WPS7082


Policy Research Working Paper                                7082




               Informing Migration Policies
                               A Data Primer

                                 Calogero Carletto
                                 Jennica Larrison
                                  Çaglar Özden




Development Research Group
Poverty and Inequality Team and Trade and Integration Team
November 2014
Policy Research Working Paper 7082


  Abstract
 Researchers in many fields, such as demography, econom-                            critical issues are the construction of proper sampling
 ics, and sociology, have established various data collection                       frames in censuses, registers, and surveys and the design
 methodologies and principles to answer a range of aca-                             of questionnaires in household, labor market, and other
 demic and policy questions on migration. Although the                              relevant surveys. The paper discusses how these data
 progress has been impressive, some basic challenges remain.                        sources can be used to answer policy questions in areas
 This paper addresses some basic, yet fundamental, ques-                            such as labor markets, education, or poverty. The focus
 tions on identification of international migrants and how                          is on how some of the existing shortcomings in avail-
 their various demographic, personal, and human capital                             ability, quality, and relevance of migration data can be
 characteristics are captured via different data sources. The                       overcome via improvements in data collection methods.




  This paper is a product of the Poverty and Inequality Team and Trade and Integration Team, Development Research
  Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to
  development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://
  econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org and cozden@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


                                                       Produced by the Research Support Team
                            Informing Migration Policies: A Data Primer 1



                        Calogero Carletto, Jennica Larrison and Çaglar Özden 2




JEL classification: F22, J61, O15,

Keywords: migration, development, survey design, data collection, data dissemination


1
  A revised version of this paper is forthcoming as chapter 2 in International Handbook of Migration and
Economic Development, edited by Robert E. B. Lucas and published by Edward Elgar Publishing, Inc. in
2015.
2
  Part of the analysis in the paper has been generously supported by the Knowledge of Change Program
(KCP)
I. INTRODUCTION



While establishing his theory of human migration, which forms the basis of much of

modern research on migration, Ernst Georg Ravenstein (1885, 1889) relied on various

data sources from over 20 countries in Europe and North America to back up his

assertions with facts. One of his main conclusions was the importance of high-quality

primary data, mainly from national sources, for demographic and geographic research.

Since then, researchers in numerous fields such as demography, economics, sociology

and political science have established various data collection methodologies and

principles via censuses, administrative registers, nationally representative or special-

purpose surveys to answer a wide range of academic and policy questions. On one hand,

the progress has been quite impressive in terms of the variety and quality of data sources

available. On the other hand, some of the basic challenges from the nineteenth century

still haunt us with all their vigor.

        This paper has two purposes. First, it answers some fundamental questions on

who international migrants are and how their numbers as well as various demographic,

personal and human capital characteristics are captured via different data sources. The

critical issues are the construction of proper sampling frames in censuses, registers,

surveys and the design of questionnaires in the relevant surveys. These are among the

challenges with which collectors and users of migration data continue to struggle from

the days of Ravenstein to date. Second, the paper discusses how these data sources on

migration can be used to answer different policy questions in various areas, such as labor

markets, education policies or economic welfare. The focus of the discussion will be on



                                              2
how some of the existing shortcomings in terms of availability, quality and policy

relevance of migration data can be overcome via improvements in data collection

methods.



II. DATA SOURCES AND CHALLENGES 3



Sources of Data



The majority of migration data come from destination countries, as it is often easier to

capture people where they currently are rather than where they left. Destination countries

use a wide range of tools to count and analyze characteristics of migrants within their

boundaries. Among these data sources are (i) censuses that are aimed at capturing all

people within borders at a given point, (ii) various surveys, such as labor market or

specialized and multi-topic surveys, that sample a smaller portion of the population but

ask more detailed questions, (iii) population registers, common in certain countries, and

(iv) various administrative data sources such as border statistics, employment and

residency permits, as well as naturalization records. Such data sources are used in

quantifying migration patterns, especially between country pairs, as well as in identifying

demographic, economic, social and cultural characteristics of migrants within a country.

In addition, the impact of migration on destination countries’ labor markets or various

other social and economic outcomes can be assessed using these data in conjunction with

other relevant data sources.


1. Some of the sections in this paper draw from Carletto and de Brauw (2008) and de Brauw and Carletto
(2012).

                                                   3
       ‘Easier’ data collection in destination countries in no way means ‘easy’, as

collecting information from migrants presents many challenges and shortcomings that

prevent researchers from answering many important policy questions. Most of these are

concerned with the impact of emigration on the families and communities that migrants

have left behind in their home countries. These effects range from the poverty alleviation

impact of remittances to the decline in health and education services when doctors and

teachers emigrate. While censuses and administrative records in origin countries may

provide clues on these issues, most relevant data come from surveys with special

migration questions or modules. The main challenge is that questions about migrants

need to be answered by a proxy, generally a family member, which introduces many

imperfections, as we discuss below.



Censuses



Censuses survey an entire population at a single point in time and are generally

conducted decennially, even though there are plenty of exceptions. Everyone needs to be

counted and the staff of the relevant National Statistics Office uses their expertise to

reach everyone with the same, short questionnaire on mainly demographic variables. The

main goal of a census is not to collect data on the migrant population, but on the whole

population; the data on migration are generally a ‘by-product’.

       An important distinction is between a ‘de jure’ census, which aims to count all

‘usually resident’, and a ‘de facto’ census, which targets all physically present at the time

of census. De facto censuses in an origin countries would generally, by definition, fail to



                                              4
count the emigrants abroad, while de jure censuses might capture some recent or

temporary emigrants. Thus it is the censuses from destination countries that can be

reliably used in migration research.

    Universal coverage is the main advantage of a census, and ‘census rounds’ are

generally conducted within ten-year periods from the middle of each decade. For

example, the 2010 round censuses are conducted between 2005 and 2014. Censuses are

expensive; resources and expertise of the national statistical agencies determine the

quality of the data and the results. Many statistical agencies just publish cross-tabulations

of the main variables of interest as they process the data according to a priority list.

Migration data, unfortunately, are among the least crucial for many countries and are

published with a significant lag. While census data provide important snapshots of

migration over time, they have two key shortcomings. First, they include only basic

variables – such as gender, age, place of birth/nationality and maybe education – and thus

cannot be used to answer many policy questions. Second, they cannot be used to analyze

recent and nuanced trends. However, almost every country conducts a census and the

questions are relatively homogeneous. 4 This degree of standardization and geographical

breadth means that census data have become the backbone of most of the available global

databases on migration stocks, especially in bilateral corridors. 5 A key challenge arises

when the census tries to capture undocumented migrants who might be reluctant to

respond due to concerns that their data will be used for identification and subsequent




4. The United Nations Department of Economic and Social Affairs Statistical Division (UNDESASD)
reports that 202 countries have completed a census since 2005, with 26 more countries planning one.
5. Censuses may be used to calculate migration flows by asking questions pertaining to migrants’ residence
in previous years and the date of their arrival in the current location.

                                                    5
deportation. As a result, migrant populations may be grossly undercounted in censuses,

unless proper corrective procedures are implemented.



Population registers



Population registers are continuous reporting systems used to enumerate the resident

population of a particular area, which typically and historically corresponds to a

municipality, parish or police precinct. Popular in many parts of Europe – especially the

Benelux, Scandinavian and Baltic countries – population registers record the names and

addresses of the residents as well as their key demographic variables, such as births,

deaths and changes in marital or residential status. A legal requirement typically exists to

register and notify any alterations in status. As such, they may be used to record both

internal and international migration, and provide detailed up-to-date demographic and

socioeconomic information. This makes them a rich, and often fairly exhaustive, source

of (migration) data, although the incentive to keep records updated varies across

individuals and subgroups of the population. In- and outflows can be tracked with much

greater frequency and depth. Although the extent of register data is also rather limited,

the continuous updating of the information makes registers more appropriate for tracking

migration and for nuanced research questions in comparison with census data.

       Population registers, unfortunately, are not widely available outside Europe, and

suffer from a higher degree of heterogeneity across countries in terms of registration

criteria as well as data scope and quality. The Nordic countries typically implement

impressively accurate registers, while the precision of those from Southern Europe is far



                                             6
worse (Redfern, 1989). Undocumented migrants are, by definition, not captured at all

since they would not want to register with local authorities. Another shortcoming is that

departures are significantly underreported since many people avoid deregistering in order

to retain residency rights, and some of the benefits that come with it (OECD, 2009).



Administrative data



There are various sources of administrative data that may provide detailed information

about international migration. Again, most of these sources come from destination

countries, as they collect data on the migrants themselves. The most common are

residency permits, which give migrants many rights and are implemented across a wide

range of countries. Many destination countries regularly publish such data, both on the

stock of migrants with permits, as well as the flow of new permits issued within a given

time frame. Data may be disaggregated by different qualification criteria, such as family

reunification, professional qualifications, humanitarian reasons and lotteries. One has to

be careful in interpreting residency permit data since issuances need not equal the number

of new immigrants. Permits might be based on issuance but taken up by migrants with an

extended delay. Another possibility is that permits are issued to those already in the

country as they change their legal status, without a real change in the number of migrants

within the country. Most importantly, the legal criteria and rights of permits vary greatly.

In some countries, residency permits lead to citizenship, while in others they are granted

for limited stays. In cases where free mobility agreements between countries exist, such




                                             7
as in the European Union, migration might not lead to issue of new permits. Students

who are issued long-term visas may or may not be included in the official statistics.

       There are other potentially useable administrative data sources, such as border

crossings, police records and social security data. There have been several innovative

papers that use social security data from European countries to analyze return and

circular migration (e.g., Borjas and Bratsberg, 1996), since all employed individuals can

be tracked for extended time periods. Ultimately, the quality of the administrative data

depends on the responsible national agency in charge of collection and dissemination,

and comparability across sources varies by country. But the use of this kind of data is

often limited by the fact that they are seldom made public by government officials.



