The Effects of Immigration on
Social Expenditure in Host Countries
Takuya Matsuyama
Tomomi Miyazaki

March 2017
Discussion Paper No.1708

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Effects of Immigration on Social Expenditure in Host Countries
Takuya Matsuyama
Tomomi Miyazaki

This paper examines the relationship between immigration and social welfare expenditure in thehost countries using OECD panel data. Particular focus is placed on the age structure andeducational level of immigrants. Empirical results show that while unskilled immigrants including
asylum seekers are not necessarily a burden to the host countries, medium and highly skilled
immigrants contribute to a decrease in social expenditure. In particular, highly skilled immigrants
mitigate the increase in social expenditure related to welfare for the elderly driven by the aging of
immigrants.

JEL classification: J15, J61, H55

Keywords: Immigration, social welfare expenditure This paper is substantially based on Matsuyama’s M.A. dissertation submitted to the Graduate School of

Economics at Kobe University. We would like to thank Bernd Hayo, Shinya Horie, and Yoichi Matsubayashi for theirinsightful comments and suggestions. Delegates at the workshop in Taipei are also acknowledged. This work has been
financially supported by the Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research #26380361 &16H03637 and Fund for the Promotion of Joint International Research (Fostering Joint International
Research) # 16KK0057). The usual disclaimer applies.
 Graduate School of Economics, Kobe University. 2-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, JAPAN. Email: mtakuya5522@gmail.com
 Corresponding Author. Graduate School of Economics, Kobe University. 2-1 Rokkodai-cho, Nada-ku, Kobe,
Hyogo 657-8501, JAPAN. E-mail: miyazaki@econ.kobe-u.ac.jp1Introduction
The attitude of nationals in developed countries toward immigration has changed
dramatically around the world. Besides Brexit, the success of US President Donald Trump also
indicates a surge in xenophobia. One reason for the change in public sentiment is the increase in
the number of immigrants, possibly leading to a threat to host economies, societies, and thenational budget.
Many researchers have examined the effects of immigration on the national budget,
especially social welfare expenditure. Recent researches have shown that the effect of immigrants
on welfare differs depending on their skills. However, as explained later, most of these researches
use a theoretical model, and there are few empirical studies.
This paper examines the effects of immigrants on social welfare expenditure in the hostcountries using the panel data of OECD countries. In particular, we focus on the age structure andthree subdomains on the educational level of immigrants. Social expenditure data is divided into
two types depending on the educational level. We then investigate the increase in social
expenditure driven by the aging of immigrants as well as the difference in the structure of thepeople who migrate to host countries. We compared OLS and IV with the pooled model and fixed
effect model, adding to year dummy variables.
Our findings are summarized as follows. Firstly, the effects of the unskilled are not strongly
observed because those disappear when we consider macroeconomic effects and that ofhumanitarian immigration. Secondly, medium skilled immigrants can contribute to reducing thesocial expenditure. Finally, considering the possible influence of the female labor force on
immigrant workers, we find that highly skilled immigrants are beneficial to the welfare system.
This paper is organized as follows. Section 1 examines the transition of immigration history
and recent immigration policy. Here we review the historical facts of migration. Although theyvary greatly from country to country, they are helpful for grasping current immigration
movements. We focus on recent entrance regulations that align with our purpose. Section 2
examines the effect of immigration on social expenditure in OECD countries. Section 3 presents
the conclusions of this paper. An explanation of abbreviated terms and detailed data source,
coefficient matrix, etc. is provided in the Appendix.

1. Survey of Historical Immigration and Entrance Regulation
Receiving policies differ depending on the different skill levels and purposes of immigrants.
In other words, every host country treats highly skilled and unskilled immigrants separately. This
structure started to develop after the Second World War and changed through the 70s. Rather than
discuss the effectiveness of each policy, this chapter organizes the transition of receiving policies
2

in main OECD countries. This is followed by a discussion on the recent entrance regulations forboth skilled and unskilled immigrants.
1.1 Immigration after the Second World War
Immigration has been promoted and restricted according to the economic and politicalsituation in host countries. Policies regulating immigration have also reacted to such factors. This
chapter provides an overview of the history of immigration in major OECD countries. Historicallyspeaking, the origin of modern immigration is the Second World War. Just after the war, thedeveloped countries, especially western Europe and the US, suffered from a serious shortage oflabor because of their rapid economic growth. Their governments promoted recruitment of
temporary workers from countries with which they had strong relationships, such as ex-colonial
or neighboring countries and those with bilateral agreements. After the economic depression
induced by the oil crisis in 1973, however, the importing countries experienced a change in theindustrial and demographic structure and started to place more importance on the economy in thelong run, while some short-term systems remained.
In Europe, the western countries welcomed temporary workers to compensate for the labor
force lost in the war and to accelerate the economic restoration after the war. In the UK, West
Germany, the Netherlands, and France, the governments and the authorities recruited anddistributed temporary workers and determined the working conditions (Castles 1986, 761-762).
Belgium and the Netherlands entered into bilateral agreements with South European countries
such as Spain, Portugal, and Italy (Messey and Liang 1989, 202). In Switzerland, employers
gathered workers but their entrance and residency were organized by the government (Castles
1986, 766-768). The oil crisis forced the countries to reduce or prohibit the inflow from outsideof the EU and EEA (Castles 1986, 773), meaning countries in the EU continued to struggle withfree movement under the Schengen treaty. Most of the unskilled workers entering the countries
after 1973 were third-world illegal immigrants, their families, and asylum seekers (Castles 2006,
773-774).
After the economic stagnation in the 70s1, the policies of these countries became more
pragmatic. The UK reduced the number of work permits issued under the Thatcher administration(Hansen 2014, 201-203). The subsequent labor government expanded the permits in response tolabor demand. At the same time, it changed some policies on the entrance of skilled immigrants
with a points system (explained later) and of the unskilled through opening its doors to the newmember states in the EU after 2000. Germany tried to legally accept a certain number of

1

In Belgium, policies are different in the two regions with different languages and cultures, so it is difficult to

discuss the immigration problem as a matter of one country (Triandafyllidou and Gropas 2016, 29-30).

3

immigrants determined by the authorities, but after the fall of the Berlin Wall in 1989, a large
number of unskilled workers or illegal immigrants managed to enter the country. Then, thegovernment imposed severe restrictions on the constitutional right of immigrants, which led to
the recent pragmatic policy (Triandafyllidou and Gropas 2016, 147-149). France experienced anumber of policy changes on the entrance and assimilation of foreigners born from 1980 to 2012
(Triandafyllidou and Gropas 2016,137). Here although the new government should follow a
different policy, the actual policy remains to be the same. In this sense, this policy is a conservative
but pragmatic. Switzerland reduced the immigration level immediately after the crisis, but it began
to allow seasonal workers to change their status to that of permanent residents because theeconomy rallied relatively quickly. After the bilateral agreement with the EU in 2002, the country
gradually placed greater restrictions on Third Country Nationals (TCNs) (D’ Amato 2014, 311315).
Even in the same region, northern countries had a different history from those in the west.
Nordic countries2 including Norway, Sweden, and Denmark accepted labor immigration very
generously from other regions, but they officially shut out migrants from the 70s up until theyjoined the EU. However, the inflow continued with another cohort such as asylum seekers andtheir families although some of them did have an economic purpose (Brochmann 2014, 281-282).
After the early 70s, the position of the three Scandinavian countries became diversified as
Denmark became the strictest, Norway remained in the middle, and Sweden became the mostgenerous.

Denmark, which accepted foreign residents from Islamic countries, questioned

whether such immigrants could assimilate in the welfare state. In the 90s, it restricted family
integration with selective systems for those who could be beneficial (Triandafyllidou and Gropas
2016, 97-99), and this lasted with further constraints until 2001 (Brochmann 2014, 292). The
government admitted that there were sufficient nationals from the new member states in 2004 tomeet the labor supply needs. Norway can be placed in the middle of the Nordic countries in termsof immigration generosity. It joined EEA in 92 and signed the Schengen Agreement in 2001.
Although the country is not a member of the EU, it faced a huge inflow from eastern Europe, andtherefore, the government required TCNs to clarify they were financially stable (Brochmann 2014,292). Sweden tried to stop the inflow until it joined the EU in 1995. On the other hand, as thewelfare policy was still generous, public opinion was such that the policy was overly generous.
After that the government adopted the same requirements as those of Norway, but it introduced amore liberal immigration policy for workers (Brochmann, 289-292).

2

Finland is located just between Europe and Russia, and has strongly restricted migration historically. The level offoreign population is quite low (see Table A1), so it is an exceptional country in the Nordic region. Thus, it has been
omitted.

4

Countries in South Europe such as Italy and Spain did not develop restrictions on thenumbers of immigrants received. In Italy, which had received numerous illegal migrants, the firstcomprehensive immigration system was passed in 1990, which set a quota on the number ofcoming in (Perlmutter 2014, 341). However, their policy changed from a generous to restrictive
one because the government coalition was composed of several parties. After 1995, thegovernment at that time introduced an efficient employment-based quota system along with the
migrants from the eastern European countries (Perlmutter 2014, 350).
Spain used to be an exporting country of immigrants until the 1st oil crisis under thenational agreements with the western countries (Hanzan 2014, 375-376). The government
encouraged or prohibited the outflow of migrants in accordance with the economic conditions.
Because of the economic depression of the receiving countries in the 70s and joining the EC in
the 80s, Spain became a net immigration country, with migration mainly from north Africa andeastern Europe (Hanzan 2014, 377). Both countries held a positive attitude towards receiving
immigrants until 2008.
While control of foreign workers in EU countries was lost since most of them failed toreturn home or family integration was prohibited, transpacific countries such as the US, Canada,
and Australia experienced a different immigration flow. The US had a bilateral program withmany countries; one of the biggest being the Bracero Accord from 1942, which recruited Mexican
workers for industries with a labor shortage (Messey and Liang 1989, 203). The original modelof current programs for unskilled workers was also formulated in the 1950s (Padilla andCachanosky, 146). After the 70s, however, the government introduced a sponsor system andstopped the nation-level restrictions even though the inflow from Mexico continued. In the 80s,
it started to regulate illegal migrants, and then in the 90s, the US began to attract people who
would not impinge on the domestic market or labor force to work in high-tech industries with theH1-B visa, (Chaloff and Lemaitte 2009, 20).
Canada and Australia in the 50s and 60s concentrated mostly on Europe as a source of labor
force to grow their economy, with a racially-motivated background. Through the crisis, however,they changed the target to skilled immigrants using a points-based system, which is explained ina later chapter (Brochmann 2014, 281-282). In 1973, the Canadian government introduced the
Temporary Foreign Worker Program, and the number of permitted workers gradually increased.
In 2008, Canadian Experience Class linking was created to promote permanent residency for
temporary workers. Between these periods, the weight of some factors in the points-based systemchanged, reflecting its economic situation (Reitz 2014, 97). Australia imitated the points system
of Canada as a main pillar of the immigration system and supplied the short-term labor force withworking holiday visas and temporary migrants with long stay visas (Castle et al. 2014, 132).

5

1.2. Restriction on and Selection of Immigrants
At first, unskilled immigrants were recruited as temporary workers to meet shortages in thedomestic labor market. Later, they were banned from entering the countries because of there being
fewer jobs due to the economic crisis. Nowadays, however, many countries are gradually starting
to regulate these immigrants again. EU countries partially use a recruitment system, but the US
restricts immigration with relatively strict systems.
After the economic depression in the 70s, the host countries focused more on the domestic
market. Temporary workers are demanded often through more flexible programs, which are
drived by employers (Padilla and Cachanosky, 145). They can be classified into three types3: thelabor market test, labor shortage list, and quota system (Padilla and Cachanosky, 136-137).
Firstly, the labor market test is a system for protecting domestic labor. Employers have toadvertise any job vacancy to nationals for a certain period, sometimes through a public
employment security office, and ensure the position cannot be filled by native workers, or EU
citizens in the case of the EU. Secondly, combined with the labor market test, the shortage list isoften created by public employment security offices, employers, and trade unions in each region.
Finally, the quota system caps the number of visas issued to immigrants. Examples of the recent
policies in main OECD countries are shown in Table1-1.
The period and work field are quite limited. Officially, immigrants are supposed to returnto their country of origin within one year while Canada and the US allow a longer period. This
indicates that the EU considers that the permanent residents of western countries provide a
sufficient workforce. As for the work field, many countries recruit unskilled workers for theservice industry and agriculture.

3

The transitional measures by the western member states against the eastern countries in the EU are being removed.

They will then face the need for new programs considering the inflow of EUA8 nationals. Therefore, the countries arelikely to renew the programs against TCNs in the near future.

