Supporting Information for

Economic Impact of Refugees

J. Edward Taylor, Mateusz Filipski, Mohamad Alloush, Anubhab Gupta,

Irvin Ruben Rojas, and Ernesto Gonzalez

Correspondence to: jetaylor@ucdavis.edu

This file includes:

Table S1

Table S2

Table S3

Table S4

Table S5

Table S6

Table S7

Table S8

Table S9

Table S10

Fig. S1

Datasets for this manuscript include the following:

Dataset S1_LEWIE_Model_Kigeme(in-kind)_forTable1.gms.txt

Dataset S2_LEWIE_Model_Gihembe(cash)_forTable1.gms.txt

Dataset S3_LEWIE_Model_Nyabiheke(cash)_forTable1.gms.txt

Dataset_S4_LEWIE_data_input_sheet_new.xlsx

Tables

Table S1: Simulated Impacts of an Additional Adult Refugee in Three Congolese

Refugee Camps in Rwanda

Refugee Impacts (US$ per

additional refugee)

A B C

Gihembe

(Cash)

Nyabiheke

(Cash)

Kigeme (in-

kind)

Real income (Inflation-adjusted) $205.71 $252.86 $145.71

[166, 260] [194, 320] [133, 161]

WFP transfer $126.31 $120.11 $113.75

Other tranfers $9.57 $6.75 $7.33

Total Spillover $69.83 $126.00 $24.64

To Refugees $28.40 $56.00 $37.50

To Host-country Households $41.43 $68.57 -$12.86

Production By Sector (Nominal)

Crop $102.86 $144.29 $48.57

Livestock $4.29 $2.86 $1.43

Retail $80.00 $82.86 $52.86

Other $51.43 $67.14 $40.00

Trade with Rest of Rwanda $54.89 $48.67 $35.39

The numbers in the table are impacts of an additional refugee within a 10 km radius of each camp and on

trade with the rest of Rwanda. 95% confidence intervals on total real-income impacts (in brackets) were

constructed by making random draws from all parameter distributions (Tables S8-S9), recalibrating the

base model, and repeating each simulation 1000 times. Dollar values calculated using an exchange rate of

700 RWF/US$.

Table S2. Set, Subset and Mapping Names Used in Model Statement

SETS

Subsets

g commodities gtv Goods locally tradable

f factors gtz Goods traded in outside markets

h or hh households gp Locally produced goods

gag Agricultural goods

gnag Nonagricultural goods

v camps fk Fixed factors

ft Locally tradable factors

Mappings ftw Factors traded in outside markets

maphv(h,v) Mapping of households to camps fpurch Purchased variable inputs

Table S3. Commodities, Factors, Households, and Camps

Commodities

Crop Local crops: produced and consumed within the cluster

Livestock Local livestock, produced and consumed within the cluster

Retail Local retailers in the cluster

Other Services and other production

Outside good Any commodity purchased outside the local economy

Factors

Labor Labor (family and hired receiving wage in cash or kind)

Land Land

Capital Capital

Input Purchased inputs

Households

Refugee Refugee households in camps

Host Host-country households within 10 km radius of camp

Camps Gihembe (cash), Nyabiheke (cash), Kigeme (in-kind)

Table S4. Variable Names Used in Model Statement

VARIABLES

Values

Consumption and income

PV(g,v) price of a good at the cluster level QC(g,h) quantity of g consumed by h

PZ(g) price of a good at the regional level Y(h) nominal household income

PH(g,h) price as seen by household h (=PV or PZ) RY(h) real household income

PVA(g,h)

price of value added net of intermediate

inputs CPI(h) consumer price index

R(g,f,h) rent for fixed factors TROUT(h)

transfers given by a household of

others

WV(f,v) wage at the cluster level SAV(h) household savings

WZ(f) wage at the regional level EXPROC(h)

household expenditures out of the

region

Production Trade

QP(g,h) quantity produced of a good by a household HMS(g,h)

household marketed surplus of good

g

FD(g,f,h) factor demand of f in production of g VMS(g,v) cluster marketed surplus of good g

