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DOI: ./agec.
ORIGINAL ARTICLE
Impacts of COVID-19 induced income and rice price shocks
on household welfare in Papua New Guinea: Household
model estimates
Emily Schmidt1Paul Dorosh1Rachel Gilbert1,2
International Food Policy Research
Institute (IFPRI), Washington, District of
Columbia, USA
Friedman School of Nutrition Science
and Policy, Tufts University, Boston,
Massachusetts, USA
Correspondence
Emily Schmidt, International Food Policy
Research Institute (IFPRI), Eye Street,
NW, Washington, DC ,USA.
Email: e.schmidt@cgiar.org
Abstract
Concerns over the potential effects of the COVID- pandemic have led to trade
restrictions by major rice exporters, contributing to an average % increase in
Thai and Vietnamese rice export prices between December and March–
September . This article assesses the consequences of these rice price
increases in Papua New Guinea (PNG), where % of rice is imported. Utilizing
data from a PNG rural household survey along with earlier national house-
hold survey data, we examine rice consumption patterns in PNG and estimate
demand parameters for urban and rural households. Model simulations indicate
that a % rise in the world price of rice would reduce total rice consumption
in PNG by % and reduce rice consumption of the poor (bottom % of total
household expenditure distribution) by %. Including the effects of a possible
% decrease in household incomes because of the COVID- related economic
slowdown, rice consumption of the urban and rural poor fall by % and %,
respectively. Maintaining functioning domestic supply chains of key staple goods
is critical to mitigating the effects of global rice price increases, allowing urban
households to increase their consumption of locally produced staples.
KEYWORDS
COVID-, household welfare, multi-market model, Papua New Guinea, rice trade
JEL CLASSIFICATION
E, F, E
1 INTRODUCTION
In response to the recent COVID- outbreak, several
major rice exporting countries implemented policies to
ensure adequate domestic supply. Vietnam suspended new
contracts for rice exports for the month of April .
In India, mobility and logistics challenges due to social
distancing regulations reduced availability of rice on the
country’s domestic market and contributed to lower export
volumes. As during the world price shocks of and
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, rice export prices rose sharply, increasing by %
for A Super in Thailand, and by % for % broken in
Vietnam between December and May , threat-
ening the food security of countries dependent on rice
imports. Although rice export restrictions eased and quar-
antine measures were loosened across the globe, Thai and
Vietnamese rice prices remained relatively high in the sub-
sequent months and were on average % and % higher,
respectively, from March through September compared to
December .
Agricultural Economics. ;–. wileyonlinelibrary.com/journal/agec 1
2SCHMIDT .
Previous research examining Vietnam and India’s
export bans during / suggest that reserving pro-
duction for domestic consumption did not protect local
consumers from rice price inflation in Vietnam and India
(Slayton, ). Panic buying in Indonesia spread to Viet-
nam, doubling rice prices in Ho Chi Minh City markets in
March , despite Vietnam’s export ban (Slayton, ;
Timmer, ).The world price shocks had medium- and
long-term policy effects, inducing rice importing coun-
tries to seek rice self-sufficiency by investing in domes-
tic rice production, contributing to a long-term trend of
rice price decline relative to other staple foods (Timmer,
). Although domestic rice production accounts for
less than % of supply, Papua New Guinea (PNG) has fol-
lowed similar policies, promoting domestic rice produc-
tion even though large-scale production and processing
are not competitive (PNG Department of Agriculture &
Livestock, ;Gibson,).
Nonetheless, PNG may be among the countries most
affected by this recent rice price shock. Although the coun-
try has invested in attaining rice self-sufficiency, economic
incentives for increasing domestic production remain
weak. The lack of comparative advantage in producing
rice, including the falling terms of trade relative to more
valuable cash export tree crops, to a large extent explains
why PNG has had little success in expanding domestic rice
production (McKillop et al., ). Since , rice imports
have almost doubled from only thousand tons/year
to an estimated thousand tons/year today (Schmidt
and Fang, ). Rice imports made up the largest share
(%) of overall value of agri-food imports; however, rice
imports comprised only about % of the total value of
imports, on average, between and (BACI trade
database; Gaulier & Zignago, ). Domestic policies to
ensure that caloric needs are met during rice price shocks
are important to avoid significant declines in overall food
consumption.
Even though PNG’s domestic food economy is dom-
inated by starchy staples such as sweet potatoes, yams
and taro, rice is an important staple for the approxi-
mately % of households that consume rice. Accord-
ing to the / Household Income Expenditure Sur-
vey (HIES), households living in urban areas depend
more on rice for overall consumption: urban per capita
consumption was nearly . times that of rural areas
Analysis by Gibson and Kim () found, however, that because of qual-
ity substitution effects, quantity elasticities are overstated.
Reliance on rice imports has also shaped trade policy and development
initiatives in other Pacific island countries (Foy, ). The Pacific region
is not alone in this policy choice; countries throughout Africa and South-
east Asia adopted similar strategies at the cost of missed opportunities to
diversify into higher value crop production (Dubois et al., ; Perez and
Pradesha, ; van Oort et al., ).
(. kg/capita and . kg/capita, respectively) in /.
Estimated rice consumption for households that do con-
sume rice is relatively high: . kg/capita/year, equivalent
to kcal/capita/day, or % of the minimum daily energy
requirement of calories per capita.
Domestic policy changes in response to the spread of
COVID- have also influenced the supply and demand
of marketed food items. Most countries, including PNG,
have implemented lockdowns and social distancing poli-
cies that require non-essential services such as restaurants,
hotels, and offices to temporarily close. In PNG, policies to
limit potential risk of contagion include restricted trans-
portation (e.g., roadblocks, permits, restricted flight trans-
portation) and closures of both formal and informal food
markets in urban centers and rural areas. These policies
have created unemployment among many urban dwellers,
as well as restricted rural to urban food supply chains for
fresh, domestic agricultural produce.
This article presents a quantitative analysis of the rice
economy of PNG utilizing data from the household
survey in several provinces of PNG along with the national
/ HIES data. We present results of simulations of
potential impacts of increases in the rice price on the wel-
fare of poor and non-poor households at both the national
and regional levels. We focus on two key outcomes of the
simulation exercise. First, we evaluate the extent of and
variation in the impact of price shocks on rice consump-
tion among urban and rural, poor and non-poor house-
holds considering the geographic and related agricultural
diversity of PNG. Second, we investigate how domestic pol-
icy changes in response to the spread of COVID- have
impacted the demand for rice in PNG.
