WORKERS’ REMITTANCES AND ECONOMIC GROWTH IN SELECTED SUB-SAHARAN AFRICAN COUNTRIES BY OKODUA, Henry CUPG040085 Department of Economics and Development Studies College of Development Studies Covenant University, Ota, Ogun State, Nigeria Being PhD Thesis/Dissertation Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Doctor of Philosophy (Ph.D) in Economics of Covenant University, Ota, Nigeria December, 2010 i DECLARATION I, Henry OKODUA, declare that this thesis is my own original work and that no portion of the work referred to in the thesis has been or will be submitted in support of an application for another degree or qualification of this or any other university or other institute of learning Signature Henry OKODUA -Date ii CERTIFICATION The undersigned certify that they have read and hereby recommend for acceptance by Covenant University a dissertation/thesis entitled: “Workers’ Remittances and Economic Growth in Selected Sub-Saharan African Countries” in partial fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in Economics of Covenant University, Ota, Nigeria Signature -Date Prof Ike, Don Supervisor -Signature Date Dr Olayiwola, W K Co-Supervisor Signature Date Prof Ike, Don Head of Department Signature Date Prof Aigbokhan, Ben External Examiner iii DEDICATION I humbly and most respectfully dedicate this work to the glory of the Most High God, JEHOVAH Almighty, who is the author and finisher of every good and perfect gift I have ever received and will ever receive in my life I also dedicate this work to the loving memories of my late father, Mr Peter S E Okodua iv ACKNOWLEDGEMENTS What appeared a huge joke at the beginning and ostensibly a journey without an end has suddenly and gloriously come to a successful completion It is still like yesterday when it all started and “just overnight”, the lord God almighty has Himself changed my title and status to a most enviable one I must therefore begin by ascribing all thanks and gratitude to Him for the gift of life, good health, soundness of mind, divine inspiration and my overall wellbeing Some individuals also showed up as instruments in the hands of the almighty God to assist me in some most invaluable ways during the course of this work They all deserve a big thank you The Chancellor of Covenant University, Bishop David Oyedepo, who God used to provide a platform for the vision that birthed Covenant University and the PhD programme, is greatly appreciated I will forever see him as one of the rarest gifts from God to my generation The Vice Chancellor, Prof Aize Obayan did not relent in speaking into my life those divinely inspired words of encouragement whenever I was privileged to meet her during the course of this work I am truly indebted to this great daughter of Zion for all the moral support which includes the SMS that she kept sending to encourage me Pastor Yemi Nathaniel was also a very great source of inspiration to me His Godly counsel and those moments of prayers for my timely and successful completion of this PhD made a whole lot of positive difference and I sincerely appreciate him for all the support My former Dean of the College of Development Studies, Prof Matthew Rotimi Ajayi was always keen on knowing the progress of this work and such interest encouraged me a great deal I really appreciate him for this My former Head of Department, Dr I O Ogunrinola was simply more than a Head of Department, he actually turned out to be a father, a pastor, a teacher, an uncle v and a friend, who was always willing and available to support in whatever decent way that was possible With every patience, humour and humility, he would sign a single document over and over again just to satisfy the requirements of the school of postgraduate studies I really appreciate him for all the prayers, words of advice and the promptness with which he treated all administrative matters relating to this