THE DETERMINANTS OF CEREAL IMPORT:

A REGRESSION ANALYSIS

ANTONIO, Czarinne Antoinette A. PRADO, Shaira Joyce V.SARMIENTO, Jan Gil G.SORIANO, Luis Andre D.

WONG, Audreynes G.

Abstract

The abandonment of the timeline of the rice self-sufficiency scheme of the Philippines

which was earlier set to 2013 raised many questions whether the country invested into the

appropriate sectors to lower cereal import.

Multiple Regression Modeling techniques were applied to determine key explanatory

variables that could sufficiently explain the variation of the dependent variable, the amount of

cereal imported by a country in 2007. The researchers computed a fitted model which included

the following variables: total population, food consumption per capita per day, total cereal

produced in 2007, land used for cereal production, and value-added industry in %GDP. It should

be noted that these variables fall under three categories: demographics, agriculture, and industry.

Exchange rate, GDP and net barter terms of trade, which are variables under the domain

of economics, were deemed insignificant, thus removed in the final specified model.

Hence, it is possible that investing in the domains of demographics, agriculture and

industry to particular values of our independent variables might be the right track to rice self-

sufficiency.

Chapter 1

The Problem and Its Background

Rationale

Rice constitutes a major part of many Filipinos’ diets. Other products, such as cereal

grains, are considered substitutes. The Food and Agriculture Organization (FAO) reported that

the Philippines is the eighth largest rice producer in the world in 2012. Ironically, it is also the

largest importer of rice in the world according to IRRI. In fact, the Philippines bought US$1.6

billion worth of cereal imported products in 2014.

It leads one to wonder that despite the Philippines being an agricultural country, it cannot

attain rice self-sufficiency. Questions such as how come the country needs to import a large

amount of cereal every year just to satisfy its demand and how come it chose to abandon its

timeline for rice self-sufficiency which was earlier set for 2013 are constantly raised.

Amarga,et al.(2008) argues that the rising shortage of cereal crops in the country caused

the Philippine government to increase the imported cereal products from other developed

countries.

However, it is also important to ask what the determinants are that explain how much

cereal a country needs to import for a particular year, not only in the Philippines, but also other

countries in general. After all, in some developing nations, grain in the form

of rice, wheat, millet, or maize forms majority of daily sustenance. In developed nations, cereal

consumption is moderate and varied but still substantial(Chattopadhyay, 2012)

Because of this pressing concern, the study aims to determine possible factors that

contribute to the amount of cereal imported by a country.

Objectives of the Study

This research aims to determine key explanatory variables of the cereal import demand in

90 countries of the world.

Specifically, the researchers have the following objectives:

to establish a structural relationship between cereal import of countries and different

factors under the domains of demographics, agriculture, and industry

to gain a better understanding of the existing relationships between these factors

using linear regression techniques; and to determine which factors significantly

explain the amount of cereal import in a particular year

to be able to offer possible explanations why the Philippines, despite being an

agricultural country, still imports cereals to satisfy the demand

Significance of the Study

The study was conducted to deem which variables significantly explain the variations in

the amount of cereal a country imports. As such, this study will greatly benefit the following:

Students

Students under the field of economics, trade, business, or statistics will be able to apply

the results of this study to their own. This study has provided the variables and its

transformation the researchers think constitutes the “best” model for a country’s import of

cereals.

Philippine Government

The government will be provided with valuable information about which factors

significantly raises the need of a country to import cereal. This will be highly useful in

selecting the optimal values for the different factors given in the study if the government

wishes to attain cereal self-sufficiency.

Other Countries

Other cereal-importing countries will be able to compare and develop strategic importing

techniques.

Scope and Limitation

Due to the nature of the study that focuses on the cereal imports of different countries, the

data used to create a regression model came from institutions such as the WorldBank,

FAOSTAT, and IndexMundi. The observations included in the creation of the model are the

countries with complete information on the regressors for the year 2007.

Countries considered to be influential outliers were not included in the model-building

process as these countries exhibit behaviour that deviates from the rest under normal conditions.

Geographic locations were not introduced to relieve the model from non-continuous data.

Since the year the researchers worked on is 2007, the study did not aim to forecast values

for the consequent years due to various inflation factors in the included variables which will not

be covered by the model.

Definition of Terms

The following terms, as defined operationally and conceptually by Worldbank and FAO,

have been given to help the readers better understand the study.