Household surveys



The final and possibly most useful and comprehensive sources of information on

migration are surveys that include, among others, Labor Force Surveys (LFS),

Demographic and Health Surveys (DHS), Living Standards Measurement Study Surveys

(LSMS), as well a variety of purpose-specific surveys. Specialized surveys are also

fielded in many countries, often to investigate specific aspects of migration. These micro-

level surveys provide a rich source of data and are essential for identifying

microeconomic linkages between migration and other facets of households’ livelihoods

and outcomes that other data sources fail to capture. They are also useful as they better

capture undercounted migrants if proper sampling frames and techniques are employed.

Finally, such surveys can be conducted in both destination and origin countries, and



                                             8
enable researchers to explore a wide range of issues. Most importantly, surveys are the

most reliable data sources in origin countries.

       There are several potential drawbacks of survey data, including the relative

variance of the difficulties faced in implementing household surveys in terms of data

collection and processing, and their comparatively small sample sizes. These limit their

potential for assessing global trends and performing comparisons. Ethnic and other

minorities, including migrants, might be underrepresented in the data and stratified

sampling can only be meaningfully applied should other nationwide data with proper

sampling, such as a census, become available. The variation in surveying practices across

countries and their relatively small sample sizes imply that they fail to capture migrants

from smaller corridors and are seldom used in the construction of databases of bilateral

migration stocks. But surveys remain an indispensable tool for studying migration, its

determinants and impacts. Where migration modules have been successfully integrated in

household surveys using appropriate sampling techniques, they have often succeeded in

capturing sufficiently large numbers of migrants for meaningful analyses. Depending on

the resources devoted, use of surveys will and should increase in the future (Center for

Global Development, 2009).



Data Challenges



As discussed above, countries use a range of statistical methods and tools – censuses,

registers, administrative records and surveys – to document and profile their populations,

including migrants. Each source has advantages and disadvantages with respect to



                                             9
coverage, detail of data collected, frequency and comparability across time and countries.

Changes in the questionnaires and data collection methods are two key determinants of

comparability over time or within a country. Some issues are more relevant for data

collected in the country of destination; others present more of a challenge when

collecting data on migrants at the origin. Below we discuss some of the more systematic

challenges faced in data collection and analysis.



Definition of a migrant



How a migrant is defined has been a challenge, and Ravenstein (1885) devoted a long

discussion to the issue. According to the United Nations Statistics Division (1998, p. 6), a

migrant is ‘any person that changes his or her country of usual residence’. The essence is

movement from one geographic location to another, which is the most relevant criterion

for economic analysis. In practice, however, migration in official statistics manifests

itself in myriad guises and is anchored to different concepts and definitions, including the

individual’s country of birth, country of citizenship, purpose of visit or visa type, place of

last permanent residence, duration of stay, and even ethnicity.

       Place of birth and nationality are the two most commonly used definitions.

Foreign-born applies to the first generation that crosses the national boundary.

Nationality, on the other hand, might apply to dependents and offspring if the destination

country’s laws are relatively restrictive in granting citizenship to migrants and/or their

children born there. Individuals may be classified as migrants or non-migrants, depending

on the definition, even in different sources within a single country. Variation in countries’



                                              10
adoption of definitions has hampered cross-country comparability of migration data.

Some destination countries grant citizenship to foreign-born people who are family

members of citizens or who satisfy certain legal and residency requirements. These

naturalized citizens continue to be recorded as migrants under the foreign-born definition,

but not under the foreign citizen definition. Many countries (for example, the USA) grant

automatic citizenship to people born within their territory regardless of the citizenship

status of the parents. Yet others, such as Japan, require at least one parent to be a citizen,

even if children were born within the country’s borders. Because of these differences in

citizenship and naturalization laws, the number of migrants will be substantially higher in

the USA if the foreign-born criterion is used. In Japan, on the other hand, the number of

migrants is higher under the foreign citizenship criterion. 6

         Most bilateral migration databases use the criterion under which census data are

collected, which tends to be the country of birth. First, this is more appropriate in

analyzing physical movements and handling the cases of former colonies and

dependencies. Second, while nationality can change, place of birth cannot. Third, the

possibility of holding multiple nationalities complicates matters further. Fourth,

naturalization laws and rates vary enormously across destination countries. Differences in

laws on citizenship criteria (for both migrants and their children born in the destination

country) do not affect data based on place of birth. Finally, when migrants, especially

refugees and asylum seekers, cannot be assigned to a specific nationality, they are often

recorded under an aggregated umbrella heading, leading to ambiguity.




6. Further confounding interpretation of the underlying definitions is the fact that countries variously apply
the foreign-born definition (Docquier and Marfouk, 2006).

                                                      11
        All of these issues plague censuses, population registers and administrative data,

as well as surveys. Thus it is critical for collectors of data to ask specific questions and

users to be aware of the differences. Ideally, both place of birth and citizenship status

should be asked about. However, many undocumented migrants will refrain from

participation in the survey for fear of identification when faced with citizenship

questions, which will bias the data collected. If a choice needs to be made, place of birth

is preferable.



Variation in census dates and missing censuses



Despite the attempts by multilateral institutions to achieve coordination, countries choose

when to conduct a census or a survey. Even though the standard procedure is to conduct

the censuses in the middle of the round, say in 2010, and every ten years, many

exceptions exist to both norms. For example, the census dates in France were 1962, 1968,

1975, 1982, 1990, 1999 and 2006. Such large variation can lead to difficulties in

comparison across countries, especially in global databases.

        The more serious problem is that many countries fail to conduct a survey or a

census at a given time or fail to include relevant questions to identify migrants. Censuses

are both expensive and demanding in terms of human resources, which make them less

attractive activities in poorer countries. Wars, civil conflict or simple public opposition

might prevent data collection. There might be political opposition since surveys and

censuses collect data on potentially controversial subjects such as ethnicity, language and

religion. Even if collected, data might not be released in detail or in a timely manner. Or



                                              12
worse, the data might be manipulated for political reasons. And, depending on the

country’s capacity and political sensitivities, adding even a minimal set of questions on

migration is often deemed impractical and undesirable, as it may have adverse

consequences for the quality of the census data. Researchers must therefore judge the

reliability of the data source, as would be the case in other areas, before using it.



Definition of a country



Even if a survey or a census asks country of birth or citizenship questions of the

participants, the definitions of countries change over time. Many countries gained

independence (Eritrea, Timor-Leste, South Sudan and many other countries in Sub-

Saharan Africa), dissolved into smaller states (the Soviet Union, Yugoslavia and

Czechoslovakia) or unified (Yemen, Germany, Vietnam) since the Second World War.

Shifting national borders creates other challenges. First, with partition, millions of

migrants are artificially created without ever moving from their homes. Those born in

Moscow, but residing in Kiev, would never have been classified as migrants under either

of the two most commonly used definitions until August 23, 1991, but are classified as

such in the following censuses. For instance, as Özden et al. (2011) show, the sudden

jump in international migrants’ numbers between 1990 and 2000 is mainly due to the

break-up of the Soviet Union.

       Changing borders pose problems when analyzing time-series data. One option is

to use the countries in existence at that point in time. Migrants from Africa who came to

the USA before the 1970s were recorded under different origin countries in different



                                              13
censuses as their birth countries gained independence. Other changes are more subtle.

The definition of an ‘Ethiopian’ included Eritreans in the 1970 census but not in 2000.

Thus there is an artificial decline in Ethiopian migrants, as some have been relabeled as

Eritreans in later years. Researchers need to keep these border changes in mind when

performing their analysis and make the necessary adjustments.



Collecting migrant data through proxy respondents



Collecting survey data on migrants in destination countries presents a number of

challenges. These difficulties center on the absence of a proper sampling frame and the

high cost of tracking down individuals who tend to form a small portion of the population

or might be present without proper documentation and want to avoid detection.

Nevertheless, most high-income destination countries have sophisticated national data

collection mechanisms, especially for surveys for labor-force, expenditure/income or

health-related issues, and can overcome these challenges in relatively satisfactory ways.

Among the most prominent examples is the micro sample of the US Census and the

American Community Survey, where a smaller sample of the American population fills

out an extensive questionnaire that covers migration-related questions such as the year of

migration, languages spoken at home and ethnicity. When these are used together with

information on education, income, occupation and demographic characteristics,

researchers can answer questions on labor market performance, poverty, cultural

integration and social outcomes. Similarly, Demographic and Health Surveys, Labor

Force Surveys or Income and Expenditure Surveys with proper sampling frames and



                                            14
detailed questions to identify migrants in the data are used extensively to analyze

linkages between migration and the applicable issues.

       The real challenge in survey design and data collection emerges in origin

countries, especially among those that lack adequate statistical capacity. The absence of

migrants from the household where data are collected only complicates matters further.

Such survey data collection operations require reliance on proxy respondents, in most

cases a family member, to answer the questions on behalf of the absent migrants. Various

concerns arise from eliciting information through proxy respondents, including whether

respondents remember or even know the answers. The information collected through

proxies in the household or community of origin can be complemented and cross-checked

with short interviews of the migrant themselves via other means, such as phone or online

interviews. Alternatively, for certain types of migration, say seasonal or circular

migration, proper timing of fieldwork may enable eliciting information at the point of

origin directly from the migrant. Finally, interviews at the origin can be administered

directly to returnee migrants, often using long recall periods.

       Deciding how to identify migrants is the first step in ensuring that desired

individuals are captured in an origin-country survey. In order to assess the impact of

overall migration patterns, one should try to identify (1) all current household members

with past experience with international migration over a given period – return migrants,

(2) all former household members who are now living abroad – current migrants, and (3)

all former household members with past international migration experience who now live

in the source country but in a different household – non-member returnees. As

mentioned, collecting information for each group presents different challenges, especially



                                             15
when combined with the necessity to use a proxy respondent (groups 2 and 3) or not

(group 1).

