Total factor productivity change in broiler insurance: evidence from Iran

Mudança na produtividade total dos fatores no seguro de frangos de corte: evidências do Irã

Abstract

Insurance is one of the primary risk management tools in the agricultural sector. In Iran, the crop insurance fund operates as a quasi-government institution, and many agricultural insurance practices have remained traditional for years without necessary innovations. In 2016, managers of crop insurance fund decided to adopt a Negotiation-Oriented insurance approach for the first time in broiler industry. In this approach, all steps including the determination of insurance premiums were conducted through consultation and coordination with the broiler industry producers’ union, rather than being dictated by government directives. This research analyzes changes in total factor productivity of broiler insurance before and after the implementation of this new approach, using the Malmquist index and Data Envelopment Analysis (DEA). Data were collected from the Iran crop Insurance fund's Comprehensive System (CS) for the period of 2010-2020. The results indicate that after the implementation of the negotiation-oriented method, total factor productivity of insurance in the broiler industry improved by 19.7% annually during 2016-2020.

Keywords:
agricultural insurance; total factor productivity change (TFPCH); broiler industry

Resumo

O seguro é uma das principais ferramentas de gerenciamento de risco no setor agrícola. No Irã, o fundo de seguro de safra opera como uma instituição quase governamental e muitas práticas de seguro agrícola permaneceram tradicionais por anos sem as inovações necessárias. Em 2016, os gerentes do fundo de seguro de safra decidiram adotar uma abordagem de seguro orientada à negociação pela primeira vez na indústria de frangos de corte. Nessa abordagem, todas as etapas, incluindo a determinação dos prêmios de seguro, foram conduzidas por meio de consulta e coordenação com o sindicato dos produtores da indústria de frangos de corte, em vez de serem ditadas por diretrizes governamentais. Esta pesquisa analisa as mudanças na produtividade total dos fatores do seguro de frangos de corte antes e depois da implementação dessa nova abordagem, usando o índice de Malmquist e a Análise Envoltória de Dados (DEA). Os dados foram coletados do Sistema Abrangente (CS) do fundo de seguro de safra do Irã para o período de 2010-2020. Os resultados indicam que, após a implementação do método orientado à negociação, a produtividade total dos fatores de seguro na indústria de frangos de corte melhorou 19,7% anualmente durante o período de 2016-2020.

Palavras-chave:
seguro agrícola; variação total da produtividade dos fatores (TFPCH); indústria de frangos

1. Introduction

The agricultural sector is inherently associated with uncertainty and risk. While these risks are inevitable, they can be managed, and Insurance is one of the primary tools for mitigating them. In the agricultural sector, insurance services protect producers against insurable events such as pests, diseases and accidents. For instance, economic damages incurred during the cultivation or throughout the agricultural year can be compensated by the insurer up to the amount committed (Crop Insurance Fund, 2020). In Iran, broiler chicken producers face significant risks not only in meat production but also in the market, leading to fluctuations in their income. These risks can cause considerable discrepancies between the actual and expected income of the producers, complicating both short-term operations and long-term planning.

Poultry insurance in Iran began from the agricultural year of 1994-1995, initially covering commercial laying hen farms and broiler chickens. Over the next 24 years, the insurance fund expanded to cover more than 1300 million chicken units across 12 types of poultry farming activities.

Until 2016 the poultry insurance followed a pre-designed method where nearly all of risk factors and diseases were insured. However, this approach had significant drawbacks. High insurance premiums were required to cover frequent and manageable risks, and the large number of claims led to low insurance commitments. Additionally, frequent site visits by the insurer agents increased the risk of disease transmission among farms, further raising operating costs and moral hazards.

Evidences showed that overcoming the above challenges, dependent on satisfaction and cooperation of all poultry stakeholders. Thus insurance fund managers decided to form a team consisting of the official representatives of poultry farm producers’ union, the Deputy Ministry for Livestock Affairs of the Agricultural Ministry, the Veterinary Organization and the crop insurance fund to investigate the issues. The reasons for switching from the pre-designed insurance approach to new approach were discussed in detailed and long-run negotiations. Finally, all members declared the obstacles and agreed to adopt new insurance method.

