The Welfare Effects of Encouraging Rural-Urban Migration

David Lagakos

Mushfiq Mobarak

Michael E. Waugh

UCSD and NBER

Yale University

NYU and NBER

May 19, 2017

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Misallocation and Cross-Country TFP Differences

• Cross-country differences in income per capita accounted for largely by

TFP, i.e. “a measure of our ignorance” (e.g. Caselli, 2005)

• Misallocation as a theory of TFP: factors of production inefficiently

allocated across firms or sectors

• Hsieh & Klenow, 2009; Restuccia and Rogerson, 2009 + enormous

follow-up literature

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Rural-Urban Income Gaps = Worker Misallocation? Large urban-rural living-standards gaps in cross section • Value added per worker (Gollin, Lagakos and Waugh, 2014) • Consumption measures (Young, 2014) • Wages (Herrendorf and Schoellman, 2015)

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Rural-Urban Income Gaps = Worker Misallocation? Large urban-rural living-standards gaps in cross section • Value added per worker (Gollin, Lagakos and Waugh, 2014) • Consumption measures (Young, 2014) • Wages (Herrendorf and Schoellman, 2015)

Rural-urban migrants increase their consumption • Bryan, Chowdhury, Mobarak (2014): Induced seasonal migrants

experienced 30% increase in consumption. • In tracking data: e.g. Beegle, De Weerdt, Dercon (2011) in Tanzania;

LSMS Panel Surveys from Tanzania and Uganda

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This Paper: Welfare Effects of Subsidizing Rural-Urban Migration Draw on unique evidence from Bangladesh • 156 million people, $900 per capita • Urban wages 80% higher than rural • Fall “lean season”: very low productivity in rural areas

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This Paper: Welfare Effects of Subsidizing Rural-Urban Migration Draw on unique evidence from Bangladesh • 156 million people, $900 per capita • Urban wages 80% higher than rural • Fall “lean season”: very low productivity in rural areas

Migration experiment of Bryan, Chowdhury & Mobarak (2014) • 100 randomly selected villages in Rangpur region, in NW Bangladesh • Treatment group offered 600 Taka ($8.50) conditional on seasonal

migration; 200 Taka extra if checked in at destination • Migration rates 58%, compared to 36% in control group • Among those offered treatment and migrated, consumption 30% higher

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60

Seasonal Migration Rates in Bryan, Chowdhury & Mobarak (2014)

20

Control

Difference

0

Migration Rate

40

Incentivized

2008

2009

2010

2011

2012

2013

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What We Do • Build two-region model with migration, featuring:

1. Sorting by comparative advantage (Roy, 1951) 2. Migration disutility that depend on migration experience 3. Incomplete markets as in Bewley-Huggett-Aiyagari - Differential urban & rural risk (Harris & Todaro, 1971) - Credit constraints as in development literature

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What We Do • Build two-region model with migration, featuring:

1. Sorting by comparative advantage (Roy, 1951) 2. Migration disutility that depend on migration experience 3. Incomplete markets as in Bewley-Huggett-Aiyagari - Differential urban & rural risk (Harris & Todaro, 1971) - Credit constraints as in development literature • Novelty: discipline model using experimental evidence

- Perform experiment within model - Match experimental moments using simulated method of moments

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What We Do • Build two-region model with migration, featuring:

1. Sorting by comparative advantage (Roy, 1951) 2. Migration disutility that depend on migration experience 3. Incomplete markets as in Bewley-Huggett-Aiyagari - Differential urban & rural risk (Harris & Todaro, 1971) - Credit constraints as in development literature • Novelty: discipline model using experimental evidence

- Perform experiment within model - Match experimental moments using simulated method of moments • Use model to interpret experiment, compute welfare gains 5 / 51

Summary of Results • To match data, model requires:

- Few rural households with strong comparative advantage in city - Substantial non-monetary disutility of migration that’s “persistent” - Seasonal migration mostly by those with lowest income and assets

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Summary of Results • To match data, model requires:

