Industrial clustering, firm performance and employee welfare evidence from the shoe and flower cluster in ethiopia

75 Tigabu Degu Getahun studied economics at the University of Copenhagen and the University of Bonn He is a Senior Researcher at the University of Bonn and a Research Fellow at the Ethiopian Development Research Institute (EDRI) in Ethiopia www.peterlang.com DEP 75_266744_Getahun_GR_A5HC PLA.indd DEVELOPMENT ECONOMICS AND POLICY Series edited by Joachim von Braun, Ulrike Grote and Manfred Zeller 75 Tigabu Degu Getahun · Industrial Clustering, Firm Performance and Employee Welfare The author examines the productivity, profitability and welfare effects of industrial clustering and a public policy promoting industrial clusters in Ethiopia He uses reliable counterfactuals as well as original enterprise and worker level data By investigating the effect of firm, time, entrepreneur and site specific factors as well as endogenous location choice issues, the author finds strong evidence for the existence of significant agglomeration economies in the Ethiopia leather footwear cluster Using primary survey data collected from firms which benefited from the cluster policy and those that did not, both before and after the implementation of the policy, the author shows the unintended negative impact of a cluster prompting policy in Ethiopia The book is essential reading for those who are interested in the gender and welfare impact of female full time labor force participation in industrial jobs Industrial Clustering, Firm Performance and Employee Welfare Evidence from the Shoe and Flower Cluster in Ethiopia Tigabu Degu Getahun Umschlaggestaltung: © Olaf Gloeckler, Atelier Platen, Friedberg Cover Design: © Olaf Gloeckler, Atelier Platen, Friedberg Conception de la couverture du livre: © Olaf Gloeckler, Atelier Platen, Friedberg ISBN 978-3-631-66744-6 11.02.16 KW 06 17:27 Industrial Clustering, Firm Performance and Employee Welfare DEVELOPMENT ECONOMICS AND POLICY Series edited by Franz Heidhues †, Joachim von Braun, Ulrike Grote and Manfred Zeller Vol 75 Industrial Clustering, Firm Performance and Employee Welfare Evidence from the Shoe and Flower Cluster in Ethiopia Tigabu Degu Getahun Bibliographic Information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the internet at http://dnb.d-nb.de Zugl.: Bonn, Univ., Diss., 2015 Library of Congress Cataloging-in-Publication Data Names: Getahun, Tigabu Degu, 1980Title: Industrial clustering, firm performance and employee welfare : evidence from the shoe and flower cluster in Ethiopia / Tigabu Degu Getahun Other titles: Development economics and policy ; v 75 Description: New York : Peter Lang, 2016 | Series: Development economics and policy ; vol 75 | doctoral Universität Bonn 2015 Identifiers: LCCN 2016006143 | ISBN 9783631667446 Subjects: LCSH: Industrial clusters—Ethiopia | Shoe industry—Ethiopia | Floriculture—Ethiopia | Women—Employment—Ethiopia Classification: LCC HC845.Z9 D54 2016 | DDC 338.70963—dc23 LC record available at http://lccn.loc.gov/2016006143 D 98 ISSN 0948-1338 ISBN 978-3-631-66744-6 (Print) E-ISBN 978-3-653-06378-3 (E-Book) DOI 10.3726/978-3-653-06378-3 © Peter Lang GmbH Internationaler Verlag der Wissenschaften Frankfurt am Main 2016 All rights reserved PL Academic Research is an Imprint of Peter Lang GmbH Peter Lang – Frankfurt am Main · Bern · Bruxelles · New York · Oxford · Warszawa · Wien All parts of this publication are protected by copyright Any utilisation outside the strict limits of the copyright law, without the permission of the publisher, is forbidden and liable to prosecution This applies in particular to reproductions, translations, microfilming, and storage and processing in electronic retrieval systems This publication has been peer reviewed www.peterlang.com Abstract Despite the number of empirical case studies on industrial clustering since the seminal work of Marshall (1920), only very few have made significant attempts to quantify its productivity, profitability and welfare effects This study examines the productivity, profitability