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Journal ArticleDOI

How important is improving food availability for reducing child malnutrition in developing countries

01 Dec 2001-Agricultural Economics (No longer published by Elsevier)-Vol. 26, Iss: 3, pp 191-204
TL;DR: Evidence is given in support of a statistically significant and strong positive impact of national food availability on child nutrition, finding that increased food supplies have resulted in significant reductions in malnutrition since the 1970s despite population increases over the period.
About: This article is published in Agricultural Economics.The article was published on 2001-12-01 and is currently open access. It has received 48 citations till now. The article focuses on the topics: Malnutrition & Developing country.

Summary (2 min read)

1. Introduction

  • Yet, for a number of reasons, estimates of the impact of national food availability on child malnutrition are critical for effective policy formulation and implementation with the goal of improving food security and nutritional status in developing countries.
  • Sustainable increases in agricultural productivity is one of the goals of a number of international institutions such as the United Nations Food and Agriculture Organization, the International Fund for Agricultural Development, the 16 research institutes under the umbrella of the Consultative Group on International Agricultural Research, and the World Bank.
  • With these aims in mind, this paper investigates the relationship between countries' per-capita DES and child underweight prevalences using country fixed-effects regression and data from 1970 to 1996 for 63 developing countries.

2. Variables, data and estimation procedure

  • A number of conceptual issues arise in the choice of empirical specification for analysis of child nutrition using national data.
  • National food availability lies at an intermediate level of causality between these two, among child nutrition's "underlying determinants".
  • The measure employed for food availabilities, daily per-capita DES, is derived from food balance sheets compiled by FAO using country-level data on the production and trade of food commodities.
  • The f.Li are unobservable country-specific, time-invariant effects, and vu is a stochastic error term.

3. Empirical results

  • All coefficients are negative and statistically significant.
  • No significant interactions between the independent variables were found.
  • The coefficient on a quadratic term for DES is significant and positive, indicating that while food availability works to reduce child malnutrition it has a declining marginal effect (column (2)).
  • An extensive grid search was undertaken to locate.

Explanatory variable Specification

  • Linear (1) the knot combinations yielding the smallest sum of squared residuals.
  • The dependent variable is the prevalence of children underweight.
  • The coefficient on the third segment remains positive and statistically insignificant even when the cut-off point is lowered considerably (making the number of data points sufficient for the estimation of a significant coefficient).
  • The first and second segments of the spline have negative and significant slopes, with the second having a much smaller slope than the first.
  • Variable ranges, based on the minimum and maximum values observed among developing countries over 1970-1995, are in column (3) .

4. Implications for policy: past contributions, future priorities

  • The authors further draw out the implications of the empirical results by asking ( 1) how much of the total reduction in the developing country child underweight rate from 1970 to 1995 can be attributed to Health Environment (19.27%).
  • Using their regression estimates, Table 5 classifies the developing countries into three groups based on the strength of impact of food availability on child malnutrition (in 1995): a "high impact" group (with DES below 2300 kcal), a "medium impact" group (between 2300 and 3120 kcal), and a "low impact" group (above 3120 kcal).
  • Most of the countries in South Asia and sub-Saharan Africa (SSA) fall into the high impact group.
  • Column (2) of Table 6 gives calculations of the absolute increase in each region's 1995 DES needed to bring about a reduction in the child malnutrition rate of 1 percentage point.
  • In prioritizing food availability across all four of the underlying determinants, two criteria must be taken into account.

5. Conclusion

  • Using a panel data set comprising 63 developing countries, this paper gives evidence in support of a statistically significant and strong positive impact of national per-capita food availability on child nutritional status.
  • The effect is very strong for countries with per-capita DES below 2300kcal.
  • Above 3120 kcal, further increases in per-capita food availability are likely to have little impact.
  • In any particular country, these factors may take priority in strategies to reduce child malnutrition either because they have a stronger impact or they are far from desirable levels.
  • Given land constraints, improvements in food availability must come from sustainable improvements in productivity.

