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Caribbean Development Report, Volume 2
THE IMPACT OF CLIMATE CHANGE ON THE
TOURISM SECTOR IN SELECTED CARIBBEAN
COUNTRIES
Sandra Sookram
Abstract
Tourism is an important source of economic growth in the Caribbean and one of the most important
industrial sectors for some countries in the subregion. The purpose of this study is to estimate the
economic impact of climate change on the tourism sector in nine countries in the Caribbean Basin:
Aruba, Barbados, the Dominican Republic, Guyana, Jamaica, Montserrat, the Netherlands Antilles,
Saint Lucia and Trinidad and Tobago.
A typical tourism demand function, with tourist arrivals as the dependent variable, is used in
the analysis. The independent variables employed in the study are: destination country Gross
Domestic Product per capita (GDPPC) and consumer price index (CPI), source country Gross
Domestic Product (GDP), and oil prices, to proxy transportation costs between source and destination
countries. Two climate variables, temperature and precipitation, are used to augment the tourism
demand function. In the first stage of the analysis, the baseline is established using annual data for the
period 1989-2007.With respect to the climatic variables, the results at this initial stage of the study
indicate a negative effect of both the temperature and precipitation variables on tourist arrivals.
In the second stage of the analysis, the cost of climate change to the tourism sector is forecast
to the end of the century under three weather scenarios: A2, B2 and Business as Usual (BAU) (the
mid-point of the A2 and B2 scenarios). The estimation is undertaken for the impact of changes in
temperature and precipitation, sea level rise, extreme events (for example, the frequency and intensity
of hurricanes) and the destruction of ecosystems. The total estimated costs to tourism for the
Caribbean subregion under the three weather scenarios at the end of the century are all very high,
ranging from US$ 43.9 billion under the B2 scenario to US$ 46.3 billion under the BAU scenario.
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INTRODUCTION
Tourism130 is an important source of economic growth in the Caribbean. Indeed, it is one of the most
important industrial sectors in some countries in the subregion.131 Furthermore, its significance to any
one country increases in accordance with the quantity of tourism-related services associated with the
sector. The World Tourism Organisation (WTO) has declared that international tourism figures
increased by 2% during 2007, and has predicted that the number of international tourists will reach the
1.6 billion mark by the year 2020.132
What needs to be considered is how many of those potential tourists would visit the
Caribbean, and what impact climate change would have on that figure. There is no doubt that climate
is an important influence on the tourism sector. Numerous studies that analyse climate data indicate
that our climate is changing; for example, the average global temperature has increased by
approximately 0.6⁰C during the twentieth century. More than that, the rate of increase in air
temperature in the Caribbean subregion has exceeded the international mean (Mimura and others,
2007).
This study attempts to determine the possible impact of climate change on nine Caribbean
countries,133 using tourist arrivals, climate (represented by temperature and precipitation) and other
economic data for the 1989-2007 period. A key objective is to estimate the economic impact of
climate change on tourism income under two climate change scenarios (A2 and B2). The main
objective of this study is to suggest adaptation and mitigation strategies for the tourism sectors of
these nine countries.
According to Braun and others (1999), environmental factors are key components when
tourists choose a holiday destination. There is convincing evidence to show that the world’s climate
will continue to change during this century. Future variations in temperature and other aspects
associated with climate change will have differing effects on different regions worldwide. Table 1
shows the major impacts of climate change and their implications for tourism destinations. It is highly
likely that most of these direct effects of climate change, and their subsequent indirect effects, would
have an impact on the Caribbean subregion.
There are many studies on the demand for tourism and modelling tourism demand. This
study uses a typical tourism model and expands the model to include two climatic variables:
temperature and precipitation. The model is then used to forecast the likely impact of changes in
temperature and precipitation on the countries in the study.
This study is organized as follows: section 2 reviews the literature related to tourism and
climate change; section 3 outlines the methodology followed in the study; section 4 presents the
results; section 5 examines the Special Report on Emission Scenarios (SRES) emission scenarios,
with particular reference to A2 and B2 and BAU scenarios which are used to forecast the impacts of
climate change for nine tourist destinations in the Caribbean; section 6 examines some of the
mitigation and adaptation strategies; and section 7 concludes.
130
According to the World Tourism Organization, tourism can be defined as “the activities of persons
travelling to and staying in places outside their usual environment for not more than one consecutive year.”
131
As an example, income from tourism in Saint Lucia ranged from 36% to 50% of Gross National
Income during the period 1989-2007.
132
See WTO website: http://www.unwto.org/index.php
133
The countries are: Aruba, Barbados, the Dominican Republic, Guyana, Jamaica, Montserrat, the
Netherlands Antilles, Saint Lucia, and Trinidad and Tobago.
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TABLE 1
MAIN IMPACTS OF CLIMATE CHANGE AND THEIR IMPLICATIONS FOR TOURISM
Impact
Warmer temperatures
Implications for Tourism
Altered seasonality, heat stress for tourists, cooling costs, changes in: plantwildlife-insect populations and distribution range, infectious disease ranges
Decreasing snow cover and
Lack of snow in winter sport destinations, increased snow-making costs,
shrinking glaciers
shorter winter sports seasons, aesthetics of landscape reduced
Increasing frequency and
Risk for tourism facilities, increased insurance costs/loss of insurability,
intensity of extreme storms
business interruption costs
Reduced precipitation and
Water shortages, competition over water between tourism and other sectors,
increased evaporation in some
desertification, increased wildfires threatening infrastructure and affecting
regions
demand
Increased frequency of heavy
Flooding damage to historic architectural and cultural assets, damage to
precipitation in some regions
tourism infrastructure, altered seasonality (beaches, biodiversity, river flow)
Sea level rise
Coastal erosion, loss of beach area, higher costs to protect and maintain
waterfronts and sea defences
Sea surface temperature rise
Increased coral bleaching and marine resource and aesthetic degradation in
dive and snorkel destinations
Changes in terrestrial and
Loss of natural attractions and species from destinations, higher risk of
marine biodiversity
diseases in tropical-subtropical countries
More frequent and larger forest
Loss of natural attractions, increase of flooding risk, damage to tourism
fires
infrastructure
Soil changes (such as moisture
Loss of archaeological assets and other natural resources, with impacts on
levels, erosion and acidity)
destination attractions.
Source: WTO-UNEP-WMO (2008) Climate Change and Tourism: Responding to Global Challenges
REVIEW OF THE LITERATURE
According to Scott and others (2004), the interrelationship between the weather and tourism has
featured in studies dating from the 1930s. In 1936, for example, Selke wrote on the geographic aspects
of the German tourist trade. So far, these studies have been few, and only in recent times has the
literature on tourism started to increase. These tourism studies, as stated by Hamilton and Tol (2007),
focused mainly on economic factors and did not include climate variables in the modelling process.
The studies had short time-horizons, and climate was taken to be a constant variable. However, there
is much evidence to show that climate will change in the long run, and that this change is being
hastened by human activities.
More recently, researchers have begun to include climatic variables and, in some cases, a
tourism climatic index. One of the first studies on climate change and tourism demand employed
temperature to estimate the effect of forecasted changes in temperature on the ski industry in
Switzerland (Koenig and Abegg, 1997). The study revealed that, under the present conditions, with
prevailing temperature and a snow line of 1,200 m,134 there was an 85 % chance that there would be
snow to keep the industry functioning. However, if temperatures were to increase by 2⁰C, then only
65% of all Swiss ski areas would be snow reliable. This would clearly have serious implications for
the growth of that sector of the industry.
The increasing volume of literature on the impact of climate on tourism demand is due to the
recognition that a more precise modelling of tourism demand must include weather and climate, since
they are significant influences on the tourism industry. The climatic factors identified as having the
most impact on tourism are temperature, sunshine, radiation, precipitation, wind, humidity and fog
(Stern, 2006; Hamilton and Lau, 2004). These factors are significant both to the tourist’s assessment
of his or her health and well-being, and to the tourism industry. It is therefore essential that these
elements be measured and evaluated, since they form an important resource for tourism.
134
In this study it was mentioned that Pfund (1993) illustrated that a minimum altitude of 1,200 m. (the
line of snow reliability) is necessary for the ski industry to be a feasible undertaking.
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The literature associating climate with tourism implies that changes in climate are likely to
affect both the length of the season for tourists and the expected environment. The literature has
shown that temperature could potentially have positive implications for the length of the season and
for the environment, while other studies have found results to indicate that it has negative implications
for tourism. Lise and Tol (2002), using cross-section data, undertook a cross-section analysis on
tourists originating in Organisation for Economic Cooperation and Development (OECD) countries,
and found that the optimal temperature for their destination countries ranged from 21⁰C to 24⁰C. The
implication of this finding is that the predicted increasing global temperatures in certain regions of the
world would have devastating effects on the tourist industries of those countries. Hamilton and others
(2005) used a simulation model to investigate the effects of climate change on international tourism
using the A1B scenario.135 They found that international tourism is expected to increase in the coming
decades, but may become sluggish later on in the century.
Another study, Berrittella and others (2006), used a computable general equilibrium model to
measure the potential effects of climate change. They employed two pathways to capture the impact
of climate change, namely, modifications in the composition of final consumption, and international
income transfers. The rationale was that spending by visitors has an impact on consumption and
income transfers in the domestic economy. The Berrittella and others (2006) study predicted that, at
the international level, changes in climate would eventually lead to a loss in welfare, and that that loss
would be disproportionately spread across the various regions of the world.
Temperature is considered to be the most important climate variable in the analysis of tourism
demand because, outside a certain range, it affects comfort. There is evidence to show that other
weather parameters are also important, for example, rain, wind and hours of sunshine (Scott and
McBoyle, 2006). If any of these parameters is to be included in the analysis of tourism flows, it must
be included as a determinant or in an index. Many studies include both temperature and precipitation
to examine the impact of climate on tourism demand (for example, see Scott and McBoyle, 2006).
There have been very few studies on the impact of climate change on tourism demand in the
Caribbean. One noteworthy microanalysis by Uyarra and others (2005) examines the significance of
environmental characteristics in influencing the choices made by tourists. The study used a selfadministered questionnaire on tourists visiting Bonaire and Barbados – 316 from Bonaire and 338
from Barbados. The study established that warm temperatures, clear waters and low health risks were
the main environmental attributes important to tourists visiting the islands. The study found that
visitors to Bonaire placed additional importance on marine wild life attributes while tourists going to
Barbados had a preference for certain beach characteristics. Uyarra and others (2005) examined the
impact of climate change by asking respondents about the likelihood of their returning to these islands
in the event of coral bleaching and sea level rise. They found that more than 80% of the visitors to
Bonaire and Barbados would be expected not to return to the islands in the event of these occurrences.
Mather and others (2005) examined the attraction of the Caribbean as a tourist destination for
travellers from North America. This study established that the Caribbean subregion is likely to be less
attractive to tourists due to factors such as increased temperatures, beach erosion, deterioration of reef
quality and greater health risks.
The climate change variables being used in this study (temperature and precipitation) are
considered to be significant determinants of tourism in the Caribbean for important reasons. Trenberth
and others (2007) have highlighted the fact that temperatures in the Caribbean subregion have been
warming at a rate ranging from 0.0⁰C to 0.5⁰C per decade for the period 1971-2000. In a related study,
Peterson and others (2002) have reported that in the Caribbean, the percentage of days with cold
temperatures has decreased since the 1950s, while the percentage of days with very warm maximum
135
The A1 emission scenario is outlined briefly in table 8. The A1B scenario is a subset of the A1
scenarios and emphasizes the technological element of the A1 scenarios; in particular A1B incorporates a
balanced weighting on all energy sources.
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or minimum temperatures has increased significantly. In relation to precipitation, it was found that
the number of heavy rainfall occurrences has been on the increase (Trenberth and others, 2007).
