Allocative Inefficiency, Tenure Systems and Poverty in Irrigated Agriculture in Pakistan

Ariel Dinar, Steven Helfand, and Sanval NasimWater Science and Policy CenterUniversity of California, Riverside

The Pakistan Strategy Support Program Competitive Grants Conference, Islamabad

9th February, 2013

Introduction

• Ineffective water-management policies in the past have affected water availability

• Institutional constraints also affect the degree of utilization of water in Pakistan’s agricultural sector.• Tenure arrangements could lead farmers to misallocate inputs

• Differences in incentives across tenure systems may explain part of Pakistan’s water-management problems.

Research Question and Objectives

Research Questions

• How are farmers using irrigation water?• Water-use efficiency

• Economic approach to measurement of water-use is through estimation of allocative efficiency

• Does this efficiency differ across land tenure systems and to what degree?• Tenure systems affect the incentives farmers face to utilize resources

efficiently

Objective

• We seek to obtain estimates of allocative inefficiency so that the degree of over (or under) utilization of water across tenure systems can be quantified in an empirically consistent manner.

• We will use our analysis to evaluate possible water policy reforms conditional on land tenure systems and political and economic feasibility, and to compare the impact of these reforms on agricultural incomes and poverty across land tenure systems.

Presentation Structure

• Stochastic Profit Model

• Data

• Preliminary Estimation Results

• Moving Forward

Stochastic Profit Model• Translog specification (Kumbhakar and Lovell, 2000):

Production function and input demand system

• X: variable inputs• Z: quasi-fixed inputs• u: technical inefficiency• ξ = input-specific allocative inefficiency

Constraints

A: vector of allocative inefficiency explanatory variablesH: vector of technical inefficiency explanatory variables

Estimation

• Iterated nonlinear seemingly unrelated regression• Maximum likelihood

Data

• Pakistan Rural Household Survey II (PRHS-II) • Information on two seasons: 2003 kharif (autumn harvest) and 2004 rabi

(spring harvest). • 887 agricultural households and 1690 plots

• Initially planned to use a panel regression with PRHS-I and PRHS-II. However, not all variables are measured consistently across datasets. Will need more time to sort this out for the final report.• We lose fixed-effects and price variation• Fixed-effects still possible with PRHS-II as long as sample is constrained to

households with multiple plots• Ease of estimation

Variables

Y: weighted output quantity index (wheat, irri-rice, basmati rice, cotton, and sugarcane)

X (variable inputs):Labor (N), fertilizer (F), and groundwater (G)

Z (quasi-fixed inputs):Capital (K) and Land (L)

Technical inefficiency explanatory variables:Relative farm size size (small if less than 10 acres)years of cultivation experience

Initially, we proposed to include surface water. However, surface water allocations and price are fixed. Consequently, it is not a variable factor of production and its allocative inefficiency cannot be estimated in the current framework. We intend to investigate explore this issue further in the final report.

Allocative inefficiency explanatory variables:Tenure dummies; total plot size; years of cultivation experience

Control variables:Land value; access to surface water dummy; location on watercourse dummies; groundwater quality dummies; district dummies; and season dummy

Also control for zero input quantities (Battese, 1997)

Initial Estimation ResultsProduction function parameter estimates

Technical inefficiency parameter estimates

Groundwater Allocative inefficiency parameter estimates

Groundwater Allocative Inefficiency Distribution Across Plot Level Characteristics

Moving Forward

• These are preliminary results. Much more to be accomplished in the future.

• Include more inputs• Separate male-female and owned-hired labor.• Include an index of minor inputs• Include surface water in a more systematic manner

• Test different model specifications

• Control for fixed-effects

• Consider system level inefficiencies (rather than the current farm level inefficiencies)

Cheers!