       The criterion of household membership, generally defined as all individuals who

normally live and eat their meals together, affects whether migrants are captured in a

survey. In the case of migration questions, additional restrictions are generally imposed to

refine the concept, for example by asking about the number of months individuals have

been absent over the previous 12 months. In most standard surveys, if the absence is

more than 3 (or 6) months, the person will no longer be considered a household member

and thus excluded from data collection past the basic household roster. However, many

of these people maintain linkages – such as through remittances – and they should be

considered in the survey, especially if the goal is to assess the impact of migration on

those left behind. Another question is whether these groups should include all former

household members (i.e. any individual who used to live in the household at any point in

time) or only members of the nuclear family. Although the latter approach may result in

an underestimation of the total number of international migrants, it may be preferable.

This method has been applied to internal migration in a nearly nationally representative

survey collected in China (de Brauw et al., 2002), and again in the Mexican National

Rural Household Survey (Richter and Taylor, 2005).

       An alternative method takes advantage of the fact that many household surveys

already contain a fertility module, in which information on all children ever born from all

female members of reproductive age is collected. A drawback is that it will miss all

children of women no longer in the household or who have passed away. A similar

approach is to list in a separate module all adult children of the head of the household



                                             16
and/or his/her spouse regardless of when they left, especially if the mother is absent or no

longer alive. These methods were used in various Albania Living Standards Measurement

Surveys (INSTAT, 2002, 2005; Carletto and Azzarri, 2007).

        In all of these techniques, the critical issue is double-counting of migrants,

especially those who can be claimed as members in other households’ rosters. The

problem of double-counting is even more acute if household rosters are further extended

to include any former household member irrespective of their relationship to the

household head. Constructing the list based on clearly defined familial relationships, such

as for children or siblings, renders the identification and recall of potential migrants

simpler and more accurate, and the sample more demographically representative.



Collecting information to assess the impact of migration



Data collection needs to be designed according to objectives. Since migration, by its very

definition, is a selective process, any analysis needs to control for the determinants of

migration and collect the necessary information for identification of both migrant

(treatment) and non-migrant (control) individuals and their households. In addition, data

on pre-migration conditions are needed. Assuming a migrant is identified based on

departure within a year, the pre-migration timing corresponds to the prior year. For

longer reference periods, ideally one would want to collect information for each single

year as the factors affecting migration are likely to have changed over time (Bilsborrow

et al., 1997).




                                              17
        In terms of impact of migration, one must first decide where the impact occurs –

on the migrant while abroad or on return, on the household left behind or on the

community. Second, the outcome of interest needs to be collected properly. For example,

if the topic of interest is poverty, the survey must collect consumption or income data

from the household. Another important issue is the identification strategy, as

unobservable factors affecting migration decisions are also likely to be correlated with

the outcome of interest. Ideally, one would rely on an experimental design in which the

treatment is randomly assigned, and before and after information are collected. However,

given the nature of migration, this is hardly ever the case. 7 Sasin and McKenzie (2007)

discuss these issues extensively.

        The information collected will depend on a number of factors, including the

length of the questionnaire, the capacity and training of fieldworkers, but, most

importantly, whether information is gathered directly from the migrant or through a

proxy respondent. Use of a proxy might severely constrain the ability to ask questions in

depth. However, a minimum set of questions can easily be asked about the emigrants,

including their basic demographic characteristics, education level, occupation abroad,

country (and location) of current residence, the year of first (and last) migration and

remittance behavior. Other questions may be asked about legal status, marital status, the

basic demographic composition of the household abroad, frequency and nature of contact

with the household, and occupation prior to migrating. Further questions that relate to the

specific objectives of the survey can also be added. An attempt could be made to collect




7, An exception is work by McKenzie et al. (2006), who take advantage of the random allocation of New
Zealand visas to Tongan residents.

                                                  18
more extensive information on past migration episodes, including timing and country of

destination.

       Finally, to ensure that household conditions prior to migration are recorded, it is

useful to collect other selected information within the recall period. For example, one

could collect information on occupations or assets in the household prior to, during and

after the migration spell of any household member who had migration experience. Other

information, often subjective, might also be worth collecting for individuals who may be

return migrants, such as whether or not they plan to leave the household again; and the

reasons for return. Some potential reasons for return, such as health of household

members, can be corroborated in other parts of the survey. While the design of a

questionnaire is important to ensuring that the ‘right’ information is gathered for a study,

a survey is only as good as its sample and sampling frame.



Tracking migrants



Another approach to collecting more exhaustive information about emigrants would be to

track them. This requires detailed contact information to reach the migrant. Such methods

have been used to examine the impacts of internal migration on welfare in Tanzania via a

health and development panel data set (Beegle et al., 2011), and in Ethiopia combining a

rural household survey with a migrant tracking survey (de Brauw et al., 2013). Given the

high mobility of migrants, the tracking survey would ideally occur within weeks of the

survey at the sending destination. Internationally, tracking surveys of this type have been

carried out in a few countries, including between Mexico and the USA, and between



                                             19
Albania and Greece. Alternatively, one can first carry out a survey in destination

countries and, using a similar approach, track down the original household in the sending

countries. An example is the aforementioned study between New Zealand and Tonga

(McKenzie et al., 2006).

       Tracking surveys can also be used as validation of information that was gathered

in the original household through proxy respondents, as well as to measure differences in

perceptions between migrants and household members left behind. While allowing for

direct interviews with the migrants, tracking presents a number of problems, which might

outweigh the benefits. It can be too costly and is characterized by a high level of attrition,

particularly when the share of undocumented migration is high.



Sampling design



Within migration surveys, a traditional probability sample based on a multi-stage cluster

design will not succeed in finding many migrants, whether this is done in origin or

sending countries. A probability sample, by assigning a known non-zero probability of

selection to each sampling unit, allows for making inferences to the whole population.

The foundation of a proper sample is an updated sampling frame. However, this is the

main stumbling block in the design of migration-focused surveys, in origin and

destination countries. As mentioned earlier, migrants with certain characteristics or from

certain origin countries might not be present in large enough numbers to be properly

captured in the sample. Furthermore, certain types of migrants might be reluctant to

participate for cultural and legal reasons. The Census Department in the USA, for



                                              20
example, uses various techniques to adjust for these biases. These include conducting

preliminary surveys to determine the extent of undocumented migration in certain areas,

and then adjusting their weight in the sample after taking their response likelihood into

account. In other cases, using listing from NGOs working with migrants is often the only,

even if incomplete, frame from which to draw a sample. Snowballing and other quasi-

random techniques are often used, but the resulting samples are seldom representative of

the migrant population as a whole.

       In origin countries, most available frames do not contain any information on the

exposure to past or current migration of the listed households, preventing ex ante

stratification of the sample based on migration status. As discussed at the beginning of

this paper, neither the population census nor available administrative records provide

adequate sampling frames for selecting emigrants in a given sending country. Nor, in

most cases, do they provide information on previous migration experience to help to

identify temporary migrants and returnees.

       As such, migration is considered a rare event, defined as an infrequent statistical

occurrence. In a normal clustered sample design typical of multi-topic surveys, the

expected number of households associated with emigration may be very low. However, a

several techniques have been proposed in the literature to better identify rare events, such

as migration (Kish, 1965; Huang, 2005). Two such approaches deemed more appropriate

in capturing data on migrants, particularly if used in combination, are disproportionate

sampling and two-phase sampling. However, both sample designs require some prior

knowledge of migration in the population.




                                             21
           Disproportionate sampling implies that primary sampling units (PSUs) with

higher migration rates are identified and oversampled. Thus the PSUs known to have a

low rate of emigration would be allocated less probability of selection than PSUs with

high migration. Representativeness would be regained through weighting. One drawback

to this method is that the migration rate might still be too low within each PSU to use

simple random sampling or systematic sampling to select households within each PSU.

           Alternatively, one can initially select PSUs using the standard method, and

then, within each PSU, oversample households known to be migrant households relative

to other households. This method is known as two-phase sampling. A random draw of

households within a PSU is unlikely to be an efficient way to select a sufficiently large

number of migrant households, even in high-migration areas. In this case, a listing

operation to clearly identify households with migrants may be a more cost-effective way

to select a more balanced sample of migrant and non-migrant households. Listing

operations are generally not very expensive and, except in special circumstances, they

add up to only about 10–15 percent of the total survey budget (Muñoz, 2007), and the

benefits may greatly outweigh the costs.

           Finally, one could combine the two methods by initially giving more weight

to PSUs with higher migration levels and then oversampling migrant households within

each selected PSU. Whichever approach is used, the primary goal is to ensure that a large

enough number of migrant households is drawn. It is important to note that using any of

these methods is predicated on having prior information about the prevalence of

migration in the population at either the area or household level. While this may be the

case if one is interested in sampling immigrants in a destination country, it is rarely the



                                              22
case for the study of emigration in a source country. For methods 1 and 3, one needs

information on the relative prevalence of emigration by PSU, and for methods 2 and 3

one needs information about emigration within PSUs.

        One further decision to make, assuming that migrants can be properly identified

in the frame, is whether to select based on the proportion of households with migrants out

of the total number of households or to select based on the proportion of migrants over

the population in the reference area. Given that a significant amount of analysis on

migration is performed at the household level, the first option may be preferable

(Bilsborrow et al., 1997). 8

        For the specific purpose of using surveys to learn about emigrants in sending

countries, the lack of a suitable sampling frame would still be an obstacle to

implementing a disproportionate sampling design. One possible modification, but a

departure from a full probability sample, would be to use alternative data sources to

identify high-emigration areas in a country. These sources may include, for example,

expert opinions, qualitative surveys or surveys in destination countries where, in addition

to the immigrant’s country of origin, the specific location of departure is asked. However,

the last method is not recommended unless most emigration from the source country has

a specific destination and all of these main destinations are covered.

        Lacking a proper sampling frame, a less than perfect alternative would be to select

all area sampling units (or clusters) at different stages with probability proportional to the

estimated size (PPES) of the overall population (or the number of occupied dwelling

units) and carry out a full listing operation only in the area sampling unit last selected.