MODELLING TOURISM DEMAND IN THE CARIBBEAN
The tourism demand function: a review of the literature
The literature on the demand for tourism indicates that tourist flows between the destination
and source countries can be explained using a demand function. A review of the literature shows
that, in order to measure tourism flows, the majority of tourism demand studies use either the number
of arrivals to the destination country, or the amount of expenditure by tourists. While some
researchers suggest that the dependent variable in the tourism demand equation should be tourist
expenditure, Crouch and Shaw (1992) demonstrate that approximately 70% of the studies that
estimate tourism demand functions have employed tourist arrivals as the dependent variable. In this
study, the number of tourist arrivals has been used as the dependent variable. The literature on
tourism demand suggests that a number of explanatory variables can be used to investigate tourism
demand. The independent variables used in this study are as follows: Gross Domestic Product per
capita (GDPPC) in the destination country (in US$ million), Gross Domestic Product (GDP) in the
source country (in constant 1990 United States dollars), the consumer price index (CPI) in the
destination country, transportation costs (in United States dollars), temperature in degrees Celsius (⁰C)
and precipitation (millimetres).
Tourists prefer to visit a country with a high per capita income, since it translates into a higher
standard of accommodation and better tourist facilities; they also prefer visiting countries where the
poverty level is low.
In tourism demand functions, income in the source country is included as a key explanatory
variable. Since travel is expensive and considered a luxury good, it is anticipated that high-income
countries would generate a higher number of travellers. Although per capita income is used in some
studies, a more general income variable (GDP) is employed in this study, since tourist arrivals include
both business and holiday visitors.
Many tourism demand studies employ the consumer price index (CPI) of the destination
country to reflect the relative prices of foreign goods and services that tourists purchase in the
destination country. These relative prices are the costs of goods and services that tourists would pay
for items such as accommodation, food, entertainment, and local transportation.
Transportation costs, usually measured by the cost of a return airline ticket between the
source country and the destination country, have been used in many tourism demand studies. Other
studies have used various proxies for the transportation cost variable, such as the cost of gasoline for a
return flight between the source country and major destination countries. In this study, oil prices are
used to proxy travel costs, due to the unavailability of travel cost data over the sample period. It is
expected that these two variables would be highly correlated.
A priori, both income variables (GDPPC in the destination country and GDP in the source
country) are expected to be positively associated with tourism demand. It is anticipated that the CPI
variable, oil prices and the two climate variables would have a negative relationship with tourism
demand.
Data
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Several sources were used to collect the data used in the study. Information on tourist
arrivals was obtained from the Caribbean Tourism Organization (Sean Smith, Statistical Specialist,
pers. comm). The income variables and the CPI were collected from the International Financial
Statistics website (http://www.imfstatistics.org/imf/).
Oil prices were obtained from the
InflationData.com website (http://www.inflationdata.com) and the two climate variables
(temperature and precipitation) were obtained from the Department of Geography Center for Climatic
Research of the University of Delaware.
Methodology and empirical model
This section outlines the economic framework and methodology used in the paper. Several
statistical techniques have been employed to estimate the demand for tourism and to forecast such
demand (see Lim, (1999) for a comprehensive review of the various techniques used). Similar to
Johnson and Ashworth (1990), Song and Witt (2000) and Bigano and others (2006), a tourism demand
model is used to determine the variables that affect tourism demand in the Caribbean.
Where, TAit is the total tourist arrivals from origin country i in period t
is Gross Domestic Product for origin countries
PCit is the per capita income in the destination country
Cpiit is the consumer price index in the destination country
opt is the price of oil
tt is the temperature
pt is the precipitation
Data were collected from nine Caribbean countries on tourist arrivals, GDP per capita, GDP
of the source countries (in this case most of the tourists came from either the United States of America
or the United Kingdom), the CPI, oil prices, temperature and precipitation. The data can be
categorized as panel data since the same information was collected for the nine countries across the
period 1989 to 2007. The panel can be defined as strongly balanced.136 Panel data permits the
estimation of a richer set of models than those that employ only time, since advantage can be taken of
both cross-sectional and temporal variations in the data. However, the estimations also become more
complicated, since there is now more heterogeneity in the data. According to Greene (2008, p.183)
“… the crucial distinction between fixed and random effects is whether the unobserved individual
effect embodies elements that are correlated with the regressors in the model, not whether these
effects are stochastic or not.”
The Hausman test was used to determine whether or not the model should be one that takes
into consideration fixed effects or random effects of the data. The results indicate that the random
effects model should be employed, in other words, an insignificant p-value was obtained (p-value >
0.05). The random effects model is employed, which uses a weighted average of the between- and
within- variation in the data, and can accommodate within-unit serial correlation.
The random effects panel data model of tourism demand (log-log specification) employed in
this study is assumed to take the following form:
136 A strongly balanced panel dataset is one in which each panel has the same number of observations and
the observations for different panels are all made at the same time. (Adapted from definition at:
http://www.stata.com/help.cgi?xt_glossary#strongly_balanced).
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where,
Caribbean Development Report, Volume 2
is tourist arrivals in the nine destination Caribbean countries (i = country; t =
time)
is the coefficient for the six independent variables
is per capita income for the destination country
is Gross Domestic Product for origin countries
is the Consumer Price Index for the destination countries
is the price of oil (proxy for travel cost)
is the temperature in the destination countries
is the precipitation in the destination countries
is the intercept
is the combined time series and cross-section error components
is the cross-section, or country-specific error component
The double log model, reported by Lim (1999), is one of the more popular model
specifications. The variables (in level) used in the model are summarized in table 2. The data
indicate that, in the present sample of nine Caribbean countries, the average annual number of tourist
arrivals over the period 1989 to 2007 was 166,600 persons. The average price of a barrel of oil was
US$ 33.27. The temperature variable is significant: the annual mean temperature experienced is 26⁰C.
The study by Lise and Tol (2002) found that the optimal temperature for comfort ranged between
21⁰C and 24⁰C, with the optimal temperature for tourists from the countries of major interest to the
Caribbean (the United States of America or the United Kingdom) being approximately 23⁰C. The
average temperature of the Caribbean is a solid 3⁰C higher than the optimal temperature for tourism of
its major source countries.
TABLE 2
SUMMARY OF CORE VARIABLES USED IN THE REGRESSION ANALYSIS
Variable
Tourist arrivals
Per capita income in
destination country
GDP in source country
Observations
171
Mean
1666
Std. Dev.
3751
Minimum
7
Maximum
21285
171
7133.637
5878.714
477
25253
171
5,298,579
3,074,121
982,323
9,393,837
CPI in destination country
170
95.68012
36.46744
8.97
263.11
Oil prices
171
33.27053
13.17715
15.52
64.93
Annual mean temperature
161
25.99453
1.407047
21.89
28.22
Annual mean precipitation
162
141.9133
84.14548
24.7
493.45
The results of the model are outlined and analysed in the following section.
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RESULTS
The results for the double-log random effects model specification are provided in table 3. Robust
standard errors were specified to control for heteroskedasticity. The results of the F-test confirm that
all the coefficients in the model are different from zero.
TABLE 3
ESTIMATION RESULTS FOR RANDOM EFFECTS MODEL
Variables
Per capita income in destination country
Gross domestic product in origin country
Consumer price index in destination country
Oil prices
Annual mean temperature
Annual mean precipitation
Constant
Wald test Chi squared value (probability in
parentheses)
Observations
Robust t-statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Tourist Arrivals
0.18718* (1.831)
-0.62628*** (2.688)
0.31431* (1.652)
-0.53791 (1.557)
-6.61282*** (4.153)
-0.54254*** (4.446)
38.78618*** (8.683)
108.33 (0.0000)
160
An examination of the results indicates that the coefficient estimates were generally in
agreement with expectations and, of importance, the results obtained for the climate variables were
highly significant.
It was found, however, that as the Gross Domestic Product in the origin countries decreases,
tourist arrivals increased to the Caribbean subregion. A straightforward explanation for this could be
that as income decreases in the origin countries (in this case, the United States and the United
Kingdom), it becomes more affordable to visit the Caribbean rather than other, more expensive
alternatives, for example Europe or Asia. Table 4 shows the top 10 tourist destinations for 2007. As a
matter of note, the Caribbean is not even mentioned in the top 50 tourist destinations.
TABLE 4
TOP TEN TOURIST DESTINATIONS FOR 2007
Country
Tourist Arrivals
(millions)
81.9
59.2
56.0
54.7
43.7
30.7
24.4
23.1
22.2
21.4
France
Spain
United States
China
Italy
United Kingdom
Germany
Ukraine
Turkey
Mexico
Source: UNWTO World Tourism Barometer 4(2), 2008
It was also found that, as the CPI in the destination country increases, so do tourist arrivals.
Again, this could be because the Caribbean, even with increasing prices, provides a still cheaper
alternative to other tourist destinations.
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The expected results were received for the two climate variables: essentially, as temperatures
increase, tourist arrivals decrease. Similarly, as rainfall increases, tourist arrivals decrease. This has
grim implications for tourist arrivals to the Caribbean due the predicted increases in temperature under
the various weather scenarios put forward by the Intergovernmental Panel on Climate Change (IPCC)
in their Special Report on Emissions Scenarios (SRES).
The results of the model indicate that
precipitation is expected to affect tourism to a much smaller degree than temperature (the model
yielded a temperature coefficient of -6.61, whereas the precipitation coefficient was -0.54254).
Furthermore, according to the IPCC predictions, precipitation is expected to decline in certain
Caribbean countries. The literature on tourism demand has pointed to the fact that tourists prefer dry
holiday locations rather than wet ones (Lise and Tole, 2002), therefore, according to the results of the
model, as the climate changes in some countries and less precipitation is observed, the impact on
tourism would be positive.
FORECASTING THE COST OF CLIMATE CHANGE
Forecasted climate change impacts at nine tourist destinations in the Caribbean
The estimated tourism demand model satisfied demand theory, passed the various hypothesis
tests, and reported significant results. The next stage of the analysis requires that the demand model
be used to make forecasts for the rest of this century for the Caribbean countries under study.
The forecasted tourist arrivals data were used to obtain a cost figure until the year 2100,
using A2, B2 and Business as Usual (BAU)137 weather scenarios. The tourism industry is very
important for most of the countries under analysis and any decline in the industry would have a
negative effect on the GDP of those countries. There are many different emission scenarios and table
5 gives a brief explanation of four SRES storylines, which includes the two that are used in this study,
namely A2 and B2.
Table 5
SRES storylines used for calculating future greenhouse gas and other pollutant emissions
Storyline
A1
A2
B1
B2
Description
Very rapid economic growth; population peaks mid-century; social, cultural and
economic convergence among regions; market mechanisms dominate.
Subdivisions: A1F1 – reliance on fossil fuels; A1T – reliance on non-fossil fuels;
A1B - a balance across all fuel sources
Self reliance; preservation of local identities; continuously increasing population;
economic growth on regional scales
Clean and efficient technologies; reduction in material use; global solutions to economic,
social and environmental sustainability; improved equity; population peaks mid-century
Local solutions to sustainability; continuously increasing population at a lower rate than
in A2; less rapid technological change than in B1 and A1
Source: Table A.2, page 107 of the United Kingdom Climate Impacts Programme UKCIP02 climate scenarios
technical report
The A2 scenario envisages that by the year 2100 the population would have reached a figure
of 15 billion, with generally slow economic and technological development. It predicts slightly lower
greenhouse gas (GHG) emissions than other scenarios. The B2 scenario forecasts a slower population
growth of 10.4 billion by 2100, with a rapidly developing economy and greater stress on
environmental protection, so producing lower emissions and less future warming
137
The Business as Usual (BAU) weather scenario in this study is simply the average values, for
temperature and precipitation, of the A2 and B2 scenarios until the end of the century. There are no
forecasted data for the BAU weather scenario for the Caribbean subregion.
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Bueno and others (2008) undertook an estimation of the cost of climate change in the
Caribbean in the absence of action by these countries to counteract the effects of climate change.
They estimate both a low-impact scenario and a high-impact scenario for the years 2025, 2050, 2075
and 2100. The low-impact scenario is the optimistic scenario, where the world takes action in the near
future and where emissions are significantly reduced by mid-century, and continue to decrease by the
end of the century. The high-impact scenario is pessimistic in nature and one in which business-asusual (BAU) takes place, meaning that greenhouse gas emissions continue to increase drastically
throughout the twenty-first century. Table 6 shows an extract of the table presented in their study.138
The data in the table reveal that, under both the high- and low-impact scenarios, all of the Caribbean
countries have much to lose in the tourism industry.
