THE EFFECT OF LAND ACCESS AND TENURE ON LIVELIHOODS UNDER HIGH FOOD PRICES ENVIRONMENT: THE CASE OF NORTHERN MOZAMBIQUE Raul Pitoro; Songqing Jin; Mywish Maredia; and Gerhardus Schultink Michigan State University, USA Paper prepared for presentation at the “2015 WORLD BANK CONFERENCE ON LAND AND POVERTY” The World Bank - Washington DC, March 23-27, 2015

OUTLINE BACKGROUND AND MOTIVATION DATA EMPIRICAL MODEL RESULTS CONCLUSIONS

BACKGROUND & MOTIVATION Poverty, hunger and malnutrition are three main constraints affecting the livelihoods of human beings; Decrease in cultivated area per adult equivalent from 2005 to 2008; Poverty reduction from 70% in 1997 to 61% in 2003 and a subsequent improvement at a slower pace to 57% in 2009; Significant improvement in the households’ income ( ), stagnant ( ); The Government aims to achieve 40% by 2015; Despite this progress 50% of the households live under poverty; 24 % of the population remains chronically food insecure

Exacerbated by: – Cyclical climatic disasters and – Drastic food prices increased in the cropping season 2007/08 in Mozambique (MSU, 2011); Significant increase in food aid 2008 – 2011 We concentrate in this period to understand: – Changes in food security and welfare and its drivers – The effect of land access Land scarcity prominent cause of poverty (Burgess, 2000). Hypothesis: high food prices  land access  high income  improve food security

Objectives Aims to answer three main research questions based on the panel data collected in rural Mozambique in 2008 and 2011: 1.How land access changed over time? 2.How income and food security changed over time? 3.What drove those changes, specifically to what extent land access have influenced these changes? 4.What role land access played in lifting people out of poverty?

FOUR strains of literature on effect of land on poverty Strong Positive effect: – Mozambique: Boughton et al.(2005), Boughton et al. (2006), Cunguara (2008); Mather et al. (2012); – Malawi: Mukherjee and Benson (2003); – Bangladesh: Mukherjee and Benson (2003); Wodom (1999); – China: Burgess (2001); – Ethiopia, Kenya, Rwanda, Mozambique, and Zambia: Bigsten et al. (2003); Jayne et al. (2003); – Mexico: Finan et al.(2005); – Nepal: Deininger (2003); Adhikari and Chatfield (2008)

Weak positive effect: – Chile, Colombia, El Salvador, Honduras, Paraguay, and Peru: Lopez and Vales(2000) No effect: – Kenya: Geda et al. (2005) ; – South Africa: Carter and May (1999) Negative effect : – Cote d’Ivoire: Grootaert (1997); – South Africa: Valente (2009)

DATA Panel household survey : Ministry of Agriculture/Michigan State University Covering: – Five provinces with high agricultural potential (Manica, Tete, Sofala, Nampula, and Zambezia) – 1,186 households (HHs) Data is representative at province level (where no district was dropped due to logistical issues) Appropriate Inverse Probability Weights (IPW) were applied

EMPIRICAL MODEL - I INCOME: Y it : per adult equivalent (AE) income (in 2011 MZN); Land it : operated land size per AE (ha); X it : a vector of exogenous household control variables; Ci: the unobserved heterogeneity, and μ it : idiosyncratic error t: =2008 and 2011 RE with robust standard errors used Implemented for each main income except remittances due to limited observations

EMPIRICAL MODEL - II FOOD (IN)SECURITY: Ψ it : perceived food available in household food req : perceived household food needs. food is : =1 if food insecure [Reported having experienced food shortage to meet household needs] Probit model with robust standard errors used

EMPIRICAL MODEL - III POVERTY: Foster-Greer-Thorbecke (FGT) measures of poverty: α: the poverty aversion parameter, N: the sample size, Yi: the predicted (from OLS regressions) household income/AE, Z: the converted international benchmark poverty line/AE, and q: the number of households with predicted income/AE below the poverty line. Poverty determinants estimated using:

DESCRIPTIVE RESULTS

The majority of those stayed non-poor also stayed food secure Relative Food security improvement while poverty worsened (proportion)

ECONOMETRIC RESULTS

No significant income variation over time in overall; Land size with positive effect on total income and off- farm/AE; Limited effect of land size on crop income  does not mean that land is not important for crop income; Self-employment and use of improved agricultural technologies are positively associated with incomes; Households living in accessible villages are better-off than their counterparts; Results are robust to potential endogeneity of land access RESULTS - I

FOOD SECURITY AND POVERTY

Food security and poverty did not vary significantly over time; Land size was found to have significant positive effect in reducing food insecurity but not on poverty; Living in non-remote areas have positive effect on both food security and poverty; Male headship, males education, and income decreases the likelihood of being food insecure and poor; Dependence ratio increases the probability of being food insecure; Asset and agricultural technology have positive welfare effect: – Livestock, land quality, improved agricultural technologies RESULTS - II

CONCLUSIONS No significant income change in total net household income, land access, and food security over time; HOWEVER, Land size was found to increase income and reduce food insecurity. BUT, it did not have significant effect on poverty; The limited effect of land size on crop income only reveal the limited change over time and NOT the importance of land size on crop income; Investments in infrastructure, land reform, and education and promoting self-employment income opportunities as key on poverty reduction.

The effect of land size on poverty is based on $1.00/day poverty line:  Local poverty lines are likely to increase the effect;  Asset-based indicator (housing, education, landholdings, durable goods); Weather controls; Transitory or chronic poverty: Short period of time to observe significant changes: – More panel data or – Generating synthetic panels as suggested by Dang et al. (2015)

NEXT STEPS POVERTY DYNAMICS: Poverty incidence (α=1) used to classify HHs: – Moved out of poverty: if yi1 z – Moved into poverty: if yi1 > z but yi2 < z – Stayed poor: if yit < z for all t – Stayed non-poor: if yit > z for all t Similar classification for food security (perceived food security status of the HHs) Probit model used to assess the effect of land size in the probability to be in each poverty/Food security group P i is the probability of being in group i

THANK YOU. MINAG Food Security Group-MSU