BIRTH RATES AND BORDER CROSSINGS:LATIN AMERICAN MIGRATION TO THE US, CANADA, SPAIN, AND THE UK

March 2011
Gordon H. Hanson, UCSD and NBER
Craig McIntosh, UCSD

Abstract. We use census data for the US, Canada, Spain, and UK to estimate bilateralmigration rates to these countries from 25 Latin American and Caribbean nations over theperiod 1980 to 2005. Latin American migration to the US is responsive to labor supply
shocks, as predicted by earlier changes in birth cohort sizes, and labor demand shocks
associated with balance of payments crises and natural disasters. Latin American
migration to Canada, Spain, and the UK, in contrast, is largely insensitive to theseshocks, responding only to civil and military conflict. The results are consistent with US
immigration policy toward Latin America (which is relatively permissive toward illegal
entry) being mediated by market forces and immigration policy in the other countries(which favor skilled workers and asylum seekers, among other groups) insulating themfrom many labor market shocks in the region.

We thank Gordon Dahl, Paul Menchik, Caglar Ozden, Dean Yang, and seminar
participants at Michigan State University, the University of Michigan, the World Bank,UC Irvine, UC San Diego, USC, University College London, and Universitat Autonoma
de Barcelona for helpful comments.

Puerto Rico . . .
Always the hurricanes blowing,
Always the population growing.
And the babies crying,
And the bullets flying.
I like to be in America!
Stephen Sondheim, Westside Story

1 INTRODUCTION
Latin America and the Caribbean have among the highest emigration rates in thedeveloping world. In 2000, 5.1% of the region’s population was living abroad, comparedwith emigration rates of 4.7% in the Middle East and North Africa, 2.6% in Sub-SaharanAfrica, 1.8% in South Asia, and 0.9% in East Asia and the Pacific.1 Due to a
combination of geography and history, the large majority of emigrants from the region
head for either one of the hemisphere’s rich countries (the US, Canada) or for one of its
former colonial powers (the UK, Spain). 2 In 2000, these four destinations were host to
75.4% of the region’s emigrants (Table 1). While Mexican migration to the US captures
most of the attention, it is by no means the only significant flow in the region. There arealso sizable flows from the Dominican Republic, El Salvador, and Haiti to the US;
Barbados, Jamaica, and Trinidad and Tobago to Canada and the UK; and Bolivia,
Colombia, and Ecuador to Spain (Fajnzylber and Lopez, 2008).
In this paper, we examine how bilateral migration from Latin America to theregion’s four primary destinations responds to demographic change, economic shocks,
and political upheaval. Our goal is to characterize how differential immigration policies
in the destination countries mediate the impact of origin country shocks on bilateral1

The highest recent emigration rates are in Eastern Europe and Central Asia, largely because of individuals
moving to Russia from former Soviet subjects following the end of the Soviet Union (Parsons et al., 2007).
2
Former French and Dutch territories in the Western Hemisphere have high migration rates to France andthe Netherlands but are too small to obtain the age-specific emigration rates necessary for our analysis.

1migration flows. We measure bilateral migration rates by year and age cohort using dataon immigrant stocks from destination country censuses and on the size of origin country
birth cohorts from the World Bank. Our main findings are that, relative to otherdestinations, migration to the US is much more sensitive to origin country demographic
and economic shocks and much less sensitive to origin country civil and military conflict.
We interpret these results to mean that greater openness to illegal migration (as in thecase of the US) makes a destination more exposed to wage shocks in origin countries,
whereas greater openness to asylum seekers (as in Canada and the UK) makes adestination more exposed to origin country civil unrest.
Among the four destination countries, there are sharp differences in immigration
policy with regards to skill, refugee status, and country of origin. These differences areimportant in light of the low skill levels of most Latin American emigrants, thepropensity of the region for economic and political instability, and the variation incountries’ colonial history. In the US, nearly half of immigration from Latin America is
undocumented, with border enforcement only partially impeding the inflow of illegal
migrants (Hanson, 2006).3 While permissiveness toward illegal entry creates ample
opportunity for low skilled immigration, the US is much less open to political refugees
from Latin America (with the exception of Cuba). Canada’s remoteness keeps its
immigration legal.4 The country uses a point system to regulate labor inflows, which
heavily favors skilled applicants, while also allotting slots to refugees and asylees. In
2000, visas to skilled workers accounted for 58% of legal immigrant inflows in Canada,
compared with 13% in the US (OECD, 2004). Outside of EU countries, the UK restricts

3
4

Throughout the paper we use Latin America to refer to Latin America and the Caribbean.
In 2002, Canada apprehended 9,500 illegal immigrants, compared to 1 million in the US (OECD, 2004).

2

immigration, with exceptions for skilled workers, asylum seekers, family members of UK
citizens, and some Commonwealth citizens. The country also has low levels of illegal
immigration compared to the US.5 In Spain, large-scale immigration is a recent event,
beginning in the late 1990s. Agreements with former colonies have enabled individuals
from these countries to enter Spain, with many ultimately obtaining work permits.6
Surging emigration from Latin America is due in part to the high frequency ofnegative shocks in the region. We focus on four sources of shocks: balance of payments
crises, natural disasters, civil and military conflict, and population-growth inducedchanges in labor supply. While there is literature on how economic and political trauma(due to currency collapses, disasters, or civil unrest) affects the region’s growth
performance (e.g., Collier et al., 2003; Raddatz, 2007; Edwards, 2008), much less work
examines its importance for labor movements in the hemisphere. Over the last three
decades, Latin America has experienced a demographic bulge, with large numbers ofyoung people coming of working age and entering the labor force (Birdsall, Kelley, andSinding, 2001). One would expect this regional increase in labor supply to have put
downward pressure on wages and raised the incentive to emigrate. In some Latin
American countries, birth rates have begun to drop sharply (Bongaarts and Watkins,
1996), but in others they have not. Whereas fertility rates in Mexico are on track to drop
below replacement level by 2020 (Tuiran et al., 2002), they remain high in Central
America and the Andes. Cross-national differences in fertility are useful empirically for
isolating the effects of labor supply on emigration.
Our approach is to estimate how labor supply and demand shocks at the time a
5

In 2001, the UK found and removed 45,000 illegal immigrants from within its borders (OECD, 2004).
As distinct from the US, Spain has frequently regularized illegal immigrants in the country, facilitating
their access to work permits (Dolado and Velasquez, 2007).