6

SAWP

Temporary Foreign Worker
Programme C(intermidiate and clerical)< 8 months

Agriculture

Sectors involved

18,000 (2006)

Number of participants

None

LimitsNone

Guarantees required
Labour marker test;
employer must pay
transportation andhousing (can deductfrom salary)

3,500 (2006)

None

None

Labour market test;
cover all recruitment
costs; help findsuitable, affordable
accommodation; pay
full transportation
costs from home
All sectors
country; provide
medical coverage
until the worker iseligible for provincial
health insuarance
coverage

17,000 (2006)

None

34,000 (2006)< 2 years

Labour market test or
shortage list;
Agriculture
employers must
guarantee housing

290,000 (2006)< 2 years

Table 1-1.Temporary work permit programmes for low-skilled workers
Programme
Maximum length of stay allowed

CANADACANADA< 6 months/annually for 3 years

Employers must
Agriculture, otherprovide housing (cantemporary
deduct from salary)

Labour market test;
cover all recruitment
costs; help findsuitable, affordable
accommodation; pay
full transportation
costs from home
All sectors
country; provide
medical coverage
until the worker iseligible for provincial
health insuarance
coverage

Seasonal Agricultural< 8 months

Temporary Foreign Worker
Programme D(elemental and labourers)

FRANCE

Bilateral Agreements

CANADAGERMANY7

< 9 months< 7 months< 9 months

Employers must
guarantee housing
Agriculture
but can deduct costs

Labour market test or All temporary
shortage list
sectors

Labour market test;
employer must
demonstrate (but notnecessarily provide)housing and pay half Agriculture
transportation costs;
employer must pay
repatriation costs foroverstayers

Demonstrateexistence of(but not necessarily
Agriculture, tourism
provide) housing;must pay repatriation
costs for overstayers

3,500 (2007)

16,000 (2005)

78,000 (2006)

5,000 (2007)

64,540 (2006) (requests)

3,500 (2007); to bephased out

Limited to Romanian/
Bulgarian citizens from
01/01/08

None

Quota of 5,000 (2007)

80,000 (2008)

SPAIN

Seasonal Work< 6 months

Employers must
guarantee housing
Food processing
but can deduct costs

ITALY

Seasonal Agricultural Worker
Scheme (SAWS)< 12 months

Contigent

Recognised Seasonal Employer

UNITED KINGDOMSector Based Scheme

NEW ZEALAND

UNITED KINGDOM

None< 10 months

50,000 (2006)

H-2A

Capped at 66,000
entries annually< 10 months,
renewable up to 3 years

UNITED STATES

H-2B

Non-agriculture,especiallylandscaping,
200,000 (2006)
cleaning, hospitality,
construction
Employer must passlabour certification
test

Employer must passlabour certification
test, pay at leastenough to counter
Agriculture
adverse wage effects,
provide housing andcover one-way
transportation costs

UNITED STATES

Source: OECD (2008)8

Unlike the development of policies for the unskilled, policies targeting skilled immigrants
have been developed in recent years. As the economic and demographic structure changed in theworld, industrialized countries could not suppress both demand-pull and supply-push factors. The
former is the “shortage of manpower and human capital and demographic decline”; the latter, the“rapid population growth combined with low rates of economic growth and high unemployment,
especially among the young” (Hollifield et al. 2014, 4). Thus, the receiving country must selectimmigrants that will be beneficial to it. The position of skilled workers differs from country tocountry based on their historical backgrounds. Australia and Canada see skilled workers as a key
factor in their economic growth, but EU countries and the US consider the protection of domestic
workers first, with a combination of skilled immigrants.
Approaches by the countries can be divided into two types: systems led by demand andsystems led by supply (Padilla and Cachanosky 2016, 134-135). The demand-led system ismentioned above. The supply-lead system, which is often used in OECD countries, is a pointsbased system, (Powel, 140-141), which originated in Canada in 1967 (Reitz 2014, 92). The systemevaluates the language skill, educational level, age, experience in the country, number of family
members in the country of origin, request by the employer, and the suitability for the job with alabor shortage (Chaloff and Lemaitre, 21-23). Detailed differences among the countries are shownin Table 1-2.
Among many EU member states, a characteristic system, called a Blue Card4, is adopted toinvite TCNs with high skills (Powel, 138-139). It permits them to reside and work inside the EU
after a continuous stay of 5-11 years, and also to override the policies of individual states. To
obtain the card, applicants must pass rigorous criteria concerning the points of occupation,
residence, and welfare independence.
Chaloff and Lemaitre compare immigration policies for the highly skilled in 10 OECD
countries. They show the advantages and disadvantages of labor-led and supply-led policies.
Although the points system attracts promising immigrants without offering employment
opportunities, it seems that, for social and demographic reasons, immigrants have more difficultyfinding jobs that match their skills (Chaloff and Lemaitre 2009, 33). The demand-driven systemis effective in linking immigrants with employment. However, if employers recruit too many
immigrants, and if they become unemployed for any reason, it can be disadvantageous to theeconomy (Chaloff and Lemaitre 2009, 32). Recently these two systems have been combined to
attract highly skilled immigrants (Chaloff and Lemaitre 2009, 42). On the other hand, policies forthe unskilled are positioned as a complement for temporary labor shortages.

The states ratifying the Blue Card are Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechoslovakia, Estonia,
Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxemburg, Malta, the Netherlands, Poland,
Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden (Padia and Cachanosky 2016,137-138).
4

9

Main temporary migration programmes
relevant for highly skilled workers#Y : maximum duration
R : renewable
LMT : labour market test
Quota

Characteristics of the Labour Market Test

Shortage occupation list

Foreign students canchange status after thecompletion of theirstudiesPossible, but no specificprogramme.

Skill Independent (880),
SOL, Employer Nomination
Australian Sponsord(ENSOL), MODL (bonus(881) & Designated Area
points for PR)
Overseas Student (882)No

Possible, but no specificprogramme.

Possible, accelerated
access to Permanent
Residence (2.5 or 1.5 YR
depending on degree)

Possible. The PostGraduation Work
Permit Program grants
up to 3YR permit towork. This is importantto acquire “Canadian
Experience” forpermanent residence.

Yes‐ A Permit (generally after 4 years of continuous
residence with a B permit over the last 10 years)‐B Permit 1YR LMT and limited to bilateralagreements (wage ≥ €33k no LMT and nocondition on nationality. UE8 nationals withNo
a job offer can get a Permit B without LMT)‐“Professional Card” for Independentpractice delivered by SPF Économie , 5 YRDanish Immigration Service consults the relevant
trade union, except for shortage list occupations.

Possible. Automatic 6Positive list of occupations. month extension after
Generally masters-level, in graduation to seek work
health, science, management, under Green Card terms.
architecture/engineering,
Study counts forlaw, etc.
permanent residence
requirement.

Employer must be authorized by Public Employment
Service, and job is checked against registeredNo
unemployed for 30 days.

B permit issued if a worker cannot be found or
trained “within a reasonable delay”. Approval iswithin 30 days of application to the responsible
labour office.

‐Skilled Worker Class (R 75) - PTS‐Provincial Nominee Class (R 87)

Temporary Foreign Worker: Labour market
opinion, with demonstration of attempts to fillposition (advertisements, etc. and public
No (target of 129
employment service), verification of prevailing wage
‐Temporary Foreign Worker (R200)
Regional Lists of000 to 142 000 inand conditions. The LMO also considers whether
limited to the duration of employment, LMT
Occupations under2007 for Skilled“employment of the foreign worker will directly
except if included in Regional Lists ofPressure (only for
Worker, Quebec
create new jobs or retain jobs for Canadians”. Trade
Occupations under Pressure
Temporary Foreign
Skilled Worker and union approval will accelerate the process. No LMO‐TN visa 1YR (NAFTA)
Workers)
Provincial Nominee) necessary for “Occupations under pressure”.
Permanent Migration: Arranged Employment
Opinion for Skilled Workers provides additional
points under the point system.

‐ General Skilled Migration Programme (GSM) PTS
Yes. Cap of 108 500
Shortage list occupations only.
‐ Employer Nomination Scheme (EN) - PTS‐ Temporary business long stay (457) 4YR for 2007-2008.
No LMT, although salary is verified.
‐ Regional Sponsored Migration Scheme (RSM) Queue spillover.
PTS‐ Permanent residence permit and unrestricted
work permit (generally after 5 years of residence and ‐Key workers permits
No registered unemployed person is available andfulfilment of the integration agreement)‐Restricted work permit 1YR LMTYes
the employer respects applicable wages and labour
EU-8 nationals after 1 year and third country nationals ‐Work permit 2YR LMT (52 weeks inlaw.
with a key worker permit after 18 months can get an employment over the last 14 months)
unlimited residence permit.

Permanent migration programmes relevant for highly
skilled workers PTS: Point system

Table 1-2. Popicies for High-Skilled immigrants in the OECD

AUSTRALIAAUSTARIA

BELGIUM

CANADACZECH REPUBLIC

DENMARK‐Long-term Residence Permit for the
purpose of employment >1YR LMT‐Permanent Residence (after 5 years of continuous‐Work Permit 1YR LMT
residence with a Long term Residence Permit; this is‐Project of Active Selection of Qualified No
shortened to 2.5 YR for qualified workers and 1.5 YR
Foreign Labour for young qualified
for highly qualified – tertiary educated – workers).
foreigners (quicker access to a permanent
resident status).
‐Work Permit 1YR LMT‐Job Card Scheme 3YR for occupations in
the “positive list” or a job offer ≥ DKK 463k
‐Green Card: 6-month job-search permit‐Permanent Residence permit (after 7 years )Noissued on the basis of points for education,
language, shortage list, experience, priorwages, experience, age. Must be converted to
Job Card permit before expiry.

10

FRANCEFINLAND‐Settlement permit (generally after 5 years ofresidence or immediately for highly qualified –researchers, university professors, those with a job
offer over EUR 65.6K annually)‐Residence permit (after 3 years for people with apermanent worker permit)‐Permanent permit P (after 4 years with a A-permit)‐A-permit 1YR LMT‐Temporarily restricted residence permit
for the purpose of employment (1YR LMT)
for people with a post secondary qualifying
education. It is subject to a local LMT and to No
Federal Employment Agency agreement.
“Tolerated” foreigners with qualifications
and‐Permanent worker permit 1YR LMT : a
job contract for unlimited duration is needed(Carte de Séjour Temporaire salarié)No‐Temporary work permit <1YR LMT(Autorisation Provisoire de Travail)‐Card “Compétences et Talents” 3YR‐A-Permit 3YR LMT‐B-Permit 1YR LMTNo

GERMANY‐Residence permit –employment (1YR but may beindefinite after 10 years)‐Long term residency permit (validity 5 years after 5
years of residence and unlimited duration after 10
years)

No

Shortage occupation list
Ineligible occupation list

Students who completed
a primary, master or
doctorate degree may bepermitted to remain in
Ireland for 6 months toseek employment.

Employers or job applicants must apply for
Foreign students who
authorisation from the Public Employment Service,earn a degree in Finland
Regional list for each of 15
which lists the job for 2-4 weeks, except for
can apply for a work
regions.
occupations on the regional shortage list. Local
permit for a maximum oflabour market authorities also check the skill level
six months.
and that the job offer satisfies collective agreements.
Student with a Frenchmaster degree, with theperspective to return in
Since 2006 there is a
Employer must publish position with the Public
Employment Service, and submit application to the shortage occupation list for their origin country, canDepartmental Labour, Employment and Vocational nationals of new EU member ask for 6 months permit
to seek work in theirstates (LMT exemption);
Training service for a discretionary review offield, and receive asince 2007 a separate,
professional qualifications, contract wage andconditions, the technological and commercial added shorter, list for 3rd country permit exempt from the
LMT. Other foreign
nationals (access only forvalue of the foreign worker, and the employer’s
students can changethese occupations).
guarantee of available housing.
status under generalrules.
Possible; used only forStudents are entitled toengineers from new EU
remain in Germany formember states, although all
Local labour market test, certain categories andup to one year after
tertiary educated EU citizens
shortage areas provide exemption from vacancy
successfully completing
will have free access fromlisting. Graduates of German secondary schools
their studies for the
1/1/2009. The “Qualified
abroad are exempt from LMT if they hold tertiary
purpose of seeking
Labour Shortage
degrees.
employment. They are
Monitoring” will also be
exempt from the LMT.
expanded.
The Law 2910/01 introduced
the possibility to respond to
local needs in labour force Possible, but no specific
Submission to the public employment service
by speciality but in practice programme(OAED) for approval
this has not been
implemented
Advertisements in the national and/or local press,
showing that the positions could not be filled fromwithin the EEA

Possible, but no specificprogramme

Yes, annual quota sets a
No, although quota contains
maximum number of
separate subcategory forconversions of study
highskilled and executives
permit to work permits(1000 in 2007).
(3000 in 2007).

Job must be submitted to the public employment
service (ADEM). If no candidates are registered, the No
application may be approved.

Yes (170 000 in2007) with some
exceptions (nurses, Listing with public employment service. Automatic
university professors, approval even without response after 21-day listing.
researchers, artists,
etc.)

No

No

GREECEIRELAND‐Work permit 1YR LMT (fix term contract)‐Residence permit (possible after 5 years of legal stay)‐Work permit 2YR LMT (open end
contract)‐Green card permit 2YR (€30k< salary < €60k and shortage occupation list or all
occupation with salary > €60k)‐Work permit 1YR LMT (salary <€30k,
occupation should not be included in theineligible occupation list)

ITALY‐Permit type C (after 5 years of residence)‐Permit type A 1YR LMT (cannot change
employer or occupation)‐Permit type B 4YR LMT (cannot change
occupation)
LUXEMBOURG

11

NETHERLANDSNEW ZEALAND‐Skilled migrant Category (SMC) - PTS‐Permanent residence permit (after 5 years ofresidence)

Work to Residence policy :‐Accredited employer (talent programme)‐Long Term Skill Shortage List
Work permits : LMT
Working holidays 1Y (work period ≤6months)No‐Labour migrant work permit 3Y LMT
non renewable. In general people are
required to take a civil immigration test in
their home country (Applicants must beNobetween the ages of 18-45)‐Highly skilled migrant 5Y (wage ≥ €33.3k
for people under 30 or wageNo, but in some cases theCentre for Work and Income must approve employer
labour market test can berequest, which must meet minimum wage to support
lifted for specificentire accompanying family.
occupations or sectors.