- Few rural households with strong comparative advantage in city - Substantial non-monetary disutility of migration that’s “persistent” - Seasonal migration mostly by those with lowest income and assets • Welfare gains mostly from targeting funds to poor, vulnerable households

- 0.4% on average; 1.5% for poorest - Better for poor than unconditional transfer program costing same

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Summary of Results • To match data, model requires:

- Few rural households with strong comparative advantage in city - Substantial non-monetary disutility of migration that’s “persistent” - Seasonal migration mostly by those with lowest income and assets • Welfare gains mostly from targeting funds to poor, vulnerable households

- 0.4% on average; 1.5% for poorest - Better for poor than unconditional transfer program costing same • Higher welfare gains in principle when, counterfactually, there is:

- Lower disutility of migration / more ”misallocated” rural workers

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Summary of Results • To match data, model requires:

- Few rural households with strong comparative advantage in city - Substantial non-monetary disutility of migration that’s “persistent” - Seasonal migration mostly by those with lowest income and assets • Welfare gains mostly from targeting funds to poor, vulnerable households

- 0.4% on average; 1.5% for poorest - Better for poor than unconditional transfer program costing same • Higher welfare gains in principle when, counterfactually, there is:

- Lower disutility of migration / more ”misallocated” rural workers • New “discrete choice” experiment: housing key component of disutility 6 / 51

Model

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Households and Preferences Unit mass of households; preferences: ∞ X

β t u(ct )u¯xt

t=0

• u(ct ) = ct1−α /(1 − α) • u¯ ∈ R is disutility of migration • xt ∈ {0, 1}, takes on value 1 iff household is “inexperienced at migration”

and in the urban area

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Experience at Migration • A household is either “experienced” or “inexperienced” at migration I

Could reflect e.g. relationship with an employer

• After each period in the urban area I

Inexperienced households remain so with probability λ, and become experienced with probability 1 − λ

I

Experienced households stay experienced

• After each period in the rural area, I

Experienced households stay experienced with probability π and loose their experience with probability 1 − π.

I

Inexperienced households stay inexperienced

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Production Technologies and Seasons One homogenous good produced in both locations, competitive Production technology in rural area: Yr = Ari Nrφ where 0 < φ < 1; Ari is rural productivity indexed by season i, with Arg > Arl • Deterministic transition: If rural productivity is Arg , then the economy

transits to Arl next period. • Idea: mimic seasonal agricultural crop cycles

Production technology in urban area Yu = Au Nu

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Household Location-Specific Productivity Each household has permanent productivity draw as in Roy (1951) • z = permanent productivity in urban area. z ∼ Pareto(θ). • all workers equally productive in rural area.

Each household experiences transitory shocks s that follow an AR(1) process: log st+1 = ρ log st + t+1

with

t+1 ∼ N (0, σs ).

Household-specific efficiency units of labor in each location: • s in the rural area. • zs γ in the urban area. • γ > 1 implies the shocks are more volatile in the urban area.

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Location / Migration Options Households in the rural area can. . . 1. Work in the rural area. 2. Pay fixed cost mT , work in the urban area the next period, return to rural. This is seasonal migration. 3. Pay fixed cost mp > mT , move to urban area next period, stay indefinitely. This is permanent migration. Households in the urban area can. . . 1. Work in the urban area. 2. Pay fixed cost mp , work in rural area for the indefinite future.

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Asset Choices Households can accumulate a non-state contingent asset, a Gross rate of return, R. Assets move with the household. For today. . . • Asset holdings can not be negative (no borrowing) • R is exogenous (storage)

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Who moves? When do they move? All else equal, migration more likely: • Among agents with higher z • In the lean season • When experienced

Policy functions for migration take the form of thresholds in: • The transitory shock, s • Asset holdings, a

Model flexible; let experimental data disciplines whether e.g. agents migrate when assets sufficiently high or sufficiently low Value Functions

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Calibration

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Calibration Overview Pre-assign some parameters Set remaining parameters using simulated method of moments 1. Cross-sectional moments: e.g. urban-rural wage gap, rural share, variances of consumption and earnings. 2. Experimental moments: Perform the Bryan, Chowdhury, Mobarak (BCM) (2014) experiment in model.

