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International Migration and Human Development

in Destination Countries: A Cross-National Analysis

of Less-Developed Countries, 1970–2005

Matthew Sanderson

Accepted: 23 March 2009 / Published online: 5 April 2009  Springer Science+Business Media B.V. 2009

Abstract   Contemporary levels of international migration in less-developed countries are

raising new and important questions regarding the consequences of immigration for human

welfare and well-being. However, there is little systematic cross-national evidence of how

international migration affects human development levels in migrant-receiving countries in

the less-developed world. This paper addresses this gap in the literature by assessing the

impact of cumulative international migration flows on the human development index, a

composite measure of aggregate well-being. A series of panel models are estimated using asample of less-developed countries for the period, 1970–2005. The results indicate that

higher levels of international migration are associated with lower scores on the human

development index, net of controls, but that the effect of international migration is

relatively small.

Keywords   Migration    Development    Human development    Globalization  Less-developed countries    Population

1 Introduction

Historically, international migration in developing countries has been directed predomi-

nately toward developed countries. More recently, however, international migration in

developing countries has become more globalized to the extent that migrant flows now

include a broader variety of both sending and receiving countries (Castles and Miller 2003;

Nyberg-Sorenson et al.  2002). The globalization of migration has coincided with a trend

toward more prevalent South-to-South movements, with persons increasingly moving

between developing countries (Nyberg-Sorenson et al.   2002). Indeed, one-half of all

migrants from developing countries now move to another developing country (Ratha andShaw   2007), and South-to-South migration has become as prevalent as South-to-North

migration (Martin and Widgren 2002).

M. Sanderson (&)Lehigh University, Bethlehem, PA 18015, USAe-mail: matthew.sanderson@lehigh.edu

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The present level of international migration in less-developed countries has raised new

and important questions regarding the impact of these movements for human development

outcomes. In a world in which international migration is produced in large part by per-

sistent cross-national differences in income levels (Hatton and Williamson   2006),

policymakers and development practitioners are searching for ways to utilize the mobilityof people to raise income levels, living standards, and promote aggregate development in

less-developed countries (Annan 2006; UN 2006; WB 2006a).

However, there is little systematic cross-national evidence regarding whether or how

international migration affects development outcomes in migrant-receiving countries in the

less-developed world. Previous cross-national studies (Frey and Al-Roumi 1999; Frey and

Field   2000; Lena and London   1993; London   1988; London and Williams   1988;   1990;

Nolan   1988; Nolan and White   1983;   1984; Shandra et al.   2004,   2005; Shen and

Williamson 1997, 1999, 2001; Wimberley 1990, 1991; Wimberley and Bello 1992) have

neglected the role of international migration as an explanation of variation in development

outcomes. Moreover, when it has been studied, the development implications of interna-tional migration have been examined almost exclusively in terms of the effects of 

remittances on migrant-sending countries (Adams Jr. and Page 2005; Cohen 2005; Durand

et al.   1996; Gammeltoft  2002; de Haas  2005; Martin and Straubhaar  2002; Massey and

Parrado 1998; Stark  2004; Taylor 1999; Taylor et al. 1996a, b; WB 2006a). The impact of 

international migration on development outcomes in migrant-receiving countries has not

been addressed systematically across countries and over time.

My aim is to assess whether and how international migration affects development

outcomes in migrant-receiving countries in the less-developed world. My analysis

advances previous efforts in several ways. First, by assessing the impact of internationalmigration, I incorporate an alternative explanation of human development into previous

models. Second, I analyze the impact of international migration alongside a variety of other

domestic and international explanations of development identified in previous studies.

Third, I examine the effect of international migration on a more extensive measure of 

development that incorporates both economic and non-economic factors. Fourth, I use

more recent data on developing countries and employ more modern estimation techniques

that address some of the methodological concerns identified in previous studies. Finally, I

examine the impacts of international migration on human development across countries

and over time. This allows a more comprehensive understanding of the relationship

between international migration and human development compared to previous studies.

2 Review of Previous Literature

2.1 Conceptualizing Development

While there have been some significant improvements over the past twenty years (Easterlin

2000), the contemporary developing world still faces an array of pressing development

challenges. Approximately 40% (2.5 billion) of the world’s population survives on lessthan $2 per day (UNDP 2005). These problems are vividly evident in sub-Saharan Africa

where a child born today in Zambia is less likely to survive to age 30 than a child born in

England in 1840 (UNDP 2005).

Historically, the most common answer to such challenges has been to improve per

capita income levels (UNDP   1990). In this respect, development has traditionally been

considered largely in material, economic terms and cross-national studies have examined

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the impact of various domestic and international explanations of per capita income levels

(Bornschier et al. 1978; Chase-Dunn 1975; Dixon and Boswell 1996; Kentor 1981, 1998,

2001; Kentor and Boswell 2003; Timberlake and Kentor 1983).

There is now, however, an emerging consensus that development is a multidimensional

concept that encompasses both economic and non-economic elements of human welfare(UNDP 2005). From this perspective, income is viewed not as an end, but as a means to

increase ‘‘human development’’ (UNDP   1990). This is a more comprehensive view

of development that considers improvements in human capabilities and expansions in

human freedoms to be as equally important for human welfare as expansions in income

(Sen 1999).

Previous cross-national studies have employed a variety of composite indicators to

measure human development, including the Physical Quality of Life Index (Ragin and

Bradshaw 1992; Shin 1989) and the Index of Net Social Progress (London and Williams

1990). However, more recent studies (Davies and Quinlivian   2006; Tsai   2006,   2007)

employ the United Nations (1990) Human Development Index (HDI), which supplementsthe material dimension of human welfare (income) with measures of the capacity to

acquire knowledge (education) and the ability to live a long and healthy life (longevity).

Composite indicators have been criticized on the grounds that they are sensitive to

the weights applied to their components. If there is disagreement about the relative

importance of weights for each component of the HDI, then the HDI might not represent a

reliable composite indicator of the quality of life, or well-being, in a country. However,

Hagerty and Land (2007) explored the robustness of the HDI, and other composite indi-

cators of well-being, to various weighting schemes. Their empirical analysis demonstrated

that the HDI is very robust to different weighting schemes: ‘‘For the HDI 2001, differentweights are simply not an impediment to agreement on a QOL (quality-of-life) index’’

(p. 473). Thus, although their utility has been disputed, the HDI is useful when the research

inquiry is interested in the ‘net’ effect of a variety of indicators, even when different

weights are assigned to its components. In this respect, the HDI is considered ‘‘excellent in

the general level of aggregation in its purpose of providing an assessment of development’’

(Hagerty et al.  2001).