Yes, people who havecompleted in New
Zealand a 3 year course
or a qualification thatwould qualify under
Skilled Migration
Category; may be granted
a work permit for a
maximum of 6 months toenable them to look for
work

Yes, international
students after graduating
can stay for up to 3
months to seek a job.

Possible, foreign
students with a job offer
after graduation may be
granted a work permit for
up to 1 year if they did
not benefit from a grantfrom their origin country
or a cooperation
programme.

For work permits: The employer must make “agenuine attempt” to recruit suitable resident
workers. The application is rejected if suitable
Immediate Skill Shortage
workers are available in New Zealand, but not
Lists (ISSL)“prepared to do the work on the terms and conditions Long Term Skill Shortage
proposed by the employer”, or if the employer could List (LTSL)“readily train” residents to do the work. Exemption
from LMT is the occupation is listed in the ISSL.

Employers are encouraged to request a labour
market assessment (LMA) from the Public
Yes for skilled
Employment Service (NAV) and enclose it with theworker specialists,
application. Otherwise, the police contact NAV for(5000 in 2007) but if
an LMA. There is a quota for skilled workers andthe quota is full, it isNo
specialists; beyond this quota, prior LMA isstill possible to grantrequired. Work permits are not granted if the posta permit following
can be filled by domestic labour, and the position
LMT
must require specific skills possessed by thecandidate.

Possible, but no specificprogramme‐Skilled worker / specialist (SWS) 1YR‐Job seeker visa (generally 3 months)30-day job listing requirement with the Public
Employment Service. Possibility of an exclusion list
No
where no authorisation is granted, although this has
not been used.

‐Permanent residence permit (after 3 years withtemporary permit)

PORTUGAL‐Work permit type II 1YR (to carry out a‐Permanent residence permit (after 5 or 8 years of scientific research activity or an activity that
residence depending whether the person is fromrequires highly qualified technical skills Yes, with some
PALOPS country –country with Portuguese as official including doctors and nurses)
exceptions
language- or not)‐Work permit type IV 1YR LMT (IEFP
list)

NORWAYSPAIN‐Work permit B type 1YR LMT (limited to
specific activities and area; can be renewed
Yes, only forfor 2 years)‐Permanent residence permit (after 5 years of legal residence)anonymous hiring‐Work permit C type 3Y LMT (after B(contingente)
type permits; no restriction)‐Permits D and E for self employed

Yes, foreign students canhave a residence and awork permit after
Regional shortage list
graduation if they have
(Catalogo de occupations de been in Spain for at least
dificil cobertura)
3 years and did notbenefit from a grant fromtheir origin country or acooperation programme.

“Negative certification” is required for General
Regime workers. Job must be listed with public
employment service for 15 days, and employers
must interview candidates sent by the Public
Employment Service, although they are allowed toreject them. However, no LMT is applied forshortage list occupations.

12

SWEDEN

SWITZERLAND‐Permanent Residence Permit (PUT)‐Work Permit 5Y LMT‐Residence permit 1YR LMT (5YR for‐ Settlement permit can be delivered after 5 years of EEA nationals)
residence for EFTA, USA and Canadian nationals or ‐Short term permit 1YR LMT once
10 years for other countries.
‐Trainee exchange schemes with about 30
countries 18 months maximum

No

The Public Employment Service authorises a work
permit only if no Swedish, EU, or EEA workers areNoavailable or who can be trained “within a reasonabletime” to fill the vacancy.

Yes, separate quotas
for longer and short Priority is given to resident workers. 21 day required
term. 7000 (<5Y)
listing with Cantonal Public Employment Service, asNo
and 4000 (<1Y)
well as EURES and other channels. Federal Office(2008). EEA
for Migration must also approve the request.
exemption.

No, as a general rule, aforeign student fromoutside the
EU/EEA/Switzerland
must leave after
completing his/herstudies.

Possible, but no specificprogramme, although
there is a quota
exemption.

Non-EEA student who
has obtained a degreelevel qualification may
The “Resident Labour Market Test” for Tier 2
apply to switch into therequires employers to advertise for an EEA worker,
relevant Tier without
submitting proof of advertisement within the past 6
leaving the UK. The
months, information on applicants and selection
Skill shortage occupation list International
process, and justification for not hiring applicants.
Graduates Scheme
The Shortage Occupation List provides anallows graduates to stay
exemption from this test for specific occupations.
up to 12 months forwork, after which theymust switch into a
relevant Tier.

‐H1B visa 2YR maximum 6Y (specialty
For EB2 and EB3 – “permanent labour
professional workers – bachelor degree or
certification”. A shortage list (“Schedule A”)
Yes, F1 visas allow
more : includes doctors and registeredprovides an exemption from certification.
graduates to stay for up
nurses). LMT in some cases. H-1B1 for
Yes for H1B (65For H1B – Internal workplace listing only: 10 day
to 12 months to pursue
nationals of Chile and Singapore (special
000). Permanent
posting at the workplace, or electronic distribution toprofessional training (6quota)
category quotas are employees, as well as to collective bargaining
Yes “Schedule A” formonths for M1 visa
‐TN visa 1YR (NAFTA), NAFTA
EB1 (40 000), EB2 representative if relevant. Labour Condition
permanent residence (EB2 holders)
occupation list includes most health(40 000) and EB3
Application is only for verification of prevailingand EB3). H-1B is available Within the H1B
professionals but physicians only for(40 000), althoughwage. For “H1B-dependent employers” there is aonly for specified specialty programme there isresearch and teaching activities“recapture” occurs. LMT consisting in attestation of “non-displacement” professions.
special quota (20000)‐J1 Visa 3YR maximum 6Y (exchange
No quota for TN, L1 of a U.S. worker within 3 months before and after
reserved for foreign
visitor skill) generally must return for 2 years or J1 visa.
request; “good faith” attempts to recruit U.S.
students with a Master orto its former country of permanent residence
workers and an offer of the job to a U.S. applicant
PhD from US academic(except if eligible to J1 waiver)
who was equally or better qualified than an H-1B
institutions‐L1 (intracompany transfer) 5-7Y maximum.
worker.

‐Tier 1 (General) High Skilled Worker
3YR (no job offer needed, points test
covering age, qualification and field, priorwage, UK experience, sufficient funds and‐Permanent residence –indefinite leave to remain
language requirement)
UNITED KINGDOM (after 5 years of legal residence with a work permit) No‐Tier 2 Skilled Worker 3YR Requires job
PTS
offer, LMT (no LMT if occupation included
the shortage occupation list), and points test
covering qualifications, expected wages,
language, sufficient funds.

UNITED STATES‐EB1 for those of “extraordinary ability” – noemployer required‐Employment based immigrant visa EB1, EB2 or
EB3‐Green card (H1B visa holders can ask for a green
card after 6 years)

Source: Chaloff and Lemaitre (2009)13

2. Empirical Analysis of Social Expenditure and Immigration
Are immigrants a net beneficiary or a net contributor to the welfare state? Thanks to social
security formed by each welfare state, people have been able to hedge potential risks and lead alife with ease. Recently, however, this has not been the case. As aging proceeds in developed
countries, that system is losing its sustainability. According to the economic literature, immigrants
are one solution to this problem. Historically, they were considered just a temporary workforce tocomplement the labor shortage in developed countries, and some countries regarded them as an
economic burden after the oil crisis in the 70s. In the late 90s, however, this outlook graduallychanged, and the contribution of immigrants to these countries has been discussed in a variety ofliterature.
2.1. Literature review
Razin and Sadka (1999) investigated the effect of immigration, especially unskilledworkers, on the pension system in the host countries. They used the two period overlapping model
accepting capital inflow, where immigrants also receive a pension after retirement, and stated that
unskilled immigrants contribute to the total welfare of natives even if they are net beneficiariesof the pension system. In many studies exploiting this result, Keminits (2003) considered theeffect on the unemployment compensation system with the pension system. They showed that
unskilled immigrants increase the unemployment rate of natives and are beneficial to the highly
skilled and pensioners. The latter is only under a low level of inflow because an overly large
inflow causes lower income per capita and higher unemployment. Lacomba and Lagos (2010)claim that immigration may be politically welcomed by the elderly, who gain a higher pension
from immigrants, but opposed by the young, who will have to share the pension with them.
From broader viewpoints, Lee and Miller (2000) examined the net present value of the
fiscal effect of immigrants. The effect differs depending on their age and skill, and types ofgovernments. Not only the young highly skilled but also the unskilled with a high fertility rate arebeneficial. They conclude that policies should not take seriously the number of immigrants
because the influence itself is quite small. Also, Storesletten (2000) calibrated the fiscal
contribution of immigrants and concluded that the U.S will obtain the highest benefit from highly
skilled foreigners aged between 40 and 44. Unskilled foreigners will always be a burden on thefiscal state if there is no fiscal policy change.
However, there are few empirical researches on this topic. Borjas (1994) started to estimate
the static effect of immigrants in the US, or the receiving rate of welfare benefits from 1970 to
90. He concluded that the ratio is increasing year by year and they are clearly receiving more
benefits than natives, although the amount itself is small. For the EU, Boeri (2010) found thatmany unskilled immigrants are net beneficiaries of non-contributory benefits. With OECD14

countries, Soroka, S. et al. (2006, 2016) tried to capture the effect of immigrants on the growth ofsocial expenditure, controlling for political movement against immigration. The results are notrobust for Soroka (2006), as it neglects serial correlation (Soroka, S. et al. 2016, 8), or significance(Soroka, S. et al. 2016, 9).
In this paper, the model of Soroka, S. et al. (2016) is expanded with consideration of theliterature of the theoretical researches described above. Immigrants are divided into threeeducational groups and the effects of each are analyzed regarding four types of social expenditure,
total, the elderly, health, and unemployment, controlling for economic growth. As a result,medium educated immigrants are found to contribute to the social expenditure.
The structure of this paper is as follows. The Section 2 defines each term used in ourestimation model based on their datasets. Section 3 discusses the model specification. Section 4shows the results of our estimation, and finally, Section 5 concludes and refers to further research.
2.2. Data
For social expenditure data, we adopted the Social Expenditure Database (SOCX) of theOrganization of Economic Co-operation Development (OECD). This data consists of nine typesof expenditure: elderly, health, unemployment, family, active labor market policy (ALMP),
unemployment, housing, survivors, disability, and others. We selected three categories for our
regression: elderly, health and unemployment. The elderly category including pension benefit isstrongly related to individual age. The benefits and services for health such as medical services
would be gradually required as aging proceeds. Unemployment benefit is irrelevant to age. Withthe three kinds of expenditure with different aspects, a different effect of immigrants can be seen.
In addition, the first two occupy about 70% of the averaged total social expenditure by the numberof sample countries (see Fig. 1), and thus, immigrants’ effect on these kinds of expenditure relates
to the entire welfare system in the receiving countries. On the other hand, we selected the othertwo, plus health expenditure, to investigate whether immigrants can be dependent on natives.
These systems are basically sustained by tax, or part of the nation’s income, while pension is
generally contributory. Thus, if immigrants are relying on these benefits or services, they are more
likely to be stigmatized.

15

Figure 2-1. The share of subdomains in SOCX of all sample countries

Survivors
4%
Incapacity

Others

ALMP

5%

3%

10%

12%
Housing

Health2%
Unemployment
6%

Family26%

Old age
32%
Source: OECD. Stat

Among the various databases that tally the number of immigrants, Brücker et al. (2013)
have a crucial advantage, with 20 countries and five interval periods from 1980 to 2010. They
consider immigrant educational levels. Immigrants are divided into low educated, medium
educated, and highly educated, and a more detailed definition is provided in Table.A2 in the
Appendix. Soroka et al. (2006, 2016) utilized UN data, comparing OECD data. Both have a longer
period and a larger number of countries, but considering the importance of educational difference,
which would capture their different economic impacts in the receiving country, the dataset of
Brücker et al. (2013) provides more significant implications.
However, this dataset has the same problem as others. The definition of immigrants isforeign-born individuals aged 25 years and older (Brücker et al. 2013)5, implying that the costand benefit of foreign children such as educational fees, future tax paid and social benefits usedby them cannot be considered. The impact of foreign born children with foreign born parents will
be measured when they reach 25 years old, but immigrant children born in receiving countries areseen as natives in many cases. Whether both or either of them should be considered are very
difficult questions. Hence, the results might overestimate or underestimate the actual impact ofimmigrants. In fact, the accumulation of immigration data today is insufficient. This paper’s

Some misclassification about female foreigners in Sweden was identified between 1980 and 1985. Female adults
who were born in and are residing in Sweden are counted as foreigners, but they are certainly Swedish. We looked atanother database compiled by AIB and did not find any change in the definition of immigrants in Sweden in these
periods. In our regression, therefore, the numbers for immigrants are treated as 0.
516contribution, therefore, is to obtain empirical analysis along with the theoretical research
considering immigrant skills.
2.3. Analysis
The model in this paper is an extension of that of Soroka et al. (2016), which empiricallyanalyzes the impact of immigration on social expenditure using political rather than economic
factors.