TABLE 6
COST OF LOW-IMPACT AND HIGH- IMPACT SCENARIOS FOR TOURISM IN SELECTED
CARIBBEAN COUNTRIES
Country
GDP
Low-impact scenario
High-impact scenario
(US$
(US$ billion)
(US$ billion)
billion)
2025
2050
2075
2100
2025
2050
2075
2100
Aruba
2.35
0.02
0.04
0.06
0.08
0.10
0.20
0.30
0.40
Barbados
2.54
0.02
0.03
0.05
0.07
0.09
0.17
0.26
0.35
Dominican Republic
20.52
0.07
0.14
0.21
0.28
0.36
0.71
1.07
1.43
Jamaica
8.77
0.04
0.07
0.11
0.15
0.18
0.37
0.55
0.74
Montserrat
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
The Netherlands Antilles
2.70
0.02
0.04
0.06
0.07
0.09
0.18
0.28
0.37
Saint Lucia
0.70
0.01
0.01
0.02
0.02
0.03
0.05
0.08
0.11
Trinidad and Tobago
12.61
0.01
0.02
0.02
0.03
0.04
0.08
0.12
0.16
Source: Bueno and others (2008)
Using low- and high-impact climate scenarios,139 and examining the impact of rising
temperatures in the subregion (for 12 CARICOM countries), a study by Margaree Consultants
Limited (2002) suggests that, on an annual basis, for the low-impact scenario the Caribbean
stands to lose US$ 715 million in tourist expenditure, while for the high-impact scenario, losses
in tourism expenditure amount to US$ 1,430 million. In terms of the cost to tourist facilities due
to sea level rise,140 it was determined that, on an annual basis, it would cost US$ 9 million, and
US$ 80 million, to replace hotels due to sea level rise under the low- and high-impact weather
scenarios, respectively. An evaluation of the loss in tourism income due to the loss of beaches
and ecosystems was also carried out in the same study, by examining the fraction of beach area
lost in conjunction with the amount that tourists spend on enjoying the ‘sun, sea and sand.’ At an
annual rate, the loss would be US$ 550 million in the low-case scenario and US$ 2.4 billion in
the high-case scenario.
138
139
140
Guyana was not included in the Bueno and others (2008) study.
Figures for temperature were based on the Intergovernmental Panel on Climate Change (IPCC) Third
Assessment Report (2001) - an increase of 2°C for the low-impact scenario and an increase of 3.3°C for
the high-impact scenario.
According to the estimates by Margaree Consultants Limited (2002), the sea level is expected to rise
between 0.5 metres (low impact scenario) and 2.0 metres (high impact scenario) by the year 2100.
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Projections for extreme events
In this section of the paper, projections regarding extreme events are made using three
weather scenarios: A2, B2 and Business as Usual (BAU).
Methodology
A tourism demand model was used to estimate the costs of temperature and precipitation to
the tourism sector in the nine Caribbean countries. This model and results of the model as relate to the
effects of temperature and precipitation were outlined in section 4 of this paper. Costing of the
climate change effects required forecasting of the variables in the model: per capita income and CPI
of the Caribbean countries included in the study, GDP of the source or origin countries (primarily the
United States and United Kingdom), oil prices, and the temperature and precipitation of the nine
countries. Forecasts up to the year 2100 of these non-climatic variables are not available for any of
the nine countries. Therefore, forecasting techniques were employed to estimate these variables.
With respect to the climate variables, forecasts for both variables were received from the Institute of
Meteorology in Cuba (INSMET). The predictions from INSMET were obtained from the European
Centre Hamburg Model (ECHAM), an atmospheric general circulation model developed at the Max
Planck Institute for Meteorology. The annual costs of climate change impacts to 2100 are estimated in
United States dollars, using 2007 as the comparator and base year. This method is frequently used in
the literature (for example, see Haites, 2002) and is considered standard.
However, apart from temperature and precipitation and its effects on the tourism sector, there
are other climate variables that have the potential to affect the tourism sector negatively, in particular,
increases in the occurrence of extreme events, sea level rise, and extreme destruction of ecosystems
due to ocean acidification. Due to lack of data, the methodology used for this part of the study was
adopted from Toba (2009). These costs do not take into consideration the indirect costs (for example,
the loss of employment) associated with changes in temperature and precipitation, extreme events, sea
level rise and ecosystem destruction. It must be noted that most of the results obtained from existing
research on economic effects of climate change in the Caribbean are not directly comparable to each
other and to this study, since many variations exist with respect to the number of countries used in the
studies, the sectors examined, and the data and methodologies employed.
Results
Table 7 shows the total costs of climate change due to temperature and precipitation changes
for the nine countries under the three climate change weather scenarios. The aggregated costing for
the Caribbean subregion is shown at four different points in the century – in the years 2025, 2050,
2075 and 2100.
TABLE 7
AGGREGATED COSTING FOR A2, B2 AND BAU SCENARIOS FOR THE CARIBBEAN
SUBREGION: TEMPERATURE AND PRECIPITATION
(Costs in $US million - 2007 dollars)
Year
A2
B2
BAU
2025
26.4
29.1
27.8
2050
111.3
116.3
115.5
2075
203.0
209.1
209.7
2100
283.9
292.7
291.8
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The table shows that, under all three scenarios, the countries under study are poised to lose
extensively from the predicted changes in temperature and precipitation under the three weather
scenarios. As shown in table 7, in total, by the year 2100, the costs to the nine countries due to
changes in temperature and precipitation are estimated to be US$ 283.9 million under the A2 scenario,
US$ 292.7 million under the B2 scenario and US$ 291.8 million under the BAU scenario.
Table 8 provides costing under the three weather scenarios for extreme events. This includes
losses due to increases in the frequency and intensity of hurricanes and the accompanying windstorms,
floods and landslides. Similar to Toba (2009), and as reported by Haites and others (2002), hurricanes
in the Caribbean are expected to increase by 27% per annum. Haites and others (2002) used the
example of 1995 hurricanes (Hurricane Luis and Hurricane Marilyn) to determine the cost in terms of
income loss from the tourism sector, and found that tourism expenditures decreased by about 17%.
Therefore, with a 27% increase in hurricanes due to climate change and an estimated 17% decrease in
tourist expenditures when a hurricane strikes, it is estimated that tourist expenditures are expected to
decrease by 21.6% due to increases in extreme events. The costs to the countries under consideration
with regard to extreme events are shown in table 8.
TABLE 8
AGGREGATED COSTING FOR A2, B2 AND BAU SCENARIOS: EXTREME EVENTS
(Costs in $US million - 2007 dollars)
Year
A2
B2
BAU
2025
9,896.6
10,057.4
10,155.9
2050
15,503.5
14,950.9
15,982.7
2075
18,394.2
17,797.4
19,181.4
2100
18,466.9
17,870.4
19,255.2
The table indicates that for extreme events occurring in the Caribbean subregion, the total
costs under the scenarios for extreme events, aggregated to 2100, are as follows: A2: US$ 18.5
billion, B2: US$ 17.9 billion and BAU: US$ 19.3 billion.
TABLE 9
AGGREGATED COSTING FOR A2, B2 AND BAU SCENARIOS: SEA -LEVEL RISE AND
DESTRUCTION OF ECOSYSTEMS
(Costs in $US million - 2007 dollars)
Year
A2
B2
BAU
2025
13,745.2
13,968.6
14,094.8
2050
21,532.7
21,654.6
22,185.8
2075
25,547.5
25,608.2
26,628.4
2100
25,648.5
25,709.5
26,731.0
Table 9 presents the loss to the tourism sector due to the predicted rise in sea level and the
destruction of ecosystems due to occurrences such as ocean acidification. Once more, similar to Toba
(2009), it is assumed that tourists spend about 30% of their total expenditure on activities related to
the sea. With rising sea levels and ecosystem destruction produced by climate change, it is assumed
that this amount would be lost due to non-participation in these activities. The costs calculated in
table 9 represent the losses that would occur when tourists refrain from sea-related activities. With
respect to rising sea levels and the destruction of ecosystems, again the Caribbean subregion is
affected negatively throughout the century, culminating by the end of the century in costs under the
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three weather scenarios as follows: A2: US$ 25.6 billion, B2: US$ 25.7 billion and BAU: US$ 26.7
billion.
A summation of all costs incurred by the tourism sector due to temperature and precipitation
change, extreme events and sea level rise and ecosystem destruction under the three weather scenarios
is shown in table 10. The figures in table 10 indicate that the tourism sector in the Caribbean countries
under examination stands to incur losses in the sums of US$ 44.4 billion (A2), US$ 43.9 billion (B2)
and US$ 46.3 billion (BAU) (2007 dollars) in total by the end of the century.
Table 10
Total costs incurred by the Caribbean subregion under A2, B2 and BAU scenarios
(Costs in $US million - 2007 dollars)
Year
A2
B2
BAU
2025
23,668.4
24,055.2
24,278.6
2050
37,147.7
36,721.8
38,283.9
2075
44,144.8
43,614.6
46,019.7
2100
44,399.5
43,872.6
46,278.7
ADAPTATION AND MITIGATION STRATEGIES
Adaptation Strategies
The UNWTO-UNEP-WMO (2008, p.81) defines adaptation as “….. an adjustment, in natural
or human systems in response to actual or expected climatic stimuli or their effects, which moderates
harm or exploits beneficial opportunities.” There is little doubt that the tourism sector will be unable
to adopt adaptation strategies to cope with changes in climate. UNWTO-UNEP-WMO (2008)
maintains that the tourism industry is dynamic and flexible enough to implement measures of an
adaptive capacity to deal with climate change. For instance, this is an industry that has had various
recent jolts (such as terrorism and severe acute respiratory syndrome (SARS)) and has shown an
ability to cope. However, the changing climate must be recognised as such and strategies must be
adopted before it is too late. Table 11 shows the projected changes in the temperature and
precipitation variables for the period 2010 to 2099.
Table 11
Projected increases in air temperature and changes in precipitation for small island regions
(Percentage)
Region
Forecast period
2010 to 2039
2040 to 2069
2070 to 2099
(a) Projected increases in air temperature
Mediterranean
0.60 to 2.19
0.81 to 3.85
1.20 to 7.07
Caribbean
0.48 to 1.06
0.79 to 2.45
0.94 to 4.18
Indian Ocean
0.51 to 0.98
0.84 to 2.10
1.05 to 3.77
Northern Pacific
0.49 to 1.13
0.81 to 2.48
1.00 to 4.17
Southern Pacific
0.45 to 0.82
0.80 to 1.79
0.99 to 3.11
(b) Projected changes in precipitation (percentage)
Mediterranean
-35.6 to 55.1
- 52.6 to 38.3
-61.0 to 6.2
Caribbean
-14.2 to 13.7
-36.3 to 34.2
-49.3 to 28.9
-5.4 to 6.0
-6.9 to 12.4
-9.8 to 14.7
Indian Ocean
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Northern Pacific
-6.3 to 9.1
-19.2 to 21.3
-2.7 to 25.8
Southern Pacific
-3.9 to 3.4
-8.23 to 6.7
-14.0 to 14.6
Source: Becken and Hay (2007), Tourism and Climate Change
TABLE 12
TOURISM STAKEHOLDERS AND CLIMATE ADAPTATION STRATEGIES
Type of
adaptation
Technical
Tourism operators/businesses
‐ Snow-making
‐ Slope contouring
Rainwater collection and
water recycling systems
‐ Cyclone-proof building
design and structure
‐
‐
‐
‐
‐
Water conservation plans
Low season closures
Product and market
diversification
Regional diversification
in business operations
Redirect clients away
from impacted
destinations
Tourism industry
associations
‐ Enable access to
early-warning
equipment (such
as radio)
operators.
‐ Develop websites
with information
on adaptation
measures.
‐
‐
‐
‐
Policy
‐
‐
Hurricane interruption
guarantees
Comply with regulations
(such as building codes)
‐
‐
Snow-condition
reports through
the media
Use of short-term
seasonal forecasts
for planning
marketing
activities.
Training
programmes on
climate change
adaptation.
Encourage
environmental
management with
firms (such as via
certification)
Coordinated
political lobbying
for GHG emission
reductions and
adaptation
mainstreaming.
Seek funding to
implement
adaptation
projects
Assess awareness
of businesses and
tourists, as well as
knowledge gaps.