6

3

cohort enters the labor market affect initial and subsequent emigration. Since individuals
are most mobile when they are young, shocks at the time of labor market entry may havelong lasting effects on migration. Much of the work on the relationship between income
and migration considers the contemporaneous correlation between living standards andlabor flows.7 By identifying how shocks to young cohorts affect migration over theirworking lives, we provide a dynamic account of how events in origin countries affectinternational labor movements. Linking changes in labor supply to particular birthcohorts requires that we aggregate across skill levels (in order to track origin country
cohorts across time and national borders), preventing us from addressing migrant selfselection, the subject of much recent literature (see, e.g., Hanson, 2010). The payoff isthat we are able to examine international migration over several decades and exploit
sizable cross-origin-country variation in labor supply and demand shocks.
Related literature includes Hanson and McIntosh (2010), who find that variationin labor supply across Mexican regions accounts for nearly a third of regional variation in
Mexican emigration rates, and Clark, Hatton, and Williamson (2007), who find thatcountries with larger populations of young people have higher rates of legal migration to
the US. Because both papers examine a single destination – the United States – they aresilent on how variation in receiving country immigration policy affects the sensitivity of
migration to events in sending countries, a feature that is central to our analysis. Mayda(2009) and Ortega and Peri (2009) find that tightening immigration policy in OECD
countries reduces bilateral migration flows. Still unknown is how immigration policy
affects the responsiveness of migration flows to different types of shocks.
In section 2, we present a simple dynamic model of migration from a given origin
7

See Hanson (2009) for a review of recent literature.

4

country to multiple destinations. In section 3, we describe data on labor supply,
migration rates, economic and political shocks, and other variables. In section 4, we
present the empirical results. And in section 5, we offer concluding remarks.

2 EMPIRICAL FRAMEWORKTo understand emigration from Latin America, we construct a simple model ofbilateral labor flows. We account for differences in worker age but not other aspects ofskill.8 In the origin country, the wage for age group i at time t is,(1)

Wit  X it ( Lit ) ,where Wit is the wage, Xit is other factors that affect the wage (including time-specificshocks, the supply of capital, and the size of other labor cohorts), Lit is the population ingroup i, and η ≤ 0 is the inverse labor-demand elasticity. The supply of labor in theorigin country is the population of group i that has not emigrated, such that(2)

Lit  Li 0  M it

where Li0 is the pre-emigration population of group i and Mit is the number of individuals
in i that have left the country by period t. Putting (1) and (2) together,(3)

ln Wit  ln X it   ln Li 0  mit ,where mit=Mit/Li0 is the fraction of group i that has moved abroad.9
An individual in the origin country has the option to stay at home or move tocountry c. In the year birth cohort i first enters the labor market, the wage in country c is,(4)

Wi 0c  X ic0 ( Lci 0 ) ,8

We ignore other aspects of skill because in order to measure net migration by age in Latin America weneed to track populations by characteristics which are invariant to time.
9
In (3), we utilize the approximation that, for small values of X/Y, ln(X+Y) ≈ lnX + Y/X.

5

where period 0 is the time of labor market entry for group i (such that no emigration has
occurred) and η is the inverse labor-demand elasticity.10 To allow for costs in labormobility between countries, we follow Blanchard and Katz (1992) and Borjas (2006) byassuming that migration from the origin country to destination country c in any period t is
an increasing function of the lagged difference in wages between the two countries:(5)

vitc   c  ln Wi ,ct 1  ln Wi ,t 1  F c  ,where vitc  M itc / Li 0 is the net emigration rate to country c for group i at t, σc ∈ 0,1] isthe supply elasticity, and Fc is the disutility that origin country nationals associate withliving in country c. In the empirical analysis, we allow the labor supply elasticity, σc, tovary by destination country, which captures differences in immigration policy.
To solve the model, define the pre-migration effective wage differential between
the origin country and destination c as,(6)

ic0  ln Wi 0c  ln Wi 0  F c   ln  ci 0  ln xic0  F c .

where ln  ci 0  ln Lci 0  ln Li 0 is initial log relative labor supply and ln xic0  ln X ic0  ln X i 0 is
initial log relative labor demand. The pre-migration wage difference is increasing in theorigin country’s relative labor supply (since η < 0) and decreasing in the origin country’s
relative labor demand. Using (3), (5), and (6), we solve for the t = 0 emigration rate anditerate forward, solving for the wage and emigration rate in each period. Approximatingthat (1+x)t ≈ 1+tx, the net migration rate from the origin country to country c at time t is,
(7)

1

1

Plugging in the determinants of the initial wage differential in (6), we obtain,
10

In later periods, we assume the wage in country c is determined by initial labor supply and subsequentinnovations to labor demand, imposing the restriction that the impact of immigration on the destination
country’s wage is negligible (which is straightforward to relax; see Hanson and McIntosh, 2010).

6

ln ℓ

(8)

1

ln

1

where c  c  0 . Equation (8) shows that migration to country c is decreasing in therelative size of country c’s initial labor supply (since θc < 0) and increasing in initial
relative labor demand (since σc > 0). The effects of initial conditions diminish as a cohort
ages, owing to wage adjustment in the origin (as captured by interactions with t). Since
these dynamic wage adjustment terms depend on the product of elasticities, their effecton migration are likely to be small (which unreported empirical results confirm).
Allowing later labor demand shocks to affect wages introduces distributed lags in these
innovations in (8). In the estimation, we control for labor market shocks that occurred
between the time a cohort comes of working age and the current period.
Equation (8) is the basis for the empirical estimation. For individual birth cohorts
in Latin American countries, we examine the correlation between the decadal rate to a
specific destination country and initial relative labor supply, initial relative labor demand,and innovations to wages. We also control for the size of other labor market cohorts. By
pooling data across cohorts, origins, destinations, and time, we are able to include a rich
set of fixed effects in the estimation to control for unobserved determinants of migration.

3 DATAThe data we require for the estimation include measures of migration rates forpairs of origin and destination countries, labor supply by birth cohort and country, andmeasures of economic shocks for origin and destination countries.