2.2 Previous Cross-National Studies of Human Development Outcomes

There is significant variation in human development levels across developing countries.While per capita income is associated with human development (Ranis et al.   2000),

countries with similar levels of GDP per capita can have considerable variation in human

development scores. For example, Albania (HDI  =   .80) has a human development score

similar to Iran (HDI  =  .76), but Iran’s per capita income level ($8,800) is much greater

than Albania’s per capita income level ($5,300) (UNDP  2007).

Discrepancies between economic and human development levels have encouraged

cross-national researchers to look beyond GDP per capita toward external, or global,

explanations of variation in human development outcomes (Easterlin 2000). These studies

acknowledge the importance of global integration for intranational processes anddynamics. In this respect, international trade and foreign direct investment have been

identified as important explanations. However, the empirical evidence linking these factors

with human development outcomes is mixed. While some studies report that trade

(Boehmer and Williamson 1996; Lena and London 1993; Shen and Williamson 2001) and

foreign investment (Lena and London 1993; London 1988; Shandra et al. 2004, 2005; Shen

and Williamson 2001; Wimberley 1990) are detrimental to human development outcomes

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in developing countries, others find that either trade or foreign investment has no effect on

human development outcomes (Brady et al.   2007; Cutright and Adams   1984; Frey and

Field 2000).

The lack of robust findings for international trade and foreign investment suggests the

need to incorporate alternative measures of global integration into cross-national models of human development outcomes. International migration could be one such measure, as

cross-national movements of people generate the very economic (Massey et al.  2002) and

non-economic (Glick Schiller et al.   1995; Portes   2001) linkages between countries that

constitute global integration. However, while cross-national research has included a variety

of demographic factors as determinants of human development outcomes (Boehmer and

Williamson   1996; Brady et al.   2007; Frey and Al-Roumi   1999; Frey and Field   2000;

London 1988; Nolan 1988; Shandra et al. 2004, 2005; Shen and Williamson 1997, 1999;

Wickrama and Lorenz 2002; Wimberley 1990) the role of international migration has been

neglected.

2.3 The Role of International Migration

Population dynamics (fertility, mortality, and migration) are vital to the prospects for

development (Barlow   1994; Crenshaw et al.   1997; Kelley and Schmidt   1995; Preston

1986). The relationship between population dynamics and development is commonly

expressed in terms of the demographic transition, which describes the shift from high

fertility and mortality toward lower fertility and mortality that occurs with economic

development and modernizing social structures (Kirk  1996). The historical experience of 

developed countries generally demonstrates that persistently elevated fertility andmortality rates are impediments in the demographic transition toward advances in devel-

opment, including improvements in economic development, literacy, and longevity (Lee

2003). In this respect, international migration is important because it is a population

dynamic that can affect the factors that promote or impede the demographic transition

toward improvements in human development: ‘‘Immigration is the wild card: it can hasten

or slow these trends’’ (Kent and Haub  2005, p. 7).

I describe the impacts of migration on human development in the following discussion.

I focus the discussion on the impacts of migration in urban areas of developing countries

for two reasons. First, international migration in developing countries has historically been

directed predominately toward urban areas (Skeldon   1997). Second, urban areas arebecoming increasingly important as loci of social change in developing countries, as these

areas are expected to experience the largest gains in population in the foreseeable future:

‘‘Nearly all the net population growth in the next 50 years will occur in the cities and towns

of less developed countries  …’’ (Kent and Haub   2005, p. 12). For these reasons, the

impacts of migration on human development outcomes are perhaps most clearly exem-

plified in the urban and urbanizing areas of developing countries.

2.3.1 Migration and the Human Influx

Historically, the ‘‘urban transition’’ (Weeks   2005, p. 99) from a predominately rural,

agricultural-based society to a largely urban, industrial-based society has been associated

with the demographic transition toward improvements in human development levels.

Rising urbanization levels have generally been associated with decreased fertility and

mortality levels and improved standards of living:  …  no country in the industrial age has

ever achieved significant economic growth without urbanization (UNFPA  2007, p. 1).

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Indeed, migration into urban areas has played a key role in promoting urbanization and the

demographic transition in developed countries (UN 2006, pp. 46–51).

Urbanization in developing countries, however, is qualitatively different from urbani-

zation in developed countries because it is shaped, at least in part, by the global political-

economic context (Roberts 1995; Smith 1996; Timberlake 1985). Urbanization patterns indeveloping countries exhibit several undesirable features related to the countries’ struc-

turally-disadvantaged positions in the global economy. Among the most pressing issues is

the problem of ‘‘overurbanization’’ (Smith 1987), which occurs when urbanization levels

increase faster than rates of economic development. In this context, population growth

outstrips the ability of the host economy and society to ‘‘adjust, absorb, and cope with the

human influx’’ (Smith 1987, p. 271), resulting in insufficient labor demand, unemployment,

and burgeoning informal sector employment levels (Evans and Timberlake 1980). Over-

urbanization has been shown to have detrimental effects on economic development in

LDCs (London 1987, 1988; London and Smith 1988; Smith 1996).

Migration fuels the ‘‘human influx’’ (Smith   1987, p. 271) that generates detrimentalurbanization patterns in developing countries. Internal, rural-urban, migration flows cer-

tainly play a key role in rising urbanization levels (Chen et al.   1998). However,

international migration into developing countries is also very important. About 80% of all

south-south international migration now occurs between countries with contiguous borders

(Martin and Widgren 2002; Ratha and Shaw 2007), and rural–urban migrations that cross

national boundaries are increasingly common, particularly where the income differential

between neighboring countries is higher (Ratha and Shaw  2007). Thus, to the extent that

immigration facilitates overurbanization, it is likely to have detrimental effects on human

development.

2.3.2 Impediments to Human Development 

The harmful effects of migration on human development are also evident in the increased

prevalence of health problems in urban areas of developing countries. While cities were

once considered, ‘‘islands of privilege’’ (Harrison  1982, p. 145), with lower fertility and

mortality rates and higher standards of living for residents, they are increasingly becoming

‘‘unhealthy islands’’ (Stephens   1996, p. 9) as the advantages of urban life for health

outcomes deteriorates. Indeed, there is renewed concern over an emerging ‘‘urban penalty’’

(Harpham and Molyneux 2001, p. 119) associated with health outcomes in urban areas of many developing countries.

Infant mortality rates are a particularly important component of the renewed urban

penalty. While infant mortality rates have fallen dramatically in developed countries, they

have remained high or stable across a large proportion of urban areas in developing

countries (Brockerhoff and Brennan 1998). This trend is particularly ominous for human

development prospects because urban areas have historically been on the leading edge of 

mortality declines in the demographic transition.

