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Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications 3rd edition [Kõva köide]

(Senior Research Fellow and Head of Capacity Strengthening, International Food Policy Research Institute, Washington, DC, USA), (Professor, Department of Economics, Department of Business Management and Education, University of Pittsbur)
  • Formaat: Hardback, 786 pages, kõrgus x laius: 229x152 mm, kaal: 1380 g
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: Academic Press Inc
  • ISBN-10: 012820477X
  • ISBN-13: 9780128204771
  • Formaat: Hardback, 786 pages, kõrgus x laius: 229x152 mm, kaal: 1380 g
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: Academic Press Inc
  • ISBN-10: 012820477X
  • ISBN-13: 9780128204771

Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications, Third Edition combines statistical data analysis and computer literacy, applying the results to develop policy alternatives through a series of statistical methods for real world food insecurity, malnutrition and poverty problems. The book presents the latest uses of statistical methods for policy analysis using the open source statistical environment R, in addition to having the original Stata files and applications. A new chapter on obesity brings in new datasets for analysis to effectively demonstrate the use of such data for addressing policy issues.

Finally, program evaluation methods which can be directly applied to the data on food security, nutrition, poverty indicators and causal factors are included. This unique, real-world data takes the reader through a "hands-on" approach toward econometric practice whereby they can also test the effects of policy and program interventions. Further, this is the first book to explore actual data with STATA and R statistical packages that also provides a line-by-line guide to the programming and interpretation of results.

  • Provides a fully revised and updated tome on the latest technology, assessment advances and policy insights surrounding food security
  • Combines case-studies with data-based analysis
  • Includes self-contained, downloadable datasets, statistical appendices, computer programs, and interpretations of the results for policy applications
Preface xxi
Introduction xxv
Section I Food security policy analysis
1 Introduction to food security: concepts and measurement
3(25)
Introduction
3(1)
Conceptual framework of food security
4(3)
Food security in the developed world
7(4)
Other policy issues in the United States
11(1)
Food security concerns in other countries
11(5)
Measurement of the determinants of food security
16(1)
Food availability
16(1)
Measuring food availability
17(1)
Measuring food access
17(1)
Food utilization
18(1)
Measuring food utilization
18(1)
Stability of availability
19(1)
Alternative approaches in measuring food security
19(2)
Conclusions
21(2)
A natural question is why is measuring food insecurity important for better program design in developing countries?
23(3)
Exercises
26(2)
2 Implications of technological change, postharvest technology, and technology adoption for improved food security--application of t-statistic
Introduction
28(2)
Review of selected studies
30(3)
Postharvest technology and implications for food security
33(3)
Food security issues and technology in the United States
36(2)
Biofuels--the Chinese experience
38(2)
US Farm Policy and food security--background and current issues
40(4)
CEO-5 and coping mechanisms for the future
44(1)
Empirical analysis--a basic univariate approach
45(1)
Data description and analysis
46(1)
Two measures of household food security are computed
47(2)
Consumption components of the food security Index
49(2)
Descriptive statistics
51(1)
Threshold of food security by each individual component
52(1)
Tests for equality of variances
52(1)
Student's f-test for testing the equality of means
53(3)
Policy implications
56(2)
Technical appendices
58(1)
Constructing the cutoff points for components of the food security Index
58(1)
Variable definitions
58(1)
Using STATA for t-tests
59(1)
Independent group t-test
59(1)
Independent sample t-test assuming unequal variances
60(3)
Exercises
63(1)
STATA exercise
64(4)
3 Effects of commercialization of agriculture (shift from traditional crop to cash crop) on food consumption and nutrition--application of chi-square statistic
Introduction
68(2)
A few concepts
70(1)
What is commercialization?