Research
‐
Site location (for
example, north facing
slopes, higher elevations
for ski areas)
‐
Education
‐
Water conservation
education for employees
and guests
‐
Public education
campaign
Behavioural
‐
Real-time webcams of
snow conditions
GHG-emission offset
‐
GHG-emission
offset
programmes
‐
217
Governments and
communities
‐ Reservoirs and
desalination plants
‐ Fee structures for
water consumption
‐ Weather
forecasting and
early warning
systems
‐
‐
‐
‐
‐
‐
Impact
management plans
(for example, the
Coral Bleaching
Response Plan)
Convention/event
interruption
insurance
Business subsidies
(for example,
insurance or energy
costs)
Financial sector
(investors/insurances)
‐ Require advanced
building design or
material (fire
resistant)
standards for
insurance
‐ Provide
information
material to
customers
‐ Adjust insurance
premiums or not
renew insurance
policies
‐ Restrict lending
to high risk
business
operations
Coastal
management plans
and setback
requirements
Building design
standards (such as.
for hurricane force
winds).
Monitoring
programmes (such
as to predict
bleaching or
avalanche risk,
beach water
quality)
‐ Water conservation
campaigns
‐ Campaigns on the
dangers of UV
radiation
‐ Extreme event
recovery
marketing
‐
‐
Consideration of
climate change
in credit risk and
project finance
assessments
Extreme event
risk exposure
‐ Educate/inform
potential and
existing
customers
‐
Good practice inhouse.
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programmes
‐
Water
conservation
initiatives
Source: World Trade Organization (2008)
As demonstrated in table 12, different types of adaptation strategies are utilized by tourism
stakeholders ranging from the technological to the behavioural. These approaches are presently being
used to cope with changes in climate in the destination country. As explained by WTO, these
approaches are rarely used singly, but are usually combined to deal with the specific climate variation
being experienced.
TABLE 13
POSSIBLE ADAPTATION MEASURES FOR TOURISM IN SMALL ISLAND COUNTRIES
AND BARRIERS TO IMPLEMENTATION
Adaptation measures
Relevance to tourism
Mainstreaming adaptation in
planning
Currently adaptation is not
mainstreamed in tourism
planning
Currently such risks are not
reflected in tourism-related
regulations
Include climate risk in
tourism regulations, codes
Institutional strengthening
Shortfall
in
institutional
capacity to coordinate climate
responses across tourismrelated sectors
Education/awareness raising
Shade provision and crop
diversification
Need to motivate and
mobilise tourism staff and
also tourists
Additional shade increases
tourist comfort
Reduce tourism pressures on
coral
Reefs are a major tourist
attraction
Reduce tourism pressures on
other marine resources
Increased productivity of
marine resources increases
well-being
of
tourismdependent communities
Many valuable tourism assets
at growing risk from coastal
erosion
‘Soft’ coastal protection
Improved insurance cover
Desalination,
rainwater
catchments and storage
Drainage
systems
and
pumping
Enhanced design and siting
standards
Tourism
activity/product
diversification
Growing likelihood that
tourists and operators will
make insurance claims
Tourist resorts are major
consumers of fresh water
Important services for tourist
resorts and for tourismdependent communities
Many valuable tourism assets
at growing risk from climate
extremes
Need to reduce dependency
of tourism on ‘sun, sea and
sand’
218
Barriers to
implementation
Lack of information on
which to base policy
initiatives
Lack of information on
which
to
base
regulatory
strengthening
Lack of clarity as to the
institutional
strengthening required
to
improve
sustainability of tourism
Lack of education and
resources that support
behavioural change
Lack of awareness of
growing heat stress for
people and crops
Reducing
pressures
without
degrading
tourist experience
Unsustainable
harvesting practices and
lack of enforcement of
regulations and laws
Lack of credible options
that
have
been
demonstrated
and
accepted
Lack of access to
affordable insurance
Lack of information on
future
security
of
freshwater supplies
Wasteful practices; lack
of information to design
adequate systems
Lack of information
needed to strengthen
design
and
siting
standards.
Lack
of
credible
alternatives that have
been demonstrated and
accepted
Measures to remove barriers
Improve targeted information,
for
example,
climate-risk
profile for tourism
Improve information, such as
climate-risk profile for tourism
Assess options and implement
the most appropriate strategies
Undertake
education./awareness
programmes
Identify,
evaluate
and
implement measures to reduce
heat stress
Improve off-island tourism
waste management
Strengthen community-based
management
of
marine
resources, including land-based
issues
Demonstration of protection for
tourism assets and communities
Ensure insurance sector is
aware of actual risk levels and
adjust premiums
Provide and ensure utilization
of targeted information, based
on climate risk profile.
Provide and ensure utilization
of targeted information, based
on climate risk profile.
Provide and ensure utilization
of targeted information.
Identify
and
evaluate
alternative
activities
and
demonstrate their feasibility.
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Source: Becken and Hay (2007), Tourism and Climate Change
The literature on adaptation strategies shows a wide range of measures that Caribbean
countries could adopt. Adoption of such measures would depend on the different climate change
impacts due to factors such as increasing temperatures, changes in precipitation, increasing intensity
of hurricanes and other extreme events, and sea level rise. There is a range of climate change
adaptation strategies that Caribbean countries could utilize to tackle the varying effects of climate
change. Becken and Hay (2007) outline some possible adaptation measures, along with the barriers
to implementation in small island countries (see table 13).
The Stern Review (2006) has emphasized that it is more cost-effective to implement
techniques that are proactive rather than reactive, and to support no-regrets measures. In the event
that there is no major change in climate, the proactive, no-regrets strategies will still be valuable and
economical. As an example, the literature on climate change risk assessment of tourism operators
(Elsasser and Burki, 2002; Scott and others 2002; Becken, 2004) has revealed that they have minimal
knowledge of climate change and that there is a subsequent lack of long-term planning in the event of
future climate changes. This indicates that there is an urgent need to educate and ensure that tourism
policymakers, who formulate policies for both the private and public sectors, are aware that the
climate is changing and that the tourism industry has to adapt to the change or face decline.
An estimation of the cost of adaptation is a complex one which depends significantly on the
determinants of the adaptive capacity of countries in the Caribbean subregion. The IPCC (2001),
drawing from Smit and others (1999), categorized determinants of adaptive capacity, including issues
such as the availability of technological resources, the organization of essential institutional and
decision-making bodies, the stock of human and social capital, information management, and public
perception.
Mitigation Strategies
Rogner and others (2007) have asserted that adaptation and mitigation can complement each
other, act as substitutes for, or be independent of one another. A discussion of mitigation measures to
cope with climate change must of necessity include technological, economic and social changes and
substitutions that can be employed to attain a reduction in GHG emissions (UNWTO-UNEP-WMO
2008; Hall and Williams, 2008). The IPCC report has asserted that human activity has been a major
contributor to climate change, which may have started as early as the mid-1700s. Carbon dioxide
(CO2) emission is just one of many GHG emissions. However, CO2 emissions become important
when released in large quantities, as can happen due to human activity such as in the burning of solid
waste, wood and wood products, and fossil fuels such as oil, natural gas, and coal. Figure 1 shows the
per capita CO2 emissions (metric tons of carbon) for the nine Caribbean countries under study in this
paper. The data reveal that Aruba, the Netherlands Antilles and Trinidad and Tobago have the highest
per capita CO2 emissions among the countries under study.
A framework for mitigation strategies must outline the mitigation tools and techniques,
policies and measures which go along with the various climate change scenarios. In addition to the
mitigation process, the potential exists for approaches in the areas of transport and accommodation
that tour operators, consumers, and destination countries can take to cope with – and perhaps alter –
the path of climate change.
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FIGURE 1
PER CAPITA CO2 EMISSIONS (METRIC TONS OF CARBON) FOR SELECTED CARIBBEAN
COUNTRIES
(1950-2005)
Data Source: USAID Development Statistics for Latin America and the Caribbean
UNWTO-UNEP-WMO (2008) has outlined four key mitigation measures that can be used to
deal with GHG emissions from tourism:
(1)
Reducing energy use/focusing on energy conservation: by changing transport
behaviour (for example, using more public transport, shifting to rail and coach instead of car and
aircraft, choosing less distant destinations), and by changing management practices, for example by
introducing videoconferencing for business tourism.
(2)
Improving energy efficiency: the use of new and innovative technology to decrease
energy demand (usually by carrying out the same operation with a lower energy input).
(3)
Increasing the use of renewable or carbon neutral energy: substituting fossil fuels
with energy sources that are not finite and that cause lower emissions, such as biomass, and hydro-,
wind- and solar energy.
(4)
Sequestering CO2 through carbon sinks: CO2 can be stored in biomass (for example,
through afforestation and deforestation), in aquifers or oceans, and in geological sinks (such as
depleted gas fields). Indirectly, this option can have relevance to the tourism sector, considering that
most developing countries and small island developing States (SIDS) that rely on air transport for their
tourism-driven economies are biodiversity-rich areas with important biomass CO2 storage function.
Environmentally-oriented tourism can play a key role in the conservation of these natural areas.
An estimation of the costs associated with mitigative action (for example, abatement costs)
depends critically on the potential of the tourism sector to implement processes associated with
mitigation. Auton (2008) summarized four phases that the tourism sector can implement to mitigate
tourism sector impact on climate, as follows:
(1)
The elimination GHG emissions by circumventing activities that can be avoided
without significant change to the tourist experience.
(2)
The reduction of GHG emissions by focusing on energy efficiency.
(3)
The substitution of practices that accounts for large GHG emissions with practices
that have a lower carbon footprint.
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Caribbean Development Report, Volume 2
The offsetting of any remaining emissions so as to achieve full carbon neutrality.
Some of these mitigation actions can include measures such as:
•
use of fuel-efficient cars by tour operators, hotels and resorts
•
employing channels such as effective ventilation and appropriate roofing to reduce
temperature in buildings
•
new buildings designed with the conservation of energy in mind
•
the use of more efficient lighting and energy-efficient appliances (less electricity
use).
CONCLUSION
In this study, tourism demand was modelled for nine Caribbean countries. The model was then used
to predict the impact of climate change on the tourism sector under three weather scenarios (A2, B2
and BAU) until the end of the century. The results of this research provide proof that the tourism
sector of the Caribbean subregion would be affected profoundly by climate change. It is therefore
very important that Caribbean countries adapt and mitigate against impending climate change to
promote and sustain growth in the tourism sector. To undertake this task, governments in the
subregion must come together to formulate policies which would ensure that the sector remains
sustainable.
The estimation of costs undertaken in this study would have benefited greatly from countryspecific data for extreme events and sea level rise due to climate change. Until such data is available,
costing figures from existing studies would have to be used to ensure that climate variables are taken
into consideration and included in any study that examines the impact of climate change on the
tourism sector in the subregion.
It is essential that Governments and policymakers become involved in the process, perhaps at
a very early stage, since climate change is an environmental, and ultimately a developmental problem.
One clear advantage of participation by these stakeholders is the recognition and acknowledgement
that for researchers to generate more accurate estimation of costs, data must be made available to these
practitioners in the field.
It is necessary that further work in this area involve a thorough investigation of adaptation
and mitigation strategies and the costs of implementing of such strategies in the Caribbean subregion.
With the formulation of mitigation and adaptation strategies and the appropriate policies in place, the
tourism sector can play a key role in dealing with climate change and encouraging sustainable growth
in the sector.
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Trenberth, K.E. and others (2007), “Observations: surface and atmospheric climate change”,
Climate Change 2007: The Physical Science Basis. Contribution of Working Group I
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Is the payoff to overeducation smaller for
Caribbean immigrants? Evidence from
hierarchical models in the United States labour
market
Dillon Alleyne
ABSTRACT
This paper examines the overeducation/undereducation and required (OUR) returns to education
hypothesis among Caribbean immigrants in the labour market of the United States of America using
the IPUMS 5% sample.140 The results show that Caribbean immigrants in the United States receive
lower returns than the native born despite being in the United States for some time. The methodology
employed is somewhat different to the traditional approach, as a hierarchical model is used to account
for the nesting of individuals within occupation, or for the presence of fixed effects due to differences
in occupation.