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Pre-Assigned Parameters

Parameter

Value

Source

Time period

Half year

—

Risk aversion, α

2.0

—

Discount factor, β

0.95

—

Gross real interest rate, R

0.96

1/ gross inflation rate

Rural seasonal productivity, Arl /Arg 50% drop in rural income Khandker (2012) Seasonal moving costs, mT

10% of rural consumption BCM (2014)

Permanent moving costs, mp

2 × mT

Very few permanent moves

Decreasing returns in rural area, φ

0.91

Akram, Chowdhury, Mobarak (2017)

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Parameters to Calibrate Remaining parameters to calibrate. . . • Productivity urban area: Au • Shape parameter, urban permanent productivity: θ • Standard deviation of transitory shocks: σs • Urban relative risk parameter: γ • Persistence of transitory shocks: ρ • Disutility of migration: u¯ • Probability of gaining experience: 1 − λ • Probability of losing experience: 1 − µ

plus two sources of measurement error. . . to match 10 moments.

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Calibration: The BCM (2014) Field Experiment The BCM (2014) field experiment • 100 randomly selected villages; 19 households in each village • Treatment group given 600 Taka ($8.50) conditional on migration; 200

Taka if they reported in at the destination. • Around two weeks of average rural wage

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Calibration: The BCM (2014) Field Experiment The BCM (2014) field experiment • 100 randomly selected villages; 19 households in each village • Treatment group given 600 Taka ($8.50) conditional on migration; 200

Taka if they reported in at the destination. • Around two weeks of average rural wage

Key results/calibration targets: • 22 percent increase in seasonal migration in treatment relative to control

(58% vs 36%). • 11% increase in migration in the subsequent year (47% vs 36%). • Induced migrants increased their consumption by 30% relative to the

average household (i.e. the LATE of migration on consumption). 19 / 51

Performing the BCM (2014) Experiment in the Model • Randomly sample rural households in the stationary distribution • Draw from bottom half of rural asset distribution, as in experiment • Assign half to treatment, half to control • Solve for optimal policies, treating this offer as one-time, unanticipated,

with no general equilibrium effects. • Treatment group offered mT , if they move. • Compute take up of offer. Compute wages of those that take up.

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Calibration Results — Targeted Moments

Moments

Data

Model

Control: Seasonal migrants

36

36

Control: Consumption increase of migrants (OLS)

10

10

Experiment: Seasonally migration relative to control

22

22

Experiment: Percent of treatment group that seasonally migrate in year 2

9

6

Experiment: Consumption of induced migrants relative to control (LATE)

30

30

1.80

1.80

63

63

Variance of log wages in urban

0.68

0.68

Variance of log consumption growth in rural

0.18

0.18

47

47

Urban-Rural wage gap Percent in rural

Percent of rural households with no liquid assets

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Migration Rates: Data and Model

Migration Rate: Treatment Minus Control

30 Data 95-5 Confidence Interval (Data) Model Prediction

25

20

15

10

5

0

-5 2008

2009

2010

2011

2012

2013

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Calibration Results — Parameters

Parameter

Value

Migration disutility, u¯

1.45

Probability gaining experience, 1 − λ

0.38

Probability losing experience, 1 − π

0.49

Shape parameter, urban talent, θ

2.08

Urban relative shock, γ

0.66

Productivity urban area Au

1.45

Standard deviation of transitory shocks

0.36

Persistence of transitory shocks

0.71

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Migration Policy: Low z, Lean Season, No Experience

Assets

P.Migrate

S.Migrate

Stay

Zero Assets

0.16 0.2

0.27 0.35 0.45 0.59 0.77

1

1.3

1.7

2.21 2.88 3.75 4.89 6.37

Transitory Shock

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Migration Policy: Moderate z, Lean Season, No Experience