and welfare effects of industrial clustering and a public policy promoting industrial clusters in Ethiopia To this end, firm-level survey data were collected from 196 leather shoe manufacturers that were part of the spontaneously emerged leather shoe cluster in Ethiopia, 86 firms that operated in a separate government created cluster, and 72 non-clustered firms located in other areas The study employs appropriate estimation strategies to disentangle the effect of industrial clustering from firm heterogeneities and other cofounders The estimation results from both the random effect model and the Abadie and Imbens (2011) bias corrected nearest neighbor matching model reveal the productivity and profitability increasing effect of industrial clustering, after controlling for the effects of site-, enterprise-, entrepreneur-, and time- specific factors The study also accounts for selection bias and endogenous location choice problem The results from the two way fixed effect impact evaluation model suggests that the implemented government cluster development program in Ethiopia has adversely impacted the productivity, profitability, growth, and innovation performance of the treated firms Due to the short span of the program, however, these findings only reflect the short-term impacts of the program The regression result from the Mincerian earning function indicates that clustered firms paid higher mean wages compared to non-clustered counterparts; implying a welfare increasing effect of industrial clustering In addition, the results of a correlation matrix analysis disclose a positive and significant correlation between firm productivity and the number of employees, again implying positive welfare effects of industrial clustering The results of a gender disaggregated analysis of employment effects also reveals a positive relationship between gender equity and industrial clustering To further explore the welfare and gender impacts of industrial clustering, the study empirically investigates the intra-household welfare impacts of formal salaried employment of women in the most female dominated cluster in Ethiopia, the cut flower industry cluster To this end, a unique quantitative survey was conducted with a random sample of 670 women working in the cut flower cluster and a control group of 182 women who applied for work in the cluster but were unsuccessful The relevance tests of the hypotheses and results of a special maximum likelihood estimation model, an endogenous binary treatment model, and difference in difference models combined with instrumental variable estimators all suggest that, compared to employment elsewhere, salaried employment of women in the cut flower cluster improved the income and consumption welfare of the working women and their household It also (i) has negative relationships with the incidence and depth of poverty; (ii) has reduced the food insecurity and hunger status of working women and their households; (iii) has improved the bargaining and decision making power of the flower working women (iv) has transformed the traditional gendered patterns of intra household time use and (v) has negative relationships with the leisure demand of the flower working women and their close substitutes The investigation of the transmission mechanisms further suggests that increases in women’s earnings derived from employment in the cut flower cluster has effected the consumption welfare of the flower working women’s household not only through the usual Marshallian income and substitution effects, but also through the distinguishing bargaining effect The qualitative findings support the quantitative findings, but unveil additional intangible benefits and costs of female labor force participation Zusammenfassung Trotz des Anstiegs von empirischen Cluster Fallstudien seit der wegweisenden Arbeit von Marshall (1920), haben nur wenige dieser Studien versucht, Produktivitäts-, Profitabilitäts-, und Wohlfahrtseffekte des industriellen Clusterings zu quantifizieren Die vorliegende Studie analysiert deshalb