Persistently elevated infant mortality rates are attributable, at least in part, to the spread

of infectious diseases in the cities of developing countries. Indeed, while the prevalence of many communicable diseases has been dramatically reduced in developed countries, these

diseases remain prevalent in the cities of developing countries: ‘‘Urban malaria, for

example, has never gone away in many areas of the world  …  and for the urban poor in the

South, tuberculosis never went away’’ (Stephens  1996, p. 23). Immigration is important

here because human mobility, by definition, increases the risk of transmitting infectious

disease: ‘‘The frequency of contact, the density of the population and the concentration and

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proximity of infective and susceptible people in an urban population promote the trans-

mission of the infective organisms. The constant influx of migrants susceptible to infection

and possible carriers of the new virulent strains of infective agents, together with the

inevitable increase in household numbers, foster the transfer of  …   microorganisms’’

(Satterthwaite  1993, p. 91).However, compounding the problem is the tendency for immigrants to face conditions

that promote the spread of infectious and communicable diseases. The relationship

between migration and HIV/AIDS exemplifies this situation (Decosas and Adrien   1997;

Mabey and Mayaud  1997; Quinn  1994). International migrants often work for extended

periods of time, usually measured in years, away from spouses in anomic social contexts

with ‘‘…limited access to health services, minimal social contact or recreational activities’’

(Lamptey et al.  2006, p. 6). These conditions increase the likelihood that migrants will

engage in risky sexual behaviors that spread HIV/AIDS (ILO 2002; Lurie 2006). Indeed,

migrants have been shown to be at a higher risk for HIV/AIDS than non-migrants (Brewer

et al. 1998; Hunt 1989; Lurie et al. 2003). Thus, by facilitating the spread of communicablediseases, including HIV/AIDS, immigration may impede declines in infant mortality rates

and reduce aggregate human development levels.

In addition to being detrimental to infant mortality, immigration may also negatively

impact human development by raising fertility rates. This may occur in one of two

ways. First, to the extent that it promotes increases in infant mortality rates, immigration

places upward pressure on fertility rates. Persistently elevated infant mortality rates

promote high fertility rates because families want to ensure that an appropriate number

of children survive. Second, immigrants generally exhibit higher fertility rates, partic-

ularly when they originate in rural areas (Brockerhoff   1995; Hirschman   1994; Zarateand de Zarate   1975). These fertility patterns are embedded in cultural norms and

expectations and can be resistant to change, at least in the short-term: ‘‘traditional

values are typically rooted in rural environments and among recent migrants to urban

areas. Cultural values, however, may persist long after the structural conditions in which

they originated have eroded’’ (Hirschman   1994, p. 216). Thus, immigration may raise

fertility rates, slowing the demographic transition, and potentially impeding human

development.

3 Method and Data

3.1 Dependent Variable

I assess the impact of international migration on human development by using the human

development index (HDI) (UNDP 1990). The HDI is a composite measure that combines

indices of three essential elements of human life: longevity, knowledge, and standard of 

living. Longevity is measured using an estimation of life expectancy at birth for the

population. Knowledge is measured as a composite score of adult literacy and gross

primary and secondary enrollment rates for the population, with adult literacy weighted at67% and school enrollments weighted at 33%. The standard of living is a proxy for the

dimensions of human development not captured by measures of longevity and knowledge.

It is measured as the level GDP per capita adjusted for purchasing power parity in U.S.

dollars. An index for each dimension of the HDI is created by identifying the minimum and

maximum levels for the dimension, and entering these values into the following formula:

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Dimension index ¼ observed country value  minimum value for all countries

maximum value  minimum value

The dimension indices are then averaged to compute the HDI:

HDI ¼ 1

3  life expectancy indexð Þ þ

 1

3  education indexð Þ þ

 1

3  GDP indexð Þ:

3.2 Key Independent Variable

The key explanatory variable is international migration. International migration is

measured as the   stock of international migrants as a percentage of the total population.

This measure is taken from the World Bank’s   World Development Indicators   database

(WB   2006b) and is logged to correct for skewness. The stock of international immi-grants is estimated from census data. These data included information on the place of 

birth or the citizenship status of the enumerated population, which allowed identification

of the foreign born and foreign population. I expect that higher levels of international

migration will be associated with lower scores on the human development index, net of 

controls.

3.3 Control Variables

Previous cross-national studies have identified international trade and foreign directinvestment (FDI) as important global-level explanations of human development out-

comes (Boehmer and Williamson   1996; Lena and London   1993; London   1988;

Shandra et al.   2004,   2005; Shen and Williamson   2001; Wimberley   1990). Thus, I

include controls for   exports per GDP  and   stocks of FDI per GDP   in order to assess the

impact of international migration on human development net of these controls.   How-

ever, I advance previous cross-national studies by also including a measure of   FDI 

stocks decomposed by economic sector (primary, secondary) per GDP. These measures

allow us to assess whether and how the impact of FDI on human development differs

across economic sectors of the host economy. The FDI data are taken from the United

Nations’   World Investment Directories   (UN   1992,   1994,   1996,   2000,   2003) and theOrganization for Economic Co-Operation and Development’s   International Direct 

 Investment Statistics Yearbook   (OECD   2001), and are logged to correct for skewness.

The exports data are taken from the World Bank’s   World Development Indicators

database (WB   2006b).

Previous studies (Dixon and Boswell 1996; Firebaugh 1992) have found that domestic

investment and foreign investment can have different effects on host social structures.

My analysis therefore includes a control for  gross domestic investment per GDP  in order

to distinguish between any differential effects of investment sources on human

development.

The analysis includes several other controls for important intranational explanations of 

human development outcomes. I control for the impact of size of the domestic market and

the aggregate level of wealth in a society by including the  level of GDP per capita. I also

include a control for the prevalence of health care resources and medical care infrastructure

in the country by including   the number of physicians per 1,000   in the population. This

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variable has been used in previous cross-national studies of health outcomes (Shen and

Williamson 1997; Wimberley 1990).

Previous studies have identified political democracy as an important intranational

explanation of human development outcomes (Frey and Al-Roumi   1999; Shandra et al.

2004, 2005). Democratic political structures are more likely to respond to public opinionand special interest groups concerned with issues related to development than more

repressive political structures. I control for this effect by including a measure of  domestic

 political structure. Values on this variable range from   -10 to   ?10, with lower scores

indicating more authoritarian political structures and higher scores indicating more dem-

ocratic political structures. These data are taken from the Polity IV dataset (Marshall et al.