We made additions to control some variables for economic growth and were more

specific regarding the immigrant educational level, as shown in the model below. Since thenumber of immigrants is counted every 5 years through the census, the other variables also havea 5-year interval. The dependent variable is the ratio of social expenditure to GDP. The
subdomains are public spending for the elderly and health. The explanatory variable is the n ofimmigrants per total population in receiving countries. The immigrants are classified into fourtypes regarding educational level: total, low educated, medium educated, and highly educated.
In the process of model specification, there were some problems. The rate of total
immigrants is strongly correlated with the other subdomain, low, medium, and highly educatedimmigrants. Moreover, the two terms of dependency ratio are also correlated. Thus, we added
those variables separately to avoid multicollinearity. The seven combinations of the explanatory
variables are shown below. For each of the seven equations, we estimated the effect on thesocial expenditure in the host countries, with two subdomains: elderly and health.
(1) total immigrants(socx)it = α + β1(total_immi)1it-1 +β2(unemp)+β3(pop65)t+β4(fm_lfp)t+β5(trd)t+β6(ex_rate)t-1 +β7(cpi)t-1+β8(ex_opne)t-1+ uit(2) unskilled immigrant(socx)it = α + β1(low_immi)1it-1 +β2(unemp)+β3(pop65)t+β4(fm_lfp)t+β5(trd)t+β6(ex_rate)t-1 +β7(cpi)t-1+β8(ex_opne)t-1+ uit(3) medium skilled immigrant(socx)it = α + β1(med_immi)1it-1 +β2(unemp)+β3(pop65)t+β4(fm_lfp)t+β5(trd)t+β6(ex_rate)t-1 +β7(cpi)t-1+β8(ex_opne)t-1+ uit(4) highly skilled immigrant(socx)it = α + β1(high_immi)1it-1 +β2(unemp)+β3(pop65)t+β4(fm_lfp)t+β5(trd)t+β6(ex_rate)t-1 +β7(cpi)t-1+β8(ex_opne)t-1+ uit

17(5) Unskilled immigrant and medium skilled immigrant(socx)it = α + β1(low_immi)1it-1 + β2(med_immi)1it-1+β3(unemp)+β4(pop65)t+β5(fm_lfp)t+β6(trd)t+β7(ex_rate)t-1 +β8(cpi)t-1+β9(ex_opne)t-1+ uit(6) medium skilled immigrant and highly skilled immigrant(socx)it = α + β1(med_immi)1it-1 + β2(high_immi)1it-1+β3(unemp)+β4(pop65)t+β5(fm_lfp)t+β6(trd)t+β7(ex_rate)t-1 +β8(cpi)t-1+β9(ex_opne)t-1+ uit(7) highly skilled immigrant and unskilled immigrant(socx)it = α + β1(high_immi)1it-1 + β2(low_immi)1it-1+β3(unemp)+β4(pop65)t+β5(fm_lfp)t+β6(trd)t+β7(ex_rate)t-1 +β8(cpi)t-1+β9(ex_opne)t-1+ uit
The models have at most seven control variables: unemployment rate, dependency ratio of
the elderly, female labor force participation rate, trade union density, exchange rate, CPI, and theratio of exports to GDP. We selected the first five variables from (Soroka et al. 2016,8), and theycapture the demographic demand for social expenditure. Despite controlling for wage levels,
which are closely related to the taxable income of individuals, all the data found in the OECD and
UN databases have limited time periods. We gave priority to the sample size so trade union density
was added, since trade unions often negotiate with employers on wages. The latter three wereadded to control for economic growth (Miyatake 2016, 38-52). This showed the effect of social
expenditure on GDP, using such control variables. The dependent variable in our model also has
GDP in its denominator, so the GDP growth itself can influence the correlation with some of the
explanatory variables. Although their impacts will be comparatively vague in the estimation, inthe general equilibrium approach, set by the theoretical researches, the dynamic mechanism of
the production factors, which also causes the economy to converge to its optimal growth, should
be considered.
Some explanatory variables have problems with endogeneity and simultaneity. Firstly, thedirection of causality between social expenditure per GDP and the variables for controlling
economic growth is ambiguous, as Miyatake (2016) uses instrumental variables for them. We
considered the possibility of reverse causality but just added a 5-year lag to the variables. This isbecause they are not the main targets of the estimation and the possibility of rejection in theoveridentification test would be high with more instrumental variables. Hence, a five-year lag ismore suitable for the regression.
Secondly, decision making by the foreign-born regarding to which country to immigrate
could be influenced by the welfare generosity of the receiving countries (Razin and Wahba
2015, 386-399). On the other hand, the number of immigrants affects social expenditure in the18

host country at the same time. This simultaneity is likely to happen only in the same year as
those immigrants who obtain a permanent residence allowance or citizenship. This is suggested
as a magnet hypothesis by (Borjas 1999, 619-624), although they conclude the effect was notrobust. This reverse causality is caused by the generosity of the welfare state as it was measured
by Razin and Wahba only in cross-section estimation.
In the regression in this paper, however, the design of the welfare state is cancelled by thepanel data analysis, so the bias is also deleted in our estimation. It is the generosity of thewelfare state to which immigrants could react, but the characteristics in each country are deleted
within the estimation. In many countries, it takes years for such rights to be bestowed on
immigrants. Thus, the 5-year lag to the explanatory variable is supposed to capture the actual
mechanism.
Finally, female labor participation6, especially for married women, should be treated as
carefully as the immigration level. In particular, it correlates with the social expenditure andsubstitution effect of immigrants (Soroka et al. 2016, 11). At the same time, it is also
endogenous, probably influenced by social expenditure. The labor supply of women is elastic totheir income with the endogeneity of taxation (Meghir and Phillips 2006, 26-27), and thisargument can be applied to their disposable income.
There are two possible examples of how social expenditure influences the labor supply.
Firstly, the decrease in benefits to a household would motivate married women to work.
Secondly, the increase in the pension premium would impose a financial burden on them, thus
encouraging them to seek employment. We cannot conclude which one overrides the other, but
considering the current aging problem, the latter would be larger. To avoid such reverse
causality, we conducted two-stage least squares analysis for factors other than the estimation forsocial expenditure for unemployment. The instrumental variables are the proportion of femaleseats in a parliament with one year lag and the length of maternity leave. The former term
indicates women’s social progress, and the latter is a proxy for companies’ consideration for
women.
Since the data for our model was panel data, we used the pooled ordinary least squares
model and fixed effects to eliminate the effect of the structure of the welfare state. As the units
are selected from OECD countries, based on the data source, they might have suffered from avariety of macro shocks such as a financial crisis at the global level. We added year dummy
variables to control for the effect of such incidents. In most of the regressions, the year dummies
are significant at the 1% level, and therefore only the result with the dummies is shown when it

Women are different from immigrants in terms of the factor affecting their labor supply. They are supposed to be
used to the welfare generosity in the country and react to policy changes, so its simultaneity problem cannot be
solved.
6

19

comes to fixed effects. As the characteristics of each country, which cannot be quantified, areassumed not to be random but related to the explanatory variable because the selection of thesample countries is not randomized, random effects are not taken into consideration. As the time
length and the variety of units are small, panel GMM are not appropriate for these models.
(Kitamura 2005, 97-101)
2.4. Results
Models are used that mainly control for some biases, with five types of estimation models
for each of the kinds of expenditure: univariate regression by OLS, multivariate regressions with
OLS and IV excluding the control variables for GDP and including them, and with OLS and IV
including them. The former three regressions, which omit some variables shown in the model
specification part, were for robustness check of the regressions with all controls. Comparing thesemodels, it seemed that the effect of immigration was captured differently depending on the control
variables.
In the results of regressions7, the significance of year dummies in fixed effects were mostly
recognized, and the Welch F test to compare fixed effects and pooled OLS indicated FE isappropriate in most of the models. In addition, many of the F values in the first regression in 2SLS
are greater than 10, indicating the instrumental variables are strongly correlated with the
explanatory variable, even though they are less than the threshold in some of the robustness
checks. The LM test also supports the irrelevance of IV with the error term of the main regression
model. The Hausman test, however, does not approve only one of the pooled OLS and two-stage
least squares, but the values of the immigration level in 2SLS differ greatly from that of OLS inmany models. Thus, it seems that we should not disregard the simultaneity of the female labor
force. The argument is the same for the fixed effect with instrumental variables, but unfortunately,
the validity of the IVs cannot be examined.

For all estimations, since the data is unbalanced panel data, we used heteroscedasticity and autocorrelationconsistent standard error.
7

20

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

fem_lfp

trd_unex_ratecpi

Dependent variable= socx

Table 2-1. Pooled OLS and FIxed Effects for social expenditure for old age

Pooled(0.008)***

0.177

Fixed

(0.205)***0.205

Pooled(0.007)***

0.183

Fixed

(0.013)***0.215

Pooled(0.008)***

0.173

Fixed

(0.014)***

0.204

Pooled(0.007)***

0.180

Fixed

(0.014)***0.209

Pooled(0.007)***

0.176

Fixed

(0.013)***

0.209

0.229
(0.071)***(0.189)***-0.5650.231

Pooled

0.202

7

Fixed

(0.012)***

6

0.172

5

Pooled(0.008)***

4

0.207

3

(0.014)***

2

0.174

1

(0.008)***-0.017(0.108)

Fixed

0.080
(0.031)**-0.447

139

20

139

0.004

20

139

0.862

20

139

0

20

139

0.862

20

139

0.034

20

139

0.000

0.866

20

139

0.006

20

139

0.000

0.000

0.865

20

139

0.050

20

139

0.000

0.000

0.870

20

139

0.063

20

139

(0.14)

0.425(0.557)(0.268)0.116(0.171)-0.5650.187(0.095)*0.286(0.239)(0.285)*

139

20

0.866

0.000

0.000

0.080
(0.146)-0.351

N

20

0.001

0.000

0.000

0.171
(0.057)***(0.304)-0.147(0.558)-0.383-0.052(0.257)(0.217)*

No. of countries

0.864

0.000

0.000

0.002
(0.002)-0.218R-squared

0.000

0.000

0.196
(0.185)(0.348)Welch F test

0.000

ex_openWald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

21

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

Dependent variable= socx

Table 2-2. Pooled OLS and FIxed Effects for total social expenditure-0.008Pooled(0.075)

0.117

Fixed

(0.042)0.012

Pooled(0.077)

0.097

Fixed

(0.044)0.011

Pooled

(0.068)*

0.122

Fixed

(0.053)-0.032Pooled(0.075)

0.118

Fixed

(0.044)0.011

Pooled

(0.066)*

0.122

Fixed

(0.042)-0.034PooledFixed(0.045)

7

0.119

6

Pooled

(0.067)*

5

0.012

4

(0.043)

3

0.116

0.008

2

(0.069)(0.079)

1

0.161

Fixed

(0.036)***0.337(0.132)***0.484(0.359)-0.037(0.218)***0.648(0.158)0.220(0.262)-0.4140.059(0.46)(0.367)0.131(0.125)***0.469(0.298)-0.128(0.213)**0.581(0.187)**0.415(0.213)***-0.771

0.432(0.179)*0.159(0.145)***-0.3910.479(0.379)0.271(0.215)*(0.127)***0.720(0.087)***

0.167

0.213

(0.224)***0.418(0.439)(0.158)0.133(0.225)*0.462(0.373)0.223(0.133)***0.656(0.113)*0.202(0.218)***0.201(0.142)0.183(0.108)*0.485(0.374)0.254(0.132)***0.710(0.24)(0.067)***

0.341

0.160

0.101

0.000

(0.095)-0.018(0.029)*0.059(0.069)0.116(0.088)-0.019(0.097)0.000(0.034)0.058(0.074)

0.114

121

(0.094)-0.008(0.114)

0.047

20

121

(0.036)0.045(0.078)*

0.145

0.901

20

121

(0.093)-0.003(0.095)-0.024

0.444

20

121

(0.028)0.046(0.09)*

0.174

0.895

20

121

(0.094)-0.020(0.098)

0.000

0.398

20

121

(0.036)0.044(0.078)*

0.145

0.901

20

121

(0.095)-0.004(0.095)-0.018

0.489

20

121

0.024
(0.03)(0.068)***

0.219

0.447

(0.181)*(0.378)(0.066)(0.092)

121

20

0.398

(0.231)*

0.473

0.668

-0.018

0.062

121

20

0.889

-0.830(0.127)***(0.253)**(0.098)(0.032)*

121

20

0.371

0.000

(0.548)

0.242

0.133

-0.001

121

20

0.895

0.000

0.003

0.118
(0.28)(0.164)(0.369)(0.091)

121

20

0.390

0.000

0.004

-0.242

0.485

0.687

121

20

0.901

0.000

0.002

(0.413)***(0.131)***(0.25)**

N

20

0.364

0.000

0.008

0.436

0.141

No. of countries

0.899

0.000

0.003

(0.199)**(0.376)R-squared

0.000

0.004

fem_lfp

Welch F test

0.001

ex_opencpi

ex_rate

trd_unWald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

22

consttotal_immi

low_immi

med_immi

Dependent variable= socx

Table 2-3. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for total social expenditure

Pooled(0.075)

0.085

Fixed

(0.097)**-0.191Pooled(0.073)

0.099

Fixed

(0.076)-0.040Pooled(0.073)

0.113

Fixed

(0.061)*-0.108Pooled-0.0197

Fixed(0.071)

6

Pooled
0.114

5

Fixed

(0.073)