8. For a worked example of a three-stage disproportionate sample of immigrants using a suitable sampling
frame, see Bilsborrow et al. (1997, pp. 280–83).

                                                   23
The method would be appropriate only in the unlikely event that the shares of migrants or

migrant households were similar across area units, but finding a sufficient number of

migrants in the select units might still be a challenge.

       Other non-probability sampling techniques may also be used to capture rare

events; for example, multiplicity methods such as snowballing have been used in the

migration literature. One use of snowballing is to gather information on undocumented

migrants, using as a starting point, or ‘seed’, a list of members of a diaspora organization

or a list of migrants assisted by an NGO in destination countries. The ‘seed’ household is

used to identify additional migrant households of the same country of origin, and so on

until the necessary number of observations is reached. However, using data from

Senegal, Beauchemin and González-Ferrer (2011) found snowballing to present a number

of selection biases, including overrepresentation of migrants with close ties to the sending

country.

       Finally, techniques such as random walks and aggregation point intercept

methods can be used to identify rare events, such as migration. Random walks using

selected households in a community may act as a starting point to identify migration

occurrences. A Brazilian survey of Nikkei population used the aggregation point intercept

method, which, together with snowballing, was compared with more traditional census-

based random sampling (McKenzie and Mistiaen, 2009). In all cases, when using these

non-probabilistic methods, it is crucial to collect ancillary information on the

implementation of the sample to be able to identify the reference population in an attempt

to make ‘educated inference’ to a larger population group. This is particularly important

given McKenzie and Mistiaen (2009) findings that non-probability methods, such as the



                                              24
aggregation point intercept, are unlikely to provide representative samples and tend to

overestimate the migrant population. However, they also show that reweighing the

intercept point estimates to account for visits by the same individual to multiple

aggregation points may generate estimates rather close to the census-based method.



III. MIGRATION AND POLICY: DATA REQUIREMENTS AND LIMITATIONS



With an understanding of the issues involved in measuring the migration process, we now

turn to the specific policy questions that are often pursued with regard to migration. Our

focus here is on examining the data requirements and identifying the data limitations

analysts are likely to face when analyzing these issues.



Welfare Impact: Poverty and Income Distribution



The interactions of migration, poverty and income distribution are of primary interest to

researchers and policy makers, and have been extensively studied in the past (e.g. Lipton,

1980; Stark et al., 1986; de Haan, 1999). Income differences are among the key

determinants of migration between countries and, conversely, migration flows tend to

change income levels and distributions in both origin and destination economies. Yet

because migration is difficult to identify statistically, few studies have been able to

convincingly demonstrate a causal relationship between migration and either poverty or

inequality. There are several fundamental challenges.




                                              25
       First, income levels and migration influence each other. Low-income levels are

among the main causes of migration. On the other hand, extreme poverty may hinder

migration as very poor people lack financial and other resources. For example, in

Nicaragua, Murrugarra and Herrera (2011) find that the socioeconomic level of a migrant

affects his choice of destination, with poorer migrants choosing destinations that have

lower transaction costs, and fewer potential gains. While income levels and poverty may

influence migration decisions, migration also leads to poverty reduction and changes in

the income distribution, especially in origin countries. In Guatemala, Adams (2005) finds

that remittances reduce the degree, depth and severity of poverty in the country. In

addition, numerous studies examine expenditure or consumption patterns of households

with migrants abroad currently or previously (Taylor and Mora, 2006; Yang, 2008;

Adams and Cuecuecha, 2010). As a result of data requirements and the difficulties

involved in identifying migration, research on the dynamics of the relationship between

migration and inequality or income growth remains relatively limited (McKenzie and

Rapoport, 2007; de Brauw and Giles, 2008; Gibson et al., 2009; Lokshin et al., 2010).

       The primary requirement to study migration, poverty and income distribution is

an accurate measure of well-being: the preferred measure is consumption, which is an

integral part of any multi-topic survey. Deaton and Grosh (2000) provide a detailed

description of the issues one faces in measuring consumption, and Deaton and Zaidi

(2002) discuss issues related to the computation of a consumption aggregate in household

survey data in detail. In order to measure the causal relationship between migration and

welfare, one would need to estimate what the migrant household’s per capita

consumption level would have been if the migrant had remained within the household.



                                            26
Panel data with information on pre- and post-migration can be most useful. However,

panel data are seldom available and cross-sectional data sets must be relied on. Although

the counterfactual is difficult (if not impossible) to ascertain in a cross-sectional study,

one can attempt to learn about it by collecting information on pre-migration conditions,

such as measures of asset holdings, that can be reconstructed using recall methods.

         The endogenous relationship between migration decisions and income outcomes

requires a proper identification strategy. Except where the migrants are chosen randomly,

for example in the case of Pacific Islanders in New Zealand (McKenzie et al., 2006), the

general approach is instrumental variables (IV) estimation. The challenge of finding valid

instruments that influence migration decisions but not the outcomes of interest, especially

economic outcomes such as income, is one of the most severe challenges faced by

researchers. Household surveys or labor force surveys with migration indicators are the

main data sources used for poverty and income distribution analysis. The instruments are

also constructed with the data in these surveys. Among the most common variables used

in the literature are current and historical social networks and diaspora linkages (Hanson

and Woodruff, 2003; Hildebrandt and McKenzie, 2005; De, 2008). In cases where

purpose-specific surveys are used or the researcher has the option to design the

questionnaire, certain questions can to be inserted that will lead to construction of

appropriate variables to be used as instruments. For example, McKenzie and Mistiaen

(2009) use the generation of the migrant when analyzing Japanese–Brazilian migration to

Japan.

         Different types of migration may have differing effects on poverty and inequality

(see Germenji and Swinnen, 2005). Furthermore, measurements at the household level



                                              27
must take into account the migrant’s absence (Barham and Boucher, 1998). If emigrants

are not accounted for in poverty estimates, poverty for some original group of individuals

can also be overestimated (Clemens and Pritchett, 2008).

       Migration may also have general equilibrium effects on the within-community

income distribution. When migration occurs, one expects local wages, either explicitly or

implicitly, to rise for the types of workers most likely to migrate, leading to more

complex effects on the income distribution than the direct effects on inequality or

income. In a cross-section, one could use questions about wages found in the labor

modules or labor force surveys to investigate whether communities with more migration

have higher increases in wages than other communities, if information on wages in the

previous period are also collected. Otherwise, panel data are necessary to investigate

general equilibrium effects.



Welfare Impact: Health and Education



The relationship between migration and human capital has also been widely examined,

particularly the effects of migration on education and health outcomes of the families left

behind (Kanaiaupuni and Donato, 1999; Cox Edwards and Ureta, 2003; Mansuri, 2006;

Acosta et al., 2007; Nobles, 2007; Amuedo-Dorantes and Pozo, 2009). These are among

the main development benefits of migration in labor-sending lower-income countries.

Migration may have positive effects on educational attainment of children in migrant

families through the provision of remittances to alleviate credit constraints. Calero et al.

(2008) find that remittances increase school enrollment and act to ensure education



                                              28
through financial shocks in Ecuador. However, parental migration may also have

negative consequences if the child must now fill the role of the absent parent by working

more at home, or if there is loss of parental involvement. In Mexico, McKenzie and

Rapoport (2007) report a decline in school attendance and attainment for boys aged 12–

18 and girls aged 16–18 with a parent abroad. Similarly, in China, de Brauw and Mu

(2011) find that the status of underweight children aged 7–12 is positively correlated with

parental migration.

       Type of migration and migrants affect welfare outcomes. Macours and Vakis

(2010) find that maternal seasonal migration positively affected early cognitive

development in Nicaragua. Gibson et al. (2011) show that children who migrate with

parents experience improvements in diet reflected in improved weight-for-age and

height-for-age, while children who remain have worsened diets and declining weight-for-

age and height-for-age.

       In examining health outcomes, a household survey could be combined with

anthropometric indicators to examine issues of nutritional status and food security as

related to migration (see Antón, 2010; and Carletto et al., 2011). Similarly, for education

outcomes, purpose-specific surveys as well as general household surveys with relevant

questions can be used. Information specifically on remittance income is also needed.

       Last, but not least, migrant selectivity is again a key concern. Health and

education outcomes are closely related to income levels, which have an endogenous

relationship with migration decisions, as discussed above. Furthermore, in many surveys,

migrants state that they choose to migrate specifically to provide the resources for their

children’s education and family’s health expenditures. Thus a natural endogenous



                                             29
relationship can emerge between migration decisions and health/education outcomes.

Since the data come from similar household survey data, parallel IV approaches can be

used, as discussed in the previous section on poverty and income.



Labor Market Impact



Migration for employment can have several effects on the economy beyond the direct

change in location of some workers. In countries of origin, at the household level,

migration may change the household labor composition as well as labor supply decisions

of family members who stayed behind. At the macro level, departure of a group of

workers, especially if they are concentrated in certain skill, occupation and age

categories, will have implications for absolute and relative wage levels as well as other

labor market outcomes. These same issues are relevant in destination countries. Labor

market implications receive extensive attention in OECD countries, where there is

significant public and political opposition to migration flows on the grounds that migrants

take jobs away from natives.

       Different data sources can be used, depending on the questions asked. Multi-topic

surveys are well suited to studying the impact of emigration on wages in specific sectors,

labor mobility across sectors, and whether or not skills acquired through migration foster

upward mobility among individuals. Aside from migration variables and data collected

for potential identification strategies, surveys should include employment status, wage

rates, job types and human capital variables in the employment module. For people with

migration experience, it is particularly useful to collect information on occupations prior



                                             30
to migration and at the destination, as well as the present occupation for return migrants

(As examples, see Schaffner, 2000; for examples in practice see Carletto and Kilic, 2011;

Mendola and Carletto, 2012). A survey that includes oversampling of migrants may be

particularly useful for statistical power, especially as the number of job types increases.