Assets

P.Migrate

S.Migrate

Stay

Zero Assets

0.16 0.2

0.27 0.35 0.45 0.59 0.77

1

1.3

1.7

2.21 2.88 3.75 4.89 6.37

Transitory Shock

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Migration Policy: Moderate z, Lean Season, Experience

Assets

P.Migrate

S.Migrate

Stay

Zero Assets

0.16 0.2

0.27 0.35 0.45 0.59 0.77

1

1.3

1.7

2.21 2.88 3.75 4.89 6.37

Transitory Shock

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The Role of Credit Constraints • Workers “stuck” in rural areas due to credit constraints?

• Policy functions in previous slides suggested otherwise.

- Low income, low asset households are more likely to migrate. - Consistent with negative selection in OLS vs. LATE estimates.

• Additional test: unconditional cash transfer

- BCM ran unconditional transfer experiment too; found negligible effects on migration - Model: negligible effects on migration

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The Role of Differential Risk • Urban area is risky and insurance is incomplete, so stay in rural?

(e.g Harris and Todaro, 1970)

• Suggests consumption of movers should be more variable than stayers

• But standard deviation of log consumption growth in fact similar:

Control Group Stay

Treatment Group

Migrate

Stay

Migrate

Data

0.39

0.43

Data

0.41

0.43

Model

0.41

0.43

Model

0.40

0.42

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Calibration Results — Identification

Moments

Data Baseline θ = ∞ u¯ = 1 λ = 0

Migration, Control

36

36

48

29

37

Migration, Experiment

22

22

31

10

16

Migration, Experiment, year 2

9

6

0

0

0

Consumption, OLS

10

10

51

2

10

Consumption, LATE

30

30

20

6

30

1.80

1.80

1.80

1.80

1.80

63

63

57

89

92

Urban-Rural wage gap Percent in rural

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Calibration Results — Identification

Moments

Data Baseline θ = ∞ u¯ = 1 λ = 0

Migration, Control

36

36

48

29

37

Migration, Experiment

22

22

31

10

16

Migration, Experiment, year 2

9

6

0

0

0

Consumption, OLS

10

10

51

2

10

Consumption, LATE

30

30

20

6

30

1.80

1.80

1.80

1.80

1.80

63

63

57

89

92

Urban-Rural wage gap Percent in rural

Migration rates help discipline productivity distribution A larger θ means less talent dispersion, more “marginal” households, more migration. Thus, migration reveals how many are on the margin or not. 29 / 51

Calibration Results — Identification

Moments

Data Baseline θ = ∞ u¯ = 1 λ = 0

Migration, Control

36

36

48

29

37

Migration, Experiment

22

22

31

10

16

Migration, Experiment, year 2

9

6

0

0

0

Consumption, OLS

10

10

51

2

10

Consumption, LATE

30

30

20

6

30

1.80

1.80

1.80

1.80

1.80

63

63

57

89

92

Urban-Rural wage gap Percent in rural Returns to Migration inform u. ¯

Consumption of new migrants must “jump” to compensate for the dislike of migration. 29 / 51

Calibration Results — Identification

Moments

Data Baseline θ = ∞ u¯ = 1 λ = 0

Migration, Control

36

36

48

29

37

Migration, Experiment

22

22

31

10

16

Migration, Experiment, year 2

9

6

0

0

0

Consumption, OLS

10

10

51

2

10

Consumption, LATE

30

30

20

6

30

1.80

1.80

1.80

1.80

1.80

63

63

57

89

92

Urban-Rural wage gap Percent in rural

Migration in year 2 informs dynamics of u. ¯ Some induced migrants keep migrating.