ID(g,gg,h) intermediate demand for production of g ZMS(g) Regional marketed surplus of a good

QVA(g,h) quantity of value added created HFMS(f,h)

factor marketed surplus from the

household

HFD(f,h) factor demand in the household VFMS(f,v)

factor marketed surplus out of the

cluster

HFSUP(f,h)

labor supply from the household (elastic

endowment) ZFMS(f)

factor marketed surplus out of the

region

Table S5. Parameter Names Used in Model Statement (GAMS)

PARAMETERS

Production Consumption

a(g,h)

Shift parameter in CD

production function alpha(g,h) consumption share parameters in the LES

beta(g,f,h)

Factor share parameters (CD

exponents) cmin(g,h) minimal consumption in the LES

vash(g,h) Value-added share of output exinc(h) exogenous income of household

idsh(gg,g,h) Intermediate input share vmsfix(g,v) fixed marketed surplus at the village level

fixfac(g,f,h) Fixed factor endowments Transfers

vfmsfix(f,v)

Factors fixed at the local level

(family, hired labor) troutsh(h) share of transfers in household expenditures

exprocsh(h)

share of expenditures outside 10 km radius

of camp

endow(f,h) Household factor endowments savsh(h) share of income saved

hfsupzero(f,h) Initial labor supply trinsh(h)

share of total transfers received by a given

household

hfsupel(f,h) Factor supply elasticity For Experiments

transfer(h) WFP transfer to household

pibudget(g,h) Liquidity constraint on inputs hfsnewref(ft,h) Refugee labor supply

pibsh(g,h) Share of pibudget to good g packsold(g) In-kind transfer sold on market

Table S6. Equation Definitions

Equation Name Description

* prices

EQ_PVA(g,h) prive value added equation

EQ_PH(g,h) market price as seen from household h

* production

EQ_FDCOBB(g,f,h) factor demands cobb douglas

EQ_FDPURCH(g,f,h) factor demands for purchased inputs - constrained or not

EQ_QVACOBB(g,h) quantity VA produced cobb douglas

EQ_QP(g,h) quantity produced from QVA and ID

EQ_ID(gg,g,h) quantity of ID needed for QP

* consumption

EQ_QC(g,h) quantity consumed

* income

EQ_Y(h) full income constraint for the household

EQ_CPI(h) consumer price index equation

EQ_RY(h) real household income equation

* transfers

EQ_TRIN(h) inter household transfers in (received)

EQ_TROUT(h) interhousehold transfers out (given)

* exogenous expenditures

EQ_SAV(h) savings (exogenous rate)

EQ_EXPROC(h) expenditures outside of the zoi (exogenous rate)

* goods market clearing

EQ_HMKT(g,h) qty clearing in each household

EQ_VMKT(g,v) market clearing in the village

EQ_ZMKT(g) market clearing in the zoi

EQ_VMKTfix(g,v) price definition in the village (camp+sourroundings)

EQ_ZMKTfix(g) price definition in the zoi

* factor market

clearing

EQ_HFD(f,h) total household demand for a given factor

EQ_FCSTR(g,f,h) fixed factors constraint

EQ_HFSUP(f,h) household elastic supply

EQ_HFMKT(f,h) tradable factor clearing in the household

EQ_VFMKT(f,v) tradable factors clearing in the village

EQ_ZFMKT(f) tradable factor clearing in the zoi

EQ_VFMKTfix(f,v)

wage determination for tradable factors clearing in the

village

EQ_ZFMKTfix(f) wage determination for tradable factors clearing in the zoi

* In case of nlp solve

EQ_USELESS trick to make gams think it's maximizing something

Table S7. Equations in the Model

Name Equation

1) HOUSEHOLD EQUATIONS

Price Block

EQ_PH(g,h).. 𝑃𝐻𝑔,ℎ = [𝑃𝑍𝑔]

𝑔∈𝑔𝑡𝑧 ∪𝑔𝑡𝑤+ [∑ 𝑃𝑉𝑔,𝑣

𝑣|𝑚𝑎𝑝ℎ𝑣(ℎ,𝑣) ]

