1. 1 Non-farm Employment, Land Tenancy Contracts and Investment in Soil Conservation Measures in Rural Pakistan Department of Food Economics and Consumption Studies University of Kiel, Germany Rakhshanda Kousar and Awudu Abdulai Annual World Bank Conference on Land and Poverty Washington DC, March 24-27, 2014

2. Outline 1.Introduction 2.Motivation 3.Conceptual Framework 4.Empirical Specification 5.Data Description 6.Empirical Results 7.Conclusions 2

3. Introduction  Low productivity problem in agriculture sector  Sustainable management of resources may increase productivity (Pretty et al., 2011)  Poor rural HHs face two bottlenecks in agriculture investments: Lack of secured property rights High degree of risks and liquidity constraints  Land tenancy arrangements influence investment in agriculture (Feder and Onchon, 1987; Deininger and Jin, 2006; Banerjee and Ghatak, 2004; Abdulai et al., 2011) 3

4. Introduction  The positive and significant impact of tenure security on investment is due to Assurance effect Transaction effect Farmer’s access to credit  Non-farm income may enhance agriculture investments by providing capital (e.g. Barrett et al., 2001; Haggblade et al., 2002; Pfeiffer et al., 2009; Chang et al., 2011)  Non-farm income contributes 35-50 percent of rural HH income across the developing world (Haggblade et al., 2010) 4

5. Motivation 5  Many empirical studies have investigated the role of non-farm work on different aspects of agricultural sector (Phimister and Roberts, 2006; Kilic et al., 2009; Lien et al., 2010; Chang and Mishra, 2012)  Similarly, there is now strong evidence that land tenancy arrangements tend to influence investment in agriculture (Feder and Onchon, 1987; Deininger and Jin, 2006; Banerjee and Ghatak, 2004; Abdulai et al., 2011)  Very few studies have analyzed the link between land rights, non-farm work and farm investment (Feng et al., 2010)

6. Objectives 6  Identifying the determinants of non-farm work and tenancy arrangements  Estimating the impact of non-farm work and tenure arrangements on the soil-improving and productivity-enhancing investments measures  Estimating the impact of non-farm work and tenure arrangements on farm productivity  Suggesting policy recommendations for improvement

7. Conceptual Framework  Household is assumed to maximize utility defined over consumption of goods C and leisure N: (1)  Utility is maximized subject to constraints  The labor supply functions can then be derived as: (2)  Non-farm work can be related to input use by Lagrangian duality theory 7

8. Conceptual Framework  We assume that the household maximizes profit and it can be expressed as (3) reflects land tenancy arrangements i.Ownership ii.Fixed-rent iii. Sharecropping  With these three types of arrangements, the cost of land can be specified as (4) 8

9. Empirical Specification  The reduced form specification for investment is: (5)  Because of the censored nature of the investment, we employ a tobit specification, expressed as: (6) (7)  Given that the errors of the individual specification may have nonzero correlation, a multivariate tobit estimation is employed  Problem of endogeneity 9

10. Empirical Specification  To deal with endogeneity, this study employed the approach suggested by Smith and Blundell (1989) Residuals from the first stage regressions are then used in the investment specification as follows: (8)  To examine the impacts of non-farm work and tenancy arrangements on farm output, we used Instrumental Variable (IV) Approach  To estimate the direct relationship between investment and productivity, we employed Propensity Score Matching (PSM) 10

11. Data Description Description of study area 11 No. of questionnaires 341 No. of districts 6 No. of sub-districts 15

12. Data Description Table 1: Descriptive statistics of variables used in the analysis 12 Variable Definition of variables MeanS.d Investment variables Organic ManureOrganic manure used per acre (kgs)280.86373.67 Green ManureLeguminous crops grown per acre0.732.37 FertilizersChemical fertilizer applied per acre (Kgs)324.87256.09 Non-farm participation variables Participation in non-farm work1 if HH members participate in non-farm work, 0 otherwise0.630.48 Migration Network1 if HH members migrated, 0 otherwise0.290.45 DisMarktDistance of market from house (km)14.0220.01 Tenancy variables Owner1 if land is under owner-cultivated, 0 otherwise0.590.50 Fix-renter1 if land is under fixed-rent contract, 0 otherwise0.260.44 Sharecropper1 if land is under sharecropping contract, 0 otherwise0.150.23 Household-level characteristics AgeHeadAge of household head (years)45.8713.30 Head1 if female is the head of household, 0 otherwise0.740.43 HeadEduYears of education of household head6.045.43 HHSizOver14No. of household members above 14 years4.323.02 Livstk1 if household has livestock, 0 otherwise0.830.38

