Report No. 78318-PY
Growth Volatility in Paraguay
Sources, Effects, & Options
June 5, 2014
Argentina, Paraguay and Uruguay Country Management Unit
Poverty Reduction and Economic Management
Latin America and the Caribbean Region
Document of the World Bank
CURRENCY AND EXCHANGE RATE
(As of June 5, 2014)
CURRENCY UNIT = GUARANIES
US$1.00 = 4421 Guaraníes
FISCAL YEAR
January 1 – December 31
Abbreviations and Acronyms
ASERCA
ARM
Supports and services for
agricultural marketing (Apoyos y
servicios a la comercialización
agropecuaria.)
Agricultural risk management
MATBA
AYII
Area-based Yield Index Insurance
AxC
Contract based agriculture
(Agricultura por contrato)
MICs
Middle Income Countries
BCP
Central Bank of Paraguay (Banco
Central de Paraguay)
OECD
Organization for Economic
Cooperation and Development
CADENA
Component Attention to Natural
Disasters in the Agricultural and
Fisheries (Componente Atencion a
Desastres Naturales en el Sector
Agropecuario y Pesquero)
PHEFA
Hemispheric Plan of
Eradication of Foot and Mouth
Disease
CBOT
Chicago Board to Trade
PEMEX
Mexico's state-owned petrol
company (Petróleos Mexicanos)
CSF
Chile’s Copper Stabilization Fund
PIT
Personal income tax
CIT
Corporate income tax
PPP
Public-private partnership
EPH
National Household Survey
(Encuesta Permanente de Hogares)
RENAMU
National registry of
municipalities (Registro
Nacional de Municipalidades)
ii
Futures and options exchange in
Buenos Aires, Argentina
(Mercado a termino de Buenos
Aires S.A.)
MERCOSUR Southern common market
(Mercado Común del Sur)
MEF
Ministry of Finance (Ministerio
de Hacienda)
FMD
Foot and mouth disease
ROFEX
GAP
Good agriculture practices
SENACSA
GDP
Gross Domestic Product
SPF
IBLIP
Index-Based Livestock Insurance
Program
Agricultural corporate income tax
National Institute of Statistics
(Dirección Nacional de Encuestas
Estadísticas y Censos)
Selective Consumption Tax
(Impuesto Selectivo al Consumo)
Latin America and the Caribbean
SPS
Futures and options exchange in
Rosario, Argentina (Mercado a
termino de Rosario S.A.
National Service of Animal's
Quality and Health (Servicio
Nacional de Calidad y Salud
Animal)
Norway’s Stabilization State
Petroleum Fund
Sanitary and Phytosanitary
VAT
WTO
Value Added Tax
World Trade Organization
y-o-y
Year-on-year
MICs
Middle Income Countries
IMAGRO
DGEEC
ISC
LAC
Vice President:
Country Director:
Sector Director:
Sector Manager:
Sector Leader:
Task Team Leaders:
Jorge Familiar Calderon
Jesko S. Hentschel
J. Humberto Lopez
Auguste Tano Kouame
Zafer Mustafaoglu
Friederike (Fritzi) Koehler-Geib
iii
Contents
Abbreviations and Acronyms ......................................................................................................... ii
Executive Summary ........................................................................................................................ 2
Chapter 1: The Sources of Volatility in Paraguay ..................................................................... 5
1.1.
Stylized facts ................................................................................................................. 6
1.2.
Sources of volatility .................................................................................................... 11
1.3.
The role of factor markets ........................................................................................... 18
Chapter 2: The effects of growth volatility in Paraguay with a focus on volatility
originating in the agricultural sector ................................................................................. 20
2.1
Propagation of shocks within agriculture ................................................................... 20
2.2
The impact of volatility originating in the agricultural sector on other sectors .......... 24
2.3
The impact of volatility originating in the agricultural sector on macroeconomic
aggregates .................................................................................................................................. 27
Chapter 3: Policy options for the management of growth volatility in Paraguay ................ 32
3.1
The macroeconomic toolbox to address growth volatility.......................................... 33
3.2
The agricultural risk management toolbox ................................................................. 38
3.3
Combining the macroeconomic and the agricultural risk management toolbox ........ 55
List of Figures Chapter 1
Figure 1.1: Real GDP growth ....................................................................................................................... 6
Figure 1.2: Volatility over time, Paraguay in regional comparison .............................................................. 7
Figure 1.3: GDP—breakpoints of volatility of quarterly y-o-y GDP growth using Inclan, Tiao (1994)...... 8
Figure 1.4: Agricultural GDP—breakpoints of volatility of quarterly y-o-y GDP growth using Inclan,
Tiao (1994).................................................................................................................................................... 8
Figure 1.5: Share of agriculture in GDP ....................................................................................................... 9
Figure 1.6: Growth volatility by economic sector ........................................................................................ 9
Figure 1. 7: Volatility of rainfall ................................................................................................................. 10
Figure 1. 8: Rainfall and agriculture GDP .................................................................................................. 10
Figure 1. 9 Exports by product.................................................................................................................... 11
Figure 1.10: Correlation between trade balance and world interest rate ..................................................... 13
Figure 1.11: Impulse response of trade balance to a shock to the world interest rate ................................. 13
Figure 1.12: Correlation between TOT and world interest rate .................................................................. 14
iv
Figure 1.14: Business cycle fluctuations in Paraguay—Government investment versus GDP .................. 15
Figure 1.15:Contribution of public and private demand components to real GDP growth ........................ 16
List of Figures Chapter 2
Figure 2.1: Wavelet analysis of rainfall and agricultural GDP ................................................................... 21
Figure 2.2: Impulse response functions linking Paraguay’s agricultural GDP to world............................. 23
Figure 2.3: Impulse response functions linking Paraguay’s agricultural GDP to the construction sector .. 25
Figure 2.4: Impulse response functions linking Paraguay’s agricultural GDP to the services sector......... 26
Figure 2.5: Wavelet analysis of agriculture and non-agricultural GDP ...................................................... 28
Figure 2.6: Wavelet analysis of agriculture and private consumption ........................................................ 28
Figure 2.7: Wavelet analysis of non-agriculture and private consumption................................................. 28
List of Figures Chapter 3
Figure 3.1: The World Bank Agricultural Risk Management Framework ................................................. 39
List of Tables Chapter 1
Table 1.1: Export by destination ................................................................................................................. 11
Table 1.2 Variance decomposition of GDP volatility ................................................................................. 12
Table 1.3 Correlations across variables ...................................................................................................... 14
Table 1.4: Variance Decomposition of Agricultural and Non-Agricultural GDP volatility ....................... 17
List of Tables Chapter 3
Table 3.1: Instruments for Managing Production Risk ............................................................................... 39
Table 3.2 Colombia’s study case. Benefits, Challenges and Considerations for Paraguay ........................ 41
Table 3.3: Mexico’s study case. Benefits, challenges and considerations for Paraguay ............................ 44
Table 3.4: Malawi’s study case. Benefits, challenges and considerations for Paraguay ........................... 45
Table 3.5: Mongolia’s study case. Benefits, challenges and considerations for Paraguay ......................... 47
Table 3.6: Instruments for Managing Market Risk ..................................................................................... 48
Table 3.7: Peru’s study case. Benefits, Challenges and considerations for Paraguay ................................ 49
Table 3.8: Subsidy Components, AxC Program, ASERCA ....................................................................... 52
Table 3.9: Mexico’s study case. Benefits, Challenges and considerations for Paraguay ............................ 52
Table 3.10 : Comparison CBOT, MATBA, & ROFEX.............................................................................. 53
Table 3.11: Argentina’s study case. Benefits, challenges and considerations for Paraguay ....................... 54
List of Annexes
v
.
Annex 1.1: Volatility over time, international comparison......................................................................... 63
Annex 1.2: Volatility breaks of macroeconomic variables in Paraguay ..................................................... 64
Annex 1.3: Graphs on volatility breakpoints Inclan Tiao (1994) by variable............................................. 66
Annex 1.4: Sectoral GDP correlations ........................................................................................................ 70
Annex 3.1: Traditional measures for agricultural risk management ........................................................... 72
Annex 3.2: Insurance products.................................................................................................................... 72
vi
Acknowledgements
This report was prepared by a team led by Friederike (Fritzi) Koehler-Geib (LCSPE) under the
overall supervision and guidance of Zafer Mustafaoglu (Lead Economist and Sector Leader,
LCSPR), Auguste T. Kouame (Sector Manager, LCSPE), J. Humberto Lopez (Sector Director,
LCSPR), Rodrigo A. Chaves (former Sector Director, LCSPR) and C. Penelope Brook (Country
Director, LCC7C). The peer reviewers were Aristomene Varoudakis (Advisor, DECOS), Cesar
Calderon (Senior Economist, DECWD), Julie Dana (Lead Financial Officer, FABLO), and
Norbert Fiess (Principal Economist/Credit Risk Head, CFRCR).
The core team included Elida Caballero Cabrera, Diana Lachy, Rei Odawara, Guillermo Cabral,
Jorge Araujo, Miriam Beatriz Villarroel, Marcelo Echague, Patricia Chacon Holt, Peter
Siegenthaler, Silvia Gulino (all LCSPE), Dante Mossi, (Country Manager, LCCPY), Gloria Dure,
Rosa Arestivo de Cuentas Zavala, (all LCCPY), and Rossana Polastri (former Country Manager,
LCCPY). Inputs and background papers were also received from Sophie Storm Theis (LCSSD),
Diego Arias Carballo (LCSAR), Hakan Berument, Julio Ramirez, Viktoria Hnatkovska (all
consultants), Andres Lajer Baron, Carolina Saizar, Hannah Nielsen, Nathalie Picarelli, Pia Maria
Zanetti, and Sona Varma (all LCSPE), Oscar Calvo-Gonzalez (LCSPR), Julian Lampietti
(LCSSD), David Gould (SARCE).
Comments and inputs were also received from many colleagues working in the Paraguay country
team, including Andrew Follmer, Carla Cutolo, Elena Feeney, Mariela Alvarez, Sabine Hader (all
LCC7C).
The team is thankful for the excellent collaboration with the Ministry of Finance, in particular with
the vice ministry of economics including the departments of economic studies, Macro Fiscal
Policies, and Debt Policy.
1
Executive Summary
Paraguay’s real GDP growth has been one of the most volatile in the region in recent years.
Between 2000 and 2011, real GDP growth in Paraguay fluctuated by 5.5 percentage points,
exceeding the volatility of most Latin American peer countries. This was not always the case.
During the period 1960-2011, growth volatility in Paraguay was lower than in other countries in
the region. It is too early to tell whether high volatility in Paraguay is temporary or permanent,
even though some structural changes as for example the increase in the weight of the agricultural
sector in GDP are in line with the idea that volatility is there to stay.
The high level of volatility is concerning because of the significant costs associated with it in
terms of welfare, economic growth, and equality. For developing countries, macroeconomic
volatility, summarized by output volatility, is reflected disproportionately in consumption
volatility, and welfare gains from reducing consumption volatility can be substantial (Loayza,
Ranciere, Serven, and Ventura (2007)). No less important is the negative impact of volatility on
economic growth. The impact arises through a decrease in productivity and various forms of
uncertainty such as economic, political, policy-related, as well as a tightening of binding
investment constraints.1 The negative link between macroeconomic volatility and equality has also
been established in the literature.2 Designing policies that help mitigate the impact of shocks to the
economy and that help increase the country’s resilience is particularly relevant in this light, also
because Paraguay still has a low per capita income compared to its neighbors and continues to
suffer from a high degree of inequity and poverty.
The high volatility of GDP growth has coincided with a volatile macroeconomic environment.
A large number of relevant economic variables and variables with economic significance have
shown high levels of volatility in recent years, including the world interest rate, Paraguay’s
nominal exchange rate, its current account balance, public consumption and investment, credit to
the private sector, agricultural GDP, rainfall and, soy prices. While the agricultural sector was
particularly affected, preceded by an increase in the volatility of soy prices and rainfall, nonagricultural GDP actually registered a decrease in volatility.
External shocks explain over 50 percent of Paraguay’s GDP growth volatility. A key factor
behind volatility in Paraguay is the strong dependence on agriculture and its concentration on few
products and few export destinations, both of which have increased over time. Of the external
shocks, foreign demand for Paraguayan output accounts for about 30 percent of GDP volatility,
the world interest rate for 20 percent, and terms of trade for 3 percent. The impact of the world
interest rate runs through its impact on portfolio reallocation, commodity prices, and economic
conditions of main trading partners. Fluctuations in commodity prices and world real interest rates
have hit all countries in the region and the world. However, their output response reflects the
interaction of these shocks with country-specific conditions that range from the strong dependence
on a few goods and services or a narrow tax base and economic policies.
1
A large body of literature has addressed this topic from various perspectives Acemoglu et al (2003), Aizenman and
Pinto (2005), Berument, Dincer, and Mustafaoglu (2011), Ramey and Ramey (1995) and Wolf (2005).
2
See for example Breen and Garcia-Penalosa (2004), Garcia-Penalosa and Turnovsky (2004) or Huang, Fang, and
Miller (2012).
2
Domestic variables explain the remaining share. 25 percent of stemming from shocks to real
GDP, 15 percent from shocks to investment, and 3 percent from pro-cyclical fiscal and monetary
policy. Pro-cyclicality is defined as a positive response of government spending to an exogenous
expansionary business cycle shock. In developing countries it is usually linked to limited access
to credit in downturns, to lax fiscal stances in good times, to and burdensome bureaucratic
processes. While Paraguay shares this pattern there is an indication that the fiscal stance was
counter-cyclical during the contractions in 2009 and 2012. In contrast to overall GDP volatility,
fluctuations of agricultural GDP originate to a large extent from domestic shocks, with weather
related shocks to agricultural output itself accounting for more than a third.
Despite a significant decrease, persistent rigidities in factor markets and limited mobility
across sectors reduce the economy’s capacity to buffer shocks and exacerbate business cycle
fluctuations and hence volatility. While labor market distortions have declined, firms’ access to
credit have improved, and agricultural efficiency has increased, important challenges remain
which suggest that shocks hitting Paraguay may rather be exacerbated than buffered. In particular,
labor and capital returns between agriculture and non-agriculture remain large, suggesting limited
factor mobility across sectors, financing constraints facing households have remained pronounced
and time-varying and the efficiency in the non-agricultural sector has shown no signs of
improvement, to the contrary has been deteriorating. These remaining frictions reduce efficiency
of the Paraguayan economy and prevent its capacity to buffer shocks that hit the economy.
Growth volatility impacts Paraguay’s economy in various ways, it impacts: i) the agricultural
sector; ii) other sectors; and iii) macroeconomic aggregates such as investment, tax revenues,
or poverty and equity. The main sources of volatility in the agricultural sector can be categorized
into shocks to production and shocks to markets. Shocks to production include variations in
rainfall, investment levels, and disease outbreaks (e.g., foot and mouth disease). Shocks to markets
include commodity price variations, the closing of markets in the case of disease outbreaks, and
fluctuations of prices of imported inputs like fertilizers and pesticides. Overall, market participants
in Paraguay report that a lack of information and knowledge on the patterns and impacts of
volatility on the economy is the biggest challenge to operating within this environment. Within the
agricultural sector sources of volatility manifest themselves through shocks that impact the level
of infrastructure and R&D investment; cause payment delays; and trigger the use of diversification
strategies. In terms of other sectors, the volatility of agricultural GDP mainly affects those
economic activities that provide inputs such as machinery or storage and transport services, but it
also affects the financial services and construction sectors. In terms of macroeconomic aggregates,
the exchange rate and levels of employment fluctuate as a consequence of shifts in agricultural
exports. There is some indication that private consumption plays a role in propagating the impact
of agricultural GDP through the economy, impacting non-agricultural GDP. Investment levels are
lower, fiscal revenues are indirectly affected, and the reduction of poverty and inequity are
generally slower than in countries with lower levels of volatility.
In terms of managing volatility, it is important to develop a comprehensive macroeconomic
risk management framework that takes all different sources of volatility and risks into
account. Sources of volatility are interrelated and taking a broader perspective allows finding
optimal ways to manage observed volatility and risks. Any policy option needs to be assessed in
terms of its fiscal implications; be it in terms of its effects on sustainability, on redistribution, or
on potential contingent liabilities.
3
Policy options are presented as: i) a macroeconomic tool set; and ii) an agricultural risk
management tool set, which need to be aligned with one another in an overall framework.
Macroeconomic policy options include the development of a strategy on the role of agriculture in
the economy and its structure; policies that render factor markets flexible; and fiscal policies such
as the introduction of fiscal rules and stabilization funds. The agricultural risk management tool
box is designed to address the shocks to production and to markets specific to the agricultural
sector. First, four case studies are presented on new tools and approaches to mitigate, cope with,
and transfer agricultural production risks: i) building animal health capacity to prevent foot and
mouth disease in Colombia; ii) introducing weather derivatives based on a rainfall index for severe
drought in Malawi; iii) establishing a weather contingency fund for the agricultural sector
(CADENA) in Mexico; and iv) implementing an index-based livestock insurance project in
Mongolia. Second, three case studies provide examples of measures to mitigate and transfer
agricultural market risks: i) developing the asparagus market in Peru; ii) introducing subsidies for
commodity price hedging contracts in Mexico; and iii) introducing agricultural commodity
exchanges in Argentina. While all case studies have been selected based on their relevance for
Paraguay, a careful assessment of their applicability to Paraguay would be required as part of an
overall assessment of agricultural risks. Government and the World Bank have been engaging in
a dialogue on this topic through the preparation of this study and with a joint agricultural risk
management assessment.
4
Chapter 1: The Sources of Volatility in Paraguay
Paraguay’s real GDP growth has been one of the most volatile in the region in recent years. This
was not always the case. During the period 1960-2011, growth volatility in Paraguay was lower
than in other countries in the region and many of these countries managed to reduce volatility (see
Table Annex 1). The high level of volatility in Paraguay is concerning because of the significant
costs associated with it in terms of welfare, economic growth, and equality.3 Designing policies
that help mitigate the impact of shocks to the economy and that increase the country’s resilience
is particularly relevant in this light, and also because Paraguay still has a low per capita income
compared to its neighbors and suffers from a persistently high degree of inequality and poverty.
The purpose of the current study is to contribute to a deeper understanding of growth volatility in
Paraguay and to provide an input into the discussion on how to better manage it. In particular,
the study will ask three questions: i) what are the sources of volatility in Paraguay? ii) How does
growth volatility, in particular that arising from the strong dependence on the agricultural sector,
impact the rest of the economy? iii) What are optimal policies for managing the types of volatility
observed in Paraguay?
This study’s quantitative analyses mainly rely on quarterly data available since the first quarter
of 1994 (earliest available) and allow insights primarily into business cycle volatility. Wherever
possible the study also shows a longer-term perspective based on yearly data. However, data
restrictions do not allow for a rich analysis of these long-term volatility trends for Paraguay, and
from a policy perspective business cycle volatility appears more relevant.
The current study seeks to provide answers to the questions identified in three chapters: i) the first
chapter covers the sources of volatility. It provides a description of stylized facts and an analysis
of the sources based on structural vector autoregression (SVAR) analysis and a business cycle
accounting exercise. ii) The second chapter addresses the effects of volatility with a particular
focus on volatility arising from a strong dependence on the agricultural sector. This chapter is
based on a qualitative analysis relying on 25 structured interviews with key players in the
Paraguayan economy, as well as on a quantitative approach based on VAR analysis and a wavelet
approach. iii) The third chapter presents policy options for managing volatility. In particular, it
provides an overview and a discussion of cases of other Governments that have successfully
managed volatility, similar to that observed in Paraguay.
3
See for example Loayza, Ranciere, Serven, and Ventura (2007), Athanasoulis and van Wincoop (2000), World Bank
(2000) on the impact of volatility on welfare, Hnatkovska and Loayza (2005) and Calderon and Schmitt-Hebbel (2003)
and Berument, Dincer, and Mustafaoglu (2011) on the growth impact, and Breen and Garcia Penalosa (2004), GarciaPenalosa and Turnovsky (2004), or Huang, Fang and Miller (2012) for the impact on equality.
5
1.1.
Stylized facts
Paraguay’s GDP growth has been one of the most volatile in the region. The high volatility of
GDP growth has coincided with a volatile macroeconomic environment. A large number of
relevant economic variables have shown high levels of volatility in recent years, including the
world interest rate, 4 Paraguay’s nominal exchange rate, its current account balance, public
consumption and investment, credit to the private sector, agricultural GDP, rainfall and soy
prices. While the agricultural sector was particularly affected, preceded by the increase in the
volatility of soy prices and rainfall, non-agricultural GDP actually registered a decrease in
volatility.
Figure 1.1: Real GDP growth
14
12
10
8
Percent
6
4
2
0
-2
-4
2013*
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
1963
1961
1959
1957
1955
1953
1951
-6
Source: Central Bank of Paraguay.
Note:*Projection.
Real growth in Paraguay has been more volatile than most other countries in Latin America
and other regions in the past decade. One commonly used definition of volatility of economic
growth is the standard deviation of real GDP growth rates or of the cyclical components of GDP. 5
According to these measures, Paraguay’s growth volatility was much lower than that of Latin
American peer countries in the period from 1960 to 2000, whereas growth has been more volatile
than most other countries in Latin America in the last decade. Real GDP growth varied by 4
percentage points in the period from 1960 to 2000, in the past decade it had a standard deviation
of 5.5 (Table Annex 1.1). In contrast, many other countries in the region managed to reduce
volatility, explaining the drop in the regional mean and median from 4.7 and 4.5 to 3.1 and 2.8
respectively. Paraguay’s MERCOSUR neighbor Brazil managed to reduce the variation of its real
GDP from a standard deviation of 4.5 from the 1960 to 2000 period to 2.3 in the past decade, a
pattern that the country shares with Bolivia, Chile, Colombia, Ecuador, Mexico and Peru. Also,
the East Asia and Pacific region, Middle East and North Africa, Sub-Saharan Africa, South Asia,
4
Measured by the U.S. 3-month treasury bill rate.
See for example Loayza, Ranciere, Serven, and Ventura (2007), Perry and Fiess (2006), Alouini and Hubert (2010),
or Furceri and Karras (2007).
5
6
and Paraguay’s peer group of lower middle income countries have all seen a reduction in growth
volatility in the past decade (Figure 1.2). While countries in Europe and Central Asia, OECD
members, and higher middle income countries share the trend of higher volatility in the period
from 2000 to 2011, Paraguay’s growth volatility exceeds the ones in these regions. In fact,
Venezuela, Argentina, and Uruguay are the only three countries in the period from 2000 to 2011
that have observed higher levels of growth volatility than that experienced by Paraguay.
Figure 1.2: Volatility over time, Paraguay in regional comparison
Panel a: Standard deviation (GDP growth)—1960-1999
Panel b: Standard deviation (GDP gap)— 1960-1999
Paraguay
Paraguay
LAC mean (excl. Paraguay)
LAC mean (excl. Paraguay)
LAC median (excl. Paraguay)
LAC median (excl. Paraguay)
East Asia & Pacific (all income…
East Asia & Pacific*
Europe & Central Asia*
Europe & Central Asia*
Middle East & North Africa*
Middle East & North Africa*
South Asia
South Asia
Sub-Saharan Africa*
Sub-Saharan Africa*
Lower middle income
Lower middle income
Upper middle income
Upper middle income
OECD members
OECD members
0
1
2
3
4
5
6
0
Panel c: Standard deviation (GDP growth)—2000-2011
1
2
3
4
5
6
Panel d: Standard deviation (GDP gap)— 2000-2011
Paraguay
Paraguay
LAC mean (excl. Paraguay)
LAC mean (excl. Paraguay)
LAC median (excl. Paraguay)
LAC median (excl. Paraguay)
East Asia & Pacific (all income…
East Asia & Pacific*
Europe & Central Asia*
Europe & Central Asia*
Middle East & North Africa*
Middle East & North Africa*
South Asia
South Asia
Sub-Saharan Africa*
Sub-Saharan Africa*
Lower middle income
Lower middle income
Upper middle income
Upper middle income
OECD members
OECD members
0
1
2
3
4
5
6
0
1
2
3
4
5
6
Source: World Development Indicators, and Central Bank of Paraguay, staff calculations.
Note:*All income levels.
While Paraguay has experienced clusters of high volatility in the past, fluctuations never
quite reached the levels observed in recent years. In 2009, the economy contracted by 4 percent,
the worst outcome in Paraguay’s recorded history, and rebounded to 13.1 percent in 2010, the best
outcome ever observed. Shortly thereafter, Paraguay has been experiencing a similar pattern of
extreme fluctuation, with a -1.2 contraction in 2012 and a projected recovery of 10.5 percent in
2013. This translates into extreme year-on-year (y-o-y) changes in terms of percentage points of
growth: drops of 10 and 9 percentage points in 2009 and 2011 respectively, and increases of 17
and 14 percentage points in 2012 and 2013 respectively. The period in the past that comes closest
to these extreme fluctuations is the end of the 1970s and the beginning of the 1980s, which
coincides with the construction and then completion of the Itaipú dam and hydro power plant (from
7
1975 to 1982). Growth dropped from 9.2 percent in 1981 to -1.4 in 1982. This period represented
a transition between a period of high growth due to the impulse of the Brazilian Paraguayan
construction project, approximately 4 times the size of Paraguay’s GDP at the time, and the period
of low growth thereafter. Yet, GDP did not oscillate between sharp contractions versus fast
expansions from year to year.
Figure 1.3: GDP—breakpoints of volatility of Figure 1.4: Agricultural GDP—breakpoints
quarterly y-o-y GDP growth using Inclan, of volatility of quarterly y-o-y GDP growth
Tiao (1994)
using Inclan, Tiao (1994)
8
5
4
2
3
2
-3
1
GDP
breaks
15
16
6
10
-4
5
-14
-24
0
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
0
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
-8
20
26
Percent growth rate
7
36
St ddev. of GDP growth
6
Stddev. of agriculture GDP growth
7
12
Percent GDP growth
25
stddev.
Agriculture GDP
Source: World Bank calculations based on Central
Bank, Paraguay.
breaks
stddev.
Source: World Bank calculations based on Central
Bank, Paraguay.
Three other past periods displayed significant y-o-y fluctuations, however not at the levels
observed in the most recent past. First, between 1955 and 1961 GDP growth rates dropped twice
by over 5 percentage points and also expanded twice by over 5 percentage points at the beginning
of the Stroessner dictatorship (1954–1989) and after a period of high inflation (Cubas, Escobar,
Franco, Olmedo, and Smith (2011)). Second, between 1966 and 1968, political uncertainties and
some elements of democratization contributed to strong variations in economic growth. Between
1995 and 2002 Paraguay underwent a period of recurrent financial crises that went hand in hand
with substantial fluctuations in growth. The biggest y-o-y change occurred between 1995 and 1996
when growth dropped from 6.8 percent in the first year to only 1.5 in the latter.
A closer look at quarterly y-o-y real growth rates reveals that high volatility is a very recent
phenomenon, with a significant increase in the fourth quarter of 2008 (Figure 1.3). The study
relies on Inclan and Tiao (1994) to identify structural breaks in the volatility of the analyzed time
series, a method that performs well with the type of data used.6 Before the break, the standard
deviation of quarterly y-o-y growth since the first quarter of 1994 amounts to 4 percentage points,
after the break it shifts up to 7 percentage points.
The dynamic of GDP growth mirrors an increase in the volatility of agricultural GDP in the
fourth quarter of 2008, a sector whose weight has increased over time. The Inclan Tiao test
6
The Inclan-Tiao test is characterized by its simplicity and independence from estimated long-run variance, which
make the test robust to time period selection, and it also performs well with shorter time series compared to other tests
such as Kokoszka-Leipus (2000) or Quandt (1960) and Andrews (1993).
8
applied to agricultural GDP growth reveals a break point in volatility also in the fourth quarter of
2008 (Figure 1.4). Before the fourth quarter of 2008, the standard deviation of real agricultural
GDP growth was 6 percentage points, afterwards it shot up to 22. While the share of agriculture in
total GDP amounted to about 12 percent in the second half of the 1990s it increased significantly
to over 18 percent in 2010 and 2011 (Figure 1.5).
Figure 1.5: Share of agriculture in GDP
Figure 1.6: Growth volatility by economic
sector
20-qurater standard deviation of
quarterly y-o-y growth
25
15
10
5
20
15
10
5
Secondary
2012:III
2011:I
2011:IV
2010:II
2009:III
2008:I
2008:IV
2007:II
2006:III
2005:I
2005:IV
2004:II
2002:I
Primary
Source: WDI.
2003:III
LAC
2002:IV
1999:IV
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Paraguay
2001:II
0
0
2000:III
Percent of GDP
20
25
Tertiary
Source: BCP.
An increase in rainfall volatility preceded the breakpoint of agricultural GDP volatility, this
is relevant given that only 2 percent of agricultural surfaces are cultivated using irrigation.
The instability of rainfall increased in 2007 and remained high until 2013 (Figure 1.7).7 Given the
agricultural production cycle in Paraguay, rainfall from December the previous year and January
of the current year are particularly relevant for the harvest. Figure 1.6 displays annual data
aggregating rain data from those months. The correlation between the cyclical component of
rainfall and agriculture GDP is 0.69, showing the high dependence of agriculture production on
weather conditions (see Figure 1.8.). Despite the high and increasing correlation between the
climate cycle and agricultural production, only 2 percent of the surface used for agricultural is
irrigated.8
7
8
No formal volatility break test was applied because the length of the yearly time series does not allow for it.
United Nations Development Program (2006).
9
Figure 1. 8: Rainfall and agriculture GDP
1800
2500
1600
2000
1400
1500
1200
1000
5
500
0
1000
800
600
400
15
10
0
-500
-5
-1000
-10
-1500
200
-15
-2000
0
Rain (cycle)
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
1991
-20
1993
-2500
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Percent of GDP
Milimeters
Standard deviation
Figure 1. 7: Volatility of rainfall
Agriculture cycle (cycle as a share of GDP, RHS)
Source: DINAC and BCP. Rain is considered for the months of December from the previous year and January of the
same year because of their importance in the main agricultural soy product. All weather stations of the Eastern part
of Paraguay are considered.
Paraguay’s agricultural sector concentrates on a few products and export destinations. Soy
and beef alone made up an average of 34 percent of total exports over the past 5 years; and exports
to Brazil and Argentina alone reached almost 50 percent of total exports in the period since 2008
(Figure 1.9 and Table 1.1).
Overall, agricultural GDP in Paraguay is much more volatile than the aggregate or than
non-agricultural GDP. Quarterly y-o-y agricultural GDP growth has fluctuated by 12 percentage
points since the first quarter of 1994, contrasting with overall GDP that showed a standard
deviation of 4.7 percentage points, and with non-agricultural GDP which varied by 4 percentage
points (Figure 1.5). In particular, the wedge between agricultural and non-agricultural GDP
volatility has increased in recent years because non-agricultural GDP is one of the few
macroeconomic aggregates that have not become more volatile but have remained relatively stable.
The Inclan Tiao test did not identify any structural break in the volatility of this variable.
Despite these facts the growth of agricultural GDP has exceeded the growth of aggregate
GDP and of non-agricultural GDP. Quarterly y-o-y growth of agricultural GDP amounted to 4
percent compared to 2.7 percent of aggregate GDP and 2.5 percent of non-agricultural GDP (Table
Annex 1.2).
Paraguay’s macro-economic environment has become more volatile with shifts in the
volatility of soy prices and world interest rates preceding those of other variables. Most
macroeconomic variables underwent an increase in volatility during 2007 and 2008, some even
increased before that (Table Annex 1.2). In light of the potential interaction between different
economic aggregates, an interesting sequence is that soy prices became more volatile in the third