𝑔∈𝑔𝑡𝑣

EQ_PVA(g,h).. 𝑃𝑉𝐴𝑔,ℎ = 𝑃𝐻𝑔,ℎ − ∑ 𝑖𝑑𝑠ℎ𝑔𝑎,𝑔,ℎ × 𝑃𝐻𝑔𝑎,ℎ𝑔𝑎

Production Block

EQ_QVACOBB(g,h).. 𝑄𝑉𝐴𝑔,ℎ = 𝑎𝑔,ℎ × ∏(𝐹𝐷𝑔,𝑓,ℎ)𝛽𝑔,𝑓,ℎ

𝑓

EQ_FDCOBB(g,f,h) [𝑅𝑔,𝑓,ℎ]

𝑓∈𝑓𝑘+ [𝑊𝑍𝑓]

𝑓∈𝑓𝑡𝑧+ [∑ 𝑊𝑉𝑓,𝑣

𝑣|𝑚𝑎𝑝ℎ𝑣(ℎ,𝑣)]

𝑓∈𝑓𝑡𝑣

=𝑃𝑉𝐴𝑔,ℎ × 𝑄𝑃𝑔,ℎ × 𝛽𝑔,𝑓,ℎ

𝐹𝐷𝑔,𝑓,ℎ

EQ_QP(g,h) 𝑄𝑃𝑔,ℎ = 𝑄𝑉𝐴𝑔,ℎ/𝑣𝑎𝑠ℎ𝑔,ℎ

EQ_ID(gg,g,h).. 𝐼𝐷𝑔𝑎,𝑔,ℎ = 𝑄𝑃𝑔,ℎ × 𝑖𝑑𝑠ℎ𝑔𝑎,𝑔,ℎ

Consumption and income block

EQ_QC(g,h).. 𝑄𝐶𝑔,ℎ =

𝛼𝑔,ℎ

𝑃𝐻𝑔,ℎ× (𝑌ℎ − 𝑇𝑅𝑂𝑈𝑇ℎ − 𝑆𝐴𝑉ℎ − 𝐸𝑋𝑃𝑅𝑂𝐶ℎ − ∑ 𝑃𝐻𝑔𝑎,ℎ × 𝑐𝑚𝑖𝑛𝑔𝑎,ℎ

𝑔𝑎)

+ 𝑐𝑚𝑖𝑛𝑔,ℎ

EQ_Y(h).. 𝑌ℎ = ∑ (𝑅𝑔,𝑓𝑘,ℎ × 𝐹𝐷𝑔,𝑓𝑘,ℎ)𝑔,𝑓𝑘

+ ∑ 𝑊𝑍𝑓𝑡𝑧 × 𝐻𝐹𝑆𝑈𝑃𝑓𝑡𝑧,ℎ𝑔,𝑓𝑡𝑧

+ ∑ ∑ 𝑊𝑉𝑓𝑡𝑣,𝑣 × 𝐻𝐹𝑆𝑈𝑃𝑓𝑡𝑣,ℎ𝑣|𝑚𝑎𝑝ℎ𝑣(ℎ,𝑣)𝑓𝑡𝑣

+ ∑ 𝑊𝑍𝑓𝑡𝑤 × 𝐻𝐹𝑆𝑈𝑃𝑓𝑡𝑤,ℎ𝑓𝑡𝑤

EQ_TROUT(h).. 𝑇𝑅𝑂𝑈𝑇ℎ = 𝑡𝑟𝑜𝑢𝑡𝑠ℎℎ × 𝑌ℎ

EQ_EXPROC(h).. 𝐸𝑋𝑃𝑅𝑂𝐶ℎ = 𝑒𝑥𝑝𝑟𝑜𝑐𝑠ℎℎ × 𝑌ℎ

EQ_SAV(h).. 𝑆𝐴𝑉ℎ = 𝑠𝑎𝑣𝑠ℎℎ × 𝑌ℎ

EQ_CPI(h).. 𝐶𝑃𝐼ℎ = ∑ 𝑃𝐻𝑔,ℎ × 𝛼𝑔,ℎ𝑔

EQ_RY(h).. 𝑅𝑌ℎ =

𝑌ℎ

𝐶𝑃𝐼ℎ

2) MARKET CLOSURE:

Market clearing block for commodities

EQ_HMKT(g,h).. 𝐻𝑀𝑆𝑔,ℎ = 𝑄𝑃𝑔,ℎ − 𝑄𝐶𝑔,ℎ − ∑ 𝐼𝐷𝑔,𝑔𝑎,ℎ𝑔𝑎

EQ_VMKT(g,v).. 𝑉𝑀𝑆𝑔,𝑣 = ∑ 𝐻𝑀𝑆𝑔,ℎℎ|𝑚𝑎𝑝ℎ𝑣(ℎ,𝑣)

+ 𝑝𝑎𝑐𝑘𝑠𝑜𝑙𝑑𝑔

EQ_ZMKT(g).. 𝑍𝑀𝑆𝑔,𝑣 = ∑ 𝑉𝑀𝑆𝑔,𝑣𝑣

EQ_VMKTfix(gtv,v).. 𝑉𝑀𝑆𝑔𝑡𝑣,𝑣 = 𝑣𝑚𝑠𝑓𝑖𝑥𝑔𝑡𝑣,𝑣

EQ_ZMKTfix(gtz).. 𝑍𝑀𝑆𝑔𝑡𝑧 = 𝑧𝑚𝑠𝑓𝑖𝑥𝑔𝑡𝑧

Market clearing block for factors

EQ_HFV(f,h).. 𝐻𝐹𝐷𝑓,ℎ = ∑ 𝐹𝐷𝑔,𝑓,ℎ𝑔

EQ_FCSTR(g,fk,h).. 𝐹𝐷𝑔,𝑓𝑘,ℎ = 𝑓𝑖𝑥𝑓𝑎𝑐𝑔,𝑓𝑘,ℎ

EQ_HFMKT(ft,h).. 𝐻𝐹𝑀𝑆𝑓𝑡,ℎ = 𝐻𝐹𝑆𝑈𝑃𝑓𝑡,ℎ − ∑ 𝐹𝐷𝑔,𝑓𝑡,ℎ𝑔

EQ_HFSUP(ft,h).. 𝐻𝐹𝑆𝑈𝑃𝑓𝑡,ℎ

ℎ𝑓𝑠𝑢𝑝𝑓𝑡,ℎ0 + ℎ𝑓𝑠𝑛𝑒𝑤𝑟𝑒𝑓𝑓𝑡,ℎ

= [∑ (𝑊𝐷𝑓𝑡,𝑑)𝜁𝑓𝑡,ℎ

𝑑|𝑚𝑎𝑝ℎ𝑑(ℎ,𝑑)]

𝑓∈𝑓𝑡𝑑

+ [(𝑊𝑍𝑓𝑡,𝑑)𝜁𝑓𝑡,ℎ

]𝑓∈𝑓𝑡𝑧∪𝑓𝑡𝑤

EQ_VFMKT(ft,v).. 𝐷𝐹𝑀𝑆𝑔,𝑑 = ∑ 𝐻𝐹𝑀𝑆𝑔,ℎℎ|𝑚𝑎𝑝ℎ𝑑(ℎ,𝑑)

EQ_ZFMKT(ft).. 𝑍𝐹𝑀𝑆𝑓𝑡 = ∑ 𝑉𝐹𝑀𝑆𝑓𝑡,𝑣𝑣

EQ_VFMKTFIX(ftv,v).. 𝑉𝐹𝑀𝑆𝑓𝑡𝑑,𝑑 = 𝑣𝑓𝑚𝑠𝑓𝑖𝑥𝑓𝑡𝑣,𝑣

EQ_ZFMKTFIX(ftz).. 𝑍𝐹𝑀𝑆𝑓𝑡𝑧 = 𝑧𝑓𝑚𝑠𝑓𝑖𝑥𝑓𝑡𝑧

For simulations with a budget constraint

EQ_FDCOBB(g,f,h)

