The Geography and Lives of the Poor: Evidence from Punjab Ali Cheema Lyyla Khalid Manasa Patnam Lahore University of Management Sciences 2008

What is to follow Identifying endemic poverty regions Changing regional socio-economic paths Poverty impact of different paths HH strategies and payoffs in different regions Where do regional differences come from

Motivation Lack of evidence on district-wise variation in poverty [World Bank 2002; Anwar, Qureshi and Ali 2004; Qureshi and Arif 2001) Some Exceptions [Jamal PDR 2005; Malik 2005 and Gazdar 1999] Putting poverty incidence in context of socio- economic change Reveal patterns not causality

Constructing the Consumption Aggregate Dataset: Punjab MICS ( ) representative at district level Money-metric measure The Aggregate Consumption Function (ACF) is constructed as follows: a. Aggregate the various sub-components b. Adjust for cost of living differences: Deflating Total Household Expenditure by Paasche’s Index to capture cost of living c. Adjust for household composition The Sub-components of ACF can be classified into four categories: i. Food items ii. Non-food items iii. Consumer durables Use Poverty line for defined by Planning Commission (Economic Survey ) and adjust it using CPI

Equivalence Factors for age/sex-specific official poverty lines Age BracketEnergy Per PersonDaily Requirement Children < Males , , , , , or more2, Females or more Source: Poverty Reduction Strategy Paper, 2003

The Geography of Poverty High poverty clustered in the South and West regions Constitute crescent of endemic poverty PoorNon Poor North Centre South West Source: MICS ( ) North: Pindi, Chakwal, Jhelum And Attock South: R.Y.Khan, Bahawalpur, Bahawalnagar, Multan, Lodhran, Khanewal And Vehari West : Mianwali, Khushab, Bhakkar, Lyyah, Muzzafargarh, DG Khan And Rajanpur Centre : All Others

The Geography of Poverty Head Count OverallHead Count Rural

The Geography of Poverty Poverty Gap OverallPoverty Gap Rural

The Geography of Poverty High poverty clustered in the South and South West districts Severity of poverty highest in these districts Deprivation index correlated with district poverty

Measuring Deprivation Deprivation Indices: Index 1  Education Illiteracy Rate (10 years and above)- female Illiteracy Rate (10 years and above)- male Proportion out of school Children – female Proportion out of school Children – male  Housing Quality Proportion of Non-Pacca houses Persons per room Percentage of housing Units with one room Percentage Non-owner households Households with no latrine facility  Housing Services Percentage of Unelectrified households Percentage of households without gas Percentage of households with no inside piped water connection Households with no telephone connection  Employment Unemployment rate [ years] Combining the indicators –Equal weights to different components of the index –Weights assigned by using principle component analysis (PCA)

Choice of Method and Sensitivity of Rankings Index1 Rank (Average)District Index1 Rank (PCA) 32Rahim Yar Khan34 31Lodhran33 34Muzaffargarh32 33Rajanpur31 28D.G.Khan30 26Bahawalpur29 27Okara28 29Bhakkar27 21Vehari26 23Bahawalnagar25 24Pakpattan24 22Khanewal23 25Jhang22 30Layyah21 20Kasur20 16Sheikhupura19 Sahiwal18 Index1 Rank (Average) DistrictIndex1 Rank (PCA) 14Multan17 13Narowal16 12Hafizabad15 11T.T.Singh14 18Khushab13 17Mianwali12 15Mandi bahauddin11 9Sargodha10 5Faisalabad9 8Gujrat8 7Jhelum7 10Attock6 4Gujranwala5 3Sialkot4 2Lahore3 6Chakwal2 1Rawalpindi1