quarter of 2003, and world real interest rates became more volatile in the fourth quarter of 2007,
and that these increases preceded the increases of volatility in the nominal exchange rate in the
first quarter of 2008; of public investment in the second quarter of 2008; of overall GDP and
agricultural GDP in the fourth quarter of 2008; and of public consumption in the first quarter of
2009. Also the increase in the volatility of the current account balance in the first quarter of 2007
was preceded by an increase in volatility in soy prices. It is also important to note that there were
additional increases, like that of inflation in the second quarter of 1995; public consumption in the
10
second quarter of 2000; or credit to the private sector in the fourth quarter of 2002. This illustrates
the need to carefully assess causalities and to take into account relevant additional factors in an
analytical assessment when searching for the sources of growth volatility in Paraguay. Such
analysis is provided in the second section of this chapter.
Figure 1. 9 Exports by product
80
40
60
30
40
20
20
10
0
0
Percent of GDP
50
average
since 2000
average
since 2008
Total
Continental Rest of the
Uruguay MERCOSUR
China
World
Argentina
Brazil
13
47
6
66
1
33
11
37
1
49
1
50
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Percent of total exports
100
Table 1.1: Export by destination
Other
Beef
Electricity
Grains
Soy beans
Total in percent of GDP(RHS)
Source: Central Bank, Paraguay.
There are only a few exceptions to this increase in volatility; the main exception is tax
revenues. Variables whose volatility has not changed over the period since the first quarter of
1994 include total and private investment, private consumption, oil prices, non-agricultural GDP,
Paraguay’s real interest rate, the real exchange rate, total Government revenues, beef prices, and
terms of trade. The only variable that displayed a decrease in volatility that was not followed by a
later increase is tax revenues. The single breakpoint is the third quarter of 2004 coinciding with
the 2004 tax policy and administration reform in Paraguay.
While it is too early to tell whether high volatility in Paraguay is temporary or permanent,
and there is no indication of a causal link explaining the increase in volatility, some structural
changes are in line with the idea that volatility is there to stay. In particular, the increased
weight of the agricultural sector and its concentration on a few products exposes the economy
more to fluctuations in rainfall which seem to be increasing in the context of climatic change. It
also renders the economy vulnerable to fluctuations in commodity prices. Yet, it is unclear whether
commodity prices will remain as volatile as they have displayed clusters of heightened volatility
in the past.9
1.2.
Sources of volatility
External shocks are the main source of growth volatility in Paraguay, with foreign demand
explaining around 30 percent of GDP volatility, 20 percent is explained by the world interest rate,
and 3 percent comes from terms of trade. Domestic variables also contribute to the volatility with
25 percent stemming from shocks to real GDP, 15 percent from shocks to investment, and 3 percent
9
See Calvo-Gonzalez, Shankar, and Trezzi (2010).
11
from pro-cyclical fiscal and monetary policy. Pro-cyclicality is defined as a positive response of
government spending to an exogenous expansionary business cycle shock. In developing countries
it is usually linked to limited access to credit in downturns, lax fiscal stances in good times, and
burdensome bureaucratic processes. While Paraguay shares this pattern there is indication that
the fiscal stance was counter-cyclical during the contractions in 2009 and 2012. In contrast to
overall GDP volatility, fluctuations of agricultural GDP originate to a large extent from domestic
shocks, with weather related shocks to agricultural output itself accounting for more than 50
percent.
About half of Paraguay’s GDP growth volatility stems from external shocks while the other
half originates in domestic shocks. These findings emerge from a variance decomposition based
on a structural VAR analysis guided by economic theory for model specification. Hnatkovska and
Koehler-Geib (2013) presents a detailed description of the model specification and a rational for
variable selection. The variables included in the VAR are chosen to capture those factors identified
in the literature as important determinants of business cycles in developing countries. External
variables are: world interest rate (as measured by the US 3 month treasury bill rate), and foreign
demand (as measured by the trade weighted GDP of Argentina, Brazil, Chile and Uruguay, the
remaining export share is allocated to the US and its GDP).10 Domestic variables are: real GDP,
total investment, Government consumption as measure of fiscal policy, domestic short-term
interest rate as a measure of monetary policy, and the real trade balance to GDP ratio. The weights
of the different variables reflect the variance decomposition at 12 quarters which is where the
percentages for most cases stabilize and is the midpoint of the of the range between 6 and 32
quarters that researchers typically define as frequencies for business cycle movements (Baxter and
King (1999)) (Table 1.2).11
Table 1.2 Variance decomposition of GDP volatility
Quarters
1
TOT
0.043
r_us
0.058
Log(yf)
0.071
Log(gc)
0.004
Log(inv)
0.248
Tby
0.047
r
0.022
Log(y)
0.506
External
0.173
Domestic
0.827
4
0.026
0.143
0.308
0.011
0.163
0.062
0.012
0.274
0.477
0.523
8
0.024
0.191
0.300
0.019
0.148
0.058
0.011
0.248
0.515
0.485
12
0.025
0.195
0.299
0.020
0.147
0.058
0.011
0.247
0.518
0.482
16
0.025
0.195
0.299
0.020
0.147
0.058
0.011
0.247
0.518
0.482
20
0.025
0.195
0.299
0.020
0.147
0.058
0.011
0.247
0.518
0.482
Source: Hnatkovska and Koehler-Geib (2013)
10
Kose, Otrok, and Prasad (2012) and Kose, Otrok, and Whiteman (2008) find a stronger role of domestic factors.
This difference appears to mainly stem from a sample period that only goes up until 2005 only. Raddatz (2007) also
finds a predominant role of external variables in a paper that examines whether the differences in output volatility
between Latin America and other regions result from volatility of external shocks or from a more pronounced
response to these. Podpiera and Tulin (2012), focusing on the role of financial external variables find a relevant role
of external factors.
11
Table 1.2 presents the results of the variance decomposition based on the estimated SVAR system at different
horizons. The variance decomposition allows quantifying the contribution of each shock to the variance of forecasting
error for output.
12
The most important external factor, foreign demand, explains around 30 percent of
Paraguayan growth volatility, and terms of trade another 3 percent, a fact that may be
related to Paraguay’s agricultural sector. The significance of these shocks may arise from the
sector’s weight in the economy and its concentration on a few export products (see Section 1.1).
As argued in Hnatkovska and Koehler-Geib (2013) concentration in terms of products and export
destinations renders countries more vulnerable to terms of trade and foreign demand shocks.
Figure 1.11: Impulse response of trade balance
to a shock to the world interest rate
.06
.15
Figure 1.10: Correlation between trade balance
and world interest rate
r_us shock
.1
TB/GDP
0
.05
.002
.02
Reat int. rate, world
.04
.004
-.02
0
0
-.05
-.04
-.002
1995q1
2000q1
TB/GDP
2005q1
0
5
10
15
20
quarters
2010q1
95% CI
Reat int. rate, world
IRF
Graphs by irfname, impulse variable, and response variable
Source: Hnatkovska and Koehler-Geib (2013)
The second most important external factor, the world interest rate, explains around 20
percent of Paraguayan growth volatility through its impact on portfolio reallocation, on
commodity prices, the economic condition of trade partners, and remittances. At a first glance
it may appear surprising that GDP growth of a country which is not heavily represented in
international capital markets varies this much with the international interest rate. Yet, there are
mainly four channels that explain the link. First, when international interest rates go up, foreign
investors may shift out of Paraguayan assets inducing a contraction or even a reversal of capital
inflows. Calvo, Leidermann, and Reinhart (1993) and Gavin, Hausmann, and Leidermann (1995)
show these “pull” effects for emerging economies. The positive correlation between Paraguay’s
trade balance and the world interest rate of 0.2, as well as the significant positive effect in the
impulse response of the trade balance to an interest rate shock controlling for other shocks, suggest
that this channel is relevant for Paraguay (Figures 1.10, 1.11 and Table 1.3).12 Second, the world
interest rate variable may pick up some of the effects of commodity price changes on the
Paraguayan economy despite controlling for terms of trade in the SVAR analysis.13 One reason
for the link between world real interest rates and commodity prices is that higher interest rates
reduce the speculative demand for commodities inducing lower prices (see for example Frankel
12
The trade balance functions as a proxy for net international financial flows. The two are closely related through
the balance of payments identity as trade has to be balanced every period in the absence of international financial
flows.
13
The recursive identification scheme does not allow for a contemporaneous correlation between the two variables.
Note that changing the order of the two variables in the SVAR will not resolve the simultaneity problem. In fact, the
results remain robust to a change in the ordering of the two variables.
13
(2008)).14 Figure 1.12 shows the negative relationship between the international interest rate and
the TOT for Paraguay. Indeed, the correlation is equal to -0.44 during the sample period (Table
1.3). Third, a higher world interest rate may also affect the Paraguayan economy indirectly by
weakening the economic conditions of its major trade partners – Brazil, Argentina, Chile, Uruguay
– who are significantly exposed to the world financial markets through sovereign borrowing. The
foreign demand variable used in the SVAR analysis may not fully capture demand shocks from
these countries if nominal rigidities exist in the goods or factor markets, if a significant informal
sector exists, or if supply and demand shocks are correlated. Fourth, a higher world interest rate,
may lead to lower inflows of remittances to Paraguayan households from abroad through its impact
on trading partners.
While external shocks like those to commodity prices and world real interest have hit all
countries, their output response reflects country specific conditions. In particular, the strong
dependence on a few goods and services, a narrow tax base, and economic policies seem to play a
role in the propagation of external shocks and their output response in Paraguay.
100
-.04
-.02
0
.02
Real int. rate, world
150
Terms of trade (Pex/Pim)
.04
200
.06
Figure 1.12: Correlation between TOT and world interest rate
1995q1
2000q1
Real int. rate, world
2005q1
2010q1
Terms of trade (Pex/Pim)
Source: Hnatkovska and Koehler-Geib (2013)
Table 1.3 Correlations across variables
GDP
GDP agri
GDP non-agri
Inv
Int rate
Int rate US
GDP foreign
TOT
Gov cons
Gov inv
GDP
1
0.39
0.97
0.89
-0.17
0.13
0.82
-0.56
0.85
0.10
GDP
agri
GDP
nonagri
Inv
Int
rate
Int
rate US
GDP
foreign
TOT
Gov
cons
Gov
inv
1
0.17
0.33
0.00
-0.15
0.30
0.08
0.01
-0.18
1
0.87
-0.18
0.18
0.80
-0.63
0.91
0.16
1
-0.23
0.00
0.74
-0.35
0.86
0.02
1
0.41
-0.23
0.25
-0.09
0.14
1
0.24
-0.44
0.09
0.02
1
-0.74
0.65
-0.03
1
-0.42
0.07
1
0.26
1
Source: Hnatkovska and Koehler-Geib (2013)
14
A large economic literature analyzes the theoretical and empirical link between world interest rates and interest
(see for example Calvo (2008), Ratnovski and Mihet (2012), Byrne, Fazio, and Fiess (2012), and Frankel and Rose
(2010)).
14
In terms of domestic variables, 25 percent of GDP volatility stems from shocks to real GDP,
15 percent from shocks to investment, and 3 percent from pro-cyclical monetary and fiscal
policies. Investment in Paraguay is one of the most volatile domestic variables (Table Annex 1.2)
and is highly pro-cyclical with a correlation between the cyclical components of investment and
GDP amounting to 0.9 (Table 1.3). Monetary policy is captured by the short-term real interest rate
and explains 1 percent of GDP volatility. Recent work shows that real interest rates tend to be
counter-cyclical in developing countries, while they tend to be pro-cyclical in developed
economies (see, for instance, Neumeyer and Perri (2005), Uribe and Yue (2005)). A prominent
explanation for pro-cyclicality includes distortions in factor markets: for example, firms may have
to pay for part of the factors of production before production takes place, creating a need for
working capital. This is also the case for Paraguay, where GDP and the real interest rate are
negatively correlated, with an unconditional correlation equal to -0.2. This correlation, however,
is somewhat smaller than the corresponding number in the other Latin American countries: it is
equal to -0.63 in Argentina; -0.49 in Mexico; and -0.38 in Brazil (see Neumeyer and Perri (2005)).
The fiscal policy stance is captured by Government consumption and investment which could be
used as tools for counter-cyclical policy. Yet data shows that they have not been counter-cyclical
in Paraguay. From 1994 to 2011 the correlation between the cyclical components of government
consumption and GDP is 0.9; this explains 2 percent of GDP volatility in Paraguay. Government
investment is less pro-cyclical and has a correlation with GDP of 0.1.
.1
GDP (log)
1995q1
2000q1
GDP (log)
2005q1
-.1
-1
-.1
-.05
-.05
-.5
0
0
.05
.05
.5
.1
Figure 1.14: Business cycle fluctuations in
Paraguay—Government investment versus
GDP
0 (log)
Gov Inv
GDP (log)
.1
0
-.1
-.2
Gov Cons (log)
.2
.3
Figure 1. 13: Business cycle fluctuations in
Paraguay—Government consumption versus
GDP
2010q1
1995q1
Gov Cons (log)
2000q1
GDP (log)
2005q1
2010q1
Gov Inv (log)
Source: Hnatkovska and Koehler-Geib (2013)
Pro-cyclicality of fiscal policy in developing countries is usually linked to limited access to
credit in downturns, lax fiscal stances in good times, and burdensome bureaucratic
processes. Pro-cyclicality is defined as a positive response of Government spending to an
exogenous expansionary business cycle shock. Gavin and Perotti (1997) showed that this is the
case in Latin America. Talvi and Végh (2005) then claimed that pro-cyclical fiscal policy is not
only a Latin American phenomenon, it is present in the entire developing world. In a recent study,
Ilzetzki and Végh (2008) revisit the evidence using a sample of 49 countries while allowing for a
reverse causality running from fiscal policy to GDP. They show that fiscal policy is indeed procyclical in developing countries. One reason for this pro-cyclicality could be frictions in
international credit markets that prevent developing countries from borrowing in bad times ((Gavin
and Perotti (1997), Caballero and Krishnamurthy (2004), Mendoza and Oviedo (2006), and
15
others); another reason originates from a political economy perspective, and proposes that good
times encourage fiscal profligacy ((Tornell and Lane (1998), Talvi and Végh (2005), and others);
the third reason rests in delays in the implementation and execution of fiscal policies in developing
economies.
While fiscal policies over the last two decades were pro-cyclical in Paraguay, a look at the
data suggests that they were counter-cyclical for a short period of time during the
contractions of 2009 and 2012. Public sector demand expanded when private demand, and as a
consequence economic growth, collapsed in all four quarters of 2009 (see Figure 1.15). The
decomposition of real growth into the components of aggregate demand reveals that public demand
components together contributed positively to real growth in the four quarters of 2009 while
private demand contracted heavily (see Figure 1.15).15 The expansion of public demand was based
on strong increases in both public investment and consumption (see Figures 1.13 and 1.14). The
reason for policies being counter-cyclical during the 2009 crisis was that Paraguay had built up
buffers through prudent fiscal policies in prior years and had access to financing, from international
institutions. Paraguay shares this pattern of a recent move towards counter-cyclical fiscal policy
with other developing countries. Vegh and Vuletin (2013) document that around one third of
developing countries were able to conduct countercyclical fiscal policy over the last decade.
However, public demand ceased to be counter-cyclical by the first quarter of 2010, when it
expanded at the same time as private demand was already recovering strongly. Only in the third
quarter of 2010 did public demand contribute negatively amidst a rapid private sector expansion.
A similar pattern can be observed in 2012 when there was the same challenge of withdrawing
expansionary expenditure fast enough as private sector growth was recovering.
Percent y-o-y real growth and
percentage contribution
Figure 1.15:Contribution of public and private demand components to real GDP growth
20
15
10
5
0
-5
-10
Public demand
Private Demand
2012 Q3
2012 Q1
2011 Q3
2011 Q1
2010 Q3
2010 Q1
2009 Q3
2009 Q1
2008 Q3
2008 Q1
2007 Q3
2007 Q1
2006 Q3
2006 Q1
2005Q3
2005Q1
2004 Q3
2004 Q1
2003 Q3
2003 Q1
2002 Q3
2002 Q1
2001 Q3
2001 Q1
-15
GDP growth
Source: Central Bank of Paraguay.
In terms of sectors, variations in agricultural GDP contribute one quarter to overall GDP
volatility; three quarters are explained by non-agricultural GDP. With the help of variance
decomposition Hnatkovska and Koehler-Geib (2013) find that a 1 percent increase in aggregate
15
Public demand components comprise public consumption, public investment, and the share of the public sector in
imports and changes in inventories.
16
GDP is accompanied by a 0.25 percent increase in agricultural GDP and 0.75 percent increase in
non-agricultural GDP. This finding is consistent with the high volatility of agricultural GDP in
recent years and the fact that agriculture has a share of about 20 percent in overall GDP. So while
more volatile, the contribution of agricultural GDP remains limited by its size in aggregate GDP.
Table 1.4: Variance Decomposition of Agricultural and Non-Agricultural GDP volatility
Agriculture
quarters
TOT
r_us
Log
(yf)
Log
(gc)
Log
(inv)
Tby
r
Log
(yA)
Log
(yNA)
Ext.
Dom.
1
0.047
0.010
0.031
0.053
0.031
0.000
0.009
0.819
0.000
0.088
0.912
4
0.038
0.023
0.208
0.149
0.042
0.015
0.010
0.513
0.001
0.270
0.730
8
0.038
0.026
0.228
0.162
0.040
0.015
0.010
0.479
0.002
0.292
0.708
12
0.038
0.026
0.229
0.162
0.040
0.015
0.011
0.477
0.002
0.293
0.707
16
0.038
0.027
0.229
0.162
0.040
0.015
0.011
0.477
0.002
0.293
0.707
20
0.038
0.027
0.228
0.162
0.040
0.015
0.011
0.477
0.002
0.293
0.707
Non-Agriculture
quarters
TOT
r_us
Log
(yf)
Log
(gc)
Log
(inv)
Tby
r
Log
(yA)
Log
(yNA)
Ext.
Dom.
1
0.185
0.037
0.036
0.014
0.191
0.059
0.061
0.006
0.412
0.258
0.742
4
0.134
0.151
0.170
0.028
0.136
0.070
0.033
0.008
0.270
0.454
0.546
8
0.120
0.221
0.159
0.027
0.124
0.068
0.030
0.010
0.243
0.500
0.500
12
0.120
0.227
0.157
0.027
0.122
0.067
0.030
0.010
0.241
0.504
0.496
16
0.120
0.228
0.157
0.027
0.122
0.067
0.030
0.010
0.240
0.505
0.495
20
0.120
0.228
0.157
0.027
Source: Hnatkovska and Koehler-Geib (2013).
0.122
0.067
0.030
0.010
0.240
0.505
0.495
In contrast to overall GDP volatility, fluctuations of agricultural GDP originate mainly from
domestic shocks, with weather related shocks to agricultural output itself accounting for
almost half. A variance decomposition based on an SVAR specification that includes agricultural
and non-agricultural GDP instead of aggregate GDP in Hnatkovska and Koehler-Geib (2013)
shows that 70 percent of the volatility of agricultural GDP can be attributed to domestic factors,
while the rest is explained by external factors. The most important domestic variable is agricultural
output itself; shocks to this variable account for 47 percent of volatility in agricultural GDP (Table
1.4). While Hnatkovska and Koehler-Geib (2013) provide evidence of a correlation of 0.36
between structural shocks to agricultural output and rainfall, a complementary analysis by
Berument (2013) goes more into detail and finds that agricultural output significantly responds to
shocks to rainfall. Moreover, the analysis shows the significant impacts of international soy and
beef prices. This is not surprising given that Paraguay is a price taker in international commodity
markets.16 Soy prices even impact non-agricultural GDP, an effect which could run through the
indirect impact of disposable income or the inclusion of soy bean processing into the value chain.
Machinery inputs were found not to be relevant. There is no data on land prices in Paraguay, and
therefore the impact of land prices on agricultural output could not be evaluated. Berument (2013)
provides a detailed description of the underlying VAR analysis of weather and price impacts on
agricultural GDP and non-agricultural GDP.
16
Section 2.1 provides a detailed description based on Berument (2013).
17
Shocks to other domestic variables such as investment, Government consumption and real
interest, show similar effects to those for aggregate GDP, except that their impacts on
agricultural GDP are larger. For instance, a 1 percent shock to Government consumption leads
to a 1 percent decline in agricultural GDP. This confirms the idea that pro-cyclical fiscal policy
may have detrimental effects on domestic conditions, especially in agriculture. Also in the case of
the external variables such as terms of trade, world interest rate, and foreign output, the qualitative
effects on agricultural GDP are the same as those on aggregate GDP. Yet quantitatively, the effects
of terms of trade and world interest rate on agricultural GDP were significantly larger. Impulse
responses show for instance that a positive shock to the terms of trade leads to a 1 percent increase
in real agricultural GDP, while it leads to about 0.2 percent increase in aggregate real GDP.
Similarly, unanticipated shocks to the world interest rate lead to a reduction in Paraguayan
agricultural GDP and the effects of these shocks are significant and larger than on aggregate GDP.
The shocks to foreign demand, in contrast, have a somewhat smaller effect on agricultural GDP
than on aggregate GDP.
1.3.
The role of factor markets
Despite a significant decrease, remaining rigidities in factor markets and limited mobility
across sectors reduce the economy’s capacity to buffer shocks and exacerbate business cycle
fluctuations and hence volatility. These findings stem from a model-based examination of the
sources of business cycle volatility in Paraguay covering the period from 1991 to 2010
(Hnatkovska and Koehler-Geib (2013)). More precisely this is a business cycle accounting analysis
based on the methodology of Chari, Kehoe, and McGrattan (2007) introducing time-varying
wedges into a standard neoclassical growth model. The wedges represent frictions and distortions
in labor and capital markets, and shocks to efficiency, government spending and trade balance.
The model is calibrated for the Paraguayan economy to quantify the frictions and evaluate their
contribution to GDP volatility. While labor market distortions have declined, firms’ access to
credit have improved, and agricultural efficiency has increased, important challenges remain
which suggest that shocks hitting Paraguay may rather be exacerbated than buffered. In particular,
labor and capital returns between agriculture and non-agriculture remain large, suggesting limited
factor mobility across sectors and the efficiency in the non-agricultural sector has shown no signs
of improvement, to the contrary has been deteriorating.
Labor market distortions have become less important in Paraguay over the analyzed period.
As argued in Hnatkovska and Koehler-Geib (2013), labor market frictions may arise from payroll
taxes, distortions due to unionization, collective bargaining, hiring and firing costs, or sticky
wages. An analysis of the Doing Business survey available back until 2006 suggests that the
indicator of hiring and firing costs has remained stable over time. At the same time, minimum
apprentice wages have increased substantially in Paraguay during this time. This would be
consistent with the idea that more young workers could be attracted into work force participation
thus reducing frictions in the market.
Firms’ access to credit has improved over time while financing constraints facing households
remain pronounced and time-varying. Financing constraints affecting firms’ investment
decisions have decreased notably with significant volatility around the trend. One way to evaluate
18
this finding would be to look at the dynamics of private credit to businesses and households. The
available data on aggregate credit to the private sector as percent of GDP shows an expansion of
35 percent between 1994 and 2011, suggesting an improvement in credit market conditions in
Paraguay during this period. Moreover the Doing Business survey also provides evidence of
improved credit conditions in Paraguay during the period between 2004 and 2012 (see Hnatkovska
and Koehler-Geib (2013) for more details).
In terms of sectors, the efficiency of agriculture has been continuously increasing, while
efficiency of non-agriculture has been decreasing. These trends reflect the fact that measured
agricultural productivity has been improving during the period from 1991 to 2010, averaging 3
percent per year; while the measured non-agricultural productivity has been falling, averaging 1.5 percent annually. This productivity measure does not only reflect total factor productivity but
also include human capital, weather conditions, omitted inputs, misallocation of resources,
institutional factors, and in fact everything that may lead to inefficient human and physical capital
stocks in each sector.
Significant distortions remain in terms of returns of labor and capital between the
agricultural and the non-agricultural sectors, suggesting that factor mobility remains limited
in Paraguay, preventing the equalization of value marginal products across sectors. The
analysis shows a 5-fold relative gap in favor of non-agricultural labor returns. While a small
improvement after 2005, suggests some recent improvements in workers’ returns in agriculture,
the gap relative to non-agricultural workers remains significant. The gap in sectoral returns to
capital shows more variability, but returns to capital remained in favor of the non-agricultural
sector.
These remaining frictions reduce efficiency of the Paraguayan economy and prevent its
capacity to buffer shocks that hit the economy. In particular, the low factor mobility between
sectors may lead to shocks being exacerbated rather than buffered.
19
Chapter 2: The effects of growth volatility in Paraguay with a focus on
volatility originating in the agricultural sector
Growth volatility impacts Paraguay’s economy in various ways, it impacts i) the agricultural
sector; ii) other sectors; and iii) macroeconomic aggregates such as investment; tax revenues;
and poverty and equity. The main sources of volatility in the agricultural sector can be categorized
into shocks to production and shocks to markets. Shocks to production include variations in
rainfall, investment levels, and disease outbreaks (e.g., foot and mouth disease). Shocks to markets
include commodity price variations, the closing of markets in the case of disease outbreaks, and
fluctuations in the prices of imported inputs like fertilizers and pesticides. Overall, market
participants in Paraguay report that a lack of information and knowledge with regard to the
patterns and impacts of volatility on the economy is the biggest challenge to operating within this
environment. Within the agricultural sector the sources of volatility manifest themselves through
shocks that impact the level of infrastructure and R&D investment; cause payment delays; and
trigger the use of diversification strategies. In terms of other sectors, the volatility of agricultural
GDP mainly affects those economic activities that provide inputs such as machinery or storage
and transport services, but it also affects the financial services and construction sectors. While the
effect of agriculture on the services and construction sectors are small, they are statistically
significant. No significant effects are found running from agriculture to mining and industry or to
the electricity and water sectors. In terms of macroeconomic aggregates, the exchange rate and
levels of employment fluctuate as a consequence of shifts in agricultural exports. There is some
indication that private consumption plays a role in propagating the impact of agricultural GDP
through the economy, impacting non-agricultural GDP. Investment levels are lower, fiscal
revenues are indirectly affected, and the reduction of poverty and inequity are generally slower
than in countries with lower levels of volatility.
The analysis of this chapter relies both on quantitative methodologies in the form of vector auto
regression (VAR) analysis and structured interviews with 25 key players in the Paraguayan
economy both from the agricultural and non-agricultural sectors. The findings of the structural
interviews are in line with the econometric findings. Yet, based on the limited sample size of the
interviews, these findings are considered as supporting evidence rather than stringent proof of the
hypotheses in the analysis. As this study focuses mainly on business cycle volatility it relies on
quarterly data which is available from the first quarter of 1994 until the fourth quarter of 2011.17
Section 2.1 will present the propagation of shocks within the agricultural sector, section 2.2 will
address the impact on other sectors, and section 2.3 will describe those effects of volatility on
macroeconomic aggregates that originate in the agricultural sector.
2.1
Propagation of shocks within agriculture
Production and market shocks associated with climate, levels of investment, sanitary
conditions in the livestock sector, and commodity prices cause volatility in agricultural GDP.
17
See Berument (2013), Berument (2013a) and Hnatkovska and Koehler-Geib (2013) for a detailed description of the
quantitative analysis.
20
Key actors in the agricultural and non-agricultural sectors are very conscious of these sources of
volatility, which are consistent with the findings of the SVAR analysis presented in section 1.2.18
Interviewees for the qualitative study were selected to represent main economic activities and
business occupations. They were grouped into the following categories: i) corporate farming; ii)
companies along the agricultural value chain including production, provision of inputs such as
machinery, and storage; iii) transport; iv) financial services including banking and insurance; iv)
utilities; v) import and export; and vi) think tanks. How do the shocks identified affect the
agricultural sector and how do they percolate through the economy?
Climate impacts agriculture through its effect on productivity and on fluvial transport
conditions. Rainfall and soil temperature create volatility in agricultural production through their
impact on productivity per hectare.19 Rainfall also has an important impact transport through its
impact on the navigability of the Paraguay and Paraná rivers, which are Paraguay’s main means
of transport for bringing agricultural exports to international markets. Wavelet analysis confirms
the impact of rainfall on agricultural output.
Figure 2.1: Wavelet analysis of rainfall and agricultural GDP
4
3
0.8
Period
6
0.6
11
8
0.4
16
0.2
10
20
30
40
50
60
Time
Source: Berument (2013a)
Rainfall leads agricultural GDP by two to three quarters and by seven to eight quarters
(Figure 2.1). These results stem from a wavelet analysis relating the level of quarterly rainfall to
agricultural GDP. Wavelet analysis resembles Fourier type analysis of time series which involves
decomposing a time series into an array of sinusoidal waves and checking for the linkages between
the series of interest at similar wavelength. This allows pinpointing at what frequencies the series
of interest move together, or at what frequencies one time series leads the other. In contrast to
Fourier type analysis, Wavelet analysis does not have an indefinite number of sinusoidal waves,
whereas, a time series is expressed in terms of wavelets (small waves) which have short
durations.20
18
In the SVAR analysis presented in section 1.2, commodity prices are reflected in the terms of trade, private
investment is included directly, and climate, innovation, and sanitary conditions are subsumed as shocks to agricultural
output itself.
19
Due to lack of data on soil temperature this variable could not be included in the quantitative analysis.
20
Berument (2013) provides more details on the methodology.
21
The heat map above summarizes the estimates of the relationship between rainfall and
agricultural GDP. The vertical axis shows the period length, for instance a period length of 4
corresponds to oscillations over 4-quarter periods and a period length of 16 corresponds to
oscillations over 16-quarter periods. The horizontal axis shows time, which is running from
1997Q1 to 2011Q4 in the subsequent analyses. The shift of colors from blue to red indicates a
strengthening relationship and an upward directed arrow indicates the second variable leads the
first one at given wavelength. The bold black contours (obtained using Monte Carlo simulations)
indicate a significant relationship. The conic envelope can be viewed as the region where estimates
have higher reliability. The map shows that rainfall cycles lead agricultural output in the short run
with a 2- to 3-quarter wavelength indicating that rainfall boosts the volume of agricultural output
within a year. The relationship that shows at 7- to 8- quarter wavelength could have to do with the
pattern that one year of high productivity is often followed by a poorer performance. It could also
be related to accumulation of water in the soil.
Secondly, volatile and relatively low levels of investment, particularly investment on
technological innovation, increase the volatility of agricultural GDP, or at least do nothing
to reduce it. As pointed out in sections 1.1 and 1.2 investment is the most volatile domestic
variable in Paraguay. The reasons behind the volatile investment environment may have to do with
uncertainty about future growth in a volatile environment as will be explained later in this section.
The volatility of investment and outcomes may therefore be a self-reinforcing cycle. Moreover,
levels of investment remain low, and therefore opportunities for reducing volatility are missed.
Genetic innovation, combined with bio-technological procedures, could reduce reliance on the
climate if crops were more resilient and if crop rotation techniques were used. Limited investment
in irrigation has a similar effect.
Third, sanitary measures in the livestock sector are an important precondition for exports
in this sector and failures in the safety measures create large fluctuations. Failure to
implement acceptable measures of hygiene and the outbreak of foot and mouth disease in 2011,
led to a drop in production and an exclusion of Paraguayan beef from Chile. As a consequence
exports dropped significantly.
Fourth, the price of soy and beef render agricultural GDP more volatile as Paraguay is a
price taker in international markets and as farmers adjust their supply to expected prices.
Impulse response functions based on a simple VAR relating Paraguay’s agricultural GDP to world
agricultural raw material prices illustrate that prices significantly impact Paraguay’s agricultural
GDP, while no significant feedback from Paraguayan agricultural GDP to world agricultural raw
material prices can be detected (Figure 2.2). This analysis is based on annual data from 1964 to
2011. Similar relationships can be identified for the prices of soy and beef with agricultural GDP
in quarterly frequency from 1994 to 2011.21 Moreover, wavelet analysis also confirms Paraguay’s
price taker role in international commodity markets.22 Increased volatility in commodities in recent
years, has also affected agricultural GDP in Paraguay. Another link between commodity prices
and agricultural GDP is explained by farmers’ supply responses to price expectations. Agricultural
producers expand or reduce production if they expect high or low commodity prices. The supply
elasticity of soy and beef production in Paraguay vis-à-vis respective commodities is relatively
21
22
Berument (2013) provides details on the analysis.
See Berument (2013a).
22
high, suggesting a fast and significant response (Favaro, Koehler-Geib, Picarelli, and Indaco