       There are numerous studies on the impact of emigration on local labor market

outcomes in origin countries. In Albania, Miluka et al. (2010) find that international

migration reduces overall labor inputs into farming. Similarly, in the Kayes area of Mali,

Azam and Gubert (2006) report that individuals left behind by international migrants can

exhibit shirking behavior in terms of working in agriculture. Within China, de Brauw et

al. (2008) find that, as internal migration increased, overall agricultural labor inputs

decreased, but the average share of agricultural labor inputs provided by women did not

change. Migration can also affect child labor: on one hand, households with out-migrants

may become wealthier, reducing the need for child labor; on the other hand, migration

could lead to labor shortages on the farm, potentially increasing the need for child labor.

Migration need not only affect the households that migrants leave; at the source,

emigration can have general equilibrium effects on wages (Hanson, 2007; Mishra, 2007).

       The macro impact of immigration in source and destination countries can be

analyzed using aggregate data as well as labor force survey data. The big debate (Borjas,

1988, 1989, 1994 and Card, 2001, 2009) on how migration affects native workers is

concerned deeply with properly identifying endogenous location choices. Migrant

workers are attracted to geographic areas and sectors where there are strong labor

demand and promising future prospects. On the other hand, arrival of migrants is likely to

cause native workers to move to other sectors and areas, causing the effect to ripple



                                              31
through the overall economy. Various authors use different methods to control for these

endogenous processes, obtaining quite different results along the way. For example, Card

(2001) uses geographic local markets, while Borjas (2003) chooses education-age cohort

cells as the unit of analysis. Among the more recent papers, Docquier et al. (forthcoming)

use the bilateral migration database of Artuc et al. (forthcoming) to analyze the impact of

immigration and emigration on labor market outcomes – both wages and employment

levels – of native workers of different skill levels in OECD countries. The analytical

framework is typically based on a general equilibrium model of labor markets and

production where the substitution elasticities between different types of labor, especially

between migrants and natives, play a critical role. Most of these studies rely on data

aggregated from labor force surveys and construct their instruments from macro data.

Özden and Wagner (2014) use the age-distribution data from origin countries interacted

with historical migration patterns as instruments of current migration patterns in

Malaysia. Borjas (2014) is an excellent survey of the issues, empirical methods and data

requirements in research on labor market implications of migration.

       An underexplored but very important issue with development implications is

return migration. Migrants obtain skills and financial resources during their time away,

which can prove useful on return (Mayr and Peri, 2008; Gibson and McKenzie, 2011).

The benefits may especially be concentrated in the labor market outcomes of return

migrants (Bijwaard et al. (2013)). A multi-topic survey should cover several important

issues in order to study the implications of return migration. In addition to identifying

return migrants in the labor force, it is also important to ask whether these individuals

plan to migrate again. Although plans are not the same as actual migration after the



                                             32
survey, individuals who answer negatively can be labeled as truly returned migrants. It is

important to ask further about the activities of a returned migrant in order to establish that

the individual is truly a return migrant committed to staying.

       Governments may want to encourage return migration, as migrants come back

with valuable human and social capital acquired abroad. If programs exist to encourage

return migration, such as job training or rural development programs, then the

questionnaire should explicitly ask about whether migrants took part in any such

programs. However, one might also want to know about the likelihood of return for

current out-migrants, which is difficult to determine without a survey in the destination

country. One can add questions to the survey about the integration of the migrant in the

destination country or area. Specifically, one can ask whether or not the spouse lives with

the migrant, if the migrant has children who were born or live at the destination, how

often the migrant returns home, and whether or not the migrant has investments at home.

If children live with migrants away from home, in cases of international migration or

countries where multiple languages are spoken, one should also ask whether or not

children living at the destination speak the native language.

       In addition, migration itself can be affected by changing opportunities at potential

destinations. As the populations of developed countries age, many developed-country

governments realize that migrants can help fill gaps in their labor market that native

workers would not fill, particularly prime-age workers. As migrants usually keep close

ties to the households and communities they leave, policies that encourage temporary

migration followed by a return can benefit both the source and destination countries.




                                              33
Brain Drain, Gain and Waste



One of the most intensely debated issues in the migration literature involves the departure

of highly skilled workers from poorer countries to wealthier ones. Often referred to as

‘brain drain’, loss of human capital in low-income countries that already suffer from

relative skill shortages can have important negative development and growth

implications. The main concern is that the positive externalities generated by high-skilled

workers will disappear with their migration. The issue takes on an even more emotional

dimension since the education of these migrants is funded through limited public

resources and their move to high-income OECD countries implies a subsidy from the

poor to the rich. Despite the extensive and old analytical literature (Bhagwati, 1979;

Bhagwati and Hanson, 2009), empirical analysis has been relatively lacking. The first

breakthrough was the construction of the bilateral migration databases for OECD

destinations based on census data (Docquier and Marfouk, 2004, 2006; Dumont and

Lemaître, 2004). These databases focused on migrant workers in the labor force, aged

15–65 generally, and disaggregated them by three education levels – primary, secondary

and tertiary – since this was the most uniformly available human capital variable that

could be used as a proxy for human capital and skill levels. Initially for the OECD

countries, these data sets highlighted, for the first time, the extent of global skilled

migration patterns.

        There had been several extensions, starting with the gender dimension (Docquier

et al., 2009, and Dumont et al., 2007). A more important issue is the age of migration,



                                               34
which is a proxy for the location of training. The issue is whether a Jamaican doctor who

migrated to the USA as a child with his family, and trained in the USA, can be

considered a real ‘loss’ for Jamaica. Beine et al. (2007) construct the database for OECD

countries and show that the initial ‘brain-drain’ levels decline considerably for many

countries as large numbers of their migrants obtained their tertiary education at the

destination and did not necessarily impose a fiscal and productivity loss on the birth

countries.

       The next extension was the addition of non-OECD destinations. OECD’s DIOC-e

database added over 30 destination countries to the original OECD-centric data set, again

using census-style sources. Artuc et al. (forthcoming) increase the number of countries to

63 in 1990 and 100 in 2000. Their main innovation is to use a gravity-type estimation

model to predict the data – by gender and skill level – for the missing destination

countries. The result is a truly global overview of human capital mobility. They estimate

that there were slightly over 110 million working-age migrants between the ages of 15

and 64, with around half in OECD countries, half female and a quarter tertiary educated.

They establish striking patterns. For example, over 70 percent of all tertiary educated

migrants in the world end up in just four countries – the USA, the UK, Canada and

Australia. The constructed database enables researchers to perform many analyses,

including the paper by Docquier et al. (forthcoming), which evaluates the impact of both

immigration and emigration in OECD labor markets.

       One of the most interesting ideas to emerge in this literature is the possibility of

‘brain gain’. The main premise is that higher expected returns to human capital in

destination countries will induce people to invest more in their human capital. If some of



                                             35
these people end up staying at home, the final human capital level in the origin country

might actually increase due to improved skilled migration opportunities. The important

issue is that this likelihood of migration can neither be too high – too many people would

leave and nobody would stay at home – nor too low – people would not invest in the their

education. Using the above-mentioned bilateral skilled migration databases, Beine et al.

(2008) show that the brain-gain effect can take place if the tertiary education level in the

labor force is between 2 and 8 percent and the skilled migration level is not very high.

The brain-gain effect has also been shown to exist using single-country survey data. For

example, Batista et al. (2009) find significant brain gain in Cape Verde, specifically

through increased education attainment of non-migrants. Similarly, using survey data in

Moldova, Böhme (2012) demonstrates that migration positively affects parental

aspirations for children’s education at the lower end of the human capital spectrum. In

their survey of the ‘best and brightest’ in Tonga and Papua New Guinea, Gibson and

McKenzie (2011) show that students who contemplated migration took additional

classes. Chand and Clemens (2008) found that ethnic Indians were more likely to invest

in their education relative to native Fijians after the 1987 coup, which led to violence

against the Indian community and increased their desire to migrate.

       The last concept to emerge in this literature is ‘brain waste’. The first type of

brain waste occurs in an origin country if it overinvests in a specific type of human

capital (e.g., engineers) and restricts labor movement. As a result, there will be high

unemployment levels for those workers and the resources spent on them will be wasted.

The source economy would theoretically benefit by allowing those individuals to




                                             36
emigrate and realize returns to their human capital elsewhere, as they might be expected

to send remittances back to the source economy.

        Second, brain waste may occur when highly skilled migrants cannot find

employment commensurate with their skills in the destination country. This is sometimes

exemplified by Eastern European physicists driving taxis in New York. This might arise

for various reasons. Their qualifications might not be recognized, such as foreign doctors

who cannot obtain licenses. They might not be trained in location-specific skills, such as

lawyers trained in a legal tradition that is different from the US legal regime. Finally,

migrants may have the bureaucratic qualifications (the diplomas), but lack the actual

human capital since their education is below the standards demanded in the destination

labor markets. Mattoo et al. (2008) show that highly skilled immigrants to the USA from

Eastern Europe and Latin America tend not to be able to find employment for which they

are trained, whereas immigrants from OECD countries and Asia are more likely to find

such employment. Their analysis shows that the main reasons are the lower quality of

education in the origin countries, as well as selection effects. Since it is easier for Latin

American migrants to enter the USA illegally or through family reunification programs,

their average human capital level might be lower than that of Asian migrants who are

forced to enter through more demanding employment-based preferences.

        In addition to census-based global bilateral databases, multi-topic surveys are well

suited to study brain drain, brain gain, and brain waste from the source-country

perspective. Through an education module, one can characterize the educational

background of a country’s (or region’s) out-migrants relative to the remaining population

(Glewwe, 2000). To study brain gain, one can attempt to relate past migration variables



                                              37
constructed from a migration history to school enrollment. If the survey includes a

detailed employment module, brain waste can be studied from the source-country

perspective by examining job types held by individuals in different educational and

experience cohorts. A survey that oversamples migrant households could be particularly

informative in studying either brain drain or brain gain, as the distribution of educational

attainment among migrants would be measured with less error. On the other hand, for

learning about the brain-drain issue from the perspective of specific occupations, a

specialized survey targeted at individuals with specific types of training (e.g. nursing

school) would be more informative.