We contribute to a sparse literature of food price shock
effects on import-dependent, island economies. In addi-
tion, given that PNG is characterized by an environment
of outdated nationally representative data, this article
demonstrates how robust analysis with limited data can
inform domestic policy during a significant global shock.
Many Pacific countries are implementing policies in reac-
tion to the COVID- pandemic with the understanding
that potential economic shocks could be devastating
for both rural and urban populations; however, these
decisions are being made without the tools to evaluate the
economic and social tradeoffs of such policies. This article
provides an example of how more thorough evaluation of
policy measures can aid in difficult decisions, even in a
data-scarce environment.
The remainder of this article is organized as follows.
Section describes the rice economy of PNG, highlight-
ing household consumption patterns and the evolution
of imports over time. Section presents an econometric
analysis of rice demand using household survey data.
Section presents model simulations of the effects of world
SCHMIDT . 3
rice price shocks and other disruptions. Section summa-
rizes and discusses policy implications.
2THE RICE ECONOMY OF PNG
Agriculture remains one of the most important sectors of
the PNG economy, with % of the population living in
rural areas and more than % of inhabitants dependent on
subsistence agriculture (Bourke & Harwood, ;Gibson,
). Given PNG’s geographic and climatic diversity, agri-
cultural production varies widely by location. Households
living in the highlands depend heavily on sweet potato pro-
duction to meet consumption demands. In addition, sales
of fruits and vegetables from rural highland areas to large
cities along the coast provide income to supplement rural
consumption. Households in the lowlands of PNG depend
on a variety of root crops including sweet potato, taro and
yam, with sago contributing an important share to overall
calorie intake in the Momase region. Peri-urban gardens
outside of major metropolitan areas are also major sources
of food supply for urban populations.
Although own production for consumption dominates
the food basket in rural PNG, both rural and urban house-
holds rely on marketed food items. An important share of
protein consumption in rural areas comes from purchased
tinned meat and fish. In select locations of the rural High-
lands region, PNG’s large mining sector is an important
source of employment, and purchased food is even more
prevalent. In addition, climatic conditions in the High-
lands facilitate production of key agri-food exports includ-
ing coffee and cocoa, with smallholder farmers reliant
on these cash crops. Throughout rural PNG, small and
medium-size informal enterprises comprise an estimated
% of the non-resource gross national domestic prod-
uct (Schmidt, Mueller et al., ; Stanley, ). With
increases in earnings from cash crops, and a growing
extractive industry sector, a greater share of households is
consuming processed foods like imported rice. Although
domestic rice production has increased over the past sev-
eral decades (primarily in the Markham and Ramu val-
leys, and Dreikiker area of East Sepik in the lowlands),
this expansion was subsidized and supported by the PNG
government, as well as foreign direct investment seeking
favorable trade terms for imported rice.
2.1 Rice consumption patterns
Detailed, nationally representative consumption and
expenditure data in PNG are sparse and dated. The most
recent nationally representative survey is the /
HIES covering households, providing detailed infor-
mation of region-level consumption of rural, urban and
metro populations.According to the / HIES data,
total consumption of rice was . thousand tons (.%
higher than average imports in and ), equal to
. kg/capita (Table ). Almost % (.) of rice in PNG
was consumed in urban areas where per capita consump-
tion was nearly . times that of rural areas (. kg/capita
and . kg/capita, respectively). In terms of the quantity
of rice consumed, overall, non-poor households (the
upper % in the per capita expenditure distribution)
consumed . kg/capita of rice, . times more than poor
households (. kg/capita).
To supplement the somewhat dated HIES (/), we
use the Rural Survey on Food Systems in PNG (PNG-
RSFS), implemented by the International Food Policy
Research Institute (IFPRI) in , to evaluate more recent
trends in household consumption. The PNG-RSFS survey
collected rural household survey data from May through
July of , in four areas of the country located in: Mid-
dle Ramu district of Madang; Maprik and surrounding dis-
tricts of East Sepik; Nuku district of West Sepik; and South
Bougainville of the Autonomous Region of Bougainville
(AROB).Three of the survey areas are located in differ-
ent provinces of the mainland Momase (lowland) region
which houses % of the country’s total population. The
PNG-RSFS data cannot be considered nationally or provin-
cially representative given its limited sampling. However,
given the geographic spread of the survey and the vari-
ety of lowland agro-ecological zones represented by the
four areas, we consider these data to provide the best
alternative for rural lowland livelihood classification short
of a nationally representative rural household survey. We
present modeling results based on the / HIES sam-
ple cluster, aggregating updated consumption estimates to
rural households in the highlands, lowlands (comprises
the Southern and Momase regions), and island regions,
and aggregating urban and metro areas in all regions,
respectively.
According to updated calculations, using the PNG-RSFS
survey and population estimates,oftheHIES
/, approximately % and % of rural poor and non-
poor calorie consumption, respectively, is derived from
roots and tubers (Figure ). Roots and tubers comprise a
smaller share of calories (% and % in poor and non-poor
The HIES / used a two-stage stratified cluster sample design with
ten strata, including rural and urban areas in four geographic regions
(Momase, Highlands, Southern, and Islands). PNG’s two largest cities,
Port Moresby and Lae were included in separate metropolitan strata.
Schmidt et al. () provides detailed information on the sampling
stratification, geographic location, and questionnaire content of the
Rural Survey on Food Systems in PNG (PNG-RSFS). Survey data are
4SCHMIDT .
TABLE 1 Estimates of rice consumption in PNG,
HIES 2009/10a2020 Estimate 2020 Population
kg/cap (’000 tons) Sharesbkg/cap (’000 tons) (thousands)
Urban
Poor . . .% . . .
Non-poor . . .% . . .
Tot al . . .% . . .
Rural
Poor . . .% . . .
Non-poor . . .% . . .
Tot al . . .% . . .
All PNG
Poor . . .% . . .
Non-poor . . .% . . .
Tot al . . .% . . .
Notes: Share of total PNG consumption. Poor is defined as households in the bottom % of the per capita expenditure distribution.
Source: Authors’ calculations using HIES – and IFPRI PNG-RSFS ().