(2013)).
Figure 2. 2: Impulse response functions linking Paraguay’s agricultural GDP to world
agricultural raw material prices23
Source: Berument (2013)
Exchange rate fluctuations and inefficiencies in the market structure were also identified as
sources of volatility, albeit of lesser concern. Beef producers pointed out that the strong
seasonality of grain production induces strong variations in the exchange rate, which imposes
financial uncertainties on producers of other products, including beef. The reason for this
uncertainty is that a strong grain harvest leads to increased inflows in dollars and hence an
appreciation of the Guaraní. This affects the beef value chain that also operates across currencies.
While reefer companies pay beef producers in Guaranís, export prices are fixed in dollars. When
the Guaraní appreciates, beef producers are negatively affected by the exchange rate change and
in some cases are not able to fulfill commitments in Guaranís . In addition, small- and mediumsized farmers point out that the monopoly position of multinational grain producers introduces
volatility, because these companies operate as price setters throughout the value chain, from the
provision of inputs such as grains, fertilizers or pesticides, through the financing of production,
and including transportation and storage.
Market participants in Paraguay report a lack of information and knowledge of the patterns
and impacts of volatility on the economy as the biggest challenge to operating in this
environment. This creates uncertainty, which in turn leads to severe disincentives to investment.
One important area where existing data is not analyzed in a systematic way is the weather. In
Paraguay, different institutions with a variety of objectives collect data on the weather. DINAC,
Dirección Nacional de Aeronáutica Civil, has the oldest and most complete database that has been
collected with the intention of monitoring weather changes for aviation. The two bi-national
23
In the figure DLCMARM stands for the annual growth rate of world agricultural raw material prices and
YAGRLCUG stands for the annual growth rate of Paraguay’s agricultural GDP.
23
hydroelectric power plants, Itaipú and Yacyreta, also monitor weather data to better control the
water level in the reservoirs. Several agricultural associations such as FECOPROD, Federación
de Cooperativas de Producción, collect climate data for agricultural purposes. Furthermore, South
American weather and climate are also carefully watched by institutions with a global scope, for
example the US National Weather Service, and the Climate Prediction Service. While all these
data sources exist they are not analyzed and used in a consistent and coordinated way. This
weakens the ability to adequately predict weather patterns for the purposes of agricultural
production and therefore impacts on the ability to forecast GDP.
Farmers and agricultural corporations reduce investment in infrastructure, innovation, and
machinery as a reaction to uncertainty and to negative shocks. Volatility renders planning
more challenging. Investment plans with fixed costs become obsolete during the production cycle,
sometimes forcing a disinvestment or a significant financial loss at the end of the production cycle.
Decisions based on a fall in production and exports affect subsequent agricultural cycles in most
cases. Livestock firms, for example, reduce investment in genetic material or pasture
improvement. Grain producers diminish investment in machinery, storage capacity, or expansion
of farm land.
Faced with a negative shock, payment delays as well as credit refinancing and restructuring
are more commonplace. Farmers and agricultural corporations find it difficult to service financial
commitments if harvests fall short of expectations. This results in payment delays, credit
refinancing and restructuring. Overall, interviewees conjecture that volatility is at the root of high
credit rates.
Family and corporate farms as well as cooperatives react to the volatile environment by
diversifying production. An important feature of the Paraguayan agricultural sector is that most
land is cultivated by large corporations. However, a significant sector of family, small corporate
farms, and cooperatives coexists with large firms. These smaller farms are engaging into
diversification strategies to reduce the dependence on a single or only a few commodities. Namely,
production is diversified into dairy products, small animal life stock, fruit and vegetables. This
raises questions of the trade-off between risk diversification and scale of production.
2.2
The impact of volatility originating in the agricultural sector on other
sectors
Fluctuations in the agricultural sector impact the service and construction sectors in a
statistically significant manner; the measurable impact is relatively small however. 24 Both,
the quantitative VAR analysis by Berument (2013), as well as the qualitative analysis with
structured interviews by Borda, Anichini, and Ramirez (2013), identify the service and
construction sectors as those most affected. The VAR analysis uses quarterly data from the third
quarter of 1994 to the fourth quarter of 2011. The specification of this simple VAR is to include
rain, agricultural GDP, and the respective sectoral GDP. Two definitions of agricultural GDP are
24
Acosta-Ormaechea (2011) finds very little spill-overs from agriculture to other sectors in a VAR analysis that
spans a very short time period from 2003 to 2010.
24
used. When a broad definition of agriculture is used in the VAR analysis, i.e., including cattle,
fishery, and forestry, the impact on services and construction is positive and significant (Figures
2.3 and 2.4). When cattle, fishery, and forestry are excluded the impact on construction becomes
insignificant. The relationship with services remains unchanged. Quantitatively, the impact is
small however. In case of the narrow definition of agriculture a 12 percent expansion of
agricultural activity induces a one percent expansion of the services sector. Irrespective of the
concept of agriculture, no significant relationships could be established between agricultural GDP
and mining and industry, or electricity and water (Berument 2013). Unconditional correlations as
presented in Annex 1.4 also confirm these sectoral interrelations with a further breakdown of
services by subsector. Together, the mining and industry, and electricity and water sectors
represent around 23 percent of GDP. The small effect of agricultural GDP on the services and
construction sectors as well as the fact that there is no significant effect from agriculture on an
important part of the economy supports the fact that agricultural GDP has become much more
volatile at the same time as the volatility of non-agricultural GDP has remained fairly stable.
Figure 2.3: Impulse response functions linking Paraguay’s agricultural GDP to the
construction sector25
Source: Berument (2013)
Companies that provide inputs to agriculture, for example, machinery or; veterinary
products including genetic material; and seeds, tend to suffer late payments, demand shifts,
and reduced capacity usage when agricultural production falls. Companies that provide
25
In the figure RAIN stands for the quarterly growth rate of rainfall, AGR2G is the quarterly growth rate of
agricultural GDP (broadly defined including cattle, fishery, and forestry), and JCONSA_log_d1 stands for the
quarterly growth rate of the value added of the construction sector; wherever necessary seasonally adjusted series
were used.
25
machinery experience delays in payment and so provide help with refinancing. Veterinary
companies experience shifts in demand towards lower quality products, for example cattle farmers
switch to less expensive breeds, and the seed sector experiences reduced physical capacity usage,
at times down to 40 percent of normal usage.
Agricultural volatility may have contributed to the relatively low level of installed, static
storage capacity in Paraguay. Storage services are divided into two categories: i) static storage
in the form of large silos, and ii) non-static storage in form of small silos (e.g., silo bags). In
Paraguay, CAPECO estimates that the static capacity of silos for soy in 2011 was close 6 million
tons while production was significantly higher at 8 million tons. Storage capacity constraint may
explain why soy beans are normally exported within a month after harvesting. Static storage
requires medium- to long-term investment and high volatility introduces disincentives to do so.
Providers of non-static storage see the demand for their products adjust as agricultural output
changes.
Figure 2. 4: Impulse response functions linking Paraguay’s agricultural GDP to the
services sector26
Source: Berument (2013)
The transport sector is impacted by strong variations in demand and as a result also by profit
margins. Faced with falling demand for transport volumes, the share of fixed costs in total costs
increases and profit margins drop. When agricultural production falls, shipping companies first
notice a reduction in the amount of fuel transported. Subsequent stages see lower shipment
26
In the figure RAIN stands for the quarterly growth rate of rainfall, AGR2G is the quarterly growth rate of
agricultural GDP (broadly defined including cattle, fishery, and forestry), and JSERSA_log_d1 stands for the
quarterly growth rate of the value added of the construction sector.
26
volumes of agricultural output. Transport companies that belong to multinational corporations are
particularly hard hit by a drop in production because they tend to specialize in the transport of a
select group of commodities. The multinational corporations to which Paraguayan transport
companies belong absorb the losses in the transport sector with earnings from other lines of
business.
The insurance sector in Paraguay experiences a strong bunching of risks, in particular
during seasons where bad outcomes are expected; this leads to high policy rates. The
insurance sector in Paraguay only offers a small range of products for the most important
commodities of the economy. Rice production is not offered in insurance policies, but insurers are
currently assessing its profitability with a view to future inclusion. A strong bunching of risks
occurs related to producers’ expectations of their harvest outcomes. Producers typically don’t
purchase insurance if they expect a good harvest, and only do so when expectations are grim. The
result of this unpredictability in insurance coverage means that policy rates are high. Following a
bad year, when there may even have been a natural disaster and the insurance companies have a
high pay-out they raise insurance policy rates for the next year.
Many producers decide against insurance and absorb the risk themselves, as they can
compensate for one bad harvest with a good harvest the following year; yet if a bad cycle
were to last for two years, the effects on producers and the economy would be severe.
Interviews with 25 key players in the Paraguayan economy reveal that many producers in Paraguay
do not apply any measures to mitigate agricultural volatility risks. In the past ten years, each bad
agricultural cycle was followed by a positive one; as such producers have been able to compensate
for the negative effects of the first cycle with a recovery the following year, without having to
resort to agricultural insurance. The risk would increase if bad weather patterns were to last for
two consecutive years, or longer, thus exhausting producers’ financial buffers. Another reason for
producers’ reluctance to insure production is that they consider policy rates are too high to be
profitable from their perspective. In the livestock sector an alternative to insurance are contractual
guarantee clauses that include ranges of volumes and operational timeframes. For small producers,
insurance is not a consideration, for them the biggest risk is credit risk in a year with a bad harvest,
their priority at that point is the need for credit refinancing. Finally, many business agreements
between companies who provide inputs and the production sectors are kept informally. Certainly
no clauses or risk prevention elements are taken into consideration.
Activity in the construction sector varies with the purchasing power stemming from
agricultural GDP. Housing construction in particular adjusts with the agricultural cycle. This comovement is also observed in other sectors that depend on Paraguayan purchasing power, for
example, the hotel and restaurant sector, or wholesale and retail trade. Companies engaged in road
construction have not been affected because they operate mainly as public sector contractors.
2.3
The impact of volatility originating in the agricultural sector on
macroeconomic aggregates
Shocks to agricultural GDP lead to a positive response in non-agricultural GDP and exports.
The impact of agricultural volatility can be measured against the rest of the economy. Impulse
responses on the basis of VAR analysis show that a shock to agricultural GDP translates into a
27
response in non-agricultural GDP with a 16:1 ratio: this is for agriculture excluding cattle, fishery,
and forestry, the effect is larger for the broader definition of agriculture (Berument 2013). A
wavelet analysis of agricultural versus non-agricultural GDP shows that agricultural GDP leads
non-agricultural GDP at wavelengths of 1 to 3 quarters. At higher wavelengths the direction of the
lead is reversed, suggesting that in the medium-term overall development of the economy feeds
back into fostering agricultural production (Figure 2.5).27 Exports respond to agricultural GDP
shocks with a ratio of 5:1 and again the effect is stronger for a broader definition of agriculture, in
this case reaching 1:1 (see Berument (2013) for details).
3
Figure 2. 5: Wavelet analysis of agriculture and non-agricultural GDP
4
0.8
Period
6
0.6
11
8
0.4
16
0.2
10
20
30
40
50
60
Time
Source: Berument (2013a)
The nominal exchange rate immediately absorbs fluctuations in agricultural export values,
explaining the stability of the real exchange rate over the period analyzed. Export revenues of
agricultural products are mainly in US dollars. As a consequence, a strong harvest generates dollar
inflows and exercises upward pressure on the exchange rate. Paraguay has a managed float
exchange rate regime whereby the Central Bank intervenes to avert abrupt changes. Exchange rate
volatility has been increasing at the same time as the volatility of the agricultural sector (Annex
1.2). Market participants from sectors other than grain production note that the agricultural
production cycle exerts an impact on the exchange rate, which renders operation in the export
sector a challenge.
Figure 2. 6: Wavelet analysis of agriculture
and private consumption
Figure 2.7: Wavelet analysis of nonagriculture and private consumption
There is some indication that private consumption plays a role in propagating the impact of
agricultural GDP through the economy, impacting non-agricultural GDP. Wavelet analysis
shows that in the middle segment of the data set, agricultural activity seems to have induced
consumption at wavelengths of 3 and 4. Toward the end of the sample, the evidence is either mixed
27
Berument (2013a) provides details on the analysis.
28
3
3
0.8
4
4
0.8
0.6
6
Period
Period
6
0.6
8
0.4
11
11
8
0.4
0.2
16
16
0.2
10
20
30
40
50
60
10
Time
20
30
40
50
60
Time
Source: Berument (2013a)
or it does not lie within the cone of reliability (Figure 2.6). Even though this is not strong evidence
it gives some indication of a relationship between agriculture and consumption. In turn, private
consumption induces non-agricultural activity at wavelengths up to four quarters (Figure 2.7).
Unemployment negatively correlates with agricultural GDP, in some sectors it is perceived
to be directly impacted by agricultural GDP and fluctuates significantly with it. The
unconditional correlation between unemployment and agricultural GDP amounts to -0.2. An indepth quantitative analysis of the relationship is restricted by the lack of quarterly data on
employment. Within agriculture, the cattle sector is relatively labor intensive and therefore
employment in this subsector varies more with the production cycle than in the case of soy, which
is more capital intensive. In terms of other sectors, the seed sector experiences strong fluctuations
in hired personnel as a consequence of variation in agricultural production.
Volatility in agriculture also impacts the public sector, through the effect that soy and beef
exports exert on fiscal revenues. A positive and significant relationship can be established
between soy and beef prices versus fiscal revenues, modeling the relationship in a two-step
approach. Favaro, Koehler-Geib, Picarelli, and Indaco (2013) find that beef and soybean exports
respond strongly to prices (using the canonical Nerlove (1959) model), they then find a positive
and statistically significant relationship between tax revenue collection and the value of exported
beef and soybean. A caveat to the analysis at the first step is that due to data restrictions export
volumes instead of production volumes are used. The response in actual production may be lower
than the estimated elasticities in this approach. The result of the second step is not trivial given the
low direct taxation of the agricultural sector. The results seem to indicate that the positive
relationship is due to value added tax. Beef and soybean production generate income that is spent
inside Paraguay for the most part. Part of this expenditure generates tax revenue via VAT and
another part generates revenue through corporate income tax. The elasticity of soy exports to price
changes exceeds that of beef, which could be linked to the limited time that soybean brokers have
to hold the crop rather than commercialize it while there is more room for timing decisions in the
case of beef. When it comes to the relationship between tax revenues versus soy and beef exports,
29
the elasticity of revenues is higher in the case of beef. This is in line with how much more labor
intensive beef is than soy and how it is more integrated into the value chain in Paraguay.28
Significant costs that overall GDP volatility imposes in terms of welfare, equity, and poverty
have been established in the economic literature, and it is likely that this also applies to
volatility originating in the agricultural sector. Poverty and distributional impacts of volatility
were not the focus of the study. Lopez-Calva, Lugo, and Barriga Cabanilas (2013), forthcoming,
are covering aspects of this topic. For developing countries, macroeconomic volatility, as
summarized by output volatility, is reflected disproportionately in consumption volatility, and the
welfare gains from reducing consumption volatility can be substantial (Loayza, Ranciere, Serven,
and Ventura (2007)). Based on the approach of Athanasoulis and van Wincoop (2000), and World
Bank (2000) estimated potential welfare gains of up to 5 to 10 percent of consumption in various
Latin American countries. The negative link between macroeconomic volatility and equity has
also been established in the literature.29 According to Breen and Garcia-Penalosa (2005) a country
like Chile could reduce its Gini coefficient by 6 points if it were to reduce its volatility to the same
level as Sweden or Norway. As argued in Lopez-Calva, Lugo, and Barriga Cabanilas (2013)
forthcoming, a reason for the link between high volatility and inequity could be that citizens at the
lower end of the income distribution have reduced access to insurance mechanisms and therefore
suffer more from negative shocks. Macroeconomic volatility may also contribute to still elevated
poverty rates; the high degree of volatility may be the weak link between solid average growth
performance and employment generation. The uncertainty resulting from volatile economic
growth may reduce the incentive for firms to employ new staff. Together, lagging employment
generation and continued high levels of inequity pose important challenges for Paraguay in
reducing poverty further.
Simulations of a negative and persistent shock to beef and soy prices illustrate the existence
of links between the agricultural sector and equity as well as poverty in Paraguay. A
Computable General Equilibrium (CGE) model is used to track the macro and micro economic
effects in Paraguay of a decrease of 25 percent in the soy and beef prices starting in 2013 and
maintained through 2018 (see Diaz-Bonilla and Cicowiez (2013) for a detailed description of the
model, the base line scenario and simulation results). In particular, a decrease in the world export
prices of soy and beef would result in slower GDP growth than under the baseline scenario.
Moreover, through a negative impact on the private sector (including reduced employment growth
and private consumption), poverty would decrease to 25.8 percent in 2018 as opposed to 24.5
percent in the baseline simulation. Inequity and the aims of the millennium development goals
would remain practically unchanged. In the case of a general decrease in all of Paraguay’s exports
, the impact would be much stronger, and would then include a negative impact on the millennium
development goals and inequity
28
It seems to be important to take into account the indirect way in which commodity prices impact fiscal revenues, in
a cointegration analysis of fiscal revenues versus beef and soy prices with yearly data from 1990 to 2010, Le Fort
(2013) cannot detect a statistically significant relationship.
29
See for example Breen and Garcia-Penalosa (2004), Garcia-Penalosa and Turnovsky (2004) or Huang, Fang, and
Miller (2012).
30
31
Chapter 3: Managing growth volatility in Paraguay
Previous chapters have identified two main types of the sources of growth volatility in Paraguay:
shocks of a macroeconomic nature and shocks specifically linked to agriculture. The main
macroeconomic sources of volatility in Paraguay are shocks to global interest rates, foreign
demand, terms of trade, investment, GDP itself, and pro-cyclical fiscal and monetary policies.
Agricultural volatility is mainly driven by shocks to production such as rainfall; investment levels;
and disease outbreaks and shocks to markets including commodity prices, the closing of markets
in the case of disease outbreaks, and prices of imported inputs like fertilizers and pesticides.
The purpose of the current chapter is to discuss policy options and tools to stabilize the economy by
rendering it more resilient to the sources of volatility, and to mitigate the impact of volatility. Sources
of volatility are interrelated and taking a broader perspective allows finding optimal ways to manage
observed volatility and risks. Therefore, it is important to develop a comprehensive macroeconomic
risk management framework that takes all different sources of volatility and risks into account and
puts forward a coherent set of measures aimed at increasing Paraguay’s capacity to prepare for and
cope with the effects of volatility. Any policy option needs to be assessed in terms of its fiscal
implications; be it in terms of its effects on sustainability, on redistribution, or on potential contingent
liabilities.
In response to the concrete shocks that previous chapters have identified, the macroeconomic tool set
in this chapter presents: i) an overall strategy with the aim of diversifying economy in a way that
renders it less dependent on products and markets that introduce volatility; ii) policies that render
factor markets more flexible; and iii) fiscal policies aimed at smoothing or at least avoiding
amplification of shocks in the economy, through tools like fiscal rules or stabilization funds.
The second set of tools contains measures of agricultural risk management designed to address
the production and market shocks specific to the agricultural sector. Given that the suggested
policy options are new and have only been applied in a few countries, this section relies on case
studies. The idea is to provide some useful tools and experiences from other countries that have
been facing similar volatility to that observed in Paraguay. However, a careful assessment of
priorities among different options, in particular also their applicability, and their fit within the
country’s comprehensive macroeconomic risk management framework is outstanding and needs
to be part of an overall assessment of agricultural risks. The Government and the World Bank are
currently collaborating on such an assessment, which aims at the adoption of an action plan of
priority measures to mitigate, transfer and absorb risks affecting Paraguay’s agricultural sector.30
First, the agricultural risk management section presents four case studies on production risks: i)
building animal health capacity to prevent foot and mouth disease in Colombia; ii) introducing
weather derivatives based on a rainfall index for severe drought in Malawi; iii) establishing a
weather contingency fund for the agricultural sector (CADENA) in Mexico; and iv) implementing
an index-based livestock insurance project in Mongolia. Second, three further case studies provide
insights into new tools to address agricultural market risks: i) developing the asparagus market
in Peru; ii) introducing subsidies for commodity price hedging contracts in Mexico; and iii)
30
See World Bank (2013b) for a detailed description.
32
introducing agricultural commodity exchanges in Argentina. To complete the presentation of
policy options for agricultural risk management traditional measures are presented in the
appendices 3.1 and 3.2.
It is important to develop a comprehensive macroeconomic risk management framework that takes
all different sources of volatility and risks into account. Sources of volatility are interrelated and
taking a broader perspective allows finding optimal ways to manage observed volatility and risks.
Section 3.1 will describe the macro-economic toolbox to address growth volatility; section 3.2 will
present the agricultural risk management toolbox mainly in the form of case studies; and section
3.3 concludes on a comprehensive macroeconomic risk management framework.
3.1
The macroeconomic toolbox to address growth volatility
The purpose of the current section is to discuss policy options for addressing volatility, which
arises from shocks to global interest rates, foreign demand, terms of trade, investment, GDP
itself, and pro-cyclical fiscal and monetary policies. Section 1.2 identified these shocks as the
main sources of volatility in Paraguay and chapter 2 discussed their impact on the agricultural
sector and on the rest of the economy. The current section discusses policy options for Paraguay
to address those shocks. It thereby complements the comprehensive analysis of “Managing Risks
for Development,” World Development Report 2014 (World Bank (2014), forthcoming)).
The recent World Development Report 2014 on Managing Risks for Development provides a
useful analytical framework for developing an effective macroeconomic policy toolbox for
Paraguay. The WDR discusses a number of country experiences with policies aimed at preparing
for or coping with risks or shocks. Transferring risks through insurance mechanisms such as the
use of sovereign bonds with pay-off structures associated with the occurrence of certain shocks is
a third category of risk policies presented by the WDR. Having macroeconomic policies in place
that safeguard macroeconomic stability is an important prerequisite for a country to face economic
shocks. Consolidating Paraguay’s important progress on macroeconomic stabilization over the
past years, in particular the control of inflation and the move to a flexible exchange rate regime is
therefore a critical for its economy to face its increasingly volatile environment. Prudent fiscal
policies in general, and specific fiscal policy instruments like fiscal rules are important policy
ingredients for both preparing an economy to face shocks and coping with them – by providing
the fiscal space to respond and avoiding amplification of the effects through pro-cyclical policies.
A key priority for policies with a more medium and long term time horizon is to diversify the
Paraguayan economy and strengthening those sectors that are less vulnerable to shocks. In the
following, policy options for Paraguay along these lines are discussed in more detail, with a focus
on policies aimed at diversifying the economy and fiscal responses. .
The diversification of the economy and the development of domestic debt markets would
reduce exposure to the global interest rate, foreign demand, and terms of trade shocks. As
discussed in chapter 1, the strong dependence on agriculture means that the economy is susceptible
to interest rate shocks. The reform the agricultural corporate income tax, IMAGRO which has been
realized in 2013 is an important first step. To level the playing field with other sectors, there is a
need to eliminate all exemptions to ensure an appropriate taxation of the agricultural sector.
Removing this distortion would level the playing field for business development in all sectors. A
33
full evaluation of the recent reform is beyond the scope of the current study; however, it appears
that further steps are needed to achieve an adequate taxation of agriculture. Also, addressing gaps
in infrastructure, health, and education would contribute to an environment for entrepreneurs to
explore new business opportunities. In addition, policies that contribute to a reduction in the
concentration of the agricultural sector in terms of products and export destinations would reduce
the exposure to foreign demand and terms of trade shocks. A diversification strategy for the
agricultural sector would be appropriate. Promoting such diversification would require investment
in human capital, access to credit and a fluid exchange of knowledge between entrepreneurs and
universities and research centers. Export promotion activities would contribute to exploring new
export markets for Paraguayan products.
There are several promising policy options forpromoting a broader growth pattern by enhancing
the regulatory and policy framework for all sectors. This includes measures that: i) improve factor
market flexibility; ii) facilitate innovation and its application, and iii) to improve forecasting of future
economic activity. As discussed in section 1.3, factor markets still show significant inflexibilities and
rigidities. There are several entry points to addressing these rigidities:
 Strengthening the domestic financial market, such as by improving access to credit for small
firms.
 Updating the legal framework for business activities with a view to reducing barriers to intersectoral factor mobility.
 Improving the flexibility of the labor market, by rendering regulations and improving the
education system with a view towards labor market needs (workers with a solid educational
background are more fungible and switch jobs more easily).
 Improving economic forecasting in Paraguay by linking universities and research centers to
the business sector and thereby fostering a fluid exchange of knowledge. This would increase
the predictability of the business environment and improve the knowledge base in the business
community.
 Finally, market participants mention the lack of information about the patterns and the effects
of volatility as a major challenge to operating in the Paraguayan economy. In this context an
important step would be to expand and improve statistics and data on weather conditions as
well as a coordinated analysis.
Dependence on international interest rates could be reduced through the development of the
domestic debt market and increased use of public pension funds for domestic investments. (see
World Bank (2013) and World Bank and IMF (2012) for a detailed description of the necessary steps).
An additional measure for reducing the exposure to international interest rate shocks consists of
ensuring that the funds of the public pension system (amounting to approximately 2 percent GDP) can
be invested in the country and increase liquidity instead of being locked in an account at the Central
Bank.
Policy instruments like fiscal rules and stabilization funds that could address the observed procyclicality of fiscal policy in Paraguay and help create fiscal space to mitigate the effects of
volatility. As stated in section 3.1, fiscal policy in Paraguay has been pro-cyclical in the last two
decades with a few exceptions in recent years. Experiences from other countries have shown that policy
instruments like fiscal rules and stabilization funds can help avoid such pro-cyclical effects. These
instruments are no panacea; however, as their effective implementation depends on their credibility,
which in turn is a function of the ability and incentives of the political decision makers to circumvent
them. The design of fiscal rules and stabilization funds, but also the general environment, such as the
34
existence of a broad political consensus, an adequate level of accountability and transparency in
political processes determine their level of credibility.
Fiscal rules are institutional mechanisms aimed at supporting fiscal discipline and attaining
sustainability of public debt, control of public spending, and contribution to cyclical stability.
There are two major categories of fiscal rules: one category defines numerical targets (i.e., ceilings or
floors) for Government balances, overall revenues or expenditures that are fixed and independent of
the business cycle (i.e. the Stability and Growth Pact in the Euro Area), and the second category aims
at stabilizing cyclically-adjusted balances, allowing for cyclical changes in actual Government
balances. Numerical targets are easier to communicate, and to verify by market participants. Structural
budget balances have the advantage of providing short term flexibility to respond to adverse shocks.
They are vulnerable though to uncertainty over the cyclical position of the economy and to overoptimistic GDP growth and budget forecasts.
Stabilization funds are designed to guard against volatility in the international markets and aim
to reduce the impact of volatile revenue on the fiscal balance and the economy. The basic concept
behind stabilization funds is that when revenues and prices are high, windfall gains are diverted as
payments into the stabilization fund. When revenues are lower than expected, payments are made out
of the fund to the budget to avoid a sudden fall in expenditure. Stabilization funds are usually
implemented in resource-rich countries that rely heavily on one or a few commodities for their fiscal
revenues.
Chile is a successful example of a country that has adopted a fiscal rule based on a cyclically
adjusted fiscal balance. In 2001, Chile adopted a cyclically adjusted Government balance rule which
links Government spending to cyclically-adjusted revenue, taking into account cycles in GDP and
mineral prices. Among the 10 countries using fiscal rules based on cyclically adjusted fiscal balances,
Chile is the only country that corrects not only for the cyclical deviation of GDP from its trend, but
also for those of copper prices from trend. An important and innovative feature of Chile’s fiscal
framework is the determination of GDP and copper price forecasting to two independent committee,
whose projections are a legally binding input into the application of the fiscal rule. Adopting the fiscal
rule has contributed to lowering the pro-cyclical bias of fiscal policy in Chile and has stabilized its
macroeconomic environment. Namely, fiscal sustainability and credibility have been increased, the
sovereign risk premium and macroeconomic uncertainty have dropped, and the volatility of GDP,
interest rates, and the exchange rate have been reduced. Moreover, the dependence on foreign financing
during downturns has been be reduced (Schmidt-Hebbel, 2012).
Adopting and effectively implementing a fiscal rule in Paraguay would require a series of fiscal
policy reforms. Difficulties in the practical application of fiscal rules have to be taken into account
and translated into a pragmatic approach that is tailored for the specific situation in Paraguay. IMF
(2009a) and Debrun, Hauner, and Kumar (2009) provide more details on the preconditions for the
successful implementation of fiscal rules. The recent introduction of a fiscal responsibility law with
the aim of strengthening the fiscal policy framework is a critical first step in this regard. An in depth
analysis of the impact of the recent reform goes beyond the scope of this study but would be warranted
given the potential impact on the economy. Overall, fiscal rules with fiscal responsibility laws are more
difficult to reverse, although it can take longer to establish them when economic and political
uncertainty exists in a country. This will have to be complemented by other reform steps:
35