Index 2 Includes Social Indicators: Under 5 Mortality Rates and Ante Natal care by skilled health workers Index2 (PCA)DistrictIndex2 (Average) 34Lodhran29 33Rahim yar Khan28 32Rajanpur33 31Muzaffargarh34 30Bhakkar31 29Bahawalpur27 28Okara26 27Bahawalnagar23 26Pakpattan25 D.G.Khan32 24Vehari24 23Khanewal21 22Jhang22 21Layyah30 20Kasur20 19Sheikhupura16 18Sahiwal17 Index2 (PCA) DistrictIndex2 (Average) 17Multan15 16Hafizabad12 15Narowal13 14Khushab19 13T.T.Singh10 12Mianwali18 11sargodha9 10Mandi Bahauddin14 9Faisalabad6 8Attock11 7Gujrat5 6Jhelum8 5Gujranwala4 4Sialkot3 3Chakwal7 2Lahore1 1Rawalpindi2

Ranking of Most Deprived Districts Index 1 & 2 combined with HCRs Index 1 1 Index 2 2 MICS SPDC Rajanpur Lodhran 2RYKhan D.GKhanMGarh 3 LodhranBhakharRajanpur 4LodhranMGarh Layyah 5D.GKhanBhlpur D.GKhan 6BhlpurD.GKhanLodhranPkpattan 7OkaraBhlnagarPkpattanRYKhan 8BhlnagarOkaraBhlnagarBhlpur 9Pkpattan RYKhanVehari 10LayyahBhakharKasurJhang

Divergent Socio-Economic Paths Access to land deteriorating sharply for landless Similar trend across all regions SharecroppedLeased (% Farm Area) North Centre West South

Divergent Socio-Economic Paths Mitigated by diversification out of agriculture in North and Centre Continued agrarian dependence in the South and West Source: Population Census (1997) and MICS ( )

The Poverty Impact Diversification out of agriculture negative correlate of poverty Limited possibilities in the South and West exacerbating problem Source: MICS ( )

The Poverty Impact Deteriorating access to land worsening matters Source: MICS ( )

The Poverty Impact Incidence of poverty much higher –Labour dependent HHs –Long-term unemployed Effect more pronounced in South and West Source: MICS ( )

Using dependents! Proportion of dependents much higher in South and West Source: MICS ( )

The Poverty Impact Related vulnerabilities in the South and West Source: MICS ( )

HH Coping Strategies Intra HH occupational diversification Similar trend across all regions Source: MICS ( )

Does it pay? Not at the same rate across all four regions! Much flatter effect in the South and West Source: MICS ( )

Creating Remittances Stark regional differences Source: MICS ( )

The Remittance Effect Strong negative correlate of poverty Source: MICS ( )

Migration and Remittances No of migrants per HH explains a large part of variation in remittances However, presence of endogeneity Prop. Remittance Income Coeff.T-Stat hh size District DummiesYes R-Squared N29258 Source: MICS ( ) Prop. Remittance Income Coeff.T-Stat hh size No. Migrants District DummiesYes R-Squared0.21 N29258

Migration and Remittances Use mean rainfall as IV for number of migrants –Controlling for HH size West and South more migrants per HH –But proportion of remittance income much less in South and West Indicates migrants from North entering a different segment of labour market Source: MICS ( ), Punjab Economic Report ( ) No. Migrants (First Stage) Coeff.T-Stat hh size Rainfall North South West R-Squared N29258 Prop. Remittance Income (Second Stage) Coeff.T-Stat hh size No. Migrants North South West R-Squared0.3 N29258

Missing Investments In part the answer lies in missing investments Source: MICS ( )

Where do the differences come from? History An earlier migration A large part still unexplained! Source: MICS ( ), Punjab Economic Report ( ) Poor Coeff.T-Stat North South West percentage displaced Canal Military Rainfall -3.5E wheat_area rice_area cotton_area tot_area_sown 9.91E tot_irrig no_factories< no_factories> R-Squared0.08 N29258

Determinants Much of the variation within district Started exploring tip of the iceberg Poor Coeff.T-Stat Regional Regional Regional R-Squared N29258 Poor Coeff.T-Stat Regional Regional Regional District DummiesYes R-Squared0.101 N29258