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Calibration Results — Identification

Moments

Data Baseline θ = ∞ u¯ = 1 λ = 0

Migration, Control

36

36

48

29

37

Migration, Experiment

22

22

31

10

16

Migration, Experiment, year 2

9

6

0

0

0

Consumption, OLS

10

10

51

2

10

Consumption, LATE

30

30

20

6

30

1.80

1.80

1.80

1.80

1.80

63

63

57

89

92

Urban-Rural wage gap Percent in rural

Multiple ways to match urban-rural wage gaps. ⇒ Urban-rural wage gap by itself hard to interpret

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Welfare

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Consumption-Equivalent Welfare Gains From Experiment

2

Welfare Gains

1.5

1

0.5

0 0

10 25 40

100 50

83

65 66

80

Income Percentile

100

50, Zero Assets

Asset Percentile

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Welfare From Unconditional Transfer (Same Cost as Experiment)

2

Welfare Gains

1.5

1

0.5

0 0

10 25 40

100 50

83

65 66

80

Income Percentile

100

50, Zero Assets

Asset Percentile

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Increase in Migration: Treatment - Control

Why? The Poor Respond More to the Migration Incentive

60 50 40 30 20 10 0 0

10 25 40

100 50

83

65 66

80

Income Percentile

100

50, Zero Assets

Asset Percentile

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Consumption-Equivalent Welfare Gains

Income Quintile

Conditional Migration Transfer

Unconditional Transfer

Welfare

Welfare | Migr.

Migr. Rate

Welfare

Migr. Rate

1

1.01

1.17

85.8

0.88

46.0

2

0.35

0.59

59.1

0.46

35.5

3

0.21

0.43

48.8

0.34

32.8

4

0.13

0.30

40.9

0.26

28.7

5

0.07

0.20

35.8

0.18

28.5

0.35

0.65

56.0

0.42

36.0

Average

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Consumption-Equivalent Welfare Gains

Income Quintile

Conditional Migration Transfer

Unconditional Transfer

Welfare

Welfare | Migr.

Migr. Rate

Welfare

Migr. Rate

1

1.01

1.17

85.8

0.88

46.0

2

0.35

0.59

59.1

0.46

35.5

3

0.21

0.43

48.8

0.34

32.8

4

0.13

0.30

40.9

0.26

28.7

5

0.07

0.20

35.8

0.18

28.5

0.35

0.65

56.0

0.42

36.0

Average

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Consumption-Equivalent Welfare Gains

Income Quintile

Conditional Migration Transfer

Unconditional Transfer

Welfare

Welfare | Migr.

Migr. Rate

Welfare

Migr. Rate

1

1.01

1.17

85.8

0.88

46.0

2

0.35

0.59

59.1

0.46

35.5

3

0.21

0.43

48.8

0.34

32.8

4

0.13

0.30

40.9

0.26

28.7

5

0.07

0.20

35.8

0.18

28.5

0.35

0.65

56.0

0.42

36.0

Average

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Migration Disutility Important for Welfare Results

Income Quintile

Welfare Gains: Surprise No u¯ After Migration Welfare

Welfare | Migr.

Welfare | Induced

1

3.40

3.84

3.99

2

1.69

2.61

3.05

3

1.12

2.14

2.77

4

0.89

1.83

2.52

5

0.48

1.29

2.30

1.51

2.60

3.21

Average

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Lack of High z Rural Workers Important for Welfare Results • Counterfactual: send representative set of urban households to rural area

– These have the relatively highest z draws in equilibrium – Also give them average assets of rural households – Re-simulate the experiment (still in partial equilibrium) • Welfare gains for this high-z group: 3.3% consumption equivalent

– Virtually all urban again within a few periods

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Additional Evidence: Discrete Choice Experiments

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Discrete Choice Migration Experiment

• New surveys of same villages as BCM (2014); conducted summer 2015

• Present two hypothetical migration options for 2015 lean season (fall

2015) to each respondent; pick Choice #1, Choice #2, or “No Migration”

• Options vary with respect to risk, amenities and wages at destination

• Goal: gauge importance of “migration disutility” relative to other

migration determinants

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Discrete Choice Migration Experiment

S.1.C.2 Given the attributes below, which option do you choose? Please evaluate each new pair of migration options independent of the ones you saw earlier. Choice #1: Migration