According to final agreement all poultry risks separated into two groups, and based on it, into two types of insurance; Basic and Supplementary. In basic insurance, only the layer of risks were covered that incurred with high intensity (in terms of severity and extent of damage) but low frequency. For instance pandemic and epidemic-prone diseases diagnosed by the veterinary organization, were classified as basic insurance (Crop Insurance Fund, 2020). In addition, other non-manageable risks with low intensity but high frequency, being supplied optionally as supplementary insurance for the applicants.

However, the representatives of poultry farmers were worried about the consequences of replacing current method with the new approach. They believed by removing the less important but frequent risks from compulsory basic insurance, the annual government compensation payment share would be decreased. It should be explained, in Iran 50 percent of insurance premiums would be paid by government as an indirect farmer support policy. To respond this concern, ultimately government accepted, to compensate the poultry farms damages up to the real damages amount completely, by significantly increasing the commitments for catastrophic damages, widespread epidemics, and newly emerging and re-emerging risks.

This new approach after this is named “the Negotiation-oriented insurance” approach was applied for the first time by quasi-government crop insurance fund in Iran. It seemed after the implementation of the negotiation-oriented insurance approach since 2016, broiler insurance performance improved considerably. As a result, the total factor productivity changes in Iran broiler insurance were analyzed before and after implementing the mentioned approach. The required data was collected from Iran crop insurance funds’ comprehensive system (CS) for the period of 2010-2020.

2. Literature Review

Productivity growth, technical progress, and productivity change have been considered in many agricultural studies. Furthermore, the productivity growth is either divided into efficiency change (EFFCH) and technological changes (TECHCH), or both. In some of these studies, parametric methods (Akbari and Ranjkesh, 2003; Heidari, 1999; Dashti and Yazdani, 1996; Seyyedan, 2003; Ghorbani, 1997; Kazem-Nejad and Koopahi, 1996; Koopahi and Darban-Astaneh, 2001; Mehrabi-Boshrabadi and Mousa-Nejad, 1996; Bottomley and Thritle, 1992; Kalirajan et al., 1996) and, in some other, non-parametric methods have been utilized (Mojaverian, 2003; Yazdani and Doorandish, 1993; Caves et al., 1982a; Färe et al., 1992, 1994a, b; 1997; Felipe, 1997; Fulginiti and Perrin, 1996; Fu, 2004; Hulten, 2000; Karadg et al., 2004; Mao and Koo, 1997; Mukherjee and Kuroda, 2003; Murillo-Zamorano, 2003; Murillo-Zamorano and Vega-Cervera, 2001; Nishimizu and Page, 1982; OECD, 2001; Ray and Desli, 1997; Rosegrant and Evenson, 1992; Seiford and Thrall, 1990; Shing, 1995; Soriano et al., 2003; Tauer, 1998; Umetsu et al., 2003).

3. Material and Methods

The common method of estimating the total factor productivity is Solow’s (1957) residual method, which denotes productivity growth after removing the share of capital and labor inputs from total product growth. It consists of four assumptions: constant economics of scale, specific production function, maximized behavior in part of the firm without productivity, and neutral technical change. The measurement of the total factor productivity will be biased if such assumptions are not satisfied. In the common method of measuring partial productivity (Nadiri, 1979; Denison, 1974; Kendrick, 1973), on the other hand, the total product index can be classified by the observed values of an input. Such an index generates some errors to determine total productivity. A more accurate method of measuring productivity is based on the total factor productivity (TFP) including all production outputs and inputs (Hulten, 2000; OECD, 2001). In the literature, many methods have been developed to indicate the growth of total factor productivity at micro and macro levels, including two main groups of frontier and nom-frontier methods. In the traditional methods of productivity measurement via non-frontier methods such as growth accounting approaches (Caves et al., 1982b; Denison, 1972; Solow, 1957) and numerical indices (Baumol, 1986; Bernard and Jones, 1996; Dollar and Wolff, 1994) all units are assumed to be efficient, further price data was required. These methods measure the growth of the total factor productivity concerning technological progress.

