scholarly journals INVESTIGATING BARGAINING POWER OF FARMERS AND PROCESSORS IN IRAN'S DAIRY MARKET

2018 ◽  
Vol 51 (1) ◽  
pp. 126-141 ◽  
Author(s):  
ZEINAB SHOKOOHI ◽  
AMIR HOSSEIN CHIZARI ◽  
MAHDI ASGARI
Keyword(s):  
Bargaining Power ◽  
Expectation Maximization Algorithm ◽  
Raw Milk ◽  
Annual Data ◽  
Bargaining Model ◽  
Government Officials ◽  
Dairy Farmers ◽  
Bilateral Bargaining ◽  
Monte Carlo Expectation Maximization ◽  
Milk Market

AbstractThe farm-gate price of raw milk in Iran is determined annually in negotiations among representatives of dairy processors, milk producers, and government officials. This study estimates the average bargaining power of dairy farmers and processors, through applying the generalized axiomatic Nash approach in a bilateral bargaining model. We employ annual data from 1990 to 2013 to estimate econometric representation of a bilateral bargaining model using a Monte Carlo expectation maximization algorithm. Results imply a higher bargaining power of 0.69 for processors, compared with 0.31 for farmers. This asymmetry of bargaining power causes unequal allocation of gains in the milk market.

scholarly journals Wage bargaining as an optimal control problem: a dynamic version of the efficient bargaining model

2021 ◽  
Author(s):  
Marco Guerrazzi
Keyword(s):  
Optimal Control ◽  
Continuous Time ◽  
Bargaining Power ◽  
Wage Bargaining ◽  
Bargaining Model ◽  
Speed Of Convergence ◽  
Wage Setting ◽  
Dynamic Version ◽  
Efficient Bargaining ◽  
Over Time

AbstractIn this paper, I develop a dynamic version of the efficient bargaining model grounded on optimal control in which a firm and a union bargain over the wage in a continuous-time environment under the supervision of an infinitely lived mediator. Overturning the findings achieved by means of a companion right-to-manage framework, I demonstrate that when employment is assumed to adjust itself with some attrition in the direction of the contract curve implied by the preferences of the two bargainers, increases in the bargaining power of the firm (union) accelerate (delay) the speed of convergence towards the stationary solution. In addition, confirming the reversal of the results obtained when employment moves over time towards the firm’s labour demand, I show that the dynamic negotiation of wages tends to penalize unionized workers and favour the firm with respect to the bargaining outcomes retrieved with a similar static wage-setting model.

Political Power and Bargaining Theory

2019 ◽  
pp. 47-82
Author(s):  
Keith Dowding
Keyword(s):  
Bargaining Power ◽  
Social Power ◽  
Power Index ◽  
Bargaining Model ◽  
Bargaining Theory ◽  
Index Approach ◽  
Study Power ◽  
Relationship Of ◽  
The Relationship ◽  
Made In

The chapter opens with some distinctions made in the study of power and semi-formally defines ‘outcome’ and ‘social’ or ‘power to’ and ‘power over’ showing the latter is a subset of the former. It argues both are legitimate ways of examining power. It argues that whilst ‘social power’ is often our concern, especially when discussing issues of freedom, domination and inequality we need to start by considering outcome power. Understanding why people can fail in their aims even when others are not acting against them – failure in their outcome power – is necessary for to understand the scope of social power. The chapter then examines the relationship between outcome power and freedom and discussesMorriss’s distinction between ability and ableness. Power is a dispositional concept and the ability that people have need to be distinguished from their exercise of their powers. It argues that if we only look at abilities we could eliminate the term power from our language since all we would need to is to look at their capacities or resources, but we also need to examine the way that agents change others incentives to act. The chapter introduces the formal aspects of the power index approach and through that discussion distinguishes power and luck. It then introduces bargaining power, formally distinguishes threats and offers and explains Harsanyi’s bargaining model of power and the extra element of reputation. It then discusses the relationship of luck and group power introducing the notion of systematic luck. It then concludes by discussing how we can study power in society.

scholarly journals Spatial Integration of the Milk Market in Poland

2020 ◽  
Vol 12 (4) ◽  
pp. 1471 ◽  
Author(s):  
Monika Roman
Keyword(s):  
Agricultural Production ◽  
Granger Causality Test ◽  
Raw Milk ◽  
Point Of View ◽  
Spatial Integration ◽  
Causality Test ◽  
Price Differences ◽  
Milk Market ◽  
The Eu