Variable Meaning

cereal7 Cereal Import is the shipment of cereals from a foreign country for use, sale,

processing, re-export, or services in the year 2007. Cereals include wheat, rice,

maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains.

arable7 Arable land (in hectares) includes land defined by the FAO as land under

temporary crops (double-cropped areas are counted once), temporary meadows

for mowing or for pasture, land under market or kitchen gardens, and land

temporarily fallow. Land abandoned as a result of shifting cultivation is

excluded.

cerealprod7 Cereal Production (in metric tons) relate to crops harvested for dry grain only.

Cereal crops harvested for hay or harvested green for food, feed, or silage and

those used for grazing are excluded.

exchangerate7 Official exchange rate refers to the exchange rate determined by national

authorities or to the rate determined in the legally sanctioned exchange market.

foodconsumption7 Food consumption (in Kcal/day) refers to the amount of food available for

human consumption as estimated by the FAO Food Balance Sheets.

gdp7 GDP at purchaser's prices is the sum of gross value added by all resident

producers in the economy plus any product taxes and minus any subsidies not

included in the value of the products. It is calculated without making

deductions for depreciation of fabricated assets or for depletion and

degradation of natural resources. Data are in current U.S. dollars.

industry7 Industry (in US $) comprises value added in mining, manufacturing (also

reported as a separate subgroup), construction, electricity, water, and gas.

Value added or economic profit is the net output of a sector by deducting cost

of capital from its operating profit

landcerealprod7 Land under cereal production refers to harvested area, although some countries

report only sown or cultivated area. Production data on cereals relate to crops

harvested for dry grain only.

netbarter7 Net barter terms of trade index is calculated as the percentage ratio of the

export unit value indexes to the import unit value indexes, measured relative to

the base year 2000.

popdensity7 Population density is midyear population divided by land area in square

kilometers.

poptotal7 Total population is based on the de facto definition of population, which counts

all residents regardless of legal status or citizenship--except for refugees not

permanently settled in the country of asylum, who are generally considered

part of the population of their country of origin. The values shown are midyear

estimates.

rainfall7 Average precipitation is the long-term average in depth (over space and time)

of annual precipitation in the country. Precipitation is defined as any kind of

water that falls from clouds as a liquid or a solid.

tariff17 Simple mean most favored nation tariff rate is the unweighted average of most

favored nation rates for all products subject to tariffs calculated for all traded

goods.

Foodexport7 Food export is the selling or shipment of food to a foreign country. Food

comprises the commodities in SITC sections 0 (food and live animals), 1

(beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC

division 22 (oil seeds, oil nuts, and oil kernels).

Chapter 2

Review of Related Literature

Past studies have shown that certain factors significantly influence a country’s cereal

import. Among these factors are food consumption, cereal production capability, land used for

cereal production, and population total. In this study, the researchers introduced another factor,

value added industry, in order to determine its effect coinciding with the previous factors.

Studies conducted in various countries such as Nigeria (Onyemauwa, 2008) yielded

something similar: “Food consumption is the sum of food production and food import less food

export (EarthTrends, 2003). Food consumption occupies a central position among household

consumption goods.” Food consumption is undeniably one of the factors affecting a country’s

cereal import.

The results show that GDP, domestic food production, relative price, and trade openness

are major determinants of food imports in the CFA zone (Seydina, et al., 2014). Further studies

under the use of Vector Error Correction Model also showed a long-running relation between

food import and food production. This was one of the bases of the researchers to find an

alternative factor to specifically explain the variation in the imports of cereal of countries.

Amarga, et al. (2008) in Understanding Philippine Cereal Import: A Time Series Analysis

found out that cereal yield, cereal productions, agriculture area, and population significantly

affect the amount of cereal import as well. Veeman T. et al (1992) attributed the amount of

cereal imported in developing countries to level of income, degree of urbanization, financial

capacity proxies and domestic grain supply variables.

Similarly, a study entitled South Asia and the global food situation: Challenges for

strengthening food security affirmed the relationship of cereal production and population.

Moreover, the paper assessed food supply, demand, and trade prospects for South Asia in the

context of a global food projections model (Sambilla, 1996).

