International Migration: a panel data analysis of the determinants of bilateral flows

Anna Maria Mayda, published online: 28 May 2009

Presented by Etienne Le Rossignol and Killian Foubert

Empirical Methods in Development Economics, October 2016

1Empirical Methods in Development Economics

Introduction

International migrations patterns vary considerably over time and across destination and origin countries

What can influence labor movements ?

Related literature about determinants of migration :

- Clark et al. (2007) and Karamera et al. (2000) focusing on the fundamentals explaining immigrant inflowsinto the US by country of origin in the last decades. Same thing for Borjas (1987), Borjas and Bratsberg(1996).

- Helliwell (1998) studied factors affecting labor movements in his investigation of the magnitude of immigration border effects

2

Investigation of the determinants of migration inflows into 14 OECD countries by country of originbetween 1980 and 1995.

She studied the effect on migration of :

- Average income and income dispersion in destination and origin countries

- Geographical factors

- Cultural factors

- Demographic factors

- The changes in destination countries’ migration policies

Three contributions :

- The analysis empasizes destination countries’ migration policies

- She uses panel data on a large and varies sample (14 OECD countries)

- The paper reviews and proposes solutions to various econometric issues

3

Introduction

Differenciation between :

- Supply side and demand side in migration flows

- Supply : People who can potentially migrate

- Demand : Countries through their migration policies

- Push and pull factors of migration flows

- Push : Income in the origin country

- Pull : Expected income in the destination country

4

Introduction

The empirical results are puzzling.

Pull proxied by GDP/Worker in country 1 potitive and ***butPush proxied by GDP/Worker in country 0 does not have the expected sign and is notsignificant!

3 possible interpretations to this:

1- Heterogeneity of the dataset2- Migration quotas are effectively not binding3- The demand side of international migration, e.g. Migration policies.

Extensions: Inequality/ Distance/ Cultural variables/ Demography/ Networks

5

Introduction

Theoretical Model – Supply side

Supply side : migrants’ decisions to move according to economic and noneconomic incentives

Probability that an individual chosen randomly from the population of a country 0 migrates to country 1 isconditional to the wages that 𝑖 receives in each of them such as:

𝜔0𝑖 = 𝛼0 + 𝜃0 × 𝑠𝑖 + 𝜖0𝑖𝜔1𝑖 = 𝛼1 + 𝜃1 × 𝑠𝑖 + 𝜖1𝑖

This can be rewrite as:

𝜔0𝑖 = 𝜇0 + 𝜗0𝑖 with 𝜗0𝑖~𝒩 0, 𝜎02 wages in country 0 for individual coming from 0

With 𝜇0 = 𝛼0 + 𝜃0 × 𝑠0

𝜔1𝑖 = 𝜇10 + 𝜗1𝑖 with 𝜗1𝑖~𝒩(0, 𝜎1

2)wages in country 1 for individual coming from 0

With 𝜇10 = 𝛼1 + 𝜃1 × 𝑠0

With 𝑠0 the mean skill level of the origin country’s population.

6

Hypothesis:

• Each individual has a Cobb-Douglas preference for the two goods produce in the world (𝑥𝐴, 𝑥𝐵) -> indirect utility function

𝑣 𝑝𝐴, 𝑝𝐵; 𝑦 = 𝐴 𝑝𝐴, 𝑝𝐵 𝑦

• Individual from country 0 moves to country 1 if the expected income in country 1 net of migration costs is greater than the expected income in 0

𝐼𝑖 = 𝜂01 × 𝑤1𝑖 − 𝐶𝑖 − 𝑤0𝑖

So the probability that an individual moves from 0 to 1 is:𝑃 = Pr 𝐼𝑖 > 0 = Pr[𝜂0𝑖 × 𝜇1

0 + 𝑣1𝑖 − 𝜇𝑐 + 𝑣𝑖𝑐 − 𝜇0 + 𝑣0𝑖 > 0]

Can be re-written as 𝑃 = 1 − 𝜙 𝑧

The probability is then the supply of emigration rate such as 𝐼01𝑠

𝑃0

7

Theoretical Model – Supply side

Theoretical Model – Demand side

8

Is the maximum number of migrants from country 0 allowed each year into country 1 = Demand for immigrants

If quotas are Not Binding =

If they are Binding <

What if the impact of on the immigration rate?

9

Theoretical Model – Demand side

What is the effect on the emigration rate from country 0 to country 1 of a change in ?