4

Pooled-0.078

Fixed

Fixed

(0.099)2
Pooled0.080

1(0.074)

Fixed-0.1380.687(0.094)(0.183)***

0.099

0.257

(0.073)(0.328)-0.0520.636(0.076)(0.186)***

0.166

0.264

(0.085)*(0.349)-0.0690.412(0.138)(0.153)***-0.4730.439(0.294)(0.302)

0.406

0.606

(0.195)***

0.569

0.160

(0.261)-0.279(0.097)***

0.470

0.003

(0.205)0.349(0.384)0.736(0.216)0.204(0.083)0.116(0.106)0.093(0.262)-0.301(0.096)***0.482(0.27)*-0.507(0.045)0.008(0.184)*0.306(0.386)0.387(0.186)0.294(0.273)**-0.690(0.085)*0.143(0.145)0.108(0.264)-0.234(0.111)***

0.431(0.038)-0.001(0.173)***0.452(0.383)**0.900(0.208)**0.448(0.082)*0.142(0.112)0.150(0.257)-0.361(0.094)***0.481(0.265)**-0.528(0.047)-0.001(0.193)*0.337(0.386)0.543(0.202)0.220(0.303)-0.48678(0.085)0.129(0.122)0.053(0.266)-0.255(0.11)***

0.441(0.295)-0.41020

78(0.036)0.003(0.15)***

0.395(0.332)0.397(0.174)***0.582(0.319)***-1.082-0.762(0.108)***(0.463)(0.115)(0.048)*

78

20

0.448

(0.547)0.320-0.169

0.343

0.133

78

20

0.653

-0.730(0.178)*(0.261)(0.174)**(0.084)

78

20

0.324

(0.377)*

0.550

0.529

0.037

0.032

78

20

0.666

-1.808(0.111)***(0.419)(0.122)(0.037)

78

20

0.378

(0.521)***-0.398

0.290

0.134

78

20

0.648

-0.548(0.278)(0.188)(0.085)

78

20

0.306

(0.449)

0.196

0.024

78

20

0.645

-1.296(0.132)(0.048)

78

20

0.288

(0.548)***

0.105

78

20

0.638

-0.695(0.084)

78

20

0.249

(0.389)*78

200.640868

high_immi

N

20

0.218

ex_opencpi

ex_rate

trd_unfem_lfp

pop65

unemp_rate

No. of countries

0.632

LM testHausman test

11.514

0.896

0.011

14.184

0.895

0.009

8.984

0.496

0.013

11.780

0.889

0.044

9.407

0.518

0.011

10.840

0.619

0.025

16.619

0.854

0.143

R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

23

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

Dependent variable= socx

Table 2-4. Pooled OLS and FIxed Effects for total social expenditure

Pooled(0.073)

0.019

Fixed

(0.036)-0.020Pooled(0.085)

0.042

Fixed

(0.053)-0.023Pooled(0.074)

0.017

Fixed

(0.053)-0.031Pooled(0.071)

0.029

Fixed

(0.051)-0.016Pooled(0.085)

0.035

Fixed

(0.052)-0.0360.345(0.106)0.139(0.136)**0.348(0.316)-0.356(0.153)0.220(0.149)***-0.436(0.142)**0.324(0.148)0.200(0.131)**0.340(0.307)-0.374-0.722(0.32)**

0.455(0.241)*(0.116)0.174(0.217)-0.294-0.423(0.534)0.355(0.312)(0.139)**0.317(0.29)-0.251

0.289(0.192)0.361(0.225)(0.184)*0.368(0.267)***-0.8700.626(0.285)**Pooled-0.051

7

Fixed(0.063)

6

0.027

5

Pooled(0.085)

4-0.033

3(0.059)

2

0.038

1

(0.082)-0.060(0.088)Fixed-0.232(0.206)(0.133)**0.281(0.255)0.500(0.312)

0.249

0.563
(0.22)**

0.199(0.294)(0.175)

0.338

0.237
(0.253)

0.314

0.422
(0.248)0.354(0.247)(0.14)**

0.301(0.307)-1.090(0.581)*

0.185

0.527
(0.136)-0.616(0.354)(0.156)0.302(0.269)-0.653(0.241)***

0.373

0.546
(0.273)*-0.710(0.333)**(0.141)**0.380(0.317)-0.009(0.109)0.006(0.075)-0.005(0.108)0.091(0.038)**(0.11)-0.0170.016(0.067)0.056(0.126)0.074(0.022)-0.007(0.056)***-0.010(0.077)0.011(0.109)(0.04)

0.067

0.029
(0.111)

0.017(0.069)0.040(0.127)0.094(0.03)***-0.001(0.107)(0.07)

0.005

0.053
(0.122)0.067(0.037)*0.020(0.102)-0.002(0.078)-0.004(0.107)0.084(0.032)**0.075(0.115)(0.28)

0.509(0.269)*0.039(0.119)0.075(0.041)*

(0.3)

0.263

fem_lfp-0.005(0.074)

0.041

pop65

trd_un(0.031)

0.093

0.046

(0.029)***(0.035)

0.096

0.069

(0.056)(0.038)*

0.081

0.042

(0.033)**(0.035)

0.127

0.067

(0.052)**(0.037)*
0.093

0.039

(0.027)***(0.033)
0.126

0.070

(0.055)**(0.036)*
0.075

0.048

(0.038)*(0.036)
0.093

0.080

(0.055)(0.024)
0.078

0.038

(0.025)(0.033)
0.129

0.094

(0.055)**(0.037)**
0.061

0.043

(0.036)121-0.047(0.023)*(0.033)0.098

121-0.027(0.037)0.091(0.058)121-0.002(0.025)(0.039)**(0.04)

0.063

121-0.029(0.032)

0.045

0.104

121

-0.052(0.03)*(0.034)(0.055)*

0.000

121

-0.035(0.033)

20

121

20

0.589

0.001
(0.027)

20

0.912

121

20

0.525

-0.016(0.037)

20

0.917

121

20

0.568

-0.007(0.016)***

20

0.916

121

20

0.516

-0.029(0.033)

20

0.911

121

20

0.514

0.000

-0.027(0.033)

20

0.915

0.000

0.051

121
20

0.498

0.000

0.015

-0.030(0.036)

20

0.911

0.000

0.027

121
20

0.486

0.000

0.052

(0.03)

0.910

0.000

0.036

121

R-squared

0.000

0.066

-0.012(0.036)ex_ratecpi

ex_openN

Welch F test

0.112

No. of countries

Wald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

24

consttotal_immi

low_immi

Dependent variable= socx

Table 2-5. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for total social expenditure-0.268Pooled(0.08)-0.048Fixed(-0.232)*-0.232Pooled

(0.064)

0.009

Fixed

(0.111)-0.139Pooled

(0.067)-0.031

Fixed(0.119)**-0.285Pooled

(0.061)-0.020Fixed(0.109)-0.165Pooled

(0.062)

0.001

Fixed

(0.094)-0.118PooledFixed(0.129)**7-0.013

6

Pooled

(0.065)

5-0.213

4(0.123)*

Fixed-0.002-0.082

2(0.067)(0.116)

1-0.589Fixed(0.257)**-0.1970.377(0.087)***-0.777(0.245)***0.224(0.288)**

0.613

0.283
(0.183)-0.510(0.176)***(0.198)0.003(0.229)-0.051

0.314(0.109)***(0.259)*0.465(0.372)***

1.125

0.377
(0.209)*(0.147)0.035(0.212)-0.2480.380(0.08)***-0.787(0.227)***-0.965(0.323)***(0.261)0.260(0.31)**

0.753

0.224
(0.185)-0.329(0.2)-0.750(0.54)(0.154)-0.127(0.228)-0.1100.307(0.098)***-0.628(0.252)**(0.226)0.304(0.301)

0.297

0.534
(0.174)***-1.052(0.273)***

0.751

0.222
(0.222)(0.231)(0.269)(0.199)***1.036-0.065(0.27)(1.036)(0.146)0.025(0.053)

0.418

0.407
(0.092)***

0.339

0.124
(0.087)0.042(0.05)

0.524

0.412
(0.197)**(0.25)-0.080(0.339)-0.006(0.059)0.052(0.026)**0.048(0.081)(0.228)**

1.094

0.185

0.121
(0.08)

0.115(0.061)*0.226(0.055)***

0.453

0.274
(0.104)***(0.396)***(0.207)-0.027(0.068)0.038(0.025)-0.002(0.097)(0.324)

0.018

0.391

0.115
(0.091)0.118(0.073)0.249(0.051)***

0.293

0.266
(0.18)(0.233)(0.288)0.004(0.06)

0.028(0.028)-0.083(0.112)(0.221)

0.959-0.1090.106(0.088)0.115(0.059)*0.228(0.056)***

0.669

0.484
(0.103)***(0.338)***(0.162)-0.024(-0.024)***0.049(0.026)*-0.006(0.108)(0.271)**-0.295

0.291

0.065
(0.094)0.143(0.143)0.218(0.055)***78-0.028(0.035)unemp_rate

pop65

fem_lfp

cpi

ex_open-1.422(0.508)***(0.249)(0.284)-0.016(0.075)0.014(0.031)-0.064(-0.064)***

78

0.029
(0.035)-0.681(0.435)

0.120

0.117
(0.092)0.156(0.071)**0.207(0.073)***

78

0.041
(0.039)-1.009(-1.009)***(0.166)-0.014(0.068)0.040(0.027)-0.139(0.136)78

0.015(0.035)-0.803(1.403)0.076(0.092)0.157(0.067)**0.213(0.057)***

78

0.024
(0.047)

med_immi

trd_un0.028(0.027)-0.088(0.125)78

0.003(0.039)high_immi

ex_rate0.213(0.058)***

78

0.037
(0.041)

20

78

20

0.029
(0.036)

20

78

20

0.056
(0.056)

20

78

20

-0.019(0.042)

20

78

20

0.040
(0.053)

20

78

20

0.504

0.015
(0.036)

20

0.783

78

20

0.433

0.059
(0.049)

20

0.806

78
20

0.416

0.012
(0.037)N
No. of countries

0.766

0.153

0.408

0.282

0.483

0.768

0.004

0.274

15.552

0.394

0.415

0.545

15.608

0.737

0.114

0.564

12.392

0.333
0.022

0.451

15.696

0.753
0.144

0.612

11.1090.348

Hausman test

0.960

13.8220.737

LM test14.122R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

25

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

fem_lfp

trd_unex_ratecpi

Dependent variable= old age

Table 2-6. Pooled OLS and FIxed Effects for total social expenditure

Fixed(0.006)***0.070

Pooled(0.004)***

0.063

Fixed

(0.006)***0.072

Pooled(0.003)***

0.061

Fixed

(0.005)***0.072

PooledPooled

0.062

7

Fixed

(0.004)***

6

Pooled

0.074

5

Fixed

(0.005)***

4

Pooled
0.061

3

Fixed

(0.003)***2
Pooled0.070

1(0.006)***

Fixed
0.063

0.085

(0.004)***(0.046)*0.067-0.004(0.005)***(0.043)

0.060

0.024

(0.004)***(0.077)

0.072

0.061

(0.006)***(0.082)

0.062-0.033(0.004)***(0.053)-0.067(0.045)-0.018-0.029(0.023)(0.023)0.181(0.215)123-0.169(0.157)123

20-0.215(0.257)

123

20

0.189

-0.264(0.212)

123

20

0.815

-0.180(0.205)

123

20

0.183

-0.175(0.133)

123

20

0.820

-0.435

123

20

0.023

(0.081)***

123

20

0.822

-0.104

123

20

0.168

(0.187)

123

20

0.815

-0.447

123

20

0.021

0.000

(0.117)***

123

20

0.820

0.000

0.000

-0.024

123

20

0.004

0.000

0.001

(0.185)

123

20

0.814

0.000

0.000

-0.368

N

20

0.046

0.000

0.000

(0.071)***

No. of countries

0.816

0.000

0.000

(0.14)

R-squared

0.000

0.000

-0.109Welch F test

0.000

ex_openWald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

26

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

Dependent variable= old age

Table 2-7. Pooled OLS and FIxed Effects for social expenditure for old age

Fixed

PooledFixed

PooledFixed

PooledPooled

0.007

7

Fixed

(0.031)

6

Pooled

0.063

5

Fixed

(0.048)4

Pooled

0.020

3

Fixed

(0.027)2
Pooled0.061

1(0.051)

Fixed

0.015

0.126
(0.068)*(0.038)-0.454

0.061

0.052
(0.03)*(0.107)***(0.049)-0.065

0.020

0.127
(0.213)(0.129)0.081(0.028)-0.391(0.081)

0.057

0.077
(0.128)(0.12)***

0.158(0.048)-0.039(0.068)**(0.02)(0.146)(0.07)

0.038

0.020

0.161

(0.05)(0.068)**

0.060

0.041

0.021

(0.086)(0.032)-0.340

0.160

0.061

(0.08)***(0.069)**(0.048)-0.005

0.024

0.026

(0.126)(0.071)(0.029)

0.158

0.060

(0.068)**(0.048)0.025-0.053(0.037)(0.025)0.025(0.017)

0.162

0.245

0.052
(0.123)(0.069)**(0.124)*0.042(0.05)

0.044

(0.2)

0.106

-0.010(0.056)(0.085)

0.251

0.044
(0.023)*0.160(0.129)*-0.238(0.313)(0.069)**

0.115

0.007
(0.163)(0.08)