(only for purchased

factors)

𝐹𝐷𝑔,𝑓,ℎ × 𝑊𝑍𝑓 = 𝑝𝑖𝑏𝑢𝑑𝑔𝑒𝑡𝑔,ℎ

Table S8. Production Function Parameter Estimates and Standard Errors

Production

Activity Parameter

Household Group

Refugee Host

Gihembe Nyabiheke Kigeme Gihembe Nyabiheke Kigeme

Crop

Shift Parameter

NAa

8.23 7.34 5.68

se 1.07 1.09 1.13

Purchased Inputs 0.21 0.18 0.42

se 0.11 0.09 0.11

Land 0.22 0.21 0.12

se 0.07 0.08 0.08

Labor 0.13 0.39 0.10

se 0.10 0.14 0.08

Capital 0.45 0.22 0.36

se b b b

N 116 147 188

R-Squared 0.37 0.22 0.41

Livestockc

Shift Parameter

NAa

6.61 6.61 6.61

se 1.49 1.49 1.49

Land 0.52 0.52 0.52

se 0.19 0.19 0.19

Labor 0.16 0.16 0.16

se 0.18 0.18 0.18

Capital 0.31 0.31 0.31

se 0.04 0.04 0.04

N 296 296 296

RMSE 0.69 0.69 0.69

Retail

Shift Parameter 6.25 6.22 5.72 6.10 5.94 5.87

se 1.26 1.31 1.34 0.66 0.69 0.70

Labor 0.65 0.65 0.65 0.71 0.71 0.71

se 0.38 0.38 0.38 0.19 0.19 0.19

Capital 0.12 0.12 0.12 0.16 0.16 0.16

se 0.06 0.06 0.06 0.03 0.03 0.03

N 65.00 65.00 65.00 178.00 178.00 178.00

R-Squared 0.29 0.29 0.29 0.26 0.26 0.26

Other

Shift Parameter 7.06 6.71 6.20 7.20 6.61 6.55

se 0.86 0.95 0.93 0.54 0.58 0.57

Labor 0.74 0.74 0.74 0.71 0.71 0.71

se 0.28 0.28 0.28 0.14 0.14 0.14

Capital 0.08 0.08 0.08 0.09 0.09 0.09

se 0.05 0.05 0.05 0.03 0.03 0.03

N 53.00 53.00 53.00 152.00 152.00 152.00

R-Squared 0.33 0.33 0.33 0.35 0.35 0.35

Source: Cobb-Douglas (double-log) production function estimates from household (crop, livestock) and business (retail,

other) survey microdata.

a Land constraints inside camps prevent refugees from carrying out crop and livestock production

b Crop production exhibits constant returns to scale

c Livestock was assumed to have Constant Returns to scale with similar technologies across regions varying on by the

shift parameter. RMSE: Root Mean Squared Error

Table S9. Expenditure Function Parameter Estimates and Standard Errors

Expenditure and

Standard Error

Household Group

Refugee Host

Gihembe Nyabiheke Kigeme Gihembe Nyabiheke Kigeme

Crop 0.75 0.51 0.41 0.22 0.18 0.20

se 0.09 0.03 0.03 0.34 0.02 0.01

R-Squared 0.28 0.72 0.42 0.00 0.40 0.53

Livestock 0.01 0.01 0.01 0.03 0.02 0.03

se 0.00 0.01 0.00 0.00 0.00 0.00

R-Squared 0.01 0.02 0.08 0.15 0.18 0.12

Retail 0.10 0.10 0.13 0.12 0.05 0.08

se 0.00 0.02 0.01 0.02 0.01 0.01

R-Squared 0.72 0.16 0.46 0.17 0.19 0.17

Other 0.07 0.07 0.08 0.20 0.08 0.10

se 0.01 0.01 0.01 0.02 0.07 0.01

R-Squared 0.32 0.31 0.15 0.43 0.01 0.27

Non-local (residual) 0.09 0.31 0.38 0.42 0.67 0.58

N 165 155 199 173 148 236

Source: Linear expenditure function estimates from household survey microdata.