Choice #2: Migration

Choice #3: No Migration

Chance of Employment

33%

33%

N/A

Daily Wage (Taka)

270

340

Wage at Home in November

Latrine Facility during Migration

Pucca Latrine in Residence

Walk to Open Defecate or Public Pay Toilet

N/A

Family Contact

See Family Every Month

See Family Every 2 Month

N/A

s16bq2_1 Your Choice (Tick Single Box)

Easy

Somewhat

Very 41 / 51

Estimated Marginal Effects on Migration Estimated Marginal E↵ects on Migration Table 1: Marginal Effects Of Migration Conditions on Migration Condition Migration Opportunity 1

Migration Opportunity 2

ME 0.000 (.)

PP 0.597*** (0.053)

66 Percent Chance of Employment

0.067*** (0.012)

–0.049*** (0.010)

0.732*** (0.046)

0.135*** (0.030)

0.200*** –0.086*** (0.045) (0.029)

100 Percent Chance of Employment

0.048*** (0.009)

–0.068*** (0.012)

0.791*** (0.040)

0.193*** (0.033)

0.161*** –0.125*** (0.038) (0.033)

Family visit once in 60 days

0.071*** (0.014)

0.000 (.)

0.760*** (0.041)

0.000 (.)

Family visit twice in 60 days

0.067*** (0.012)

–0.004 (0.008)

0.732*** (0.046)

–0.027 (0.024)

0.200*** 0.032 (0.045) (0.023)

Family visit 4 times in 60 days

0.058*** (0.012)

–0.013* (0.007)

0.763*** (0.049)

0.003 (0.028)

0.179*** 0.010 (0.046) (0.028)

Walk to Open Defecate or Public Pay Toilet

0.067*** (0.012)

0.000 (.)

0.732*** (0.046)

0.000 (.)

Pucca Latrine in Residence

0.029*** (0.006)

–0.038*** (0.008)

3449

–0.001*** (0.000) 3449

1

Raw Daily Wage(Taka) - Migration Opportunity 2 Observations

ME 0.000 (.)

At Home

PP 0.116*** (0.018)

33 Percent Chance of Employment

0.906*** (0.021)

0.174*** (0.032)

3449

0.004*** (0.000) 3449

PP 0.286*** (0.055)

0.169*** (0.040)

0.200*** (0.045)

ME 0.000 (.)

0.000 (.)

0.000 (.)

0.065*** –0.136*** (0.019) (0.032)

3449

–0.002*** (0.000) 3449

Standard errors in parentheses. * p < .10, ** p < .05, *** p < .01. Standard errors are adjusted for 2,566 clusters in hhid. PP columns represent predicted probabilities of migrating at given condition, and ME columns represent marginal effects of chaning migration conditions in each category. PP and ME are measured while fixing 1st migration conditions (wage, employment chance, family visit, latrine) at the worst, and fixing 2nd migration condition at median. Analysis sample includes only those households in the control group.

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42 / 51

Estimated Marginal Effects on Migration Estimated Marginal E↵ects on Migration Table 1: Marginal Effects Of Migration Conditions on Migration Condition Migration Opportunity 1

Migration Opportunity 2

ME 0.000 (.)

PP 0.597*** (0.053)

66 Percent Chance of Employment

0.067*** (0.012)

–0.049*** (0.010)

0.732*** (0.046)

0.135*** (0.030)

0.200*** –0.086*** (0.045) (0.029)

100 Percent Chance of Employment

0.048*** (0.009)

–0.068*** (0.012)

0.791*** (0.040)

0.193*** (0.033)

0.161*** –0.125*** (0.038) (0.033)

Family visit once in 60 days

0.071*** (0.014)

0.000 (.)

0.760*** (0.041)

0.000 (.)