In a study entitled “Population and Food in the Early Twenty-First Century: Meeting

Future Demand of an Increasing Population”, it showed the positive relationship between cereal

import demand and population growth. It forecasted that about 90% of the rate of increase in

aggregate food (cereal) demand from 2002-2010 would be due to population increase. The result

of a decrease in the rate of population growth combined with a modest increase in per capita

income will be a slow growth in per capita cereal consumption (Islam, 2002).

In “Supply and Demand for Cereals in Nepal, 2010-2030” by Prasad, et al., the estimates

showed that the large growth in the direct demand for rice is driven mainly by the high growth in

population. Also, in a paper explaining why Africa became a net food exporter by Rakotoarisoa

et. al(2011), they found out that as local food production grew sluggishly, agricultural imports

have grown consistently faster than agricultural exports.

The production argument, initially proposed by Mah (1971), attributes grain imports

primarily to the inadequacy of domestic cereal production. It is to be expected since domestic

food supply is also likely to increase with agricultural growth. In most models, agricultural

growth is often indexed by labour force in the agricultural sector or the per cent GDP allotted in

the agricultural sector of a certain country.

It is important to note however that there was a study conducted by Bautista in 1990 that

corroborates with other past studies wherein they saw a positive correlation between agricultural

growth in developing countries and increases in their food imports. Agricultural growth in

developing countries affects food imports through the induced increase in national income,

which affects food demand, and through the likely expansion in domestic food supply. However,

as they only dealt with developing countries in their analysis, their results aren’t valid once we

extended our scope to world data. Furthermore, Bautista also said explicitly that: “It is also not

necessarily the case that relationships based on past observations will continue to hold in the

future.”

Chapter 3

Methodology

A. Data Collection

Data about the countries used in this research paper was obtained from the online

databases of World Bank, Food and Agriculture Organization of the United Nations

(FAO), and IndexMundi.

Specifically, the data on the following regressors came from World Bank:

Arable land (hectares)

Average precipitation in depth

(mm/year)

Population density (people/km2 land

area)

Total Population

Official exchange rate (LCU per US$,

period average)

Land under cereal production (hectares)

GDP (current US$)

Tariff rate, most favored nation, simple

mean, manufactured products (%)

Net barter terms of trade index

(year2000=100)

Cereal production (metric tons)

Industry, value added (current US$)

Food exports (% of merchandise

exports)

The data on Food Consumption (kcal/person/day) was from FAO Statistics Division,

while the data on Imports of Cereals (US$) came from IndexMundi.

The researchers used the data for the year 2007 because it has the most complete

record on the different variables.

B. Data Cleaning

There were a total of 248 countries listed in the World Bank datasets, a total of 174

countries listed in the FAO Statistics Division dataset on food consumption, and a total of

177 countries in the IndexMundi dataset on cereal import. The researchers excluded the

countries with missing data on at least one of the primary regressors used. Also, some

countries were named differently between the three sources so the researchers had to

standardize them and to merge the datasets accordingly. The researchers chose the year

2007 for it had the most number of observations with complete information. The data

cleaning resulted to a dataset with 90 countries with the following variable names and

meaning:

Country_Name: Country Name

cereal7: Cereal Import ($)

arable7: Arable land (hectares)

cerealprod7: Cereal production (metric tons)

exchangerate7: Official exchange rate (LCU per US$, period average)

landcerealprod7: Land under cereal production (hectares)

netbarter7: Net barter terms of trade index (2000 = 100)

popdensity7: Population density (people per sq. km of land area)

poptotal7: Total Population

rainfall7: Average precipitation in depth (mm per year)

tariff17: Tariff rate, most favored nation, simple mean, manufactured products (%)

gdp7: GDP (current US$)

foodconsumption7: Food Consumption (kcal/person/day)

foodexport7: Food exports (% of merchandise exports)

industry7: Industry, value added (current US$)

C. Model Building Process

The researchers set up the level of significance to be 0.05.

The initial model was:

cereal 7 = β0+β1 arable7+ β2 cerealprod 7+β3exchangerate 7+ β4landcerealprod 7+β5 netbarter 7+β6 popdensity 7+β7 rainfall 7+β8tariff 17+β9 gdp 7+β10 foodconsumption 7+β11 foodexport 7+ β12industry 7+ε

Performing the regression analysis resulted in a significant ANOVA F-test and an R2= 0.78.

However, it resulted to a lot of problems—non-linearity, non-normality, heteroskedasticity,

multicollinearity, influential outliers, and too few significant variables.