Trade, Foreign Direct Investment and Migration



The main premise of this area of literature is that migration can facilitate integration

between countries in other areas, mainly through trade and foreign direct investment

(FDI) flows. Migrants possess knowledge of specific conditions, regulations and product

features in both import and export markets. By using their knowledge, they can lower

transactions costs, match parties in a relationship and increase economic integration.

Migrants also are consumers of specific products from their home countries. Starting with

Rauch (1999), an extensive literature mentioned these information and demand channels

as key to the relationships between trade, migration and FDI flows.

       The main analytical framework consists of gravity models and focuses on OECD

countries due to data availability. Most papers analyze a single country and its multiple

trading partners (such as Head and Ries, 1998; Dunlevy and Hutchinson, 2001; Girma



                                              38
and Yu, 2002; Blanes-Cristóbal, 2003; Hatzigeorgiou, 2010). The main exceptions are

Felbermayr and Toubal (2008), who perform cross-section analysis, and Felbermayr and

Jung (2009), who use panel data. Other papers focus on the trade and migration links

between province/regions of a country and its trading partners (Wagner et al, 2002; Co et

al., 2004; Bardhan and Guhathakurta, 2005; Herander and Saavedra, 2005; Dunlevy,

2006; Bandyopadhyay et al., 2008; Peri and Requena-Silvente, 2010).

       The empirical analysis requires the matching of bilateral trade, migration and FDI

data. Migration data are generally obtained from macro sources such as censuses, while

trade and FDI data come from relevant customs and financial administrations. Single-

country studies find a positive relationship, but are likely to suffer from omitted-variable

biases as we cannot implement country fixed effects due to the nature of the data, an

issue emphasized strongly in the literature on gravity models (Anderson, 1979).

Felbermayr and Jung (2009) control for country-pair unobserved heterogeneity (and

multilateral resistances) using a panel for 1990 and 2000, but they can cover only North–

South trade. These authors find a significant and positive effect as well. In a more recent

paper, Parsons (2012) uses the Özden et al. (2011) database of global bilateral migration

for 1960–2000. The long-term panel structure of the data, combined with the fact that

they cover all countries, enables Parsons (2012) to control for pairwise heterogeneity as

well as to instrument properly for migration patterns by using historical stocks.

       The parallel analysis can be extended to linkages between FDI and migration

flows. Similar macro sources are used for bilateral migration and FDI data. Among the

important examples, Kugler and Rapoport (2005) use a standard gravity model while

Javorcik et al. (2010) use the US data with education levels to analyze the role of skilled



                                             39
migration. In addition, they use geographic variables and passport costs as instruments to

control for potential endogeneity problems.



Forced Displacement



The study of forced displacement is often associated with people fleeing from conflict.

This may include, but is not limited to, generalized violence, armed conflict or

persecution. As defined by the 1951 Convention on Refugees, a refugee is ‘a person who

is outside his or her country of nationality or habitual residence; has a well-founded fear

of being persecuted because of his or her race, religion, nationality, membership of a

particular social group or political opinion; and is unable or unwilling to avail him- or

herself of the protection of that country, or to return there, for fear of persecution’

(UNHCR, 2007, p. 3). While many individuals fleeing persecution may seek assistance

across international borders either in the form of asylum or through temporary assistance,

others may fear being returned and choose to remain anonymous.

       Accurately recording refugees in data is extraordinarily difficult. The level of

accuracy often depends on the destination of the refugees, the size of the displacement,

and whether the refugee chooses to integrate into the destination community. The United

Nations High Commissioner for Refugees (UNHCR) maintains a statistical database of

refugees across the world. The data are usually reported by local UNHCR offices and

reflect host-country data. In the developing world, this information largely incorporates

refugees in camps, but may not include those who have integrated into society. In 25

industrialized countries, the data are estimated based on individual-country recognition of



                                              40
the refugee population over a ten-year period. 9 Furthermore, in OECD countries, refugees

are most often integrated into society and may be registered in the census.

        Forced displacement is not limited to those fleeing conflict, but may also include

individuals fleeing natural disasters or environmental change. Such migration may be

international or domestic, and therefore, while forced, environmental migrants often do

not fit the legal definition of a refugee. Individuals fleeing natural disasters, such as

Haitians responding to the 2010 earthquake by fleeing to the Dominican Republic, may

do so temporarily or on a longer-term basis. Documenting the temporary migration may

be done through administrative data, such as information on border crossings, or through

targeted surveys at the destination shortly after the natural disaster. Alternatively, longer-

term migration may be captured in census data or through multi-topic surveys.

Individuals forced to migrate due to environmental change are unlikely to be able to

return. Researchers are devising a numbers of ways to link multiple types of data in order

to assess and anticipate environmental migration. In Ghana, Van der Geest et al. (2010)

link census data to vegetation data to examine the connections between internal migration

and vegetation dynamics. Feng et al. (2010) examine the connection between crop yields

and migration by merging crop data with census data. Others are modeling climate

change patterns in an attempt to understand future migration trends (Barbieri et al., 2010;

Hunter et al., 2011; McLeman, 2013).

        Forced migration may also include displacement due to development policies or

projects. These could include the introduction of conservation areas or biosphere projects,

or large infrastructure projects, such as the construction of dams or deforestation. Similar


9. See the UNHCR website (http://www.unhcr.org/4a01417d6.html) for a list of the 25 industrialized
countries and a complete explanation of the database.

                                                   41
to many environmental refugees, those displaced by development projects/policies

remain largely within their own country, and do not meet the legal requirements to be

considered refugees. Surveys specifically targeting those who may be affected by such

policies would be most beneficial. Moreover, in designing surveys to answer questions

about all types of forced migration, it is important to include questions regarding where

individuals lived previously and why they moved.



IV. CONCLUSIONS



The study of migration and its importance as a driver and consequence of many

socioeconomic processes has long been a key component in many countries’

development debates. However, the availability of data has been a major constraint

hampering the formulation of proper policies in both destination and origin

countries. A number of data sources exist, including censuses, various

administrative data and surveys, but their thematic and geographic coverage

remains inadequate and inconsistent.

       Collecting migration data poses a number of technical and logistical

challenges requiring special methods and techniques to properly sample and

interview migrants. Furthermore, because migration is a highly selective decision,

assessing the true impact of migration on the outcomes of choice remains

particularly difficult and demanding in terms of data. The objective of this paper was

to give readers of this volume a primer on the issues surrounding the collection of




                                            42
migration data and on the data requirements to address some important questions

at the center of national and global policy debates.

       Each of the different data sources reviewed has its own strengths and

weaknesses, depending of its specific use, but improving the linkages across these

different data sources and the various available databases is probably an aspect of

the migration data agenda that deserves more attention and would result in

substantial benefits to policy analysts. Ultimately, no single data source will be able

to meet all data demands; thus countries need to think systemically about how the

various sources are integrated and complement each other.

       The issue of representativeness, or the lack thereof, of many migration

studies also highlights the difficulties associated with drawing suitable samples of

migrants at both the origin and destination. Several techniques were highlighted,

but the lack of complete and updated sampling frames is bound to continue to

hamper future efforts.

       Migration is innate to human nature, and both internal and international

flows will undoubtedly continue to be a natural part of the policy debates of most

countries. Some processes, like increasing rural–urban migration, are inevitable and

come with countries’ development and households’ evolution. Other emerging

issues, like climate change, are bound to accelerate these processes. And others, like

the aging of population in developed countries, are sure to generate additional

demand for unskilled and skilled labor from sending countries. However, how

migration is managed and its positive impact fully exploited will ultimately depend

on the availability of accurate and timely data on the different facets of the


                                           43
phenomenon. From Ravenstein’s day much has changed in terms of global migration

patterns, and our knowledge has steadily increased. Yet some of the same data

challenges remain to this day, and this requires a concerted effort among

researchers and policy makers to resolve.




REFERENCES



Acosta, P., P. Fajnzylber and J.H. López (2007), ‘Remittances and household behavior:

       evidence for Latin America’, in P. Fajnzylber and J.H. López (eds.), Remittances


                                          44
       and Development: Lessons from Latin America, Washington, DC: The World

       Bank, pp. 133–70.

Adams, R.H. (2005), ‘Remittances and poverty in Guatemala’, World Bank Policy

       Research Working Paper No. 3418, Washington, DC: The World Bank.

Adams, R.H. and A. Cuecuecha (2010), ‘Remittances, household expenditure and

       investment in Guatemala’, World Development, 38(11), 1626–41.

Amuedo-Dorantes, C. and S. Pozo (2009), ‘New evidence on the role of remittances on

       health care expenditures by Mexican households’, Discussion Paper No. 4617,

       Bonn, Germany: Institute for the Study of Labor (IZA).

Anderson, J. E. (1979), ‘A theoretical foundation for the gravity equation’,

       The American Economic Review, 69(1), 106–16.

Antón, J. (2010), ‘The impact of remittances on nutritional status of children in Ecuador’,

       International Migration Review, 44(2), 269–99.

Artuc, E., F. Docquier, Ç. Özden and Christopher Parsons (forthcoming), ‘A global

       assessment of human capital mobility’, World Development.

Azam, J.P. and F. Gubert (2006), ‘Migrants’ remittances and the household in Africa: a

       review of the evidence’, Journal of African Economies, 15(2), 426–62.



Bandyopadhyay, S., C. Coughlin and H. J. Wall (2008), ‘Ethnic networks and US

       exports’, Review of International Economics, 16(1), 199–213.