The results suggest that overeducation, though common to both the native born and to
Caribbean immigrants, is rewarded less among immigrants and represents an underutilization of
resources. It was also observed that Caribbean immigrants have higher levels of undereducation
relative to the native born.
The paper suggests ways in which immigration policies could be crafted to improve the return
to education of immigrants who bring considerable pre-immigration experience to the labour market,
to create win-win situations for both sending and receiving countries. With respect to sending
countries, it is argued that improved returns to overeducation add to immigrant wealth which allows
those who wish to return to the Caribbean, often with improved skills and expertise, to do so much
140
I thank Robin Kraft at the Centre for Global Development, Washington DC, for invaluable research
assistance. I also wish to thank colleagues in the Department of Economics, University of the West Indies,
Mona, Jamaica, Karoline Schmidt of the ECLAC Subregional Headquarters, Port of Spain and an
anonymous referee for helpful comments.
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earlier. Secondly, this translates to a possible increased flow of remittances and other resources to the
Caribbean. For receiving countries, improved returns to immigrant overeducation reduce the
misallocation of resources at the level of the labour market.
INTRODUCTION
There has been significant movement of labour globally despite rigidities imposed through
immigration laws and regulations, as workers move to host countries in anticipation of improved
economic opportunities. In the case of the Caribbean, there has been significant emigration since the
1970s, especially to the United States of America. Official statistics show that there were some 1.2
million immigrants to the United States from the Caribbean by 1980, 1.9 million by 1990, and 2.95
million by 2000.141 The Caribbean- born accounted for almost 10% of the 31.1 million foreign-born
population by 2000, and Cubans accounted for 29% of the overall Caribbean-born population in 2000,
followed by the Dominican Republic accounting for 23.3 %, Jamaica for 18.8%, and Haiti for 14.2%.
The Caribbean diaspora in the United States of America is varied, with migrants coming from the
English-, Spanish-, Dutch- and French-speaking Caribbean, and with major concentrations in Florida,
New York, New Jersey, Massachusetts and California. Despite a relatively long period of migration,
with well-established networks, there is anecdotal evidence of overeducation among Caribbean
migrants in the United States labour market.
Overeducation occurs when employed individuals have more education than is required for
the job. This is a form of underemployment or labour underutilization, which imposes costs both on
the individual and on the economy. In an environment in which there is increasing competition for
skilled labour (Alleyne, 2008), barriers to the full utilization of skills of migrants represent a waste of
resources and a limitation on the ability of migrants to reach their goals quickly and efficiently.
This paper investigates the extent of overeducation and undereducation among Caribbean
immigrants to the United States relative to native workers. It also points to the factors which explain
such a phenomenon, and seeks to identify policy initiatives that might be pursued based on the
assumption that overeducation is a major problem for immigrants.142 While some work has already
been done by Chiswick and Miller (2005) in examining overeducation among Caribbean migrants, the
focus has been on a broader set of countries which incorporated the Caribbean.
This paper employs the large Integrated Public Use Microdata Series data set (IPUMS 5 %
USA) to examine this issue, and distinguishes between returns to the native born, the Caribbean-born
residing in the United States for more than five years, and the Caribbean-born residing in the United
States for five years or less. This paper is organized as follows: Section II briefly reviews the relevant
literature. Section III discusses various approaches to measuring overeducation. Section IV examines
the modelling approaches to the OUR hypothesis versus the standard returns to education model.
Section V examines the actual model used and relevant descriptive statistics. Section VI discusses the
results from the hierarchical models used and section VII concludes with some policy
recommendations.
141
These are the only official statistics. See Campbell Gibson and Kay Jung (2006).
It is also recognized that overeducation is also a feature of the internal labour market of many
industrialized countries. For example, in the United Kingdom, some studies have found the incidence of
overeducation to be about 30%, while in the United States of America, it is about 45% (Groot, W and H.
Maassen van den Brink, 2000). In the case of Australia, the estimates of overeducation were between 10%
and 30% (Green and others, 2005; Linsey, 2005).
142
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REVIEW OF THE LITERATURE
There is no general theory of overeducation in the literature but there are a number of explanations,
each drawing on the theory of labour market behaviour (Hartog, 2000). An important aspect of this
problem is whether overeducation is a temporary or a permanent feature of the labour market, whether
it is due to labour market inefficiency, or discrimination, and how it affects individual earnings and
productivity. In this section, various explanations for overeducation, among them the human capital
theory, the career mobility approach, the job competition approach and the theory of discrimination,
are discussed briefly.
THE HUMAN CAPITAL THEORY
The explanations build on the basic human capital theory of Becker (1964), Becker and Cheswick
(1966) and the particular context in which the phenomenon is observed. The human capital model
approach to investment in education assumes that some optimal investment is made with the
expectation of positive returns to that education. As a result, earnings increase with the level of
education, as the individual is willing to forego earnings and incur education investment costs in the
short run, in return for increased future benefits. The model implicitly assumes that all education
acquired by a worker is acquired to perform the duties expected in the worker’s job.
Since human capital also consists of labour market experience and firm-level training,
workers who are overqualified would be more likely to be less experienced and to have less job
training. Thus, in the case of immigrants, overeducation is evidence of a compensation for other kinds
of training and skills acquired in the labour market. This may reflect the lack of host country labour
market skills which, over time, as immigrants remain in the host country, would be compensated for
by increased experience. The prediction is that there should be less overeducation143 when migrants
hail from jurisdictions that are similar to that of the host country, in terms of education system,
language and the institutional workings of the labour market.144
According to this approach, overeducation reflects a lower quality of formal education
obtained by migrants in their home country and therefore, migrants who acquire additional training in
the host country should have better matches between skills and education.145 To push the argument
further, migrants from countries that have a lower standard of education should have lower matches
between job requirements and education level, and with increasing assimilation there should be better
matches. In addition, education of immigrants may contain large elements of region-specific skills that
are less easily transferrable across national boundaries. Of course, when the cultural and institutional
differences are large, the effects are greater.
The human capital model sees overeducation as arising from an increase in educational
attainment of workers causing relative wages of highly-skilled workers to fall. Thus, within the human
capital framework, overeducation is not a permanent phenomenon. If there is an increase in the
number of educated workers relative to labour market requirements, employers can substitute away
from poorly-educated to better-educated workers as the wages of educated workers fall. The model
predicts a positive relationship between returns to overeducation and earnings.
143
This would mean that the degree of overeducation should be similar to that of host country workers.
Caribbean economies are strong exporters of qualified labour and family ties; geographical proximity
and the use of the same language make the United States of America and Canada and, to a lesser extent, the
United Kingdom, preferred destinations for migrants.
145
Bratsberg and Terell (2002), using 1980 and 1990 United States census data, show that the income
effect of education is positively related to indicators of education quality in the home country.
144
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THE CAREER MOBILITY APPROACH
The career mobility approach is another spin off from human capital theory, in which overeducation is
seen as a temporary phenomenon largely experienced by immigrants new to the labour market.
According to this approach, newly-arrived immigrants are willing to take up positions below their
level of education while acquiring experience and the specific training required for future
advancement in the labour market (Linsley, 2005). Of course, this assumes that immigrants can be in
occupations which are in line with their training, and that the additional experience acquired helps to
advance their careers. While some immigrants can make this leap, others may be stuck in areas totally
removed from their professional training and may demonstrate very little advancement along their
previous career path. As Nielsen (2008) points out, the validity of the career mobility theory would
require that the overeducated worker make the leap to a better-matching job within a reasonable
period of time. In fact, many migrants fail to make this transition (Büchel, Felix and A. Mertens,
2000).
THE JOB COMPETITION THEORY
The approaches set out so far examine the labour market from the supply side. On the demand side,
job competition theory argues that firms compete for highly-productive workers who represent
training costs to the firm. Since training costs should be lower for more educated workers, firms match
these workers to high-paying jobs. At the same time, if there is a significant increase in the educationlevel of workers, lower-skilled workers are forced down the queue, to be replaced by more highlyeducated workers. Because of the oversupply, educated workers are prepared to accept a mismatch
rather than be unemployed. Thus, if overeducation is due to structural factors, skilled employees will
continue to invest in education in order to maintain their position in the queue. The implication is that
overeducation is not temporary, and causes overinvestment in education.146
THE THEORY OF DISCRIMINATION
Theories of discrimination also explain the degree of overeducation, and the difference in returns to
education between the native born and immigrants in the labour force. One approach is often termed
statistical discrimination, according to which employers have little or no knowledge of the skills and
productivity levels of applicants and, therefore, proceed on the basis of notions of ethnicity and
gender, to name a few, in making hiring decisions. The idea is that employers feel that such
characteristics are correlated with performance. In the face of discrimination, immigrants may find it
more difficult to get a job equivalent with their skills and may then prefer to take other jobs. Thus, the
expectation is that there ought to be much more overeducation among immigrants relative to native
workers. If there is statistical discrimination, this should be reduced over time, as employers receive
more information about the productivity of immigrants.
Overeducation may also occur due to higher search- and mobility- costs among immigrants,
who may not have much local support and may take time to get relevant labour market information.
With time, immigrants would gather such information, thus reducing the monopolistic influence of
employers in the labour market. Nonetheless, if employers have a taste for discrimination against
immigrants in the labour market, and are willing to maintain such discrimination, then overeducation
would persist. The impact of product market competition over time, however, should cause a decline
in such practices.
ISSUES ARISING
146
The assignment approach is also set out in the literature, in which both demand- and supply- side factors
are examined. It assumes that not all workers with similar education are equally productive, so that there is
a mismatch of skills which adjusts over time (Sattinger, 1993).
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Two issues arise here. The first is that there is no recognized body that properly compares host
country skills to those of immigrants, for various levels of education in the United States labour
market, except for a few professions. The second is that skills and experience acquired abroad are
often not easily transferred to the host country. Thus, imperfect information and the unwillingness to
incur search costs by employers may be important factors in explaining relative overeducation among
immigrants. The current immigration policy of many developed countries also favours immigrants
with host-country training over those who have been trained abroad, especially in developing
countries.147 Whether there are greater mismatches among Caribbean-born workers versus native-born
workers in the United States, is an empirical question. If there is a mismatch due to quality
considerations, then one should see lower mismatches among Caribbean migrants who have acquired
some education in the United States, relative to recent arrivals. These several explanations, though
useful, offer only a partial explanation of the reasons for overeducation, and their predictions are not
consistent across explanations.
MEASURING OVEREDUCATION IN THE MODIFIED HUMAN
CAPITAL MODEL
The view of education in the labour market along the lines of Becker (1964) and Mincer (1974) has
come under challenge by proponents of the Overeducated/Undereducated/Required (OUR)
hypothesis. Under this approach, each job is perceived as having a required level of education and
training that is necessary for its satisfactory completion. At the same time, in each job there are
workers with levels of education greater or less than that reference level. The former are called
overeducated and the latter are called undereducated. While the concepts are straightforward, the
measurement issues are not.
There are three approaches to measuring these, which are: job analysis (JA), worker selfassessment (WSA), and realized matches (RM) procedures. Job analysis uses evaluations of the
required level of education for the job titles in an occupational classification by job analysts, and
compares these with the actual educational attainment of the worker. Despite its apparent objectivity
in using and evaluating job classifications, job analysis has several shortcomings (Hartog, 2000).
Usually, occupational categories in the regular data sets are broad, giving rise to heterogeneity among
workers who are in the same occupational category. In many instances, job classifications may be
dated, providing a mismatch between such classification and workers’ educational level. Nielsen
(2007, p.13) also argues that it may be difficult to translate job requirements into years of schooling,
since many jobs do not require significant formal schooling. Also, such assessments may reflect the
characteristics of workers currently in jobs, rather than characteristics needed to perform tasks
required in the job.
In the worker self-assessment (WSA) approach, surveys are used to ask workers about the
level of education and schooling that is required to perform their job. The responses are then
employed to compare the actual level of education with the workers’ perceived requirement for the
job. While this would incorporate current information, it is very subjective, especially since
individuals will tend to exaggerate reported education requirements for their job.