10

Theoretical Model – Demand side

Inpact of the second moment of the income distributions, of the origin country’s population at home and abroad.We assume that so we obtain :

If 𝜎0

𝜎1< 1 and 𝜌01 > 𝜎0/𝜎1 then d𝜎0 > 0 or d𝜎1 < 0 will increase the immigration rate.

Similarly, if 𝜎0

𝜎1> 1 and 𝜌01 > 𝜎1/𝜎0 then d𝜎0 > 0 or d𝜎1 < 0 will decrease the immigration rate.

Data

• Data on yearly immigrant inflows into 14 OECD countries by country of origin between 1980-95 -> International Migration Statistics (IMS) for OECD countries

Problem: only covers legal immigration through population registers, residence and work permits

• Sample : 79 origin countries with per worker GDP levels ranging from $1.000 to $55.000

Problem: sum by country of origin is not equal to 100% of the total flow into each destination country.

The dataset includes zero flows in correspondence of some country pairs (for example Italy -> US)

11

DataImmigration:

- International Migration Statistics (IMS) based on (SOPEMI)

Macroeconomic variables:

- 2001 World Development Indicators from the World Bank- Penn World Tables

Geographic and cultural information:

- Glick and Rose’s dataset, (2002)

Schooling years:

- Barro and Lee’s dataset, (2000)

Gini Coefficient:

- Deininger and Squire dataset, (1996)

Population:

- The United Nations dataset. 12

13

14

Data

Empirical Model

• The estimating equation includes

• Dependent variable = emigration rate

• Explanatory variables = mean wage of the origin country’s population in the origin and destination countries -> proxy: log level of per worker GDP (Purchasing Power Parityadjusted) in the two countries

• Expectations = Pull effect positive and push negative (if not binding), 0 otherwise

• Another determinant of flows = cost 𝐶𝑖• Distance

• Common land border

• Linguistic and cultural similarity

• Past colonial relationships

• Demographic determinant of migration flows = share of population in the origin country between15 and 29 years old

• Extension of the basic model = the individual has expectations

15

16

Empirical Model

• Endogeneity in time series dimension

• Reverse causality. Wage -> emigration rate BUT EMPIRICALLY we find that emigration rate -> wage

Immigration -> decreasing wages in the destination country

Emigration -> increasing wages in the origin country

• This issue can be solved

• Relate current emigration rates to lagged values of log per worker GDP at home and abroad

17

Empirical Model

Empirical Results

18

• Investigate whether per worker GDP of origin and destination countries is a good proxy for mean income opportunities

• Not a direct measure of wages (depends on rates of return to both capital/labor and endowments)

• Test this proxy with robustness checks

• Column 4 control for the average schooling level in bothcountries

• Results = pull: negative and significant ; push: samethan before

• Column 5 control for the mean skill level and per workercapital endowment in destination and origin countries

• Results = right sign but not significant + prior findingson pull and push factors robusts

19

Empirical Results

• Column 6: only include significant determinants fromregression 1,2,3 -> log distance and share of youngpeople (in origin country)

• Consistent with previous results

• Column 7: robustness check for the asymmetrybetween push and pull factors to use a tobitspecification -> larger in magnitude than OLS

• Column 8: Fixed effects for each combination of originand destination countries

• Confirmation that pull and push factors have an asymmetric effect in terms of magnitude and significance levels

• Column 9: Investigate the interaction between changes in destination countries’s migration policies and pull/push factors

• Pull factors bigger when migration policies not binding

• Once migration restrictions are relaxed: push factors turn negative and significant

• Adding log distance and share of young people: both turn more pronounced when a host country’sbecomes less restrictive (negative and positive) 20

Additional Results

21

Conclusion

1. Pull factors increase the size of emigration rates.

2. Effect of Push factors is seldom negative, and when it is the size of the effect is smaller than for Pull factors and insignificant.

3. Push effects suggest that migration quotas are more binding then Pull effects do.

4. Role played by migration policies

5. Distance negative significant effect

6. Demographic share of young positive and significant

22

Criticism & Open question

• In the theoretical framework, the author assumes that eachindividual knows the wage level 𝑤1𝑖 and 𝑤0𝑖he would get in each location, the migration cost 𝐶𝑖 and the probability 𝜂01

• To solve the reverse causality issue, Mayda uses lagged independent variable, saying that migration flowsonly affect wages at t+n

Open question: reflexion about illegal immigration

23