Nishimizu and Page (1982) described another important source of the total factor productivity's growth, the technical efficiency changes. Once the adoption of technical innovation progress, shifts the potential production frontiers upwards, the change in the efficiency enhances production through available inputs and technology.

The initial point for measuring technical efficiency and productivity is estimation of the frontier production utilized to determine technological progress. Most studies on this issue are on the basis of parametric or non-parametric approaches. The estimation technique is among the important issues; thus, some researchers (Heidari, 1999; Dashti and Yazdani, 1996; Seyyedan, 2003; Ghorbani, 1997; Kazem-Nejad and Koopahi, 1996; Koopahi and Darban-Astaneh, 2001; Mehrabi-Boshrabadi and Mousa-Nejad, 1996; Berger, 1993) have employed the parametric method while others (Färe et al., 1994a; Fu, 2004; Hulten, 2000; Mao and Koo, 1997; Mukherjee and Kuroda, 2003; Murillo-Zamorano, 2003; OECD, 2001; Ray and Desli, 1997; Seiford and Thrall, 1990) have utilized the non-parametric method.

A major weakness of non-parametric approaches’ like Data Envelopment Analysis (DEA) is their deterministic character, inability to separate technical inefficiencies from statistical noise. Analyzing productivity based on long-term data, requires high-quality and accurate data, which might be influenced by economic or environmental changes over time.

Parametric frontier functions, on the other hand, require a functional form for the technology and inefficiency error terms, possibly being difficult to estimate (Murillo-Zamorano and Vega-Cervera, 2001). Non-parametric methods, in contrast, don’t require a functional form for the efficient frontier and can measure productivity changes directly without making strong assumptions.

Caves et al. (1982a) first developed the Malmquist index (MI) taken from the work of Malmquist (1953), who had previously described the input value index as a ratio of distance functions.

In this paper, the index was developed by Färe et al. (1994b), meaning the change in productivity as the two Malmquist productivity indexes' geometric mean, in such a way that changes in productivity are related to changes in efficiency and technology (Caves et al., 1982b). This index is well-suited for analyzing productivity changes across different industries and allows for a detailed examination of productivity with high accuracy. Another advantage of this index is that it does not require price information, making it valuable in cases where prices are distorted and censored. Moreover, productivity measurement in multi-input, multi-output is plausible. Meanwhile, this index can be calculated for each firm (Caves et al., 1982a).

In order to express the productivity change's Malmquist index, the product distance function should be determined (Deaton, 1979). Based on the view of Shephard (1970) and Caves et al. (1982b), the distance function in period t is expressed as below (Equations 1 and 2):

$D_0^t\left(x^t,y^t\right)=\text{inf}\left\{\theta :\left(x^t,y^t/\theta \right)\in S^t\right\}$ (1)

where S^t indicates the production technology for the time t=1,..., T. Such a technological set converts the input vector $x^t=\left(x^t,\dots \mathrm{.,}{x}_M^t\right)\in R_M^{+}\text{into the output vector}{y}^t=\left(y^t,\dots \mathrm{.,}{y}_N^t\right)\in R_N^{+}$

$S^t=\left\{\left(x^t,y^t\right):x^t\to y^t\right\}$ (2)

The distance function D₀^t(.) reciprocally shows the maximum development vector y^t at the input level x^t in the condition that the observations at the frontier of the period t. According to Farrell’s (1957) theory, this function completely determines the technology that $D_0^t\left(x^t,y^t\right)\le 1$ if and only if $(x^t,y^t)\in S^t$. Moreover, $D_0^t\left(x^t,y^t\right)=1$ if and only if the observations are efficient technically. For completing the Malmquist productivity index, it is vital to outline the mentioned distance function regarding two periods, i.e., $D_0^t\left(x^{t+1},y^{t+1}\right)$ and $D_0^{t+1}\left(x^t,y^t\right)$. In terms of periodic compound cases, the distance function value can exceed 1. This case arises when the analyzed unit in one period is unavailable in another period. If $D_0^t\left(x^{t+1},y^{t+1}\right)>1$ there is a technical improvement, while if $D_0^{t+1}\left(x^t,y^t\right)>1$ there is the technical regress. According to the product distance functions well-defined above with variable returns to scale, Caves et al. (1982b) offered data-oriented Malmquist productivity indexes for times t and t+1 as follows (Equations 3 and 4):

