Effects of migration and remittances on poverty and inequality A comparison between Burkina Faso, Kenya, Nigeria, Senegal, South Africa, and Uganda Y. Bambio F. Cissé D. Gaye Consortium pour la Recherche Economique et Sociale Dakar, June 5 th 2014

Outline Objectives and hypotheses Context Theory Methodology Preliminary results Main remarks

Objectives and hypotheses Objective: Evaluate impact of migration and remittances on poverty and inequality in Burkina Faso, Kenya, Nigeria, Senegal, South Africa and Uganda. Hypotheses: – Migration and remittances decrease poverty, and increase inequality. – The impact of international remittances on poverty and inequality is greater compared to that from internal remittances.

Context Increase in migration and remittances in Africa – Stock of emigrants: 22 millions (2.5% of population) [World Bank, 2010], – Of which 30% are doctors and nurses (World Bank, 2010)

International Remittances Recipients By Region in 2008 (%)

Top Ten International Remittances Recipient Countries in Africa, 2008 (US$ million)

Context (ctd) Increasing interest in research on impacts of migration and remittances. Challenge in identifying micro impacts. – Availability of appropriate data. – Appropriate econometric methods, etc. Several approaches exist.

Theory Several theories on migration and remittances – Micro analysis: migration results essentially from individual or household rational choice, and social capital. – Neoclassical economy, New migration economy. – Close link between migration and remittances. – Maximization of household income and relative “privation” – Various reasons for remittances: altruism, risk sharing, inheritance, etc. Theoretical basis: maximization of joint utility under budget constraint.

Methodology  Some approaches Experimental Methods Difference-in-Difference Propensity Score Matching Instrumental Variables Before/After method Etc.

Methodology (ctd)  Model and estimation Counterfactual approach – Combination of Propensity Score Matching and Instrumental Variables methods. – Objective of this combination: reduce both biases from observables et aux non-observables characteristics. – Main potential risk: limits in sample sizes and appropriate instruments.

Methodology (ctd) Following Garip (2007):  Model and estimation (ctd)

Methodology (ctd)  Model and estimation Appropriate treatment of errors: – Consider predicted error for non-migrants households, – Generate an error using weighted cumulative probability function, and a random error term for migrant households. Calculate and compare poverty and inequality indicators from the 2 groups.

Methodology (ctd)  Data Source: Data from Migration and Remittances Household Surveys in Burkina Faso, Kenya, Nigeria, Senegal, South Africa, and Uganda (Africa Migration Project – World Bank) Contents: – Cross-section household data ( ), – About 2,000 households per country, – Same data collection methodology, – Various information, including migration, remittances, social- economic characteristics (education, marital status, housing conditions, labor force participation, assets, access to finance, etc.).

Table 1: Determinants of international migration Burkina FasoNigeriaSenegal Age of household head0.007*0.006**-0.014*** Squared age of household head *** Sex of household head (1 = male) **-0.156*** Own house/building (1 = yes) ***0.130*** Own agricultural land (1 = yes ***-0.098*** Highest education of household head: primary level (1 = yes) Highest education of household: head: secondary level (1 = yes) **0.056* Highest education of household head: tertiary level (1 = yes) Log household size0.132***-0.028***0.193*** Household residence: in most urban region (1 = yes) Constant-0.303** F (.)17.7***18.98***41.64*** F test of excluded instruments: F (.)23.03***35.89***43.33*** Angrist-Pischke multivariate F test of excluded instruments: F (.) * Number of observations2,0832,2011,950 Preliminary results

Table 2: Determinants of international remittances Burkina FasoNigeriaSenegal Age of household head0.010***0.006***-0.017*** Squared age of household head **0.0002*** Sex of household head (1 = male) *** Own house/building (1 = yes) ***0.085*** Own agricultural land (1 = yes **-0.089*** Highest education of household head: primary level (1 = yes) **-0.081*** Highest education of household: head: secondary level (1 = yes) Highest education of household head: tertiary level (1 = yes) Log household size0.116***-0.025***0.210*** Household residence: in most urban region (1 = yes) ** Constant-0.485***-0.091*0.282** F (.)19.34***14.87***43.94*** F test of excluded instruments: F (.)26.29***28.32***45.58 Angrist-Pischke multivariate F test of excluded instruments: F (.) *** Number of observations2,0832,2011,950 Preliminary results (ctd)

Table 3: Effects of international migration and remittances on per capita expenditure Burkina FasoNigeriaSenegal Migrant household *** Remittance receiving household Own agricultural land (1 = yes-0.311* Highest education of household head: primary level (1 = yes) Highest education of household: head: secondary level (1 = yes)0.620***0.119*0.276*** Highest education of household head: tertiary level (1 = yes)1.504**0.583***0.375** Log household size-0.130*-0.329***-0.688*** Household residence: in most urban region (1 = yes)0.646**0.539**0.246*** Constant11.965***11.576***13.247*** F (.)9.23***46.14***79.94*** Number of observations2,0832,2011,950 Preliminary results (ctd)

Table 4: Identification and instrument tests Burkina FasoNigeriaSenegal Underidentification test **14.67*** Weak identification test Weak-instrument-robust inference Anderson-Rubin Wald test [F (.)]4.08***83.11***18.96*** Stock-Wright LM S statistic [Chi-sq (.)]16.29***290.08***73.41*** Overidentification test (Sargan)] ***3.345 Preliminary results (ctd)

Table 5: Average Treatment Effect of international migration on per capita expenditure Burkina FasoNigeriaSenegal Nearest Neighbor Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Stratification Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Kernel Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Direct Matching using Nearest neighbor Coefficient (SATT) ** Number of observations2,0832,2081,953 Number of matches111 Preliminary results (ctd)

Table 6: Average Treatment Effect of international remittances on per capita expenditure Burkina FasoNigeriaSenegal Nearest Neighbor Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Stratification Matching Number of observations, treatment Number of observations, comparison1,6491,8971,371 Average treatment effect on the treated (ATT) t-Statistics Kernel Matching Number of observations, treatment Number of observations, comparison16491, Average treatment effect on the treated (ATT) t-Statistics Direct Matching using Nearest neighbor Coefficient (SATT) *0.2014** Number of observations2,0832,2081,950 Number of matches111 Preliminary results (ctd)

Table 7: Determinants of internal migration Burkina FasoNigeriaSenegal Age of household head *** Squared age of household head ** Sex of household head (1 = male) *** ** Own house/building (1 = yes) Own agricultural land (1 = yes 0.120***0.041* 0.079*** Highest education of household head: primary level (1 = yes) ** *** Highest education of household: head: secondary level (1 = yes) *** Highest education of household head: tertiary level (1 = yes) *** Log household size *** *** Household residence: in most urban region (1 = yes) * *** Constant *** F (.)1.87**13.36***14.61*** F test of excluded instruments: F (.)2.47**26.58***1.85 Angrist-Pischke multivariate F test of excluded instruments: F (.) ***1.48 Number of observations2,0832,2011,950 Preliminary results (ctd)

Table 8: Determinants of internal remittances Burkina FasoNigeriaSenegal Age of household head ** Squared age of household head ** Sex of household head (1 = male) 0.078***-0.107*** *** Own house/building (1 = yes) 0.154***0.069*** Own agricultural land (1 = yes 0.114*** *** Highest education of household head: primary level (1 = yes) ** Highest education of household: head: secondary level (1 = yes) 0.089* Highest education of household head: tertiary level (1 = yes) Log household size Household residence: in most urban region (1 = yes) *** Constant ** ** F (.)4.63***9.75***18.80*** F test of excluded instruments: F (.)8.64***23.51***14.65*** Angrist-Pischke multivariate F test of excluded instruments: F (.)2.54*3.72**11.75*** Number of observations2,0832,2011,950

Preliminary results (ctd) Table 9: Effects of internal migration and remittances on per capita expenditure Burkina FasoNigeriaSenegal Migrant household ** Remittance receiving household *** 2.128*** Own agricultural land (1 = yes *** Highest education of household head: primary level (1 = yes) ** Highest education of household: head: secondary level (1 = yes) 0.687***0.557*** 0.307*** Highest education of household head: tertiary level (1 = yes) 1.864***0.840*** 0.464** Log household size ***-0.568*** *** Household residence: in most urban region (1 = yes) 0.584***1.091** 0.375* Constant ***12.112*** *** F (.)15.49***9.95***68.17*** Number of observations2,0832,2011,950

Preliminary results (ctd) Table 10: Identification and instrument tests Burkina FasoNigeriaSenegal Underidentification test ***5.39 Weak identification test Weak-instrument-robust inference Anderson-Rubin Wald test [F (.)] ***18.96*** Stock-Wright LM S statistic [Chi-sq (.)] ***73.41*** Overidentification test (Sargan)]7.031** *

Preliminary results (ctd) Table 11: Average Treatment Effect of internal migration on per capita expenditure Burkina FasoNigeriaSenegal Nearest Neighbor Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Stratification Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Kernel Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Direct Matching using Nearest neighbor Coefficient (SATT) ** Number of observations2,0832,2081,950 Number of matches111

Preliminary results (ctd) Table 12: Average Treatment Effect of internal remittances on per capita expenditure Burkina FasoNigeriaSenegal Nearest Neighbor Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Stratification Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Kernel Matching Number of observations, treatment Number of observations, comparison Average treatment effect on the treated (ATT) t-Statistics Direct Matching using Nearest neighbor Coefficient (SATT) * Number of observations2,0832,2081,950 Number of matches111

Quick remarks Model instruments need to be improved, particularly for some countries; Data are collected on a year that is specific for most of countries; Challenge in using external data on poverty (e.g. poverty line); Both migration and remittances seem to have positive impact on per capita expenditure; with different amplitude per country.

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