What the researchers did next was to remove variables that give similar information and

variables that seem to have little effect on cereal import. The following variables were

removed: arable7, exchangerate7, and rainfall7.

However, problems were not solved through this. Condition indices and Variance Inflation

Factors still hinted multicollinearity.

Based on the partial plots of the previous model, the researchers removed the variables

netbarter7 and foodexport7 because they showed plots that indicate they did not have much

contribution to the goodness of the model.

Problems were still not resolved. Different combinations of variables were tried but the

problem of multicollinearity was still present. The researchers then decided to include

cerealprod7, landcerealprod7, popdensity7, foodconsumption7, and industry7—

regressors presumed to be primarily affecting cereal import. The problem on

multicollinearity was resolved but exhibited a low R2 (0.5152). The researchers replaced

popdensity7 with poptotal7. The value of R2 improved(0.7288), there was no severe

multicollinearity, but there were the problems of non-normality, heteroskedasticity, and

influential outliers.

One possible remedy of heteroskedasticity is transformation of variables. The researchers

hope that by serendipitous effect, that the problem of non-normality be solved. The

researchers then created variables which are transformations of the original variables. This

includes:

natural logarithm: l_<varname>

inverse: i_<varname>

square-root: sqrt_<varname>

square: sq_<varname>

inverse square-root: isqrt_<varname>

The researchers tried different combinations of the untransformed and transformed variables

—both dependent and independent, basing on partial and sequential sum of squares, residual

plots, partial regression plots, and partial residual plots (using Stata). Even the previously

omitted variables were included in the search.

Statistics on Argentina:

Internal studentized residual= -3.579

External studentized residual= -3.8679

Cook’s D= 0.078 (cutoff=0.0449)

DFFITS= -0.7384 (cutoff=0.519)

The researchers decided to remove Argentina from the final model due to possible

complications and its effect on the R square of our model. Unremoved, the fitted model

computed from the data with Argentina produced a lower R2. Furthermore, the tests on

normality disagreed with each other. The deletion of Argentina as an observation is justified

as follows:

On early March 2007, Argentina imposed export quotas or quantitative restrictions on

wheat exports. This policy explicitly aimed to limit price increase of wheat, which is a

common household ingredient in a typical Argentinean family. It also aimed to keep an

adequate supply of grains in the domestic market in a setting of growing international

demand and weather-induced national shortages. (Calvo). Naturally, this policy had a

huge impact on the cereal import value of Argentina for that year.

Wheat and maize alone accounted for 7.5 percent of total Argentine exports. As of 2007-

2008 data of FAO, wheat was the top 4 exported good of Argentina. Restricting exports

would mean that this surplus would be available in the domestic market, making

domestic price detached from the ever growing international prices of cereal on that year.

On that year, Argentina successfully achieved a relatively lower price for cereals

compared to world market. Thus, cereal produced by other countries imported in

Argentina couldn’t compete with the low price offered by the domestic food grain

suppliers; making cereal import value of Argentina significantly lower compared to other

observations from our data.

D. The Final Model:

l _ cereal 7=β0+β1l _ foodconsumption7+β2l _ cerealprod 7+β3√ _ landcerealprod 7+β4 l _ poptotal7+β5 l _ industry7+ε

ε Normal (0 , σ2 )

E. Diagnostic Checking

Linearity

Residual Plots:

The residual plots did not exhibit any alarming pattern to conclude that linearity was not

captured. Every plot appeared to be random and contained in a horizontal band.

Partial Regression Plots

The partial regression plots also indicated that the linearity of each regressors given all

other regressors were captured. Both the plots of residuals did not show any systematic

departure from 0. There was no problem of non-linearity.

Normality

The following SAS output displays the result of the four tests for Normality of the

residuals

All of the tests did not reject the hypothesis that the error terms are normally distributed.

This means that there is no reason to doubt the normality of error terms. We can assume

Normality.

Homoskedasticity

Both White’s Test and Breusch-Pagan test did not reject the hypothesis that the error

terms have constant variance. We can assume homoskedasticity.

Multicollinearity

Variance Inflation Factor:

Condition Indices with Variance Proportion:

Almost all figures indicated that there is no strong correlation among the regressors. The

5th condition index was not far from the general cut-off 30. Backed up by the low VIFs,

and insignificantly large variance proportions, the researchers can assume there was no

multicollinearity with l_cerealprod7 and l_poptotal7. The highest condition index was

just the result of l_foodconsumption7 having values very close to each other that it

became slightly correlated with the intercept. However, it had a low VIF, far from the

general cut-off of 10.