$M_0^t\left(x^t,y^t,x^{t+1},y^{t+1}\right)=\frac{D_0^t\left(x^{t+1},y^{t+1}\right)}{D_0^t\left(x^t,y^t\right)}$ (3)

$M_0^{t+1}\left(x^t,y^t,x^{t+1},y^{t+1}\right)=\frac{D_0^{t+1}\left(x^{t+1},y^{t+1}\right)}{D_0^{t+1}\left(x^t,y^t\right)}$ (4)

It should be mentioned that each of the product-based productivity indices generates a different productivity index, if the source of technology is hicks neutral. To avoid applying such limitations or deciding on one of the technologies, some researchers have defined the added productivity index as the two indexes' geometric mean (Fisher, 1922). In other words, the Malmquist productivity change index on the basis of the product can be developed as the two Malmquist productivity indices' geometric mean as below (Equation 5):

$M_0^t\left(x^t,y^t,x^{t+1},y^{t+1}\right)={\left[\left(\frac{D_0^t\left(x^{t+1},y^{t+1}\right)}{D_0^t\left(x^t,y^t\right)}\right)\left(\frac{D_0^{t+1}\left(x^{t+1},y^{t+1}\right)}{D_0^{t+1}\left(x^t,y^t\right)}\right)\right]}^{1/2}$ (5)

where $M_0^t$ is a combined geometric mean of the two Malmquist productivity index. The first is examined on the basis of the technology in period t, whereas the second is evaluated based on the technology in period t+1 (Caves et al., 1982b). This equation expresses the productivity of the point $\left(x^{t+1},y^{t+1}\right)$ relative to $(x^t,y^t)$. Note that values ​​above 1 show positive growth in the total factor productivity in the period t+1. The Malmquist index below 1, indicates performance drop over time. Färe et al. (1994b) indicated that the Malmquist index ability to decompose total factor productivity changes is into technological changes and efficiency changes. This decomposition is expressed as Equation 6 for the product-oriented mode:

$M_{0c}^t\left(x^t,y^t,x^{t+1},y^{t+1}\right)=\frac{D_{oc}^{t+1}\left(x^{t+1},y^{t+1}\right)}{D_{oc}^t\left(x^t,y^t\right)}{\left[\left(\frac{D_{0c}^t\left(x^{t+1},y^{t+1}\right)}{D_{0c}^t\left(x^t,y^t\right)}\right)\left(\frac{D_{0c}^t\left(x^t,y^t\right)}{D_{0c}^{t+1}\left(x^t,y^t\right)}\right)\right]}^{1/2}$ (6)

where the first ratio indicates the relative efficiency change between the period t and t+1, in which the $M_{0c}^t\left(.\right)$value above 1 shows increased total factor productivity between periods t and t+1. This increase may be clarified on the basis of improving either technical efficiency or technological progress. To better understand, Figure 1 depicts the above decomposition for a product, denoting an input with constant returns to scale technology; i) the technological progress $(S^t\subset S^{t+1})$ from t to t+1 occurs.

Figure 1
Malmquist productivity.

In Figure 1, the unit operates in the set of inputs and outputs $(x^t,y^t)$ and $\left(x^{t+1},y^{t+1}\right)$ at times of t and t+1, respectively. The observations are lower than the frontier of technological productivity $(S^t,S^{t+1})$ in the two time periods t and t+1, indicating non-technical efficiency combinations. Concerning y-axis distances, the decomposition of the above relationship is equivalent to technical efficiency and technological change, i.e. (Equations 7 and 8),

$Efficiencychange=\frac{\raisebox{1ex}{$y^{t+1}$}\!\left/ \!\raisebox{-1ex}{$y^{t+\mathrm{1,}t+1}$}\right.}{\raisebox{1ex}{$y^t$}\!\left/ \!\raisebox{-1ex}{$y^{t,t}$}\right.}$ (7)