Barbieri, A.F., E. Doningue, B.L. Queiroz, R.M Ruiz, J.I. Rigotti, J.A.M. Carvalho, and

       M. F. Resende (2010), ‘Climate change and population migration in Brazil’s

       Northeast: scenarios for 2025–2050’, Population Environment, 31, 344–70.



                                            45
Bardhan, A. D. and S. Guhathakurta (2005), ‘Global links of subnational

       regions: coastal exports and international networks’,

       Contemporary Economic Policy, 22, 225–36.

Barham, B. and S. Boucher (1998), ‘Migration, remittances, and inequality:

       estimating the net effects of migration on income distribution’, Journal of

       Development Economics, 55, 307–31.

Batista, C., A. Lacuesta and P. Vicente (2009), ‘Micro evidence of the brain gain

       hypothesis: the case of Cape Verde’, Banco de España Working Paper No. 0902,

       Madrid: Banco de España.

Beauchemin, C. and A. González-Ferrier (2011), ‘Sampling international migrants with

       origin-based snowballing method: new evidence on biases and limitations’,

       Demographic Research, 25, 103–34.

Beegle, K., J. de Weerdt and S. Dercon (2011), ‘Migration and economic mobility in

       Tanzania: evidence from a tracking survey’, Review of Economics and Statistics,

       93(3), 1010–33.

Beine, M., F. Docquier and H. Rapoport (2007), ‘Measuring international skilled

        migration: a new database controlling for age of entry’, ULB Institutional

        Repository 2013/10411, Université Libre de Bruxelles.

Beine, M., F. Docquier and H. Rapoport (2008), 'Brain drain and human capital

       formation in developing countries: winners and losers,' Economic

       Journal, 118(528), 631-52.

Bhagwati, J. (1979), ‘International migration of highly skilled: economics, ethics and

       taxes’, Third Word Quarterly, 1(3), 17–30.



                                            46
Bhagwati, J. and G. Hanson (2009), Skilled Immigration Today: Prospects, Problems,

       and Policies, New York: Oxford University Press.

Bijwaard, G. E., C. Schluter and J. Wahba (2013.), ‘The impact of labor market

       dynamics on the return migration of immigrants’, The Review of Economics

       and Statistics, online.

Bilsborrow, R., G. Hugo, A.S. Oberai and H. Zlotnick (1997),

       International Migration Statistics: Guidelines for Improving Data Collection

       Systems, Geneva: International Labour Organization.

Blanes-Cristóbal, J.V. (2003), ‘The link between immigration and trade in

       Spain’, Seville, Universidad Pablo de Olavide, Paper presented at XXVIII

       Simposio de Análisis Económico, 11–13 December, Seville.

       http://www.revecap.com/vieea/autores/B/62.pdf.

Böhme, M. (2012), ‘Migration and education aspirations – another channel of brain

       gain?’, Kiel Working Paper No. 1811, Kiel: Kiel Institute for the World

       Economy.

Borjas, G.J. (1988), ‘Self-selection and the earnings of immigrants’,

       National Bureau of Economic Research, Inc. Retrieved from

       http://ideas.repec.org/p/nbr/nberwo/2248.html.

Borjas, G.J. (1989), ‘Economic theory and international migration’, International

       Migration Review, 23(3), 457–85.

Borjas, G.J. (1994), ‘The economics of immigration’, Journal of Economic Literature,

       32(4), 1667–717.

Borjas, G. J. (2003), ‘The labor demand curve is downward sloping:


                                           47
       reexamining the impact of immigration on the labor market’, Quarterly

       Journal of Economics, 118(4), 1335–74.

Borjas, G.J. (2014), Immigration Economics, Cambridge, MA: Harvard University Press.

Borjas, G.J. and B. Bratsberg (1996), ‘Who leaves? The outmigration of the foreign-

       born’, The Review of Economics and Statistics, 78(1), 165–76.

Calero, C., A.S. Bedi and R. Sparrow (2008), ‘Remittances, liquidity constraints and

       human capital investments in Ecuador’, World Development, 37(6), 1143–54.

Card, D. (2001), ‘Immigrant inflows, native outflows, and the local market

       impacts of higher immigration’, Journal of Labor Economics, 19(1), 22–64.

Card, D. (2009), ‘Immigration and inequality’, American Economic Review, 99(2),

       1–21.

Carletto, C. and C. Azzarri (2007), ‘Migration in Albania: trends, determinants and

       impact on poverty’, Background Paper to the 2007 Albania Poverty Assessment,

       mimeo, Washington, DC: The World Bank.

Carletto, C., K. Covarrubias and J.A. Maluccio (2011), ‘Migration and child growth in

       Guatemala’, Food Policy, 36(February), 16–27.

Carletto, C. and A. de Brauw (2008), ‘Measuring migration using

       household surveys’, Migration Operational Vehicle, Operational Note No. 2,

       Washington, DC: The World Bank.

Carletto, C. and T. Kilic (2011), ‘Moving up the ladder? The impact of migration

       experience on occupational mobility in Albania’, Journal of Development

       Studies, 47(6), 846–69.

Center for Global Development (2009), ‘Migrants count: five steps toward better



                                           48
       migration data’, Report of the Commission on International Migration Data,

        Washington, DC: Center for Global Development.

Chand, S. and M.A. Clemens (2008), ‘Skilled emigration and skill creation: a quasi-

       experiment’, International and Development Economics Working Papers idec08-

       05, International and Development Economics.

Clemens, M. and L. Pritchett (2008), ‘Income per natural: measuring

       development for people rather than places’, Population and Development

       Review, 34(3), 395–434.

Co, C. Y., P. Euzent and T. Martin (2004), ‘The export effect of

       immigration into the USA’, Applied Economics, 36, 573–83.

Cox Edwards, A. and M. Ureta (2003), ‘International migration,

       remittances, and schooling: evidence from El Salvador’, Journal of

       Development Economics, 72(2), 429–61.

De, P.K. (2008) ‘Identifying the impacts of international migration on women’s

       contraceptive use in Mexico’, mimeo, New York: New York University.

De Brauw, A. and J. Giles (2008), ‘Migrant opportunity and the educational

       attainment of youth in rural China’, Policy Research Working Paper Series

       4526, The World Bank.

De Brauw, A., J. Huang, S. Rozelle, L. Zhang and Y. Zhang (2002),‘The

       evolution of China’s rural labor markets during the reforms’, Journal of

       Comparative Economics, 30(2), 329–53.

De Brauw, A. and C. Carletto (2012), ‘Improving the measurement and policy relevance

       of migration information in multi-topic household surveys’, mimeo.



                                           49
       http://siteresources.worldbank.org/INTLSMS/Resources/3358986-

       1199367264546/Migration_Data_v14.pdf.

De Brauw, A. and R. Mu (2011), ‘Migration and the overweight and underweight status

       of children in rural China’, Food Policy, 36(February), 16–27.

De Brauw, A., V. Mueller and T. Woldehanna (2013), ‘Does internal migration improve

       overall well-being in Ethiopia?’, Ethiopia Strategy Support Program II (No. 55).

       Retrieved from http://essp.ifpri.info/2013/06/20/does-internal-migration-improve-

       overall-well-being-in-ethiopia/.

De Brauw, A., Q. Li, C. Liu, S. Rozelle, and L. Zhang (2008), 'Feminization of

       Agriculture in China? Debunking Myths Surrounding Women’s Participation

       in Farming', China Quarterly, 194:327–334.

De Haan, A. (1999), ‘Livelihoods and poverty: the role of migration – a critical

       review of the migration literature’, Journal of Development Studies, 36(2), 1–47.

Deaton, A. and M. Grosh (2000), ‘Consumption’, in M. Grosh and P.

       Glewwe (eds), Designing Household Survey Questionnaires for Developing

       Countries: Lessons from 15 years of the Living Standards Measurement Study

       Volume 1. Washington, DC: The World Bank, pp. 91–134.

Deaton, A. and S. Zaidi (2002), Guidelines for Constructing Consumption

       Aggregates for Welfare Analysis, Washington, DC: The World Bank.

Docquier, F. and A. Marfouk (2004), ‘Measuring the international mobility of skilled

        workers (1990–2000) : release 1.0’, Policy Research Working Paper Series 3381,

       Washington, DC: The World Bank.

Docquier, F. and A. Marfouk (2006), ‘International migration by educational attainment



                                            50
       (1990–2000)’, in Ç. Özden and M. Schiff (eds), International Migration,

       Remittances and the Brain Drain, Basingstoke, UK: Palgrave-Macmillan, ch. 5.

Docquier, F., B.L. Lowell and A. Marfouk (2009), ‘A gendered assessment of

       highly skilled emigration’, Population and Development Review, 35(2), 297–

       321.

Docquier, F., Ç. Özden and G. Peri (forthcoming), ‘The wage effects

       of immigration and emigration’, The Economic Journal.

Dumont, J.C. and G. Lemaître (2004), ‘Counting immigrants and expatriates in OECD

       countries: a new perspective’, mimeo, Paris: OECD.

Dumont, J.C., J.P. Martin and G. Spielvogel (2007), ‘Women on the move: the

       neglected gender dimension of the brain drain’, Discussion Paper Series No.

       2920, Institute for the Study of Labor.

Dunlevy, J. A. (2006), ‘The influence of corruption and language on the protrade

       effect of immigrants: evidence from American states’, Review of Economics

       and Statistics, 88(1), 182–6.

Dunlevy, J. A. and W. K. Hutchinson (2001), ‘The pro-trade effects of

       immigration on American exports during the period 1870 to 1910’,

       Vanderbilt University Working Paper 01-W25.

Felbermayr, G. J. and B. Jung (2009), ‘The pro-trade effect of the brain

       drain: sorting out confounding factors’, Economics Letters, 104(2), 72–5.

Felbermayr, G. and F. Toubal (2008), ‘Revisiting the trade–migration nexus:

       evidence from new OECD data’, mimeo.