Adequate public financial management systems are prerequisites for effective implementation
of fiscal rules. In Paraguay a careful evaluation of these systems would have to precede further
steps.
An independent fiscal council could help in the formulation and implementation of sound fiscal
policies. In particular, a fiscal council can complement the role played by existing institutions
and enhance the effectiveness of fiscal rules.
Fiscal rules also need to include accountability, transparency, monitoring, external control,
auditing, and enforcement mechanisms.
Along with Chile, and Mexico, Norway is an example of a country that has managed revenue
volatility through the implementation of a stabilization fund. Established in 1990 and activated
in 1995, Norway’s Stabilization State Petroleum Fund (SPF) is designed to manage accumulated
budgetary surpluses from oil revenues and has flexible operation principles with no specific rules
for accumulation or withdrawal. The SPF effectively finances the overall budget balance by
transferring net oil revenues from the budget to the SPF and in turn, financing the budget’s nonoil deficit through a reverse transfer. In addition, an overall budget surplus will be transferred to
the fund and a budget deficit is financed by the fund. The accumulation of assets in the SPF, which
include the returns on the fund's capital, represents Government net financial saving. The amount
actually saved depends on oil prices and the fiscal outturn that contains the non-oil fiscal deficit.
Controlled by the ministry of finance and managed by the central bank, the SPF assets have a high
level of transparency and accountability. The size of accumulated funds reached close to 20 percent
of GDP at end-1999 and has been increasing rapidly.
Chile’s Copper Stabilization Fund (CSF) has helped the Government resist expenditure
pressures during the increases in copper prices, reducing the cyclicality of fiscal policy.
Established in 1985 following a sustained increase in the international copper price, the CSF's
accumulation and withdrawal rules are based on a reference copper price determined annually by
the authorities.31 The resources of the CSF have grown substantially since 1987, although in 199899 there were significant withdrawals, partly on account of a sharp decline in copper prices. In
recent years, CSF resources have been used to subsidize domestic gasoline prices through credits
to the Oil Stabilization Fund. The establishment of the CSF has allowed the Government to resist
expenditure pressures during increases in the copper prices in the late 1980s and mid-1990s and to
escape pro-cyclical fiscal policy. Davis, Ossowski, Daniel, and Barnett (2001) found a negative
correlation between a copper price increase and Government spending. Sound fiscal and
macroeconomic policy in Chile seems to have played a key role in helping the effective
implementation of the CSF.
Through a new Fiscal Responsibility Law enacted in 2006, Chile has modernized its stabilization
fund and strengthened its link to the overall fiscal policy framework. The Law created two sovereign
wealth funds: (a) the new Pension Reserve Fund (PRF), created to finance future pension liabilities by
the government. (b) the Economic and Social Stabilization Fund (ESSF). The Law established that, in
31
No explicit formula is used to calculate the reference price. In practice, however, the reference price followed a tenyear moving average until the mid-1990s; more recently, the reference price has been set somewhat lower than the
moving average. When the price of copper exceeds the reference price by between $0.04 and $0.06 a pound, 50 percent
of the resulting state copper company's revenues is deposited in the CSF; above $0.06 per pound, 100 percent. The
rules for withdrawals are symmetric (OECD, 2009).
36
good times, fiscal surpluses in excess of the structural target (and after contribution to the PRF) are
channeled to the ESSF. In bad times, resources may be withdrawn from ESSF to finance budget
deficits, including payments into the PRF.32
Mexico’s Oil Stabilization Funds are an example that illustrates that stabilization funds can
produce limited results due to excess revenues allocation rules and capped savings. In order
to reduce oil-related volatility in the budget, Mexico established three oil revenue stabilization
funds: one by the Federal Government, a second one by the state-owned petrol company
(PEMEX), and a third one by the State Governments. The first one was established in 2000 and
the other two in 2006. The rules of the funds were updated in the 2006 Fiscal Responsibility Law
and in the 2009 budget.33 The Federal Government fund is managed by the Ministry of Finance
and has a target level for savings, which was 0.5 percent of GDP in 2008 and was almost doubled
in the budget for 2009.34 As determined by law, 90 percent of excess revenues are allocated to
those three funds (40 percent to the Federal Government fund, and 25 percent to the PEMEX and
State Government funds each) and the remaining 10 percent to states for investment. Once the
funds have reached their limit, 75 percent of excess revenues are allocated to investment, and 25
percent to a fund to support the restructuring of pension systems. At end-2008, the funds’
cumulative reserves were equivalent to 1.2 percent of GDP. Due to the cap on their size, the
Mexican funds have accumulated a limited amount of savings and have therefore showed limited
success in reducing volatility (OECD, 2009).
With the adoption of the fiscal responsibility law, a pre-condition for establishing a
stabilization fund in Paraguay has been fulfilled, but other conditions, such as the creation
of an advisory committee would need to be put in place. A functioning stabilization fund
requires a number of conditions to be in place: i) effective and transparent corporate governance;
ii) transparent information of the transfers between the budget and the stabilization fund; iii)
portfolio composition determined by maturity concerns (determined by the length of commodityprice and output cycles) and the Government’s degree of risk aversion, and iv) efficient portfolio
management using transparent guidelines and closely monitored by the Government and the
public, independent of political consideration (Schmidt-Hebbel, 2012). Other critical conditions
for the effective operation and implementation of a stabilization fund include the adaptation of
legislation and institutions that define investment policies and management principles of their
funds. The status of these preconditions would have to be carefully assessed in the case of
Paraguay.
Establishing a stabilization fund is most effective in combination with the introduction of a
fiscal rule. One important factor that strengthens the effectiveness of stabilization funds is the
introduction of fiscal rules. Countries can establish stabilization funds with and without fiscal rules
for expenditure and revenue smoothing. However, adopting fiscal rules when stabilization funds
are established is critical; without fiscal rules regarding liquidity constraints, stabilization funds
are unable to stabilize expenditure directly and Governments could finance spending through
32
Schmidt-Hebbel (2002) documents in detail the institutional aspects of fiscal policy in Chile and compares them with those of
Norway.
33
The Law also included provisions for setting a reference price for oil and transfers to the funds.
Before transferring excess revenues to the funds, some items are deducted, which include shortfalls in revenues with
respect to the budget, changes in energy costs that are not fully reflected in domestic electricity tariffs, costs of natural
disasters and outlays resulting from changes in non-programmable expenditures due to changes in interest or exchange
rates (OECD, 2009).
34
37
borrowing bypassing the operations of the stabilization fund. Expenditure smoothing therefore
requires additional fiscal policy decisions besides the operation of the fund.
3.2
The agricultural risk management toolbox
The purpose of this section is to present some useful tools and experiences from other
countries that have been managing volatility similar to that observed in Paraguay. First, the
section introduces the agricultural risk management framework used by the World Bank. It then
presents four case studies on new tools and approaches to mitigate, cope with, and transfer
agricultural production risks: i) building animal health capacity to prevent foot and mouth disease
in Colombia; ii) introducing weather derivatives based on a rainfall index for severe drought in
Malawi; iii) establishing a weather contingency fund for the agricultural sector (CADENA) in
Mexico; and iv) implementing an index-based livestock insurance project in Mongolia. Second,
three case studies provide examples of measures to mitigate and transfer agricultural market risks:
i) developing the asparagus market in Peru; ii) introducing subsidies for commodity price hedging
contracts in Mexico; and iii) introducing agricultural commodity exchanges in Argentina. To
complete the presentation of policy options for agricultural risk management traditional measures
of risk management are presented in the appendix to the chapter.
Each case study highlights its relevance to Paraguay and indicates benefits and limitations
associated with the given approach. The case studies identify directions to guide further research
to determine whether the program is appropriate for Paraguay.
However, a careful assessment of priorities among different options, their applicability, and
their fit within the country’s comprehensive macroeconomic risk management framework is
outstanding and needs to be part of an overall assessment of agricultural risks. Case studies
cannot be applied immediately to Paraguay. A careful assessment of viable policy options is
provided in World Bank (2013b). For all new policies and programs in agricultural risk
management, an informed decision-making process relies on a sector-wide risk assessment to
identify hazards, vulnerability, and exposure to risk, followed by cost-benefit analyses to weigh
different options. It is also important to link it to the overall macroeconomic environment because
the suggested solutions may have implication on fiscal sustainability, redistribution, and
contingent liabilities for the Government.
Overall, the objective of an explicit agriculture risk management strategy as part of a
comprehensive macroeconomic risk management framework is to move from ad-hoc, expost responses to adverse shocks to agriculture, to the establishment of an ex-ante risk
management framework. This allows the Government to better manage fiscal exposure
(revenues and/or expenditures) in case of systemic shocks to agricultural production. In recent
years, the Bank has developed a framework for supporting Governments in defining their
agriculture risk management strategy. This framework is described below. This strategy fits well
into a broader set of fiscal policy tools to manage volatility that may arise from fluctuations in
agricultural GDP, such as the establishment of a fiscal rule and stabilization funds.
Agricultural risk management framework
38
Agricultural GDP volatility can derive from risks associated with production, market, and
the enabling environment. For the agriculture sector of Paraguay, production risk and market
risk are the most important sources of risk: i) production risks arise from rainfall, investment
levels; and disease outbreaks; ii) market risks arise from fluctuations in the prices of export
commodities like soy, beef, and maize; fluctuations in the prices of imported inputs; the closing
of markets (such as the border closings due to foot and mouth disease outbreaks); and volatility
in the prices of imported inputs like fertilizers and pesticides.
There are three main strategies that comprise an integrated agricultural risk management
strategy and the case studies of this section are categorized accordingly (Figure 3.1): i)
mitigation: activities designed to reduce the likelihood of an adverse event or reduce the severity
of actual losses (e.g. diversification, animal and plant health investments; ii) transfer: the
transfer of the potential financial consequences of particular risks from one party to another, for a
fee or premium (e.g. commercial insurance and hedging); iii) coping: improves resilience to
cope with (respond to) events, through ex-ante preparation (e.g., social safety net programs,
buffer funds, savings, strategic reserves, contingent financing, etc.)
Figure 3.1: The World Bank Agricultural Risk Management Framework
Source: World Bank (2013a).
Case studies on managing production risks
Table 3.1: Instruments for Managing Production Risk
Strategy
Mitigation
Transfer
Problem
Foot and mouth
(foot and mouth
disease) outbreak in
the Andean Region
threatens the
important cattle/beef
export sector of
Colombia.
Severe droughts
pose significant
Instrument
Public-private
partnership (PPP)
investments in
Animal Health
Public sector
purchase of an
39
Description
Joint PPP for investing in
sanitary measures, standards
regulation, certification, etc. to
mitigate risk of disease
outbreak and remain free of
foot and mouth disease in order
to maintain access to export
markets.
Financial contract (derivative)
by which payment to the
Case Study
Colombia:
Building animal
health capacity to
prevent foot and
mouth disease and
support the
livestock sector
Malawi: Weather
derivative based on
food insecurity
problems for the
vulnerable
population segments
of Malawi.
Transfer
Coping
Severe weather
events make it
difficult for small
farmers to invest
and exist the
poverty cycle.
Harsh winters force
the Government to
respond with aid to
low-income herders
that have lost a large
amount of livestock
Index-based weather
derivative to gain
access to quick and
appropriate level of
resources to
respond.
Federal and
subnational
Governments
purchase Indexbased insurance to
obtain additional
fiscal resources to
compensate farmers
after an adverse
weather event
Contingency lines of
credit to fund
emergency response
activities and
payments.
Malawi Government is
provided when rainfall in a prespecified period falls within a
pre-specified threshold. With
the payout, the Government
purchases food aid.
Emergency fund financed by
Government savings and indexbased insurance that provides
direct payments to small
farmers in a given municipality
affected by catastrophic
weather event.
rainfall index for
severe drought
Provides fiscal resources after a
harsh winter (dzud) in order for
the Government to make
catastrophic payments and
provide assistance to herders.
Mongolia: Indexbased Livestock
Insurance Project
Mexico: Weather
Contingency Fund
for the Agriculture
Sector (CADENA)
Source: authors.
Case studies on managing production risks—case study 1: building animal health
capacity to prevent foot and mouth disease and support the livestock sector in
Colombia
Instrument: Public-private investments in animal and plant health
ARM Strategy: Mitigation
Relevance for Paraguay: In 2011 an outbreak of foot and mouth disease in Paraguay led to the
mandatory slaughter of 1,000 head of cattle. Further outbreaks have been reported since then
(January 2012). Paraguay’s status as free of foot and mouth disease with vaccination has been
suspended by the OIE. 35 Chile, which had previously purchased roughly a third of Paraguayan
beef exports, banned Paraguayan beef. Total beef exports dropped 16.5 percent in 2011. Paraguay
is strengthening investments in a sanitary and phytosanitary (SPS) system and is establishing a
national biosafety laboratory level 3.
Colombia’s efforts to strengthen the National Agricultural Science and Technology and
Sanitary and Phytosanitary (SPS) systems via public and private sector participation
improved the access of Colombia’s export products to international markets. Colombian
agricultural and agro-entrepreneurial sectors accounted for 21 percent of aggregate GDP, 25
percent of export revenues, and 30 percent of job creation in the country, employing more than 4.5
million. The livestock sector was extremely vulnerable to lapses in quality standards. Venezuela,
the principal market for Colombian beef, closed its border with Colombia and sent an extreme
shock through the sector. Furthermore, on the brink of joining a Free Trade Agreement with the
35
World Organization for Animal Health (2013).
40
United States, strengthening SPS standards was a necessary step to ensure competitiveness in
international markets.
The SPS strengthening strategy involved national disease-free certification, low-tech
implementation of good agricultural practices (GAP), and the approval of export protocols
with many countries. The country was certified free of foot and mouth disease without
vaccination, and several plant and animal disease-free areas were established (among the most
important, Brucelosis, Tuberculosis, Bactrocera, controlled fruit fly). With respect to the
eradication of foot and mouth disease, the country has complied with the commitments of the
Hemispheric Plan of Eradication of Foot and Mouth Disease (PHEFA).
To maintain foot and mouth disease-free status by the OIE and PHEFA, Colombia decided
to establish an in-country Biosafety Level 3 Agriculture Laboratory. As part of the
strengthening of the SPS laboratory network, Colombia needed to respond to the rising threat of
foot and mouth disease to the livestock industry. Such a laboratory serves an important role in a
prevention system by analyzing samples and monitoring standards control. The investment was
justified from the point of view of the large returns to the local livestock industry (local
consumptions and exports), but also for the Region. This laboratory is the only one of its level of
biosafety in the Andean Region. The availability of a national biosafety level 3 agriculture
laboratory for Colombia and for the Andean Region is a resource that can yield large economic
returns by allowing for early and precise surveillance, control and monitoring of exotic or
emerging animal health issues.
Table 3. 2 Colombia’s study case. Benefits, Challenges and Considerations for Paraguay