0.010(0.204)-0.164(-0.164)***

0.162

0.310

0.067
(0.101)(0.07)**(0.152)*0.043(0.045)(0.2)-0.051(0.05)

0.001(0.017)

0.107

0.021
(0.041)-0.025(0.046)

116

0.265

-0.045(0.053)0.007(0.017)116

20(0.125)**0.000(0.06)-0.029(0.047)

116

20

0.330

0.119

-0.050(0.053)0.019(0.019)

116

20

0.880

(0.198)0.022(0.041)-0.024(0.045)

116

20

0.301

0.309

-0.037(0.053)0.008(0.017)

116

20

0.879

(0.309)-0.007(-0.007)***-0.027(0.045)

116

20

0.198

0.115

-0.045(0.053)(0.02)

0.020

116

20

0.880

(0.202)-0.016(0.046)-0.027(0.044)

116

20

0.295

0.297

-0.050(0.05)

0.022(0.018)

116

20

0.878

(0.153)*-0.011(0.041)-0.023(0.044)

116

20

0.348

0.000

0.107

-0.048(0.052)0.020(0.018)

116

20

0.879

0.000

0.090

(0.199)fem_lfp-0.024(0.044)

116

20

0.179

0.000

0.075

0.300

trd_un

116

20

0.880

0.000

0.087

(0.144)*

N

20

0.203

0.000

0.032

(0.2)

No. of countries

0.879

0.000

0.054

0.110

R-squared

0.000

0.038

pop65

Welch F test

0.027

ex_opencpi

ex_rateWald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

27

consttotal_immi

low_immi

Dependent variable = old age

Table 2-8. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for old age-0.076Pooled(0.036)*

0.063

Fixed

(0.043)-0.054Pooled(0.034)**

0.079

Fixed

(0.032)-0.027

Pooled(0.038)

0.060

Fixed

(0.047)*-0.092Pooled(0.035)**

0.077

Fixed

(0.034)-0.033Pooled(0.034)**

0.081

Fixed

(0.032)*-0.057

PooledFixed(0.044)*

7

0.064

6

Pooled(0.036)*

5-0.041

4(0.037)3

0.107-0.0452(0.038)***(0.048)1-0.430Fixed(0.151)***-0.300

0.170

0.700

(0.091)

0.071(0.125)0.066(0.045)***0.151(0.137)**-0.293-0.084(0.19)(0.078)0.042(0.127)***0.558(0.077)-0.004(0.095)**-0.236(0.308)(0.042)-0.012(0.058)-0.035(0.124)0.022(0.051)***

0.193(0.146)***-0.394(0.012)0.010(0.058)0.060(0.129)***

0.529

0.108
(0.08)(0.096)***-0.4270.226(0.079)***(0.055)***(0.143)***-0.0660.009(0.089)**-0.2850.091(0.053)(0.019)-0.263(0.134)**0.018(0.08)(0.133)

0.091

0.003

(0.157)*

0.151

0.515

(0.077)(0.044)0.203(0.045)***(0.149)***-0.0230.008(0.102)**-0.0060.076(0.073)(0.016)-0.144(0.087)(0.123)0.032-0.012(0.177)

0.149

0.645

(0.073)(0.046)0.125(0.047)***(0.139)(0.05)-0.0720.012(0.077)

0.119

0.120

0.057

(0.017)-0.106(0.096)(0.128)(0.085)-0.002(0.152)

0.165

0.611

-0.051(0.042)-0.240(0.053)***(0.186)***(0.055)(0.02)

0.009

unemp_rate

74

20

74

0.421

20

74

0.526

20

74

0.431

20

74

0.544

20

74

0.511

20

139

-0.681

0.108

0.050

-0.009

74

20

0.467

(0.27)**

0.026(0.08)(0.128)(0.076)(0.045)

74

20

0.422

-0.1200.572-0.039

0.021

74

20

0.527

(0.232)(0.166)***(0.062)(0.016)

74

20

0.376

-0.5220.030-0.016

74

20

0.472

(0.256)***(0.05)***(0.079)(0.045)

74

20

0.328

-0.040-0.054(0.13)

0.017

74

20

0.472

(0.197)(0.06)

0.017(0.019)med_immi

pop65

0.004

high_immi

fem_lfp(0.041)

0.214

trd_unex_rate

cpi

N

20

0.330

ex_openNo. of countries

0.542

LM testHausman test

11.186

0.382

0.249

13.926

0.289

0.217

8.337

0.555

0.237

11.639

0.346

0.680

8.645

0.505

0.251

10.498

0.435

0.579

16.555

0.228

0.761

R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

28

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

Dependent variable= old age

Table 2-9. Pooled OLS and FIxed Effects for social expenditure for old age

0.048

Pooled(0.05)

0.034

Fixed

(0.045)0.049

Pooled

(0.043)

0.026

Fixed

(0.046)

0.047

Pooled

(0.042)

0.028

Fixed

(0.043)0.052

Pooled(0.051)

0.032

Fixed

(0.042)0.046(0.075)-0.075(0.04)**

0.107(0.093)-0.078-0.470(0.119)***(0.058)-0.053(0.114)**-0.313-0.093(0.277)(0.05)*

0.101(0.109)-0.0070.132(0.109)(0.075)0.032(0.16)***-0.6250.394(0.152)**PooledFixed(0.048)

7

0.026

6

Pooled

(0.042)

5

Fixed(0.05)

0.032

4Pooled(0.05)

0.032

3

0.043

2

(0.048)

1

0.036

Fixed

(0.047)-0.344

0.104

0.242
(0.151)(0.071)***(0.047)**0.028(0.116)-0.061(0.053)0.093(0.142)-0.127(0.129)0.133(0.104)-0.059-0.042-0.582(0.285)*(0.103)(0.076)-0.453(0.131)0.117-0.356(-0.356)***(0.041)**-0.466(0.115)***(-0.09)-0.090-0.0390.107(0.082)(0.04)**

0.101

0.136
(0.112)(0.05)*

0.229(0.173)-0.0680.173(0.137)(0.079)0.186(0.127)

0.129

0.262
(0.138)*(0.047)**0.197(0.135)0.192(0.129)0.167(0.136)

0.262

0.203
(0.169)0.230(0.174)0.244(0.244)0.230(0.153)-0.002(0.045)0.041(0.023)*-0.100(0.066)-0.024(0.041)-0.024(0.038)0.051(0.023)**-0.108(0.069)-0.015(0.036)0.014(0.041)0.044(0.044)-0.106(-0.106)***-0.028(0.042)-0.017(0.038)0.036(0.021)-0.072(0.062)-0.015(0.037)0.011(0.041)

0.055(0.019)**-0.108(0.068)-0.017(0.038)0.013(0.041)

0.036(0.021)*-0.075(0.058)-0.024(0.043)-0.024(0.038)0.048(0.017)**-0.047(0.061)(0.16)

0.171(0.149)fem_lfp(0.04)-0.029pop65

trd_un(0.02)

0.060-0.012(0.017)***-0.0030.058(0.012)(0.032)*

0.007

0.050

(0.022)(0.02)**-0.0070.078(0.011)(0.031)**

0.012

0.056

(0.022)(0.018)***-0.0080.079(0.011)(0.031)**

0.008

0.048

(0.022)(0.022)**-0.001

0.055(0.012)(0.031)*

0.019

0.048

(0.019)(0.048)-0.0080.079(0.011)(0.032)**

0.026

0.037

(0.024)(0.022)-0.0030.058

116-0.064(0.017)***(0.012)(0.032)*116-0.038(0.012)***

0.023

0.040

116

-0.031(0.017)*(0.024)(0.024)116-0.045(0.009)***-0.0010.059

116-0.061(0.02)***(0.012)(0.031)*116-0.046(0.011)***

20

116

20

0.520

-0.030(0.018)

20

0.903

116

20

0.403

-0.033(0.01)***

20

0.912

116

20

0.400

-0.037(-0.037)***

20

0.912

116

20

0.401

-0.045(0.01)***

20

0.902

116

20

0.348

0.000

-0.044(0.022)*

20

0.912

0.000

0.001

116
20

0.318

0.000

0.000

-0.039(0.013)***

20

0.903

0.000

0.004

116
20

0.325

0.000

0.001

-0.028(0.019)

0.903

0.000

0.004

116

R-squared

0.000

0.005

-0.030(0.01)***

ex_ratecpi

ex_openN

Welch F test

0.011

No. of countries

Wald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

29

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

fem_lfp

trd_unex_ratecpi

ex_openN
No. of countries

Dependent variable = old age

Table 2-10. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for old age

Pooled(0.029)

0.006

Fixed

(0.047)**-0.112Pooled(0.027)

0.015

Fixed

(0.052)-0.062Pooled(0.028)

0.020

Fixed

(0.041)-0.035Pooled-0.056

7

Fixed(0.054)

6

Pooled
0.018

5

Fixed

(0.027)

4

Pooled-0.0893

Fixed(-0.089)*

2
Pooled0.014

1(0.034)

Fixed-0.1150.428(0.051)**(0.115)***0.011-0.134(0.029)(0.119)-0.0950.283(0.055)*(0.126)**0.018-0.122(0.026)(0.134)-0.0120.181(0.053)(0.116)-0.406-0.019(0.107)***(0.117)

0.182

0.703

(0.072)-0.0250.053(0.103)***0.292(0.044)***

0.123

0.017

(0.127)0.034(0.152)***0.761(0.077)

0.033

0.008

(0.05)**-0.124(0.083)0.034(0.128)***0.334(0.045)***0.121(0.064)0.064(0.015)0.015(-0.01)***-0.010(0.686)0.686(-0.052)-0.052(0.025)***0.079(0.013)0.006(0.038)**-0.094(0.058)0.022(0.099)**0.254(0.036)***0.116(0.108)**-0.214(0.054)0.019(0.024)**0.053(0.024)0.024(0.126)-0.046(0.113)***0.538(0.083)-0.032(0.075)***-0.197(0.026)***0.093(0.015)-0.012(0.039)**-0.094(0.08)

0.104(0.101)**0.233(0.045)***0.140(0.046)-0.011(0.024)**0.049(0.027)0.015(0.112)0.054(0.151)***0.740(0.077)0.013(0.024)***0.096(0.013)-0.002(0.036)**-0.076(0.06)

0.042(0.093)**0.213(0.035)***0.115(0.102)**-0.236(0.048)0.023(0.024)**0.052(0.025)0.021(0.122)-0.038(0.113)***0.569(0.076)-0.052(0.091)-0.138(0.025)***0.074(0.013)0.007(0.039)**-0.097(0.063)0.041(0.102)**0.223(0.042)***0.138(0.119)**-0.272-0.050(0.039)0.050(0.017)0.009(0.019)**0.039(0.098)-0.010(0.114)***0.365(0.075)0.079(0.125)***-0.510-0.258(0.039)***(0.144)***(0.066)(0.029)(0.021)-0.001-0.056(0.279)0.121-0.1080.067(-0.001)***-0.007-0.358(0.098)(0.13)-0.009(0.041)***(0.023)***0.061-0.062(0.156)**

0.109

0.015

0.000

(0.039)-0.022-0.584(0.059)*(0.027)(0.013)-0.041(0.06)-0.072139(0.018)***(0.269)**-0.087

0.072

0.077

-0.05774(0.016)***-0.439(0.035)**(0.025)***(0.027)***-0.00174(0.019)(0.204)**-0.001-0.022-0.06174(0.016)***-0.359(0.012)(0.059)-0.01174(0.021)(-0.359)***0.083-0.06574(0.018)***0.342(0.023)***

0.002

74

-0.007(0.02)(0.572)-0.06074

20(0.016)***

20

74

20

(-0.001)***

20

74

20

(0.022)***

20

74

20

(0.024)

20

74

20

0.622

(0.016)***

20

0.750

74

20

0.497

(0.022)

20

0.778

74

20

0.542

(0.014)***

20

0.735

0.825

0.480

0.614

0.995

0.751

0.218

0.817

15.678

0.487

0.513

0.835

16.500

0.631

0.936

0.945

13.186

0.478
0.391

0.984

15.869

0.713
0.903

0.306

11.4590.454

Hausman test

0.559

13.776

0.775

LM test14.182R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

30

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

fem_lfp

trd_unex_ratecpi

Dependent variable= health

Table 2-11. Pooled OLS and FIxed Effects for social expenditure for health

0.046

Fixed

(0.007)*0.007

Pooled(0.002)***

0.044

Fixed

(0.003)***0.051

Pooled(0.002)***

0.042

Fixed

(0.003)***0.052

Pooled(0.002)***

0.043

Fixed

(0.003)***0.051

Pooled(0.002)***

0.042

Fixed

(0.003)***0.050

PooledPooled(0.002)***

7

0.052

6

Fixed

(0.003)***

5

0.042

4

Pooled(0.002)***

3

0.051

2

(0.002)***

1

0.042

Fixed

(0.002)***0.040(0.016)**0.007(0.032)0.035(0.052)0.070(0.02)***

0.065(0.023)**0.043(0.005)***0.046(0.034)122

0.068(0.01)***

122

20-0.046(0.109)0.041(0.137)

122

20

0.090

0.098
(0.053)*0.011(0.057)

122

20

0.860

0.752
(0.752)***

122

20

0.092

-0.024(0.069)

122

20

0.854

0.145

122

20

0.026

(0.069)*

122

20

0.860

-0.011

122

20

0.089

(0.092)