70(4)
Review of selected studies
74(5)
Organic farms and commercialization in the United States
79(2)
Organic farming in a global context
81(1)
Empirical analysis
82(1)
Data description and analysis
83(1)
Descriptive analysis: cross-tabulation results
84(3)
Chi-square tests
87(2)
Chi-square tests using STATA
89(4)
Conclusion and policy implications
93(2)
Technical appendices
95(1)
Pearson's chi-square (Χ2) test of independence
95(1)
Student's t-test versus Pearson's chi-square (Χ2) test
96(1)
Limitations of the chi-square procedure
96(1)
Descriptive analysis: cross-tabulation results
97(2)
Chi-square tests using R
99(2)
Exercises
101(1)
STATA exercise
102(3)
4 Effects of technology adoption and gender of household head: the issue, its importance in food security--application of Cramer's V and phi coefficient
Introduction
105(2)
Review of selected studies
107(2)
Female farm operators in Kenya and Ethiopia: recent evidence
109(1)
Rights, norms, and institutions: beyond technology adoption
110(2)
Female farm operators in the United States
112(1)
Women in agriculture: the global scene
113(3)
Uganda's coffee market: a case study
116(2)
Empirical analysis
118(1)
Data description and analysis
119(1)
Descriptive analysis: cross-tabulation results
120(1)
Cramer's V and phi tests
121(2)
Conclusion and policy implications
123(1)
Section highlights: Covid-19, women's burden, and the digital divide
123(3)
Cramer's V in STATA
126(2)
Technical appendices
128(1)
Phi coefficient and Cramer's V
128(1)
Applications in R
128(3)
Exercises
131(1)
STATA exercise
131(4)
5 Changes in food consumption patterns: its importance to food security--application of one-way ANOVA
Introduction
135(3)
Determinants of food consumption patterns and its importance to food security and nutritional status
138(1)
Impact on food security
139(1)
Review of selected studies
140(1)
Food consumption patterns for developing countries
141(3)
Nutritional and economic outcomes
144(1)
Food consumption patterns in the United States
145(2)
Food consumption patterns in India and China
147(3)
Empirical analysis and main findings
150(1)
Data description
150(1)
Analysis method
150(1)
Results
150(3)
One-way ANOVA in STATA
153(5)
Conclusion and policy implications
158(1)
One-way ANOVA
159(1)
Underlying assumptions in the ANOVA procedure
160(1)
Decomposition of total variation
160(1)
Number of degrees of freedom
161(1)
F-test and distribution
161(1)
Relation of F to 7-distribution
161(1)
STATA workout
162(1)
Fruit intakes per week for three income groups {Fit and F3)
162(2)
Compute mean square between and mean square within
164(1)
Compute the calculated value of F
164(1)
One-way ANOVA in R
165(3)
Exercises
168(2)
6 Impact of market access on food security--application of factor analysis
Introduction
170(1)
Assessing the linkages of market reforms on food security and productivity
171(2)
Review of selected studies
173(2)
Food deserts in the United States
175(3)
Access, information, and food security in Africa
178(2)
The role of the informal sector and food security
180(1)
Food security issues in Middle East and North Africa
180(1)
Food insecurity in South Asia: case studies of India and Afghanistan
181(4)
Empirical analysis
185(1)
Technical concepts
186(1)
Eigenvalues and eigenvectors
186(1)
Properties of eigenvalues
187(1)
Data description and methodology
188(1)
Food indicators
189(1)
Asset indicators
189(1)
Yield/technology indicators
189(1)
Market access indicators
189(1)
Household level characteristics
190(1)
Factor analysis by principal components
190(1)
Step 1 Computing the observed correlation matrix
190(2)
Step 2 Estimating the factors
192(1)
Principal components analysis
193(1)
Examining eigenvalues
193(1)
Scree plot
193(4)
Step 3 Making the factors easier to interpret: rotation procedure
197(1)
Varimax orthogonal rotation
197(1)
Step 4 Computing factor scores
198(3)
Principal components analysis in STATA
201(3)
Conclusion and policy implications
204(2)
Technical appendices
206(1)
Factor analysis decision process
206(5)
Exercises
211(1)
STATA workout
212(10)
Section II Nutrition policy analysis
7 Impact of maternal education and care on preschoolers' nutrition--application of two-way ANOVA
Introduction
222(1)
Conceptual framework: linkages between maternal education, child care, and nutritional status of children
223(1)
Possible linkages
223(1)
Conceptual and measurement issues on child care
224(1)
Measurement issues
225(2)
Review of selected studies
227(2)
Maternal education and nutrition status in the United States
229(3)