The realized matches (RM) procedure is not subject to bias and is based on the actual
educational attainment of each worker in the sample under consideration. A number of approaches
have been used in the literature, and the most popular employ the mean and standard deviation
(Verdugo and others, 1989) or the modal education level of workers as the reference level. Thus,
workers whose education level is above one standard deviation of the mean are overeducated, while
those whose level is one standard deviation below are undereducated. Those whose education level is
within that range of plus or minus one standard deviation of the mean years are termed adequately
147
This is the case for Canada, Australia and New Zealand.
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educated.148 In the case of the mode, workers whose actual level of education is the modal level for
that occupation are adequately educated for the job. Those whose education is above the mode are
overeducated, and those below are undereducated.149 The RM procedure imposes strong symmetry on
the matching distribution.
While it is argued that empirical results are not sensitive to the approach used, the methods
are very different (Chiswick and Miller, 2005; Hartog, 2000) and are often based on data availability.
This must be taken into account when drawing conclusions from such analyses.
OVEREDUCATION AND ITS DETERMINANTS
This paper adopts the RM approach to matching, under which workers whose years of education are
one standard deviation above the mean occupational level are categorized as overeducated, while
workers whose educational attainments are one standard deviation below the mean are categorized as
undereducated. Those workers whose educational attainments are within one standard deviation below
and above the mean are categorized as having the required education. It is recognized that either the
use of the mode or the mean imposes strong symmetry conditions on the data: however, there is no
information in the data set that allows for any other approach to the analysis. The estimation procedure
employs a specification which modifies the standard human capital model along the lines referred to
as the (OUR) or Overeducated/Undereducated/Required model.
In the standard Mincer approach to human capital investment, the natural logarithm of annual
earnings per worker (ln Yi ) is regressed on a constant, and a linear term in actual years of schooling
(s i ) and linear and quadratic terms in proxies for years of experience such as x t and x 2t , respectively.
Additional variables employed are the personal characteristics of workers, hours worked and
occupation effects, plus a random error term.
The equation for this specification is as follows:
ln Yi = α 0 + α ssi + α 2si2 + β1x i + β2 x i2 + .... + εi ………………....... (1)
where ε i ~ i.i.d(0, σ )
2
This basic equation with modifications has been important to understanding the return to
education for a range of countries and situations. On the one hand, although derived from a clear
theoretical perspective, namely, human capital theory, its premise lies in some simplifying
assumptions. The OUR framework, on the other hand, disaggregates the education variable to
incorporate the issues of overeducation, undereducation and those workers who have the required
level of education for the job, as given in equation (2).
ln Yi = α 0 + α s (s iR + s Oi − s iU ) + β1x i + β 2 x i2 + ... + ε i ....................( 2)
= α 0 + α1s iR + α 2s Oi − α 3s iU + α 2s i2 + β1x i + β 2 x i2 + ... + ε i
148
Some authors, for example, Nielsen (2007), employ a modified RM procedure; thus, an individual
is adequately employed as follows:
⎛ Median − 25%decile ⎞
Median − ⎜
⎟ * (2 * Std.Dev ) ≤ Sactual ≤ Median +
⎝ 75%decile − 25%decile ⎠
⎛ 75%decile − Median ⎞
⎜
⎟ * (2 * Std.Dev )
⎝ 75%decile − 25%decile ⎠
149
See Cohn and Khan (1995), Kiker and others (1997)
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In this equation, the education variable is disaggregated such that
s = s R + sO − s U
where
s R is required schooling or the years of education required for the job
s O is over-schooling, or years of education in excess of that required for the job and
s U is under-schooling.
The equation collapses to
s = sR
for adequately matched workers
s = s R + sO
for overeducated workers and
s = sR − sU
for undereducated workers.
Unlike the standard ordinary least squares (OLS) formulation in equation (2) however, the
equation estimated in this paper accounts for the occupation effects, especially since Caribbean
migrants are concentrated in particular occupations. The 2000 United States census data suggest, for
example, that 24.6% of Caribbean-born workers were in sales and office occupations, as against
19.9% for the foreign born, and 24.4% were in service occupations as against 20.0% for other groups.
To capture occupational effects, a hierarchical model is employed (Bryk, A.S and Raudenbush, 1992).
The model is set out as follows:
ln Yij = α 0 + α s (s ijR + s Oij − s ijU ) + β1x ij + β 2 x ij2 + ... + μ oj + ε ij ....................(3)
ε ij ~ N (0, σ 2 ) , μ oj ~ N (0, τ oo )
where i represents the returns to individuals, and j refers to the occupation of individuals.
Thus u oj accounts for shifts in the regression line at the occupation level (fixed effects) and ε ij is the
random error term at the individual level. Hierarchical models allow for nested data structures, and
correct for biases in parameter estimates due to the clustering of the data structure. The model allows
2
for the estimation of the individual level ( σ ) and the occupational level ( τ 00 ) variances which can
be used to determine how much of the total variation in returns can be accounted for by individuals or
by their occupation.150
MODEL FORMULATION AND DESCRIPTIVE STATISTICS
The discussion in the literature suggests that there are no clear-cut explanations for overeducation
among migrants relative to native workers. The usual approach is to employ a set of variables based
on expectations about the factors that influence the degree of overeducation in the labour market
among immigrants and native workers. Among the usual variables employed are experience in the
150
ˆ )
The variation (interclass correlation) accounted for at the individual level is τˆ 00 /( τˆ 00 + σ
2
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labour market, age, language skills and source of education, whether acquired in the host country or
elsewhere, to capture a notion of the quality of education. The issue of heterogeneity among workers
is also important, as workers with similar education-levels may display varying efforts to achieve job
matches (Chiswick and Miller, 2008). Such motivations may be unobservable and, in addition, some
workers may have better networks than others, which may make it easier to access information about
job opportunities in the host country.
It is not clear whether overeducation may reflect the desire to have just any employment or
whether it is due to lack of ability to find suitable employment. Furthermore, there may be structural
factors which allow for greater overeducation among immigrants relative to native workers. Chiswick
and Miller (2005), using the 1% Integrated Public Use Microdata Series (IPUMS), found that the
mean years of schooling for employed males for the native born and Caribbean migrants were 13.5
years and 12.4 years respectively. The percentage of overeducation, undereducation and correctlymatched individuals were 11.9%, 8.04% and 80.04% for the native born, while it was 8.06%, 18.16%
and 73.77% for Caribbean-born immigrants. Thus, the degree of overeducation was lower and the
degree of undereducation was much higher, than for the native born. The results also suggested that
the Caribbean-born workers were among the highest of the correctly matched individuals, and among
the lowest of the overeducated of any region.
In the light of these results, workers of Caribbean origin were divided into two groups. In the
first were Caribbean-born who had been in the United States of America for more than five years and,
in the second, were those who had migrated to the United States within the last five years. The
minimum age for the second group was 20 years of age, on the assumption that such individuals
would have acquired most of their education in the Caribbean. This would provide ample opportunity
to test for quality differences in education levels.
Equation (4) sets out the general formulation, where N is for native-born, C is for the
Caribbean-born who have lived in the United States for more than five years, while RC represents
Caribbean nationals who have arrived within the last five years at age 20 or older. For Caribbean-born
workers, the linear and quadratic terms on years in the United States was also employed to capture the
impact of experience acquired abroad. Following Chiswick (1997), the weeks and hours worked have
both been placed on the right hand side to avoid misspecification of the income equation. The model
to be estimated within the hierarchical modeling framework is as follows:151
151
Experience - potential experience defined as: (age-years of schooling -5)
Gender- a dummy variable with 1 for males, and zero otherwise.
Married- is a dummy variable with 1 for being married and zero otherwise.
South- a dummy variable with 1 for living in a Southern state and zero otherwise.
Speaks no English-a dummy variable with 1 if an individual does not speak English and zero
otherwise.
Speaks poor English- a dummy variable with 1 if the individual speaks English poorly and zero
otherwise.
Veteran-a dummy variable with 1 for veterans and zero otherwise.
Metropolitan- a dummy variable with 1 for living in a metropolitan area and zero otherwise.
Black is 1 if the individuals belongs to the black race and 0 otherwise.
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ln Yij( N ,C , RC ) = α 0 + α 1s ijR + α 2 s Oij − α 3s ijU + β 1 Experience
β 4 Married
β 8 Veteran
ij
ij
ij
+ β 2 Experience
2
ij
/ 100 + β 3 Gender ij +
+ β 5South ij + β 6 Speaks no English ij + β 7 Speaks English Poorly ij +
+ β 9 Metropoli tan ij + β 10 Black + β 11 Years sin ce migration ( YSM ) +
β 12 YSM + β 10 Log of weeks worked ij + β 11 Log of hours worked ij +
2
μ oj + ε ij .......... .......... .......... ..( 4 )
In the model estimation, the years since migration and the square of that variable were
restricted to zero in the equation for native workers.152 The data set is the 5 % IPUMS for both males
and females from 20 to 65 years of age. This is a relatively large data set, with over 4 million
observations for the native born, 57,000 observations for persons of Caribbean origin residing in the
United States in excess of five years, and 6,650 for Caribbean-born persons who have migrated within
the last five years.
Figure 1 below shows the distribution of income for each of the three groups. It is clear that
the distribution of income for recent arrivals is very much to the left of the other two groups. The
native-born distribution is slightly to the right of the Caribbean-born living more than five years in the
United States. Thus, immigrants from the Caribbean who have been in the United States in excess of
five years have a somewhat similar distribution relative to the native born, but they are not identical.
.4
0
.2
Density
.6
.8
Figure 1: Kernel Density Estimates
4
6
8
10
Log of annual earnings
12
14
Native born
Caribbean born > 5 years in US
Caribbean born <=5 years in US
On the assumption that returns to education might vary by the level of education, which
reflect different labour market segments, as was found by Bratsberg and Ragan Jr. (2002), figure 2
plots the mean education and log of mean earnings for the groups at various education levels. The
results are striking, for they suggest non-linearities in the relationship between earnings and education,
especially for Caribbean immigrants in the United States five years or less. Thus, differences in
education level may be an important source of differences in earnings, as shown above.
152
In the standard formulation, an additional variable, the mean occupational wage, was also used. See
table 8.
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Figure 2: Mean Earnings and Mean Years of Education
Caribbean Born >5 years in US
10
9.5
5
10
15
20
10.5
11
Caribbean born <= 5 years in the US
9.5
10
Log of Mean Earnings
10.5
11
Native Born
5
10
15
20
Mean Years of Education
Table 1 reports the percentage distribution of employed individuals by occupational groups,
after removing missing values and zero-income categories.
TABLE 1
PERCENTAGE DISTRIBUTION OF EMPLOYED BY THREE-DIGIT OCCUPATION GROUPS
Codes
in
IPUMS
1-43
50-73
80-95
100-124
130-156
160-196
200-206
210-215
220-255
260-296
300-354
360-365
370-395
Management, business and financial operations
Business operations specialists
Financial specialists
Professional and related occupations
Architecture and engineering occupations
Life physical and social science occupations
Community and social services occupations
Legal occupations
Education, training and library occupations
Arts, design, entertainment, sports and media
occupations
Healthcare
practitioners
and
technical
occupations
Healthcare support occupations
Protective service occupations
234
Native born
Caribbean
born with
more than 5
years in US
Caribbean
born 5 years
or less in US
10.78
2.55
2.65
2.73
2.39
1.14
1.92
1.58
7.44
2.12
7.72
1.89
2.95
1.86
1.65
0.67
1.99
0.94
4.58
1.29
3.62
1.34
1.47
1.74
1.44
0.51
1.35
0.32
3.29
1.54
5.65
7.67
3.54
1.77
2.15
6.92
2.29
6.79
2.34
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400-416
420-425
430-465
470-496
500-593
600-613
620-676
680-694
700-762
770-896
900-975
980-983
Caribbean Development Report, Volume 2
Food protection and servicing occupations
Building and ground cleaning and maintenance
occupations
Personal care and service occupations
Sales occupations
Office and administrative support occupations
Farming, fishing and forestry occupations
Construction, extraction and maintenance
occupations
Extraction workers
Installation, maintenance and repair workers
Production occupations
Transportation and material moving occupations
Military occupations
2.93
2.25
3.13
4.70
5.86
7.41
2.30
10.28
15.27
0.38
4.74
3.28
9.00
15.66
0.21
3.94
3.97
10.01
12.28
0.47
6.58
0.10
3.94
7.32
5.32
0.29
0.01
4.10
6.69
6.61
0.22
0.01
4.07
11.06
8.40
0.08
Source: 5 % IPUMS USA.