122

20

0.849

0.170

122

20

0.326

0.000

(0.067)**

122

20

0.847

0.000

0.000

0.030

122

20

0.024

0.000

0.000

(0.095)

122

20

0.860

0.000

0.000

0.151

N

20

0.052

0.000

0.000

(0.063)**

No. of countries

0.859

0.000

0.000

0.081

R-squared

0.000

0.000

(0.122)

Welch F test

0.000

ex_openWald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

31

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

Dependent variable= health

Table 2-12. Pooled OLS and FIxed Effects for social expenditure for health

Fixed(0.014)0.014

Pooled(0.018)**

0.039

Fixed

(0.01)

0.014

Pooled(0.016)**

0.046

Fixed

(0.012)0.002

Pooled(0.016)**

0.044

Fixed

(0.01)

0.014

Pooled(0.016)***

0.047

Fixed

(0.01)

0.006

Pooled0.044

7Pooled(0.017)**

6

Fixed

(0.01)

0.006

5

0.046

4

Pooled(0.016)**

3

0.010

2

(0.009)

1

0.045

Fixed

(0.017)**0.081(0.034)**0.120(0.048)**0.070(0.029)**0.063(0.028)**0.035(0.019)*0.078(0.03)**-0.054(0.119)-0.163(0.117)0.096(0.068)0.005(0.063)0.040(0.016)**0.065(0.024)**0.009(0.009)*

0.069

0.095
(0.066)(0.047)-0.011

0.023(0.063)(0.027)-0.041

0.040

0.035

(0.069)(0.104)(0.042)

0.084

0.021

(0.069)(0.025)(0.08)
0.031-0.003(0.031)

0.062

0.026

(0.061)(0.028)

0.071

0.071

(0.049)(0.049)

0.021

0.022

-0.006(0.099)(0.024)(0.026)0.100(0.049)*(0.04)

0.055-0.0180.028(0.046)0.103(0.055)*

0.026

0.026

0.000
(0.104)(0.027)(0.027)0.097(0.097)0.090(0.053)-0.006(0.099)-0.019(0.098)0.091(0.056)0.109(0.052)**-0.018(0.099)

trd_unfem_lfp

116-0.014(0.024)0.009(0.031)20

116-0.026(0.007)***0.063(0.016)***

0.855

20

116

-0.013(0.024)0.007(0.031)

0.394

20

116

-0.028(0.007)***0.071(0.017)***

0.849

20

116

-0.019(0.022)0.015(0.028)

0.332

20

116

-0.027(-0.027)***0.065(0.065)

0.000

0.844

20

116

-0.017(0.024)(0.03)

0.025

0.345

20

116

-0.025(0.009)***0.059(0.019)***

0.008

0.000

0.855

20

116

-0.013(0.024)0.007(0.032)

0.425

20

116

-0.031(0.006)***0.082(0.021)***

0.014

0.000

0.849

20

116

-0.019(0.024)0.015(0.029)

0.349

20

116

-0.024(0.009)**0.059(0.02)***

0.001

0.000

0.856

20

116

-0.014(0.025)0.007(0.032)

0.394

20

116

-0.029(0.008)***0.073(0.018)***

(0.1)

0.095
(0.059)

N

20

0.368

0.000

0.004

(0.1)

-0.014

No. of countries

0.853

0.000

0.015

pop65

R-squared

0.000

0.004

ex_opencpi

ex_rateWelch F test

0.006

\

Wald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

32

consttotal_immi

Dependent variable= health

Table 2-13. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for social expenditure for health

Fixed

PooledFixed

PooledFixed(0.017)-0.011PooledPooled

0.052

7

Fixed

(0.02)**

6

Pooled

0.001

5

Fixed

(0.019)4

Pooled

0.051

3

Fixed

(0.02)**

2
Pooled(0.02)-0.021

1

0.044

Fixed

(0.022)**0.137(0.043)***0.007-0.072(0.073)(0.017)-0.057(0.093)0.107(0.044)**0.052-0.183(0.087)**0.059(0.02)**-0.262(0.14)*

0.055(0.03)*(0.062)

0.007

0.076
(0.078)-0.177(0.022)-0.018(0.111)0.028(0.05)(0.074)**0.044-0.162(0.08)**

0.098(0.021)**

0.046(0.027)*(0.076)(0.02)-0.168-0.0150.087(0.044)**(0.073)**

0.044

0.112

(0.021)**

0.042(0.032)(0.055)**-0.0020.007(0.074)-0.134(0.016)-0.160(0.079)**0.052(0.046)(0.077)*

0.086

0.046
(0.026)*(0.08)

0.052(0.023)***-0.165(0.03)

0.029(0.048)(0.072)**

0.034

0.070

(0.09)

0.045(0.027)(0.077)-0.147-0.138high_immi(0.039)***0.110(0.032)0.005(0.051)

0.101(0.029)-0.004-0.034(0.011)***(0.045)**

0.094

74

0.041
(0.026)(0.042)

0.000

20

74

-0.039(0.009)***(0.041)***

0.127

0.824

20

74

0.047
(0.025)*(0.031)-0.003

0.308

20

74

-0.037(0.012)***(0.049)**

0.105

0.827

20

74

0.044
(0.025)*(0.034)

0.004

0.343

20

74

-0.035(0.009)***(0.038)***

0.112

0.153
(0.043)***0.109(0.045)**(0.074)*

0.002

0.046
(0.025)*

74

20

0.394

0.021
(0.102)0.073(0.036)-0.038(0.011)***

74

20

0.809

0.120
(0.039)***0.042(0.031)(0.067)

0.098

0.041
(0.026)

74

20

0.270

0.017
(0.088)-0.137

low_immi

0.076(0.043)*(0.074)*(0.041)**-0.033(0.009)***

74

20

0.824

-0.293(0.139)**

0.061

0.029

0.042
(0.026)

74

20

0.286

0.019
(0.133)0.067(0.03)**(0.075)(0.036)-0.033(0.01)***

74

20

0.809

-0.173(0.158)***-0.1990.046(0.025)*

74

20

0.341

0.007
(0.114)

unemp_rate(0.077)**

med_immi

pop65

74

20

0.809

fem_lfp

N

20

0.292

ex_opencpi

ex_rate

trd_unNo. of countries

0.823

LM testHausman test

11.186

0.730

0.115

13.926

0.666

0.193

8.337

0.994

0.235

11.639

0.818

0.087

8.645

0.945

0.237

10.498

0.925

0.041

16.555

0.670

0.204

R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

33

consttotal_immi

low_immi

med_immi

high_immi

unemp_rate

pop65

Dependent variable= health

Table 2-14. Pooled OLS and FIxed Effects for social expenditure for health

Fixed(0.013)0.012

Pooled(0.02)

0.030

Fixed

(0.014)0.011

Pooled(0.023)

0.031

Fixed

(0.013)0.006

PooledPooled

0.027

7

Fixed

(0.021)

6

Pooled

0.007

5

Fixed

(0.014)

4

Pooled
0.033

3

Fixed

(0.023)2
Pooled0.013

1(0.013)

Fixed
0.027

0.111
(0.08)(0.021)

0.108

0.005

(0.05)**(0.014)-0.2090.029(0.133)(0.023)0.164-0.1970.006(0.074)**(0.073)**(0.014)0.080-0.2600.031(0.049)(0.151)(0.023)-0.1550.027(0.078)(0.024)-0.107-0.0280.098(0.06)(-0.107)***(0.069)0.117(0.046)**-0.192-0.028(0.078)**(0.082)(0.03)

0.003-0.0310.038(0.071)(0.042)

0.122
0.051

-0.005(0.054)**(0.047)(0.029)-0.082-0.0740.037(0.064)(0.069)(0.047)0.058(0.03)

0.007(0.053)0.028(0.03)(0.028)-0.0060.004

0.013(0.029)(0.038)

0.055

0.042

(0.051)(0.033)-0.0070.029-0.016(0.029)(0.017)(0.009)*

0.045

0.038

0.028

-0.020(0.049)(0.052)(0.031)(0.019)

0.010

0.041

0.047

-0.0180.009(0.031)(0.102)(0.021)**(0.009)**(0.009)0.059(0.05)
0.039-0.018(0.01)

0.009

0.012

(0.028)(0.017)0.014(0.094)0.041-0.014(0.009)0.044(0.02)*(0.008)0.008(0.043)0.035-0.015(0.009)

0.034
(0.1)

(0.033)(0.017)

0.004

0.047

0.035

-0.019(0.009)(0.056)(0.019)*(0.01)*

0.007

0.019

0.041

-0.023(0.009)(0.101)(0.041)(0.019)

0.015

0.021

0.031

0.048

-0.021(0.008)(0.01)**(0.031)(0.022)**(-0.021)***(0.01)

0.010

0.005

0.018

-0.0160.009(0.016)(0.098)0.039(0.03)(0.017)(0.009)

0.021

0.043

0.029

-0.0160.007(0.011)*(0.056)(0.017)(0.009)*(0.009)

0.012

0.045

0.037

-0.0190.011(0.016)(0.103)(0.032)(0.018)(0.009)

0.028

0.044

0.039

-0.0180.008(0.01)**(0.058)(0.021)*(0.009)*(0.009)

0.013

0.017

-0.0210.014(0.016)(0.104)(0.019)(0.008)0.017

fem_lfp(0.01)

0.010(0.011)

trd_unex_rate0.003-0.009(0.015)-0.0250.022-0.002(0.022)-0.0250.013-0.016(0.017)-0.028

0.020

0.000

(0.01)*-0.020

0.005

0.001

116

(0.008)(0.016)-0.025116(0.015)0.017-0.008116(0.006)(0.011)-0.025116(0.014)0.005-0.002116(0.008)*(0.016)-0.019116(0.015)*cpi

ex_open116

20(0.007)

20

0.451

116

20

0.866

(0.016)

20

0.453

116

20

0.869

(0.001)*

20

0.505

116

20

0.869

(0.014)*

20

0.420

116

20

0.863

(0.008)

20

0.867

0.000

116

20

0.915

0.000

0.000

(0.015)

20

0.450

0.000

0.000

116
20

0.866

0.000

0.000

(0.007)

20

0.424

0.000

0.000

116

0.862

0.000

0.000

(0.016)R-squared

0.000

0.000

N

Welch F test

0.001

No. of countries

Wald test

Each cell contains coefficient and (standard error) blow. The value of all tests is p value, ***p<0.01; **p<0.05; *p<0.1

34

Dependent variable= health

Table 2-15. Two Stage Least Squares and FIxed Effects with Instrumental Variabless for social expenditure for health

Fixed(0.032)-0.021Pooled(0.024)

0.020

Fixed

(-0.006)-0.006Pooled(0.02)

0.023

Fixed

(0.03)-0.017

Pooled(0.022)

0.018

Fixed

(0.027)-0.019Pooled(0.021)

0.024

Fixed

(0.027)-0.023Pooled(0.02)

0.024

Fixed

(0.027)-0.010

0.031

0.038
(0.038)(0.072)*-0.1260.020(0.027)-0.184(0.079)**(0.084)

0.080

0.055
(0.045)0.041(0.049)(0.076)-0.0850.015(0.034)(0.086)

0.062

0.076
(0.04)*(0.072)*-0.1300.020(0.027)-0.187(0.079)**-0.016(0.121)(0.081)

0.113

0.035
(0.043)0.102(0.058)*-0.266(0.128)**(0.074)*-0.1370.028(0.031)-0.204(0.086)**-0.047(0.087)(0.083)

0.022

0.092
(0.045)**-0.064(0.072)0.143(0.061)**Pooled0.017

7Pooled(0.021)

6-0.0185(0.032)

4

0.027

3

(0.02)

0.020(0.023)2-0.1481

const(0.084)*

Fixed

total_immi

0.025(0.031)(0.031)

0.169(0.052)***-0.055

(0.1)

0.087
(0.046)*(0.089)

0.046

0.073

0.112
(0.048)**0.017(0.032)(0.097)0.039(0.084)-0.085

low_immi

0.065(0.045)(0.074)-0.327(0.127)***

0.077

0.014
(0.153)0.046(0.031)(0.091)-0.192(-0.192)***-0.1550.328(0.399)unemp_rate(0.077)**high_immi

med_immi

pop65(0.046)-0.028(0.014)**(0.069)0.080(0.073)0.033(0.028)0.009-0.007(0.047)-0.036(0.016)**0.019(0.009)**0.017(0.027)0.078(0.065)0.036(0.028)0.023(0.011)**0.027(0.019)-0.007-0.016(0.06)-0.032(0.016)**0.018(0.01)*

0.011(0.029)74(0.01)

0.099(0.077)0.026(0.029)0.006(0.012)0.030(0.018)74

0.000(0.012)-0.003(0.043)-0.033(0.016)**0.014(0.011)0.021(0.028)74

0.008(0.011)

0.069(0.062)0.034(0.028)0.024(0.011)**(0.02)

0.030

74

0.000
(0.012)-0.014(0.058)-0.033(-0.033)***0.019(0.009)**0.006(0.033)74-0.008(0.011)

0.062(0.073)0.007(0.035)0.015(0.015)0.029(0.018)74-0.005(0.013)-0.007(0.048)-0.031(0.016)*0.021(0.015)0.027(0.027)74

0.008(0.011)0.087(0.076)0.025(0.029)0.017(0.012)0.012(0.027)74

0.000(0.012)0.003(0.046)-0.032(0.017)*

0.013

0.005
(0.033)74

0.005(0.005)

0.073

0.030
(0.027)0.023(0.011)**(0.02)

0.030

74-0.003(0.015)0.031

trd_un0.013(0.009)0.005(0.034)74-0.001(0.011)

fem_lfp

ex_rate

0.030(0.018)74-0.006(0.012)

20

20

20

20

20

20

20

20

20

0.856

20

0.398

20

0.857

20

0.395

20

(0.01)

cpi

74

0.006
(0.012)20

0.457(0.01)

ex_open74-0.005(0.011)N
No. of countries

0.838

0.374

0.347

0.346

0.758

0.856

0.137

0.413

15.678

0.380

0.561

0.328

16.500

0.803

0.803

0.800

13.186

0.382
0.284

0.267

15.869

0.838
0.503

0.946

11.4590.351

Hausman test

0.884

13.7760.850

LM test14.182R-squared

F testEach cell contains coefficient and (standard error) blow. The value of all tests other than F test is p value, ***p<0.01; **p<0.05; *p<0.1. F test is for the 1st stage of 2SLS, and the value is F statistics. LM test is the Overidentification test.