0
500
1,000
1,500
2,000
2,500
3,000
3,500
Rural Urban Rural Urban
Poor Nonpoor
Kcal / person / day
Others
Roots
Wheat
Maize&Sorghum
Rice
FIGURE 1 Estimated total calories per person per day by food category and location in . Source: Authors’ calculation from the PNG
Household Income Expenditure Survey (/) and IFPRI () *Note: ‘‘Others’’ contains the remaining food items including meat, dairy,
vegetable and fruit [Color figure can be viewed at wileyonlinelibrary.com]
households, respectively) per person per day in urban
households, whereas rice comprises % and % of calo-
ries per person per day for urban poor and non-poor
available for download at: https://dataverse.harvard.edu/dataset.xhtml?
persistentId=doi:./DVN/ZXRDN
The population estimates for PNG are sourced from World Bank
published estimates.
households, respectively. As expected, non-poor house-
holds (both rural and urban) consume a greater share of
‘‘other’’ calories outside of the major staple foods, includ-
ing meat, vegetables, and fruit.
Table focuses on household rice consumption in .
For rural households in the Momase region, estimates
are based on data from the PNG-RSFS conducted in
SCHMIDT . 5
TABLE 2 Average annual rice consumption (kg/capita) by expenditure quintile
Expenditure quintile
Survey area
Household
samplea1 (poorest) 2 3 4 5 (wealthiest) Total
AROB Consuming . . . . . .
All . . . . . .
East Sepik Consuming . . . . . .
All . . . . . .
Madang Consuming . . . . . .
All . . . . . .
West Sepik Consuming . . . . . .
All . . . . . .
MomasebConsuming . . . . . .
All . . . . . .
Total Consuming . . . . . .
All . . . . . .
Notes:
aConsuming sample are households that reported consuming rice, while All refers to the entire survey sample regardless of whether they report eating rice or not.
bMomase includes households from survey sites in East Sepik, Madang and West Sepik.
Source: Authors’ calculations using IFPRI PNG-RSFS ().
TABLE 3 Household budget shares by expenditure quintile
Expenditure quintiles
Food type Q1 Q2 Q3 Q4 Q5 Poor Non-poor All hhds
Wheat/flour products . . . . . . . .
Rice . . . . . . . .
Starch . . . . . . . .
Protein (animal) . . . . . . . .
Fruit . . . . . . . .
Veg eta bles . . . . . . . .
Fats . . . . . . . .
Other (including dairy) . . . . . . . .
Food share of total expenditure . . . . . . . .
Note: Poor is defined as households in the bottom % of the per capita expenditure distribution.
Source: Authors’ calculations using IFPRI PNG-RSFS ().
(Schmidt Gilbert, et al., ). Rice consumption in the
other regions is estimated using shares of total rice con-
sumption in the non-Momase regions from the /
HIES. Note that rural per capita consumption in the
Momase region in the PNG-RSFS ( kg/capita/year for all
households) is similar to a per capita consumption esti-
mate derived as the share of the region in PNG national
consumption in – (.) times total con-
sumption of thousand tons (i.e., . thousand tons,
. kg/capita/year) (Table ).
Rice consumption in the sampled households of the
RSFS () echo consumption patterns reflected in the
HIES (/), whereby households with greater expen-
For the % of the sample in Momase residing in rice consuming house-
holds, per capita consumption is . kg/person (Table ).
ditures consume greater quantities of rice per capita
(Table ). For example, the poorest rice-consuming house-
holds in the East Sepik sample area near Maprik eat, on
average, about kg of rice per person per year, whereas
the least poor rice-consuming households eat more than
four times the amount of the poorest. Including house-
holds that do not eat rice, average consumption of poor and
non-poor households is and kg per person per year,
respectively. In addition, Table demonstrates the varia-
tion in rice consumption by region, whereby households
in ARoB consume substantially more rice per capita in
all expenditure quintiles compared to households in other
survey areas.
Food accounts for more than three-quarters of over-
all household expenditures in poor and non-poor house-
holds (Table ). The PNG-RSFS () underlines the
6SCHMIDT .
FIGURE 2 Rice consumption by kilometer distance to nearest
major market town. Note: Major market towns for each area
include: Wewak (East Sepik), Maprik (East Sepik), Nuku (West
Sepik), Madang (Madang), Kieta (Bougainville), Arawa
(Bougainville), Buka (Bougainville); Households with implausible
rice consumption per capita have been excluded. Source: Authors’
calculations using IFPRI–RSFS () [Color figure can be viewed at
wileyonlinelibrary.com]
importance of locally produced crops in the consumption
basket. Almost half of total household expenditure com-
prises starches, predominantly roots, tubers and bananas
(Table ). Given the higher cost of animal-source protein,
the second largest item in terms of expenditure share is
meat, fish, and eggs in both poor and non-poor households
(. and ., respectively). Although quantity of rice con-
sumed diverges between poor and non-poor household
groups, the share of household expenditure dedicated to
rice is similar (.% and .%, respectively).
Following Bennet’s law, the share of expenditure on
starchy staples in the food basket decreases as incomes
rise. Although rice is considered a starchy staple in many
countries in South and East Asia, it is predominantly
imported and relatively more expensive than domestically
produced root or tuber-based staples (e.g., sweet potato
or taro), especially in rural areas of PNG. Similar to the
HIES / data, the IFPRI survey data suggest that as
household expenditure rises, the food budget share for rice
remains relatively stable across income quintiles (Table ).
Overall, the total share of expenditure on food remains
relatively unchanged across income quintiles as well,
reflecting a strong reliance on subsistence agriculture.
It is important to note, however, that estimating household expendi-
ture values from reported food consumption data can introduce mea-
surement error given the significant challenges to valuing the true cost
of consumed items in subsistence and low-income households. Previ-
ous work has shown that consumption recall of subsistence households,
as well as lack of data on direct purchases of staple crops within sub-
sistence agriculture households may lead to measurement error result-
Using the PNG-RSFS sample, we evaluate rice consump-
tion differences by proximity to a major market town.
Similar to the HIES, the PNG-RSFS data suggest that
households that are further away from a market consume
less rice (Figure ). The household sample in Madang
district is located in the remote, Middle Ramu sub-district,
and illustrates how rural consumption baskets differ based
on access to a market. Recognizing the dependence on
locally produced food items in rural areas of PNG, an
(imported) food price shock will have, on average, larger
effects on urban and peri-urban households.