PhD work My supervisor and incubent Head of Department, Prof Don Ike and co-supervisor, Dr Wumi K Olayiwola were simply wonderful There was really no support I needed that they never offered me and beyond my expectations these two excellent academics supported me I must never forget those moments that Dr Olayiwola even went out of his way by helping out in search of relevant literatures for my use May the good lord continue to bless them and all members of their families I also specially appreciate Prof (Prince) Famous Izedonmi, for the role he played in the hands of God to locate me for my assignment in Covenant University Professors: Fadayomi, T O., Rudrappan, D., Omideyi, A.K Olutunla, G.T., Ige, C S Otokiti, S.O all made very useful contributions to ensure that the very best came out of this work I remain greatly indebted in gratitude to these very eminent academics The adjunct lecturers who taught and imparted me tremendously during the course work component of the PhD programme are very much appreciated In this group are Professors: Oladeji, S.I., Olomola, P.O and Drs: Adebayo, A A and Ekanem, O T They were simply wonderful as they imparted me with so much knowledge that continues to sharpen and shape my mind I must not also forget to appreciate Drs Ebiai, A and Enyi, E P for their tremendous inputs into the initial draft of this thesis Members of my academic department turned out to be truly an academic family Every one simply believed in me even in moments I was not delivering vi satisfactorily on their expectations Dr Alege, P O and my brother and friend Mr Urhie, Ese were very helpful to me in the area of modeling and clarification of theoretical framework respectively Mr Urhie, Ese also helped in printing copies of the final draft of this thesis I really appreciate these two friends for supporting me so immensely Mr Osabuohien, Evans is sincerely appreciated for clarifying to me a particular STATA command that I so much needed Elder Iyoha, Francis of the Department of Accounting kept pouring out his elderly counsels in torrents and I really appreciate him for this My good friend Mr Folarin, Sheriff of the Department International Relations was also very helpful with his positive comments and I truly appreciate him for his support Mr Wogu, Power of the Department of Human Resource Development was really available to help in changing the slides during the proposal presentation and I do thank him for this friendly gesture I am indeed very grateful to all individuals (academic and non-academic members of staff as well as my undergraduate students) in the Department of Economics and Development Studies and of course, in the College of Development Studies for every contribution made to ensure the best of this work is produced Members of my immediate family led by my most loving wife, Mrs Ngozi H Henry-Okodua were ever there to support me with their prayers, precious time and very kind words of encouragement My mother, Mrs S O Okodua, My sisters Mrs Margaret Ajibola, Grace (Mama Emma), Mrs Christiana Nkanga and my brothers, Clifford and Philip were all very supportive with their prayers and I thank them from the very bottom of my heart I do always realize that I am from one of the best families on earth and I consider myself very privileged and fortunate to be a part of this family I am thankful to my parents-in-law, Sir and Mrs Lawrence Amazue who were never tired of praying for the successful completion of this work and always sending me kind words of encouragement vii to finish on time My sisters-in-law, Ugochi, Chidinma, Onyekachi and Ijeoma were a most formidable team of encouragers I simply cannot thank these lovely sisters enough My brothers-in-law, Messrs Bankole Ajibola and Dave Nkanga were nonetheless supportive particularly with their prayers, I really appreciate them I remain grateful to all other family members who supported in one