There is a striking similarity between the distribution of workers among the native-born and
the Caribbean-born living for more than five years in the United States, by occupational category.
Interestingly, in some occupations, the percentage of Caribbean-born workers arriving within the last
five years is higher than for the native born. These major categories are health support occupations,
protective services, food protection and servicing, building and ground cleaning and maintenance,
personal care and service, transport and material moving occupations, and production operations,
many of these being lower level occupations. The highest concentration of recently-arrived
immigrants was in production operations, 11.06%, sales, 10.01%, and office and administrative
support, 12.28%.
TABLE 2
MEAN YEARS OF EDUCATION BY OCCUPATIONAL GROUP
Codes in
IPUMS
1-43
50-73
80-95
100-124
130-156
160-196
200-206
210-215
220-255
260-296
300-354
360-365
370-395
400-416
Management, business and financial
operations
Business operations specialists
Financial specialists
Professional and related occupations
Architecture and engineering occupations
Life, physical and social science
occupations
Community
and
social
services
occupations
Legal occupations
Education,
training
and
library
occupations
Arts, design, entertainment, sports and
media occupations
Healthcare practitioners and technical
occupations
Healthcare support occupations
Protective service occupations
Food
protection
and
servicing
occupations
235
Natives
Caribbeanborn with
more than 5
years in US
Caribbean-born
with 5 years or
less in US
14.47
14.21
13.93
14.38
15.06
14.85
14.85
16.30
14.18
15.01
14.77
15.02
15.99
13.57
15.67
15.36
15.07
15.84
15.64
15.13
14.50
16.89
15.95
16.16
15.69
15.24
15.28
14.65
13.82
13.61
15.24
14.90
14.53
12.51
13.24
12.25
11.83
12.61
11.01
12.20
11.93
11.30
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420-425
430-465
470-496
500-593
600-613
620-676
680-694
700-762
770-896
900-975
980-983
Caribbean Development Report, Volume 2
Building and ground cleaning and
maintenance occupations
Personal care and service occupations
Sales occupations
Office and administrative support
occupations
Farming, fishing and forestry occupations
Construction,
extractions
and
maintenance occupations
Extraction workers
Installation, maintenance and repair
workers
Production occupations
Transportation and material moving
occupations
Military occupations
Overall mean
11.85
10.55
10.81
12.77
13.37
12.96
11.75
12.63
12.81
11.75
12.72
12.61
11.79
12.08
9.44
11.33
9.59
11.27
11.88
12.33
10.82
12.02
12.00
11.90
12.15
12.10
11.04
11.48
11.51
11.67
13.48
13.53
13.27
12.72
12.44
12.28
Table 2 examines the mean years of education by occupational group. The overall mean years
of education was higher for the native born and for persons of Caribbean origin with more than five
years in the United States, but slightly lower for those who had come in the last five years. The
differences in the overall mean years of education were statistically significant at the 5% level. The
mean years of education for natives varied between 11.79 and 16.89 years of education while, for
persons of Caribbean origin with more than five years in the United States, the mean years of
education varied between 9.4 and 16.1 years and, for recent arrivals, the mean was about the same.
Interestingly, recent arrivals had a slightly higher average age of education relative to other groups in
the occupational groups of professional and related, architect and engineering, and extraction workers.
The overall results suggest no striking difference between the two Caribbean groups. Thus,
significant differences in returns for new immigrants would reflect some discounting of local
experience and education, plus problems of integrating quickly into the local labour market.
TABLE 3
DISTRIBUTION OF LEVEL OF EDUCATION FOR NATIVE BORN, CARIBBEAN-BORN
IN THE UNITED STATES MORE THAN FIVE YEARS, AND CARIBBEAN-BORN IN THE
UNITED STATES FIVE YEARS OR LESS
11th grade
or less
12th grade no
diploma, some
College-no
diplomas, high
school graduate
or GED
Natives
5.81
Caribbean-born with more than five
14.89
years in US
Caribbean-born and less than five
20.11
years in US
Caribbean born with five years or less in the United States
Cuba
16.59
The Dominican Republic
34.29
Haiti
19.66
Jamaica
18.91
Antigua and Barbuda
15.86
Associate
degree
Bachelor’s
degree
Masters’
degree,
professional
degree, or
Doctorate
56.20
54.77
8.15
9.11
19.62
13.44
10.12
7.78
56.74
5.75
10.22
7.18
54.34
47.67
65.00
58.41
58.14
4.75
3.53
5.50
7.17
2.92
11.99
7.96
7.30
10.76
13.91
12.32
6.55
2.54
4.75
9.18
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Bahamas (the)
Barbados
Dominica
Grenada
Saint Kitts and Nevis
Saint Lucia
Saint Vincent and the Grenadines
Trinidad and Tobago
Guyana
Caribbean Development Report, Volume 2
7.41
8.50
18.09
19.27
13.68
11.24
13.10
7.88
18.04
46.69
50.44
61.04
62.34
65.01
66.37
73.26
68.14
59.81
16.88
19.15
6.34
8.08
3.37
12.43
12.03
5.61
9.03
22.40
14.43
11.04
9.50
14.03
9.96
0.00
11.35
9.78
6.62
7.48
3.49
0.08
3.91
0.00
1.60
7.02
3.33
Source: IPUMS USA, 5 % Sample.
Table 3 examines the percentage of individuals by education level, for each of the three major
groups and for individual Caribbean countries. The results are interesting, showing that 5.81% of
native born workers, 14.89% of Caribbean–born workers living more than five years in the United
States, and 20.11% of Caribbean-born workers living for five years or less in the United States, have a
level of education to eleventh grade or below. Except for Associate degrees, the native born had
significantly higher shares than the Caribbean-born in the higher degree categories. In terms of
professional and other degrees, the percentage of Caribbean-born workers in the United States for
more than five years was 7.78%, which was much closer to the 7.18% for recent arrivals. The countrylevel data are reported for individuals from several Caribbean countries that were living in the United
States for a maximum of five years. Cuba, the Dominican Republic and Haiti had the highest
percentage of persons in the lower educational levels. In terms of the Caribbean populations, it
appears that, on arrival in the United States, individuals upgrade their skills by acquiring higher levels
of education.153
Table 4 examines the percentage of overeducation, undereducation and required levels of
education, using the RM procedure. The overall results show that, among the employed, native born
workers have the highest levels of required education (76.36%), and recent arrivals from the
Caribbean have the lowest (66.81%). The percentage of Caribbean-born workers in the United States
for more than five years having the required levels of education was 70.79%, which means that
Caribbean nationals did not catch up completely over time.
TABLE 4
PERCENTAGE DISTRIBUTION OF OVEREDUCATED, REQUIRED AND
UNDEREDUCATED WORKERS
Percentage
overeducated
Native-born
13.91
Caribbean-born with more than 5 years in United States
11.77
Caribbean-born with less than five years in United States
13.19
Caribbean-born with five years or less in the United States by country of origin
Cuba
20.95
The Dominican Republic
12.72
Haiti
8.60
Jamaica
8.29
Antigua and Barbuda
7.51
Bahamas (the)
16.25
Barbados
10.45
153
The results may vary by age and gender.
237
Percentage with
required
education
Percentage
undereducated
76.36
70.79
66.81
9.37
17.44
20.00
61.93
54.76
73.30
74.78
75.80
78.31
73.16
17.12
32.51
18.10
16.93
16.69
5.44
16.39
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Caribbean Development Report, Volume 2
Dominica
Grenada
Saint Kitts and Nevis
Saint Lucia
Saint Vincent and the Grenadines
Trinidad and Tobago
Guyana
17.09
6.66
3.91
4.54
10.16
8.65
7.14
69.37
72.56
79.93
80.97
68.45
78.44
72.02
13.53
20.78
16.16
14.50
21.39
12.91
20.83
Interestingly, both Caribbean-born groups had lower shares of overeducation relative to the
native born, but a higher share of undereducation. This may reflect the heavy concentration in such
areas as sales, production occupations, and transport and material moving operations. Similar results
are found in Chiswick and Miller (2005).
For individual Caribbean countries, the highest percentages of overeducated workers are to be
found among Cubans, with a share of 20.9%, the Bahamas with 16.25%, Dominica with 17.0%, and
the Dominican Republic with 12.7%. Interestingly, Cuba and the Dominican Republic are Spanishspeaking countries, and Chiswick and Miller (2005) found that English proficiency was important for
labour-market performance.
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TABLE 5
INCIDENCE OF OVEREDUCATION ACROSS POPULATION SUBGROUPS, AS A
PROPORTION OF ALL PERSONS IN POPULATION SUBGROUP
(Percentage)
Age Group
20 to less than 30
30 to less than 40
40 to less than 50
50 to less than 60
Greater than 60 years
Native born
Caribbeanborn residing
more than 5
years in the
US
12.19
13.90
14.72
15.03
13.27
10.23
12.39
12.75
10.73
10.58
Caribbeanborn residing
5 years or
less in the
US
10.93
14.67
13.43
13.67
11.89
In Table 5, the incidence of overeducation is examined by age groups with a range of ten
years between age groups 20 to 60 years of age. In the case of the native born, the shares rose from
12.19% to 15.03%, and then fell to 13.27%. With respect to persons of Caribbean origin residing more
than five years in the United States, the percentages were much lower with increasing age than those
of natives. In the case of persons of Caribbean origin who have been five years or less in the United
States, the percentage rose steeply, from 10.93% in the first age group to 14.67% in the second, and
then fell to 11.8% in the last group. This means that the Caribbean-born who have migrated within the
last five years have a higher share of overeducation, by age group, relative to those that have migrated
prior to that period.
TABLE 6
INCIDENCE OF UNDEREDUCATION ACROSS POPULATION SUBGROUPS, AS A
PROPORTION OF ALL PERSONS IN POPULATION SUBGROUP
(Percentage)
Age group
20 to less than 30
30 to lese than 40
40 to less than 50
50 to less than 60
Greater than 60 years
Native born
8.40
9.05
9.40
11.82
16.00
Caribbean-born,
residing more
than 5 years in
the US
11.81
14.15
17.95
24.28
30.88
Caribbean-born,
residing 5 years
or less in the US
15.33
18.22
22.53
32.53
43.91
Table 6 examines the incidence of undereducation by the same age groups reported in table 5.
At each reported age group, undereducation among the native born is much lower than for Caribbeanborn workers who are residing in the United States in excess of five years, or those who have been in
the United States for five years or less. For the last group, the percentage of undereducation rose from
15.33% to 43.91%. In summary, workers who are Caribbean-born and residing five years or less in the
United States, have higher levels of undereducation than the other two groups, at all age levels.
Some studies have found that English proficiency has a considerable impact on the ability of
migrants to integrate, in labour markets for which the language of communication is English. Given
the significance of many Spanish-, Dutch- and French-speaking groups of Caribbean origin, it would
be useful to examine the degree of over- or undereducation for different levels of English proficiency.
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TABLE 7
DISTRIBUTION OF OVEREDUCATION, UNDEREDUCATION AND REQUIRED
EDUCATION FOR CARIBBEAN-BORN, BY DEGREE OF FACILITY IN SPEAKING
ENGLISH
Does not speak English
Speaks only English
Speaks English very well
Speaks English well
Speaks English, but not
well
Caribbean-born, more than five years in
the United States
Caribbean-born, five years or less in the
United States
Overeducated
Undereducated
Required
Overeducated
Undereducated
Required
4.51
11.93
14.76
10.13
6.84
56.38
13.72
11.48
18.90
38.23
39.11
74.34
73.76
70.97
54.93
9.97
8.24
16.51
17.76
18.08
40.03
17.30
15.02
11.57
19.92
50.18
78.77
78.47
70.66
62.01
The results in table 7 show that, for recent arrivals, only 50.18% of workers who speak no
English had the required education for their jobs, relative to 39.11% of those Caribbean-born workers
residing for more than five years in the United States.154 Interestingly, those who spoke English but
not well had the next highest percentage of individuals with the required education.