2.2 PNG rice imports
Estimates of PNG rice imports are available from
to via BACI, a database that attempts to reconcile
export and import data from national data sources (Gaulier
&Zignago,). BACI data for quantity and value of
PNG rice imports show a long-term upward trend, inter-
rupted by years of low volumes and value of imports
from through (Figure ). Informal data from
the PNG rice trade suggests that current (early ) rice
imports are about thousand tons per month, equiv-
alent to approximately thousand tons/year, consis-
tent with the overall trend in Figure . Vietnam, Thai-
land, and the United States were the major exporters of
rice to PNG between and , accounting for %,
%, and % of the quantity of PNG rice imports in this
period.
International rice prices have increased in world mar-
kets in . Between December and May , the
export price (fob) of Bangkok A Super (a commonly
traded medium quality rice) rose by % and the export
price of Vietnam % broken rose by %. These price
increases in early reflected Vietnam’s announcement
of rice export restrictions and Cambodia’s ban on white
rice exports in response to the COVID- outbreak. Like-
wise, despite India’s large rice stocks and no formal export
ban, logistics challenges due to shelter-in-place policies to
curb COVID- contagion substantially delayed rice ship-
ments and supply chain logistics. The increases in rice
price in both Vietnam and Thailand persisted in sub-
sequent months as well: the average price of rice from
these two sources from March through September
was % higher than the price in December . In
the next section, we evaluate household demand for rice
to estimate own-price elasticity of rice demand which
is incorporated in the simulation model presented in
Section .
ing in an underestimate of household consumption values (Zezza et al.,
).
SCHMIDT . 7
-
50,000
100,000
150,000
200,000
250,000
2001 2003 2005 2007 2009 2011 2013 2015
(Metric tons)
Australia PR China Thailand Taiwan USA Vietnam Other
FIGURE 3 Quantity of PNG rice imports, –. Source: Authors’ calculations using BACI data [Color figure can be viewed at
wileyonlinelibrary.com]
3DETERMINANTS OF HOUSEHOLD
RICE DEMAND
3.1 Empirical specification
We employ Heckman’s two-stage model to estimate the
marginal effect of income on rice consumption using
the PNG-RSFS dataset (Heckman, ).Approxi-
mately one-third of the households in the PNG-RSFS
reported that they did not consume rice during the -day
recall period. Given that over % of rice consumed in
PNG is imported rice, we hypothesize that the majority of
zero expenditures are due to household inability to pur-
chase rice because either it is cost-prohibitive via direct
costs (current rice price) or indirect costs related to mar-
ket access.Under this hypothesis, we assume that non-
consuming households may have a latent demand for rice,
due to potential household and geographic characteristics
that restrict consumption.
We only observe household rice demand (measured as
rice expenditure as a share of total household expenditure)
when it is positive, with the remainder of household obser-
vations censored at zero. Heckman () developed a two-
stage estimator to mitigate the potential sample selection
bias that arises from a non-random sample comprises non-
zero, positive values. For the case presented here, stage
For evaluations of the Heckman two-stage approach in household bud-
get studies, see McDonald and Moffit (); Heien and Wesseils ();
Hoffman and Kassouf (); Adusah-Poku and Takeuchi ().
We calculate total expenditure on rice based on reported consumption
data, and hence the argument of infrequent purchases is less valid assum-
ing household-level consumption of rice does not fluctuate dramatically
between weeks during the survey period ( months).
one of the Heckman two-stage estimator employs a probit
model that determines the probability that a given house-
hold consumes rice. Predicted values from the first-stage
probit equation are retained as to allow an estimation of
the inverse Mills ratio for each observation which is used
as an instrument in the second stage.
The desired consumption (share of expenditure on rice)
equation which causes sample selection, is specified as
𝐶∗
𝑗=𝛾
′𝑧𝑗+𝑢
𝑗()
where 𝐶∗
𝑗is household consumption on rice, 𝑧𝑗is a vec-
tor of variables associated with rice consumption, which is
only observed if the household reported eating rice, and 𝑢𝑗
is the error term with a bivariate normal distribution with
zero mean. The variable 𝐶∗
𝑗is not observed, but households
report whether they consumed rice or not, so that
𝐶∗
𝑗=1if𝐶
∗
𝑗>0
and
𝐶∗
𝑗=0if𝐶
∗
𝑗≤0
Let 𝑤𝑗represent the share of total expenditure dedicated
to rice by each household:
𝑤𝑗=𝛽
′𝑥𝑗+𝜀
𝑗,()
whereby, 𝑥𝑗is the vector of variables affecting house-
hold rice consumption and 𝜀𝑗is the error term with a
bivariate normal distribution with zero mean. Under the
assumption that the error terms are jointly normal, the
8SCHMIDT .
second-stage linear regression is defined as
𝐸𝑊𝑗𝐶𝑗=1
=𝐸𝑊𝑗𝐶∗
𝑗>0
=𝐸(𝑊
𝑗𝑢𝑖−𝛾
′𝑧𝑗
=𝛽
′𝑥𝑗+𝐸𝜀𝑗
𝑢𝑗−𝛾
′𝑧𝑗
=𝛽
′𝑥𝑗+𝜌𝜎
𝜀𝜆𝑗(𝛼𝑢)
()
To correct for potential sample bias, the ordinary least
squares (OLS) regression represented in Equation ()
includes a vector of independent variables 𝑋and the
inverse Mills ratio 𝜆𝑗(𝛼𝑢)as regressors in order to esti-
mate 𝛽.𝜌is the correlation between the error terms (i.e.,
between unobserved determinants of the probability of eat-
ing rice, 𝑢, and the unobserved determinants of the share
of rice expenditure in household total expenditure, 𝜀), and
𝜎is the standard deviation of the error term 𝜀.
3.2 Results
In total, the PNG-RSFS sample comprises households,
of which had sufficient data for analysis of rice con-
sumption. The household survey collected detailed con-
sumption and expenditure information on weekly con-
sumption of a detailed list of food items, as well as monthly
and yearly non-food expenditures. Households were asked
whether they consumed rice, and if so, how much rice was
consumed during the last days and where they sourced
their rice (harvested, purchased, or gifted). Data collec-
tion spanned months (May–July ) and was located
in areas of the country that have non-seasonal agricultural
production practices.