35

Firstly, the impact on the total social expenditure differs depending on whether the model
controls for the effect of the exchange rate, CPI, and the ratio of exports. The models without
them in Table 2 show that the increase in unskilled immigrants, and the medium and the highly
skilled in some models will enhance the expenditure, approving fixed effects. On the contrary,while Table 4 adopts most of the fixed effects model, other than that of total expenditure, it
indicates that the medium educated immigrants have a significant effect indicating that they
reduce the ratio of SOCX by around 0.7 if their level is one point higher. As for the other types ofimmigrants, no significant effect is shown. Considering the simultaneity in Tables 3 and 5, theresults changed. In Table 3, the impact of the unskilled remained significant, but the Hausman
test did not reject the null hypothesis, so there was no significant effect. In Table 5, the pooledmodel also supports the financial contribution of the medium and highly educated, with asignificant positive coefficient of low educated immigrants, but the result did not pass theHausman test. The result of fixed effects with IV differs depending on the model, but models 4,6, and 7 show that highly educated immigrants reduce the expenditure.
As for the expenditure for the elderly, the results of regression with all variables weresimilar to that of the total expenditure while the estimate without the macroeconomic terms was
slightly different. In Table 7, fixed effects were also recommended by the F test and the models
showed that the unskilled had negative values. In Table 9, the regression also supported fixed
effects. Two out of three models with medium educated immigrants were significant, but theabsolute values of the coefficient were slightly smaller than those in Table 4 by 0.25. In Table 8
with IV, some 2SLS models had some consistent values of immigration, but they were notsignificant. As for the Panel IV estimation, the highly skilled had a positive value. The Hausman
test of all models in Table 10 indicates that OLS had consistency. FE with IV showed again thathighly educated foreigners negatively affected the elderly social expenditure.
Finally, the significant effects in the estimation for total social expenditure and the elderly
held in the regression with health as well. The results of the regressions with social expenditure
for health are shown in Table 14 and 15. Table 5 adopts fixed effect in all patterns, but thedifference from the former two was significant in lower educated immigrants. In two out of threemodels, they had a significant positive value of about 0.1. The medium educated were alsosignificant with negative values of 0.2. In Table 6, although the consistency of 2SLS could not befound even with values following the result of Table 5, the fixed effects model showed thecontribution of the highly educated to health expenditure.
2.5. Discussion
Organizing these effects of immigrant educational levels on social expenditure, there are
four stable results throughout the total, the elderly, and health expenditure. The first two are in the36models with macroeconomic control variables, while the second is that of multivariate regressions
with all variables. The last one holds in fixed effects with instrumental variables whether thevariables are in the model or not.
Firstly, there is the positive value of unskilled foreign born people in fixed effects, but thisholds under the condition of excluding the effect of CPI, exchange rate, and the ratio of exports.
If these are considered in Tables 4, 9, and 14, the value of the unskilled becomes insignificant. In
the three tables, most of those three terms have positive coefficients, so it could partially capture
their effects. The unskilled would include the majority of asylum seekers and refugees in receiving
countries. The result captures the effect not only of those who immigrated for economic reasons,but also of humanitarian immigrants.
Secondly, if the variables are included in the estimation, medium educated immigrants
could decrease the ratio of SOCX per GDP. There are two possible ways of causing this effect.
The first interpretation is that they are net contributors to the welfare system, which indicates they
pay more directly and indirectly than they use the social services and benefits. The other
implication is intergenerational. The inflow of medium educated immigrants might contribute
more to the welfare system than the old immigrants receive. Their effect on the receiving nation’s
economy exceeds that of the pensions used by the migrants who entered the host countries
previously. The 1% increase in medium educated immigrants will induce less social expenditure
for the elderly by 0.4% of the GDP. That is in line with the result of Razin and Sadka (1999). For
the other kinds of expenditure, the highly educated may also make some contribution, but theresult is not stable through the regressions.
Finally, considering the possible reverse causality of female labor, the highly skilled would
contribute to the social welfare system. In the estimations with instrumental variables, theoverestimate of the effect of female labor participation on SOCX can be removed as was argued
previously. Suppose the recent increase in social expenditure in OECD countries is largely driven
by aging, women and married women in particular will gain an incentive to work in order tocompensate for the high premium or possible unsustainability of the welfare state. Women are
more likely to work part time8, and these jobs can complement the jobs of highly skilled people
including immigrants. This reverse causality from the rise in SOCX to the higher promotion ofhighly skilled immigrants might have made fuzzy their “true” economic contribution to thewelfare state in OLS estimates. Comparing the regression tables of IV with OLS, there is a clear
increase in the absolute value of the highly skilled.
In spite of the possible positive and negative effects, it seems that the impacts cannot be themain solution to the problem of the current welfare system. The size of the impact is quite small
8

Labor Force Statistics in OECD. stat https://stats.oecd.org/Index.aspx?DataSetCode=LFS_SEXAGE_I_R#

37

considering the historical increase in the number of immigrants. A 5% increase of SOCX per GDP
has taken about 20 or 30 years in OECD countries. The appropriate interpretation would be that
immigration, at least that of medium and highly skilled immigrants, should not be denied in termsof the social welfare system.

3. Conclusion
The recent trends in immigration policy are a combination of supply-led and demand-led
systems. Both systems have been developed since the oil crisis because the host countries realized
the need to match immigrants more closely with the needs of the domestic economy. The former
systems are the labor market test and shortage list, and for the latter, the points system is used.
Our findings are summarized in three ways. Firstly, low educated immigrants including
asylum seekers might be a burden on the welfare state, under the condition of disregarding some
economic factors such as inflation and trade. However, since the multiple regression results
including them show some of the terms are extremely significant, the results are not robust.
Secondly, disregarding the possible bias of female workers, the medium skilled can contribute to
the welfare state. Finally, if that bias truly exists, highly skilled immigrants will reduce social
expenditure that mainly increases by aging.
For future research, we will explore the following three points. Firstly, we will study eachcountries’ welfare system. As there are several pension systems such as the Beveridgean andBismarckian schemes, immigrants might affect them differently (Locomba and Lagos 2010, 285).
Secondly, we did not consider the large inflow of migrants from eastern countries to the EU in
1991, when the collapse of the Soviet Union occurred, and in 2000, when the EU was expanded.
Since newcomers to the EU are allowed to move around the area under the Schengen Agreement,
there might be unique influence to the welfare system by them. Finally, further research will
include the accumulation of better data. Currently there is a trade-off between the specificationand the year length. In fact, our analysis failed to consider longer periods, and thus, it could notdivide old and new immigrants, who entered under different policies.

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41

Appendix

Proportion of seats held by women in national parliaments

Share of Internatioal Exports in GDP

Comsumer Price Index

Reral Effective Exchange rate

Trade Union Density

Female Labor Participation Rate

Dependency Ratio of Population Older Than 65

Dependency Ratio of Population Younger Than 15

Unemployment Rate

Total Population

Immigration Stock

Social Expenditure per GDP

Data

1990~2014

1990~2014

1985~2014

1985~2014

1985~2014

1985~2014

1985~2014

1985~2014

1985~2014

1985~2014

1985~2014

1980~2010

1985~2014

Periods

Employment

World Development Indicators

OECD FACTBOOK 2015/2016

Consumer Price IndicesOECD FACTBOOK 2015/2016

Trade Union

Labor Force Suvery

Population Statistics

Population Statistics

Labor Force Suvery

Population Statistics

The IAB brain-drain data

Social Expenditure

Database

OECD.Stat

THE WORLD BANK

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

OECD.Stat

AIB

OECD.Stat

WebsiteTable.A1 Data Souces

Length of Maternity Leave

42

Table.A2 The definitions of variables
Variables

Definition

socx

Total social expenditure as percentage of GDP

healthSocial expenditure for heallth as percentage of GDP

oldageSocial expenditure for old age as percentage of GDP

low_immi

The share of immigrants with lower secondary, primary and no schooling, per total population

med_immi

The share of immigrants with high-school leaving certificate, per total population

high_immi

The share of immigrants with higher than high-school leaving certificate, per total population

total_immi

All immigrants including the three above

unemp_rate

Unemployment rate

pop15

Dependency ratio of people younger than 15 on all ages

pop65

Dependency ratio of people older than 65 on all ages

fem_lfp

Female labor force participation rate

trd_unRatio of wage and salary earners in trade unions to all earners

ex_rateReal effective excahnge rate (base year is 2010)

cpi

Cunsumer Price Index (base year is 2010)

ex_openInternational exports in goods and services per GDP

par_seats

Proportion of seats held by women in national parliaments

maleave

Lenght of maternity leave

Table.A3 Descriptive statistics
mean

min

max

s.d.

sample

socx

0.205

0.087

0.319

0.051

139.000

health

0.054

0.024

0.084

0.013

122.000

oldage

0.066

0.024

0.119

0.023

123.000

unemployment

0.011

0.000

0.038

0.008

118.000

low_immi

0.039

0.003

0.190

0.035

140.000

med_immi

0.022

0.001

0.061

0.016

140.000

high_immi

0.023

0.001

0.132

0.024

140.000

total_immi

0.084

0.005

0.265

0.062

140.000

unemp_rate

0.077

0.000

0.029

0.043

132.000

pop15

0.190

0.000

0.011

0.033

140.000

pop65

0.145

0.000

0.021

0.029

140.000

fem_lfp

0.533

0.001

0.043

0.095

132.000

trd_un

0.352

0.077

0.831

0.206

128.000

ex_rate

0.989

0.682

1.495

0.123

140.000

cpi

0.800

0.122

1.135

0.213

140.000

ex_open

0.416

0.070

2.033

0.306

139.000

par_seats

0.210

0.000

0.453

0.139

116.000

maleave

0.291

0.087

0.607

0.115

92.000

43

socx
1.000

0.651

family1.000

Table. A4 Correlation matrix

0.556

0.231

0.211

0.474

0.082

0.555

0.385

-0.218-0.044

0.262

0.333

0.331

0.091

0.279

-0.277 oldage

0.191 health0.029 family-0.146 socx

par_seats maleave
0.539

0.179

-0.212-0.015
0.310

ex_open
0.207

0.033

0.424
-0.4230.281

cpi-0.310

0.304

0.429
0.149

-0.434ex_rate-0.093-0.302-0.293

0.349

0.108

0.114

-0.008

trd_un-0.181
0.259

0.327

0.022

-0.268fem_lfp-0.061
0.329

0.401

-0.0140.005-0.404

pop65

0.477
0.135

unemployment low_immi med_immi igh_immi total_immi unemp_ratepop15
0.169-0.182

0.742
0.165

oldage0.070-0.110

0.631
0.322
0.198

health0.322
0.133

0.170 low_immi-0.165 unemployment

0.339

0.042

1.000

-0.148

1.000

0.702

0.210 med_immi-0.0330.192 high_immi

0.223-0.014-0.012-0.083

0.190

0.165

-0.062

0.246

0.063

0.533

-0.1460.421-0.142-0.091-0.097-0.1730.123-0.187-0.057-0.2030.235 total_immi-0.0900.251-0.409 unemp_rate-0.063

0.262

0.243

-0.123

0.686

0.243

0.553

-0.327-0.015-0.3090.868-0.1990.429-0.254-0.099-0.0530.471-0.142-0.126-0.212-0.141-0.019

0.381

0.698

-0.211-0.3170.806-0.203

1.000

0.566

0.109

-0.1591.000-0.368

1.000
1.000

0.023

0.449 pop15

0.109-0.359 pop65

0.006

0.313

-0.259-0.126-0.0841.000-0.016-0.2850.510-0.511

1.000

0.060

0.214 fem_lfp-0.105-0.244 trd_un

0.040

0.078

-0.114-0.203

0.268

0.104

-0.046-0.304

0.445

1.000

-0.154-0.802

0.298

1.000

0.295

0.142 cpi

-0.019 ex_rate

0.460

0.276

1.000

0.423

1.000

0.259

-0.0900.191 ex_open

0.138-0.227 par_seats

1.000

1.000

-0.221

1.000

1.000

1.000 maleave

44

Figure A1. The ratio of foreign-born individuals aged 25 years

and older to the total population in the sample OECD countries

45

46

47

48

49

50

Source: Brücker et al. (2013) and OECD.Stat51