$Technologicalchange=\frac{\raisebox{1ex}{$y^{t+1}$}\!\left/ \!\raisebox{-1ex}{$y^{t+\mathrm{1,}t}$}\right.}{\raisebox{1ex}{$y^{t+1}$}\!\left/ \!\raisebox{-1ex}{$y^{t+\mathrm{1,}t+1}$}\right.}\times \frac{\raisebox{1ex}{$y^t$}\!\left/ \!\raisebox{-1ex}{$y^{t,t}$}\right.}{\raisebox{1ex}{$y^t$}\!\left/ \!\raisebox{-1ex}{$y^{t,t+1}$}\right.}$ (8)

where $y^{t,t}$, $y^{t,t+1}$, $y^{t+\mathrm{1,}t}$ and $y^{t+\mathrm{1,}t+1}$ are the maximum level for x^t and x^t+1 of input for each technological set $(S^t,S^{t+1})$. Thus, the Malmquist productivity index is calculated by the non-parametric linear programming (Data Envelopment Analysis: DEA). Färe et al. (1994a) indicated that this index could be estimated with constant returns to scale and the availability of suitable longitudinal data. Suppose k=1,..., K of the firms, n =1,..., N inputs $x_n^{k,t}$ are observed in each time t=1,..., T. The inputs are employed to yield m=1,..., M products $y_m^{k,t}$. In order to calculate the change in total factor productivity for each firm between times t and t1, it is required to resolve four different equations of linear programming models (Distance functions) $D_{0c}^t\left(x^{t+1},y^{t+1}\right)$, $D_{oc}^{t+1}\left(x^{t+1},y^{t+1}\right)$, $D_{0c}^{t+1}\left(x^t,y^t\right)$, $D_{0c}^t\left(x^t,y^t\right)$) as follows (Equations 9 to 12):

${\left[D_{0c}^t\left(x^t,y^t\right)\right]}^{-1}=ma{x}_{=\varphi ,\lambda }\varphi$,(9)

st

$-\varphi y^{it}+Y^t\lambda \ge \mathrm{0,}$

$x^{it}-X^t\lambda \ge \mathrm{0,}$

$\lambda \ge 0$

${\left[D_{oc}^{t+1}\left(x^{t+1},y^{t+1}\right)\right]}^{-1}=ma{x}_{=\varphi ,\lambda }\varphi$,(10)

st

$-\varphi y^{it+1}+Y^{t+1}\lambda \ge \mathrm{0,}$

$x^{it+1}-X^{t+1}\lambda \ge \mathrm{0,}$

$\lambda \ge 0$

${\left[D_{0c}^t\left(x^{t+1},y^{t+1}\right)\right]}^{-1}=ma{x}_{=\varphi ,\lambda }\varphi$,(11)

st

$-\varphi y^{it+1}+Y^t\lambda \ge \mathrm{0,}$

$x^{it+1}-X^t\lambda \ge \mathrm{0,}$

$\lambda \ge 0$

${\left[D_{0c}^{t+1}\left(x^t,y^t\right)\right]}^{-1}=ma{x}_{=\varphi ,\lambda }\varphi$,(12)

st

$-\varphi y^{it}+Y^{t+1}\lambda \ge \mathrm{0,}$

$x^{it}-X^{t+1}\lambda \ge \mathrm{0,}$

$\lambda \ge 0$

where $\varphi$ denotes a scalar, and $\lambda$ denotes a $n\times 1$ vector of fixed numbers, representing the weights of the reference set.

The statistical population of this research is insurer agents as the representatives of crop insurance fund in 31 provinces of the country. The number of issued insurance policies per year entered as output. Labor force in terms of man-days including insurers, assessors and inspectors for broiler farms insurance per year, total capital related for broiler insurance in terms of million rials for every year, the number of insurance plans and the number of damaged cases totally as inputs entered in the model. The data are collected from the crop insurance fund's Comprehensive System (CS) of Iran for the period 2010-2020. The object of this research is analyzing the broiler insurance performance of the country through calculation the changes in total factor productivity before and after the new insurance approach.