Table summarizes values of the relevant variables
used for this analysis. The selection of variables used in
the Heckman two-stage procedure is suggested by other
studies and adapted to the unique characteristics of PNG
(Alderman, ; Burton et al., ; Hoffmann & Kas-
souf, ; Kojima et al., ; Madden, ). In the first
(probit) equation of the Heckman two-stage model, partic-
ipation (whether a household consumed rice in the past
days) largely depends on household characteristics. We
hypothesize that access to markets and ability to travel to
a market are important indicators of participation. Poor
security within PNG (particularly for women), lengthy
travel times, and inadequate transportation infrastructure
suggests that the sex and age of the household head, and
Bourke and Harwood () provide a detailed description of geogra-
phy and associated crop production differences, considering rainfall and
elevation across the landscape of PNG. Most lowland areas in PNG, and
all of the survey areas included in the PNG-RSFS are considered non-
seasonal, whereby sweet potato, yam, sago, and other staple crops arepro-
duced and harvested year around. This was also reflected during survey
scoping visits and focus-group data collection activities.
distance to the nearest major market town would affect the
decision of whether a household travels to purchase rice
and other foodstuffs. Recognizing time-use tradeoffs of
traveling to a market, we include household size and num-
ber of household dependents in the first-stage regression
as well. We also assume that total household expenditure,
educational attainment of the household head, and the
price of rice is associated with household participation via
household income and opportunity costs. We include the
unit price of sweet potato in both the first and second stage
of the regression to consider substitution effects among
staple crops. Finally, we include a set of province dum-
mies to control for geographic variations and consumption
preferences.
In the second-stage (quantity of rice consumption) equa-
tion, total household expenditure and unit prices of rice
and sweet potatoes are the principal covariates we evalu-
ate for this analysis. Total household expenditure and unit
rice price coefficients derived in the second-stage regres-
sion are used to calculate the expenditure and own-price
elasticities, respectively, employed in the partial equilib-
rium model discussed below.
Total household expenditure includes the value of both
non-food and food (purchased and non-purchased) con-
sumption and expenditure, and is used as the proxy for
household income for several reasons. First the IFPRI sur-
vey comprises predominantly rural households whereby
over half of the surveyed households reported that their
sole source of income was from subsistence farming (barter
and trade remain common practice in rural communities
in PNG). Second, other forms of income earned via wage
work (reported by less than % of households) and non-
farm enterprises (approximately one-third of households)
fluctuate based on the opportunity costs of working off-
farm versus planting and harvesting food for the house-
hold. Finally, given the rural nature of the household sur-
vey data, most households reported that they had no access
to credit or savings options via lenders or banks.
The unit price of rice is constructed from data col-
lected in the household survey on purchased food items.
For households that consumed and purchased rice, the
reported unit cost (PGK/kg after conversion to kilogram
units) is used. For households that did not purchase rice,
we use the median of all (consistent unit) price data by
food item within a community and assign each household
The inclusion of these variables may be justified on several grounds. For
example, age may be associated with consumer preference, whereby older
households prefer less labor-intensive crops (i.e., purchase rice compared
to harvesting customary staple crops).
Previous studies have shown, particularly in contexts such as PNG, that
total expenditure is a more reliable measurement of household income
(Browning et al., ; Schmidt, Gilbert et al., ; Zezza et al., ).
SCHMIDT . 9
TABLE 4 Descriptive statistics of covariates included in Heckman model by rice and non-rice consuming households, Mean (SD)
All
households
Rice consuming
households
Non-rice consuming
households T-test
Household budget share of total expenditure for
rice
. . . .
(.) (.) (.)
Log of total household expenditure
(PGK/capita/year)
. . . .
(.) (.) (.)
Log of household-level unit rice cost (PGK/g) . . . .
(.) (.) (.)
Log of household-level unit sweet potato cost
(PGK/g)
. . . .
(.) (.) (.)
Household size . . . .
(.) (.) (.)
Sex of household head is female . . . .
(.) (.) (.)
Age of household head . . . .
(.) (.) (.)
Years completed education of household head
(-)
. . . .
(.) (.) (.)
Number of household dependents ( >age >) . . . .
(.) (.) (.)
Euclidean distance to major market town (km)a. . . .
(.) (.) (.)
Bougainville (/) . . . .
(.) (.) (.)
East Sepik (/) . . . .
(.) (.) (.)
Madang (/) . . . .
(.) (.) (.)
West Sepik (/) . . . .
(.) (.) (.)
Number of Households 1012 671 341
Note:PGK=Papua New Guinea Kina; T-test p-value is derived from a t test of equal variances, standard deviations are presented below in brackets.
aMajor market towns for each area include: Wewak (East Sepik), Maprik (East Sepik), Nuku (West Sepik), Vanimo (West Sepik), Madang (Madang), Kieta
(Bougainville), Arawa (Bougainville), Buka (Bougainville); USD . =PGK . in June .
Source: Authors’ calculation using IFPRI PNG-RSFS ().
within the respective community the same community rice
price.
In addition to total household expenditure and unit
price, we also include household size and number of
A similar exercise is used to construct sweet potato prices. However,
most households produce sweet potato for own-consumption and thus
less data were available to construct community level prices. When less
than purchase price observations were available within a community,
we used the median price at province level.
dependents assuming that household composition and
size may affect purchase volume. Finally, we maintain the
set of province-level dummies in the second-stage regres-
sion to control for area-specific factors that may drive the
quantity of rice consumption.
To correct for selection bias using the Heckman two-
stage model, an exclusion restriction is required whereby
at least one variable which appears in the first-stage par-
ticipation equation is absent in the second-stage levels
equation. Throughout all specifications we assume that
10 SCHMIDT .
distance to a market is associated with a household’s deci-
sion to consume rice, but once that decision is made, dis-
tance does not directly influence the amount purchased
or consumed. Additional exclusions include the charac-
teristics of the household head (sex and age). In addi-
tion, we assume that years of education of household head
may be associated with whether to purchase rice (related
to the opportunity cost of labor for own production of
staple crops), but not on the amount purchased or con-
sumed. We test these exclusion assumptions and results
show that adding these covariates iteratively to the second-
stage regression has little effect on overall results (sensi-
tivity results to the exclusion restriction are provided in
Appendix Table ).
Table presents the coefficients for the first-stage
probit equation and the second-stage levels equation. We
compare the coefficients for the levels equation estimated
without correcting for selection bias (column B), and
estimated by Heckman’s procedure (column C). The
coefficients for the uncorrected consumption equation
(column B) consider only those households that consumed
rice, not correcting for sample selection bias—hence the
total observations are equal to the censored sample of
households. Correcting for selection bias via the Heckman
procedure (column C) suggests that the factors influencing
households’ decision to consume rice may differ from the
factors influencing how much rice households consume
(measured as the share of total expenditure spent on
rice). Conditional and unconditional marginal effects
calculated at the mean are presented in columns D and E,
respectively. The conditional marginal effect estimates the
effect of a covariate among consumers (those that reported
a positive value of rice expenditure) during the survey
period. The unconditional marginal effect reflects the sum
of the effect of a covariate, accounting for the increased
probability of consumption and correcting for sample
selection bias using the Heckman two-stage estimator.
Results from the probit equation show a positive and
significant coefficient for total household expenditure
and household head education (Table ). Higher total
expenditures are associated with a greater probability of
consuming rice. This is expected given that almost all con-
sumed rice in PNG is purchased. As expected, the probit
equation demonstrates that the probability of consuming
rice decreases as the unit rice price increases. A higher
level of education attained by the household head is asso-
ciated with a higher probability of consuming rice, which
suggests that households with higher levels of education
face greater opportunity costs of dedicating labor to own-
harvest agricultural or food preparation activities.
The distance to the nearest major market town is neg-
atively correlated with the probability of consuming rice;
however, quantitatively this association is small and it
loses significance when we test for joint significance with
the quadratic term. This is most probably due to the
collinearity of the region-level dummy given the clustered
survey sampling approach used to randomly select sur-
vey communities for data collection. Given limited infor-
mation on market towns with reliable rice supply, we
use a distance measure to larger market towns; however,
it is possible that more detailed information about mar-
ket accessibility, including smaller markets, could improve
this estimate. Further evaluation of rice consumption by
survey area and distance to a market suggests that the
Bougainville sample may be driving this result. Almost
all households in Bougainville (% of households in
Bougainville, comprising % of rice-consuming house-
holds in the total sample) depend on rice to satisfy calorie
needs regardless of distance to a market.
We include the unit price of sweet potato (an important
staple crop in much of rural PNG) as a covariate in the first
and second-stage regression to test for potential substitu-
tion effects that may be present in the decision to consume
rice. While sweet potato prices do not have a significant
effect in the selection (probit) equation of whether to con-
sume rice, an increase in sweet potato prices does have a
positive and significant relationship on rice share of total
expenditure in the second-stage levels regression suggest-
ing that households would face trade-offs in what staple to
buy depending on price (controlling for other factors).
The region-level dummy coefficients in the probit
regression are also telling in terms of controlling for vari-
ation in household consumption baskets. Two factors may
be at play here. First, compared to the most remote survey
area in Madang (for which the dummy variable was omit-
ted), South Bougainville is the most connected survey site
to major markets. Second, agricultural production in South
Bougainville is more export oriented. South Bougainville
is heavily focused on cocoa and copra production, neces-
sitating substitution of otherwise harvested staple foods.
Similar to Madang survey sites, West Sepik households are
remote and rely predominantly on subsistence agriculture.
Results suggest that an increase in unit rice price has
a statistically significant inverse relationship (-.) with
the rice share of total household expenditure when evalu-
ating the unconditional marginal effects. Similarly, as total
household expenditure increases, the share of total house-
hold expenditure dedicated to rice decreases. This is con-
sistent with Bennet’s law whereby increases in household
income lead to a shift away from starchy staples in lieu
Eliminating the quadratic term from the second stage regression results
in the linear distance measure becoming insignificant.
Gibson and Rozelle () used a detailed rural road dataset and travel
time analysis to show that improved rural road access has a significant
and positive impact on household income, particularly in poor house-
holds, in Papua New Guinea.
SCHMIDT . 11
TABLE 5 Heckman sample selection model for rice expenditure share of total household budget
Consumption equation Marginal effects
Participation
equation (Probit)
Without
correctionb
Heckman
procedure
Conditional
marginal effects
Unconditional
marginal effects
Varia bles A B C D E
Log of total household expenditure
(PGK/capita/year)
.*** −.*** −.*** −.*** −.**
(.) (.) (.) (.) (.)
Log of household-level unit rice price
(PGK/g)
−.*** .*.** . −.
(.) (.) (.) (.) (.)
Log of household-level unit sweet potato
price (PGK/g)
−. . .*. .
(.) (.) (.) (.) (.)
Household size . −. −. −. −.
(.) (.) (.) (.) (.)
Sex of household head is female −. −. −.
(.) (.) (.)
Age of household head (years) −. −. −.
(.) (.) (.)
Years completed education of household
head (–)
.** .** .**
(.) (.) (.)
Number of household dependents
( >age >)
. −. −. −. .
(.) (.) (.) (.) (.)
Euclidean distance to major market town
(km)a
−.*−.*−.*
(.) (.) (.)
Euclidean distance to major market town
(km)asquared
. . .
(.) (.) (.)
Region dummy (Madang omitted)
Bougainville (/) .*** .*** . .** .***
(.) (.) (.) (.) (.)
East Sepik (/) .** −. −. −. .
(.) (.) (.) (.) (.)
West Sepik (/) . . −. −. .
(.) (.) (.) (.) (.)
Inverse Mills (Lambda) −.
(.)
Constant . .*** .***
(.) (.) (.)
N Observations
Note: Standard errors in parentheses.
aMajor market towns for each area include: Wewak (East Sepik), Maprik (East Sepik), Nuku (West Sepik), Vanimo (West Sepik), Madang (Madang), Kieta
(Bougainville), Arawa (Bougainville), Buka (Bougainville). bNot corrected using Heckman procedure using censored sample of non-zero observations of rice
expenditure.
***p<..
**p<..
*p<..
Source: Authors’ calculation using IFPRI PNG-RSFS ().
12 SCHMIDT .
of more protein and vitamin-rich (and more expensive)
foods. The coefficient on household head education is pos-
itively associated with the share of rice expenditure, sug-
gesting that its inclusion in the second-stage regression
may be necessary. We test this specification (see results in
Appendix Table ) and find that the education of the house-
hold head is not significant with regard to the rice budget
share when controlling for other factors.
The coefficient βof . on the logarithm of household-
level unit rice price in the Heckman second-stage regres-
sion (Table ) implies an own-price elasticity of -. (equal
to βdivided by the average rice budget share (w) of con-
sumers of .% minus one). In the simulations that follow,
we use the βcoefficient and the mean budget shares (w)of
poor and non-poor households to calculate own-price elas-
ticities (=β/w - ) of -. and -. for the poor and non-
poor, respectively. We use the coefficient on the logarithm
of total expenditures βY(-.) to calculate expenditure
elasticities (=+βY/w) of . and ., for the poor and
non-poor, respectively.
4 IMPACTS OF WORLD PRICE
SHOCKS AND INCOME SHOCKS ON
HOUSEHOLD CONSUMPTION
4.1 Model structure
We use a simple partial equilibrium model of PNG’s rice
economy adapted from Dorosh ()andCoadyetal.
() to estimate the effects of world price changes and
other shocks on rice consumption and household welfare.
The model simulations presented here assume an inte-
grated rice market for PNG in which percentage changes in
domestic rice prices are the same for all households across
the country.
In the model, the domestic price of rice is set equal to the
exogenous import price of rice plus fixed domestic market-
ing costs. In each simulation, the percentage changes in
rice consumption for each household are calculated as the
household’s own-price elasticity of demand for rice times
the percentage change in the rice price. Total demand for
rice, equal to the sum of demand by all households, deter-
mines the volume of imports. Given that domestic produc-
tion of rice accounts for only about six thousand tons, or
approximately .% of total supply, changes in domestic pro-
duction have little effect on the results in the simulations
presented below, reducing imports by less than .%. House-
hold incomes are set exogenously given that incomes from
rice production account for a negligible share of income
Thus, although the estimated values of the coefficient βYare negative,
the expenditure elasticities are positive.
of any of the household groups specified in the model.
(Appendix Table lists the equations and variables in the
model.)
We estimate COVID- shocks to household incomes
using mobility data from Google () that provide an
indication of activity in various sectors. Using the per-
centage change in mobility in various categories relative
to January to February , , we construct low and
high estimates of income shocks for broad household
groups (Appendix Figure and Appendix Table ). Thus,
for the urban poor, we use the average reduction in activ-
ity observed between January and the average of March
through August in retail and recreation, parks and transit
stations (.%) as the low estimate of their income loss. We
use the reduction in activity in transit stations (.%) as
the high estimate of their income loss. For the urban non-
poor, we use the average reduction in transit stations and
workplaces (-.%) as the low estimate for income losses;
for rural households, our low estimate is zero. For both the
urban non-poor and rural households, the high estimate is
.% (the same as the low estimate for the urban poor).
4.2 Simulation results
Table presents the simulation results of a % increase in
the world price of rice, equal to the increase in world prices
from December to the average March-September
price. Simulation models the effects of the price hike with
no income shock and Simulations and model the effects
of low and high estimates of household income shocks dis-
cussed above. Simulation shows the effects of the shocks
modeled in Simulation , but uses more inelastic demand
parameters (equal to . times those in Simulation ), since
the econometric estimates using cross-section data may
overstate responsiveness of demand in the short run.
The % increase in the world price of rice with no
household income shock results in a .% decline in
demand for rice and an increase in the rice import bill from
US$ . million to US$ . million (Simulation ). Poor
consumers (those in the bottom % of the income distri-
bution) suffer a net welfare loss of US$ . million (US$
. per capita, equivalent to .% decrease of a US dollar
per day income). The household groups with the largest
rice consumption, Rural Lowlands (Southern and Momase
rural areas) and Other Urban (comprise urban areas of all
See Sampi and Jooste () for another example of the use of Google
mobility data to estimate economic losses.
At the prevailing exchange rates of (USD . =PGK .), the
average income of households below the poverty line in the IFPRI
survey was . Kina (. US$/day). Approximately % of the popula-
tion (from the PNG-RSFS) and % of the population (from the HIES
/) live at or below the poverty line.
SCHMIDT . 13
TABLE 6 Effects of increases in world rice prices: PNG model simulation results
Base Sim 1 Sim 2 Sim 3 Sim 4
Household Income Shock (%) .% −.% −.% −.%
Production Rice (’ tons) . . . . .
Imports (’ tons) . . . . .
Total Supply (Demand) (’ tons) . . . . .
Rice Consumption (% change) — −.% −.% −.% −.%
Urban Poor . −.% −.% −.% −.%
Urban non-poor . −.% −.% −.% −.%
Rural Poor . −.% −.% −.% −.%
Rural non-poor . −.% −.% −.% −.%
Value of imports (mn $) . . . . .
Net Benefits (Poor HHs) (mn$)
Metro —−. −. −. −.
Other Urban — −. −. −. −.
Rural Lowlands - Main —−. −. −. −.
Rural Highlands - Main — −. −. −. −.
Rural Islands — −. −. −. −.
Total Poor — −. −. −. −.
US$/capita (poor households) —−. −. −. −.
As share of $/day income — −.% −.% −.% −.%
Notes: Own-price elasticities of demand: (poor: -., non-poor: -.); Income elasticities of demand: (poor: ., non-poor: .).
Source: Model simulations.
regions except Port Moresby and Lae metro centers) suffer
losses of US$ . million and US $. million, respectively.
Including the effects of low (-.%) and high (-.%)
estimates of income shocks due to the slowdown in the
PNG economy linked to COVID- restrictions on move-
ment of people and goods, rice consumption falls by .%
and .%, respectively. Rice consumption of the urban
poor drops more steeply than for other household groups (-
.% in Simulation ) because of the larger income shock
for these households. The welfare losses due to the shocks
for all poor households together are slightly smaller than in
Simulation (e.g., US$ . million in Simulation as com-
pared with US$ . million in Simulation ) because of the
downward shift in demand (i.e., lower rice consumption)
caused by the income shock.
With a more inelastic price elasticity of demand, rice
consumption of poor consumers falls less, by only .% in
Simulation , in comparison with a decline of .% in Sim-
ulation . Welfare losses for the poor, equal to US$ . in
Simulation , are only slightly larger (.%) than in Simu-
lation , however.
We measure the change in welfare related to rice consumption (includ-
ing both price and income effects on rice consumption). These calcula-
tions do not include other welfare losses due to price and income shocks.
5POLICY IMPLICATIONS AND
CONCLUSIONS
Since December , just before the onset of the COVID-
pandemic, international rice prices rose steeply—by %
for Bangkok A Super rice and % for Vietnam % broken
rice. Given that essentially all of PNG’s rice supply comes
from rice imports (estimated to be about thousand
tons/year), the domestic price of rice in PNG has increased
in local urban markets.
Although rice is not the major staple in PNG, on aver-
age, it still accounts for % of total calories and about % of
total household expenditures (including the value of own
consumption of other foods) in the IFPRI rural house-
hold survey. Furthermore, the – HIES indicates
that despite the considerable marketing costs for deliver-
ing imported rice from seaports to local markets, .% of
the population in that year lived in households that held
rice stocks.
In this article , we use data on the shares of regional
household rice consumption from the – HIES,
household rice consumption in lowland rural areas from
the IFPRI Rural Survey on Food Systems (PNG-
RSFS) and international trade data on imports to esti-
mate household rice consumption for various regions
14 SCHMIDT .
of PNG. The PNG-RSFS data are also used to
econometrically estimate the responsiveness of house-
hold rice demand to changes in incomes and rice
prices.
Model simulations indicate that a % rise in the world
price of rice (approximately equal to the actual increase
in world rice prices through September ) is expected
to decrease overall rice consumption in PNG by %, and
reduce rice consumption of both poor and non-poor house-
holds by a similar percentage. Including the effects of esti-
mated % to % declines in household incomes because
of the COVID- related economic slowdown, total rice
consumption falls by as much as %. For the urban poor
whose incomes likely are most affected, the drop is steeper:
%. Because domestic rice production in PNG is only .
million tons (.% of supply), however, changes in produc-
tion have little effect on imports or incomes. Overall, the
welfare loss to poor households is about US$ million.
Note that this estimate focuses on the welfare effect associ-
ated with a rice price increase; however, the overall welfare
effect due to a contraction in the economy (considering all
sectors) would be much larger.
While PNG produces a very small share of rice for
domestic consumption (PNG does not export rice), a vari-
ety of research on rice production potential in PNG sug-
gests that significant rice production expansion is unlikely
to be profitable or sustainable. It is unclear whether it
is economically or politically feasible to expand rice pro-
duction due to: difficulties in accessing land that is not
under customary tenure, opportunity costs of developing
rice production in lieu of higher value tree-crop exports
(Gibson, ), and tradeoffs of lost employment in less
labor-intensive rice production compared to other export
crop activities (Gibson, ).
Investment in improved data systems are also needed to
provide real-time analysis on market supply and demand.
Currently, although a variety of institutions are collecting
food price data, there is no central repository or database
that can be easily drawn upon to monitor food price
changes in the market over time. Small surveys of whole-
sale traders and regular monitoring of prices of rice and
other food commodities would also enhance the ability of
all actors in rice and other key food value chains to respond
to shocks quickly and efficiently. Finally, there is a need
for an updated national household survey to provide up-
to-date information on household incomes and expendi-
However, PNG’s largest supplier of rice, Trukai Industries Ltd., has
partnered with PNG to provide technical expertise and support to expand
domestic rice production (particularly in the East Sepik and Markham
valley). Similarly, PNG has explored opportunities to link with Indone-
sia’s Naima Agro Industries to develop large-scale mechanized rice farms
in return for favorable trade terms for rice imports (with promises of
reserving up to % of rice imports for the Indonesian company).
tures across all geographies of PNG to better inform policy
and development assistance. Given that per capita rice con-
sumption has risen by over % since the / national
household survey, updated data on regions outside the
areas covered in the IFPRI survey are needed for fur-
ther analysis of rice markets, as well as broader national
food policy.
A targeted food or cash transfer could also be consid-
ered, not only to offset negative impacts of a rice price
shock, but other potential shocks, as well. Currently, PNG
does not have a social safety net program for vulnerable
households. Instead, almost % of the rural households
in the IFPRI survey reported that they rely on assis-
tance from their wantok (kinship group) as their main cop-
ing strategy. Some form of targeted safety net, perhaps with
a work requirement for households with members able
to work (such as in Ethiopia’s Productive Safety Net Pro-
gram) may be a workable model that could be tested, ini-
tially through small projects that include rigorous impact
evaluations.
Finally, lack ofinformation dissemination to rural farm-
ers regarding travel restrictions has disrupted agricultural
trade as goods transported on major corridors are confis-
cated and farmers and traders are prevented from reaching
their destination. Disruptions of supply chains of locally
produced foods threaten to magnify the negative impacts
of world rice price increases on household access to food.
Appropriate mechanisms to implement public health mea-
sures restricting mobility may be needed; however, these
policies should try to minimize domestic market disrup-
tions where possible.
The worldwide COVID- crisis has affected food secu-
rity for households in PNG both through disruptions in
trade in international markets, as well as reduced eco-
nomic activity and resulting income losses domestically.
Although, PNG’s food economy is heavily dependent on
non-traded starchy staples, significant consumption of
imported goods, particularly rice, make it vulnerable to
international price shocks. A return to lower international
rice prices will benefit poor households, particularly those
in urban areas, but longer-term investments in produc-
tion, information systems, markets and broader safety nets
are needed to achieve sustained improvements in food
security.
ACKNOWLEDGMENTS
This work was supported by the Australia Department of
Foreign Affairs and Trade (DFAT), the Regional Strate-
gic Analysis and Knowledge Support System for Asia
(ReSAKSS-Asia) funded by United States Agency for Inter-
national Development (USAID), and the CGIAR Research
Program on Policies, Institutions, and Markets, led by the
International Food Policy Research Institute (IFPRI). The
SCHMIDT . 15
authors are grateful to Harold Alderman, Xinshen Diao,
and two anonymous reviewers for comments on previous
versions of this manuscript.
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SUPPORTING INFORMATION
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in the Supporting Information section at the end of the
article.
How to cite this article: Schmidt E, Dorosh P, &
Gilbert R. Impacts of COVID- induced income
and rice price shocks on household welfare in
Papua New Guinea: Household model estimates.
Agricultural Economics. ;–.
https://doi.org/./agec.