The production of milk is an essential branch of agricultural production in Poland. There have been considerable changes in the milk market in Poland over the last 20 years (such as, e.g., the adjustment of the market to the EU requirements, which had an impact on the functioning of this market as well as its spatial integration. This research mainly aimed to assess the processes of spatial integration on the Polish milk market in the period 1999–2018. In order to process the material collected, the author applied the analysis of price differences, the analysis of correlation, the Johansen test of cointegration and the Granger causality test. As a result of the research conducted, it was found that there is a long-term balance between prices in various voivodeships in Poland. Moreover, the closer the voivodeships were to one another, the greater the co-variability of prices was between them. In addition, it was indicated which voivodeships were crucial from the point of view of the process of revealing and determining raw milk prices in Poland by two distinguished periods (1999–2008 and 2009–2018).

scholarly journals A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data

2018 ◽  
Vol 12 (3) ◽  
pp. 253-272 ◽  
Author(s):  
Chanseok Park
Keyword(s):  
Monte Carlo ◽  
Expectation Maximization ◽  
Likelihood Function ◽  
Expectation Maximization Algorithm ◽  
Parametric Models ◽  
Maximum Likelihood Estimates ◽  
Computational Technique ◽  
Stable Convergence ◽  
Explicit Integration ◽  
Monte Carlo Expectation Maximization

The expectation–maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation–maximization is best suited for situations where the expectation in each E-step and the maximization in each M-step are straightforward. A difficulty with the implementation of the expectation–maximization algorithm is that each E-step requires the integration of the log-likelihood function in closed form. The explicit integration can be avoided by using what is known as the Monte Carlo expectation–maximization algorithm. The Monte Carlo expectation–maximization uses a random sample to estimate the integral at each E-step. But the problem with the Monte Carlo expectation–maximization is that it often converges to the integral quite slowly and the convergence behavior can also be unstable, which causes computational burden. In this paper, we propose what we refer to as the quantile variant of the expectation–maximization algorithm. We prove that the proposed method has an accuracy of [Formula: see text], while the Monte Carlo expectation–maximization method has an accuracy of [Formula: see text]. Thus, the proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation–maximization algorithm. The improved performance is illustrated through the numerical studies. Several practical examples illustrating its use in interval-censored data problems are also provided.

Marketing Raw Milk from Dairy Farmers before and after the 2008 Milk Scandal in China: Evidence from Greater Beijing

Agribusiness ◽  
2014 ◽  
Vol 30 (4) ◽  
pp. 410-423 ◽  
Author(s):  
Xiangping Jia ◽  
Hao Luan ◽  
Jikun Huang ◽  
Shengli Li ◽  
Scott Rozelle
Keyword(s):  
Raw Milk ◽  
Dairy Farmers ◽  
Before And After

QUALITY CONTROL COMMITTEE LABORATORY OF THE MINNEAPOLIS-ST. PAUL MARKET1

1970 ◽  
Vol 33 (8) ◽  
pp. 316-318
Author(s):  
Roy E. Ginn
Keyword(s):  
Quality Control ◽  
Raw Milk ◽  
Steering Committee ◽  
University Of Minnesota ◽  
Health Agencies ◽  
Processing Plants ◽  
The Cost ◽  
The University ◽  
Milk Market

The Quality Control Committee laboratory is a unique organization which was started approximately 32 years ago by Dr. Harold Macy of the University of Minnesota. The dairy industry operates a laboratory which does most of the official testing for the health agencies in the Minneapolis-St. Paul market. With higher costs of operations many health agencies are trying to find ways of saving money, and still have a satisfactory laboratory program to protect the public's health. Some health agencies are using industry laboratories, and the cost is passed on to the customer rather than the taxpayer. The laboratory functions are to evaluate the quality of the raw milk supply from 4238 Grade A producers, and the finished products from 17 processing plants. The laboratory also does the official butterfat testing for the Federal Milk Market Administrator for Order 68. This organization is supervised by a Steering Committee of nine individuals who represent the University of Minnesota; the producer cooperatives, who supply the raw milk; and the Grade A fluid milk processors from the Minneapolis-St. Paul market. All of the routine results from the laboratory are provided to the health agencies. The health agencies and laboratory manager have a close working relationship to coordinate the program. In order for an organization like this to work, it takes cooperation from all parties involved.

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