2006).

The analysis also controls for a number of demographic factors that could impact

human development. The age composition of the population has important implications

for development. Populations with relatively young age structures are characterized by a

dependency burden that can impede human development outcomes (Barlow   1994). Icontrol for the dependency burden by including the   infant mortality rate, which proxies

the size of the youth population. As birth cohorts age, they enter the labor force and

transition from net resource consumers to become net resource producers, producing a

‘‘demographic windfall’’ (Crenshaw et al.   1997) effect as the size of the labor force

expands relative to the other segments of the population. Higher levels of the population

in the prime working-age category should thus produce positive spillover effects for

human development. I control for the demographic windfall effect by including the

 percentage of the total population in the 20–29 age category. All of the age compo-

sition variables are taken from the United Nations’ (2007)   World Population Prospectsdataset.

The analysis also includes a control for the spatial distribution of the population. Higher

levels of urbanization may be associated with reduced development levels in developing

countries (London and Smith   1988; Smith   1987,   1996). As a result, there is renewed

discussion of an ‘‘urban penalty’’ (Harpham and Molyneux  2001) associated with health

outcomes in the cities of many developing countries. However, urbanization is also con-

sidered to be an important prerequisite for economic development and rising levels of 

human development (UNFPA   2007, p. 1). Rising levels of urbanization allow for

agglomeration of industries, and economies of scale, promoting economic development

(McNicoll 1984). Similarly, higher levels of urbanization may also allow for health to betreated for efficiently and effectively, thus reducing the prevalence of factors that increase

morbidity and mortality. Finally, urbanization levels are closely associated with past levels

of rural–urban, or internal, migration levels (White and Lindstron  2006). Internal migration

and international migration are qualitatively different types of population movements

(White and Lindstron 2006). It is important not to confound these two types of movements

in assessing the impact of international migration on human development. The analyses of 

economic and human development therefore control for   the percentage of population in

urban areas. These data are taken from the World Bank’s (2006b)   World Development 

 Indicators database.The analysis also includes controls for the status of women. The status of women in

society is an important explanation of human development outcomes (Boehmer and

Williamson   1996; Shen and Williamson   1997,   1999). I measure the status of women

with two measures, each of which captures a unique aspect of the status of women in

society.

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Education is a crucial determinant of fertility behavior (Caldwell 1982) and rising levels

of education among females strongly reduces fertility rates and improves a variety of 

human development outcomes (Cleland and Hobcraft   1985). Rising levels of education

also improve the relative status of women in society, which is associated with decreases in

fertility and infant mortality and increases in positive health outcomes for women(Caldwell   1993; Hirschman   1994; Subbarao and Raney   1995; Wickrama and Lorenz

2002). The analysis controls for the aggregate level of female education by including a

measure of the  gross secondary school enrollment ratio for females. The gross secondary

school enrollment ratio is the ratio of total school enrollment, regardless of age, to the

population of the age group that officially corresponds to the secondary level of education.

These data are taken from the United Nations’ (UN   2005)   Population, Resources, and 

Environment  database.

I also control for a second aspect of women’s status:   the female labor force partici-

 pation rate. The female labor force participation rate is the percentage of the total

economically active population who are females. The economically active populationrefers to all employed and unemployed women, including those seeking work for the first

time, persons working on their own account, employees, unpaid family workers, members

of producers’ cooperatives and members of the armed forces. Like the rate of female

education, higher levels of female participation in the labor force may indicate that women

have obtained a higher level of status in society. If so, female labor force participation

would be expected to be associated with improvements in human development. However,

higher levels of female labor force participation may have a detrimental effect on infant

mortality rates because working mothers may not have sufficient time to devote to caring

for infants (Hobcraft et al. 1984). Thus, it is necessary to distinguish between the impact of education and labor force participation for women, as they may have different effects on

human development. These data are taken from the United Nations’ (2005)  Population,

 Resources, and Environment  database.

Finally, in order to capture   time-specific effects   that may affect the level of human

development but are not explicitly controlled for in the analysis (e.g. military conflicts,

famines, refugee crises), the analysis includes dummy variables for each of the 10-year

periods, or waves, examined in the analysis (Wooldridge 2006).

3.4 Panel Analysis

This study uses cross-national panel data to examine the impact of international migration

on human development in developing countries. The panel dataset includes data measured

at ten year intervals over the period, 1970–2005. The dependent variable is lagged ten

years in each wave in order to allow hypothesized effects to become evident.

Unobserved effects are central to the problem of causal inference, and panel data

provide an advantage over cross-sectional data in addressing the issue of unobservable

influences (Halaby 2004). Panel models have traditionally included a lagged endogenous

variable on the right hand side of the equation and used ordinary least-squares to estimate

the model. Including the lagged endogenous variable is considered to at least partiallycontrol for the unobserved effects of omitted variables on the dependent variable (Finkel

1995).

Fixed effects models and random effects models represent more sophisticated approa-

ches to the problem of unobserved heterogeneity. Between-unit variation is the source of 

heterogeneity bias in OLS (Stimson 1985, p. 921). Ordinary least squares, however, cannot

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address the problem because it assumes observations are homogenous on the level of the

dependent variable; that is, observations are constrained to a common intercept (Stimson

1985, p. 921). To correct for heterogeneity bias, fixed effects and random effects models

‘‘simulate’’ (Alderson and Nielsen 1999, p. 616) time-invariant country-specific effects by

allowing each country to have a unique intercept, or effect.The two approaches differ, however, in how each treats country-specific effects. Fixed

effects models introduce a series of dummy variables to allow each country to have a

unique effect (Wooldridge   2006). It treats the country-specific effects as ‘fixed’ while

retaining the classic OLS error structure. Fitting individual intercepts effectively removes

all between-unit variation from the data, thereby removing the source of heterogeneity bias

(Stimson 1985). The fixed effects model is specified as follows:

 yit  ¼ ao þ ai þ b0 xit  þ  eit 

where  ao is the mean overall intercept;  ai is the country-specific intercept, or the country-

specific deviation from the overall mean intercept for country  i that explicitly controls for

unobserved heterogeneity;  xit  is a vector of covariates for country  i at time  t ; and eit  is the

familiar error term from OLS.

Random effects modeling treats the country-specific intercepts as part of the error term

and considers them as ‘random’ draws from a larger population (Wooldridge   2006).

Because the country-specific effects are considered to be random, the distribution

parameters (mean and variance) are of more interest than the individual ‘fixed’ country-

specific effects (Stimson 1985, p. 923). Random effects modeling corrects for unobserved

heterogeneity by specifying the bias and modeling it as part of a complex error structure.