Family visit twice in 60 days

0.067*** (0.012)

–0.004 (0.008)

0.732*** (0.046)

–0.027 (0.024)

0.200*** 0.032 (0.045) (0.023)

Family visit 4 times in 60 days

0.058*** (0.012)

–0.013* (0.007)

0.763*** (0.049)

0.003 (0.028)

0.179*** 0.010 (0.046) (0.028)

Walk to Open Defecate or Public Pay Toilet

0.067*** (0.012)

0.000 (.)

0.732*** (0.046)

0.000 (.)

Pucca Latrine in Residence

0.029*** (0.006)

–0.038*** (0.008)

3449

–0.001*** (0.000) 3449

1

Raw Daily Wage(Taka) - Migration Opportunity 2 Observations

ME 0.000 (.)

At Home

PP 0.116*** (0.018)

33 Percent Chance of Employment

0.906*** (0.021)

0.174*** (0.032)

3449

0.004*** (0.000) 3449

PP 0.286*** (0.055)

0.169*** (0.040)

0.200*** (0.045)

ME 0.000 (.)

0.000 (.)

0.000 (.)

0.065*** –0.136*** (0.019) (0.032)

3449

–0.002*** (0.000) 3449

Standard errors in parentheses. * p < .10, ** p < .05, *** p < .01. Standard errors are adjusted for 2,566 clusters in hhid. PP columns represent predicted probabilities of migrating at given condition, and ME columns represent marginal effects of chaning migration conditions in each category. PP and ME are measured while fixing 1st migration conditions (wage, employment chance, family visit, latrine) at the worst, and fixing 2nd migration condition at median. Analysis sample includes only those households in the control group.

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Estimated Marginal Effects on Migration Estimated Marginal E↵ects on Migration Table 1: Marginal Effects Of Migration Conditions on Migration Condition Migration Opportunity 1

Migration Opportunity 2

ME 0.000 (.)

PP 0.597*** (0.053)

66 Percent Chance of Employment

0.067*** (0.012)

–0.049*** (0.010)

0.732*** (0.046)

0.135*** (0.030)

0.200*** –0.086*** (0.045) (0.029)

100 Percent Chance of Employment

0.048*** (0.009)

–0.068*** (0.012)

0.791*** (0.040)

0.193*** (0.033)

0.161*** –0.125*** (0.038) (0.033)

Family visit once in 60 days

0.071*** (0.014)

0.000 (.)

0.760*** (0.041)

0.000 (.)

Family visit twice in 60 days

0.067*** (0.012)

–0.004 (0.008)

0.732*** (0.046)

–0.027 (0.024)

0.200*** 0.032 (0.045) (0.023)

Family visit 4 times in 60 days

0.058*** (0.012)

–0.013* (0.007)

0.763*** (0.049)

0.003 (0.028)

0.179*** 0.010 (0.046) (0.028)

Walk to Open Defecate or Public Pay Toilet

0.067*** (0.012)

0.000 (.)

0.732*** (0.046)

0.000 (.)

Pucca Latrine in Residence

0.029*** (0.006)

–0.038*** (0.008)

3449

–0.001*** (0.000) 3449

1

Raw Daily Wage(Taka) - Migration Opportunity 2 Observations

ME 0.000 (.)

At Home

PP 0.116*** (0.018)

33 Percent Chance of Employment

0.906*** (0.021)

0.174*** (0.032)

3449

0.004*** (0.000) 3449

PP 0.286*** (0.055)

0.169*** (0.040)

0.200*** (0.045)

ME 0.000 (.)

0.000 (.)

0.000 (.)

0.065*** –0.136*** (0.019) (0.032)

3449

–0.002*** (0.000) 3449

Standard errors in parentheses. * p < .10, ** p < .05, *** p < .01. Standard errors are adjusted for 2,566 clusters in hhid. PP columns represent predicted probabilities of migrating at given condition, and ME columns represent marginal effects of chaning migration conditions in each category. PP and ME are measured while fixing 1st migration conditions (wage, employment chance, family visit, latrine) at the worst, and fixing 2nd migration condition at median. Analysis sample includes only those households in the control group.