way or the other Finally, I appreciate the almighty God once again for giving me strength and courage to walk on this terrain that is reserved for real men throughout all ages He alone has by this rare out-pouring of divine favour and privileges given me a new name that opens additional doors and windows of opportunities Thus, He has empowered me to walk in the paths of the chosen and privileged few Henry Okodua December, 2010 viii TABLE OF CONTENTS Title page…………………………………………………………… …… i Declaration ………………………………………………………………….ii Certification… ………………………………………………………… …iii Dedication …………………………………………………………… ……iv Acknowledgements ………………………………………………….…… v Table of Contents ………………………………………………….…… …ix List of Abbreviations ………………………………………………………xiii List of Figures………………………………………………………… ….xiv List of Tables ……………………………………………………… ……xv List of Appendices ………………………………………………………….xvi Glossary of Terms …………………………………………………………xvii Abstract ……………………………………………………………………xxi CHAPTER ONE: INTRODUCTION 1.1 Background to the Study ……………………………….…………… 1 1.2 Statement of the Problem ………………………………….……… … 8 1.3 Research Questions …….………………………………… … ….… 14 1.4 Objectives of the Study ………………………………… ……… …14 1.5 Statement of Research Hypotheses……………… …………… ……15 1.6 Scope of the Study……………………………………… ……….……15 1.7 Justification of the Study………………………………… ……….… 16 ix 1.6 Structure of the Study………………………………………………….18 CHAPTER TWO: PATTERNS AND TRENDS OF REMITTANCES AND ECONOMIC GROWTH IN SSA 2.0 INTRODUCTION……………………… …………………………… 19 2.1 Patterns and Trends of Output Growth in SSA…………… ………… 19 2.2 Patterns and Trends of Domestic Investment in SSA……… ………… 23 2.3 Patterns and Trends of Foreign Trade in SSA………………… … ….26 2.4 Patterns and Trends of Workers’ Remittances flow to SSA…… …… 29 2.5 Trends in Workers’ Remittances and Growth Indicators in SSA… … 32 2.6 Sources and Destination of Remittance Flows …………………….… 34 2.7 Country Level Analysis of Remittance Flows to SSA ……….……… 35 CHAPTER THREE: REVIEW OF THE LITERATURE 3.1 Conceptual and Measurement Issues………………… ……………… 51 3.2 Review of Theoretical Issues…………………………… …………… 57 3.3 Review of Methodological and Empirical Issues………… ……… 64 3.4 Modeling Issues in the Remittances Literature………… ………… 78 CHAPTER FOUR: THEORETICAL FRAMEWORK AND METHODOLOGY 4.1 Theoretical Framework…………………………………….………… 81 x APPENDIX 4: Correlation Coefficients for the One-Step System GMM Dynamic Panel Data Estimation (YGR) correlate resid l.ygr ll lk reer inf inv wr yr* (obs=133) resid resid ygr L1 ll lk reer inf inv wr yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 L ygr ll lk reer inf inv wr 1.0000 0.0966 1.0000 0.0257 0.5340 1.0000 -0.0287 0.0620 -0.1640 1.0000 -0.0941 0.1537 -0.0137 -0.1631 0.1004 0.4077 0.8616 -0.2448 0.1135 0.3662 0.4227 -0.1362 -0.1485 -0.0238 -0.1762 0.0816 0.0153 -0.0159 -0.1519 0.0674 -0.0411 -0.0078 -0.0657 0.0233 -0.0186 0.0001 0.0163 0.0061 0.0436 0.0079 0.0748 0.0074 0.0210 0.0156 0.1145 0.0070 0.1283 0.0239 0.1882 -0.1928 1.0000 0.0124 -0.1257 -0.0574 0.0482 -0.0872 -0.0252 0.0275 0.0849 0.0092 1.0000 0.4120 -0.1572 -0.1592 -0.0895 -0.0082 0.0503 0.1042 0.2595 1.0000 -0.1475 -0.1198 -0.0822 -0.0077 0.0378 0.1095 0.2099 yr6 yr7 yr8 1.0000 -0.1667 1.0000 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 1.0000 0.0681 0.0004 -0.0095 -0.0100 0.0287 -0.0332 -0.0196 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 -0.0000 yr1 yr2 yr3 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 yr4 yr5 164 APPENDIX 5: LSDV Linear Regression Result (YGR) areg ygr l.ygr ll lk reer inf inv wr yr*, absorb(countryid) robust (dropping yr1 because it does not vary within category) Linear regression, absorbing indicators ygr Coef ygr L1 ll lk reer inf inv wr yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 0314063 -9.40942 -.8126612 0038955 -.1532422 0011784 0003433 (dropped) -2.040307 -1.913353 -1.905739 -.9680189 -.8609837 4679434 (dropped) 19.06527 countryid absorbed Robust Std Err Number of obs F( 13, 101) Prob > F R-squared Adj R-squared Root MSE t P>|t| = = = = = = 133 3.45 0.0002 0.4772 0.3167 3.0981 [95% Conf Interval] 1292819 19.82997 1.642863 003133 0412981 0007783 0028244 0.24 -0.47 -0.49 1.24 -3.71 1.51 0.12 0.809 0.636 0.622 0.217 0.000 0.133 0.904 -.2250541 -48.74675 -4.07166 -.0023195 -.2351664 -.0003656 -.0052597 2878667 29.92791 2.446338 0101105 -.0713179 0027224 0059462 2.910312 2.122244 1.887166 1.651482 1.18654 9945623 -0.70 -0.90 -1.01 -0.59 -0.73 0.47 0.485 0.369 0.315 0.559 0.470 0.639 -7.813583 -6.123314 -5.649368 -4.244114 -3.21476 -1.505001 3.732969 2.296608 1.83789 2.308076 1.492793 2.440887 22.63085 0.84 0.402 -25.82824 63.95879 (19 categories) 165 APPENDIX 6: OLS Linear Regression Result (YGR) reg ygr l.ygr ll lk reer inf inv wr yr*, robust Linear regression Number of obs F( 13, 119) Prob > F R-squared Root MSE ygr Coef ygr L1 ll lk reer inf inv wr yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 3395813 6411125 -1.687579 -.0002664 -.0909555 0011134 -.0002634 (dropped) (dropped) -.7256122 -.4956724 2135507 -.0372017 1.136257 0324898 13.16178 Robust Std Err t P>|t| = = = = = 133 4.24 0.0000 0.2774 3.3555 [95% Conf Interval] 1359736 3606137 9112346 0018056 0350804 0005007 0016269 2.50 1.78 -1.85 -0.15 -2.59 2.22 -0.16 0.014 0.078 0.067 0.883 0.011 0.028 0.872 0703399 -.0729387 -3.491914 -.0038418 -.1604182 000122 -.0034848 6088227 1.355164 1167568 0033089 -.0214928 0021048 002958 1.32397 1.066972 1.288655 1.083236 1.074868 1.003711 5.677946 -0.55 -0.46 0.17 -0.03 1.06 0.03 2.32 0.585 0.643 0.869 0.973 0.293 0.974 0.022 -3.347204 -2.608384 -2.338115 -2.182116 -.9920885 -1.954959 1.918876 1.89598 1.617039 2.765216 2.107713 3.264603 2.019939 24.40468 166 APPENDIX 7: Two-Step System GMM Dynamic Panel Data Estimation (INV) xtabond2 inv l.inv wr intr inf fd yr*, gmm( inv l.inv wr, lag(3 3) collapse equation(level > )) iv( intr inf fd yr*) small robust twostep artests(3) Favoring space over speed To switch, type or click on mata: mata set matafavor speed, perm yr1 dropped due to collinearity yr8 dropped due to collinearity Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate optimal weighting matrix for two-step estimation Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, two-step system GMM Group variable: countryid Time variable : year Number of instruments = 13 F(11, 20) = 260.57 Prob > F = 0.000 inv Coef inv L1 wr intr inf fd yr2 yr3 yr4 yr5 yr6 yr7 _cons 1.522043 -2.09074 -4.809234 9547287 8771388 -204.5687 -302.9658 -56.01824 -57.57085 -208.501 -218.9 -70.6041 Number of obs Number of groups Obs per group: min avg max Corrected Std Err .0799817 1346747 22.00037 3.861875 4840851 136.043 149.0769 119.1201 115.592 170.9897 133.9801 178.967 t 19.03 -15.52 -0.22 0.25 1.81 -1.50 -2.03 -0.47 -0.50 -1.22 -1.63 -0.39 P>|t| 0.000 0.000 0.829 0.807 0.085 0.148 0.056 0.643 0.624 0.237 0.118 0.697 = = = = = 147 21 7 7.00 7 [95% Conf Interval] 1.355205 -2.371666 -50.7012 -7.101001 -.1326449 -488.3494 -613.9347 -304.4984 -298.6915 -565.1793 -498.3776 -443.9228 1.688882 -1.809813 41.08274 9.010458 1.886923 79.21205 8.003134 192.4619 183.5498 148.1772 60.57767 302.7146 Instruments for first differences equation Standard D.(intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8) Instruments for levels equation Standard _cons intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 GMM-type (missing=0, separate instruments for each period unless collapsed) DL3.(inv L.inv wr) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Arellano-Bond test for AR(3) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(1) = 26.49 but not weakened by many instruments.) overid restrictions: chi2(1) = 1.26 can be weakened by many instruments.) -0.59 -1.29 -1.21 Pr > z = Pr > z = Pr > z = 0.554 0.196 0.225 Prob > chi2 = 0.000 Prob > chi2 = 0.262 167 APPENDIX 8: Correlation Coefficients for the Two-Step System GMM Dynamic Panel Data Estimation (INV) correlate resid l.inv wr intr inf fd yr* (obs=147) resid resid inv L1 wr intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr3 yr4 yr5 yr6 yr7 yr8 L inv wr intr inf fd yr1 yr2 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 -0.1040 -0.0204 -0.1034 -0.0404 0.0617 0.0180 -0.0687 -0.0682 0.0039 0.1097 0.0017 0.0035 1.0000 0.7618 0.2224 0.1835 -0.1983 -0.0745 -0.0537 -0.0509 -0.0193 0.0208 0.0678 0.1099 1.0000 0.0970 0.0331 -0.0551 -0.0558 -0.0523 -0.0502 -0.0269 -0.0087 -0.0011 0.1950 1.0000 0.6208 0.0035 0.1672 0.0618 0.0355 -0.0247 -0.0285 -0.1000 -0.1113 1.0000 -0.0978 -0.0654 0.0672 -0.0923 -0.0198 0.0547 0.0713 -0.0157 1.0000 -0.0183 -0.0097 0.0001 0.0049 0.0541 -0.0179 -0.0132 yr3 yr4 yr5 yr6 yr7 yr8 1.0000 -0.1667 1.0000 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 168 APPENDIX 9: One-Step System GMM Dynamic Panel Data Estimation (INV) xtabond2 inv l.inv wr intr inf fd yr*, gmm( inv l.inv wr, lag(3 3) collapse equation(level > )) iv( intr inf fd yr*) small robust artests(3) Favoring space over speed To switch, type or click on mata: mata set matafavor speed, perm yr1 dropped due to collinearity yr8 dropped due to collinearity Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate robust weighting matrix for Hansen test Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, one-step system GMM Group variable: countryid Time variable : year Number of instruments = 13 F(11, 20) = 2378.62 Prob > F = 0.000 inv Coef inv L1 wr intr inf fd yr2 yr3 yr4 yr5 yr6 yr7 _cons 1.598502 -2.211787 -22.14462 -1.51909 1.338188 -118.2094 -376.0929 -145.5531 -58.18054 -71.65533 -275.3189 -131.8035 Number of obs Number of groups Obs per group: min avg max Robust Std Err .07937 1239623 26.45814 4.505236 6378451 146.5662 155.3274 142.1335 104.0838 207.0516 138.2551 162.038 t 20.14 -17.84 -0.84 -0.34 2.10 -0.81 -2.42 -1.02 -0.56 -0.35 -1.99 -0.81 P>|t| 0.000 0.000 0.413 0.739 0.049 0.429 0.025 0.318 0.582 0.733 0.060 0.426 = = = = = 147 21 7 7.00 7 [95% Conf Interval] 1.432939 -2.470368 -77.33532 -10.91685 0076665 -423.9411 -700.1002 -442.0384 -275.2955 -503.5575 -563.714 -469.8089 1.764065 -1.953207 33.04609 7.878668 2.66871 187.5223 -52.08567 150.9322 158.9344 360.2468 13.07615 206.2019 Instruments for first differences equation Standard D.(intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8) Instruments for levels equation Standard _cons intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 GMM-type (missing=0, separate instruments for each period unless collapsed) DL3.(inv L.inv wr) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Arellano-Bond test for AR(3) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(1) = 26.49 but not weakened by many instruments.) overid restrictions: chi2(1) = 1.26 can be weakened by many instruments.) -0.98 -1.45 -1.53 Pr > z = Pr > z = Pr > z = 0.326 0.146 0.127 Prob > chi2 = 0.000 Prob > chi2 = 0.262 169 APPENDIX 10: Correlation Coefficients for the One-Step System GMM Dynamic Panel Data Estimation (INV) correlate resid l.inv wr intr inf fd yr* (obs=147) resid resid inv L1 wr intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr3 yr4 yr5 yr6 yr7 yr8 L inv wr intr inf fd yr1 yr2 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 -0.2338 -0.0253 -0.0057 0.0271 0.0185 0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 1.0000 0.7618 0.2224 0.1835 -0.1983 -0.0745 -0.0537 -0.0509 -0.0193 0.0208 0.0678 0.1099 1.0000 0.0970 0.0331 -0.0551 -0.0558 -0.0523 -0.0502 -0.0269 -0.0087 -0.0011 0.1950 1.0000 0.6208 0.0035 0.1672 0.0618 0.0355 -0.0247 -0.0285 -0.1000 -0.1113 1.0000 -0.0978 -0.0654 0.0672 -0.0923 -0.0198 0.0547 0.0713 -0.0157 1.0000 -0.0183 -0.0097 0.0001 0.0049 0.0541 -0.0179 -0.0132 yr3 yr4 yr5 yr6 yr7 yr8 1.0000 -0.1667 1.0000 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 1.0000 1.0000 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 170 APPENDIX 11: OLS Linear Regression Result (INV) reg inv l.inv wr is intr inf fd yr*, robust Linear regression Number of obs F( 12, 134) Prob > F R-squared Root MSE inv Coef inv L1 wr is intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 1.492146 -2.040912 1690263 -17.55043 3.819515 1.073328 (dropped) (dropped) -251.7033 -6.962524 81.52272 81.68152 -100.1055 55.34653 -225.1606 Robust Std Err t P>|t| = = = = = 147 95.80 0.0000 0.9721 601.43 [95% Conf Interval] 0672773 1030068 0526384 11.83806 6.265001 2620202 22.18 -19.81 3.21 -1.48 0.61 4.10 0.000 0.000 0.002 0.141 0.543 0.000 1.359083 -2.244642 0649168 -40.96405 -8.571565 5550982 1.625209 -1.837183 2731358 5.863192 16.2106 1.591559 145.1386 134.8978 100.377 204.77 160.4065 138.3133 106.7409 -1.73 -0.05 0.81 0.40 -0.62 0.40 -2.11 0.085 0.959 0.418 0.691 0.534 0.690 0.037 -538.7622 -273.7669 -117.0055 -323.3178 -417.3617 -218.2132 -436.2754 35.35552 259.8418 280.051 486.6808 217.1507 328.9062 -14.04577 171 APPENDIX 12: LSDV Linear Regression Result (INV) areg inv l.inv wr is intr inf fd yr*, absorb(countryid) robust (dropping yr1 because it does not vary within category) Linear regression, absorbing indicators inv Coef inv L1 wr is intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 1.66606 -2.199281 2287761 3.64175 3.78888 -.2479393 (dropped) (dropped) -248.3985 1.684435 73.07941 45.9227 -217.4991 -17.93526 -476.7028 countryid absorbed Robust Std Err Number of obs F( 12, 114) Prob > F R-squared Adj R-squared Root MSE t P>|t| = = = = = = 147 108.82 0.0000 0.9805 0.9750 544.96 [95% Conf Interval] 1124533 093338 095027 10.9598 5.517684 2997467 14.82 -23.56 2.41 0.33 0.69 -0.83 0.000 0.000 0.018 0.740 0.494 0.410 1.443291 -2.384183 0405284 -18.06952 -7.141609 -.8417352 1.88883 -2.014379 4170239 25.35302 14.71937 3458566 148.9315 148.7504 99.2898 174.4334 157.3187 154.8281 150.9538 -1.67 0.01 0.74 0.26 -1.38 -0.12 -3.16 0.098 0.991 0.463 0.793 0.170 0.908 0.002 -543.4306 -292.989 -123.6129 -299.6285 -529.1462 -324.6486 -775.7411 46.6336 296.3579 269.7717 391.4739 94.14808 288.778 -177.6644 (21 categories) 172 APPENDIX 13: Two-Step System GMM Dynamic Panel Data Estimation (REB) xtabond2 reb l.reb wr cab intr open reer yr*, gmm(l.reb wr cab, lag( 2) collapse equatio > n(both)) iv( intr reer open yr*) small robust twostep artests(3) Favoring space over speed To switch, type or click on mata: mata set matafavor speed, perm yr1 dropped due to collinearity yr8 dropped due to collinearity Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate optimal weighting matrix for two-step estimation Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, two-step system GMM Group variable: countryid Time variable : year Number of instruments = 19 F(12, 20) = 3994.26 Prob > F = 0.000 Number of obs Number of groups Obs per group: min avg max = = = = = 146 21 6 6.95 7 Corrected Std Err t P>|t| [95% Conf Interval] 3344472 055633 6.01 0.000 2183989 4504955 -.220629 6342295 -9.845849 -82.02106 0381551 220.0341 165.0725 153.8099 161.446 104.2631 128.1684 -184.3704 0725187 0260773 10.46355 131.248 1548049 203.2634 153.4548 115.6127 111.203 88.58727 89.07543 273.769 -3.04 24.32 -0.94 -0.62 0.25 1.08 1.08 1.33 1.45 1.18 1.44 -0.67 0.006 0.000 0.358 0.539 0.808 0.292 0.295 0.198 0.162 0.253 0.166 0.508 -.3719003 5798331 -31.67243 -355.7995 -.2847622 -203.966 -155.0285 -87.35405 -70.5193 -80.52671 -57.63964 -755.4425 -.0693577 6886259 11.98073 191.7574 3610724 644.0342 485.1735 394.9739 393.4113 289.0529 313.9765 386.7017 reb Coef reb L1 wr cab intr open reer yr2 yr3 yr4 yr5 yr6 yr7 _cons Instruments for first differences equation Standard D.(intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(L.reb wr cab) collapsed Instruments for levels equation Standard _cons intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.reb wr cab) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Arellano-Bond test for AR(3) in first differences: z = Pr > z = Pr > z = Pr > z = 0.330 0.378 0.267 Prob > chi2 = 0.000 Prob > chi2 = 0.219 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(3) = 6.38 Prob > chi2 = Difference (null H = exogenous): chi2(3) = 1.88 Prob > chi2 = 0.094 0.597 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(6) = 203.17 but not weakened by many instruments.) overid restrictions: chi2(6) = 8.26 can be weakened by many instruments.) -0.97 -0.88 1.11 173 APPENDIX 14: Correlation Coefficients for the Two-Step System GMM Dynamic Panel Data Estimation (REB) correlate resid l.reb wr cab intr open reer yr* (obs=146) resid resid reb L1 wr cab intr open reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr2 yr3 yr4 yr5 yr6 yr7 yr8 L reb wr cab intr open reer yr1 1.0000 0.2648 0.0001 0.0862 -0.0760 0.0969 0.0035 0.0239 -0.0336 -0.0038 0.0384 -0.0086 0.0065 -0.0227 1.0000 0.8083 0.7850 0.1471 0.0319 -0.0298 0.0468 -0.0053 -0.0518 -0.0448 0.0273 -0.0730 0.0993 1.0000 0.7270 0.0894 -0.0447 -0.0202 -0.0510 -0.0475 -0.0453 -0.0217 -0.0032 -0.0353 0.2033 1.0000 0.1746 0.0157 -0.0351 -0.0440 -0.0539 -0.0324 0.0430 0.0908 -0.0654 0.0606 1.0000 -0.0202 -0.1734 0.1694 0.0638 0.0374 -0.0229 -0.0267 -0.1136 -0.1097 1.0000 -0.2851 -0.0024 0.0047 0.0016 -0.0162 -0.0077 -0.0191 0.0387 1.0000 0.0760 0.0625 0.0205 0.0047 0.0076 0.0089 -0.1801 yr2 yr3 yr4 yr5 yr6 yr7 yr8 1.0000 -0.1680 -0.1680 -0.1680 -0.1680 -0.1633 -0.1680 1.0000 -0.1680 -0.1680 -0.1680 -0.1633 -0.1680 1.0000 -0.1680 -0.1680 -0.1633 -0.1680 1.0000 -0.1680 -0.1633 -0.1680 1.0000 -0.1633 -0.1680 1.0000 -0.1633 1.0000 174 APPENDIX 15: One-Step System GMM Dynamic Panel Data Estimation (REB) xtabond2 reb l.reb wr cab intr open reer yr*, gmm(l.reb wr cab, lag( 2) collapse equatio > n(both)) iv( intr reer open yr*) small robust artests(3) Favoring space over speed To switch, type or click on mata: mata set matafavor speed, perm yr1 dropped due to collinearity yr8 dropped due to collinearity Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate robust weighting matrix for Hansen test Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, one-step system GMM Group variable: countryid Time variable : year Number of instruments = 19 F(12, 20) = 5881.31 Prob > F = 0.000 Number of obs Number of groups Obs per group: min avg max = = = = = 146 21 6 6.95 7 Robust Std Err t P>|t| [95% Conf Interval] 3260587 0550705 5.92 0.000 2111836 4409338 -.2074022 6383358 -20.42885 54.7389 0900303 334.3751 180.5206 205.9587 264.3118 137.9955 181.4249 -301.3717 0739689 0196731 11.84277 177.831 2102059 209.6102 198.7275 144.034 144.1988 127.7382 106.9296 291.5617 -2.80 32.45 -1.73 0.31 0.43 1.60 0.91 1.43 1.83 1.08 1.70 -1.03 0.011 0.000 0.100 0.761 0.673 0.126 0.374 0.168 0.082 0.293 0.105 0.314 -.3616985 5972985 -45.13244 -316.2101 -.3484516 -102.8641 -234.0176 -94.49102 -36.48155 -128.4617 -41.62634 -909.5588 -.0531058 6793732 4.27474 425.6878 5285122 771.6144 595.0589 506.4084 565.1052 404.4527 404.4761 306.8153 reb Coef reb L1 wr cab intr open reer yr2 yr3 yr4 yr5 yr6 yr7 _cons Instruments for first differences equation Standard D.(intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(L.reb wr cab) collapsed Instruments for levels equation Standard _cons intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.reb wr cab) collapsed Arellano-Bond test for AR(1) in first differences: z = -1.22 Pr > z = 0.223 Arellano-Bond test for AR(2) in first differences: z = -1.09 Pr > z = 0.277 Arellano-Bond test for AR(3) in first differences: z = 1.45 Pr > z = 0.146 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(6) = 203.17 Prob > chi2 = 0.000 but not weakened by many instruments.) overid restrictions: chi2(6) = 8.26 Prob > chi2 = 0.219 can be weakened by many instruments.) Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(3) = 6.38 Prob > chi2 = 0.094 Difference (null H = exogenous): chi2(3) = 1.88 Prob > chi2 = 0.597 175 APPENDIX 16: Correlation Coefficients for the One-Step System GMM Dynamic Panel Data Estimation (REB) correlate resid l.reb wr cab intr open reer yr* (obs=146) resid resid reb L1 wr cab intr open reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr2 yr3 yr4 yr5 yr6 yr7 yr8 L reb wr cab intr open reer yr1 1.0000 -0.2851 1.0000 -0.0024 0.0760 0.0047 0.0625 0.0016 0.0205 -0.0162 0.0047 -0.0077 0.0076 -0.0191 0.0089 0.0387 -0.1801 1.0000 0.2701 -0.0008 0.0849 0.0135 0.0091 -0.0090 0.0001 0.0001 0.0003 0.0009 0.0022 -0.0026 -0.0011 1.0000 0.8083 0.7850 0.1471 0.0319 -0.0298 0.0468 -0.0053 -0.0518 -0.0448 0.0273 -0.0730 0.0993 1.0000 0.7270 0.0894 -0.0447 -0.0202 -0.0510 -0.0475 -0.0453 -0.0217 -0.0032 -0.0353 0.2033 1.0000 0.1746 0.0157 -0.0351 -0.0440 -0.0539 -0.0324 0.0430 0.0908 -0.0654 0.0606 1.0000 -0.0202 -0.1734 0.1694 0.0638 0.0374 -0.0229 -0.0267 -0.1136 -0.1097 yr2 yr3 yr4 yr5 yr6 1.0000 -0.1680 -0.1680 -0.1680 -0.1680 -0.1633 -0.1680 1.0000 -0.1680 -0.1680 -0.1680 -0.1633 -0.1680 yr7 yr8 1.0000 -0.1680 1.0000 -0.1680 -0.1680 1.0000 -0.1633 -0.1633 -0.1633 1.0000 -0.1680 -0.1680 -0.1680 -0.1633 1.0000 176 APPENDIX 17: OLS Linear Regression Result (REB) reg reb l.reb wr cab intr open reer yr*, robust Linear regression Number of obs F( 12, 133) Prob > F R-squared Root MSE reb Coef reb L1 wr cab intr open reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 5484697 -.4474112 6007272 -22.06792 -13.98695 079638 (dropped) (dropped) -85.90667 8.837841 98.45347 -91.45896 4.528824 -154.7017 57.0378 Robust Std Err t P>|t| = = = = = 146 135.08 0.0000 0.9606 530.29 [95% Conf Interval] 0928024 1011547 0724322 11.17454 69.58047 1236633 5.91 -4.42 8.29 -1.97 -0.20 0.64 0.000 0.000 0.000 0.050 0.841 0.521 3649102 -.6474913 457459 -44.17074 -151.6144 -.1649632 7320293 -.2473311 7439954 0348897 123.6405 3242392 187.8241 107.8441 123.1865 130.3115 156.0889 188.431 124.1483 -0.46 0.08 0.80 -0.70 0.03 -0.82 0.46 0.648 0.935 0.426 0.484 0.977 0.413 0.647 -457.4155 -204.4735 -145.2046 -349.21 -304.2089 -527.411 -188.5228 285.6022 222.1492 342.1115 166.2921 313.2665 218.0076 302.5984 177 APPENDIX 18: LSDV Linear Regression Result (REB) areg reb l.reb wr cab intr open reer yr*, robust absorb( countryid) (dropping yr1 because it does not vary within category) Linear regression, absorbing indicators reb Coef reb L1 wr cab intr open reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 _cons 3663364 -.2239559 6587274 7.782394 147.7737 -.6012485 (dropped) (dropped) -106.6473 -76.12383 -10.63278 -157.1491 -81.68543 -332.4017 -171.8505 countryid absorbed Robust Std Err Number of obs F( 12, 113) Prob > F R-squared Adj R-squared Root MSE t P>|t| = = = = = = 146 60.27 0.0000 0.9820 0.9769 389.07 [95% Conf Interval] 0926983 1016109 0639868 8.989553 265.9012 4330388 3.95 -2.20 10.29 0.87 0.56 -1.39 0.000 0.030 0.000 0.388 0.579 0.168 1826844 -.4252654 531958 -10.02753 -379.0246 -1.459176 5499885 -.0226464 7854968 25.59232 674.5719 2566794 153.3732 101.6442 107.5454 116.07 138.6195 226.7708 169.0538 -0.70 -0.75 -0.10 -1.35 -0.59 -1.47 -1.02 0.488 0.455 0.921 0.178 0.557 0.145 0.312 -410.5072 -277.4992 -223.6996 -387.1047 -356.3156 -781.6757 -506.7767 197.2127 125.2516 202.4341 72.80655 192.9447 116.8722 163.0756 (21 categories) 178 ... of remittances in 2008 …….… 2.1: Trends in Workers’ Remittances and Selected Economic Growth Indicators in SSA……………………………….………………… 33 2.2: Remittance Receipts and other Growth Indicators in Nigeria…... examples of industrialization and growth are thus underpinned by rising rates of savings, investment and exports While African countries have in the past experienced surges of investment and growth, ... Remittance Receipts and other Growth Indicators in Guinea… …….46 2.7: Remittance Receipts and other Growth Indicators in Djibouti …… …47 2.8: Remittance Receipts and other Growth Indicators in Lesotho