PANEL DATA APPROACHES TO ESTIMATING IMPACTS 27


Table 5.1 Estimating the impact of access to rural infrastructure and extension services in Nepal using a generalized method of moments (GMM) approach, 1996


Consumption


Variable growth Columns 1 and 2 variable: Ln of 1996


consumption per capita


Column 3 variable: Ln of 1996 agricultural income per capita


Extension visit in 1996 (1 = Yes) Irrigation access in 1996 (1 = Yes) Ln travel time in hours Ln landholdings in hectares in 1996 Ln killings per district (2000–02) Ln district population in 1996 Ln district land size Ln percentage of Brahman ethnicity in district 0.091


(0.087) 0.044


(0.037)


–0.052*** (0.016) –0.007 (0.017) –0.048* (0.027) 0.044


(0.032)


–0.130*** (0.037) 0.109


(0.095)


Constant 7.678*** (1.700)


Observations 730 R-squared 0.323 F statistic


Hansen–Sargan statistic


32.60*** 20.55***


–0.050 (0.058) –0.007 (0.035)


0.051*** (0.014) –0.018 (0.016) 0.033


(0.027) –0.006 (0.029)


0.099*** (0.033) –0.138 (0.092) –0.016 (1.518)


730 0.152


32.60*** 3.93


0.157


(0.130) 0.136


(0.084)


–0.101*** (0.029) 0.009


(0.029) 0.102** (0.045) –0.128** (0.056) –0.143* (0.079) –0.209 (0.167) 14.37*** (4.344)


647 0.359


15.37*** 2.45


Source: Authors’ calculations. Notes: Robust standard errors are in parentheses. Ln means log normal; *** means p < 0.01; ** means p < 0.05; * means p < 0.1. The panel is restricted to the rural subsample, and several observations have been dropped due to missing data. All estimates have been made using a GMM-IV approach in which ln of consumption and agricultural income per capita in 1996 were instrumented by ln of asset holdings in 1996, ln of arable land- holdings in 1996, and the difference in district elevation. Ln number of working men in household, ln number of working women in household, education of the household head, and indicators of female-headed household, rainfall, and price shocks were included in the regression but not displayed in the table.


–0.547*** (0.109)


Poverty –0.260***


(0.094) Agricultural


income growth –0.844***


(0.154)


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