.337, 3.540) 

Year: 2015 

3.411*** 

1.260* 

2.855*** 

(2.103, 4.718) 

(-0.081, 2.600) 

(1.551, 4.159) 

Observations 

157 

157 

157 

Country  fixed  effects  included  in  the  regression  but  not  shown  here.  Significance  thresholds  are:  <0.01  ***, 
<0.05 **, <0.1 *. 95% confidence intervals are in parentheses. 

31 

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doi:10.2760/836439 

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