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Conclusions and Future Work

• General-equilibrium model of migration; novelty: disciplined using

experimental evidence

• Welfare gains from migration subsidies mostly from targeting poorest

• Not about reducing misallocation of workers across space

• Future work: comparison to rural employment guarantees; effects of

scaling up migration subsidies

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Extra Slides

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Rural Household’s Problem back

Problem of household in rural area with productivity z:  v (a, r , s, x, i, Nr ) = max v (a, r , s, x, i, Nr | stay),  v (a, r , s, x, i, Nr | perm), v (a, r , s, x, i, Nr | seas)

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Rural Household’s Problem back

Problem of household in rural area with productivity z:  v (a, r , s, x, i, Nr ) = max v (a, r , s, x, i, Nr | stay),  v (a, r , s, x, i, Nr | perm), v (a, r , s, x, i, Nr | seas)

Valuing of staying in rural area, v (a, r , s, x, i, Nr | stay) is:

  max u(Ra + wr (s, i, Nr ) − a0 ) + β E[v (a0 , r , s 0 , x 0 , i 0 , Nr0 )]

a0 ∈A

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Rural Household’s Problem back

Problem of household in rural area with productivity z:  v (a, r , s, x, i, Nr ) = max v (a, r , s, x, i, Nr | stay),  v (a, r , s, x, i, Nr | perm), v (a, r , s, x, i, Nr | seas)

The value of permanently moving, v (a, r , s, x, i, Nr | perm), is:   0 0 0 0 0 0 max u(Ra + w r (z, s, i, Nr ) − a − mp ) + β E[v (a , u, s , x , i , Nr )] 0

a ∈A

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Rural Household’s Problem back

Problem of household in rural area with productivity z:  v (a, r , s, x, i, Nr ) = max v (a, r , s, x, i, Nr | stay),  v (a, r , s, x, i, Nr | perm), v (a, r , s, x, i, Nr | seas)

The value of seasonally moving, v (a, r , s, x, i, Nr | seas), is:

  max u(Ra + wr (s, i, Nr ) − a0 − mT ) + β E[v (a0 , seas, s 0 , x 0 , i 0 , Nr0 )]

a0 ∈A

where v (a0 , seas, s 0 , x 0 , i 0 , Nr0 ) is:   0 00 x 00 00 00 00 00 max u(Ra + w ¯ + β E[v (a , r , s , x , i , N )] u (z, s) − a )u r 00 a ∈A

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The Role of Information • BCM did an information experiment too

- Treatment group instruction on types of jobs in urban areas - Also information on average wages, and where/how to find these jobs - Result: precise zero effect on migration

• We did follow-up surveys on same villagers on wage expectations, 2014

- Ratio of perceived Dhaka wages to rural Rangpur wages = 2.4 - Averages from Household Income and Expenditure Survey = 2.2

• Consistent with model’s assumption of rational expectations

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Wage Expectations

Section 15: Wage Expectations Enumerators: Please tell the respondent to answer these questions for employment inclusive of any fringe benefits. For example, if an agricultural laborer cash-in-hand payment is 100 taka/day and also receives food valued at 20 taka/day their daily wage would be 120 taka/day.

Q# Question s15q1 How much do you think you would make per day if you were to stay at home in November?

Units Taka/Day

s15q2 How much do you think the average permanent resident of Dhaka makes per day in November?

Taka/Day

s15q3 How much do you think the average seasonal migrant in Dhaka makes per day in November?

Taka/Day

Response

s15q4 If you were to migrate to Dhaka this November, Taka/Day how much do you think you would make per day?

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Fraction

.2

.3

Perceived Ratio of Dhaka Wages to Own Wages (2014)

0

.1

Mean = 2.42 Median = 2.33

0

1

2

3

4 Ratio

5

6

7

8

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Actual Urban-Rural Wage Gaps in HIES (2010)

Mean

Median

All urban / All rural

1.80

1.72

All urban / Rangpur rural

2.12

1.87

Dhaka / Rangpur rural

2.20

2.01

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