AGRICULTURAL POLICY AND DEVELOPMENT INDICATORS
FOR AFRICA
Concept Note for Rome Workshop
This note contains preliminary suggestions on what types of indicators might be constructed for
the APDI, explains their potential use, and discusses some of the measurement issues that will
need to be confronted.
1. What types of indicators will be developed?
The Agricultural Policy and Development Indicators (APDI) project seeks to develop a suite of
measures of value to policymakers in African countries.
The proposed APDIs are to be compiled for a range of African countries on a consistent and
comparable basis, and to be computed on a regular basis for purposes of policy monitoring.
Three types of indicator are envisaged:
(i) Measures of distortions. These distortions will be calculated for all markets affecting
agriculture, including commodity markets, factor markets (land, labour, credit, purchased inputs),
and foreign exchange markets. The distortions fall into two categories: (i) policy distortions, i.e.
those that arise directly from government policies such as taxes and subsidies; and (ii) ‘implicit’
distortions that are a result of excessive costs or rents in the system, and that could be reduced
through appropriate investments or institutional reforms. The latter can be interpreted as a
development gap that needs to be bridged by suitable policies and investments.
(ii) Development indicators. The purpose of these indicators is to provide internationally
comparable data that can help provide context for policy analysis and decision-making. Indicators
may be provided in the areas of food balances (production, consumption, trade and stocks);
market structures; incomes; poverty and inequality; food security; productivity; and state of
natural resources. Where possible, these indicators would draw from existing sources (e.g.
Countrystat).
(iii) Measures of budgetary expenditures. These measures will track budgetary transfers, with
suitable distinctions across areas that affect agricultural development (including payments to
agriculture directly and to non-agricultural areas, such as infrastructure and education, which may
have an important impact on agricultural development). They will also measure relevant aid flows
to African countries, which will be mapped onto national expenditures. The budget expenditures
will be broken down so as to reflect the differing economic impact of alternative types of
expenditure (for example, making a distinction between spending on private versus public goods).
2. The use of APDIs
The development of APDIs would provide African governments, aid donors, international
organisations and researchers with hitherto missing information that is vital to effective policy
decisions, aid allocations and research into policy effectiveness.
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APDIs serve two core functions: first they are useful for policy benchmarking – by measuring
distortions and development progress in multiple domains they can identify the most urgent areas
for action; second, they form an essential input into more formal kinds of policy analysis. APDIs
should make it possible to diagnose policy biases, identify the main development challenges
confronting the agricultural sector and rural populations, and – in conjunction with policy analysis
– suggest appropriate policy responses.
How are the three types of indicators linked?
The three types of APDI are complementary:
1. Measures of distortions represent potential areas for policy action. In the case of explicitly
distorting policies there may be a need for assessing their effectiveness in reaching given
objectives and possibly reforming them, while in the case of implicit distortions arising from
market failures or high transactions costs, there may be a case for institutional or regulatory
changes (e.g. a curbing of monopoly powers), or for new investments in public goods to reduce
costs and bridge the development gap.
2. The development indicators would include measures linked to the size of implicit distortions,
such as the condition of rural infrastructure, the share of farm operations receiving credit, and
measures of the functioning of land markets or water allocation. Changes in these measures would
provide further information on progress in reducing implicit distortions.
3. The third type of indicator, government expenditures, would make it possible to contrast the
actual allocation of money with areas of need. Thus there would be a link between the
development gap and efforts to bridge that gap.
How would this work in practice? Taking the output market as an example, producer prices may
be high / low relative to landed border prices due to two kinds of “distortion” [level 1]: (a) price
policies – an explicit distortion; or (b) high transport & other transactions costs – an implicit one.
Factors that may explain the output price wedge include policies (such as tariffs and non-tariff
measures such as SPS regulations) and a lack of development, which may be revealed by
indicators such as the percentage of rural roads that is paved, access to price discovery
mechanisms such as mobile phones, and the availability of electricity [level 2]. Indicators of
efforts to bridge that development gap would include absolute and relative spending levels on
roads and other elements of farm & rural infrastructure [level 3].
By measuring distortions across multiple domains, it should be possible for policymakers to
identify where the distortions are greatest and where the most important priority areas are, be they
in the area of commodity policies, macro policies, structural policies or regulatory policies. It
should also facilitate comparative analysis, so that countries can share experiences on the basis of
a common analytical framework.
3. Measuring distortions
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The following sections set out possible indicators, data requirements, and methods of calculation
in each of these domains. A key guide reference is the OECD’s method for calculating its
Producer Support Estimate and related indicators, fully explained in a recently released manual
(OECD, 2008).
Output market distortions
The calculations of output market distortions should be made in a manner that is consistent with
the OECD’s calculations of market price support (MPS), which are available for OECD and a
number of non-OECD countries. This will provide an important basis for comparing policies and
their impacts in developed and developing countries.
The practicalities of calculating MPS include decisions on (i) commodity selection, (ii) the
comparability of domestic and internationally traded products; (iii) the choice of reference prices;
and (iv) adjustments for transport costs, marketing margins and transport costs; (v) specific
adjustments for livestock products (feed adjustment, comparing domestic milk prices with trade
prices in dairy products). OECD practice would be followed as far as possible.
The degree of market price support is typically revealed through price comparisons. Prices
received by domestic producers are compared with international (traded) prices received by
foreign producers. These prices are adjusted for quality differences and for transport and other
costs, in order to bring them to the “point of competition” – i.e. to capture the opportunity cost of
domestic provision.
In developing countries, transport and other transaction costs are often high, and could be reduced
significantly through suitable investments, e.g. in physical infrastructure, and through institutional
reforms. An important aim of the proposed measures is to calculate the impact that a lowering of
these costs would have on producers’ incentives. These could be called “indirect costs” or
“opportunity costs” of policy neglect (or inattention?) to the extent that they can be reduced
through public investments and other policies.
Thus, for example, producers may be effectively subsidised through price supports on the one
hand, while poor infrastructure may implicitly tax potential exporters or implicitly subsidise
import-competing suppliers. Note that a similar distinction can be made between direct and
indirect policies when examining distortions in input markets (a point taken up later).
The OECD methodology would be used to calculate the direct impact of policies given current
costs. Estimating the scope for reducing these costs may be difficult, but is an important task.
Such information could be obtained directly from local experts, or inferred from the data, e.g. by
looking at regional variations in prices and marketing margins. The practicalities for breaking
costs down into two components – an “efficient” level, and one representing a “development gap”
– may mean that this is not possible for all commodities covered by the MPS calculations, but 2-3
commodities per country would provide valuable information.
A further issue that is important in developing countries is the treatment of “non-tradables”. In
practice, there is a degree of local tradability for most developing country staples, while that local
tradability may be encouraged or impeded by price policies and transaction costs. Again, price
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policies, and actionable and non-actionable costs all need to be estimated. One possibility is to
consider regional variations in local prices, another is to use a reference price for an
internationally traded substitute and adjust this with a conversion factor (reflecting quality and
other factors). This issue has not really been confronted in the context of the OECD’s MPS
calculations, as most products are traded, and, for those developing countries where calculations
have been made, it has been possible to drop non-tradables without losing too much in terms of
sectoral coverage. [Ask for elaboration from DAI project].
The potential importance of transport and other transaction costs complicates MPS calculations
when those costs are so high that they effectively prohibit trade [need a technical note to develop
this].
The basic calculation for market price support follows from the (adjusted) law of one price, with
prices in domestic and international markets equalised once adjusted for (a) support to the
domestic market, and (b) the cost of getting goods to the point of competition. In the case of an
import, the identity can be written as:
Pd = Pw × XR + MPS + C1 + C2
where C1 and C2 are, respectively, the efficient and excess costs of getting exports to the point of
competition. The objective is to obtain estimates MPS and C2, given data on prices, the exchange
rate, and total cost (C1 + C2). Given data on prices, the exchange rate and costs, MPS can be
calculated as a residual, and the remaining challenge is to split up C1 and C2. [If the only policy in
place is a tariff, C1 + C2 can be calculated as a residual, and compared with direct estimates].
For export markets, the law of one price gives the following relationship:
Pd = Pw × XR + MPS  C1  C2
But the principles for calculating explicit and implicit distortions are the same.
The aim is to calculate MPS on an annual basis. It is unlikely to be able to estimate C1 and C2 with
the same frequency, so some measurement error will be absorbed in MPS.
Data needs:
 domestic producer prices
 domestic wholesale prices
 tariffs and other border measures (SSGs, NTMs, TRQs etc.)
 transport and other transaction costs
 information on trade volumes (to check law of one price can be applied)
 technical conversion factors
 costs of efficient transport and other transaction.
These indicators form the basis for derived indicators, such as nominal rates of protection (NRPs)
and nominal rates of assistance (NRAs). The can ultimately be linked to net indicators that also
account for policies that affect producers’ incentives via input markets (e.g. adjusted NRPs,
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effective rate of protection (ERPs) and effective rates of assistance (ERAs) [for definitions, see
Josling and Valdés (2004)].
Many input policies are applied at the sectoral level, rather than on a commodity-specific basis.
These policies are to be measured in the first instance at the level at which they are applied (see
APIs for input markets). Attributing these policies to specific outputss requires an understanding
of the production technology in each case, plus a consideration of aggregation issues (i.e. how
consistency is to be ensured between the total subsidy/tax and the implied total revealed by (i) per
unit input subsidies and (ii) estimates of input use in each market). Such a mapping would provide
valuable sectoral information, but the computation of ERPs may be too ambitious at this stage,
given data availability in most African countries.
Intersectoral distortions
The focus here is on how government policies discriminate in favour of or against agriculture
compared with other sectors. Two important ways in which they may do this are through
exchange rate policy (which influences the relative prices of tradables compared to non-tradables)
and in the setting of agricultural tariffs relatives to tariffs in other sectors.
For calculating these distortions, the methodology employed in the DAI project would be
employed (Anderson et al., 2007), and calculations from that project would be harnessed to the
extent possible.
These annual measures could be benchmarked against cross-section measures that account for all
sectors and (in principle) tax/subsidy policies across economic sectors. For this, an IO Table,
SAM and CGE model would all be helpful.
Input and factor market distortions
The basic aim here would be the same as for the commodity market module, i.e. to identify the
agents at whom those policies are directed (e.g. producers, or input suppliers), measure an input
price distortions that can be attributed to (i) formal policies and (ii) excessive costs due to market
failure or a “development gap”. [Similarly, there would be an effort to find indicators of the extent
of excessive cost in the input market and of policy effort to reduce those costs.]
As with output markets, there would be a need for some within-country disaggregation in the data
collected. For example, some farmers may have access to credit at commercial rates (possibly
from overseas) while others may be charged much higher interest rates or, more likely, have no
access at all. It is the spread of distortions within each country that can provide a good gauge of
the extent to which these distortions can potentially come down with improved policies.
Measuring implicit distortions to input markets may be too difficult in many cases. However,
some measures of the underperformance of input markets should be sought.
In principle, measures of explicit distortions across output and input markets should lead to
nominal and effective rates of protection at the commodity level and for the sector as a whole. In
some cases, it should be possible to calculate effective rates of protection. Measures of implicit
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distortion could be similarly aggregated to produce commodity-specific development gaps, an
agricultural market development gap (summing across sub-sectors), and a sectoral development
gap (adding in the cost of underdevelopment to input markets).
4. Development indicators
The contextual information of value to policymakers would be harnessed primarily from
secondary sources, and coordinated with the Countrystat initiative to the extent possible.
Indicators would be developed in the following four areas (the examples are at this stage
suggestive):
(i)
Sectoral performance
a. Production, consumption, trade and changes in stocks. These figures will in any
event need to be collected in order to compute policy distortions. In general, data
are much easier to obtain for crops than for livestock products, which is a serious
deficiency in many African economies.
b. Crop yields, and value added as a share of livestock production (Countrystat core);
c. Productivity/efficiency of water use
d. Agriculture as a share of rural and overall economic activity.
(ii)
Poverty, inequality and food security
a. The rural poor as a % of total poor;
b. Rural relative to urban per capita incomes;
c. Gini coefficient and rural gini coefficient;
d. Share of urban households which are food insecure; share of rural / farm
households that are food insecure;
(iii)
Costs in output markets
a. Share of rural roads that is paved;
b. Extent of rural electrification;
c. Share of farm households with mobile phones;
d. Development of storage infrastructure
(iv)
Costs in input markets
a. Irrigated land as a share of cropland (Countrystat core);
b. Share of farms with access to credit;
c. Share of land for which there is legally recognised land tenure (Countrystat core).
4. Government expenditures
The main task is to break down budgetary expenditure by various categories and sub-sectors.
Another task is to map aid flows onto national receipts and expenditures. Under the Creditor
Reporting System (CRS), aid is identified by sector. The agriculture-specific categories are listed
in Table 1, while the broader categories are presented in Table 2 (with some breakdown).
It is important to note here that, for the purposes of agricultural policy analysis, the CRS
breakdown may not be the most useful. Many of the expenditures of greatest relevance to
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agricultural development may not be specific to agriculture, but could fall into other categories
(such as investments in rural infrastructure, or in banking and financial services).
In order to facilitate economic analysis, the following distinction is proposed:
1. Between agriculture-specific, agriculture-supportive, and non-agricultural expenditures;
2. Within agriculture-specific, the OECD’s distinction between payments to farmers and
payments not to farmers but to the sector more generally should be preserved.
The distinction between payments to farmers and payments to the sector as a whole provides a
rough division between the provision of private and public goods. This division is worth retaining,
but it does mean that the use of aid falling into some CRS categories may need to be further
investigated. For example, support for agricultural development, land and water resources could
fall into either the private or the public category.
In terms of payments to farmers, there is no need to follow the OECD’s detailed disaggregation,
which has evolved to reflect the complexity of programmes in the OECD area, and the importance
that is attached to gauging degrees of decoupling. However, a useful distinction might be made
between income support and extension services.
Within the OECD’s General Services Support Estimate, the following categories of support are
included: research and development, agricultural schools, inspection services, infrastructure,
marketing and promotion, and public stockholding. These categories might be added to the direct
payments to farmers.
The OECD categories may not exhaust the forms of agricultural spending in African countries,
and some merger of these with the CRS categories identified in Tables 1 and 2 might be
appropriate.
At the national level, it may not be possible to track all the relevant sub-categories that are
identified at the donor level. It will be important to keep track of the gap between donor
information on commitments and disbursements on the one hand, and national receipts and
expenditures on the other.
Should a PSE be measured?
On the one hand this would be straightforward if the above indicators were calculated and would
provide clear comparability with OECD countries, and the significant number of developing
countries that OECD now covers. In an African context, however, it seems appropriate to focus on
price distortions in output and input markets; but the inclusion of direct payments that are
included in the PSE, such as extension services and targeted input provision, could easily be
misconstrued. Aggregating potentially good and bad policies into the PSE may be
counterproductive.
Moreover, the aim of the APDI project is fundamentally to assist African policymakers with their
decision making, and to help establish policy dialogues between countries on the basis of
comparable information. The use of a politicised measure, which has evolved to address the
policy concerns of OECD members, may be inappropriate.
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Table 1. CRS Agriculture-Specific Categories
31120
31130
AGRICULTURE
Agricultural policy and administrative
management
Agricultural development
Agricultural land resources
31140
Agricultural water resources
31150
31161
Agricultural inputs
Food crop production
31162
Industrial crops/export crops
31163
31164
31165
Livestock
Agrarian reform
Agricultural alternative development
31166
31181
31182
Agricultural extension
Agricultural education/training
Agricultural research
31191
Agricultural services
31192
Plant and post-harvest protection and
pest control
31193
Agricultural financial services
31194
31195
Agricultural co-operatives
Livestock/veterinary services
31110
Agricultural sector policy, planning and
programmes; aid to agricultural
Integrated projects; farm development.
Including soil degradation control; soil
improvement; drainage of water logged
areas; soil desalination; agricultural land
Irrigation, reservoirs, hydraulic
Supply of seeds, fertilizers, agricultural
Including grains (wheat, rice, barley,
maize, rye, oats, millet, sorghum);
horticulture; vegetables; fruit and
Including sugar; coffee, cocoa, tea; oil
seeds, nuts, kernels; fibre crops;
Animal husbandry; animal feed aid.
Including agricultural sector adjustment.
Projects to reduce illicit drug cultivation
through other agricultural marketing and
Non-formal training in agriculture.
Plant breeding, physiology, genetic
resources, ecology, taxonomy, disease
control, agricultural bio-technology;
Marketing policies & organisation;
storage and transportation, creation of
Including integrated plant protection,
biological plant protection activities,
supply and management of
Financial intermediaries for the
agricultural sector including credit
Including farmersÕ organisations.
Animal health and management,
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Table 2. Broad CRS Categories
110
120
130
140
150
160
210
230
240
250
311
312
313
321
322
323
331
332
400
410
430
43010
43030
43040
43050
43081
43082
500
510
51010
520
52010
530
53030
53040
600
700
910
920
930
998
EDUCATION
HEALTH
POPULATION POLICIES/PROGRAMMES AND REPRODUCTIVE
HEALTHSUPPLY AND SANITATION
WATER
GOVERNMENT AND CIVIL SOCIETY
OTHER SOCIAL INFRASTRUCTURE AND SERVICES
TRANSPORT AND STORAGE
ENERGY GENERATION AND SUPPLY
BANKING AND FINANCIAL SERVICES
BUSINESS AND OTHER SERVICES
AGRICULTURE
FORESTRY
FISHING
INDUSTRY
MINERAL RESOURCES AND MINING
CONSTRUCTION
TRADE POLICY AND REGULATIONS AND TRADE-RELATED
ADJUSTMENT
TOURISM
MULTISECTOR/CROSS-CUTTING
General environmental protection
Other multisector
Multisector aid
Urban development and management
Rural development
Non-agricultural alternative development
Multisector education/training
Research/scientific institutions
COMMODITY AID AND GENERAL PROGRAMME ASSISTANCE
General budget support
General budget support
Developmental food aid/Food security assistance
Food aid/Food security programmes
Other commodity assistance
Import support (capital goods)
Import support (commodities)
ACTION RELATING TO DEBT
HUMANITARIAN AID
ADMINISTRATIVE COSTS OF DONORS
SUPPORT TO NON- GOVERNMENTAL ORGANISATIONS (NGOs)
REFUGEES IN DONOR COUNTRIES
UNALLOCATED/ UNSPECIFIED
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