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Thư viện số Văn Lang: Food Price Volatility and Its Implications for Food Security and Policy

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Nguyễn Gia Hào

Academic year: 2023

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Based on survey data, most small households perceive prices as too unpredictable. About a fifth of respondents also considered prices from the previous harvest period in forming their price expectation.

Table 20.1 Impact of structural parameters on the quality of the price signal Impact in optimum
Table 20.1 Impact of structural parameters on the quality of the price signal Impact in optimum

Empirical Model

Estimation Technique

This indicates that some farmers predicted prices correctly, and the dependent variable has natural zero values. The marginal effects must be interpreted in percentage points in the case of the relative measures of the dependent variable (RMPPE and RIPPE).

Results and Discussion

Robustness Checks

Thus, the 'luck factor' can probably explain some of the remaining variation in smallholder forecasting errors. The variance of the residual is household specific and normally distributed; it measures the forecast error of the household.

Conclusions

In addition, the results for the variance regression were mostly consistent with the QML Poisson and OLS estimation results. Using a primary survey data set eliciting smallholder price expectations for the next harvest period, we empirically evaluated the impact of access to ICT and grain markets, as well as other variables of interest, on the accuracy of smallholder price forecasts.

Table 20.6 Factors that affect price prediction accuracy of smallholders, MLE
Table 20.6 Factors that affect price prediction accuracy of smallholders, MLE

Appendix. OLS Estimation Results

Acknowledgments We would like to thank Maxim Torero for his helpful comments on an earlier version of this chapter and for some key foundational ideas for the theoretical model framework. We also thank Judge Tambo, Jan Brockhaus and Lukas Kornher for helpful discussions and Tobias Heimann for his research assistance.

Table 20.7 (continued)
Table 20.7 (continued)

Kalkuhl Open Access This chapter is distributed under the terms of the Creative Commons Attribution-Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which prohibits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Ramakrishna G, Demeke A (2002) An empirical analysis of food insecurity in Ethiopia: the case of North Wello.

Coping with Food Price Shocks in Afghanistan

Introduction

Much of the literature has emphasized the impact of food price increases on poverty rates. The evidence indicated that Afghan households, across the distribution, experienced a decline in the quantity and quality of food consumed as a result of the 2008 wheat flour price increases.

Background: Afghanistan Circa 2007/2008

3The indices were compiled by the Afghanistan Central Statistical Organization and are based on data from six urban areas. Our calculations using price data collected in the NRVA also showed a 60% increase in food prices in urban areas over this period, with an overall increase of 40% nationally.

Figure 21.2 depicts the consumer price indices (CPI) for food and nonfood items in urban areas from 2005 to 2011
Figure 21.2 depicts the consumer price indices (CPI) for food and nonfood items in urban areas from 2005 to 2011

Household Coping Strategies

In this section, we discuss the current literature on eating and non-eating responses to high food prices. 2010) for a more detailed review of the literature on the effects of the economic crisis on well-being and see Compton et al. 2010) for a thorough review of the literature on the impacts of the 2007/2008 food price crisis.). Most recent studies of high food prices have examined the consequences for poverty rates rather than specific household coping responses. This chapter contributes to the smaller body of literature examining the impact of recent high food prices on nutrition-related outcomes.

Tandon and Landes (2014) found that in response to increases in food prices, Indian households reduced dietary diversity and delayed purchases of health goods, clothing, and durable goods. 2009) showed that households in Bangladesh reduced the number and quality of meals when food prices increased; they also found that households reduced expenditure on clothing, health, transport and cooking fuel. And finally, Hadley et al. 2012) provided qualitative evidence from urban Ethiopia that food price increases can affect important cultural practices such as funerals because households can no longer afford standard cultural practices at their socioeconomic level.

Data

  • Measures of Nonfood-Based Coping Responses
  • Measures of Food-Based Coping Responses
  • Summary Statistics
  • Price Data

If the amount of wasted food is negatively correlated with food prices (as one might expect), then the coefficients for the price of wheat flour in the regressions examining food consumption and caloric intake will have positive biases.8 However, we maintained the assumption that the waste is low and any potential bias is small. The price data is taken from a district price survey, which included current prices for the food items included in the consumption section, as well as domestic and imported grain and fuel. 8 The sign of the bias is determined by the product of the correlation coefficients for (1) food waste and wheat flour prices and (2) food waste and food consumption or caloric intake.

To further explore how the food security status of the most vulnerable households was affected by the wheat flour price increase, we controlled for heterogeneity. According to this categorization, approximately 80% of the population in Afghanistan has acceptable diets, which is consistent with food security assessments conducted by WFP in several other developing countries in recent years.

Table 21.1 Household characteristics
Table 21.1 Household characteristics

Methodology

  • Model Estimation

25 The imputed housing value is the log of the imputed monthly rental value based on a hedonic model of housing structure. 27 By construction, OLS estimates are constant throughout the distribution of the dependent variable and thus cannot account for heterogeneous effects for subgroups of households. By design, the expectation of RIF is the value of the distribution statistic, or more formally, E(RIF(Y;,Fy))D(Fy).29.

For our analysis, we estimated marginal effects across all deciles (10th, 20th, : : :, 90th) of the food-based coping response distribution while controlling for covariates in our model specification. An alternative to UQR is the conditional quantile regression (hereafter CQR) estimator (Koenker and Bassett 1978), which allows behavioral responses to vary along the distribution of the dependent variable after conditioning on observed covariates (e.g., see Chamberlain 1994).

Results and Discussion

  • Supplemental Results

Furthermore, we found no effect of the price increases on education, which is a good child. We observed the largest percentage decrease in food consumption and calories for the Afghan households at the top of the respective distributions, with smaller decreases observed as one. Given the importance of food in the budget of Afghan households, it is plausible that even those households at the top of the handouts could have been significantly affected by the wheat flour price increases.