4. Results

Table 1 reports the technical efficiency of Iran's broiler industry in the first step, assuming of variable and constant returns, relative technical efficiency changes in years t-1, t, and t+1. Efficiency as a comparative concept in economics does not have any unit of measurement. Technical efficiency figure indicates the performance of an agent in utilizing inputs compared to other agents or its previous performance. As can be observed, the average technical efficiency of broiler insurance with the assumption of variable return to scale for the years 2010-2020 was calculated to be 100%.

Table 1
The technical changes in broiler industry insurance.

Year	Technical efficiency under CRS 1 technology compared to the change in technical efficiency in the year			Technical productivity under VRS 2 technology
	t-1	t	t+1
2010	0.000	1.000	16.769	1.000
2011	0.951	1.000	1.153	1.000
2012	1.123	1.000	0.949	1.000
2013	1.576	1.000	1.180	1.000
2014	12.095	1.000	10.232	1.000
2015	1.072	1.000	2.437	1.000
2016	10.756	1.000	1.112	1.000
2017	1.337	1.000	1.438	1.000
2018	1.413	1.000	1.576	1.000
2019	1.996	1.000	1.386	1.000
2020	1.271	1.000	0.000	1.000

• ¹
  Constant return to scale;
• ²
  Variable return to scale. Source: Research findings.

High similarity of insurer agents in terms of size, structure and operating process is observed in Iran. These similarities led to insurer agents set on production frontier in different years. Above explanation clearly indicates the main reason for technical efficiency was calculated 100% with no changes over time.

Table 2 indicates the technological changes between the two periods, determining whether technical progress occurs in the crop insurance fund's input-output combination in broiler insurance. The obtained values associated with the technical change confirm that before implementing the new approach in broiler industry insurance in 2016, the advancement of insurance technology occurred only in 2013 and 2014. Before 2013, damage assessments and insurance policies were issued offline, through agricultural bank branches in all over the country. For this reason, the ratio of compensation payments to insurance premiums was very high (greater than 1). From the mentioned year and before the full implementation of negotiation-oriented insurance method in 2016, crop insurance fund managers decided that, all broiler insurance affairs being managed online, through a designed comprehensive electronic platform named SABKA. This mechanization of insurance practices was the main reason for total productivity growth of broiler insurance in the years 2013 and 2014. However, for the period 2010-2015, before implementing the negotiation-oriented insurance approach, total factor productivity of broiler insurance declined by an average of 21.4% annually, as shown in Table 2. On the other hand, during the years 2016-2020, after implementing the new insurance approach, total factors productivity of industry insurance improved almost in all over the years, by an average of 19.7% annually. It is necessary to explain that, the negotiation-oriented insurance method has been the most effective broiler insurance policy since 2016.

Table 2
The total factor productivity changes in insurance of the broiler industry.

Efficiency change	Technical change	Change of total factor productivity of insurance	Year
-	-	-	2010
1/000	0/238	0/238	2011
1/000	0/987	0/987	2012
1/000	1/228	1/228	2013
1/000	3/202	3/202	2014
1/000	0/324	0/324	2015
1/000	2/101	2/101	2016
1/000	1/097	1/097	2017
1/000	0/991	0/991	2018
1/000	1/125	1/125	2019
1/000	0/958	0/958	2020
1/000	0/974	0/974	Average

• Source: Research findings.

Accordingly, it can be concluded that in future plans for broiler insurance, paying attention to the technical efficiency improvement should be considered alongside with the technological advancement (Equation 13).

$\overline{\%TFPC{H}_L}={\left[\left({\pi }_{T=1}^{t_l}TFPC{H}_t\right)-1\right]}^{l/n_l}\times 100$ (13)

where $\overline{\%TFPC{H}_L}$ denotes the total factor productivity's geometric average annual growth rate for L= 1, 2; before and after implementing the new insurance approach for the broiler industry in the considered years t=1,2,..5. Therefore, the performance of insurance is at a favorable level, based on which the movement of the insurance system has been in the direction of sustainability.

Figure 2 depicts the trend of the number of insurance policies issued using two pre-designed and negotiation-oriented approaches. As can be seen, the highest number of insurance policies issued in the pre-designed method was in 2010, then a downward trend took place. In 2015, this amount decreased to 26340 insurance policies. Further, in spite of increasing the number of active broiler farmers for 2010-2015, reducing the number of issued insurance policies was observed. However, by applying the new negotiation-oriented insurance approach, the desirability of insuring for broiler producers has increased since 2016. Thus, the number of issued insurance policies has started to increase, reaching 62061 in the agricultural year 2020.