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Citations
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TL;DR: A theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification.
Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

4,284 citations

Book
01 Jan 2002
TL;DR: The FAO's latest assessment of the long-term outlook for the world's food supplies, nutrition and agriculture is presented in this paper, where the projections cover supply and demand for the major agricultural commodities and sectors, including fisheries and forestry.
Abstract: This report is FAO's latest assessment of the long-term outlook for the world's food supplies, nutrition and agriculture. It presents the projections and the main messages. The projections cover supply and demand for the major agricultural commodities and sectors, including fisheries and forestry. This analysis forms the basis for a more detailed examination of other factors, such as nutrition and undernourishment, and the implications for international trade. The report also investigates the implications of future supply and demand for the natural resource base and discusses how technology can contribute to more sustainable development. One of the report's main findings is that, if no corrective action is taken, the target set by the World Food Summit in 1996 (that of halving the number of undernourished people by 2015) is not going to be met. Nothing short of a massive effort at improving the overall development performance will free the developing world of its most pressing food insecurity problems. The progress made towards this target depends on many factors, not least of which are political will and the mobilization of additional resources. Past experience underlines the crucial role of agriculture in the development process, particularly where the majority of the population still depends on this sector for employment and income.

1,643 citations


Cites background from "How important is improving food ava..."

  • ...Future food security will be determined largely by the interplay of a number of factors such as political and socioeconomic stability, technological progress, agricultural policies, growth of per capita and national incomes, poverty reduction, women’s education, drinking-water quality (Smith and Haddad, 2001), and increased climate variation....

    [...]

Journal ArticleDOI
TL;DR: It is revealed that studies in developed and developing countries have used a variety of food and food-group classification systems different numbers of foods and food groups and varying reference period lengths.
Abstract: Dietary diversity is usually measured using a simple count of foods or food groups over a given reference period. Our overview however revealed that studies in developed and developing countries have used a variety of food and food-group classification systems different numbers of foods and food groups and varying reference period lengths (ranging from 1 to 15 days). Research should be conducted to validate and compare indicators based on different methodological approaches. It would also be useful to continue to explore whether indicators based on food groups (a simpler approach) perform as well as those based on single foods in predicting outcomes of interest. (excerpt)

468 citations

Journal ArticleDOI
TL;DR: There are inextricable links among different strategies for improving child health and that policy planners, associating benefits with these strategies, must take into account the strong moderating impact of national context.

240 citations

References
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Book
28 Apr 2021
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

10,363 citations

Book
01 Jan 1993
TL;DR: In this paper, the authors propose a nonlinear regression model based on the Gauss-Newton Regression for least squares, and apply it to time-series data and show that the model can be used for regression models for time series data.
Abstract: 1. The Geometry of Least Squares 2. Nonlinear Regression Models and Nonlinear Least Squares 3. Inference in Nonlinear Regression Models 4. Introduction to Asymptotic Theory and Methods 5. Asymptotic Methods and Nonlinear Least Squares 6. The Gauss-Newton Regression 7. Instrumental Variables 8. The Method of Maximum Likelihood 9. Maximum Likelihood and Generalized Least Squares 10. Serial Correlation 11. Tests Based on the Gauss-Newton Regression 12. Interpreting Tests in Regression Directions 13. The Classical Hypothesis Tests 14. Transforming the Dependent Variable 15. Qualitative and Limited Dependent Variables 16. Heteroskedasticity and Related Topics 17. The Generalized Method of Moments 18. Simultaneous Equations Models 19. Regression Models for Time-Series Data 20. Unit Roots and Cointegration 21. Monte Carlo Experiments

4,912 citations

Posted Content
TL;DR: A theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification.
Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

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TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Abstract: We draw attention to two problems associated with the use of instrumental variables (IV), the importance of which for empirical work has not been fully appreciated. First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation. Second, in finite samples, IV estimates are biased in the same direction as ordinary least squares (OLS) estimates. The magnitude of the bias of IV estimates approaches that of OLS estimates as the R 2 between the instruments and the endogenous explanatory variable approaches 0. To illustrate these problems, we reexamine the results of a recent paper by Angrist and Krueger, who used large samples from the U.S. Census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment. We find evidence that, despite huge sample sizes, th...

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"How important is improving food ava..." refers background in this paper

  • ...This set passed both relevance (Bound et al., 1995) and over-identification (Davidson and Mackinnon, 1993) tests....

    [...]