Analysis on Agriculture Productivity and Climate Change in Pakistan
Team Lead: Dr. Abid Aman Burki (LUMS)
Dr. Mushtaq A. Khan (LUMS), Muhammad Raza Mustafa Khan (LUMS), Verda Arif (LUMS), Muhammad Abubakar Memon (LUMS), Dr. Shabbir Ahmad (University of Queensland).
Agriculture has been the mainstay of Pakistan’s economy, which contributes nearly 20% to the Gross Domestic Product (GDP) and accounts for more than 40% of the employed labor force. Moreover, the agriculture sector stimulates demand for manufactured goods and services, provides raw material to agro-based industries, and plays a strategic role in achieving food security by ensuring improved availability of food items to the rapidly growing population by increasing agricultural productivity. Thus, improvements in agriculture are essential for sustained improvements in living standards.
The aim of this project is to produce two research papers on “Analysis on Agriculture Productivity and Climate Change in Pakistan,” which will serve as background papers to the World Bank’s Pakistan Country Economic Memorandum 2.0 report to be published in 2022.
Paper 1: Recent Trends in Agricultural Productivity in Pakistan
There have been very few studies measuring total factor productivity (TFP) of agriculture sector in Pakistan and those that have been carried out have used national level time series data, which does not provide enough insights on regional trends (Wizarat, 1981; Khan, 1994; Kemal et al., 2002; Ali, 2005, among others. Moreover, these studies have not decomposed TFP growth into smaller components in order to understand what is driving the trends in TFP growth. The unavailability of coherent estimates of technical change and efficiency change components can misguide policy-making (O’Donnell, 2011). Hence policymakers will be unable to evaluate whether pay offs from improving the rate of technical progress, e.g., through increasing expenditure in research and development, are more or less likely to be greater than improving levels of efficiency, e.g., human capital accumulation, or scale and mix efficiency, e.g., using taxes or subsidies to change relative prices (O’Donnell, 2011).
Regional and district level analyses of TFP growth is required to frame effective agricultural policy. None of the studies carried out so far account for TFP growth across districts due to paucity of data. This data is not readily available but has to be compiled from various sources. Murgai et al. (2001) was the first attempt in this direction, but their analysis covers the period between the 1960s and 1990s.
The aim of this paper is to measure the trends in TFP across districts and to pinpoint factors that allow some districts to be more productive than others. It will measure productivity in Pakistan, its four provinces, and agro-climatic zones by using district-level aggregate data for the period between 1992 and the latest available year. A major task is to generate a consistent time series data of all crop outputs and inputs at the district level for all four provinces. The paper will employ the Fare-Primont index, which fulfills transitivity property, to measure TFP. The paper will also investigate the drivers of productivity change for policy interventions including: 1) human capital investment, health (life expectancy, infant mortality; 2) investment in physical capital (road density); 3) urbanization; 4) adverse events (floods, earthquakes); 5) inequality (measured by land Gini index); 6) soil quality.
Paper 2: Impact of Climate Change on Productivity of Major Crops in Pakistan’s Punjab
How Pakistan can produce more food and raw material by increasing the productivity of major crops has not yet been reliably quantified. Increased variability of temperature and precipitation associated with continued emissions of greenhouse gases brings changes in crop yields and productivity. However, reliable estimates on the impact of climate change on farm-level productivity of major crops are not available.
This paper aims to investigate the impact of climate change on TFP of major crops in Pakistan. The paper will apply a modified Levinsohn and Petrin (2003) method to farm level panel data to generate consistent production parameters and productivity. To dispel endogeneity concerns, semi-parametric crop production functions will be estimated that will correct for choice of input bias to obtain TFP. The paper will also disentangle productivity gains that arise from weather changes from those that arise from farm management practices. Farm-level unbalanced panel data of wheat, rice, sugarcane and cotton farms in Punjab Pakistan will be used for eight consecutive years, 2013 – 2020, augmented by temperature and precipitation data in phenological stages of the crop development.
The farm level data to estimate productivity of the farms will be taken from the Agriculture Department of the Government of the Punjab. The Crop Reporting Service (CRS) of the Agriculture Department annually collects this data from randomly sampled farms on the basis of a survey questionnaire administered to the farms to collect accurate acreage and production estimates for Kharif and Rabi crops each year. As per sampling exercise, same set of farms are approached for a period of 10-years.
District level weather data will be collected from “Reliable Prognosis” which is an online data source. Currently, the website provides weather forecasts for 172,500 locations, along with reporting information on observational data sourced from various weather and coastal stations. This database reports hourly data on weather forecasts and information on the actual weather conditions. The data is available for various stations of Punjab, however, weather data available on nearby districts will be used as a proxy for the districts on which information on weather variables is not reported.
The paper will take seasonal stages of crop development and construct the three weather change variables around these biological stages of crop development. Weather changes will be measured by three set of variables namely, growing degree days (GDD), killing degree days (KDD) and precipitation. Each of these variables will be measured for its seasonal phenology to capture seasonal biological phenomenon, which is sensitive to small variations in climate, especially to temperature and precipitation. GDD accounts for the beneficial effects of temperature on the growth of the crop by measuring the number of days when heat index is within an optimal temperature range for each of the phenological stages. By contrast, KDD is a measure of accumulated heat above an optimal temperature threshold, indicating heat stress. The precipitation variables will be included to examine the impact of rainfall in biological stages of crop growth.
Other variables to be included are farm-yard manure, pesticide sprays, and incidence of disease, which are dummy variables. Three soil type variables measure the quality of land namely, chikny, sandy and kalrathy.