3.1 Bilateral migration rates
To calculate bilateral migration rates we use the number of immigrants by age and7origin country in each destination county’s census count, and the size of the relevant birthcohorts in the origin country, as measured by the World Development Indicators. The
bilateral net migration rate for a given birth cohort and origin-destination pair is then thechange in the stock of immigrants in that cohort from a particular origin country in aparticular destination, divided by the size of the original birth cohort in the origin. In all
regressions, the dependent variable is the annualized bilateral net migration rate for abirth cohort over the relevant time period (in most cases the ten years between censuses).
For the US, we are able to measure age-specific stocks of immigrants from all but
the very smallest Latin American and Caribbean countries in 1980, 1990, 2000, and2005, using data from decennial censuses and the American Communities Survey(2005).11 For Canada, we have similar measures from decennial censuses for 1981, 1991,
and 2001, provided by Statistics Canada. Data for the UK and Spain are more
problematic. For the UK, we have country specific immigration stocks aggregated by
five year birth cohorts in 1981, 1991, and 2001, based on data provided by the UK
Census Commission. For Spain, we have similar data for 1981, 2001, and 2007 (the1991 census reports region rather than country of birth for many countries in the sample).
One problem is that the UK provides incomplete data on immigration stocks for nonCommonwealth countries in the region, as does Spain for countries that are not former
colonies. Consequently, UK and Spanish data are a mix of stocks for individual origin
countries and aggregates of remaining countries in the region. In both cases, the residual
aggregates are very small in size, indicating that few individuals from former Spanish
colonies migrate to the UK or vice versa.

11

We can measure immigrant stocks for the US in earlier years as well, but this is of no use since our dataon births do not begin until 1960 (meaning we cannot measure source-country labor supply before 1976).

8

To gauge the magnitude of emigration from Latin America and the Caribbean,
Table 1 reports total emigration rates in 2000 by origin country, as well as the fraction ofemigrants residing in the US, Canada, Spain, and the UK, using data from Parsons at al.
(2007). Excluded are Cuba, which severely restricts emigration, and countries with fewer
than 200,000 inhabitants in 2000, on which we have incomplete data.
Evident in Table 1 is variation in the attractiveness of the four destinations toemigrants from the region. In the Caribbean and Central America, the share of emigrants
going to the four destinations is above 50 percent in all countries, except Nicaragua,12 andabove 70 percent in all other countries except Haiti, a former French colony, and Antigua
and Barbuda. In more remote South America, the share of emigrants going to the four
destinations exceeds 50 percent for only two countries, Ecuador and Guyana. For
Bolivia, Chile, Paraguay, and Uruguay, neighboring Argentina is an important
destination; the share of emigrants going to the four destinations plus Argentina is above
60 percent for each country. For Colombia, neighboring Venezuela is an important
destination; the share of its emigrants going to the four destinations plus Venezuela isover 80 percent. Thus, in South America nearby rich nations compete for migrants withmore distant high-income countries. We also see that Argentina and Brazil – SouthAmerica’s largest nations – have low emigration rates, under 2 percent in either case. For
this reason we exclude from the countries in Table 1 Argentina, which in the sample
period is more a destination for migration than an origin, and Brazil, which as a former
Portuguese colony sends few migrants to the US, Canada, the UK, or Spain. We focus on
migration rates for individuals aged 16 to 40, as these are peak years for migration(Hanson and McIntosh, 2010).
12

In 2000, 43% of Nicaragua’s emigrants resided in neighboring Costa Rica.

9

To gauge the variation in migration rates for the sample cohorts, Table 2 shows
the annualized average changes in migration rates across cohorts by origin anddestination country pair in the latest available period. Aside from a few small Caribbean
countries, migration to the US rises most rapidly for nearby Mexico and Central America(El Salvador, Honduras, Guatemala), migration to Canada rises most rapidly for former
British colonies (Antigua and Barbuda, Grenada, Guyana, Jamaica, Trinidad andTobago), and migration to Spain varies across origins, with Bolivia and, particularly,
Ecuador being strong outliers.13

3.2 Labor market shocks
The first labor market shock we consider is the change in labor supply, as
predicted by differences in birth rates across countries. We measure labor supply using
the number of live births in each country, as reported in World Development Indicators,
which begin in 1960. Assuming that individuals enter the labor force at age 16, thenumber of individuals born, say, in El Salvador in 1970 would indicate the number ofindividuals coming of working age in 1986. Column 1 of Table 2 shows substantial
variation in the growth rate of birth cohorts across origin countries during the period 1960
to 1985, which corresponds to the growth rate of cohorts coming of working age from1976 to 2001. The size of birth cohorts grew rapidly in many poor countries (e.g.,
Guatemala, Haiti, Honduras, Paraguay) but grew slowly or declined in the region’s richer
countries (e.g., Antigua and Barbuda, Barbados, Chile, Uruguay), though a few richer
countries had high birth rates into the mid 1970s (e.g., Mexico, Venezuela).
In using number of births to measure labor supply, we ignore variation across13

On Ecuadoran migration to Spain, see Bertoli, Fernández-Huertas Moraga, and Ortega (2010).

10source countries in both mortality rates and labor force participation rates, data on which
we cannot obtain by age and year. While cross-country variation in mortality rates is aconcern, there are two reasons why it is unlikely to be a significant problem for ouranalysis. One is that we focus on migration of those of prime migration age, which is 16
to 40. For individuals out of childhood but not yet in middle age, variation in mortality
across Latin American countries is low. More importantly, much of the variation in
mortality rates is absorbed by the country and time dummies that we include in theestimation. In unreported regressions of annual mortality rates for nations in Latin
America on country dummies and year dummies, the adjusted R squared is 0.94 for infantmortality, 0.95 for under-5 mortality, and 0.86 for adult mortality. Thus, most of thecross-country variation in mortality can be removed by removing country-specific means
and time-specific means from the data, which we do in the empirical analysis.
An important question is whether the factors that produce variation in fertility
across countries are correlated with emigration, potentially confounding our empiricalanalysis. The literature associates national differences in levels and changes in fertility
with a large set of determinants (see, e.g., Dasgupta, 1995; Galor, 2005; Lehr, 2009).
Because realizations on emigration are observed between 16 and 40 years after the shifts
which caused the changes in birth cohort size, we take these changes to be predetermined for our analysis. We assume that, given country, year, and cohort fixed
effects, the most plausible explanation for correlation between country-level birth cohort
size and subsequent migration is the cohort size itself.
To control for how changes in labor demand affect migration, we include in theestimation of equation (8) per capita GDP in the year a cohort entered the labor market,