The random effects model is specified as follows:

 yit  ¼ ao þ b0 xit  þ ui þ eit 

where ao is the mean overall intercept; xit  is a vector of covariates for country  i  at time t ; ui

is the country-specific effect, or the country-specific deviation from the overall mean

intercept for country   i   that explicitly controls for unobserved heterogeneity; and   eit   is

disturbance term.

3.5 Sample Composition

The countries included in the analyses are selected on the basis of data availability.

Countries are included in the analysis if they have information on the endogenous variable

at time t  and the exogenous variables at time t-10. For example, a country is included in the

analysis if it has data available on the endogenous variable at time   t   (measured in 1980,

1990, 2000, or 2005) and on all of the exogenous variables at time  t-10 (measured in 1970,

1980, 1990, or 1995). On the contrary, a country would not be included in the analysis if it

is missing data on the endogenous variable at time  t  or any of the exogenous variables at

time   t-10.

Because countries are included in the analyses based upon data availability, the sampleis not, strictly speaking, a random draw from the population of all less-developed countries.

However, two issues are worth noting. First, the sample includes countries from each

region of the less-developed world, with countries in the Latin America and Caribbean

region being overrepresented. There are six countries from the East Asia and Pacific

region, five countries from the South Asia region, seventeen countries from the Latin

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America and Caribbean region, and four countries from the Eastern Europe and Central

Asian region. Thus, to the extent that the results of the analyses are generalizable, they

likely are more representative of Latin American and Caribbean countries than of countries

in other regions of the less-developed world. Appendix 1, lists the countries included in the

analysis.Second, the measures of central tendency for the endogenous variable (HDI) and the

key exogenous variable (international migrants per capita) in the sample are similar

when compared to the total population of countries for which data was available.

Appendix 2   presents correlation coefficients and basic descriptive statistics for the vari-

ables and   Appendix 3   provides a comparison of basic descriptive statistics for the

endogenous variable and key exogenous variable for the sample and the total population

of countries.

3.6 Robustness Checks

In order to assess the robustness of the estimates, several checks were implemented. First,

bivariate plots were inspected for the presence of outliers. Armenia, Estonia, Georgia,

Kazakhstan, and Lithuania—five successor states of the former Soviet Union—and

Slovakia—a successor state of the former Czechoslovakia were outliers with very large

stocks of international migrants. These countries were excluded from the analysis because

the international migration data for these countries reflects substantial error from re-

classification of international migration. I note, however, that excluding these countries

does not change the substantive findings of the analyses.

Second, the models were estimated using two different estimation techniques forpanel data: random and fixed effects modeling. There is considerable debate over

whether fixed effects or random effects modeling is a more appropriate method of 

addressing the problem of unobserved heterogeneity. The choice over whether to employ

random or fixed effects modeling in the analysis is commonly resolved by using the

Hausman test to test for statistical differences in the random and fixed effects estimators

(Halaby   2004). Where it was possible to run them, Hausman tests favored the fixed

effects models over the random effects models in my analysis. However, the findings

were robust to both random and fixed effects specifications. I present estimates from both

random and fixed effects models in order to assess the robustness of the findings acrossdifferent modeling techniques.

Third, although the analyses incorporate data on the largest possible sample given data

availability, the estimates might be sensitive to sample composition. Thus, two different

resampling techniques were used to estimate the standard errors for the coefficients:

bootstrapping and jackknifing. Both bootstrapping and jackknifing are data-dependent,

nonparametric, approaches to estimating standard errors from the observed distribution

of the sample (Davison and Hinkley   1997; Efron   1979; Mooney and Duval   1993).

Bootstrapping constructs samples of the standard errors by taking random draws of   N 

observations from a   N -observation dataset. For this analysis, the bootstrapped standard

errors were calculated based upon 1,000 repetitions of sample size 34.Jackknifing repeatedly calculates the statistic by omitting one randomly-selected

observation from each sample. As a result, jackknifing has been used to check the

robustness of the estimates to influential observations in addition to its utility as a

resampling technique (Gould   1994). Because the data are clustered, one country (i.e.,

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observation) was omitted from each sample when calculating the jackknife standard errors

for the estimates.

The estimates are presented below with standard errors estimated using random and

fixed effects modeling. However, these estimates were robust to both the bootstrap and the

 jackknife resampling techniques. Results from these supplemental analyses are availableupon request.

4 Results

The central finding of the analysis is that international migration has a negative effect on

human development levels over a 10-year period, net of controls. The negative impact of 

international migration is robust across both random and fixed effects modeling techniques

and in the presence of all control variables. However, it is important to note that the effectof international migration is relatively small: because the migration variable is logarith-

mically transformed, a 1% increase in international migration as a proportion of the

population is associated with a decline of 0.01 in the human development index.

While no other variable has a consistent effect on human development across random

and fixed effects models, four variables are robust across random effects models net of 

controls. Levels of economic development are positively associated with human devel-

opment, while infant mortality rates have a negative effect on human development across

random effects models. I also find that the two measures of women’s status have con-

trasting effects. Female labor force participation rates have a negative impact on human

development, but female education rates have a positive impact on human development.The models also provide partial evidence that stocks of FDI have a positive effect on

human development, and that there is a trend toward rising levels of human development

since 1970 across developing countries.

The complete results are given in Table  1, which includes four models.

Model 1 includes international migration along with controls for important intra-

national characteristics of the country: levels of economic development, female education

rates, prevalence of medical resources, female labor force participation rates, levels of 

democratic development, and infant mortality rates. International migration has a negative

effect on human development, net of these control variables. Higher levels of internationalmigration are associated with lower levels of human development. This effect is robust

across random and fixed effects models. This finding provides support for Malthusian-type,

population pessimist theories that view immigration as a detriment to human development.

Higher levels of female labor force participation rates also have a negative effect on human

development. This effect is robust across modeling techniques. However, while higher

rates of female participation in the labor market have a detrimental impact on human

development, higher rates of female education are positively associated with human

development. Economic development also has a positive effect on human development

levels. Infant mortality rates are also negatively associated with human development.

However, the effects of female education, economic development, and infant mortality arenot robust across modeling techniques. The prevalence of medical resources and the level

of democratic development are not associated with human development in this model. The

results do, however, find partial evidence that human development levels have consistently

increased since 1970, as each of the time trend variables in the fixed effects models is

positive and significant.