EARNINGS AND JOB MATCHING
The models were estimated for the full sample of Caribbean immigrants but, because of the large size
of the native-born sample, a random sample was taken to reduce the sample size for the native born.155
An initial, multilevel unconditional or random intercept- only model156 (the results are not reported)
was first run for each sample to get initial values for the variability in wages between the occupation
and individual- level variances, after which equation (4) was run, with the explanatory variables added
but accounting only for returns to actual education.
With respect to the null model, the proportion of the variance in returns due to occupations
was 18.4% for the native born, 17.7% for the Caribbean-born residing for more than five years in the
United States, and 13.0% for recent arrivals.157 This suggests that there is some clustering of returns
within occupations, and that OLS would lead to misleading results.158
154
It is important to note that workers who did not speak English, or spoke English but not well, were a relatively
small percentage of the population. English proficiency is important, since Cuba, the Dominican Republic and
Haiti constitute 63.45% of Caribbean-born workers residing for five years or less in the United States.
155
The sample size was reduced to 74,061 observations.
156
The null model is as follows: y ij = γ 00 + μ 0 j + ri where γ 00 is the grand occupation mean income, μ 0 j is
the mean occupation income and ri is the individual error term.
When the decline in the variance due to adding explanatory variables is computed as
τˆ 00 ( Model 1) − τ 00 ( Model 2) / τˆ 00 ( Model 1) , where model 1 is the null model, the results suggest
157
[
]
that the decline in the occupational variance was 98% for natives, 95% for Caribbean-born residing for more than
5 years in the United States, and 90% for recent arrivals.
158
The variance component among individuals is 6 times the variance component among occupations for recent
arrivals.
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In table 8,159 the returns to an additional year of education were 9.1% for the native born,
which were higher than the returns found by Chiswick and Miller (2005),160 after controlling for
occupation effects using OLS. The returns to education for the Caribbean-born residing for more than
five years in the United States were 5.5%, and for recent arrivals, 2.8%.161
With respect to the experience variable, the linear and quadratic variables were significant in
all the equations. For the native born, when evaluated at 10 years of experience, the increment to
earnings with experience was 2.0%, while for recent arrivals it was 1%.
TABLE 8
REGRESSION ESTIMATES OF EARNING EQUATION, NATIVE-BORN, CARIBBEAN-BORN
WITH MORE THAN FIVE YEARS IN THE UNITED STATES, AND CARIBBEAN-BORN
RESIDING FOR FIVE YEARS OR LESS IN THE UNITED STATES.
Native born
0.091***
(70.88)
Caribbeanborn >5 years
in United
States
0.055***
(44.77)
Caribbeanborn
=<5years in
United
States
0.028***
(8.83)
Native born
with linear
Spline
Caribbeanborn
>5years in
United
States with
linear
Spline
Experience
0.032***
(39.87)
0.020***
(20.86)
0.017***
(5.88)
0.034***
(41.56)
0.023***
(24.4)
0.019***
(6.36)
Experience2/100
-0.049***
(-28.38)
-0.031***
(-16.85)
-0.029***
(-4.68)
-0.054***
(-30.55)
-0.040***
(-21.28)
-0.033***
(-5.32)
Gender
0.270***
(46.19)
0.190***
(30.32)
0.203***
(10.96)
0.264***
(45.21)
0.186***
(29.79)
0.202***
(10.91)
Marriage
0.090***
(17.51)
0.072***
(12.81)
0.037*
(2.20)
0.089***
(17.41)
0.069***
(12.43)
0.036*
(2.14)
South
-0.061***
(-12.30)
-0.122***
(-22.32)
-0.106***
(-6.44)
-0.065***
(-13.11)
-0.119***
(-21.93)
-0.104***
(-6.33)
Does not speak English
-0.042
(-0.30)
-0.161***
(-9.75)
-0.142***
(-5.38)
-0.164
(-1.15)
-0.210***
(-12.74)
-0.148***
(-5.60)
Speaks English poorly
-0.029
(-0.79)
-0.139***
(-14.17)
-0.110***
(-5.09)
-0.048
(-1.31)
-0.161***
(-16.48)
-0.113***
(-5.24)
Veteran
-0.013
(-1.80)
0.016
(1.30)
0.133*
(2.57)
-0.005
(-0.68)
0.019
(1.56)
0.136**
(2.62)
Years of education
159
Caribbeanborn =<5
years in
United
States with
linear
Spline
The table reports the well known Bic (Bayesian information criterion), where smaller values reflect better
model specifications. The Wald Chi(k) test is a “F” test in a non-linear setting with (k) representing the number
of coefficients being estimated. In all cases, the models are well specified, as the null hypothesis of no
relationship is comprehensively rejected. N is the number of observations.
160
Chiswick (2007) found returns of 5.8% for the native born and 2.3% for the foreign born.
161
By setting H1:α1 = α2 = - α3 in the OUR model, the traditional Mincer equation is obtained. This restriction is
rejected, which suggest that the OUR formulation holds.
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Metropolitan
0.185***
(35.32)
0.094***
(5.42)
0.071
(1.41)
0.183***
(34.97)
0.098***
(5.66)
0.068
(1.35)
Black
-0.043***
(-5.17)
0.030***
(5.03)
0.084***
(4.57)
-0.041***
(-4.89)
0.035***
(6.03)
0.092***
(4.95)
Log of weeks worked
0.846***
(127.16)
0.803***
(121.58)
0.860***
(59.65)
0.850***
(127.92)
0.804***
(122.41)
0.860***
(59.67)
Log of hours worked
0.871***
(123.99)
0.650***
(80.20)
0.589***
(27.18)
0.869***
(123.87)
0.644***
(79.84)
0.588***
(27.16)
0.011***
(9.37)
0.01
(0.41)
0.010***
(8.75)
0.009
(0.35)
-0.000
(-1.47)
0.003
(0.62)
-0.000
(-0.97)
0.003
(0.69)
Years since migration (YSM)
YSM2
Years of education <= 11 years
0.015***
(3.60)
0.008**
(3.29)
0.009
(1.53)
Years of education > 11 years
0.102***
(72.32)
0.082***
(49.80)
0.042***
(8.92)
Constant
1.772***
(32.21)
3.316***
(54.23)
3.668***
(28.99)
2.561***
(37.18)
3.749***
(61.18)
3.836***
(28.87)
.0427***
.043***
.061***
.041***
.036***
.056***
0.40924***
0.3997***
0.4247***
0.4073***
0.3956***
0.4239***
** p<0.01
*** p<0.001
74061
57158
6650
74061
57158
6650
Bic
144420.3
110251.4
13506.38
144093
109677.8
13507.69
Wald Chi (k)
_62159.7
36426.22
57916.65
62801.35
37411.47
5819.61
τ̂ 00
σ̂
2
* p<0.05
N
Bic: Bayesian information criterion
The linear spline function confirms that workers constitute two heterogeneous groups, as the
returns to education at eleven years or less was much lower than the returns above eleven years of
education. More important, however, is the fact that the differences in returns persist for the Caribbean
groups at both levels of education.
Among the other variables of interest, gender is positive and significant, reflecting higher
returns for males, while speaking English poorly has a significant negative effect on earnings. In
addition, the coefficients with respect to those who do not speak English, or do not speak it well, are
also negative and significant for the Caribbean-born. Chiswick (2007) has shown that, after
controlling for occupation, some of the earnings disadvantage of immigrants with limited proficiency
is due to this deficiency, placing them in lower-earning occupations.
For those Caribbean-born who have been in the United States for five years or less, the
coefficient on the linear and quadratic terms on years since migration is not statistically significant,
while the coefficient on the linear terms is significant, though small, for the other Caribbean group.
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This result is important, since it means that, for recent arrivals, there is no significant relationship
between experience gained abroad and income.
For the native born, the elasticity of earnings with respect to weeks worked was 0.84 while
for hours worked, it was 0.87. These are likely to have different effects, especially for recent
immigrants, since weeks worked may differ from the number of hours worked. Individuals may work
similar hours in a week but fewer weeks, for example. The elasticities with respect to weeks worked
were similar for all groups, but the returns to hours worked were much less for the Caribbean-born
relative to the native born.
When account is made of occupational status, the impact of experience on such status is
negative for immigrants (Chiswick, 2007). The results of a quantile regression (not reported here) run
with the average income for each occupation used as the status variable, show that the returns to
experience, though small, decline as incomes rise. These results suggest that the impact of nontransferability of human capital skills is more important for those who are higher up in income
distribution; however, the impact is fairly small. Chiswick (2007) pointed out that foreign workers
with experience tend to be channelled into lower-paying occupations (captured by mean occupational
earnings) but, within that occupation, receive a relatively high rate of pay.
Table 9 reports the results for the OUR model, which show that the variance due to
occupation effects still accounts for 3.1% of the variation for the native born, 2.1% for the Caribbeanborn residing for more than five years in the United States, and another 2.3% for recent arrivals. With
respect to the native born, the returns to an additional year of required education were 15%, which is
some 5.9% higher than that obtained when the actual years of education variable is used in the
standard Minceran equation. This difference is accounted for by the fact that the returns to actual years
of education are a combination of earning increments to correctly matched job requirements, and to
years of education that are not matched with job requirements. Thus, once mismatches are accounted
for, the returns to years of schooling tend to be much higher.
The returns to required education for the Caribbean-born with more than five years in the
United States, and those who have been in the United States for less than five years, were basically
13% and 2% lower than returns to the native born.
The table also reports two types of mismatches, overeducation and undereducation. In terms
of the native born, the return to overeducation was 9.2%, while for the Caribbean-born with more than
five years in the United States, it was 5.3% and, for more recent arrivals, the return was 2.0%. Thus,
there are wide variations between the groups in terms of rewards to overeducation, even when
occupation effects are included. Hence, while the percentage of overeducated workers is higher among
the native born, the returns are also greater. In contrast, the Caribbean-born living five years or less in
the United States are least rewarded for their overeducation. One other important result is that, in the
models for the Caribbean, people of African descent receive a positive return relative to other ethnic
groups while, for the host country model, they receive a negative return relative to such groups.
The results are in line with other findings that the returns to required schooling are higher
than the returns to actual education. This follows from a comparison between tables 8 and 9. In
addition, the returns to overeducation are positive, but smaller than the returns to required education,
while the returns to undereducation are negative (Hartog, 2000).
The results also suggest that the labour market discounts experience gathered elsewhere, as
the increment to experience for the native born was 3.3%, for the Caribbean-born living more than
five years in the United States it was 2.2% and, for recent arrivals from the Caribbean, 1.9%.
243
ECLAC – Project Documents collection
Caribbean Development Report, Volume 2
TABLE 9
REGRESSION ESTIMATES OF EARNING EQUATION, NATIVE-BORN, CARIBBEAN-BORN
WITH MORE THAN FIVE YEARS IN THE UNITED STATES, AND CARIBBEAN-BORN,
RESIDING FOR FIVE YEARS OR LESS IN THE UNITED STATES, FOR THE OUR
EDUCATION MODEL BASED ON THE MEAN LEVEL OF EDUCATION
Native born
Caribbean-born
living more than
5 years in the
United States
Caribbean-born
living 5 years or
less in the
United States
0.033***
0.022***
0.019***
Experience /100
-0.052***
-0.037***
-0.033***
Overeducation
0.092***
0.053***
0.020*
Undereducation
-0.085***
-0.039***
-0.022***
Required education
0.150***
0.133***
0.128***
Gender
0.271***
0.186***
0.200***
Marriage
0.089***
0.070***
0.039*
South
-0.065***
-0.121***
-0.105***
Does not speak English
-0.138
-0.200***
-0.143***
Speaks English poorly
-0.036
-0.152***
-0.095***
Veteran
-0.006
0.018
0.118*
Black
-0.043***
0.037***
0.087***
Log of weeks worked
0.848***
0.803***
0.858***
Log of hours worked
0.866***
0.643***
0.583***
0.010***
0.018
0
0.001
0.988***
2.261***
2.346***
τ̂ 00
.031***
.021***
.023***
2
.406***
.394***
.418***
74061
57158
6650
Bic
143873.1
109518.2
13426.56
Wald Chi(k)
63262.41
37752.56
60239.93
Experience
2
`
Years since migration(YSM)
YSM2
Constant
σ
* p<0.05,
** p<0.01
*** p<0.001
N
244