11

as well as contemporaneous per capita GDP, for both the origin and destination country.
(As we control for origin and destination country fixed effects in the regressions, percapita GDP effectively picks up how differential income values in a given year affectmigration.) Over our time period, Latin America experienced multiple balance of
payments crises, frequent natural disasters, and episodes of intense civil unrest, events
that disrupt the lives of individuals, reducing their income and wealth and often
displacing them from their homes. While these shocks are temporary, they are often
severe in nature, leading to temporary or permanent emigration. We construct measuresof the incidence of these shocks equal to the number of events that occur in a country
over a given time period divided by the number of years in the period, which we refer toas the annualized shock incidence, as reported in an appendix.
To capture balance of payments crises, which are typically followed by a banking
crises and collapse in GDP, we use the measures of sudden stops in Cavallo (2007),
which indicates whether a country has a large decline in its current account, with foreign
capital inflows suddenly reversing and becoming capital outflows. Calvo (1998)
associates such episodes with a loss in investor confidence in a country, as occurs wheninvestors downgrade expectations about a country’s capacity to service its debts ormaintain a pegged exchange rate. Cavallo’s definition of a sudden stop is whether acountry experiences a decline of greater than two standard deviations in a current accountsurplus in successive years, where he measures the standard deviation four different
ways. We take the average incidence across the four measures between census intervals.
Mexico, Colombia, and Ecuador are the countries most prone to capital inflow reversals,with 11 other economies experiencing at least one sudden stop in recent years. Nine

12

countries experience no sudden stops, with seven of these being Caribbean nations.
Natural disasters are a common occurrence in Latin America, given its proximity
to the Ring of Fire and exposure to tropical storms in both the Caribbean and Pacific.
Following Yang (2008), we define a serious natural disaster as an earthquake over 7.5 on
the Richter scale, a windstorm (e.g., hurricane) lasting a week or more, or a landslide or
volcanic eruption that affects more than 1000 people. We count the number of events
that occur between census intervals. Data on these events are from the International
Emergency Event Database (http://www.emdat.be/). Mexico, Ecuador, Nicaragua, andHonduras have the highest incidence of natural disasters, with only seven countries
escaping a serious disaster during the sample period.
The last three decades have been a time of political transition in Latin America,
with military coups displacing democratically elected governments during the 1960s and1970s, followed by a return to democracy in the 1980s and 1990s. Armed insurgencies
have occurred in over a half dozen countries, with these conflicts involving thousands ofcasualties and lasting for a decade or more. We measure conflict as the fraction of years
between census intervals in which a serious conflict exists (be it extra-state, intra-state,internal, or internationalized internal in nature) that resulted in the deaths of over 1,000
people. The source is the CSCW Monadic Armed Conflict Database from theInternational Peace Research Institute (http://www.prio.no/). Colombia, El Salvador,
Guatemala, and Nicaragua are the most conflict prone countries, with each country being
subject to a conflict of some type in one quarter or more of the sample years.

3.3 Immigration policy in receiving countries
The four main receiving countries for Latin American emigration differ

13considerably in their immigration policies. The US, which is the most important
destination for Latin American emigrants, manages immigration through granting
permanent residence visas and temporary work visas, and enforcing US territory against
illegal immigration. The 2,000 mile long US border with Mexico makes illegal entry an
attractive option for migrants from Latin America. In 2005, the last year of our US
sample, there were 18.9 million immigrants from Latin America and the Caribbean
residing in the US, of whom 46.2% were estimated to be in the country illegally (Passel,
2006). The majority of legal immigrants from Latin America enter as family members of
US citizens and residents. In 2005, family sponsored visas accounted for 76.6% of US
legal inflows from the region, with employer sponsored visas (the majority of whom areskilled workers) accounting for 13.6% and refugees and asylees accounting for only 1.9%
(DHS, 2005).14 While the US is relatively open to inflows of low-skilled labor from
Latin America, few individuals in the region qualify as skilled workers and fewer still(outside of Cuba) as refugees or asylees. The latter outcome is the consequence of US
policies that tend to favor asylum claims from individuals fleeing left-wing regimes overclaims from individuals fleeing right-wing ones (see, e.g., Kirkpatrick, 1982).
Canada has long managed its immigration policy through a regime that favors
skilled workers, the legal basis for which was established in 1976 and modified several
times since (Mayda and Patel, 2004). Individuals earn points for entry depending on their
youth, education, work experience, ability to speak English or French, and having a job
offer from a Canadian employer. In 2001, the last year in our Canadian sample, skilled
immigrants accounted for 60.6% of permanent immigration visas, family members of
Canadians 26.6%, and refugees and asylees 11.3% (OECD, 2004). Because of the
14

These figures exclude Cuba, for which 90.0% of immigrants are refugees or asylees.

14emphasis on skills, Latin America, where education levels remain relatively low,
accounts for a small share of Canadian immigration, comprising 8.0% of legal inflows in
2001. If an individual from Latin America cannot qualify for a Canadian visa on thebasis of skills or family, the primary means of entry would be through asylum.
The UK belongs to the European Union and allows for the unrestricted movementof EU citizens. Outside of the EU, immigration is limited to family members of UK
citizens, skilled workers, temporary workers with a job offer from a UK employer,
citizens of Commonwealth countries with UK ancestry, and refugees and asylees.
Commonwealth citizens aged 17 to 30 who lack UK ancestry may qualify for a “working
holiday” in which they spend two years in the UK, with eligibility to work for one ofthese.15 Some individuals may abuse such visas by staying on in the country and working
illegally. In 2001, the last year of our UK sample, asylum seekers accounted for 24.5%
of immigration admissions, temporary foreign workers 22.8%, and EU citizens 16.2%,
with the remainder made up by family members of UK citizens and skilled workers(OECD, 2004). In 2002, which is after our UK sample period, the UK implemented apoint system intended to expand skilled immigration (Mayda and Patel, 2004). For Latin
America, opportunities to migrate to the UK would appear to be limited primarily to
Commonwealth citizens and refugees and asylum seekers.
Spain’s immigration policy is somewhat difficult to specify. As an EU member,it allows the unrestricted movement of EU citizens. Until the late 1980s, the country was
primarily a source of emigration. Following the sudden increase in immigration inflows
in the 1990s, government policy responded slowly, being concerned initially with how totreat those who had already found a way into the country. It appears that a large fraction
15

See http://www.ukvisas.gov.uk.

15

of non-EU immigrants who entered Spain in the 1990s and 2000s did so illegally or as
visitors (Dolado and Velasquez, 2007). For those able to obtain employment, thegovernment has been relatively permissive in granting legal work permits, offering
multiple amnesties to undocumented workers in the last two decades. The mostsignificant barrier to migrants from Latin America entering Spain may not be obtaining avisa but the cost associated with travel, establishing residence, and finding initial
employment as an undocumented worker. Recently, Spain has expanded the number ofwork visas it supplies in an attempt to direct immigration through legal channels,
requiring prospective migrants to line up a job before entering the country.

4 RESULTS
4.1 Basic Regressions
Table 3 provides an initial view of the results by estimating the migration effectsof labor supply and demand shocks. The dependent variable in all specifications is theannualized percent net migration rate for a given birth cohort and origin-destination pair(i.e., the change in the stock of immigrants in that cohort from a particular origin country
in a particular destination, divided by the size of the original birth cohort in the origin).
Let N ic be the number of children born in origin i for birthyear cohort c. When we use
the censuses of destination country j in year t , we count M ijct , the number of migrants
from each origin cohort in the destination at that time. The stock migration rate at a
M
moment in time is then mijct  100*  ijct
 and the flow migration rate between two
N
ic 


census intervals is dmijct  mijct  mijct 1 . This rate will be different depending on theamount of time having elapsed between censuses, and so we divide it by the time interval
16to arrive at our annualized dependant variable, admijct . The specification for our coreregression is then:admijct   1 log( N ic / N jc )   2 log(GDPic / GDPjc )   X ict   i   j   c  t   ijct .

ˆ1 is the core parameter of interest; this gives the extent to which average migration
flows are increased when the origin cohort is large relative to the destination cohort,
controlling for a set of origin cohort/time level characteristics and for fixed effects fororigin, destination, birth cohort group and census year group.
The first four columns of Table 3 present impacts by destination country and thelast column pools observations across destinations. Data are for 5-year birth cohorts.
Initially, we exclude the size of younger or older cohorts from the regression. Column 5pools data across destinations and includes destination country fixed effects as well.
The analysis features variables that enter at the origin country level, the origin
birth cohort level, and the destination birth cohort level. The data therefore have a nonnested multi-level structure, and it is not clear how we should handle the standard errors.
The number of clusters is relatively small across most of the primary dimensions (25birth countries, 4 destinations, 4 destination census waves, and 7 aggregated birthcohorts), and so our ability to estimate consistent cluster-level covariance terms islimited.16 As a conservative approach to estimating standard errors that nonethelessprovides sufficient observations for consistent estimation, we cluster our analysis at the
dyad level (origin * destination), for 71 dyads in a dataset of 804 observations.17 In thefirst four columns of Table 3, this is equivalent to clustering by origin country.
16

Cluster asymptotics are based on the number of clusters and not the number of observations per cluster(Cameron, Gelbach, and Miller 2008).
17
We have experimented with different clustering structures, and all results discussed here are robust to thealternative strategy of clustering by origin country.

17

For the US, we find a strong impact of a demographic push created by large origin
birth cohorts. In Table 3, the log birth cohort size ratio (birth cohort size in origin/birthcohort size in destination) enters positively and highly significantly. Emigration rates arehighest for the middle age group, comprising individuals 23-34 years old. Further,emigration is weakly increasing in the initial income of a cohort and strongly decreasing
in distance to the origin country. The marginal effects indicate that a 10% increase in therelative size of the origin birth cohort will lead to a 0.36 percentage point increase in the
net migration rate to the US over the course of 10 years, similar to the number foundusing cross-Mexican state variation in Hanson and McIntosh (2010).18 Put differently,
the 52% decrease in total fertility rates that occurred in Mexico between 1978 and 2002
(from 5 to 2.4, Tuiran et al. 2002) would alone cause the fraction of a Mexican birthcohort that migrates to the US each decade to drop by almost 2 percentage points.
Canada displays patterns that are similar but considerably muted relative to the
US. For Canada, the marginal effect of a given labor supply shock for migration is 1/24th
as large, and insignificant, and the marginal effect of distance is 1/13th as large, andsignificant. Results for Spain, for which we have 96 observations, show that migration
rates are insignificantly correlated with initial cohort size and decreasing in distance tothe origin. For the UK, for which we have only 40 observations, we obtain a negative
correlation between birth cohort size and migration and, again, a negative correlation
between migration rates and distance to the origin.
18

Is it reasonable that such similar marginal effects are found in Hanson & McIntosh (2010) using crossstate Mexican variation, and in this paper using cross-country variation? On the one hand we might expect
lower marginal effects in this sample because we include distant countries with low sensitivity to labor
supply shocks. On the other hand, the impact of cross-state variation on migration propensities will be
dampened by migration within Mexico, which provides a safety-valve for states that see radically different
population growth than their neighbors. The similarity of the marginal effects suggest that these two forcesbalance out in aggregate.

18

The final column of Table 5 pools observations across countries and allows
coefficients for the regressors to differ for the US. As suggested by the results in the first
four columns, the impact of initial birth cohort size is significantly larger for the US, as is
the impact of distance to the origin country. Thus, for the US we see greater sensitivity
to labor supply shocks in origin countries and greater sensitivity to proximity to origin
countries. A 10% increase in the size of an origin birth cohort will result in about 0.1
percentage point more of that cohort migrating to the US over a decade than to the otherdestinations, and for every 1000 km distance from the destination differential migration
to the US falls by 0.7 percentage points relative to other destinations. That birth cohort
size and distance matter relatively more for bilateral migration to the United Statesreflects the greater importance of low-skilled flows of illegal workers in total US
immigration from Latin America.
In Table 4, we introduce two changes to the specification. First, we allow
coefficients to differ for birth cohort size in the origin and in the destination country, incontrast to the regressions in Table 3, which impose homogeneity of the coefficients.
Second, we introduce the size of younger and older cohorts into the estimation, to allow
for the possibility that younger and older workers are an important source of competition
for jobs in the origin country labor market. We again examine results for each
destination separately and then for the four destinations pooled.
For the US, the coefficient on origin country birth cohort size is positive andsignificant and on destination birth cohort size is negative and significant, consistent withthe model in section 2. While the absolute value of the origin country coefficient isroughly twice as large as for the destination country, we cannot reject that the coefficients19are equal and opposite in sign. The relatively small coefficient on destination country
birth cohort size may reflect the modest variation left in this variable, once birth cohortand census wave fixed effects are removed from the estimation. The coefficients onolder and younger birth cohorts are insignificant for the US, as they are for the othercountries. In later regressions, we will exclude these variables from the analysis.
For Canada, we see coefficient estimates that are qualitatively similar to the US –the coefficient on origin country birth cohort size is negative and on destination country
birth cohort size is positive – but much smaller in quantitative magnitude. For Spain andthe UK, we have insufficient observations to include destination country birth cohort size
as a regressor, given that the regressions already have controls for destination country,
birth cohort, and census wave fixed effects. The final column of Table 4 shows that,similar to Table 3, relative birth cohort size in the origin country contributes to migration,with significantly stronger effects in the United States.

4.2 Additional labor market shocks
We next consider how a broader set of shocks affect migration. Our data provide
an intuitive way to examine the impact of shocks on migration because we have longtime series over many countries, and so observe a sufficiently large number of shocks inthe data to estimate precise impacts. The three shocks we consider in Table 5 are theannualized number of serious natural disasters (earthquakes, windstorms, landslides,
volcanic eruptions), the annualized number of sudden stops (severe negative reversals in
the current account), and the annualized incidence of civil and military conflict (years inwhich there are more than 1,000 conflict-related deaths). Table 5 takes the pooled data
structure to the analysis of origin-country shocks in driving emigration from the20

Americas, similar to column 5 of Tables 3 and 4.
In column 1, the incidence of natural disasters has a negative and insignificant
effect on migration to the four destinations as a group, but a positive and strongly
significant effect on migration to the United States. The positive effect on US migrationis seen by adding the coefficient on natural disasters to the coefficient on the interaction
between natural disasters and the US dummy variable, a sum which is positive andsignificant (as shown by the second to last row of the table). Similarly, in column 2
sudden stops have a negative effect on migration to the four destinations overall but apositive effect on migration to the US, although the total effect of sudden stops on US
migration is only marginally significant. These results stand in contrast to column 3, in
which the incidence of civil conflict has a positive and significant effect on migration to
the four destinations but a negative effect on migration to the United States.
Whereas negative economic shocks in the form of disasters or balance of
payments crises contribute to greater migration flows to the US, they do not contributesignificantly to migration to Canada, the UK, or Spain. The reverse holds true forpolitical shocks in the form of civil unrest, which leads to greater migration to Canada,
the UK, and Spain but not to the United States. These estimates indicate that a one
standard deviation increase in the incidence of natural disasters (e.g., the Bahamas
instead of Costa Rica in 2005) would increase migration to the US by about 8 percentage
points over ten years and decrease migration to the other destinations by a similar
amount. These results are consistent with Yang’s (2008) findings that a Latin Americancountry experiencing a serious windstorm (i.e., hurricane) is followed by a substantial
increase in remittances from immigrants abroad. A one standard deviation increase in21

annualized civil conflict, on the other hand (e.g., Peru instead of Honduras in the 1990s)
would increase migration to the other destinations by about one third of a percentage
point over 10 years while decreasing migration to the US by a similar amount.
The difference in these results reflects the differences in migration policies in thedestinations. The US is relatively open to economic migration of low skilled workers
from Latin America through illegal entry but relatively closed to political refugees,
except for those coming from left-wing countries, which in the sample period applies
only to Cuba (which is not in our data) and Nicaragua. Canada, the UK, and Spain, in
contrast, are less open to illegal immigration and more open to asylum seekers, insulating
their exposure to economic shocks in Latin America but enhancing their exposure topolitical shocks in the region.
Having examined the effects of labor supply and labor demand shocks
independently, we conclude our empirical analysis by testing whether ‘American
exceptionalism’ is also found in the intersection of these two types of shocks. Table 6
interacts labor supply shocks (birth cohort ratios) with labor demand shocks (natural
disasters, sudden stops, and conflict) and uses a triple-interaction with a US dummy totest for heterogeneity of effects across destinations. This triple-interaction further
reinforces the uniqueness of the US as a destination. Large cohorts are particularly prone
to migrate to the US when they face a sudden stop, and this effect is significant within the
US alone and strongly different from the pattern relative to other destinations. Largecohorts facing civil conflict, on the other hand, are somewhat more likely to migrate toother destinations but significantly less likely to migrate to the US. These results
demonstrate that the impact of labor demand shocks is modulated by cohort-level labor22

supply in a manner that is strongly heterogeneous across destinations.

4.3 Robustness Checks
One concern could arise in our analysis if it were the case that countries that had
shocks in one period always had them in later periods, or if the impact of the shocks
themselves displayed sufficient persistence. In unreported results, we calculateannualized shock variables over the preceding census interval and include them in a
specification similar to Table 5. We find natural disasters to be the only type of shockwith any persistence in migration impacts, but the lagged effects are always of the same
sign and with a reduced magnitude from the original shock. The heterogeneity
observable in the response to shocks for migration to the US is very similar in the
response to lagged shocks. Inclusion of these lags does not change our overall read on
the results, which is that natural disasters disproportionately increase migration to the US
and political shocks increase it to the other destinations.
In other unreported results, we examined whether the results may be driven by
Mexico, which is the largest source country for immigrants in the US. All of our results
are robust to dropping Mexico from the estimation sample. We also considered whether
the importance of agriculture may mediate the impact of labor market shocks. Since thesupply of agricultural land is relatively inelastic, increases in labor supply may have amore negative effect on wages in agriculture dependent economies than in economies
specialized in manufacturing (which is intensive in the use of relatively elasticallysupplied capital). We found no evidence that the level of agricultural development inorigin countries matters for emigration.

23

5 DISCUSSION
We intersect data on the size of birth cohorts in origin countries with data on thesize of immigrant stocks by age and origin country in the US, Canada, Spain and UK to
examine factors associated with emigration from 25 Latin American and Caribbean
countries over the period 1980 to 2005. Our cohort panel data cover a long time spanover a broad set of countries and therefore provide a good platform for examining how
large shocks contribute to migration. We find that for migration to the US labor supply
shocks, in the form of abnormally large or small birth cohorts, are a significant pushfactor, while they are weakly correlated or uncorrelated with migration to Canada, Spain,or UK. Additionally, natural disasters and balance of payments crises increase migration
to the US, but not to the other destinations, whereas civil conflict has the reverse effect,
decreasing migration to the US but raising it to Canada, Spain and the UK.
These results draw a picture of one destination, the US, that is uniquely engaged
in a demographic dance with its neighbors. Inaccessibility by land, along withimmigration regimes that are more formulaic and asylum-based, have effectively turned
off a susceptibility to labor supply-driven migration in Canada, the UK, and Spain. The
United States displays a similar insensitivity with respect to the far-off countries of SouthAmerica. With its close neighbors, migration rates to the US respond strongly toshocks; larger or richer cohorts are most likely to migrate to the US, with this sensitivity
heightened by economic volatility in the destination.

24

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26

Table 1: Emigration Rates in Latin America and the Caribbean, 2000

Origin Country
Antigua & Barbuda
BahamasBarbados
Dominican Republic
GrenadaHaiti
Jamaica
Trinidad & Tobago
MexicoBelizeCosta Rica
El Salvador
GuatemalaHonduras
Nicaragua
PanamaArgentina
Bolivia
BrazilChile
Colombia
Ecuador
GuyanaParaguay
Peru
Uruguay
Venezuela
Total

Emigration rate
0.625
0.124
0.401
0.111
0.678
0.096
0.371
0.258
0.105
0.214
0.03
0.163
0.055
0.058
0.107
0.066
0.017
0.047
0.006
0.036
0.04
0.058
0.503
0.079
0.029
0.076
0.015
0.051

Share of emigrants
from US, Can, Spain,
UK
0.562
0.895
0.852
0.828
0.711
0.643
0.884
0.878
0.928
0.857
0.736
0.871
0.835
0.822
0.448
0.82
0.41
0.188
0.304
0.249
0.443
0.768
0.84
0.053
0.491
0.233
0.558
0.754

The emigration rate is the share of emigrants (as measured by Parsons
et al., 2007) in the total population. Very small countries in Latin
America and the Caribbean are excluded.

27

Table 2: Average Annualized Percent Change in Migration by Origin
and Destination, Latest Available Year

Origin Country:
Antigua-Barbuda
BahamasBarbados
BelizeBolivia
Chile
Colombia
Costa Rica
Dominican Republic
EcuadorEl Salvador
GrenadaGuatemala
GuyanaHaiti
Honduras
Jamaica
MexicoNicaragua
PanamaParaguay
Peru
Trinidad & Tobago
Uruguay
Venezuela

Birth cohort
Destination Country:
growth rate in
Origin, 1960-1985: Canada Spain
UK
USA-29.1%
0.073-0.281
90.4%
0.001
0.224
-8.8%
0.031
0.346 0.179
110.3%
0.020
0.027 -0.280
39.2%
0.001
0.624
0.032
3.5%
0.001
0.081
0.021
15.5%
0.003
0.212
0.027
26.0%
0.003
-0.013
0.180
14.3%
0.002
0.366
0.186
44.0%
0.003
1.347
0.132
18.8%
0.009
0.014
0.670
0.434
0.976
61.2%
0.004
-0.003
0.380
20.0%
0.173
0.107 0.974
71.8%
0.014
0.129
63.7%
0.002
0.047
0.462
30.1%
0.081
0.481 0.215
45.7%
0.002
0.000
0.505
63.0%
0.001
0.021
-0.02026.7%
0.000-0.0280.059
76.5%-0.001
0.242
0.023
44.0%
0.003
0.250
0.076
33.6%
0.081
0.230 0.347
-4.5%
0.003
0.499
0.247
65.3%
0.002
-0.0980.038

Notes: The years for which the columns 2-4 correspond are 2001 for Canada and theUK, 2005 for the US, and 2007 for Spain.

28

Table 3: Impact of Labor Supply Shocks
Five-year Birth Cohorts
Dependent Variable:
Annualized migration rate overcensus interval, percent

USA

CAN

SPN

UK

PooledLog Birth Cohort Size Ratio

0.355

0.015

-0.202-0.4500.120(4.09)**(1.15)(1.10)(3.68)*(2.74)**

0.148

0.014

-0.039-0.0090.059(2.22)*(1.74)(0.82)(0.18)(1.27)

0.032-0.0191.532-1.091

0.102(0.42)(1.11)(4.66)**(4.33)*(1.15)-0.079-0.004-0.0360.022-0.063(2.58)*(1.29)(1.23)(0.95)(2.70)**-0.1290.001-0.065-0.098-0.060(1.84)(0.28)(2.02)(2.56)(1.15)-0.306-0.023-0.472-0.805-0.052(5.47)**(2.71)*(4.65)**(4.48)*(1.09)Log GDP pc Ratio at Age 16

Log GDP pc in year of census
Young (16-22)
Older (35-40)
Dyadic Distance, (000 km)
Log Birth Cohort Size Ratio * US

0.085
(3.04)**

Distance * US

-0.070(5.05)**

Observations

426

242

96

40

804

Birth country, birth cohort, and census wave fixed effects included in all specifications.
Destination fixed effects included in column 5. Regressions are weighted by the size of theorigin birth cohort.
* significant at 95%, ** significant at 99%, t-statistics in parentheses and SEs clustered byorigin/destination dyad.

29

Table 4: Controlling for Adjacent Cohort Size
Dependent Variable:
Annualized migration rate overcensus interval, percent

USA

CAN

SPN

UK

All

Log Origin birth cohort size

0.464

0.028

-0.392-0.3980.155(4.16)**(1.30)(1.60)(1.56)(3.16)**-0.226-0.021-0.106(2.17)*(1.05)(1.78)

0.195

0.016

-0.030-0.0060.077(4.07)**(1.72)(0.56)(0.09)(1.84)

0.010-0.0191.803-1.0220.090(0.15)(1.12)(4.28)**(5.73)**(1.13)

11.4305.777-7.1332.129-2.745(0.33)(1.31)(1.80)(1.15)(0.44)-46.971-5.2765.695-0.425-4.232(1.27)(1.40)(1.58)(0.17)(0.59)-0.071-0.006-0.027-0.477-0.047(2.53)*(1.35)(0.98)(7.29)**(2.65)*-0.0670.004-0.0660.000-0.035(1.69)(1.26)(1.80)(.)(0.98)-0.362-0.032-0.608-0.772-0.053(5.30)**(2.22)*(4.15)**(5.28)**(0.98)Log Destination birth cohort size
Log GDP pc Ratio at Age 16
Log GDP pc in year of census
Log origin birth cohort size, (next/this)Log origin birth cohort size, (this/last)
Young (16-22)
Old (35-40)
Dyadic Distance (000 km)
Log Origin cohort size * US

0.086
(2.99)**Log Destination cohort size * US

0.116
(1.33)

Distance * US

-0.070(5.01)**

Observations

425

242

80

30

777

All regressions calculated using five-year birthyear cohorts. Birth country, birth cohort, and census
wave fixed effects included in all specifications, plus Destination country FE in column 5. Regressions
are weighted by the size of the origin birth cohort.
* significant at 95%, ** significant at 99%, t-statistics in parentheses and SEs clustered byorigin/destination dyad.

30

Table 5: The Impact of Labor Demand Shocks
Shocks:Dependent Variable:
Annualized migration rate overcensus interval, percent.
Natural Disasters * USAnnualized # of
Serious Natural
Disasters(per '000 square
km.)

Annualized # of Annualized Civil
Sudden Stops
Conflict

61.51967.482(2.79)**(2.66)*

Sudden Stops * US

0.626

0.716

(1.48)(1.83)

Civil Conflict * USNatural Disasters
Sudden Stops
Civil Conflict
Log Cohort Size Ratio
Cohort Size Ratio * USAll

-0.334-0.332(3.49)**(3.37)**-30.087

25.86827.452-36.489(1.33)(3.29)**(3.66)**(1.39)

0.154-0.2070.171-0.390(0.94)(0.93)(1.00)(1.49)

0.000

0.001

0.193

0.215

-0.01

(0.03)(3.06)**(3.06)**

0.086

0.101

0.084

0.103

(1.09)(1.49)(1.04)(1.37)

0.089

0.041

0.066

0.062

(3.52)**(2.40)*(3.21)**(3.59)**Log GDP Ratio

0.084

0.091

0.096

0.092

-1.52

(1.76)(1.74)(1.60)

Dyadic Distance ('000 km)

0.077

0.085

0.054

0.039

(1.10)(1.14)(1.04)(0.88)

Distance * US

-0.059-0.079-0.071-0.059(4.69)**(6.51)**(7.06)**(7.32)**

642

642

642

642

0.00

0.10

0.09

Observations
p-value on F-Test that the shock issignificant in U.S.:

Mean of the Shock Variable:
0.00060.07240.1201
All regressions calculated using five-year birthyear cohorts, with birth cohort, birth country, destination country,
and census wave fixed effects included in all specifications. Regressions weighted by origin birth cohort size.
Cohort age dummies included as controls but not reported in the table.
* significant at 95%, ** significant at 99%, t-statistics in parentheses and SEs clustered by origin/destination dyad.
# of Serious Natural Disasters is the sum, over census intervals, of earthquakes over 7.5 Richter, windstorms lastinga week or more, or landslides or volcano eruptions affecting more than 1000 people in sending country.
# of Sudden Stops is the the sum, over census intervals, of Sudden Stops 1-4 from Cavallo data, defined as a fall in
FA surplus of at least 2 SD from sample mean, with standard deviations calculated four different ways.
Civil Conflict is from CSCW Monadic Armed Conflict data, calculated as the number of years between census
intervals in which a serious conflict exists (Extra-state, Intra-state, Internal, or Internationalized Internal) that killedover 1000 people in sending country.

31

Table 6: Interacting Supply- and Demand-side Shocks
Shocks:Dependent Variable:
Annualized migration rate over census
interval, percent.
Cohort Ratio * Disasters * USAnnualized # of
Serious Natural
Disasters(per '000 squarekm.)
28.537

Annualized # of
Sudden Stops

Annualized Civil
Conflict(1.15)
Cohort Ratio * Sudden Stops * US

1.239
(4.03)**

Cohort Ratio * Conflict * US

-0.408(5.75)**

Cohort Ratio * Disasters-5.208(0.31)

Cohort Ratio * Sudden Stops-0.941(4.02)**

Cohort Ratio * Conflict

0.091(1.22)

Disasters
Disasters * US
Sudden Stops
Sudden Stops * US
Conflict
Conflict * US
Log Cohort Ratio
Log Cohort Size Ratio * US
Log GDP Ratio
Dyadic Distance ('000 km)
Distance * US
Observations
p-value on F-Test that the interaction
between the shock and the cohort size
effect is significant in U.S.:-39.986-39.891(1.16)(1.72)-22.120(1.03)

143.780

66.13548.462(2.21)*(3.10)**(2.34)*-0.4010.166-0.466-1.56(0.95)

-1.82

0.727

0.507

0.819

(1.91)(1.43)(2.27)*

0.211

0.194

0.151

(3.07)**(4.34)**(2.65)*-0.317-0.268-0.943(3.38)**(3.34)**(4.51)**

0.101

0.195

0.083

(1.39)(2.51)*(1.04)

0.055-0.0130.070(2.89)**(0.46)(4.16)**

0.095

0.107

0.084

(1.64)(1.73)(1.45)

0.039

0.021

0.010

-0.88

(0.55)-0.35-0.059-0.033-0.056(7.50)**(3.42)**(7.04)**

642

642

642

0.13

0.05

0.00

Mean of the Shock Variable:
0.00060.07240.1201
All regressions calculated using five-year birthyear cohorts, with birth cohort, birth country,
destination country, and census wave fixed effects included in all specifications. Regressions areweighted by the size of the origin birth cohort. Cohort age dummies included but not reported.
* significant at 95%, ** significant at 99%, t-statistics in parentheses and SEs clustered byorigin/destination dyad.

32

Appendix: Annualized values of shocks

Origin Country:
Antigua-Barbuda
Bahamas
BelizeBolivia
Barbados
Chile
Colombia
Costa Rica
Dom. Republic
Ecuador
GrenadaGuatemala
GuyanaHonduras
Haiti
Jamaica
MexicoNicaragua
PanamaPeru
Paraguay
El Salvador
Trin. & Tobago
Uruguay
Venezuela# of Serious
NaturalDisasters# of Sudden
Stops

0.05
0.04
0.05
0.13
0.00
0.16
0.02
0.10
0.04
0.34
0.00
0.15
0.00
0.18
0.04
0.01
0.42
0.20
0.00
0.18
0.00
0.12
0.00
0.00
0.03

0.00
0.00
0.00
0.03
0.03
0.11
0.09
0.06
0.00
0.07
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.03
0.04
0.04
0.04
0.01
0.03
0.04
0.04

Fraction ofyears inwhich Civil
Conflict
0.00
0.00
0.00
0.00
0.00
0.42
0.00
0.04
0.00
0.26
0.00
0.08
0.00
0.00
0.00
0.23
0.00
0.31
0.00
0.31
0.00
0.00

Number of Serious Natural Disasters: The sum, over census intervals, of earthquakes
over 7.5 Richter, windstorms lasting a week or more, or landslides or volcano
eruptions affecting more than 1000 people.
Number of Sudden Stops: The sum, over census intervals, of Sudden Stops 1-4 from
Cavallo (2007), defined as a year-on-year fall in the current account surplus of at least
two standard deviations from the sample mean, with the standard deviation calulated
four alternative ways.
Civil Conflict: Calculated as the number of years between census intervals in which a
serious conflict exists (Extra-state, Intra-state, Internal, or Internationalized Internal)that killed over 1000 people in a country.

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