Benefits
Disease free
certification is
mandatory for export
markets with higher
quality standards
Early and precise
surveillance of animal
and plant disease
Multi-faceted approach
combines lowtechnology extension
for good agricultural
practices with advanced
technology




Challenges
Coordination between
private and public actors
and clear definition of roles
Integration between
components
Up-front costs of
establishing a new
laboratory; operational
costs of collecting and
analyzing samples
Targeting investments for
cost-effectiveness




Considerations for Paraguay
Disease prevention reduces livestock loss,
requisite slaughter of sick animals, and increases
access to export markets
Multiple outbreaks of foot and mouth disease
indicate need for further
intervention/investments. Interventions must be
specified to target the unique gaps and
weaknesses in the existing system. An analysis
to determine the causes of outbreaks is
necessary to guide future investments.
Paraguay is in the process of establishing a
similarly advanced certified laboratory.
To regain foot and mouth disease-free national
status, coordination with the OIE is necessary.
What would the gains to Paraguay be if it were
to regain foot and mouth disease-free national
status? How sensitive are current consumers of
Paraguayan beef to foot and mouth disease
concerns?
Source: authors.
Case studies on managing production risks—case study 2: introducing a weather
contingency fund for small farmers (CADENA) in Mexico
41
Instrument: Federal and state Governments purchase index-based insurance to obtain additional
fiscal resources to compensate farmers after an adverse weather event.
ARM Strategy: Transfer
Relevance for Paraguay: A majority – 83.5 percent (nearly 242,000) – of Paraguay’s farms are
less than 20 hectares. Such smallholder farmers cannot qualify for commercial agricultural
insurance. Index insurance has a number of advantages over traditional insurance and traditional
disaster response programs for covering small farmers, but there are numerous difficulties in
implementation at the same time.
CADENA (Componente Atencion a Desastres Naturales en el Sector Agropecuario y Pesquero)
is a macro-level catastrophe crop and livestock insurance program that is specifically
designed to provide a social safety net for vulnerable smallholder farmers that do not qualify
for commercial agriculture insurance. The CADENA program is designed to replace the
Government’s traditional ad-hoc disaster relief schemes. Instead, States purchase parametric crop
and livestock insurance to cover a pre-registered rural population, which receive automatic
payments in the case of a catastrophic disaster, regardless of their individual, farm-level losses.
CADENA is designed to quickly provide income-compensation to smallholder farmers to
help them recover from a catastrophic event and continue production. Under CADENA index
insurance, farmers are not compensated for their actual losses and instead receive payments based
on whether their location was affected by a disaster. CADENA actively promotes pre-registration
of farmers so that payments are fast and transparent.
Under CADENA, smallholders do not pay any part of the premium. Rather, the Ministry of
Agriculture subsidizes either 80 or 90 percent of the insurance premiums, depending on the degree
of marginalization of farmers in the state, and the State Government pays the remainder.
Beneficiaries are eligible if they meet certain criteria for smallholder producers in terms of size of
property, number of livestock, etc.
Mexican states are incentivized through federal Government premium subsidies to contract
agricultural insurance. States can either directly contract insurance from a private insurer or
Agroasemex, sharing the cost of the premiums with the Ministry of Agriculture in the proportions
indicated above; or if they decline to contract insurance cover, States can still benefit from
CADENA’s Direct Support program in the event of a catastrophe, but the state must shoulder 50
percent of the cost of the total estimated damages, with the Ministry of Agriculture compensating
50 percent of the costs. If a State declines insurance coverage, the Ministry of Agriculture is
entitled to purchase insurance cover and pay 100 percent of the premiums, exclusively using
insurance from Agroasemex, the public re-insurance company in order to hedge their exposure in
case they need to provide direct payments.
Mexican states must choose between parametric/weather index insurance from Agroasemex
and area-based yield index insurance (AYII). Agroasemex is the only company that offers
parametric weather insurance products. Private insurers only offer AYII. Neither form of insurance
is indemnity-based, meaning that producers are not individually compensated for the specific
quantity of damages incurred on their farm. The difference between parametric/weather index
insurance and AYII is that payouts from a parametric insurance are triggered by a pre-established
42
weather variable that is correlated with agricultural losses, while AYII requires actual in-field
sampling of crop yields to establish the actual average municipality-level yield loss. The
parametric weather index covers a restricted number of risks while AYII covers multiple risks,
including natural, climatic, and biological causes of crop production or yield loss. The two kinds
of livestock insurance available are a parametric remote sensing pasture index, using a NDVI and
traditional catastrophe livestock insurance.
Since its inception in 2003 the CADENA program has expanded fast in terms of coverage
and budget allocation. In 2011 approximately 8 million hectares of crops were insured in 27
states with over 2.5 million insured farmers (beneficiaries). This represents about 56 percent of
this target group (4.5 million subsistence smallholders farming 16.5 million hectares). Overall the
CADENA crop and livestock insurance programs in 2011 covered 2,362 municipalities in 30 out
of Mexico’s 32 states36 with Total Sum Insured (TSI) of 12 billion (Ministry of Agriculture 2012).
The Federal Government’s CADENA budget has increased significantly through the Ministry of
Agriculture for support to catastrophe crop insurance premiums and direct compensation
payments. In 2012 it reached US$ 232.7 million, of which 153.6 million (66 percent of total) was
allocated to premium subsidies and the remainder of US$ 80 million for direct payments. For 2013,
the Ministry of Agriculture has therefore significantly increased the federal Government financial
budget for the CADENA Program to about US$ 400 million (representing an increase of about 72
percent on the 2012 budget).
Weather index insurance structured like CADENA avoids many problems of traditional
insurance, including: i) adverse selection: All farmers in a given region that qualify are
automatically opted in to the insurance product. In the CADENA case, farmers do not pay directly
for the premiums to the insurance so there is no willingness-to-pay obstacle; ii) moral hazard:
farmers still have an incentive to try to save their crops, as the indemnity payout is perceived as an
additional bonus regardless of actual losses; iii) high correlated risks: natural disasters typically
strike entire communities, wiping out local coping mechanisms such as informal lending within a
community; iv) transaction costs: index insurance can reduce or eliminate the need for in-field
damage assessments.37 Traditional multiple or single peril crop insurance relies on surveys of field
damage to determine the appropriate indemnity payment.
CADENA also has several advantages compared to an ex-post disaster compensation
program, like its precursor program: i) insurance payouts can be made rapidly to State
Governments, and State Governments have some degree of autonomy over how to allocate
resources in the case of a disaster; ii) insurance payouts can be made rapidly to farmers where
there is an ex-ante farmer registry; iii) Transparency and standardization of payout rules; iv)
subsidies for a public-private partnership may be less of a fiscal burden, and at the very least a
more consistent fiscal burden, than an ex-post program; v) index insurance makes it possible to
layer risk and enable risk transfer (reinsurance in this case). The maximum liability can be
quantified in advance and transferred out of the fiscal budget to local and international insurance
and reinsurance markets.
36
37
Mexico has 31 states plus 1 District Federal and a total of 2,445 municipalities.
See annex 3.2 for a comparison of principle agricultural insurance products.
43
Challenges for CADENA consist of the ensuring that the state distributes payments quickly
to affected farmers in insured locations and to address the high basis risk. An external
evaluation by the Universidad de Chapingo found that the average time post-event is 89 days for
beneficiaries to receive payouts.38 Furthermore, CADENA has had difficulties monitoring how the
state has transferred payouts or used the resources. High basis risk, the difference between the
value of the insurance payout and the value of the beneficiary farmer’s actual loss, is large for
index insurance. For many farmers, CADENA payouts are inadequate to cover their costs invested
in agricultural production.
Table 3.3: Mexico’s study case. Benefits, challenges and considerations for Paraguay
Benefits
 Avoids many limitations of
traditional/commercial
insurance products (adverse
selection, correlated risk,
moral hazard, transaction
costs)
 Advantageous over an expost emergency fund
(private sector contributes
to cost-sharing, designed to
increase speed of payouts,
possible to transfer risk
instead of retaining all risk
in the fiscal budget)