13. Data Description 13 VariableDefinition of variables MeanS.d TTwellNumber of tube well0.660.97 NONLABUnearned income (Rs)5.5515.74 Credit1 if HH has access to credit, 0 otherwise0.360.48 Exte0off1 if HH has contact to extension agent, 0 otherwise0.210.41 Farm-level characteristics TCultiLandTotal cultivated land in acres22.8338.71 SoiFertQuality level of soil13.5135.91 Residence1 if landlord reside in village where farm is located, 0 otherwise0.540.43 ADisFieldDistance of plot from owner’s residence (km)1.994.12 Location dummies Location11 if household resides in Lahore district, 0 otherwise0.150.36 Location21 if household resides in Sahiwal district, 0 otherwise0.200.39 Location31 if household resides in M.Garh district, 0 otherwise0.300.46 Location41 if household resides in Layyah district, 0 otherwise0.020.13 Location51 if household resides in Sialkot district, 0 otherwise0.250.43 Location61 if household resides in Khushab district, 0 otherwise0.080.27 Table 1: Descriptive statistics of variables used in the analysis….......continue

14. 14 Table 2: Multivariate Tobit estimates of extent of investment in soil-conservation and productivity-enhancing measures Note: standard errors are in parentheses and p-values in squared brackets. Significance of t-statistics of mean difference is at the *10%, **5% and ***1% levels. District fixed effects included in the estimation, but not reported here. VariableOrganic Manure Green ManureFertilizer Participation in non-farm work762.609(451.52)*356.986(202.59)*-444.531(198.39)** Own-cultivated658.964(233.66)***417.658(125.41)***-307.909(168.03)* Fix-rented-8.942(2.96)***-4.360(1.58)***1.772(1.04)* HeadEdu9.801(3.36)***1.007(0.50)**16.752(5.72)*** livstk7.291(1.56)***1.496(0.63)**-0.452(0.26)* TCultiLand1.885(1.13)*0.019(0.01)*-3.927(1.82)** NONLAB0.993(0.571)*0.031(0.02)*1.971(1.13)* SoiFert4.588(2.61)*0.037(0.01)*0.929(1.059) Residual NF-0.155(0.19)-0.345(0.970)0.117(0.84) Residual Owner-0.273(0.33)-0.779(0.66)-0.104(0.16) Residual Fix-renter-0.338(0.43)-0.237(0.66)-0.709(0.56) Intercept1633.371(319.60)***-0.9160(1.51)426.146(120.74)*** Number of observations341 Cross-equation correlations 0.218(0.06)*** 0.137(0.07)** 0.724(0.06)*** Likelihood ratio test of12.70(0.00) -statistics for joint significance of residues 0.681.290.83 - statistics for overidentification 0.570.620.91 Empirical Results

15. 15 Table 3: Instrumental variable estimates of determinants of land productivity Note: statistics of mean difference is at the *10%, **5% and ***1% levels. Predicted values of non-farm participation and tenancy arrangement variables are used. Wald test for the joint significance of the non-intercept exogenous variables against a critical value of The instrument used in the non-farm equation is migration status. In tenancy arrangement equations, distance and location are used as instruments. VariableCoefficientt-value Participation in non-farm work0.135**2.24 Own-cultivated1.309***7.61 Fix-rented1.067***5.32 Organic Manure0.050***4.16 Fertilizers0.011*1.72 TCultiLand0.329*1.68 Equipments0.688**2.30 Family labor0.164*1.85 Hired labor0.422***3.50 HeadEdu0.275*1.70 livstk3.593***7.47 location1-0.0112-0.02 location2-1.274*-1.86 location3-0.602-0.96 location4-1.29-1.82 location50.6491.10 Constant6.656***6.18 R2R2 0.2984 Adjusted R 2 0.2637 Wald-statistics36.61 F-value121.29 Prob>F0.00 Number of obsevations341 Empirical Results

16. 16 Table 4: Average treatment effect for organic manure, green manure and fertilizer Note: Numbers in parentheses are t-values. Significance of t-statistics of mean difference is at the *10%, **5% and ***1% levels. ATT is the average treatment effect for the treated. NNM stands for Nearest neighbor matching and KBM stands for Kernel based matching. MatchingOutcomeATT Nr of treated Nr of controls Common support imposed Balancing property satisfied Organic Manure NNM Output value per acre 254002.08**(2.31) 192147Yes KBM230887.26*(1.97)192147Yes Green Manure NNM Output value per acre 34130.03**(2.03)27168Yes KBM27542.06***(2.96)27168Yes Fertilizer NNM Output value per acre 266991.271**(2.33)30629Yes KBM234394.563**(2.21)30629Yes Empirical Results

17.  Investments are the keystone to increase agricultural productivity and farm income  Non-farm work induces a shift toward investment in soil-improving measures with long-term benefits, and away from static inputs such as chemical fertilizer with short-term benefits  Non-farm work can contribute to higher farm productivity which helps to reduce rural poverty  Strengthening of tenure security, either through land reforms to improve ownership or improving tenancy contracts through longer tenure durations can have positive impacts on investment and productivity Conclusions 17

18. 18 Thank you