Food Insecurity & Poverty in Pakistan: A Micro & Macro-Econometric Evidence

In this study, we try to indirectly quantify the welfare of people in Pakistan through measuring the food insecurity, malnutrition, and poverty during the last decade (2005-14). For this purpose, we use nationally representative data from the Household Income and Expenditure Survey (HIES) from 2005- 2014. The Pakistan Bureau of Statistics (PBS) collected this data in five rounds: 2005-06, 2007-08, 2010-11, 2011-12, and 2013-14, which comprises 81102 households. We start our analysis with an estimation of food insecurity status of households and then move on to an estimation of poverty status, with the help of headcount ratios. We extend our analysis, with the application of econometric models to estimate the determinants of food insecurity and poverty and relationship between them. Results from the headcount ratios suggest that over the period, food insecurity trends of the households increased from 58% to 77% and poverty rates declined from 29% to 19%. However, we find urban households are more food insecure over the time than rural households. In qualitative terms of food insecurity, we use two food diversification measures: dietary diversity score and share of staple food in total calories consumed per household, which suggest that households’ dietary diversity score is good on average for 9 out of 10 food groups and on the whole it has slightly improved from 8.8% to 9% in the previous 10 years. Interestingly, when we analyze the share of staple food in total calories, the results suggest that a major portion of a household’s diet consists of staple food (wheat, rice and cereals), which increased from 53% to 57% from 2005 to 2014. To find out the determinants of food insecurity and poverty, we use Heckman’s two-stage model. In the first stage, we estimate the binary logistic model for food insecurity status to produce inverse mills ratios (IMR) and then sub-divide the data into two groups: food secure and food insecure based on the standard minimum caloric intake per day i.e. 2350. In the second stage, we estimate two separate models using ordinary least square (OLS) regression with time dummies to find the determinants of household caloric intake for the food insecure group. Results suggest that head of the family’s education and other household members, female head of household, livestock ownership, consumption of food crops and livestock produced at home, farming, and foreign and domestic remittances play a significant positive role in caloric intake of food insecure households. Urban region of residence and year dummies depict a significant negative role in caloric intake and food insecurity. However, it is important to note that the years 2007-08 and 2010-11 show a significant increase in caloric intake, while, year 2011-12 and 2013-14 show deterioration in caloric intake as compared to the base year 2005-06. Similarly, we estimate headcount ratios for poverty estimation and divide the sample into poor and non-poor groups based on the official poverty line, which is different for 5 years. Here again, we use the Heckman two stage model to find the determinants of poverty, and the results suggest that expenditure per adult equivalent(AE) (a proxy for poverty) has a significant positive relationship with head of the family’s education level and family members with basic and higher education level assets value, safe water, toilet type and electrification; while, household head’s age, household size and rural region of residence have a significant negative relationship with expenditure per AE. Importantly, all the year dummies show a significant negative relationship with expenditure per AE; expenditure per AE declined in 2013-14 as compared to 2005-06. To identify if there is a relationship between food security and poverty, we use the two stage least square (2SLS) that also overcomes the problem of endogeneity. Results reveal that expenditure per AE, female headship of family, percentage of earners in household, percentage of household members with higher and professional education, livestock ownership, consumption of food crops and livestock produced at home, farming, have a significant positive impact on caloric intake over the time. For long run estimates of food insecurity and poverty, we use the Auto Regressive Distributive Lag Model (ARDL) model that uses data from 1973-2013 and 1974-2016 for both models, respectively. The results demonstrates that food insecurity has a significant long run relationship with milk and wheat production and trade openness, while poverty has a significant long run relationship with Gini coefficients, inflation, unemployment, and agricultural growth rate. The results suggest that women’s empowerment, education and livestock production should be promoted through different sophisticated policies. However, as urban regions are more food insecure while rural ones are more income poor, separate policies are needed.