Rural Development Policies and Sustainable Land Use in the ...

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34 CHAPTER 3 Key socioeconomic elements of the survey at the household level included household composition, education, asset ownership, labor use, sources of income, sales of crop and livestock products, participation in credit markets, membership of organizations, participation in training and extension programs, and collective action. Information collected at the parcel and plot levels included land tenure, cropping patterns, crop production, land management technologies including use of labor and other inputs, and conservation practices and investments. To be better able to analyze the adoption of conservation practices and suggest policies for sustainable land use, the survey collected detailed biophysical data for a (randomly drawn) sample of two plots on each farm. These included landscape attributes, plot size, type of soil parent material, erosion status, and presence of physical conservation structures. Soil samples were also taken and analyzed in a local soil laboratory and resulted in data regarding pH, nutrient content, organic matter content, and texture. These data were used mainly for the calculation of soil moisture availability, 33 soil fertility, 34 and erosion risk. Soil fertility and erosion risk served as a basis for the construction of a soil quality variable (Wielemaker 2002). Finally, the survey data were supplemented by adding secondary information regarding rainfall, population density, market access, and road density. Most of these data were obtained from CIAT. Econometric Analysis Ideally, we would like to estimate the system represented by equations (1)–(4) and (6)–(8) using a systems approach such as three-stage least squares or full information maximum likelihood to deal with endogenous explanatory variables and account for correlation of error terms across the different equations. However, three-stage least squares estimation is not appropriate because there are many limited dependent variables in this system, and joint maximum likelihood is not feasible due to the large number of dependent variables and error terms. Instead, we use single-equation estimators appropriate to the nature of each dependent variable. L hpt are left-censored continuous variables (censored below at 0); hence we use a Tobit estimator to estimate equation (2). IN hpt , LM hpt , and P ht are dichotomous choice variables; we use probit models to estimate equations (3), (4), and (7). LS ht is a polychotomous choice variable; we use a multinomial logit model to estimate equation (6). y hpt and I ht are continuous uncensored variables; thus least squares regression is feasible and used for equations (1), (5), and (8). 33 Besides rainfall, moisture availability in the soil is critical for crop growth and as such constitutes another indicator of agricultural potential. Moisture availability is soil specific and takes into account not only rainfall but also evapotranspiration, temperature, and soil characteristics. We used the data from our soil samples and operationalized moisture availability as crop water deficits for annual crops (for maize in the main and secondary growing seasons) and permanent crops (coffee). Water deficits were calculated on the basis of data for monthly temperature, effective rainfall (taking runoff into account as determined mainly by slope, slope direction, contour curvature, profile curvature, and position on slope), evapotranspiration, and soil characteristics including depth, texture, and organic matter content. Only moisture availability for the second season was considered because the data indicated very few cases of main season water deficits. Moreover, moisture availability for coffee is highly correlated with moisture deficit for maize in the secondary growing season. For more details, see Wielemaker (2002). 34 Soil fertility is yet another indicator of agricultural potential. We approximated soil fertility by potential maize yield (nutrient-limited but not water-limited) using the QUEFTS (QUantitative Evaluation of soil Fertility and response To Fertilizers) model (Janssen 1990). For a given plot this model calculates potential yield on the basis of the soil’s nitrogen content, pH, and available potassium and phosphorus. We had data for each of these variables from the analyses done in the soil laboratory of the FHIA (Honduras Foundation for Agricultural Research), a private agricultural research institute.
METHODS AND MODELS 35 Inclusion of endogenous explanatory variables in equations (1)–(5) and (8) could result in biased estimates. We use instrumental variables (IV) estimation to address the endogeneity problem in equations (1), (5), and (8). Because the dependent variables in equations (2)–(4) are limited dependent variables [censored in equation (2) and binary in equations (3) and (4)], it is not technically appropriate to use IV estimation for these equations. However, we tested linear OLS versus IV versions of these models using a Hausman (1978) exogeneity test, to test whether endogeneity of the livelihood strategies (LS ht ) and participation in programs and organizations (P ht ) could be biasing our results. For equations (3) and (4), this amounts to assuming a linear probability model rather than a nonlinear probit model (only for the purposes of the exogeneity test). For equation (2), we tested for exogeneity using a truncated version of the regression for family labor, dropping the observations with zero family labor used on the plot. We did this only for the family labor regression, since there were few censored observations for family labor input (only 35 out of 1635 observations), implying that the truncation should have little effect on the validity of the results (there were many censored observations for the other types of labor input). We also tested for exogeneity of these variables (i.e., LS ht and P ht ) in equations (5) and (8) using a Hausman test. In no case did we reject exogeneity of the livelihood strategies and participation variables at the 10 percent level. In all cases, the instrumental variables used were found to be highly significant predictors of the endogenous explanatory variables (hence the instrumental variables are “relevant”) and the validity of the exclusion restrictions was accepted using Hansen’s J test (Davidson and MacKinnon 2004) (the results of these tests are reported in the discussion of the econometric results in Chapter 5). These results give us confidence that our results in estimating equations (2)–(5) and (8) are not biased by endogeneity of the livelihood strategies and participation variables. Based on these results, we treat livelihood strategies and participation in programs and organizations as exogenous variables in estimating equation (1). We estimate equation (1) several ways. First we estimate the full model using ordinary least squares (OLS) and IV estimation, including all of the variables specified. In the full IV model, predicted values of L hpt , and predicted probabilities of LM hpt and IN hpt from estimation of equations (2)–(4) are used as instrumental variables. 35 To identify additional instrumental variables and improve the performance of the models, we tested the joint significance of subsets of the village-, household-, and parcel-level variables using Wald tests in both the full OLS and IV models, and then estimated reduced OLS and IV models that excluded variables that were highly statistically insignificant (p-values at least 0.20) in both of the full models. In the reduced IV model, we tested the relevance of the excluded instrumental variables using joint significance tests for the firststage regressions, and the validity of the exclusion restrictions using Hansen’s J test (see Davidson and MacKinnon 2004 for a description of these tests). The excluded instrumental variables were highly significant predictors of all of the endogenous righthand side variables in the annual crops regression, and for most of the endogenous variables in the perennial crops regression. In both regressions, Hansen’s J test failed to reject the exclusion restrictions. Thus we have confidence that the reduced models are valid. Hausman tests comparing the reduced versions of the OLS and IV models were conducted, and failed to reject the OLS model in both the annuals and perennials regressions. Thus, the OLS model is preferred as the more efficient model in both cases, 35 The full IV model is identified by the nonlinearities in equations (2)–(4).