empirisch Produktivitäts- und Profitablitätseffekte des industriellen Clusterings sowie einer staatlichen Politik zur Föderung von industriellen Clustern in Äthiopien unter Verwendung von neu erhobenen Umfragedaten auf Firmenebene in 196 Lederschuhe produzierenden Firmen, die in einem spontan entstandenen Lederschuhcluster in Äthiopien operieren; außerdem 86 Firmen, die in einem von der Regierung initiierten Cluster operieren sowie 72 Firmen, die außerhalb von Clustern operieren Die Studie nutzt verschiedene Schätzstrategien, um die Effekte des Clusterings von Firmen-Heterogenitäten und anderen Faktoren zu trennen Schätzergebnisse aus dem Random Effect Modell und dem von Abadie und Imbens (2011) Bias-korrigierten Nearest Neighbor Matching Schätzer zeigen einen zunehmenden Effekt des industriellen Clusterings auf Produktivität und Profitabilität, nachdem für Effekte von natürlichen Gegebenheiten sowie Firma-, Unternehmer- und zeitabhängigen Faktoren kontrolliert wird Die Studie berücksichtigt sowohl Selektionsverzerrungen als auch endogene Standortwahlprobleme Schätzergebnisse des am weitesten verbreiteten Impaktevaluationsmodells, dem Difference in Difference-Modell, zeigen an, dass das implementierte Cluster-Entwicklungsprogramm der Regierung adverse Effekte bezogen auf Profitabilität, Wachstum, Innovation und Produktivität hat Jedoch bezieht sich dieses Resultat, aufgrund der kurzen Zeitspanne des Programms, nur auf den unmittelbaren kurzfristigen Effekt des Programms Regressionsresultate aus Mincer-Typ-Einkommensfunktionen zeigen, dass Cluster Firmen im Durchschnitt höhere Löhne für ihre Arbeiter zahlen als entsprechende Nicht-Cluster Firmen, was auf einen positiven Wohlfahrtseffekt des industriellen Clusterings hinweist Außerdem zeigt die Analyse der Korrelationsmatrix eine positive und signifikante Korrelation zwischen der Produktivität der Firma und der Höhe der Beschäftigung an, was wiederum positive Wohlfahrtseffekte des industriellen Clusterings widerspiegelt Die Disaggregation des Beschäftigungseffekts nach Geschlecht deutet auf eine Reduzierung der Geschlechterdisparität hin Um den Effekt auf Wohlfahrt und Geschlecht weiter zu erkunden, untersucht die Studie den Intra-Haushaltseffekt der Lohnbeschäftigung von Frauen in einem der von Frauen am meisten dominierten Cluster in Äthiopien – dem Blumencluster Dafür wurden einzigartige quantitative Umfragedaten mittels Zufallsstichprobe erhoben von (i) 670 Frauen, die im Blumencluster eine Beschäftigung aufgenommen haben und (ii) einer Kontrollgruppe von 182 Frauen, die sich beworben hatten, aber keine Beschäftigung aufgenommen haben Der Relevanztest der Hypothese und die Schätzergebnisse des Maximum Likelihood-Schätzmodells, des Treatment Effect-Modells, des mit Matching kombinierten Difference in Differences-Modells und des Instrumentalvariablenschätzers zeigen alle an, dass Lohnbeschäftigung von Frauen im Blumencluster, verglichen mit sonstiger Beschäftigung, den arbeitenden Frauen und deren Haushalten dazu verhilft (i) die Inzidenz und den Schweregrad der Armut zu reduzieren, (ii) Hunger und Nahrungsmittelunsicherheit bei den beschäftigten Frauen und deren Haushalten zu verringern, (iii) die Verhandlungsposition und Entscheidungsbefugnisse der Frauen zu verbessern and (iv) die Nachfrage der im Blumensektor beschäftigten Frauen nach Freizeit zu verringern Interessanterweise deutet die Analyse zusätzlich darauf hin, dass die Erhöhung des Einkommens der Frauen aufgrund der Beschäftigung im Blumencluster signifikant die kollektive Haushaltsnachfrage beeinflusst und zwar nicht nur durch einen gewöhnlichen Marshall’schen Einkommens- und Substitutionseffekt, sondern auch durch einen differentiellen Verhandlungseffekt Während die qualitativen Resultate die quantitativen Resultate unterstützen, decken sie zusätzlich weitere, schwieriger zu greifende Nutzen und Kosten der weiblichen Teilnahme an der Erwerbstätigkeit auf Acknowledgement It is a great pleasure to thank God, the many people and institutes who made this book possible First and foremost glory to the Almighty