Challenges
High basis risk
Difficulties in
implementation (speed and
transparency of indemnity
payouts)
Indemnity payouts do not
completely cover production
costs and instead serve to
help get farmers “back in
production.”
PPP challenges: imperfect
competition between public
insurance agency and private
sector





Source: authors.
38
Universidad de Chapingo, External Evaluation to the PACC, 2010.
44
Considerations for Paraguay
Adequate insurance market: Is such
a program feasible given the current
technical level of local insurance
companies? Is there technical
expertise in the market to offer
index-based, low-cost insurance?
Issues with data and
implementation: Is there sufficient
weather data information to design a
macro-level agriculture insurance
product? Registration of farmers
may be difficult or given widespread
land tenure insecurity.
Paraguay’s natural hazards: Does
the frequency and severity of natural
hazards in Paraguay justify the
transfer of such risks through
insurance, or absorbing and
diversifying the risk is more viable?
Integration with existing policies:
How would such a program interact
with other social safety net
programs for small farmers and rural
households in place? How can it be
linked to or replace the current
system for coping with disasters?
What would be the fiscal burden
comparison between state subsidies
for premiums and a state emergency
fund?
Would land tenure insecurity
complicate farmer registration and
indemnity payouts?
Case studies on managing production risks—case study 3: index-based weather
derivatives in Malawi
Instrument: Fiscal risk management via index-based weather derivative delivers timely and
guaranteed contingent funds in case of emergency
ARM Strategy: Transfer
Relevance for Paraguay: Paraguay faces low frequency, severe production risks like drought that
necessitate occasional large and urgent Government expenditures. In January 2012, the
Government declared a state of food emergency in southeastern Paraguay due to drought, and the
Ministry of Agriculture estimated that 30 to 50 percent of agricultural production would be lost.39
Food insecurity was exacerbated by difficulties in transport of food, as low water levels limited
commercial shipping along Paraguay’s rivers and canals. Even though Paraguay is a net foodexporting country, like Malawi, it has a high number of farmers that rely on their own production
for food security and faces food shortages and pressure for emergency responses.
Malawi’s index-based weather derivative transfers the financial risk of severe and
catastrophic national drought to the international risk markets with the World Bank as
intermediary. Malawi has a high exposure to the risk of drought and food shortage. For a foodimporting country with a high portion of the population dependent on agriculture, Malawi faced
widespread hunger in 2005 when a severe drought struck. Millions of farmers needed food aid.
The Government of Malawi spent $200 million responding to the crisis and donors contributed
similar funds.
Instead of waiting for international relief funds to mobilize, the Government of Malawi
receives a payout from the World Bank if the index hits the pre-determined trigger. The
derivative gave nearly immediate access to Malawi to funds to respond to the crisis, thereby
reducing the country’s dependence on humanitarian aid. Weather-risk management transactions
can be customized according to countries’ specific needs, the type of weather hazard, level of
protection, and estimated financial loss associated with a severe and catastrophic event. Drought
can be predicted and yield loss correlates closely with rainfall in the case of Malawi. The
Government of Malawi has stopped purchasing a derivative but is now considering financing
through international financial institutions including a draw down option.
Pre-requisites for a weather derivative contract: i) index: an index that dependably captures
national hazard (e.g. drought) risk; ii) data: high quality historical weather data and reliable realtime communication; iii) premium: an annual, non-refundable premium must be paid by the
“insured” party or a donor; iv) integration: into a larger risk-management strategy.
Table 3.4: Malawi’s study case. Benefits, challenges and considerations for Paraguay

39
Benefits
Payout is timely and
guaranteed in time of need
because it is index-based and
is independent of actual
production assessments

Challenges
Basis Risk: the potential
mismatch between the
contract payout and the
actual maize production
losses whereas the payout
USDA (2012).
45

Considerations for Paraguay
Which catastrophic events is
Paraguay most vulnerable to, and
what are the current coping
measures?

Creates opportunities to
access the market for risk
transfer. Systemic risk can be
transferred from a low-income
country to investors.
 Cost savings: through early,
more efficient and planned
response to weather shocks
due to predictable crisis
financing.
 Strengthens Government’s
ability to finance responses to
natural disasters, reducing the
country’s reliance on
humanitarian emergency
appeals.
 Less fiscal volatility via
improved budget planning
Source: authors.

does not adequately
indemnify the Government
for losses. The index also
only covers losses from a
certain pre-specified
shock. Indexed risks: the
contract only covers risks
that can be indexed – not
other natural and manmade risks to food
production. Setting up an
index requires historical
crop and weather data and
an adequate network of
weather data stations.
Premium: these
transactions have an
upfront cost.




Does Paraguay face comparably
severe and frequent risks? Will costsavings from weather derivative
justify this choice of instrument?
Malawi counted on donors to help
finance the premium for the weather
derivative; can Paraguay garner such
support?
Does Paraguay have availability of
historic weather data to build index?
Paraguay is a larger country than
Malawi and may be better suited to
diversify risks across Departments or
sectors, rather than transfer them.
What are other, less costly measures
to ensure food security?
Case studies on managing production risks—case study 4: index-based livestock
insurance program in Mongolia
Instrument: Contingency lines of credit to fund emergency response activities and payments,
based on livestock mortality index insurance.
ARM Strategy: Transfer
Relevance to Paraguay: Paraguay livestock production is concentrated in the Chaco, a region that
faces high exposure to weather shocks like drought.
In Mongolia, harsh winters occur roughly once every five years, killing millions of livestock
and devastating the basis of the livelihood for nearly half Mongolia’s population. Roughly a
third of aggregate GDP derives from the agriculture sector, of which nearly 80 percent comes from
herding. The rural population relies heavily on livestock for income, employment, food security,
and a means to invest wealth. Recent dzud events occurred in December 2009 and January and
February 2010.
Beginning in 2006, the World Bank helped the Government of Mongolia develop the IndexBased Livestock Insurance Program (IBLIP), which is a combination of self-insurance,
market-based insurance, and social safety net. Layers of risk are allocated to different actors
depending on severity. Herders assume small, frequent losses. Larger losses are transferred to the
private insurance industry, for which herders pay a market premium rate. The Government of
Mongolia bears the cost for the catastrophic loss risk layer.
Since the project’s inception, insurance policies have become more and more popular among
herders. Increasing numbers of farmers are purchasing insurance. After the first phase of the
project in 2010, over 14,000 insurance policies had been sold. In 2009, indemnity payments were
made to all 2,117 herders who were eligible following livestock losses. Local insurance firms
remain committed to selling the product.
46
As an index-insurance product, insurance payouts do not compensate for individual livestock
losses, but rather are triggered for a micro-region when the livestock mortality rate in the
region exceeds a specific threshold. Good data makes this product possible. Since the insurance
is not linked to the dzud event, the program relies on Mongolia’s three decades of time-series data
on animal mortality per micro-region and for all species of livestock. After a specified “exhaustion
point” that varies based on species and location, insurance companies are not liable and the
Government financed and operated safety net program is mobilized.
Index-based mortality insurance was chosen for its relative simplicity, low cost, and low risk
of moral hazard and adverse selection. Alternatives considered include individual insurance
coverage to herders and index-based weather insurance. Individual coverage has not been
successful in Mongolia due to moral hazard, adverse selection, high administration costs, and an
immature private insurance market. Index-based weather insurance was also considered but
Mongolia does not have the historical weather data necessary to design a weather index. The dzud
events themselves are also complex phenomena that are influenced by summer rainfall, winter
snowfall, temperature, and wind.
The Government of Mongolia was able to turn to international markets with a Contingent
Debt Facility to finance these risks. By pooling risk, the Government of Mongolia could obtain
global reinsurance on the pool. Such a contingency line of credit funds the Government’s
emergency response. This is considered to be a more efficient way to provide subsidy.
Furthermore, the partnership with the private insurance sector makes it possible for the insurance
to stand on its own. If the Government of Mongolia decides to end the subsidy, the livestock risk
insurance can still be sold.
Table 3.5: Mongolia’s study case. Benefits, challenges and considerations for Paraguay






Benefits
Risk layering
Relatively simple
Reduced risk of
moral hazard and
adverse selection
Ex-ante budget
planning to reduce
fiscal exposure to
emergency events
Promote good
management
practices for herders
Program was
piloted to test
several hypotheses
before full scale
implementation




Challenges
Willingness to pay
Willingness to pay: given the risk
layering approach, farmers pay
for small, frequent risks but are
covered for larger losses.
Substantial outreach was
necessary to educate about this
new program and encourage
farmers to purchase the insurance.
Domestic insurance market: The
domestic insurance market is very
small and highly concentrated,
with the largest insurance
company at a market share of 74
percent. The IBLIP invested in
capacity building and eventually
helped develop the market and
encourage new insurance
companies to enter and stronger
products.
Significant outreach necessary to
educate about insurance project
47




Considerations for Paraguay
What incentives to purchase insurance
do farmers (in particular smaller ones)
face? Does insurance increase access to
credit in Paraguay? What is the
willingness to pay of Paraguayan
farmers for such a product?
Same insurable asset across territory:
Mongolia’s reliance on livestock
production is unique. To have a large
number of producers in different
regions across the country with the
same insurable asset (livestock)
facilitated risk pooling. In contrast, the
majority of livestock raised in Paraguay
is on large ranches and does not have
the same significance to rural
livelihoods.
State of the insurance and reinsurance
market; would similar risk layering be
appropriate, or do farmers prefer other
types of coverage?
Is saving/borrowing the appropriate
tool for the public sector fiscal


Significant capacity building
necessary in the insurance sector
Premium on transferring the risk
at the catastrophic layer
management (rather than transfer of
risks) given the scale of catastrophic
events in Paraguay?
Source: authors.
Case studies on managing market risks
Table 3.6: Instruments for Managing Market Risk
Strategy
Mitigate
Problem
Peru’s exports have
been historically
concentrated in few
commodities (minerals
and fishmeal),
influencing
agricultural GDP
volatility
Problems with
Transfer
enforcing forward
contracts and domestic
price formation.
Transfer Reduce income
volatility for growing
agriculture supply
chains.
Source: authors.
Instrument
Diversification of
Agriculture Sector
Subsidies for
commodity price
risk hedging
contracts
Development of
Agriculture
Commodity
Exchange
Description
Historically dependent on
traditional exports of raw
materials, Peru diversified into
high-value non-traditional
agricultural exports through public
private partnerships. Now the
world’s leading producer of
asparagus.
Subsidize premiums on options
contracts bought in international
markets in order to encourage
physical forward contracts.
Develop local commodity
exchange to offer local
futures/options contracts accessible
to local agribusiness
Case Study
Peru:
Development
of the
Asparagus
Market
Mexico:
AxC
Argentina
(ROFEX and
MATBA
exchanges)
Case studies on managing market risks—case study 1: development of the
asparagus market in Peru
Instrument: Diversification of agricultural sector by promoting non-traditional export products
ARM Strategy: Mitigation
Relevance to Paraguay: Soy and beef alone comprise over a third of Paraguay’s total exports.
Given this high concentration of economic activity in two commodities, Paraguay is exposed to
adverse shocks in terms of trade. Volatility in price (both of inputs and exports) and exchange and
interest rate volatility increase revenue uncertainty. Paraguay could mitigate risk by diversifying
its portfolio of exports. Other countries like Peru have employed different methods in the past to
diversify production and support non-traditional exports.
Peru’s economy has suffered from strong swings in terms of trade due to concentration of
export products. Exports in Peru have historically concentrated on a few primary products,
mainly fishmeal and minerals. Export diversification is a strategy to reduce market risk. Resourcerich, Peru has tried a number of policies to diversify the export base, moving from protectionist
policies in the 1970s and 1980s towards liberal reforms in the 1990s.40
40
Illescas, Javier and Jaramillo (2011).
48
Non-traditional agricultural exports have increased and diversified significantly in the past
decade. Peru is expanding exports of products that have not been previously exported as well as
increased the export of products to new destination markets. From 2000 to 2005, the average
annual rate of growth for non-traditional agricultural exports was 20 percent and reached a value
of $1.02 billion in 2005.41
Asparagus has been one of the most successful non-traditional agricultural exports, with a
25 percent share of the total value of all non-traditional exports in 2005.42 Public and private
sector cooperation in the development of the asparagus industry, coupled with favorable
exogenous economic conditions and opportunities, contributed to investments in quality
improvements, product safety, logistics efficiency, and coordination of actors along the supply
chain. The Government lent support to the expansion of drip irrigation which was necessary for
asparagus to take off. The Peruvian export promotion agency, helped establish a non-profit, the
Peruvian Institute of Asparagus (IPE), which went on to negotiate for preferential US tariffs for
Peruvian asparagus and develop integrated pest management and sanitary certifications. The
public sector also helped with coordination issues between importers and exporters and
improvements in logistics efficiency like a cold chain organized by Frio Aereo. 43 Today, Peru is
the world’s leader in exports of green asparagus.
There are many instruments for Governments to promote export diversification. Such
measures include decoupled subsidies to promote diversification (in compliance with the WTO)
and removing subsidies for traditional crops.
Table 3. 7: Peru’s study case. Benefits, Challenges and considerations for Paraguay


Benefits
Non-traditional
exports can represent
a high-value niche
market and be
especially profitable
given first-mover
advantages.
Diversifying
production can offset
volatility in the
markets for other
products.