Outliers

Based on the studentized residuals, both internal and external, no observation was

considered an outlier. Because the regression assumptions do not appear to be violated,

the researchers had no further reason to investigate possible influential outliers.

F. The Fitted Equation

l _ cereal7 =−25.39010+2.99514 (l _ foodconsumption 7 )−0.26293 (l _ cerealprod 7 )−0.00055202 (√_ landcerealprod7 )+1.33888 (l _ poptotal7 )+0.15330(l _ industry 7)

Chapter 4

Discussion and Results

I. Fitted Model

Upon validation of assumptions and diagnostic checking, the final model specification is

given by:

l _ cereal7 =−25.39010+2.99514 (l _ foodconsumption 7 )−0.26293 (l _ cerealprod 7 )−0.00055202 (√_ landcerealprod7 )+1.33888 (l _ poptotal7 )+0.15330(l _ industry 7)

For ease of interpretation, the final model specification can be expressed as:

cereal 7 = exp {−25.39010 }∗foodconsumption 72.95514∗cerealprod 7−0.26293∗exp {−0.00055202∗√¿ landcerealprod 7 }∗poptotal 71.33888∗industry 70.015330

The result from the ANOVA test yielded a p-value less than the specified 0.05 level of

significance. The test concluded that there is at least one independent variable in the model that

has a significant contribution in estimating the mean of the response variable, cereal import. A

coefficient of multiple determination (R2) equal to 0.8673 was produced which implies that

86.73% of the variation in Cereal Import can be explained by the regressors in the model

specification. The model also produced a relatively high Adjusted R2 equal to 0.8592.

II. Determinants

One of the objectives of this study is to establish a structural relationship between cereal

import of countries and different factors under the domains of demographics, agriculture, and

industry. As such, the analysis is decomposed to the analysis of the contributions of the

determinants to our dependent variable, cereal import, under these domains.

The table below displays the parameter estimates of our fitted model, standard errors and

individual t-tests generated by SAS.

A. Demographics

The variables population total and food consumption are both statistically significant at α

= 0.05. The positive sign of the coefficients suggests that population total and total food

consumption of a country is directly proportional to the cereal import of a country. Holding other

regressors constant, a unit increase in the natural logarithm of the population total will result in a

1.33888 increase in the estimated mean of the natural logarithm of cereal food import of a

country. The same applies to the natural logarithm of Total Food Consumption with a 2.99514

increase in the estimated mean of the natural logarithm of cereal food import of a country ceteris

paribus.

This is consistent with what appeals to reason since cereal is a staple food in

almost any country’s diet. This means that we would expect a higher demand of cereals

as the population of a country or its food consumption per capita steadily grows. On the

other hand, assuming that a country’s agricultural capacity to produce cereal remains

constant, the total cereal supply available for consumption in that country would

eventually be scarce as population total and food consumption steadily increase. Hence,

the dependence of that country on imports of cereal to feed the masses is likely to

increase.

B. Agriculture

Intuitively, one would expect cereal import to be closely associated with the agricultural

capability of a country to produce cereals. In the cereal import fitted model, cereal production

and land allotted for cereal production are specified to be additional explanatory variables, and

both their coefficient estimates determined to be statistically significant. Our model also shows

that a unit increase in the natural logarithm of cereal production, holding other regressors

constant, results in a 0.26293 decrease in the estimated mean of the natural logarithm of cereal

food import. A 0.00055202 decrease in the estimated mean of the natural logarithm of cereal

food import of a country is expected for every unit increase in the square root of land cereal

production. The estimated results show that cereal import varies inversely with cereal production

and land allotted for cereal production.

C. Industry

There are studies that claim that a developing country, as indexed by its industry output,

will fuel cereal import. The study, by Veeman et. al., attributes this to an increase in the income

share of the poorest 40 percent of a country’s population. They continue to argue that mean

financial capacity of an individual or purchasing power in a certain country reflects greatly in the

cereal demand of a country. Rising national income leads to changes in both the volume and

composition of food consumption according to the existing income distribution. (Bautista)

Another study made by FAO, validates their results. Average cereal consumption per person in

developing countries has risen steadily throughout the past four decades (FAO).