for enabling me to accomplish my writing successfully My boundless gratitude goes to my first Professor Dr. Joachim Von Braun Throughout my thesis-writing period, he provided encouragement, sound advice and lots of good ideas I would have been lost without him It has been an honor to be his student I am also very grateful for my second Professor Dr Ulrich Hiemenz and my Dr. Marc Müller who took time to review my work I am also greatly indebted to my research partner and friend Dr Espen Villanger for his continued encouragement, support and inspiration I am heavily indebted to H.E Ato Newai Gebre-ab, EDRI director and chief economic advisor to the Ethiopia government prime minister, for his invaluable advice, encouragement, moral support and easy yes for all my difficult requests I would also like to wholeheartedly thank Mrs Rosemary Zabel, Mrs Maike Retat Amin and Dr Gunther Manske for their excellent admin and logistic assistance I would also like to take this opportunity to gratefully acknowledge the funding sources that made my doctoral study possible The first three years of my study was primarily funded by the German Academic Exchange Service while part of my last year study was funded by BMZ through ZEF My field research was mainly funded by the Dr Hermann Eiselen grant program of the Fiat Panis foundation Part of my work was also financially supported by IDRC through EDRI My time at Bonn was made enjoyable due to the many friends, fellow students and colleagues Daniel Ayalew, Lukas Kornher, Million, Negash, Christine Husmann and Robert Poppe thank you so much for making my time in Germany more enjoyable Christine and Robert thank you so much for the German translation of my abstract Nuru Yasin, Hussein Ahmed, Abdurrahman Ali, Ibrahim Worku, Feiruz Yimer, Biruk Teklie, Dr. Girum Abebe, Dr Seid Nuru, Ato Mezgebe Mihretu, Bedilsh, Desta Solomon, Laura Kim and friends and colleagues at EDRI, please accept my sincerest appreciation for being always with me in all my difficult times; and for providing all the emotional supports and comraderies Dr Seid and Dr Assefa, many thanks for believing on me and recommending me to study in the University of Bonn, Center for Development I hope I have not failed you Van der, L (2003) Industrialization, value chain and linkages; the leather footwear sector in Addis Ababa, Ethiopia, Working Paper, Institute of Social Studies, Netherlands Venables, A, (2010): economic geography and African development, Regional science 89(3) Visser, E.J (1999) A comparison of clustered and dispersed firms in the small-scale clothing industry in Lima, World Development, vol 27, No 9, pp 1553–1570 Villanger, E., T 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Ethiopia, Tanzania, Zambia, China and Vietnam World bank Available at: http://siteresources.worldbank.org/DEC/Resources/VolumeIICh4.pdf World Bank 2011 World Development Report 2012: Gender Equality and Development Washington D.C: World Bank World Bank 2015 Ethiopia poverty assessment 2014 Washington DC: World Bank World Bank data base (2014) Official exchange rate (LCU per US$, period average) available at http://data.worldbank.org/indicator/PA.NUS.FCRF Workneh T (2007) An Assessment of the Working Conditions of Flower Farm Workers: A case study of four flower farms in oromiya region Addis Ababa, Ethiopia Yusof, A and Duasa, J (2010) Household Decision-Making and Expenditure Patterns of Married Men and Women in Malaysia, Journal of Family and Economic Issues, 31(3): 371–381 Zeng, D (2008) Knowledge, technology and cluster-based growth in Africa, World Bank, Washington DC 224 Annexes Annex 1.1:  The Evolution of the flower cluster in Ethiopia Year Domestic owned 2000 2001 2002 2003 2004 20005 2006 2007 2 15 20 24 Foreign owned 1 18 28 Joint venture 0 0   15 All 3 10 31 53 67 Source: Author computation based on EDRI_GRIPS survey (2009) data Annex 2.1:  Correlation Matrix of Firm Performance Output VA Per GP Per Profit Per Worker Worker Per Worker Worker Output Per Worker Profit Quantity Capacity Return util on Cap 1.00 VA Per Worker 0.55 1.00 GP Per Worker 0.53 0.99 1.00 Profit Per Worker 0.52 0.98 0.99 1.00 Profit 0.09 0.58 0.56 0.58 1.00 Quantity of output 0.25 0.27 0.23 0.25 0.71 1.00 Capacity utilization 0.22 0.27 0.24 0.26 0.19 0.21 1.00 Return on Capital 0.16 0.45 0.48 0.50 0.51 0.36 0.32 1.00 Note: VA stands for Value added, GP stands for gross profit Annex 2.2:  Number of days required to hire skilled worker in 2013 Mean p25 p50 p75 max Non-clustered 1 30 Cluster 1 1 P-value for t test§ 0.000** Note: § Ho: Mean (Non-clustered)-Mean (clustered) =0; * p < 0.05, ** p < 0.01 225 Annex 3.1:  Percentage of Firms who received the listed Service List of Services Business Association Provide mechanism for joint training Provide mechanism for joint marketing Provide platform for joint business promotion Facilitate access to finance Low tax Facilitate access to land Lobbying government Government Treated Control Treated Control 15 10 2 36 21 5 37 0.5 14 Note: All treated firms except one and 110 out of the 195 control firms are members of business association Annex 3.2: The Percentage distribution of the relative location advantage of the Mercato Cluster Advantage of being located in Mercato over Better access to Skilled labor Proximity to Raw material supplier Proximity to Machine service provider Proximity to customer Cheap rent of Working Premise Access to larger working premise Proximity to other Shoe Makers Better access to designer Yeka Rimo 70 83 20 85 – – Percentage of firms 71 86 29 79 – – 18Mazoria 33 50 83 – – 0 Annex 3.3: Correlation between Firm performance and Network density Permanent Client Permanent Supplier Backward collaborator Forward collaborator Horizontal collaborator Total collaborator Ln(VA) Ln(PfeVA) Ln(GP) Ln(pfeGP) Ln(profit) Ln(pfeprof) Ln(design) 0.0649 0.0247 0.0683 0.0386 0.0605 0.0227 0.1924 0.1947 0.0996 0.1819 0.0896 0.1715 0.0882 0.2115 0.0365 0.0241 0.0559 0.0527 0.0644 0.0489 0.1357 0.3197 0.1334 0.3084 0.1297 0.2947 0.1335 0.2162 0.2766 0.1101 0.2523 0.0867 0.2253 0.0722 0.0794 0.2141 0.0944 0.2175 0.1074 0.2104 0.0986 0.1906 Note: VA, pfeVA, GP, pfeGP, pfeprof, desugn stands for value added, value added per worker, gross profit, gross profit per worker, net profit per worker and number of new design respectively 226 Annex 4.1: Food Security Scale Values and the Corresponding Number of Affirmative Responses for Households with Complete Responses Number of affirmative response Food Security Status Level§ (out of 18) Household with children (out of 10) Household without children The 1998 USDA Food security scale values 0.0 1.0 1.2 1.8 2.2 2.4 2 10 11 12 13 14 15 10 16 17 18 3.0 3.4 3.7 3.9 4.3 4.4 4.7 Category Food secure Food Insecure without Hunger Food Insecure with hunger 5.0 5.1 5.5 5.7 5.9 6.3 6.4 6.6 7.0 7.2 7.4 7.9 8.0 8.7 9.3 Note: § “(i) Food secure: Households show no or minimal evidence of food insecurity (ii) Food insecure without hunger: Food insecurity is evident in household members’ concerns about adequacy of the household food supply and in adjustments to household food management, including reduced quality of food and increased unusual coping patterns Little or no reduction in members’ food intake is reported and (iii) Food insecure with Hunger: Food intake for adults and /or children in the household has been reduced to an extent that implies that adults and /or children have repeatedly experienced the physical sensation of hunger.” (Bickel, 200) 227 general and food consumer price indices Annex 4.2:  Food and General Consumer Price Index (2000 GC is the base year) 1400 641 1200 501 1000 800 354 360 267 289 313 2008 2009 2010 343 600 175 214 100 100 103 102 131 120 135 124 153 138 157 185 2001 2002 2003 2004 2005 2006 2007 400 200 417 518 2011 2012 Year CPI food CPI Source: Own Computation using FAO stat data Annex 4.3a:  Average Monthly Expenditure and Consumption Monthly (in birr) Before Participation After Participation comparison participant comparison participant Per adult expenditure Real per adult expenditure Per adult consumption Real per adult consumption Per adult food consumption