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    F    D    I   s   e   c   o   n    d   a   r   y   s    t   o   c    k

   p   e   r

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    1    9    9    0

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    0 .    0    8    6

    4    *    *    *

    (    0 .    0    2    4    5    )

    0 .    0    0    2    9

    (    0 .    0    1    7    8    )

    0 .    0    8    1    7    *    *    *

    (

    0 .    0    2    0    7    )

    0 .    0    0    2    2

    (    0 .    0    1    8    1    )

    0

 .    1    0    8    1    *    *    *

    (    0 .    0    2    4    1    )

    1    9    9    5

    0 .    0    0    2    5    *

    (    0 .    0    2    0    1    )

    0 .    1    0    1    4    *

    *    *

    (    0 .    0    2    3    9    )

    0 .    0    0    8    5    *

    (    0 .    0    2    4    4    )

    0 .    0    9    5

    6    *    *    *

    (    0 .    0    2    6    8    )

   -    0 .    0    1    4    8

    (    0 .    0    2    0    4    )

    0 .    0    8    3    7    *    *    *

    (

    0 .    0    2    4    7    )

   -    0 .    0    1    4    0

    (    0 .    0    2    0    5    )

    0

 .    1    1    6    0    *    *    *

    (    0 .    0    2    8    5    )

    C   o   n   s    t   a   n    t

    0 .    6    9    0    0    *    *    *

    (    0 .    0    4    3    7    )

    0 .    6    6    7    2    *

    *    *

    (    0 .    0    3    8    4    )

    0 .    6    8    6    6    *    *    *

    (    0 .    0    8    5    0    )

    0 .    6    3    1

    1    *    *    *

    (    0 .    0    8    6    0    )

    0 .    6    8    1    8    *    *    *

    (    0 .    0    4    5    8    )

    0 .    6    6    1    3    *    *    *

    (

    0 .    0    4    1    0    )

    0 .    6    5    5    1    *    *    *

    (    0 .    0    5    2    1    )

    0

 .    7    0    0    8    *    *    *

    (    0 .    0    5    6    2    )

     R       2

    0 .    8    8    2

    0 .    4    4    4

    0 .    8    8    9

    0 .    3    2    5

    0 .    8    6    2

    0 .    5    4    1

    0 .    8    8    9

    0

 .    1    8    8

     N

    3    4    /    6    6

    3    4    /    6    6

    3    4    /    6    6

    3    4    /    6    6

    3    4    /    6    6

    3    4    /

    6    6

    3    4    /    6    6

    3

    4    /    6    6

    *    *    *   p     \

 .    0    0    1   ;    *    *   p     \

 .    0    1   ;    *   p     \

 .    0    5    (   o   n   e  -    t   a    i    l   e    d    t   e   s    t   s    )

72 M. Sanderson

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In Model 2, I include controls for the age composition and the spatial distribution of the

population. Neither of these variables has an effect on human development in these

models. International migration remains negative and significant, net of these control

variables, and is robust across random and fixed effects models. The opposing effects of 

women’s status remain apparent in this model. Higher levels of female labor force par-ticipation rates have a negative effect on human development, but higher rates of female

education are positively associated with human development. However, compared to

Model 1, female labor force participation rates are no longer robust across random and

fixed effects models. Infant mortality rates are also negatively associated with human

development, but this effect is not apparent in the fixed effect model. Similarly, economic

development has a positive impact on human development, but only in the random effects

model. The prevalence of medical resources and the level of democratic development are

not associated with human development in this model. The time trend variables indicate

rising levels of human development since 1970, but this effect is not robust across

modeling techniques.In Model 3, I add controls for international trade and FDI to assess the impact of 

international migration on human development net of these important international

variables. I also include domestic investment to control for the differential effects of 

foreign and domestic investment on human development. International migration has a

negative effect on human development, net of these controls, and it is robust across

random and fixed effects models. Contrary to the global political economy literature, I

find that stocks of FDI have a positive effect on human development. This effect is

robust across random and fixed effects models. International trade and domestic

investment, however, do not have an effect on human development in these models. Thetwo measures of women’s status have contrasting effects, as higher levels of female

labor force participation rates are associated with lower levels of human development,

but higher levels of female education rates are associated with higher levels of human

development. Of these two variables, however, only the negative effect of female labor

force participation rates is consistent across random and fixed effects models. Infant

mortality rates are also negatively associated with human development, but this effect is

not apparent in the fixed effect model. The prevalence of medical resources and the level

of democratic development are not associated with human development in this model.

The models again report partial evidence that human development levels have consis-

tently increased since 1970, as each of the time trend variables in the fixed effectsmodels is positive and significant.

In Model 4, I substitute controls for FDI decomposed across economic sectors of the

host economy for the aggregated FDI variable used in Model 3. International migration

still has a negative effect on human development, net of the disaggregated FDI measures

and other controls. This effect is robust across random and fixed effects models. Neither

FDI primary stocks nor FDI secondary stocks are robust across modeling techniques.

However, the analysis does indicate that the aggregate FDI stocks measure may conceal

contradictory effects of FDI on human development. In the random effects model, stocks

of FDI in the primary sector have a negative impact on human development, but FDIsecondary stocks has no effect. In the fixed effects model, stocks of FDI in the secondary

sector has a positive effect on human development, but FDI primary has no effect.

International trade and domestic investment do not have an effect on human develop-

ment in these models. Female education rates are positively associated with human

development, but are only significant in the random effects model. Female labor force

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participation rates are negatively associated with human development, but are only

significant in the fixed effects models. Infant mortality rates are also negatively asso-

ciated with human development, but this effect is not apparent in the fixed effect model.

Similarly, economic development has a positive impact on human development, but only

in the random effects model. The prevalence of medical resources and the level of democratic development are not associated with human development in this model. The

time trend variables indicate rising levels of human development since 1970, but this

effect is not robust across modeling techniques.

5 Discussion

This study suggests that international migration is an important explanation of cross-

national variation in human development levels. This analysis finds that higher levels of international migration are associated with lower human development levels over a 10-year

period. This finding is robust across two different modeling techniques that include con-

trols for important international and internal economic, demographic, and political

explanations of human development. Developing countries with larger shares of interna-

tional migrants as a proportion of the population face several impediments to human

development as a result of the international immigration. While the harmful impacts of 

migrant streams into developed countries tend to be diminished by relatively robust

economies, political structures, and social welfare infrastructures, it is likely that migration

into less-developed countries places excess burden on the relatively minimal infrastruc-

tures that support social welfare and human well-being. As a result, immigration intodeveloping countries tends to undermine, rather than promote, human development and

social well-being.

This analysis contributes to our understanding of the structural impacts of international

migration on development outcomes in less-developed countries. As the destinations of 

migrant flows becomes increasingly diverse (Castles and Miller 2003) and a broader array

of developing countries are more deeply enmeshed into global processes, understanding

the structural consequences of international migration will only become more important

both for social theory and for development policies.

It is worth stressing, however, that the findings reported here suggest that although theeffect of international migration is statistically significant, it is also relatively small in

size: a 1% increase in the stock of international immigrants as a proportion of the

population is associated with a .01 decrease in the human development index. Thus,

these findings lend credence to the importance of international migration as an expla-

nation of human development outcomes in cross-national research. In this respect, much

more research along these lines is needed. For example, there is reason to believe that

the effect of international migration might be larger than is indicated by the findings

presented here, as the indicators of international migration stocks in less-developed

countries likely undercount, quite significantly, the actual size of the foreign born

populations (Zlotnik   1998). Most migrants who move into less-developed countries arefrom younger age cohorts, which increases the likelihood that they will enter the host

country without documents, or illegally (McKenzie   2008). Thus, to the extent that

censuses and population registers in less-developed countries undercount undocumented

migration, the effect of international migration could be larger than these findings sug-

gest. Hence, the need for future research. Regardless, however, the results presented here

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do not support the position that international migration is   the   strongest, nor   the   most

important, explanation of human development levels in migrant-receiving countries in

the less-developed world.

Nevertheless, the findings do advance cross-national scholarship on human devel-

opment in several ways. Previous studies have relied primarily on economic measuressuch as international trade and foreign direct investment to assess the impact of global

integration on human development. By demonstrating the explanatory power of inter-

national migration, I have identified an important non-economic dimension of global

integration that exerts a significant effect on human development levels across countries

and over time. Thus, this study suggests that incorporating international migration into

theoretical and empirical models would further enhance our understanding of global-

ization as a multidimensional phenomenon that includes both economic and non-

economic components. Studies that consider only economic forms of global integration

offer less complete answers to increasingly complex questions about the consequences

of globalization.Another contribution of this analysis is to provide additional insights into the effect of 

foreign direct investment on development. Compared to previous studies, I examine the

effect of foreign direct investment on a broader measure of development and over a

longer time horizon. The analysis controls for a wider array of variables and uses more

sophisticated modeling techniques that address methodological problems inherent in

panel analysis. I also disaggregate foreign direct investment by economic sectors in

addition to assessing the impact of the total stock of foreign direct investment. When

measured in the traditional manner as total stocks, foreign direct investment has a

positive impact on human development levels. This finding is contrary to some of theprevious research in this area, which reports a negative impact of foreign investment

(Lena and London 1993; London 1988; Shandra et al.  2004, 2005; Shen and Williamson

2001; Wimberley   1990). However, when foreign direct investment is disaggregated

across economic sectors, the results are more mixed. Foreign investment in the primary

sector has a negative effect on human development and foreign investment in the sec-

ondary sector has a positive effect. While neither of these effects is robust across

modeling techniques, this analysis does provide a more nuanced understanding of the

relationship between foreign direct investment and human development and suggests an

important area for future research. Previous studies that report that foreign investment

has a detrimental impact on human development outcomes might be capturing the effectof foreign investment in the primary sector. Thus, if samples are comprised largely of 

countries in which primary sector foreign investment is predominate, the findings may be

a partial artifact of sample composition. Subsequent studies on the relationship between

sectoral foreign investment and development outcomes are therefore particularly

worthwhile.

Two other notable findings are the relatively minimal impact of per capita income levels

and the relatively robust impacts of women’s status on human development. While the

traditional answer to human development problems is to raise per capita income levels, this

analysis suggests that this strategy may have limited impact. Economic development cer-tainly promotes social well-being, as these findings indicate, but there are several other non-

economic factors that have larger impacts on human development levels, including

migration.

In this respect, the status of women in society is another such factor. The analysis

suggests that women’s status has opposing effects on human development. Countries with

higher proportions of educated women tend to have higher human development levels.

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However, human development levels tend to be lower where women represent larger

proportions of the labor force. These findings suggest the importance of educating women

for development prospects but also provide evidence of the importance of women in

providing child care and basic needs for households in developing countries. These find-

ings deserve serious attention in future research in that they may provide additionalinsights into the expanding role of women in development processes.

Finally, this analysis has implications for development policies. Although international

migration remains the ‘‘step-child’’ (Bouvier et al.  1997; Goldstein 1976) of demographic

research and development policy, these findings suggests the need to more thoroughly

incorporate international migration into development frameworks. At the national level,

migration clearly has an impact on labor markets, political structures, and social welfare

infrastructures. While I acknowledge the constraints placed upon policy construction by

transnational social structures (Meyers   2000), it remains incumbent upon national poli-

cymakers to account for the impact of international migration, regardless of the size of its

relative effect on host social structures and development outcomes.Economic growth and human development are correlated, and raising per capita

incomes is important for development prospects. However, there is increasing evidence

that suggests that improvements in human development outcomes are more important for

increasing living standards (Ranis et al.   2000). Country performance in raising living

standards depends on two factors: the time sequencing of the relationship and the strength

of the association (Ranis et al.  2000).

In terms of the timing of development policies, human development seems to be more

important than economic development. Development policies that emphasize economic

growth, without concomitant improvements in human development, tend to lead countriestoward a ‘‘dead end’’, while policies that promote human development can lead countries

toward virtuous, mutually-reinforcing cycles that promote both economic and human

development: ‘‘Economic growth itself will not be sustained unless preceded or accom-

panied by improvements in human development (Ranis et al.  2000, pp. 212–213). Thus, to

the extent that migration is detrimental for human development, it is likely to also

undermine the prospects for economic development. In response, policymakers should

attempt to strengthen the relationship between economic and human development. In this

respect, these results provide evidence that states could potentially minimize the detri-

mental impact of international migration by providing sufficient resources for health and

education, particularly for women.

Appendix 1

See Table 2.

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Appendix 2

See Table 3.

Table 2   Countries included in cross-national analysis of human development

1970–1980 1980–1990 1990–2000 1995–2005

Argentina x x

Bangladesh xBulgaria x

Bolivia x

Brazil x x x

Chile x x

Colombia x x x

Costa Rica x x x

Dominican Republic x x

Ecuador x x x

Guatemala xHonduras x x

Hungary x x

Indonesia x x

India x x

Sri Lanka x

Mexico x x

Mongolia x

Nigeria x x

Nepal x xPakistan x x

Panama x

Peru x x x x

Philippines x x

Papua New Guinea x x

Poland x x

Paraguay x

El Salvador x x

Thailand x x x

Trinidad and Tobago x x

Turkey x x x

Tanzania x

Venezuela, RB x x x

Vietnam x

 N  =  34, 66 observations

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      T    a      b      l    e      3

    Z   e   r   o  -   o   r    d   e   r   c   o   r   r   e    l   a    t    i   o   n   s   a   n    d    b   a   s    i   c    d   e   s   c   r    i   p    t    i   v   e

   s    t   a    t    i   s    t    i   c   s    f   o   r   v   a   r    i   a    b    l   e   s   u   s   e    d    i   n   c   r   o

   s   s  -   n   a    t    i   o   n   a    l   a   n   a    l   y   s    i   s   o    f    h   u   m   a   n    d   e   v   e    l   o   p   m   e   n    t    i   n    d   e   x

    (   a    )

    (    b    )

    (   c    )

    (    d    )

    (   e    )

    (    f    )

    (   g    )

    (    h    )

    (    i    )

    (    j    )

    (    k    )

    (    l    )

    (   m    )

    (   n    )

    (   o    )

    (   a    )    H    D    I

    1 .    0    0    0

    (    b    )    M    i   g   r   a    t    i   o   n

    0 .    0    4    5

    1 .    0    0    0

    (   c    )    G    D    P

    0 .    7    2    9

    0 .    3    6    4

    1 .    0    0    0

    (    d    )    F   e   m .   e    d   u   c .

    0 .    8    0    4

   -    0 .    0    2    0

    0 .    5    5    7

    1 .    0    0    0

    (   e    )    D   o   c .   p   e   r    1    0    0    0

    0 .    6    5    1

    0 .    1    6    5

    0 .    6    2    2

    0 .    7    3    1

    1 .    0    0    0

    (    f    )    F   e   m .    L .    F .    P   a   r    t .

   -    0 .    0    3    6

   -    0 .    1    7    1

   -    0 .    1    3    4

    0 .    1    9    1

    0 .    1    4    8

    1 .    0    0    0

    (   g    )    D   e   m   o   c   r   a   c   y

    0 .    3    7    6

    0 .    3    4    4

    0 .    3    6    5

    0 .    3    4    5

    0 .    3    7    0

    0 .    0    4    1

    1 .    0    0    0

    (    h    )    I   n    f   a   n    t   m   o   r    t .

   -    0 .    8    6    9

   -    0 .    1    2    4

   -    0 .    6    0    9   -

    0 .    7    5    8

   -    0 .    5    9    1

   -    0 .    1    4    7

   -    0 .    4    5    1

    1 .    0    0    0

    (    i    )    U   r    b   a   n    i   z   a    t    i   o   n

    0 .    7    0    0

   -    0 .    0    2    2

    0 .    6    6    0

    0 .    5    3    7

    0 .    6    9    7

   -    0 .    2    1    7

    0 .    2    7    6

   -    0 .    5    2    0

    1 .    0    0    0

    (    j    )    A   g   e   c   o   m   p .

    0 .    0    8    3

   -    0 .    1    7    5

   -    0 .    0    5    6

    0 .    0    2    4

   -    0 .    2    3    1

    0 .    0    4    2

    0 .    0    2    9

   -    0 .    1    9    1

   -    0 .    0    2    2

    1 .    0    0    0

    (    k    )    E   x   p   o   r    t   s

    0 .    3    3    1

    0 .    1    8    0

    0 .    1    9    9

    0 .    3    2    8

    0 .    1    6    6

    0 .    1    3    4

    0 .    3    2    9

   -    0 .    5    3    9

   -    0 .    0    6    3

    0 .    2    3    5

    1 .    0    0    0

    (    l    )    F    D    I   s    t   o   c    k

    0 .    1    7    6

    0 .    1    2    9

    0 .    2    1    4

    0 .    1    2    7

   -    0 .    0    5    3

   -    0 .    0    0    8

    0 .    1    9    8

   -    0 .    3    1    8

   -    0 .    1    7    9

    0 .    2    4    3

    0 .    5    1    1

    1 .    0    0    0

    (   m    )    F    D    I    P   r    i   m   a   r   y

    0 .    1    1    6

   -    0 .    1    4    2

    0 .    0    6    7

    0 .    1    0    5

    0 .    0    3    8

    0 .    0    0    0

    0 .    1    0    9

   -    0 .    2    7    9

   -    0 .    0    1    8

    0 .    1    0    1

    0 .    3    8    4

    0 .    7    0    6

    1 .    0    0    0

    (   n    )    F    D    I   s   e   c   o   n    d   a   r   y

    0 .    1    4    3

    0 .    0    6    2

    0 .    2    1    8   -

    0 .    0    3    7

   -    0 .    0    4    2

   -    0 .    3    6    7

    0 .    0    5    1

   -    0 .    1    8    9

    0 .    1    1    9

    0 .    2    2    7

    0 .    2    1    7

    0 .    4    5    5

    0 .    2    8    9

    1 .    0    0    0

    (   o    )    D   o   m   e   s    t    i   c    I   n   v .

    0 .    1    8    0

   -    0 .    0    9    5

   -    0 .    1    3    5

    0 .    2    2    6

   -    0 .    0    1    1

    0 .    3    0    3

    0 .    0    8    0

   -    0 .    3    0    5

   -    0 .    0    2    5

    0 .    3    3    1

    0 .    2    0    2

    0 .    0    1    9

    0 .    0    2    8

   -

    0 .    0    8    8

    1 .    0    0    0

    M   e   a   n

    0 .    7    0

    0 .    0    2

    2    0    9    1 .    3    9

    4    3 .    4    5

    0 .    9    0

    3    3 .    7    7

    4 .    6    1

    5    8 .    3    1

    4    7 .    9    1

    0 .    1    7

    2    5 .    1    0

    0 .    1    1

    0 .    0    5

    0 .    0    3

    2    1 .    8    0

    M   e    d    i   a   n

    0 .    7    4

    0 .    0    1

    1    6    8    7 .    6    9

    4    0 .    7    5

    0 .    7    1

    3    3 .    2    0

    7 .    0    0

    4    6 .    0    0

    5    1 .    4    0

    0 .    1    7

    2    4 .    5    4

    0 .    0    7

    0 .    0    1

    0 .    0    3

    2    1 .    5    7

    S    D

    0 .    1    3

    0 .    0    2

    1    7    1    1 .    5    4

    2    5 .    3    9

    0 .    8    2

    8 .    0    0

    5 .    6    9

    3    7 .    5    7

    2    3 .    1    8

    0 .    0    2

    1    4 .    2    8

    0 .    1    2

    0 .    1    0

    0 .    0    3

    6 .    4    4

     N

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

    6    6

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Appendix 3

See Table 4.

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 N    66 932 66 1,426

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