We observed decreases in nutrient intake as a result of the price increases for wheat flour (table 21.9). We found decreases in the intake of iron, retinol and beta-carotene for most households, with the lowest deciles of the distribution being an exception (and for beta-carotene also those in the upper deciles).

Table 21.8 Effects of wheat flour price increases on food security across the distribution
Table 21.8 Effects of wheat flour price increases on food security across the distribution

Conclusion

While we found that households at the top of the distribution did experience a significant decrease in calorie intake. Short-term interventions can play a potentially important role in protecting the population from negative consequences of food price shocks in the long term. In their guidelines for assessing food security at the household level, the Food and Agriculture Organization of the United Nations and the World Food Program (2009) suggested the construction of a food consumption score, estimates of food expenditure and calorie intake.

The key point is that policies designed to be triggered by reducing caloric intake below subsistence levels will fail to detect the onset of food insecurity in time. This view is supported by recent literature comparing different measures of food security (Headey and Ecker 2013; Tiwari et al. 2013).

Appendix

Bibi S, Cockburn J, Coulibaly M, Tiberti L (2009) The impact of the increase in food prices on child poverty and the policy response in Mali. Brinkman H-J, de Pee S, Sanogo I, Subran L, Bloem MW (2010) High food prices and the global financial crisis have reduced access to nutritious food and worsened nutritional status and health. Hadley C, Stevenson EGJ, Tadesse Y, Belachew T (2012) Rapidly increasing food prices and the experience of food insecurity in urban Ethiopia: impact on health and well-being.

Sari M, de Pee S, Bloem MW, Sun K, Thorne-Lyman AL, Moench-Pfanner R, Akhter N, Kraemer K, Semba RD (2010) Children 0–59 months of age in Indonesia: implications of rising food prices. Wodon QT, Tsimpo C, Backiny-Yetna P, Joseph G, Adoho F, Coulombe H (2008) Potential impact of higher food prices on poverty: summary estimates for a dozen West and Central African countries.

The Impact of Cereal Banking in the Gambia 22

  • Introduction
  • Context
  • Methodology
    • Propensity-Score Matching
    • The Propensity-Score Matching Results
  • Impact Evaluation
    • Empirical Strategy
    • Comparison of Means: Treated and Control Villages
    • Estimating Treatment Effect on the Treated
  • Conclusion

78% of the active population in The Gambia engage in rain-fed subsistence agriculture as a source of income and food. As a result, the problem of food insecurity is more seasonal than chronic in rural Gambia. Food-deficit households and communities in The Gambia experience food shortages due to non-availability or high cost of food during the lean period.

Some of the lowland villages are also capable of double cropping rice (von Braun et al.1989; Carney1992). Mid-season changes in the prices of the three major crops in The Gambia7 are a proxy for food availability.

Fig. 22.1 Crop production and rainfall variability, 1991–2012. Source: Department of Water Resources, Gambia
Fig. 22.1 Crop production and rainfall variability, 1991–2012. Source: Department of Water Resources, Gambia

Stocks and Storage Behavior of Traders in Ghana: Insights from a Trader Survey

Introduction and Motivation

On the one hand, time series econometric approaches are used to explain the dynamics and variability of wholesale market prices (Alderman and Shively, 1996; Shively and spatial market integration (Badiane and Shively, 1998; Abdulai, 2000). All of the above studies focus on maize. the most important domestic crop in Ghana.On the other hand, market analyzes based on survey data emphasize the role of the different actors in the value chain.

None of the existing studies examines the storage behavior of major wholesalers and firms to predict national inventory trends, which is the main objective of this chapter. The present work fills this gap in the literature by evaluating primary data collected from July to November 2013.

Price Instability and Trade Patterns

This suggests a temporal shortage of supplies at the end of the production year as a result of traders' inventories (Shively2001). A few large importing companies share the majority of the market between them (Kula and Dormon2009). Transporting goods from surplus regions in the middle belt and the northern part of the country to consumption and industrial centers is the major challenge for a long-distance trader.

Compared to the well-understood structure of the value chain, research on how marketing and trade flows change over the course of a year is still lacking. In contrast, the observed increase in the market purchases made by farmers indicates that commodities must be stored somewhere and then sold to farmers at the end of the marketing year (GSS2007; Chapoto et al.2014).

Fig. 23.1 Seasonality of local staples. Source: Authors’ computation based on SRID (2014)
Fig. 23.1 Seasonality of local staples. Source: Authors’ computation based on SRID (2014)

Storage Behavior .1 Description of the Data

  • Motives for Trader Storage
    • Speculative Storage
    • Safety Stocks
    • Aggregation Stocks
  • Operational Costs
  • Aggregated Results: Seasonality in Storage and Trade
  • Micro Results: Heterogeneity of Traders

However, inventory levels are likely to increase by the end of the production year as low availability makes input supply uncertain. 6The figures must be interpreted with caution in relation to the total size of the reported costs. This roughly corresponds to harvest time and thus the time of year when prices are lowest.

Maize stocks were accumulated during the year and distributed towards the new harvest. None of the traders completely emptied their stocks during the observed period.

Table 23.1 Transport costs on selected roads in May–June 2011
Table 23.1 Transport costs on selected roads in May–June 2011

Hình ảnh

Table 20.1 Impact of structural parameters on the quality of the price signal Impact in optimum
Fig. 20.2 Illustration of prediction error using self-reported prices Table 20.3 Consistency of
Fig. 20.3 Summary statistics and distribution of the dependent variable
Table 20.4 Factors that affect price prediction accuracy of smallholders Dependent variable: relative mean/index price prediction error
+7

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