Children's nutrition and maternal education in Africa
232(1)
Kenya
232(1)
Uganda and Nigeria
232(1)
Substantive findings from Asia and Latin America
233(1)
Empirical analysis
234(1)
Data description
234(1)
Cross-tabulation of weight for height with mothers' educational levels
235(2)
Two-way ANOVA results
237(1)
Definition of main effect
237(1)
Partitioning sum of squares
237(4)
Interpreting the interaction effect and post hoc tests
241(1)
Two-way ANOVA in STATA
242(6)
Conclusion
248(1)
Technical appendices
249(1)
Scoring system used to create the care index (Ruel et al., 1999) care Index by age group
249(1)
Post hoc procedures
249(1)
ANOVA in R
250(3)
Exercises
253(1)
STATA workout
254(5)
8 Indicators and causal factors of nutrition--application of correlation analysis
Introduction
259(2)
Review of selected studies
261(3)
Food insecurity and nutrition in the United States
264(5)
Food insecurity in Brazil
269(1)
Global Monitoring Report on Nutrition and Millennium Development Coals
269(1)
Impact of food price spike and domestic violence in rural Bangladesh
270(1)
Malnutrition and chronic disease in India
271(1)
Malnutrition in Guatemala
272(1)
Empirical analysis and main findings
273(1)
Data description and methodology
274(1)
Concepts in correlation analysis
275(1)
Inference about population parameters in correlation
276(1)
Descriptive analysis
277(1)
Main results
278(1)
Correlation analysis of the outcome variables
279(2)
Estimating correlation using STATA
281(2)
Conclusion and policy implications
283(3)
Estimating correlation using R
286(3)
Exercises
289(1)
STATA workout
289(7)
9 Effects of individual, household, and community indicators on child's nutritional status--application of simple linear regression Introduction
296(40)
Conceptual framework and indicators of nutritional status
297(1)
Household utility maximization framework
297(2)
Core indicators of nutritional status
299(4)
Review of studies on the determinants of child nutritional status
303(4)
Child's nutritional status in the United States
307(1)
The economics of double burden
308(1)
AIDS and double burden in Africa
309(1)
Malnutrition and mortality in Pakistan and India
310(2)
Social participation as social capital, women empowerment, and nutrition in Peru
312(1)
Social capital and policy during the pandemic
313(3)
Empirical analysis and main findings
316(1)
Data description
316(2)
Incidence of stunting and wasting
318(1)
Normality tests and transformation of variables
319(2)
Regression results
321(4)
Simple regression in STATA
325(2)
Conclusion
327(1)
Simple regression in R
328(2)
Exercises
330(1)
STATA workout
331(5)
10 Maternal education and community characteristics as indicators of nutritional status of children--application of multivariate regression
Introduction
336(1)
Selected studies on the role of maternal education and community characteristics on child nutritional status
337(5)
Community characteristics and Children's nutrition in the United States
342(3)
Community characteristics and child nutrition in Kenya
345(1)
Financial crisis and child nutrition in East Asia
345(1)
Double burden within mother--child pairs: Asian case
346(1)
Empirical analysis
347(3)
Data description and methodology
350(2)
Descriptive summary of independent variables
352(1)
Main results
353(1)
Step 1 Estimating the coefficients of the model
353(2)
Step 2 Examining how good the model predicts
355(2)
Step 3 Hypotheses Testing
357(5)
Step 4 Checking for violations of regression assumptions
362(6)
STATA Output
368(3)
Conclusions
371(1)
Multiple regression in R
372(4)
Exercises
376(6)
Section III Special topics on poverty, nutrition, and food policy analysis
11 Predicting child nutritional status using related socioeconomic variables--application of discriminant function analysis
Introduction
382(2)
Conceptual framework: linkages between women's status and child nutrition
384(1)
Linkages between women's status and child nutrition
384(1)
Review of selected studies
385(1)
Direct linkage studies between women's status and children's nutritional status
385(3)
Indirect linkages between women's status and child's nutritional status
388(2)
US DA nutrition assistance programs: a case study from the United States
390(3)
Case studies of women's status and child nutritional status from Africa, Asia, and Latin America
393(1)
Food security and welfare in Africa: social customs, technology, and climate change
393(3)
Childhood undernutrition and climate change in Asia
396(1)
Adaptive strategies and sustainability lessons from Latin America
397(1)
Can garden plots save Russia?