Page 1 and 2: Rural Development Policies and Sust
Page 3 and 4: Contents List of Tables iv List of
Page 5 and 6: TABLES v 5.6 Determinants of extern
Page 7 and 8: Boxes 1.1 Defining “Hillsides,”
Page 9 and 10: Acknowledgments The authors acknowl
Page 11: SUMMARY xi the adoption of sustaina
Page 14 and 15: 2 CHAPTER 1 Box 1.1 Defining “Hil
Page 16 and 17: 4 CHAPTER 1 will allow Honduras to
Page 18 and 19: 6 CHAPTER 1 In addition to providin
Page 20 and 21: 8 CHAPTER 1 econometric analysis to
Page 22 and 23: 10 CHAPTER 2 1998 and the coffee cr
Page 24 and 25: 12 CHAPTER 2 Box 2.1 Land Issues in
Page 26 and 27: 14 CHAPTER 2 Table 2.3 Yield of pri
Page 28 and 29: 16 CHAPTER 2 Table 2.7 Intersectora
Page 30 and 31: 18 CHAPTER 2 Figure 2.2 Terms of tr
Page 32 and 33: CHAPTER 3 Methods and Models In thi
Page 34 and 35: 22 CHAPTER 3 Figure 3.1 Sustainable
Page 36 and 37: 24 CHAPTER 3 financial organization
Page 38 and 39: 26 CHAPTER 3 decide the number of c
Page 40 and 41: 28 CHAPTER 3 The reduced form versi
Page 42 and 43: 30 CHAPTER 3 Table 3.1 Variables in
Page 44 and 45: 32 CHAPTER 3 of years of formal sch
Page 48 and 49: 36 CHAPTER 3 although we report the
Page 50 and 51: 38 CHAPTER 4 Table 4.1 Natural capi
Page 52 and 53: 40 CHAPTER 4 Box 4.1 Remittances Re
Page 54 and 55: 42 CHAPTER 4 Table 4.6 Access to pu
Page 56 and 57: 44 CHAPTER 4 strategy (clusters 1 a
Page 58 and 59: Table 4.8 Mean household net income
Page 60 and 61: 48 CHAPTER 4 Table 4.9 Summary stat
Page 62 and 63: 50 CHAPTER 4 Box 4.2 Gender in the
Page 64 and 65: 52 CHAPTER 5 The socioeconomic expl
Page 66 and 67: 54 CHAPTER 5 explanatory variables
Page 68 and 69: 56 CHAPTER 5 technology training) a
Page 70 and 71: 58 CHAPTER 5 Table 5.3 Determinants
Page 74 and 75: 62 CHAPTER 5 the basic grains/lives
Page 76 and 77: 64 CHAPTER 5 Table 5.5—Continued
Page 78 and 79: 66 CHAPTER 5 ambiguous effects, dep
Page 82 and 83: 70 CHAPTER 5 more livestock focus m
Page 86 and 87: 74 CHAPTER 5 Geographic determinant
Page 92 and 93: 80 CHAPTER 5 when they purchase it
Page 94 and 95: 82 CHAPTER 5 Table 5.10 Summary of
Page 96 and 97:
84 CHAPTER 5 able to compensate for
Page 98 and 99:
86 CHAPTER 5 On the other hand, edu
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88 CHAPTER 5 and wage labor. Nevert
Page 102 and 103:
90 CHAPTER 5 productivity. 62 Incre
Page 104 and 105:
CHAPTER 6 Summary of Findings, Conc
Page 106 and 107:
94 CHAPTER 6 zero/minimum tillage,
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96 CHAPTER 6 Develop and promote yi
Page 110 and 111:
98 REFERENCES Boucher, S., B. Barha
Page 112 and 113:
100 REFERENCES Hornby, A. S. 1974.
Page 114 and 115:
102 REFERENCES Pino, H. N., P. Jim
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