Diversity in a Globalized World

Lecture 3: People on the Move

Bogdan G. Popescu

Tecnológico de Monterrey

Three numbers

  • 281 million people live in a country they were not born in
  • That is 3.6% of humanity — one person in twenty-eight
  • Money they send home is worth more than all foreign aid combined

Monday’s hit American towns did not empty out. Mexico’s exposed regions were different — people moved.

Today: why they leave, where they go, and what follows them back.

Learning objectives

  • Describe the global pattern of migration and remittances
  • Explain why development can increase emigration
  • Explain why migration clusters in networks
  • Assess what the evidence says about wages in receiving countries

Most people guess too high

Surveys in rich countries find people overestimate the share of migrants around them, usually by more than a factor of two.

Alesina, Miano & Stantcheva (2023), six-country survey.

The true global figure is 3.6%.

The interesting fact is not the size. It is the distribution.

Where migrants live

Figure 1

The extremes are small and rich: Qatar 78%, the UAE 76%, Kuwait 69%. The United States is 15%. Mexico is 1%.

Mexico looks untouched

Table 1
Country Migrants (%) Net migration Remittances (% GDP)
United Arab Emirates 76.0 92,541 NA
Germany 18.0 203,468 0.5
United States 14.9 329,769 0.0
Mexico 1.1 -147,456 3.9

Only 1% of people in Mexico were born abroad. But net migration is negative and remittances are 4% of GDP.

So what?

  • Migration is small globally, concentrated locally
  • One map only shows the countries people arrive in
  • A country can be shaped by migration without receiving anyone

To see the other half, we first need to ask why people leave.

II. Why People Leave

The wage gap is enormous

The same worker, doing the same job, earns several times more in a rich country than in a poor one.

Clemens (2011) — today’s reading.

Clemens (2011) calls the unrealised gains from this gap “trillion-dollar bills on the sidewalk.”

If money were all that mattered, far more people would move than actually do.

What stops them

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flowchart LR
  A["Wage gap<br/>abroad"] --> D{"Decide"}
  B["Cost of<br/>travel"] --> D
  C["Visas and<br/>borders"] --> D
  E["Someone<br/>already there"] --> D
  D --> F["Migration"]
Figure 2

Author’s illustration. The gap creates the incentive; the other three decide who can act on it.

It is not the poorest who leave

  • Moving costs money: transport, documents, months without earnings
  • The very poorest cannot pay those costs
  • So emigration is low in the poorest countries

As a country grows richer, more people can afford to leave — before rising wages at home eventually make them stay.

Who gains and who loses

Figure 3

What that graph cannot show

  • Net migration is arrivals minus departures
  • A country can send many people abroad and still gain people overall
  • So we cannot read emigration off it

Research that does measure emigration finds the hump: it rises with income across poor countries, then falls (Clemens, 2014; Dao et al., 2018). The mechanism is the one we just built — the poorest cannot pay the cost, and the richest no longer need to.

Why migration clusters

  • The first migrant faces the highest cost and the greatest risk
  • The second follows a known route, to a known contact
  • Each arrival lowers the cost for the next

Munshi (2003) shows this for Mexican migrants: those with larger established networks in the United States were more likely to be employed on arrival.

Exercise 1 — Who leaves?

Two villages, same country. Going abroad costs 50,000 pesos, paid before you leave.

  • Village A: a family earns 20,000 pesos a year
  • Village B: a family earns 60,000 pesos a year

Quick first: which village sends more people abroad?

Now the real question. A new road halves the cost, to 25,000 pesos. Which village changes more?

Five minutes, in pairs.

So what?

  • Wage gaps create the incentive; costs decide who can act
  • Development first raises emigration, then lowers it
  • Networks make migration self-reinforcing once it starts

Everyone who leaves is still connected to the place they left. That connection has a price, and it is measurable.

Remittances: the second map

Figure 4

Almost the opposite map

  • Map 1 was dark in the Gulf, North America, Australia
  • Map 2 is dark in Central America, the Caucasus, West Africa
  • Same phenomenon, seen from each end

Mexico is pale on the first map and clearly visible on the second.

The most dependent economies are small ones: Kyrgyzstan 32% of GDP, Tajikistan 27%, Nepal 24%.

Mexico, 1990 to 2023

Figure 5

What that money does

  • It goes to households, not governments
  • It rises when the sending household is in trouble
  • It is more stable than foreign investment during crises

Yang (2011) reviews the evidence: remittances raise consumption and schooling, and cushion shocks.

The other side of the money

Remember who leaves: not the poorest. Leaving costs money.

  • So the money comes back to households that could already afford to send someone
  • Inside a village, remittances can widen the gap they are meant to close

Two other costs have their own literatures: the loss of skilled workers, smaller and more mixed than “brain drain” suggests (Docquier & Rapoport 2012), and what years apart cost families raising children (Parreñas 2005).

So what?

  • Migration binds two economies together, not one
  • The receiving country gains workers; the sending country gains income
  • Neither map alone describes what happened

Which leaves the question everyone actually argues about.

The question everyone asks

Do migrants lower wages for workers already there?