398(1)
Empirical analysis and main findings
398(1)
Data description and analysis
399(2)
Descriptive statistics
401(2)
Testing the assumptions underlying discriminant analysis model
403(1)
Box's M Test
403(1)
Tests of equality of group means
404(1)
Summary of main findings
405(2)
Relative impact of the predictor variables on ZWHNEW
407(1)
Correlation between the predictor variables and discriminant function
408(1)
Classification statistics
409(1)
Classification function based on equal and unequal prior probabilities
410(2)
Canonical discriminant analysis using STATA
412(4)
Conclusions
416(1)
Technical appendix: discriminant analysis
417(1)
Discriminant analysis decision process
417(7)
Canonical discriminant analysis using R
424(2)
Exercises
426(1)
STATA workout
426(8)
12 Measurement and determinants of poverty-- application of logistic regression models
Introduction
434(1)
Dimensions and rationale for measuring poverty
435(1)
Defining and measuring poverty
435(1)
Monetary approach
435(1)
Basic needs approach
436(1)
Capability approach
436(1)
Participatory poverty approach
437(2)
Rationale for measuring poverty
439(1)
Indicators in measuring poverty
439(1)
Income measure
440(1)
Consumption expenditure
440(2)
Construction of poverty lines using food energy intake and cost of basic needs approaches
442(1)
Poverty lines in theory
442(1)
Absolute and relative poverty
443(1)
Referencing and identification problems
443(1)
Deriving a poverty line
444(1)
Food energy intake method
444(2)
Cost of basic needs method
446(3)
New measures of poverty based on the engel curve
449(1)
Measures of poverty
449(5)
Selected review of studies on determinants of poverty
454(4)
Poverty and welfare in the United States
458(5)
Agriculture and poverty in Laos and Cambodia
463(2)
Financial crisis and poverty in the Russian Federation
465(1)
Poverty in Europe
465(2)
Poverty in developing countries: China and India
467(1)
Determinants of poverty--binary logistic regression analysis
468(1)
Dichotomous logistic regression model
469(1)
An example with the Malawi dataset
469(1)
Expected determinants of household welfare
470(2)
Empirical results
472(1)
Measuring model fit
472(1)
Log-likelihood ratio
472(1)
Hosmer--Lemeshow goodness-of-fit test
473(1)
Generalized coefficient of determination
474(1)
Classification table
475(1)
Interpreting the logistic coefficients and discussion of results
476(2)
Estimating logistic regression models in STATA
478(1)
Example 1
478(4)
Example 2
482(3)
Conclusions and implications
485(1)
Technical appendices
486(1)
Technical notes on logistic regression model
486(2)
Estimating logistic regression models in R
488(1)
Exercises
489(1)
STATA workout
490(3)
13 Classifying households on food security and poverty dimensions--application of K-Means cluster analysis
Introduction
493(2)
Food hardships and economic status in the United States
495(4)
Food security, economic crisis, and poverty in India
499(2)
Cluster analysis: various approaches
501(1)
Hierarchical clustering method
501(1)
Single linkage (nearest neighbor method)
502(1)
Complete linkage (farthest neighbor method)
502(1)
Average linkage method
503(1)
K-means method
503(1)
Review of selected studies using cluster analysis
503(3)
Empirical analysis: K-Means clustering
506(1)
Data description
507(1)
Initial partitions and optimum number of clusters
507(1)
Descriptive characteristics of the cluster of households
508(2)
Cluster centers
510(3)
Cluster analysis in STATA
513(3)
Conclusion and implications
516(1)
Cluster analysis in R
517(2)
Exercises
519(1)
STATA workout-1
519(4)
STATA workout-2
523(4)
14 Household care as a determinant of nutritional status--application of instrumental variable estimation
Introduction
527(2)
Review of selected studies
529(5)
Federal nutrition programs and children's health in United States
534(2)
Parental unemployment and children's health in Germany
536(2)
Food security using the Gallup World Poll
538(1)
Empirical analysis
539(1)
Stage 1 Estimating Child-care practices
540(1)
Stage 2 Estimating the determinants of child health (weight-for-age Z-Scores)
541(2)
IV estimation using STATA
543(2)
Conclusions
545(1)
Instrumental variable estimation using R
546(1)
Exercises
547(1)
STATA workout 1
548(4)
STATA workout 2
552(5)
15 Achieving an ideal diet--modeling with linear programming
Introduction
557(2)
Review of the literature
559(4)
Linear programming model
563(2)
Solution procedures
565(1)
Graphical solution approach
565(2)
Some qualifications about the optimum
567(1)
Using solver in excel to obtain an LP solution
568(1)
Step 1 Setting the problem in excel
568(2)
Step 2 Solving the parameters of the model
570(1)
Step 3 Deriving the results
570(2)
Summary
572(1)
Exercises
573(2)
16 Food and nutrition program evaluation
Introduction
575(2)
Recent developments
577(1)
Randomization
577(2)
Instrumental variables
579(4)
Difference-in-difference
583(2)
Regression discontinuity design
585(2)