Real per adult food consumption 368 185 446 230 318 147 347 180 433 221 303 138 489 137 566 158 361 168 574 160 712 199 483 227 DifferenceinDifference 98*** 26*** 138*** 44*** 119*** 58** Note:* p < 0.05, ** p < 0.01, *** p < 0.001, consumption is the sum of expenditure and market value of home produced goods Annex 4.3b:  The density function of the log of real per adult equivalent consumption after before 228 log of real per adult consumption 4.4a:  The Selection and the Two wage equations of the DID_3SLS Estimate of Consumption group ii_9farmdist w2info -.000638 4106216 0003164 0383149 -2.02 10.72 0.044 0.000 -.0012581 3355257 -.0000178 4857175 lrwearn L1 -.2901525 0584978 -4.96 0.000 -.4048061 -.1754989 lrhearn L1 .320363 101672 3.15 0.002 1210895 5196365 lrearnH_W L1 -.2876116 0864693 -3.33 0.001 -.4570883 -.118135 lrynl L1 -.0053837 0076531 -0.70 0.482 -.0203836 0096161 chdbelow5 young workingage WOMAGE WOMAGEsqr AgeH_W AgeH_Wsqr v_4yrschoolwf EducH_W mrtstatus II_3Urbrth Orthodox -.0513872 0617596 0309774 -.0074957 0001742 -.0001881 0001553 -.0019046 0732476 -.0360325 0069145 -.0200478 0311067 0232693 0246236 0156876 000213 0059826 0002541 0065381 0446924 0695316 0405214 0495147 -1.65 2.65 1.26 -0.48 0.82 -0.03 0.61 -0.29 1.64 -0.52 0.17 -0.40 0.099 0.008 0.208 0.633 0.413 0.975 0.541 0.771 0.101 0.604 0.865 0.686 -.1123553 0161526 -.017284 -.0382428 -.0002432 -.0119137 -.0003427 -.014719 -.0143478 -.1723119 -.072506 -.1170948 0095809 1073667 0792388 0232514 0005917 0115375 0006534 0109097 1608431 100247 086335 0769993 parentland L1 .0560837 0492477 1.14 0.255 -.04044 1526073 _cons 67165 3308267 2.03 0.042 0232415 1.320058 L_lrwearn litracyW Educ3 Educ6 WOMAGE WOMAGEsqr _cons 0344072 -.2764737 -.1513726 1041865 -.0009036 -.2980621 3012079 3260644 2815596 0669631 0009426 1.144016 0.11 -0.85 -0.54 1.56 -0.96 -0.26 0.909 0.396 0.591 0.120 0.338 0.794 -.5559495 -.9155483 -.7032193 -.0270589 -.0027512 -2.540293 6247638 3626008 4004741 2354318 0009439 1.944169 D_lrhearn litracyH AGEHUS AGEHUSsqr _cons 0433373 -.0578337 0003416 1.354087 1165486 02974 000346 608094 0.37 -1.94 0.99 2.23 0.710 0.052 0.324 0.026 -.1850937 -.1161229 -.0003366 1622444 2717683 0004556 0010198 2.545929 Table 4.4b:  Minimum Eigen Value Test Minimum eigenvalue statistic = 88.5613 Critical Values Ho: Instruments are weak 2SLS relative bias 2SLS Size of nominal 5% Wald test LIML Size of nominal 5% Wald test # of endogenous regressors: # of excluded instruments: 5% 10% 19.93 8.68 10% 20% (not available) 15% 11.59 5.33 20% 8.75 4.42 30% 25% 7.25 3.92 229 Annex 4.5: Treatment Effect Model with Endogenous binary treatment: Household Consumption Consumption demand Equation Selection Equation Jackknife Coef Std Err Jackknife t P>t Coef Std Err −0.004 0.001 1.469 0.166 −0.068 0.046 −1.49 0.138 0.144 0.190 0.76 0.447 −0.221 0.161 −1.37 0.171 −0.021 0.035 −0.61 0.539 chdbelow5 −0.263 0.131 −2 Farm distance Lag of women Real earning Info source −0.015 0.010 −1.56 Lag od women earn 0.060 0.027 2.26 P>t −2.77 0.006 8.83 0.118 Growth in Husband earn t 0.024 Lag of real husband Lag of spouse Earning gap earning −0.034 0.018 −1.88 0.061 Lag of spouse Growth in non labor income Earning gap 0.024 0.007 3.35 0.001 Lag of non-labor Lag of Parental Land holding income 0.01 0.047 Hh size −0.084 0.040 −2.1 0.036 young 0.144 0.102 1.41 0.16 Women age −0.007 0.020 −0.34 0.732 Working age 0.157 0.125 1.25 0.21 Age square 0.000 0.000 −0.29 0.775 Woman age −0.207 0.066 −3.14 0.00 Husband age 0.023 0.016 1.43 0.154 Women age square 0.003 0.001 3.21 0.00 Age square 0.000 0.000 −0.55 0.582 Spouse age gap 0.002 0.032 0.06 0.95 Age gap 0.000 0.001 0.06 0.96 Gap square 0.000 0.032 0.027 1.19 0.23 230 0.11 0.915 Age gap square 0.000 −0.72 0.472 Wife schooling 0.05 Consumption demand Equation Selection Equation WageEduc1 0.003 0.002 1.44 0.15 WageEduc6 0.000 0.002 0.23 0.819 Married women HageEduc1 −0.001 0.002 −0.79 Education gap Education gap 0.319 0.217 1.47 0.14 −0.300 0.304 −0.99 0.32 0.099 0.208 0.47 0.64 −0.134 0.227 −0.59 0.55 0.421 0.205 2.06 0.04 3.779 1.237 3.06 0.002 −0.004 0.054 −0.08 0.935 0.432 Urban born 0.103 0.078 1.32 Married −0.151 0.058 −2.59 0.187 Orthodox 0.01 Orthodox −0.004 0.052 −0.07 0.944 Lag of Born in Urban −0.018 0.041 −0.45 0.655 Parental land Oromo ethnic 0.404 −0.040 0.048 −0.84 Emp duration −0.147 0.010 −15.52 Participant 0.254 0.085 2.98 0.003 constant 0.504 0.331 1.52 0.129 lambda constant Note: the dependent variable in the consumption demand equation is the log difference of real per adult consumption while it is participation dummy in the selection equation Table 4.6:  Leisure hour on Last Month as of the survey Date Wife Participant Comparison Mean difference Husband Oldest Son Oldest Daughter 77 108 97 36 95 −59*** 86 119 124 −9* −11* −27** Note: * p < 0.05, ** p < 0.01, *** p < 0.001 231 Annex 4.7a:  FIML estimate of Wives’ Leisure demand equation lhearn lynl exprfix adehhsz WOMAGE WOMAGEsqr AGEHUS AGEHUSsqr AgeH_Wsqr WageEduc1 WageEduc6 HageEduc1 EducH_W Orthodox II_3Urbrth Oromo mrtstatus parentland _cons Participant 1.191 −1.356* −0.332 0.491 −4.635*** 0.046* −1.359 0.025 −0.034 0.040 0.069 0.050 3.917 −1.084 2.186 −8.921* −4.803 3.033 140.978*** Standard errors in parentheses Nonparticipant −15.168 (10.50) 0.712 (1.87) −0.145 (2.27) −8.583 (13.18) −0.237 (7.84) 0.006 (0.13) −0.701 (3.82) −0.005 (0.07) 0.061 (0.15) 0.067 (0.50) 0.047 (0.45) −0.154 (0.40) −15.698 (20.16) 0.789 (12.21) −2.864 (10.28) 12.051 (15.38) 28.322 (14.99) −17.934 (10.03) 219.527 (134.37) (1.46) (0.59) (0.66) (3.28) (1.26) (0.02) (1.04) (0.01) (0.03) (0.20) (0.14) (0.15) (6.52) (4.03) (3.40) (3.57) (5.23) (4.17) (22.30) * p < 0.05, ** p < 0.01, *** p < 0.00 Annex 4.7b:  FIML Estimate of Husbands’ Leisure Demand Equation lhearn lynl exprfix adehhsz WOMAGE WOMAGEsqr AGEHUS AGEHUSsqr AgeH_Wsqr WageEduc1 WageEduc6 HageEduc1 EducH_W Orthodox II_3Urbrth Oromo mrtstatus parentland _cons Participant equation −4.095 (2.41) −0.488 (1.03) 1.531 (1.11) 2.690 (5.52) 0.582 (2.16) −0.030 (0.03) −6.782*** (1.77) 0.099*** (0.02) −0.076 (0.04) −0.745* (0.33) 0.461 (0.24) 0.314 (0.25) −16.366 (11.00) −5.251 (6.77) 5.011 (5.76) −1.189 (6.04) 0.327 (8.82) 16.594* (7.27) 198.144*** (37.18) Standard errors in parentheses 232 Nonparticipant Equation −9.142 (8.90) 0.138 (1.49) 0.123 (1.91) −11.515 (11.06) −9.481 (6.35) 0.110 (0.11) 0.826 (3.20) 0.017 (0.05) −0.024 (0.12) 0.966* (0.42) −0.501 (0.38) −0.878** (0.34) 28.467 (16.66) 2.688 (10.26) 10.880 (8.65) 15.749 (12.66) 3.037 (12.41) −4.561 (8.20) 278.688* (109.95) * p < 0.05, ** p < 0.01, *** p < 0.001 Annex 4.7c:  FIML Estimate of Daughters` Leisure Demand Equation lhearn lynl exprfix adehhsz WOMAGE WOMAGEsqr AGEHUS AGEHUSsqr AgeH_Wsqr WageEduc1 WageEduc6 HageEduc1 EducH_W Orthodox II_3Urbrth Oromo mrtstatus parentland _cons Participant Equation −2.997 (5.75) 1.445 (2.03) 3.130 (2.22) −1.786 (10.90) −18.394*** (4.86) 0.213** (0.07) −2.882 (3.74) 0.021 (0.05) 0.047 (0.07) −0.053 (0.62) 0.785 (0.50) −0.053 (0.48) 6.634 (21.51) −18.584 (15.72) 10.424 (12.03) 5.249 (14.84) −11.545 (21.88) −8.515 (16.60) 560.808*** (96.81) Standard errors in parentheses Non-participant Equation −7.283 (11.35) 2.570 (1.83) 5.207* (2.33) −15.843 (12.92) −9.702 (10.45) 0.120 (0.16) 0.618 (4.15) 0.018 (0.07) −0.144 (0.15) −0.886 (0.46) −0.139 (0.46) 0.634 (0.37) −38.443 (20.11) 57.379*** (13.45) 4.028 (11.29) 25.488 (19.13) 4.996 (21.93) −4.287 (10.80) 254.074 (152.36) * p < 0.05, ** p < 0.01, *** p < 0.001 Annex 4.7d:  FIML Estimate of Sons` Leisure Demand Equation lhearn lynl exprfix adehhsz WOMAGE WOMAGEsqr AGEHUS AGEHUSsqr AgeH_Wsqr WageEduc1 WageEduc6 HageEduc1 EducH_W Orthodox II_3Urbrth Oromo mrtstatus parentland _cons Participant equation 1.532 (4.60) 0.174 (2.03) 5.953** (2.21) −18.690 (11.56) −3.349 (5.28) 0.018 (0.07) −5.089 (4.64) 0.058 (0.06) −0.034 (0.11) 0.580 (0.65) −0.004 (0.50) −0.611 (0.51) 10.646 (22.94) 26.684 (15.68) 31.781* (12.56) −5.020 (13.74) −4.112 (25.70) 21.775 (15.96) 277.348** (95.50) Standard errors in parentheses Non-participant Equation −36.814** (13.61) 1.030 (2.29) 3.566 (3.08) −10.066 (14.81) 14.894 (10.18) −0.271 (0.16) −7.959 (4.70) 0.131 (0.07) −0.273 (0.16) 0.000 (0.61) 0.855 (0.58) −0.648 (0.49) −11.957 (25.58) −5.621 (19.46) 1.354 (13.45) 3.122 (19.71) 23.797 (30.38) −34.625* (13.66) 322.034 (165.35) * p < 0.05, ** p < 0.01, *** p < 0.001 233 Annex 4.8:  The DID and FE estimate of the wives work load (DID_COV) domstk (FE) domstk time −19.15*** (3.80) −21.40*** (3.68) group −0.86 (3.32) Group#time −29.80*** (4.52) −26.26*** (3.41) N 1265 1427 adj R2 0.271 0.481 Note: Marginal effects; Standard errors in parentheses (d) for discrete change of dummy variable from to Annex 4.9:  Women’s Control over Important household purchase Type of Expenditure Comparison group Participant Mean difference Vegetable /Fruit (%) 58 94 36** Women’s cloth (%) 54 78 24** Medicine for the Women (%) 58 76 18** Note:* p < 0.05, ** p < 0.01, *** p < 0.001 Figure 4.10a:  Actual Spending Pattern of Women Earned incomes 1,66 3,18 0,91 food and other non-durables 8,02 food & children good 32,22 food and saving to buy food items only 22,09 to buy food items and others 31,92 234 buy children goods save Figure 4.10b:  Hypothetical Women’s Preferred spending pattern94 100% 18,3 38,7 80% 66,7 60% 99,3 40% 20% No 81,7 61,3 Yes 33,3 0% 0,7 Alcohol Remitance Children expenditure Durbles at home Figure 4.11:  Deriver(s) of the observed Living standard Improvements Woman employment in commercial farm 75 Employment and other household income Driver Other factor ( decrease in hhsize) Increment in Other household income Employment and other factor Improvement in harvest Employment and harvest Harvest and employment 20 40 60 80 Percentage of women 94 had the women could spend part of their salary on anything they want without the husband figured it out 235 75 Tigabu Degu Getahun studied economics at the University of Copenhagen and the University of Bonn He is a Senior Researcher at the University of Bonn and a Research Fellow at the Ethiopian Development Research Institute (EDRI) in Ethiopia DEVELOPMENT ECONOMICS AND POLICY Series edited by Joachim von Braun, Ulrike Grote and Manfred Zeller 75 Tigabu Degu Getahun · Industrial Clustering, Firm Performance and Employee Welfare The author examines the productivity, profitability and welfare effects of industrial clustering and a public policy promoting industrial clusters in Ethiopia He uses reliable counterfactuals as well as original enterprise and worker level data By investigating the effect of firm, time, entrepreneur and site specific factors as well as endogenous location choice issues, the author finds strong evidence for the existence of significant agglomeration economies in the Ethiopia leather footwear cluster Using primary survey data collected from firms which benefited from the cluster policy and those that did not, both before and after the implementation of the policy, the author shows the unintended negative impact of a cluster prompting policy in Ethiopia The book is essential reading for those who are interested in the gender and welfare impact of female full time labor force participation in industrial jobs Industrial Clustering, Firm Performance and Employee Welfare Evidence from the Shoe and Flower Cluster in Ethiopia Tigabu Degu Getahun Umschlaggestaltung: © Olaf Gloeckler, Atelier Platen, Friedberg Cover Design: © Olaf Gloeckler, Atelier Platen, Friedberg Conception de la couverture du livre: © Olaf Gloeckler, Atelier Platen, Friedberg www.peterlang.com DEP 75_266744_Getahun_GR_A5HC PLA.indd 11.02.16 KW 06 17:27 ... Congress Cataloging -in- Publication Data Names: Getahun, Tigabu Degu, 1980Title: Industrial clustering, firm performance and employee welfare : evidence from the shoe and flower cluster in Ethiopia /... Clustering, Firm Performance and Employee Welfare Evidence from the Shoe and Flower Cluster in Ethiopia Tigabu Degu Getahun Bibliographic Information published by the Deutsche Nationalbibliothek... between gender equity and industrial clustering To further explore the welfare and gender impacts of industrial clustering, the study empirically investigates the intra-household welfare impacts of