The fitted model also supports this conclusion. A unit increase in the natural logarithm of

industry output of a country results to a 0.15330 increase in the estimated mean of the natural

logarithm of cereal imports of a country, holding other variables constant. We have a significant

t-test with a p-value of 0.0202 which is less than 0.05.

III. The Philippines

The Philippines is the 67th observation in our data set. The original and transformed

values of the dependent variables and independent variables for the said observation are shown

below.

cereal7 1146429042 l_cereal7 20.85991777

cerealprod7 22977304 l_cerealprod7 16.9500175

landcerealprod7 6921286sqrt_landcerealprod

72630.833708

poptotal7 88875548 l_poptotal7 18.30274761

foodconsumption7 2580 l_foodconsumption7 7.855544678

industry7 49367998680 l_industry7 24.62256825

Also shown below is a SAS generated output containing the values of the predicted

value, standard error and the residual for the 67th observation. We also requested a 95%

prediction interval.

Chapter 5

Summary, Conclusions and Recommendations

Summary and Conclusions

On the basis of the results, the following can be concluded:

1. The final specified model was significant in the ANOVA F-Test with a computed R 2 of

0.8673

2. Population total, food consumption per capita, total land for cereal production, cereal

produced in a year, and value-added industry were included in the model to explain

the amount of cereal imported by the country. The individual t-tests on these

variables were all significant at α = 0.05

3. Population total had the greatest effect on the dependent variable in terms of

estimated standardized coefficient compared to the other regressors in the fitted

model

4. Transformations were done to remedy problems of nonlinearity and

heteroskedasticity

5. the model constructed a prediction interval for the Philippines which captured

both the predicted value and the actual value of cereal import

Recommendations

The researchers recommend the following:

1. That future researchers verify above conclusions for years other than 2007 and check

if the conclusions made about 2007 are still valid for the other years

2. That future researchers incorporate more variables to further improve the model,

specifically, in terms of R2.

3. That future researchers could also introduce dummy variables to indicate if the

country is undeveloped, developing and developed

4. That the government look into the domains of demographics, agriculture and industry

in terms of population total and food consumption, cereal produced and land for

cereal production and value-added industry respectively to try solving the problem

of rice self-insufficiency

Bibliography

Jerardo, A. (2012, May 30). USDA ERS - U.S. Agricultural Trade: Import Share of Consumption. Retrieved May 26, 2015, from USDA ERS: http://www.ers.usda.gov/topics/international-markets-trade/us-agricultural-trade/import-share-of-consumption.aspx

Amarga, A., Damay, A., & Lope, D. (2008). Understanding Philippine Cereal Import: A Time Series Analysis. Quezon City: unpublished.

Veeman, T., Sudol, M., Veeman, M., & Dong, X.-Y. (1992). Cereal Import Demand in

Developing Countries. EconPapers.

C.S. Onyemauwa, M.A.C.A. Odii, J.I. Lemchi, C.C. Eze, U.C. Ibekwe and C.A. Emenyonu, 2008. Analysis of Household Consumption of Cereals in Owerri Municipality, Imo State, Nigeria. International Journal of Agricultural Research, 3: 273-280.

Seydina Ousmane Sene. Food Imports Under Foreign Exchange Constraints in the CFA’s Franc Zone of Sub-Saharan Africa (SSA), 2014.

Islam, N. (1995). Population and Food in the Early Twenty-First Century: Meeting Future Food

Demands of an Increasing Population. Washington, D.C.: International Food Policy

Research Institute.

Bautista, R. M. (1990). Agricultural Growth and Food Imports in Developing Countries: A Reexamination. Economic Development in East and Southeast Asia: Essays in Honor of Professor Shinichi Ichimura .

Dong, X.-y., Veeman, T. S., & Veeman, M. M. (1995). China's grain imports: a empirical study. Food Policy , 20 (4), 323-328.

Mah, F. (1971). Why China imports wheat. China Quarterly , 128-129.

Prasad, S. K., Pullabhotla, H., & Ganesh-Kumar, A. (2011). Supply and Demand for Cereals in Nepal, 2010-2030. New Delhi: Internation Food Policy Research Institute.

Rakotoarisoa, M. A., Iafrate, M., & Paschali, M. (2011). Why has Africa become a Net Food Importer? Rome, Italy: Food and Agriculture Organization.

Appendix

The following SAS output presents 95% CI for the 88 observations: