Training Manual
For
Analyzing indicators of Food and Agriculture
By
Samir Jrad
Chief of Agricultural and Food Policies Division
Damascus, June, 2011
1. Background
This manual was accomplished by the Agricultural and Food Policies Division (AFD) of the National Agricultural Policy Center (NAPC), which relates to the Ministry of Agriculture and Agrarian Reform (MAAR). It focuses mainly on enhancing the analytical ability of the AFD staff, especially the new employees. The members of the other divisions of the NAPC as well as of the MAAR can also benefit from this guideline to enhance their analytical skills. Therefore, this contribution of the AFD complies with the orientation of the Government in general and the instructions of the MAAR in particular that intend to improve the capabilities of the public sector employees.
2. Proposed methodology for analyzing a statistical event
This manual introduces a proposed general methodology to analyze indicators related to the agricultural and food sector in Syria relying on various analysis tools. This methodology can be adopted to assess the development of the selected measures and its underlying factors in the short, medium and long terms. To do so, some analysis tools are used such as descriptive analysis, changes between periods, growth rates, index numbers, and regression analysis. Based on these tools, the suggested methodology comprises the following footsteps to assess the dynamic of a statistical event:
1. Describing a statistical event using several measures such as the mean, minimum, maximum, coefficient of variation (both mean and trend based)[1]. This is illustrated in Table A1, Table A2, Table A3, Figure A1 and excel file sheets (summary statistics, weighted average and trend line)[2].
2. Introducing a figure in the text showing the annual changes of the event during the considered period (Figure A2 and excel sheet: change).
3. Inserting a figure in the text illustrating the changes in the index number of the event as compared to the base year (Figure A3 and excel sheet: change).
4. Presenting a table in the text clarifying the event changes during the considered period as compared to the base year. The base year can be a single year (original observation and trend estimated) and three years average. Based on these estimates, the table has also to include: average change using three years average, simple annual growth rate (calculated between the target year and base year using the original observations), and average annual growth rate using three years averages and trend estimates (Table A4 and excel sheet: changes). These estimations have to be presented at current and constant prices when data are available.
5. Assessing the impact of various factors on the event when it is considered as a compound occurrence like production, GDP and value of production and inputs (for example, studying the influence of quantity and prices on the value of production). This is illustrated in Table A5, Table A6 and excel sheet: analysis of index numbers.
6. Drawing a figure that shows the disaggregation of the event values for both base and target years by sectors and governorates (Figure A4 and excel sheet: disaggregation and comp). This illustration will be more powerful when it is presented at both current and constant prices. This, of course, relies on data availability.
7. Introducing a table clarifying the summary statistics of the event by sectors and governorates for both base and target years relying on data availability. The summary statistics have to comprise: the mean, minimum, maximum and coefficient of variation (Table A7).This analysis has to be supported by estimating the matrix of correlation coefficients among governorates (Table A8 and Table A9) and drawing the Lorenz curve (Table A10 and Figure A5). More details are presented in excel sheets: disaggregation and comp. and Gini.
8. Comparing the event level with that of other countries (for example, productivity, income and nutrition). This is illustrated in Table A11 and excel sheet: disaggregation and comp.
9. Conducting a regression analysis to study the long-term impact of various independent variables on the event (Table A12, Table A13, Figure A6, Table A14, Table A15, Table A16, Table A17, Table A18, Table A19, Table A20, Figure A7 and excel sheets: extended trend line and production possibility frontier).
10. Using various methods for the estimation of index numbers so that the power of the short, medium and long term analysis is strengthened (Table A21, Table A22,Table A23 and excel sheets: index numbers and nutrition).
The above-mentioned steps are not applicable for all statistical events. Therefore, the analysis stages are selected according to data availability.
3. Comparative advantage
Comparative advantage analysis is the framework by which the financial and economic profitability of any activity is determined via assessing the returns of this activity in the absence of market distortions. In other words, comparative advantage analysis focuses on the calculation of the real costs (economic costs), relying on the international prices, or the opportunity cost to determine the probability of the activity to be profitable when the policies causing the divergence between domestic and world prices are not present.
Comparative advantage of a production system is assessed through the Policy Analysis Matrix (PAM) by which the impact of governmental interventions (current policies) and market distortions is evaluated considering the social prices (Table 1). The PAM is the tool used to analyze market failures and policy interventions by studying the impact of these policies considering all the agents of the commodity chain from the farm gate until the wholesale market or where the foreign and local products are unified.
Table 1.The Policy Analysis Matrix
Item / Revenues / Costs of tradable inputs / Costs of domestic factors / ProfitPrivate prices / A / B / C / D
Social prices / E / F / G / H
Divergence / I / J / K / L
Source: Elaborated from NAPC database.
According to Table 1, the PAM consists of three rows and 4 columns. The first row of the matrix presents the aggregate budget of the system at market prices. The second row illustrates this budget at social prices by considering the distortions, either by adding them to or subtracting them from the first row. The social budget is assessed by using the world prices as reference for outputs, and eliminating the taxes from the value of tradable inputs and adding the subsidy to the value of the latter (Table A24, Table A25, Table A26 and Table A27). The third row includes the divergences between social and private prices. The columns discriminate between tradable goods (final and intermediate) and domestic resources. By definition domestic resources are items that are not traded internationally such as labour, land and capital. The measures (revenues, costs, profit) of the PAM are calculated at both private and social prices. The revenues represent the sales. The profit is calculated by subtracting the costs (tradable goods and domestic resources) from the revenues.
To assess the performance of the agents and to compare between their efficiencies, several indicators derived from the PAM are used as illustrated in Table 2. Some of these indicators can also be used for analyzing statistical events.
Table 2.Indicators of the PAM
Indicators / Formula / Meaning1. Financial Profitability (FP) / [D = A - B - C] / Absolute value of the profit generated by the system at private price
2. Financial Cost-Benefit Ratio (FCB) / [(C+B) /A] / Indicator of the competitiveness of the system. If FCB<1, the system is competitive, if FCB>1 the system is not competitive, FP is negative
3. Social Profitability (SP) / [H = E - F - G] / Absolute value of the profit generated by the system at social price.
4. Domestic Resource Cost (DRC) / [G / (E - F)] / Indicator of the comparative advantage of the system. If DRC<1, the system has comparative advantage, meaning that less value of domestic factors (labor, capital…) is used than the added value generated (VA= E-F), if DRC>1 the system has no comparative advantage, SP is negative.
5. Social Cost-Benefit Ratio (SCB) / [ (F + G) / E ] / Another indicator for measuring the comparative advantage of the system. It takes into account the full cost of production (F + G) instead of the Domestic factors only. It is a more appropriate ratio to rank the relative position of different systems when they have different cost structures (i.e. tradable and non-tradable), because the DRC is biased in favor of the system that has a high share of tradable.
6. Transfers / [L = I - J - K] / Absolute value of the transfer between the economy and the system
7. Nominal Protection Coefficient (NPC) / [A / E] / Indicates the level of protection for the main output, if NPC> 1, the system benefits from a protection, if NPC<1 the system is taxed.
8. Effective Protection Coefficient (EPC) / [(A - B) / (E - F)] / Indicates the total level of protection taking into account the effect of the policy on the private value of the tradable output and tradable input.
9. Profitability Coefficient (PC) / [D / H] / Measures the impact of the policy on the profitability of the system. If PC>1, the system benefits from a net transfer from the economy, if PC<1, the economy benefits from a net transfer from the system.
10. Producers Subsidy Ratio (PSR) / [L / E] / Indicator of the impact of the policy/market distortion on the increase (+) or reduction (-) of the total revenue of the system at social price. i.e. magnitude of the divergence from the reference situation at social price to the current situation at market price
11. Equiv. Producer Subsidy (EPS) / [L / A] / Indicator of the impact of the policy/market distortion on the increase (+) or reduction (-) of the total revenue of the system at market price. Equivalent to the Producer Equivalent Subsidy (PSE) as defined by OECD for trade negotiations. If + it is producer subsidy, if – its consumer subsidy.
Source: Elaborated from NAPC database.
References
Binger Brain R. and Hoffman Elizabeth. 1998. Microeconomics with Calculus. Second edition, Addison – Wesley.
Beyer H. and Walter E. 1977.Analysis in Industrial Firm. Verlag Die Wirtschaft, Berlin, Germany.
Grad Samir and Fayez Mansour. 2008. Analysis of Supply Response for Selected Food Groups in Syria. NAPC, Damascus Syria.
Grad Samir and Mouzad Karkout. 2008. Demand Analysis of Selected Food Groups in Syria. NAPC, Damascus, Syria.
Heady Earl O., et al. 1961. Agricultural Supply Functions. The Iowa State University Press.
Hazell P. B. R. and Norton R. D. 1986. “Mathematical Programming for Economic
Analysis in Agriculture.” MacMillan Publishing Co., New York, 400 pp.