Challenges
Coordination and clarity in roles between
public and private sector actors to align
incentives, knowledge transfer, and
marketing. Identifying market failures
(and Government failures) help
determine roles for the public and private
sector.
Switching to non-traditional, high-value
export products require complementary
investments in logistics, technology, and
inputs. Access to credit and markets are
also important factors for success.
Niche markets are also subject to
volatility in demand and supply and will
evolve over time as other producing
countries enter the market.
Equitable access to new agricultural
technology and crops can be difficult; not
all farmers are able to switch due to
capital constraints and lack of market
integration.
41
Rios (2007).
Ibid.
43 Shimizu (2006).
42
49




Considerations for Paraguay
Analyze current subsidies and
export engagement to ensure
alignment with development
objectives
Select the appropriate new
products to fit Paraguay’s
development goals, assets, and
comparative advantage
Define public sector
engagement. Subsidies should
meet criteria for efficiency and
be compliant with WTO
regulations.
The ecological impact of
expanding new crop should be
considered (Peru is facing
issues with water consumption
of asparagus production).
Poorly managed natural
resources can increase risk in
the medium to long term.
Source: authors.
Case studies on managing market risks—case study 2: support for forward
contracting and price hedging in Mexico
Instrument: Promotion of forward contracts and subsidies for premiums on options (derivative)
contracts bought in international commodity exchanges
ARM Strategy: Transfer
Relevance for Paraguay: The three main agriculture commodities in Paraguay (soy, meat and
maize) have liquid futures/options markets where coverage against price fluctuations can be
bought. Currently, agro-exporters are (for the most part) the ones in Paraguay who purchase these
price hedging contracts, as well as some large and integrated farmers (in particular those selling
through the Brazilian market). However, the Government and small farmers are left retaining the
risk of such commodity price volatility, undermining shared prosperity and their capacity to
accumulate capital and smooth income.
Agricultura por Contrato (AxC) is a price risk management program initiated in 2001 as
part of a broader program called “Programa de Prevencion y Manejo de Riesgos”. This larger
program included sub-programs to support the production of specific crops, the commercialization
and export of specific crops, quality certification, access to grains for animal production, and
contract farming for livestock producers. The two main subprograms are: i) establishment of fixed
bases (differentials) over Chicago Board of Trade (CBOT) futures prices for each of the two main
agricultural seasons in a given year and provision of compensation to producers and consumers
when prices moved away from those fixed bases, hereafter referred to as the bases compensation
program; and ii) co-financing of the purchase of options (puts and calls) used to hedge the physical
forward contracts agreed between producers and consumers, hereafter referred to as the hedging
program.
Mechanically, the risk management programs provide compensation which protects
participants from volatility in the physical price of key commodities for a select set of
Government-supported forward contracts, called AxC contracts. This is designed to
encourage producers and consumers to engage in more forward contracting, which in turn supports
the commercialization of agricultural trade.
Forward contracts for the central commodities covered by the program (yellow maize,
wheat, sorghum) are typically priced by taking the CBOT futures prices and adding a
premium. 44 The premium is calculated as a differential (bases) that reflects local supply and
demand conditions, and the costs of logistics, insurance, and financing. Producers and consumers
agree on these contracts at the beginning of a production cycle (pre-harvest), but do not deliver
and settle the contracts until the end of the production cycle three to six months later (post-
44
Cotton, coffee, orange juice, pork, beef, and white maize were added in 2010.
50
harvest).45 In between, the bases component of the price can fluctuate, creating risk for both the
producer and the consumer. The diversity of Mexican agricultural production/consumption
patterns, along with its size and geography, create high levels of differentiation between markets.
This means that the number of bases differentials, which correspond to individual commodities
and production/consumption zones, is high.
The bases compensation program targets the estimated bases price levels, above the CBOT
futures price, for each of the two main harvest seasons (spring/summer and winter/fall).
Estimated bases levels for specific commodities produced in targeted states are announced at the
beginning of the season, and are used to establish the pricing for the physical AxC contracts agreed
between the producers and consumers. The bases levels are derived using a formula that starts with
the CBOT futures price, and then adds the costs for physical delivery of maize from the US to a
specific consumer zone in Mexico (referred to as the Standardized Base Consumer Zone). Costs
for physical delivery from a local production zone to the consumer zone (referred to as the
Standardized Base Production Zone) are then subtracted to determine a producer price. At the end
of the season, actual bases price levels are calculated using the average of prices for physical
transactions observed during the first fifteen days of the harvest (for each corresponding crop and
production cycle) and the actual transportation costs observed during the same time window. The
program then provides compensation to producers and consumers for negative movements
between the estimated bases and the actual bases which may have occurred during the period
between agreement (pre-harvest) and settlement (post-harvest) of the forward contract. When the
actual calculation of the bases price is higher than the estimated level, payment goes to the
producer, thereby ensuring that he/she is being compensated for the increase in prices reflected by
the current market at harvest time. When the actual calculation of the bases price is lower than the
estimated level, payment goes to the consumer thereby ensuring that he/she is able to take
advantage of the decrease in prices reflected by the current market at harvest time.
The hedging program targets the CBOT futures price which is used as the price reference
for negotiation between producers and consumers of the AxC physical contract. Under the
program previously managed by ASERCA, producers and consumers entering into AxC contracts
were provided with option contracts which hedged the CBOT futures price level fixed in the AxC
contract. The main objective of this approach was to reduce the incentive to default on the forward
AxC contracts in the event of favorable price movements (up for producers, down for consumers)
which could occur in between the time of contract agreement (pre-harvest) and contract
settlement/delivery (post-harvest). The hedging program therefore provided producers with a call
option (which would provide a payout if market prices increased) and consumers with a put option
(which would provide a payout if market prices decreased). ASERCA took responsibility for
purchase of the option contracts, which were also settled by ASERCA at the end of the season.
Table 3.8 summarizes the levels of subsidy support provided to producers and consumers under
the program as managed by ASERCA. It is important to differentiate between the two different
types of subsides: for the bases compensation program, Government provides a cash payment
which offsets movements in the bases price between pre- and post-harvest period; for the hedging
Throughout this chapter the word “consumer” refers to buyers of the physical commodity, generally local and
international agribusinesses, trading companies, and market intermediaries. The word “final consumer” refers to
individual who is a purchaser of a food product, typically purchasing that product at further points along in the supply
chain.
45
51
program, Government provides a subsidy to cover a portion of the cost (premium) used to purchase
a CBOT option contract. Recently the ASERCA program has been reactivated and now coexists
with a newer program.
Table 3. 8: Subsidy Components, AxC Program, ASERCA
Component
Bases compensation program
Producer
Differential between estimated
standardized bases and actual, if
actual > estimated
Hedging program
80 percent coverage for purchase of
a call option
Source: adapted from materials provided by ASERCA, August, 2012.
Consumer
Differential between estimated
standardized bases and actual, if
actual < estimated
70 percent coverage for purchase of
a put option
Table 3. 9: Mexico’s study case. Benefits, Challenges and considerations for Paraguay


Benefits
Income smoothing for
both buyers and sellers of
agriculture commodities
in Mexico
The effects of an increase
in the use of forwards in
a market can change
investment decisions by
buyers and sellers of the
commodity.




Challenges
Very costly program, which is
undergoing reforms due to budget
constraints.
The program (as currently design)
does not promote the development of
a local futures/options contract.
Basis risk (price differential in prices
between CBOT and domestic
markets).
Program eligibility is broad, and thus
most benefits go to larger
producers/buyers who may not need
the support.
Considerations for Paraguay
Who would be the target
beneficiaries of such program
in order to ensure the
progressivity of the subsidy?
 How to promote the use of the
price hedging instruments by
farmers (education)?
 What to do about the price
differential between Paraguay
and the Brazilian, Argentinean
and Chicago market prices?

Source: authors.
Case studies on managing market risks—case study 3: development of a local
futures and options contract in Argentina
Instrument: Soy futures contract at ROFEX, Rosario’s Agricultural Futures and Options
Exchange
ARM Strategy: Transfer
Relevance for Paraguay: As in Argentina, Paraguay exports soy and most of soy exporters of
Paraguay are a subsidiary or part of the same company that operates in Argentina. On the other
hand, small farmers in Paraguay do not have access to such hedging instruments, while in
Argentina they do through cooperatives operating in local exchanges.
The Bolsa de Comercio de Rosario (BCR) (established in 1884), is a not-for-profit
organization based in Rosario, Province of Santa Fe, Argentina. BCR is the main grain cash
market of Latin America, larger than Kansas [KCBT] and second in rank to Minneapolis [MPLS]
in the U.S.46 It serves as a forum for the conduct of trade negotiations in several markets. It hosts
four Divisions, Markets, or Exchanges: (i) the BCR itself as a huge cash market in several
agricultural products and other services, like modern grain and oilseed laboratories; (ii) cámara
46
In 2010 KCBT delivered 775,200 tons; MPLS 234.2 million tons # 1 in tonnage delivered worldwide.
52
Arbitral de Cereales de la Bolsa de Comercio de Rosario S.A.; (iii) mercado a Término de Rosario
S.A. -Rosario Futures Exchange – ROFEX- [1909]; and (iv) mercado de Valores de Rosario S.A.
[The Rosario Securities Exchange]. Another recent market could be added (v) an independent
virtual Cattle Market via TV weekly sessions.
The BCR’s physical or grain cash-market is the most important in Argentina and in Latin
America, in terms of weight and value, also providing reference prices for the main national
and international grain markets. The BCR additionally operates a huge complex of laboratories
through other of its affiliations, Cámara Arbitral de Cereales de Rosario S.A. [CACBCR], where
thousands of samples are (anonymously) analyzed using code bars, providing certainty to clear
disputes and quality certifications for all type of agricultural products, by-products, and soil and
water analysis, as well.
Based on a dynamic and well established cash market, ROFEX was founded in 1909 as the
“Mercado General de Productos Nacionales” (General Exchange of National Commodities).
In 1924 this market traded futures and forwards of linen (1.3 million tons); wheat (3.7 million
tons); and corn (3.3 million tons in 1929). The market activity went on until trading was hampered
by interventionist Governments (1932 recession, World War II, etc.) Government intervention and
subsequent chronic inflation. ROFEX has traditionally been a futures exchange for commodities.
Between the late 1930s and 1980s, the Exchange was used by Government as a regulatory
agency and for official grain purchases. At the start of the nineties (1991-1993) the Government
allowed the negotiation of live-cattle and grain futures and options contracts in US dollars, the
second being a cash-settled soybeans contract, named “Índice Soja Rosafé” or ISR,47 the main
agricultural hedging instrument in Rosario, among other standardized delivery contracts (corn,
wheat, soybeans, and sunflower) traded at the Buenos Aires Futures Exchange (Mercado a
Término de Buenos Aires S.A., MATBA), located at the Buenos Cereal Exchange (Bolsa de
Cereales de Buenos Aires, BCBA).
Table 3.10 : Comparison CBOT, MATBA, &
ROFEX
CBOT
136
Tons
U$s 3.375,Margin
u$s 1,90
Fee
u$s 10,Spread
Source: authors.
MATBA
100
u$s 1.632,0,5% s/MC
u$s 20,-
ROFEX
30
u$s 1.632,u$s 12,20
u$s20,-
The success in launching a new futures/options contract on agricultural commodities is the
value added to market participants. In the case of Argentina and ROFEX, this was not only
based on the price differential between the Argentine Soy Market and CBOT, but also on the size
of the CBOT contracts. ROFEX offers a smaller contract, which makes it possible for smaller
market participants (farmers, cooperatives, intermediares, buyers) to access such price hedging
products and in the local currency (Table 3.10).
By the end of 2010 the ISR represented more than 75 percent of all Rofex’s contracts. In 1991 and 1992 the ISR’s
options on futures was the first of its kind in Argentina.
47
53
There are some pre-conditions that are important for Paraguay to consider in terms of
having a local futures contract for soy or any other agriculture commodity (livestock, maize).
The main ones include: i) market size, liquidity and minimum volume of contracts: a reduced
number of agribusinesses (i.e. monopsonies) reduces the benefit from trading using standard
contracts or from making the price of financial or physical products public. There need to be
sufficient contracts for physical commodities (or financial products) so that there is competition
for buying, selling, and keeping long or short open positions, so that price volatility is reduced due
to the law of large numbers; ii) institutional and regulatory framework: A set of clear and
transparent rules for market participants and potential entrants is important. The lack of transparent
rules for entrants, for instance, has stifled competition in some countries. Regulations about
membership procedures and limits, or minimum capital and trade requirements, should be
negotiated by participants and published. Furthermore, exchanges may attract both local and
international capital. Consequently, financial regulations will be required up to international
standards. Sound financial management is also required, since international investors may demand
dollar or euro-based contracts; iii) committed agribusinesses: agribusinesses are essential for the
development of agricultural futures contracts, as they are the main beneficiaries. Supermarkets,
processing and trading plants, food exporters/importers, agricultural input suppliers, and farmers
must see the benefit of such contracts. Some of the elements that agribusinesses consider for
deciding whether to support or not trading through the exchange are (the list is not exhaustive): (i)
tax benefits; (ii) benefits from having a locally publicly known reference price; (iii) benefits from
a third party quality control mechanism; (iv) in some cases the quality standards, controls and
grades agreed to be traded through the exchange; and (v) arbitration mechanism. Another aspect
for agribusiness is the value added of the contract versus contracts already being traded in other
exchanges nearby. There needs to be a differentiation of contracts to avoid competition and to
lower basis risk. 48 Because international agricultural commodity exchanges already carry a wide
variety of standard agriculture commodity contracts, a futures contract in Paraguay will struggle
if it replicates an already existing contract, in particular for commodities whose prices are highly
correlated with international markets.
Table 3.11: Argentina’s study case. Benefits, challenges and considerations for Paraguay
Benefits
Challenges
Considerations for Paraguay





Increase access to agriculture price
hedging instruments by all market
participants (in particular smaller
buyers and sellers).
Reduce the cost and basis risk of
commodity price hedging for all
market participants, in particular in
relation to exchange rate risk and
price differentials.
Promoting local agriculture price
formation


Achieve enough liquidity to
sustain the local
futures/option
Strengthen the commodity
exchange and the regulator
to operate and supervise the
market.
Educate the potential market
participants in the usage and
operation of futures/options
contract.
48


Examine price differentials
between domestic and
international commodity
markets.
Discuss value added of new
contracts with agribusinesses
and agroexporters.
Identify what commodities
could be initially traded in
order to create success stories
to gain momentum.
Basis risk here is defined by the difference in price movements between the international market (i.e. CBOT) and
the domestic market that would trade such commodity. If the basis risk is low, agribusinesses would not see the need
to develop a local commodity exchange, and would thus use the international market to trade/hedge. In many cases
exporters and arbitrageurs would go long buying cash, forward or futures contracts in one market and, simultaneously
or at a later time, would sell or go short in the delivery market.
54
Source: authors.
3.3
A comprehensive approach to managing volatility
It is important to develop a comprehensive macroeconomic risk management framework
that takes all different sources of volatility and risks into account. Different sources of
volatility do not occur in isolation; the analysis in this study has shown that there are many
interrelations between different variables; some may lead others, or there may be self-reinforcing
mechanisms at play as for example in the case of investment and growth volatility. In addition,
policy options available to address these different sources of volatility also feed back into the
system, partly in the desired way and partly with side effects. For example, what would be the
budget implications for Paraguay if the Government were to introduce a contingency fund for
small farmers similar to the Mexican CADENA program? How big would the fund have to be to
effective and to have a distributional impact? How big would be potential contingent liabilities
and how would this impact fiscal sustainability? What would be the distributional impact of an
introduction and how would that fit with the overall design of the social protection system? These
interrelations and examples illustrate the need for a careful assessment of choices and emphasize
the need for a comprehensive macroeconomic risk management framework to develop priorities
under the constraints of overall macroeconomic management.
Taking a broader perspective is recommended because it allows for finding optimal ways to
manage the observed volatility and risks. For instance, looking narrowly at ways of managing
commodity price volatility one would find a set of useful tools, and yet, this approach would not
include considering a set of policy options which could be effective in the medium- and long-run
and could at the same time also alleviate additional challenges that the Government in Paraguay is
facing: Government taxation itself contributes to a situation in which agriculture weighs heavy in
Paraguayan GDP and exposes the country to the fluctuations of commodity prices. The fact that
the agricultural sector is practically exempt from taxation, despite being one of the most important
corporate sectors of the country, introduces a distortion into the economy which sets disincentives
for entrepreneurs to explore business opportunities in other sectors. Moreover the low taxation of
agriculture contributes to the low tax-to-GDP ratio in Paraguay, which is a major challenge to the
public provision of social services. Certainly, addressing tax distortions is complementary to
looking for more targeted ways to manage a specific type of volatility. And the strategy of
assessing possibilities to diversify the economy is also linked back to specific tools of agricultural
risk management as illustrated by the Peruvian case study and the development of asparagus
market.
The policy options presented in this study are intended as a basis for a dialogue on possible
steps towards further improved management of growth volatility in Paraguay. The analytical
background, which provides some information on the sources and the effects of growth volatility
in Paraguay is intended as a starting point for further research on the topic and a dialogue on
possible policy options. The options presented in this chapter need to be carefully assessed from
the vantage point of applicability to Paraguay. They are an invitation for discussion and further
investigation. Questions whether the institutional framework in Paraguay is adequate for hosting
a fiscal rule or a stabilization fund, or whether weather data has sufficient quality to allow for the
creation of weather related derivatives need to be assessed. The Government and the World Bank
55
have been engaging in a dialogue on this topic through the preparation of this study and with a
joint agricultural risk management assessment.
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Annex 1.1: Volatility over time, international comparison
Standard deviation of Paraguay’s GDP growth and output gap in international comparison
std dev (GDP growth)
std dev (GDP gap)
1960196020001960196020002011
1999
2011
2011
1999
2011
Argentina
5.83
5.56
6.73
5.66
5.20
6.86
Bahamas, The
7.16
7.91
2.62
7.87
8.84
2.95
Barbados
4.54
4.61
3.51
4.46
4.69
3.62
Belize
4.03
4.17
3.56
4.81
5.30
2.60
Bolivia
3.52
3.93
1.31
3.98
4.49
1.37
Brazil
4.11
4.51
2.29
3.84
4.30
1.63
Chile
4.64
5.21
2.02
4.50
5.05
1.73
Colombia
2.21
2.35
1.77
2.31
2.21
2.56
Costa Rica
3.34
3.49
2.85
3.32
3.49
2.77
Cuba
6.36
7.02
3.65
6.52
7.27
3.90
Dominican Republic
5.26
5.75
3.36
4.63
4.98
3.41
Ecuador
3.55
3.82
2.48
3.17
3.25
2.94
El Salvador
4.18
4.66
1.84
4.63
5.20
1.89
Guatemala
2.49
2.73
1.46
2.56
2.83
1.40
Guyana
5.22
5.74
2.84
5.18
5.69
2.61
Honduras
3.04
3.24
2.42
3.09
3.17
2.92
Jamaica
5.03
5.18
0.33
5.20
5.31
0.26
Mexico
3.78
3.78
3.34
3.25
3.39
2.82
Nicaragua
6.23
7.06
1.96
5.70
6.41
2.12
Panama
4.40
4.56
3.67
4.14
4.34
3.43
Paraguay
4.28
3.88
5.50
4.31
4.22
4.45
Peru
5.03
5.39
3.14
5.01
5.53
2.69
Puerto Rico
3.55
3.10
2.78
2.79
2.73
3.06
Suriname
5.24
5.69
2.10
4.50
5.15
2.68
Trinidad and Tobago
4.99
4.70
5.71
5.36
4.98
6.62
Uruguay
4.44
4.26
5.12
5.37
5.28
5.53
Venezuela, RB
5.32
4.36
7.90
5.17
3.90
8.24
LAC mean
(excluding Paraguay)
4.52
4.72
3.11
4.50
4.73
3.18
LAC median
(excluding Paraguay)
4.49
4.59
2.81
4.57
4.98
2.80
Mercosur
(excluding Paraguay)
3.53
3.68
3.10
3.37
3.47
2.99
East Asia & Pacific
(all income levels)
2.77
2.90
1.83
2.01
2.12
1.64
Europe & Central Asia
(all income levels)
1.89
1.71
2.22
1.61
1.47
2.04
63
Middle East & North
Africa (all income levels)
South Asia
3.78
4.35
1.65
3.11
3.53
1.64
2.62
2.58
2.05
1.89
1.93
1.79
Sub-Saharan Africa
(all income levels)
Lower middle income
Upper middle income
OECD members
2.13
2.21
1.37
1.71
1.76
1.59
1.66
2.12
2.01
1.58
1.95
1.77
1.50
2.22
2.06
1.45
1.77
1.48
1.44
1.63
1.37
1.54
2.24
1.85
Source: World Development Indicators, and Central Bank of Paraguay.
Annex 1.2: Volatility breaks of macroeconomic variables in Paraguay
Variable
Date
Direction
of change
in volatility
Coefficient
of variation
Standard Deviation
Mean
Entire Period Before Break After Break*
Entire Period Before Break After Break*
GDP
single breakpoint
2008-IV
Increase
1.8
4.8
0.0
4.0
0.0
7.0
0.0
2.7
0.0
2.6
0.0
3.2
0.0
Agriculture sector
single breakpoint
2008-IV
Increase
3.1
11.9
0.0
6.3
0.0
22.5
0.0
3.9
0.0
4.9
0.0
0.4
0.0
Non -agriculture sector
No change
No change
1.6
4.2
0.0
-0.0
-0.0
2.5
0.0
-0.0
-0.0
Total investment
No change
No change
6.3
14.5
0.0
-0.0
-0.0
2.3
0.0
-0.0
-0.0
Private investment
No change
No change
6.6
22.2
0.0
-0.0
-0.0
3.4
0.0
-0.0
-0.0
Private consumption
No change
No change
1.8
5.4
0.0
-0.0
-0.0
3.0
0.0
-0.0
-0.0
Inflation
single breakpoint
1995-II
Increase
0.5
4.6
0.0
3.2
0.0
3.7
0.0
8.9
0.0
18.7
0.0
8.0
0.0
Soy price
single breakpoint
2003-III
Increase
3.2
27.1
0.0
18.2
0.0
31.6
0.0
8.5
0.0
0.9
0.0
14.8
0.0
Oil prices
No change
No change
2.1
36.8
0.0
-0.0
-0.0
17.5
0.0
-0.0
-0.0
Beef price
No change
No change
2.3
14.9
0.0
-0.0
-0.0
6.3
0.0
-0.0
-0.0
first breakpoint
2001-III
Increase
7.2
8.6
11.5
35.1
second breakpoint
2003-II
Decrease
6.2
6.9
third breakpoint
2008-I
Increase
0.0
0.0
12.6
0.0
0.0
0.0
-1.4
0.0
RER
No change
No change
8.3
10.5
0.0
-0.0
-0.0
1.3
0.0
-0.0
-0.0
TOT
No change
No change
26.1
16.0
0.0
-0.0
-0.0
0.6
0.0
-0.0
-0.0
2007-I
Increase
3.4
146.6
0.0
97.0
0.0
202.9
0.0
43.3
0.0
18.4
0.0
74.7
0.0
-1.3
0.0
Nominal Exchange rate
Current account balance
single breakpoint
World real interest rate
single breakpoint
Public consumption
Public investment
-5.9
2007-IV
Increase
2.2
2.4
0.0
1.9
0.0
2.4
0.0
1.1
0.0
1.8
0.0
No change
0.7
7.9
0.0
-0.0
-0.0
12.0
0.0
-0.0
-0.0
first breakpoint
2002-IV
Decrease
11.4
8.1
-16.6
second breakpoint
2004-II
Increase
0.0
14.2
0.0
16.5
0.0
12.3
0.0
26.3
0.0
No change
No change
0.7
9.6
0.0
-0.0
-0.0
13.2
0.0
-0.0
-0.0
single breakpoint
2004-III
Decrease
1.0
15.0
0.0
21.2
0.0
8.7
0.0
14.4
0.0
14.6
0.0
14.2
0.0
2.1
7.5
0.0
3.1
6.2
0.0
6.9
0.0
55.6
17.0
Total revenue
Tax revenue
14.5
No change
Interest rate
Credit to private sector
2.1
first breakpoint
2000-II
Increase
second breakpoint
2009-I
Increase
first breakpoint
2002-II
Decrease
second breakpoint
2008-II
Increase
1.1
3.3
Source: Central Bank of Paraguay.
64
17.4
0.0
38.4
38.3
3.7
0.0
0.1
1.7
0.0
13.6
0.0
14.9
11.5
5.1
16.6
65
Annex 1.3: Graphs on volatility breakpoints Inclan Tiao (1994) by variable
Current account balance and volatility breakpoints
16
Stddev. of Beef
15
10
5
8
6
200
150
100
-15
4
-100
-25
2
-200
-35
0
-300
50
stddev.
Sample statistics for entire series: Mean=6.3; Standarddeviation=14.9
4
3
3
2
1
0
Private consumption
breaks
Jan-12
Jan-11
Jan-10
Jan-09
Jan-08
Jan-07
Jan-06
Jan-05
Jan-04
23
7
18
6
13
5
8
4
3
3
-2
2
-7
1
-12
0
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
-7
8
Percent growth rate
Percent growth rate
5
-2
stddev.
Public consumption and volatility break-points
Stddev. of oil private consumption
6
8
breaks
Sample statistics for entire series: Mean=43.3; Standarddeviation=146.6
Private consumption and volatility break-points
13
Jan-03
Current account Balance
stddev.
Public consumption
Sample statistics for entire series: Mean=3.0; Standarddeviation=5.4
breaks
Sample statistics for entire series: Mean=3.7; Standarddeviation=7.5
Source: authors.
66
stddev.
Stddev. of oil public consumption
breaks
0
Jan-00
Jul/ 97
Jul/ 98
Jul/ 99
Jul/ 00
Jul/ 01
Jul/ 02
Jul/ 03
Jul/ 04
Jul/ 05
Jul/ 06
Jul/ 07
Jul/ 08
Jul/ 09
Jul/ 10
Jul/ 11
Jul/ 12
Beef price
100
0
Jan-02
-5
200
300
12
USD million
25
Percent gropwth rate
250
400
14
Jan-01
35
Stddev. of current account balance
Beef price and volatility break-points
-7
4
-17
2
-27
0
Credit
GDP
Interest rate
breaks
Sample statistics for entire series: Mean=16.5; Standarddeviation=17.4
12
7
6
5
2
4
3
-3
2
-8
breaks
Sample statistics for entire series: Mean=2.7; Standarddeviation=4.8
30
25
7
20
6
5
15
4
10
3
5
2
1
0
0
breaks
stddev.
Sample statistics for entire series: Mean=12.0; Standarddeviation=7.9
67
Jan/ 94
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
4
8
0
9
2
2
stddev.
GDP and volatility break-point
stddev.
Domestic Interest rate and volatility breakpoints
2
0
1
-2
1
-4
0
International interest rate
Inflation
Private investment
breaks
7
22
17
12
7
1
2
breaks
breaks
Stddev. of GDP
16
4
3
3
3
3
8
50
25
30
20
10
15
-10
10
-30
5
-50
0
Sample statistics for entire series: Mean=3.4; Standarddeviation=22.2
Stddev. of Inflation
3
6
Interest rate
53
Stddev. of private investment
8
Jan/ 94
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
13
Percent
10
Stddev. of credit to private sector
12
Percent growth rate
23
St ddev. of GDP
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Percent growth rate
33
Stddev. of interest rate
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Percent growth rate
43
14
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan/ 94
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Interest rate
Credit to private sector and volatility breakpoints
International interest rate and volatility
break-points
6
3
3
stddev.
Sample statistics for entire series: Mean=1.1; Standarddeviation=2.4
Inflation and volatility break-points
4
4
3
3
stddev.
Sample statistics for entire series: Mean=8.9; Standarddeviation=4.6
Private investment and volatility break-points
stddev.
Public consumption and volatility break-points
50
70
40
50
30
30
10
20
-10
-30
10
-50
0
breaks
10
10
6
-10
2
-30
0
Total investment
breaks
stddev.
Sample statistics for entire series: Mean=2.3; Standarddeviation=14.5
Nominal exchange rate and volatility breakpoints
Non-agriculture GDP and volatility breakpoints
14.00
10.00
17
8.00
6.00
(3)
4.00
(13)
2.00
(23)
0.00
Jan-92
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
7
Nominal exchange rate
breaks
4
7
3
2
1
1
-8
0
Non- agriculture
breaks
stddev.
Sample statistics for entire series: Mean=2.5; Standarddeviation=4.2
RER and volatility break-points
26
35
21
30
16
105
85
65
25
45
20
25
15
5
10
-15
Percent growth rate
40
125
Stddev. of oil prices
145
12
10
8
11
6
6
1
4
-4
-9
5
-14
-55
0
-19
2
0
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
-35
breaks
2
-3
Oil prices and volatility break-points
Oil prices
3
2
stddev.
Sample statistics for entire series: Mean=6.9; Standarddeviation=14.5
4
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
27
Percent growth rate
12.00
37
5
12
Stddev. of nominal exchange rate
47
Percent growth rate
4
-20
stddev.
Sample statistics for entire series: Mean=3.7; Standarddeviation=7.5
Percent growth rate
8
0
Stddev. of non-agriculture GDP
Public investment
12
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
-70
14
20
RER
stddev.
Sample statistics for entire series: Mean=17.5; Standarddeviation=36.8
breaks
stddev.
Sample statistics for entire series: Mean=1.4; Standarddeviation=10.5
68
Stddev. of RER
Percernt grosth rate
90
16
30
Percent growth rate
110
Stddev. of Investment
60
Stddev. of oil public consumption
130
Total investment and volatility break-points
Fiscal revenue and volatility break-points
Soy price and volatility break-points
35
25
23
Stddev. of Revenue
33
15
13
10
3
-7
Percent growth rate
20
5
25
43
20
23
15
3
10
-17
5
-37
0
-17
Fiscal revenue
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan/ 12
Jan/ 11
Jan/ 10
Jan/ 09
Jan/ 08
Jan/ 07
Jan/ 06
Jan/ 05
Jan/ 04
Jan/ 03
Jan/ 02
Jan/ 01
Jan/ 00
0
Jan/ 99
-27
breaks
Soy price
stddev.
Sample statistics for entire series: Mean=14.4; Standarddeviation=15.0
stddev.
Sample statistics for entire series: Mean=8.5; Standarddeviation=27.1
TOT and volatility break-points
Revenue and volatility break-points
18
50
12
40
10
30
8
20
6
10
4
0
2
-10
0
12
4
10
8
-6
6
-16
Stddev. of TOT
14
14
4
-26
2
0
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
Jan/ 95
Jan/ 96
Jan/ 97
Jan/ 98
Jan/ 99
Jan/ 00
Jan/ 01
Jan/ 02
Jan/ 03
Jan/ 04
Jan/ 05
Jan/ 06
Jan/ 07
Jan/ 08
Jan/ 09
Jan/ 10
Jan/ 11
Jan/ 12
-36
Percent growth rate
16
24
Percent growth rate
breaks
Stddev. of Revenue
Percent growth rate
30
63
43
Stddev. of Soy price
53
Total revenues of government
TOT
breaks
breaks
stddev.
stddev.
Sample statistics for entire series: Mean=13.2; Standarddeviation=9.6
Sample statistics for entire series: Mean=0.6; Standarddeviation=16.0
Source: authors.
69
Annex 1.4: Correlation of sectoral GDP growth
Agriculture Minning
Agriculture
Mining
Industry
Construction
Electricity and water
Transport
Communications
Wholesale and retail trade
Finance
Housing
Services to entreprises
hotels and restaurants
Services to houses
General government
Tax to products
Bi-nationals
GDP
1.00
Industry
0.10
1.00
0.25
0.21
1.00
Construction
0.43
0.81
0.30
1.00
Electricity
and water
-0.02
0.25
0.30
0.11
1.00
Transport
0.67
0.25
0.15
0.34
0.03
1.00
Wholesale
Communicati
and retail
ons
trade
0.01
0.64
-0.11
0.38
0.19
0.44
-0.18
0.58
0.21
0.41
0.09
0.62
1.00
0.03
1.00
Finance
Housing
0.09
0.24
0.28
0.39
0.29
0.09
0.29
0.18
1.00
Services to Hotels and Services to General
Tax to
entreprises restaurants houses
government products
-0.31
-0.44
-0.01
-0.58
0.18
-0.32
0.04
-0.30
-0.49
1.00
-0.16
-0.10
0.21
-0.06
0.26
-0.36
0.00
-0.11
0.42
-0.13
1.00
0.50
0.26
0.53
0.42
0.47
0.32
0.33
0.54
0.61
-0.20
0.21
1.00
-0.08
0.50
0.06
0.47
0.03
0.05
0.15
0.13
0.57
-0.63
0.38
0.35
1.00
-0.38
0.27
0.54
0.22
0.34
-0.38
0.26
-0.01
0.54
-0.12
0.46
0.30
0.43
1.00
0.46
0.22
0.45
0.30
0.61
0.23
0.21
0.71
0.09
0.18
0.05
0.49
-0.18
0.08
1.00
Bi-nationals GDP
-0.04
0.08
0.10
-0.04
0.21
0.15
0.35
0.28
0.06
0.13
-0.31
0.06
-0.04
0.14
0.42
1.00
Source: Central Bank of Paraguay, World Bank staff calculations.
Annex 2.1: List of interviewees and interview guide (Borda, Anichini, and Ramirez (2013))
List of interviewees
Area
Actor
Agricultural Production
Asociación Productores de Soja
FECOPROOD
CAPPRO: Cámara Paraguaya de Exportadores de Aceite
Seeds
COPATIA
Relmo Paraguay
Aprosemp
Inputs
Diagro S.A.
Agrofield
Equipment and Machinery
COMAGRO-ROCKING
Campos del Mañana
Storage
CAFI
SILOMAQ
Trasport
Naviera Mercosur
Multimar
Technical assistance and consultrancy
Agrotec
Finance
Banco Nacional del Fomento
70
0.81
0.33
0.50
0.55
0.30
0.67
0.25
0.86
0.33
-0.32
-0.10
0.67
0.14
0.03
0.71
0.41
1.00
Cooperativa Caapibary
Cooperativa Ycua Bolaños
Reefers
Frigorifico Concepción
Input providers: veterinary products, salts,
minerals
Biotechnology: Embryonic reproduction
Agricultural Insurance
Ciavet
LASCA
Gyba SA
Sancor
Garantia de Seguros.
General Insurance
La Agricola Seguros Generales
La Consolidada
Water and Electricity
ESSAP
Investment and Export
Think tanks
REDIEX
CADEP
CECTEC
Guía de entrevistas
a)
¿A qué atribuye la gran volatilidad de la producción agropecuaria/ del PIB agropecuario
y como afecta a este sector?
b)
¿Cómo cambian las decisiones de negocio en el sector agropecuario en anos malos y
buenos del sector?
c)
¿Cual es el impacto de las fluctuaciones del sector agropecuario sobre las inversiones en
la economía, el consumo, el empleo, la tasa de cambio?
d)
¿Cuál es la naturaleza y el nivel de relacionamiento con el sector agropecuario de su
negocio?
e)
¿Cuáles son la consecuencias de la volatilidad en su sector/ comercio/ empleo/ ingreso/
actividad económica?
f)
¿Cómo cambian en su sector/ comercio/ empleo/ ingreso/ actividad económica las
decisiones de negocio debido a las fluctuaciones en el sector agropecuario?
g)
¿Por favor especifica los impactos directos y indirectos a su sector/ comercio/ empleo/
ingreso/ actividad económica de las fluctuaciones en el sector agropecuario?
h)
¿Cómo y en qué grado afecta la volatilidad del PIB agropecuario a su sector? 10% ?
100%?
i)
En su opinión, además de su sector ¿A quién más y como les afecta la volatilidad?
j)
¿Qué Riesgos y Oportunidades usted ve en la volatilidad que origina en el sector
agropecuario?
k)
¿Qué mecanismos existen para asegurarse en contra de/ para transferir/ para evitar estos
riesgos?
71
l)
¿Usa Usted cualquier mecanismo de aseguro contra los riesgos? ¿Si no, porque no? ¿Que
necesitaría cambiar para que puedan usar un aseguro? ¿Si usa mecanismos de aseguro cuales
son? ¿Cómo funcionan?
m)
¿Qué se puede hacer en términos de política pública y/o intervención de los agentes
económicos para mitigar/evitar la volatilidad/ el impacto de la volatilidad?
Annex 3.1: Traditional measures for agricultural risk management
Table: Potential Risk Management Mechanisms
Risk Management Strategy
Mitigation
Transfer
Household/Community
 Sharecropping
 Water resource management
 Soil drainage
 Use of resistant seeds
 Crop calendars
 Crop diversification
 Income/labor diversification
 Savings in livestock
 Food buffer stocks
 Farmer self-help groups
Markets
 New technology
 Improved seed
 Formal savings
Governments
 Irrigation infrastructure
 Extension
 Agricultural research
 Weather data systems
 Diversification of export
markets
 Diversification of agricultural
production






Risk pooling (peers, family
members)
Money lenders
Insurance
Hedging
Trading
State support for insurance
Derivatives or macro-level

insurance
 State-sponsored hedging
 Commodity exchange
Coping
 Sale of assets
 Formal lending
 Disaster relief
 Migration
 Risk sharing (input
 Humanitarian aid
suppliers, wholesalers)
 Contingent financing
Source: Adapted from Weather Index Insurance for Agriculture: Guidance for Development Practitioners (2011)
Annex 3.2: Insurance products
Traditional products
Table: Comparison of Insurance Products
Product
Summary
Perils
Benefits
Challenges
Named peril
crop
insurance


Specific perils
Damage-based policy
measures percent damage
in field
Agreed sums insured
Typically unsubsidized
and run by private sector


Hail, fire
Suited to
localized,
independently
occurring,
sudden perils


Simple policy
Limited farmer details
needed at point of sale
Transparent loss
assessment
Manageable adverse
selection and moral
hazard

All perils
Yield-based policy
measures harvested yield

Wide list,
difficult to
exclude risk

More easily made into a
“universal” product
type



Multiple Peril
Crop


72




Individual farmer
loss assessment
Loss assessment
cost in small
farmer systems
Not suited to
complex perils,
especially drought
or pest
Individual farmer
loss assessment,
major loss
Insurance
(MPCI)



compared to average
yield
Costly; often requires
subsidy
Problematic for small
farms
Successful in a couple of
developed countries


Source of loss
not identified
May include
quality loss,
price risk



Limited technical
adaptation required for
diff. crops
Farmers typically want
and understand this
insurance
Indemnifies each farmer
according to yield





Area yield
index
insurance



Index-based products

Weather
Index
Insurance
(WII)




Farmers in given district
all treated equally
MPCI but on area
average yield
Effective where similar
exposures affect whole
district
NAIS is largest program
(India)


Payouts based on
weather station
measurement
Index trigger, exit,
increments set to
expected loss of yield
Can be complex to
design
Limited experience to
date


Wide list
Source of loss
not identified
May include
quality loss,
price risk






Rainfall
deficit and
excess;
temperature
Basis risk
minimized for
gradual
events




No adverse selection,
moral hazard,
individual farmer loss
adjustment
Low administrative
costs
Can address catastrophe
perils affecting group
Farmer enrollment easy
Captures all causes of
yield loss

No adverse selection,
moral hazard,
individual farmer loss
adjustment
Can address catastrophe
perils affecting group
Transparent, objective
meteorological service
data (MET)
Easier to reinsure






adjustment task,
impartial loss
adjustment
difficult
Adverse selection
(worst farmers
benefit)
Moral hazard
(exploitation of
policy)
Major work to set
up yield history
for each farmer,
poor data
High premium
and
administrative
cost
Not suited where
farms are small
Local perils will
not result in
payout
Yield history at
local district level
often not
available or
reliable
Basis risk at local
level depends on
district area and
peril
Basis risk is key
challenge
Setting up index
parameters is
technically
complex
Need good
meteorological
and agronomic
data, crop
modeling
Difficult to
correlate damage
for sudden-impact
weather
Source: Adapted from Weather Index Insurance for Agriculture: Guidance for Development Practitioners (2011)
73
Map
74