The theory is simple: more workers, same jobs, lower pay.

Testing it is hard, because migrants choose where to go — they move to places already doing well.

A natural experiment

  • April 1980: Cuba briefly allowed emigration from the port of Mariel
  • About 125,000 people left for Florida in six months
  • Miami’s labour force grew roughly 7% almost overnight
  • Miami’s labour supply jumped for reasons unrelated to Miami’s economy

Mariel, 1980

Cuban refugees arriving in Florida during the Mariel boatlift, 1980. Source: Wikimedia Commons, public domain.

Two economists, one event

  • Card (1990): Miami against four similar cities → no measurable effect on wages
  • Borjas (2017): only non-Hispanic men, 25–59, without a high-school diploma → a large fall
  • Peri & Yasenov (2019); Clemens & Hunt (2019): same data, re-examined → the fall does not survive

So the argument is not about what happened in Miami. It is about which workers you count.

Borjas’s group is a few dozen men a year in the survey. The share of Black workers in that small sample rose sharply after 1980, and Clemens and Hunt show that alone can produce the drop.

Why they disagree

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  A["Same event:<br/>Mariel 1980"] --> B["Which<br/>workers do<br/>you count?"]
  A --> C["Which<br/>cities do you<br/>compare?"]
  B --> D["Different<br/>answer"]
  C --> D
  D --> E["The answer<br/>depends on<br/>those choices"]
Figure 6

Author’s illustration, after Card (1990) and Borjas (2017).

What we can say

  • Average effects on wages are small in most studies
  • Effects concentrate on workers most similar to arrivals
  • The answer depends heavily on how you define the comparison group

National Academies (2017) reviews the US evidence: effects on natives overall are very small; where negative effects appear, they fall on earlier immigrants and on workers without a high-school diploma.

That is not a dodge. It is the honest state of a genuinely contested literature.

Exercise 2 — Design the test

125,000 people arrive in Miami in four months, and wages change. Did they cause it? You need a second city — one that shows what Miami would have done anyway.

1. Which city would you use? What goes wrong with it?

Atlanta Los Angeles Tampa
no big arrivals; economy growing fast in the 1980s a gateway city — new arrivals every year same state; smaller, older, more retirees

2. Whose wages would you look at? all workers · no high-school diploma · construction workers · Cubans already in Miami

Five minutes, in pairs.

Exercise 2 — Responses

The city. Card used four averaged together — Atlanta, Houston, Los Angeles, Tampa — so no single city’s quirks decide the answer.

Whose wages Verdict
Cubans already in Miami closest substitutes — where Card looked
No high-school diploma right idea, tiny sample — the Borjas problem
Construction workers moves with Miami’s own building boom
All workers 125,000 arrivals vanish in a big market

A good comparison is not a city that looks like Miami. It is one that was moving like Miami until 1980.

Takeaways

  1. Migration is 3.6% of humanity, very unevenly placed
  2. Development raises emigration before it lowers it
  3. Networks make migration self-reinforcing
  4. Remittances bind sending and receiving economies together
  5. Wage effects are small on average, contested at the margins

A country can be transformed by migration without a single migrant arriving.

References

Alesina, A., Miano, A., & Stantcheva, S. (2023). Immigration and redistribution. The Review of Economic Studies, 90(1), 1–39.

Borjas, G. J. (2017). The wage impact of the Marielitos: A reappraisal. Industrial and Labor Relations Review, 70(5), 1077–1110.

Card, D. (1990). The impact of the Mariel boatlift on the Miami labor market. Industrial and Labor Relations Review, 43(2), 245–257.

Clemens, M. A. (2011). Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives, 25(3), 83–106.

Clemens, M. A., & Hunt, J. (2019). The labor market effects of refugee waves: Reconciling conflicting results. ILR Review, 72(4), 818–857.

Clemens, M. A. (2014). Does development reduce migration? In R. E. B. Lucas (Ed.), International Handbook on Migration and Economic Development (pp. 152–185). Edward Elgar.

Dao, T. H., Docquier, F., Parsons, C., & Peri, G. (2018). Migration and development: Dissecting the anatomy of the mobility transition. Journal of Development Economics, 132, 88–101.

References (cont.)

Docquier, F., & Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681–730.

National Academies of Sciences, Engineering, and Medicine. (2017). The Economic and Fiscal Consequences of Immigration. Washington, DC: National Academies Press.

Munshi, K. (2003). Networks in the modern economy: Mexican migrants in the U.S. labor market. Quarterly Journal of Economics, 118(2), 549–599.

Peri, G., & Yasenov, V. (2019). The labor market effects of a refugee wave: Synthetic control method meets the Mariel boatlift. Journal of Human Resources, 54(2), 267–309.

Parreñas, R. S. (2005). Children of Global Migration: Transnational Families and Gendered Woes. Stanford University Press.

United Nations, Department of Economic and Social Affairs. (2021). International Migrant Stock 2020. New York: United Nations.

World Bank. (2024). World Development Indicators. Washington, DC: World Bank.

Yang, D. (2011). Migrant remittances. Journal of Economic Perspectives, 25(3), 129–152.