Propensity score matching and pipeline comparisons
587(1)
Randomization and development policy: applying the methods
588(3)
Summary and conclusions
591(1)
Section highlights: nobelprize worthy
591(1)
STATA workout 1
592(2)
STATA workout 2
594(8)
17 Multidimensional poverty and policy
Multidimensional child poverty and gender inequalities
602(1)
Multidimensional energy poverty
603(1)
Financial exclusion and Multidimensional Poverty Index
604(1)
The Alkire--Foster method
604(5)
STATA implementation
609(5)
STATA workout
614(5)
Section IV Technical appendices
Appendix 1 Introduction to software access and use
619(2)
Appendix 2 Software information
621(2)
Appendix 3 SPSS/PC+ environment and commands
623(10)
Appendix 4 Data handling
633(8)
Appendix 5 SPSS programming basics
641(14)
Appendix 6 STATA--a basic tutorial
655(8)
Appendix 7 Anthropometric indicators--computation and use
663(6)
Appendix 8 Elements of matrix algebra
669(6)
Appendix 9 Some preliminary statistical concepts
675(4)
Appendix 10 Instrumental variable estimation
679(6)
Appendix 11 Statistical tables
685(10)
References 695(40)
Index 735
Suresh C Babu is a Senior Research Fellow and Head of Capacity Strengthening at the International Food Policy Research Institute (IFPRI), Washington D.C. Before joining IFPRI in 1992 as a Research Fellow, Dr. Babu was a Research Economist at the Division of Nutritional Sciences, Cornell University, Ithaca, New York. Between 1989 and 1994 he spent 5 years in Malawi, Southern Africa on various capacities. He was Senior Food Policy Advisor to the Malawi Ministry of Agriculture on developing a national level Food and Nutrition Information System; an Evaluation Economist for the UNICEF-Malawi working on designing food and nutrition intervention programs; Coordinator of UNICEF/IFPRI food security program in Malawi; and a Senior Lecturer at the Bunda College of Agriculture, Lilongwe University of Agriculture and Natural Resources (LUANR). He has been coordinator of IFPRIs South Asia Initiative and Central Asia Program. His past research covers a range of developmental issues including nutrition economics and policy, economics of soil fertility, famine prevention, market integration, migration, pesticide pollution, groundwater depletion, and gender bias in development. He has published more than 18 books and monographs and 80 peer reviewed journal papers. He has been on the advisory board of World Agricultural Forum and a Coordinating Lead Author of Millennium Ecosystem Assessment. He currently conducts research on Capacity Development including Economic Analysis of Extension and Advisory Services; Reforming of National agricultural Research Systems; Understanding Policy Process; and Institutional Innovations for Agricultural Transformation. He is or has been a Visiting as Honorary Professor of Indira Gandhi National Open University, India, American University, Washington DC, University of Kwazulu-Natal, South Africa, and Zhejiang University, China. He currently serves or has served on the editorial boards of the following journals Food Security, Food and Nutrition Bulletin, Agricultural Economics Research Review, African Journal of Agricultural and Resource Economics, African Journal of Management, and African Journal of Food, Nutrition, and Development. Dr. Babu was educated at Agricultural Universities in Tamil Nadu, India (B.S. Agriculture; M.S. Agriculture) and at Iowa State University, Ames, Iowa (M.S. Economics and PhD Economics). Shailendra Gajanan is a Professor in the Department of Economics, and is the Chair of the Department of Business Management and Education at the University of Pittsburgh, Bradford. He received his BA and MA in Economics from the Presidency College and the Loyola College, University of Madras, India. He received and MA (1985) from the University of Akron, Ohio and PhD (1991) in Economics from the University of Pittsburgh. He joined the faculty of the Social Sciences Division at the University of Pittsburgh in 1991. He served previously as the Chair of the Social Sciences Division. Dr. Gajanan has served as the Program Director for Economics and has taught lower and upper level theory and policy classes in economic theory, econometrics, game theory, as well as applied classes in global hunger and environmental economics. He has also taught courses in India and West Germany. He supervises and directs International Study Abroad Programs and is slated to coordinate the Pitt-in-Himalayas in the summer of 2016. He has also served as an external evaluator on several thesis committees from universities in India and Malaysia. Dr. Gajanans research focuses on applied microeconomics dealing with water depletion within the commons, capacity utilization in manufacturing, the premium in the labor market for skills with English, and the impact of Category Management on product assortment in retail. He is the co-author of Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications (Elsevier 2014).