Feng, S.F, A.B. Krueger and M. Oppenheimer (2010), ‘Linkages among climate


                                            51
       change, crop yields and Mexico–US cross-border migration’, Proceedings of

       the National Academy of Sciences USA, 107(32), 14257–62.

Germenji, E. and J. Swinnen (2005), ‘Human capital, market imperfections, poverty and

       migration: evidence From Albania’, Licos Discussion Papers, No. 175, Leuven:

       Centre for Institutions and Economic Performance.

Gibson, J. and D. McKenzie (2011), ‘The microeconomic determinants of emigration

       and return migration of the best and brightest: evidence from the Pacific’, Journal

       of Development Economics, 95(1), 18–29.

Gibson, J., D. McKenzie and S. Stillman (2009), ‘The impacts of international migration

       on remaining household members: omnibus results from a migration lottery

       program’, World Bank Policy Research Working Paper No. 4956, Washington,

       DC: The World Bank.

Gibson, J., D. McKenzie and S. Stillman (2011), ‘What happens to diet and child health

       when migration splits households? Evidence from a migration lottery program’,

       Food Policy, 36(February), 7–15.

Girma, So. and Z. Yu (2002), ‘The Link between immigration and trade:

       evidence from the U.K.’, Weltwirtschaftliches Archiv, 138(1), 115–30.

Glewwe, P. (2000), ‘Human capital’, in M. Grosh and P. Glewwe (eds), Designing

       Household Survey Questionnaires for Developing Countries: Lessons from 15

       years of the Living Standards Measurement Study Volume 1. Washington, DC:

       World Bank, pp. 143–76.

Hanson, G. (2007), ‘Emigration, labor supply, and earnings in Mexico’, in

       G. Borjas (ed.), Mexican Immigration, Chicago, IL: University of Chicago Press



                                           52
       and the National Bureau of Economic Research, pp. 289–328.

Hanson, G.H. and C. Woodruff (2003), ‘Emigration and educational attainment in

       Mexico’, mimeo, San Diego, CA: University of California.

Hatzigeorgiou, A. (2010), ‘Does immigration stimulate foreign trade?

       Evidence from Sweden’, Journal of Economic Integration, 25(2), 376–402.

Head, K. and J. Ries (1998), ‘Immigration and trade creation: econometric

       evidence from Canada’, Canadian Journal of Economics, 47–62.

Herander, M. G. and L. Saavedra (2005), ‘Exports and the structure of

       immigrant-based networks: the role of geographic proximity’, Review of

       Economics and Statistics, 87(2), 323–35.

Hildebrandt, N. and D. McKenzie (2005), ‘The effects of migration on child health in

       Mexico’, Economía, Fall, 257–89.

Huang, W. (2005), Methods to Extract Rare Events (Degree of Doctor of Philosophy).

       Los Angeles, CA: University of California.

Hunter, L.M., W. Twine and A. Johnson (2011), ‘Adult mortality and natural resource

       use in rural South Africa: evidence from the Agincourt Health and

       Demographic Surveillance Site’, Society of Natural Resources, 24, 256–75.

INSTAT (2002), Albania Living Standards Measurement Survey,

       www.worldbank.org/lsms.

INSTAT (2005), Albania Living Standards Measurement Survey,

       http://www.worldbank.org/lsms.

Javorcik, B., Ç. Özden, M. Spatareanu and C. Neagu (2010), ‘Migrant networks and

       foreign direct investment’, Journal of Development Economics, 94, 231–41.


                                           53
Kanaiaupuni, S. and K. Donato (1999), ‘Migradollars and mortality: the effects of

       migration on infant survival in Mexico’, Demography, 36(3), 339–53.

Kish, L. (1965), Survey Sampling, New York: John Wiley.

Kugler, M. and H. Rapoport (2005), ‘Skilled emigration, business networks and foreign

       direct investment’, CESifo Working Paper Series 1455, ECSifo Group, Munich.

Lipton, M. (1980), ‘Migration from rural areas of poor countries: the impact on

       rural productivity and income distribution’, World Development, 8, 1–24.

Lokshin, M., M. Bontch-Osmolovski and E. Glinskaya (2010), ‘Work-related migration

       and poverty reduction in Nepal’, Review of Development Economics, 14(2), 323–

       32.

Macours, K. and R. Vakis (2010), ‘Seasonal migration and early childhood

       development’, World Development, 38(6), 857–69.

Mansuri, G. (2006), ‘Migration, school attainment and child labor: evidence from rural

       Pakistan’, mimeo, Washington, DC: The World Bank.

Mattoo, A., I.C. Neagu and Ç. Özden (2008), ‘Brain waste? Educated immigrants in the

        US labor market’, Journal of Development Economics, 87(2), 255–69.

Mayr, K., and G. Peri (2008), ‘Return migration as channel of brain gain’, CReAM

       Discussion Paper Series No. 0804, Centre for Research and Analysis of Migration

       (CReAM), Department of Economics, University College London.

McKenzie, D., J. Gibson and S. Stillman (2006), ‘How important is selection?

       Experimental versus non-experimental measures of the income gains from

       migration’, World Bank Policy Research Paper No. 3906, Washington, DC: The

       World Bank.



                                           54
McKenzie, D. and J. Mistiaen (2009), ‘Surveying migrant households: a comparison

       of census-based, snowball and intercept point surveys’, Journal of the Royal

       Statistical Society, Series A, 172(2), 339–60.

McKenzie, D. and H. Rapoport (2007), ‘Network effects and the dynamics of

        migration and inequality: theory and evidence from Mexico’, Journal of

       Development Economics, 84(1), 1–24.

McLeman, R. (2013), ‘Developments in modeling of climate change-related

       migration’, Climatic Change, 117, 599–611.

Mendola, M. and C. Carletto (2012), ‘International migration and gender differences in

       the home labour market: evidence from Albania’, Labour Economics, 19(6): 870–

       80.

Miluka, J., G. Carletto, B. Davis and A. Zezza (2010), ‘The vanishing farms? The impact

       of international migration on family farming’, Journal of Development Studies,

       46(1), 140–61.

Mishra, P. (2007), ‘Emigration and wages in source countries: evidence from

       Mexico’, Journal of Development Economics, 82(1), 180–99.

Muñoz, J. (2007), ‘Sampling: what you don’t know can hurt you’, presented

       at the Multi-topic Household Survey course, January,Washington, DC: World

       Bank.

Murrugarra, E. and C. Herrera (2011), ‘Migration choices, inequality of opportunities,

       and poverty reduction in Nicaragua’, in E. Murrugarra, J. Larrison and M.

       Sasin (eds), Migration and Poverty: Toward Better Opportunities for the Poor,

       Washington, DC: World Bank, pp. 101–24.



                                            55
Nobles, J. (2007), ‘Parental migration and child health in Mexico’, Conference paper

        presented at the Population Association of America Meetings, New York, March

        29–31.

OECD (2009), International Migration Outlook. Paris OECD.

Özden, Ç. and M. Wagner (2014), ‘Immigrant versus natives? Displacement

        and job creation’, World Bank Policy Research Working Paper 6900.

Özden, Ç., C. Parsons, M. Schiff and T. Walmsley (2011), ‘Where on earth is

       everybody? The evolution of global bilateral migration 1960–2000’, The World

        Bank Economic Review, 25(1), 12–56.

Parsons, C. R. (2012), ‘Do migrants really foster trade? The trade-migration

       nexus, a panel approach 1960-2000’, Policy Research Working Paper Series

       6034, The World Bank.

Peri, G. and F. Rquena-Silvente (2010), Rauch The trade creation effect of

       immigrants: evidence from the remarkable case of Spain’, Canadian Journal of

       Economics, 43(4), 1433-59.

Rauche J. (1999), ‘Networks versus markets in international trade’, Journal of

       International Economics, 48, 7–35.

Ravenstein, E. G. (1885), ‘The laws of migration’, Journal of the Statistical

        Society, 46, 167–235.

Ravenstein, E. G. (1889), ‘The laws of migration: second paper’, Journal of the

       Royal Statistical Society, 52, 241–305.

Redfern, P. (1989), ‘Population registers: some administrative and statistical pros and

       cons’, Journal of the Royal Statistical Society, Series A (153), 1–41.


                                            56
Richter, S. and J.E. Taylor (2005), ‘Policy reforms and the gender dynamics of rural

       Mexico-to-U.S. migration’, Davis, CA: University of California at Davis.

Sasin, M. and D. McKenzie (2007), ‘Migration, poverty and human capital’, Migration

       Operational Vehicle Operation Note No. 1, Washington, DC: World Bank.

Schaffner, J.A. (2000), ‘Employment’, in M. Grosh and P. Glewwe (eds), Designing

       Household Survey Questionnaires for Developing Countries: Lessons from 15

       years of the Living Standards Measurement Study Volume 1, Washington, DC:

       World Bank, pp. 217–50.

Stark, O.J., E. Taylor and S. Yitzhaki (1986), ‘Remittances and inequality’, Economic

       Journal, 101, 1163–78.

Taylor, J.E. and J. Mora (2006), ‘Does migration reshape expenditures in rural

       households? Evidence from Mexico’, World Bank Policy Research Working

       Paper No. 3842.

United Nations High Commissioner for Refugees (2007), Convention and Protocol

        Relating to the Status of Refugees, Geneva: UNHCR.

United Nations Statistics Division (1998), Recommendations on Statistics of

       International Migration Revision 1, New York: United Nations.

Van der Geest, K., A. Vrieling and T. Dietz (2010), ‘Migration and environment in

        Ghana: a cross-district analysis of human mobility and vegetation dynamics’,

        Environment and Urbanization, 22(1), 107–24.

Wagner, D., K. Head and J. Ries (2002), ‘Immigration and the trade of provinces’,

       Scottish Journal of Political Economy, 49(5), 507-25.




                                           57
Yang, D. (2008), ‘International migration, remittances and household investment:

       evidence from Philippine migrants’ exchange rates shocks’, The Economic

       Journal, 118(April), 591–630.




                                          58