Table S10. Sensitivity Analysis of Model Assumptions in Simulations of an Additional

Refugee (US$/refugee, US$1=700 RWF)

Model Assumptions: (A) Base

Model

(B) No

Labor

market

impact

(C) Least

constrained

(D) Most

constrained

Elasticity of labor supply 100 100 100 0

Labor Market Impact

Considered Yes No Yes No

Inputs constrained No No No Yes

Fixed capital Yes Yes No Yes

Iterations 1000 100 100 100

Camp

Gihembe (Cash)

Real income (Inflation-

adjusted) 206 199 221 79

To Refugees 164 124 166 113

To Host-country Households 41 74 56 -34

Nyabiheke (Cash)

Real income (Inflation-

adjusted) 253 243 214 80

To Refugees 183 127 173 116

To Host-country Households 69 114 41 -36

Kigeme (in-kind)

Real income (Inflation-

adjusted) 146 144 199 81

To Refugees 159 110 170 101

To Host-country Households -13 34 29 -20

(A) Base model used in the main text (one additional refugee brings WFP transfer, other private transfers, and increases

labor supply)

(B) Base model without labor market impact (wage + business income of a refugee)

(C) Base model + capital holdings increase by the same value as the labor market impact of one refugee (wage +

business income of a refugee)

(D) Base model with dramatically reduced elasticity of labor supply, no labor market impacts, and an added constraint

on input purchases.

Results of sensitivity analysis: Participation in labor markets allows refugees to capture more of

the spillovers they create, but it does not change the overall size of the spillover as long as the labor

supply around the camp is elastic. Local capital investment brings higher multipliers, particularly

in the in-kind camp. Only in the most constrained scenario, where the economy has no ability to

increase local production and capture spillover benefits, do we see significantly reduced overall

spillovers and negative impacts on host economy households.

Fig. S1: Increase in real income for one extra refugee at different values of elasticity

of labor supply (variations on Base Model)

Notes: As far as we know, there are no estimates of wage elasticities of labor supply for

Rwanda. Typical estimates for European countries range from 0.03 to 0.6 (1). Estimates

for African countries vary widely. Abdulai and Delgado (2) estimate a wage elasticity of

0.32 for men and 0.66 for women in Ghana. In rural Malawi, an experimental study

measured this elasticity from the change in probability of working on a given day as

wages change. This yielded an estimated labor supply elasticity of 0.16; however, 74

percent of individuals chose to work at the lowest wage offer (3). None of these studies

involves a change in labor supply due to the presence of refugees or other immigrants.

Given the lack of work opportunities inside the refugee camp, it is likely that local

elasticities of labor supply in our study areas are higher than most national estimates.

High elasticities of labor supply (ε) reduce the labor market benefit of adding an

additional refugee worker to the local workforce. This labor-supply effect overwhelms

any positive impacts, for example, via higher wage income; thus, the relationship

between the elasticity of labor supply and the total real-income impact is decreasing and

converges to those at the bottom of Fig. 2 as ε increases. Fig. S1 illustrates this between ε

= 0 and ε = 4, at which point most of the convergence is complete. The decrease is

sharper at the in-kind camp where, with high ε, wages do not decrease to compensate for

lower output prices resulting from refugee sales of food aid. If ε < 100, actual impacts are

larger than in our simulations using the base model.

References for Supplementary Materials:

1. Bargain O, Orsini K, Peichl A (2014) Comparing Labor Supply Elasticities in

Europe and the United States: New Results. J Human Resources 49(3): 723-838.

2. Abdulai A, Delgado CL (1999) Determinants of Nonfarm Earnings of Farm-based

Husbands and Wives in Northern Ghana. AJAE 81(1): 117-130.

3. Golderg J (2016) Kwacha Gonna Do? Experimental Evidence about Labor Supply

in Rural Malawi. American Economic Journal: Applied Economics 8(1): 129-149.