Johnson Stanley R., Hassan Zuhair A. and Green Richard D. 1984.Demand Systems Estimation. The Iowa State University Press, Ames, Iowa.
Kmenta Jan. 1986.Elements of Econometrics. Second edition, Macmillan Publishing Company, New York, United States of America.
Little Arthur D. 1985.Economic Analyses of Advertising Effectiveness. General methodological perspective.
MAAR. The Annual Agricultural Statistical Abstract. Various issues.
Nerlove Marc. 1985. The Dynamic of Supply. The Johns Hopkins Press.
Oezcan Kivilcim Metin, DellalIlkay and Tan Sibel. Basic Food Consumption in Turkey: Effects of Income, Price and Family Size in Urban Areas. Ankara Turkey. .
Perali Federico. 2003. Partial Equilibrium Analysis of Policy Impacts. Part I, training materials, NAPC, Damascus, Syria.
Pomboza Ruth and Mbaga Msafiri. 2007. The Estimation of Food Demand Elasticities in Canada. Canada.
Raunikar Robert and Huang Chung – Liang. 1984. Food Demand Analysis. Iowa State University Press, Ames, Iowa.
Sadoulet Elisabeth and de Janvry Alain. 1995. Quantitative Development Policy Analysis. The Johns Hopkins University Press, Baltimore and London.
Sadiddin Ahmad and Atiya Basima. 2009. Analysis of Agricultural Production for Selected Crops: Wheat, Cotton and Barley. NAPC, Damascus, Syria.
Salvatore Dominick and Diulio Eugene A. 1996.Principles of Economics. Second edition, McGraw – Hill Companies, Inc., and Math Soft, Inc.
Tamhane Ajit C. and Dunlop Dorothy D. 2000.Statistics and Data Analysis. Prentice Hall, Upper Saddle River, NJ 07458, United States of America.
United Nations (Economic and Social Commission for West Asia). 1995. Evaluation of Agricultural Policies in Syria (Results of the Policy Analysis Matrix). New York, United States of America.
Annex
3
3
3
3
Parity prices
Parity prices are estimated as import parity prices (cost for insurance and freight (cif)) and as export parity prices (free on board (fob)). This is illustrated by the following formulas and tables.
IPP = OPPcif *ER + HCP + TCBM + MC – TCFM – TPC
Where:
IPP:Import parity price.
OPPcif: Price at the observed port.
ER: Exchange rate.
HCP: Operational cost at the entry port.
TCBM: Transportation cost from border to market.
MC: Marketing cost.
TCFM: Transportation cost from farm to market.
TPC: Total processing cost at the factory.
EPP = OPPfob * ER – HCP – TCBM – MC – TPC – TCFM
Where:
EPP: Export parity price.
OPPfob: Price at the observed port.
Table A24. Import parity price of selected products
Item / Unit / Wheat / FertilizerCifLattakia / US$/tonne / 212.70 / 261.74
Exchange rate / US$/Syrian Pound / 45.50 / 45.50
CifLattakia / Syrian Pound/tonne / 9,677.85 / 11,909.13
Insurance (1.5%) / Syrian Pound/tonne / 145.17 / 178.64
Interest for 180 days / Syrian Pound/tonne / 677.45 / 823.64
Operational cost / Syrian Pound/tonne / 43.75 / 43.75
Custom fees / Syrian Pound/tonne / 12.00 / 12.00
General fees / Syrian Pound/tonne / 126.52 / 126.52
Loss (0.5% of total cost) / Syrian Pound/tonne / 53.41 / 65.52
Interest of working capital for three months (7.5%) / Syrian Pound/tonne / 201.30 / 246.92
Profit (3% of total cost) / Syrian Pound/tonne / 328.12 / 402.48
Distance between port and farm / Kilometer (km) / 100.00 / 100.00
Transportation price from mills or farm to port / Syrian Pound/km / 0.6 / 0.6
Transportation cost / Syrian Pound/tonne / 62.00 / 62.00
Import parity price at plant / Syrian Pound/tonne / 11,327.6 / 13,880.6
Import parity price at plant / Syrian Pound/kg / 11.33 / 13.88
Transportation cost from farm to plant / Syrian Pound/kg / 0.06 / 0.06
Import parity price at farm gate / Syrian Pound/kg / 11.3 / 13.8
Conversion factor from wheat to flour / 0.7
Import parity price for wheat flour / Syrian Pound/kg / 16.14
Source: Elaborated from United Nations (1995).