Figure 2
Trend of the insured policies in two pre-designed and negotiation-oriented approaches.

As can be observed in Figure 3, during the agricultural years 2010-2013, by applying a pre-designed insurance method, the number of insured one day chicks was almost constant despite the increase in the hatching rate in the country's broiler farms, in which this amount was decreased to 509 million one day chicks in 2015. This indicated that in the pre-designed method, the willingness of broiler producers for insurance services has decreased every year, and in turn, the number of one day insured chicks has decreased. Nonetheless, since the agriculture year 2016, by applying a new negotiation-oriented insurance approach, the desirability of insurance for broiler farms has increased. It has reached 1258 million one day chicks in the agriculture year 2020, indicating a favorable performance in the negotiation-oriented method.

Figure 3
Trend of the number of one-day chicks insured in the pre-designed and negotiation-oriented methods.

Figure 4 exhibits the compensation trend paid in two pre-designed and negotiation-oriented approaches. As can be seen, during the agricultural years 2010-2015, by applying pre-designed insurance method, the compensation paid was very high compared to the insurance performance, reaching the amount of 2214 billion Rials in 2010. Meanwhile, in these years, the desire of broiler producers for insurance services has decreased Figure 3.

Figure 4
Trend of compensation paid in the pre-designed and negotiation-oriented approaches.

Yet, since 2016 using the new negotiation-oriented insurance approach, the desirability of insurance for broiler producers has increased. According to Figure 1, there is an increasing trend in the number of broiler insurance policies issued. However, the compensation paid in these years has decreased significantly compared to the years before implementing the new approach. This is due to the compensation payment for minor, manageable and frequent risk layers have been removed by crop insurance fund. According to Figure 4, with the decrease in the amount of insurance premium received, the amount of compensation has also decreased, and only broiler farms damaged due to catastrophic risk factors could receive the compensation.

Figure 5 illustrates the trend of the ratio of commitments to the amount of insurance premium received by crop insurance fund in two pre-designed and negotiation-oriented insurance approaches. This figure shows smaller ratio of commitments to insurance premiums for 2010-15. However, the reduction of insurance premiums and the growth of commitments can be observed after implementing the negotiation-oriented insurance approach. For this reason, the ratio of commitments to insurance premiums has had a completely upward trend. This research clearly shows the success of the new insurance method in improving the productivity insurance of the broiler industry.

Figure 5
Trend of commitments to insurance premiums ratio.

5. Discussion & Suggestion

Applying the negotiation-oriented approach broiler industry insurance not only improved insured satisfaction, but also reduced crop insurance Fund costs and permanently decreased moral hazards. The results indicate that the changes made in the insurance method, by eliminating manageable frequent minor risks and reducing the insurance premiums paid by broiler meat producers, enabled the insurer to significantly increase its commitment, decrease loss assessment costs and minimize moral hazards. As a result, the number of issued insurance policies grew from 26340 units in 2015 before implementing new approach to 50277 units in 2020. Furthermore, data analysis shows that total factor productivity of broiler industry insurance improved by approximately 19.7% annually from 2016 to 2020 due to the negotiation-oriented approach. The findings also indicate that managing changes in insurance methods and risk layering increases both the effectiveness and productivity of crop insurance. Based on these findings:

Risk Layering: Risk layering in agricultural insurance should be carefully considered. A cost-effective risk profile of risks should be developed, where minor, frequent, and manageable risks are covered by farmers, while catastrophic but infrequent risks are fully covered by the insurance fund.

Expansion to Livestock Farms: It is recommended to implement a similar insurance scheme be for livestock farms. Before introducing any new insurance plan, it is essential to secure the persuasion and cooperation of livestock producer’s unions with the involvement of quasi-government crop insurance Fund and the government officials.

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Publication Dates

• Publication in this collection
  14 Mar 2025
• Date of issue
  2025

History

• Received
  25 May 2024
• Accepted
  18 Dec 2024