Stepping Stone to Better Life? Internal Migration in Ethiopia: The Case of Production Workersin Cut Flower Industry
Eun Jin Ryu, PhD candidate, Department of International Studies, Graduate School of Frontier Sciences, University of Tokyo, Email:1024703477@edu.k.u-tokyo.ac.jp (corresponding author)
Aya Suzuki, Associate Professor, Department of International Studies, Graduate School of Frontier Sciences, University of Tokyo

Internal migration within developing countries has received less attention compared internationalmigration and empirical evidence of the internal migrants’ wage discrimination and performance inthe labor market in sub-Saharan Africa is scarce. Considering the importance of the nonagriculturallabor market for poverty reduction, it is important to understand the welfare and possible wage
discrimination migrants may face. This paper aims to analyze differences in wages and productivity
between migrants and non-migrants using primary data from 710 production workers in cut flower
industry in Ethiopia and to investigate whether there exist wage differential and discrimination against
migrants. The Mincer-type wage regression is estimated, and chow test and the Blinder-Oaxaca
decomposition approach is used to test for wage discrimination. We find that earnings aresignificantly different between migrant and non-migrant and migrants are less likely to be paid.
However, migrant group is more likely to be productive and less likely to be absent. Chow test also
supports that there is significant difference in earnings and productivity. The Blinder–Oaxaca
decomposition result reveals that around 44 percent of wage differentials is unexplained even when
non-cognitive skills are controlled, supporting the idea of wage discrimination against migrants in cut
flower industry in Ethiopia. With field observation and descriptive statistics on financial behavior ofmigrants, this study will discuss motivations of internal migration even if they may facediscrimination in the labor market. This study contributes to the literature on discrimination by
providing rich empirical evidence using various earnings and productivity outcome variables.
Table. Estimation Results of Earnings and Productivity Regression (OLS Estimates)
(1)

(2)

(3)

(4)

Log of monthly wages

Log of bonus-0.006-0.422**-0.011Monthly average
productivity
0.024*(0.013)(0.210)(0.013)(0.013)Age

0.015***-0.0570.014***

0.008*(0.005)(0.062)(0.005)(0.004)Age squared-0.000***0.001-0.000***-0.000*(0.000)(0.001)(0.000)(0.000)= 1 if female-0.050***-0.030-0.054***-0.103***(0.016)(0.231)(0.016)(0.016)0.004***

0.043*

0.005***-0.001(0.002)(0.025)(0.002)(0.002)

VARIABLES= 1 if migrantYears of schoolingYears of working experienceLog of total income-0.001

0.270***

0.002

0.018**(0.009)(0.093)(0.008)(0.008)Years of working experience

0.003***-0.018***0.003***-0.001squared(0.001)(0.005)(0.001)(0.001)= 1 if married

0.018

0.087

0.020

-0.001(0.014)(0.186)(0.013)(0.013)0.027-0.320

0.023

0.010

(0.021)(0.241)(0.021)(0.020)0.006-1.960***-0.0190.021

one year(0.014)(0.274)(0.014)(0.018)Constant

6.597***

0.678

9.094***

0.587***(0.075)= 1 if Oromo ethnic group= 1 if new worker less than

(0.082)(1.083)(0.083)Observations

710

710

710

677

R-squared

0.494

0.437

0.509

0.176

Note: 1. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. 2. Each regression includes farm fixed effects.