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Spatial price transmission

Spatial price transmission

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Spatial price transmission refers to the process by which price changes in one geographic market influence prices in another market, often due to factors such as transportation costs, market integration, and supply chain dynamics. It is a key concept in economics and agricultural economics, analyzing how prices adjust across different locations.
lightbulbAbout this topic
Spatial price transmission refers to the process by which price changes in one geographic market influence prices in another market, often due to factors such as transportation costs, market integration, and supply chain dynamics. It is a key concept in economics and agricultural economics, analyzing how prices adjust across different locations.

Key research themes

1. How does consumer information affect spatial price transmission and adjustment speed in retail markets?

This research area investigates the influence of consumer awareness and search behavior on how quickly and symmetrically prices adjust to cost shocks across spatially separated retail markets. Understanding the role of consumer information is critical because it shapes firms' strategic pricing responses and market efficiency in transmitting cost changes to retail prices, shaping phenomena such as asymmetric price adjustments.

Consumer Information and Price Transmission: Empirical Evidence*
by Jens-Peter Loy

2023, The Journal of Industrial Economics

Key finding: Using detailed commuting data as an exogenous proxy for consumer information in the Austrian retail gasoline market, this study demonstrates that a higher share of informed consumers significantly increases the speed and... Read more
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Spatial price competition with uninformed buyers
by Jean Gabszewicz

2023

Key finding: Extending earlier two-firm models, this paper analytically characterizes spatial price competition wherein buyers are imperfectly informed and face search costs proportional to distance. In symmetric multi-firm setups, the... Read more
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2. What are the dynamics and efficiency implications of spatial market integration and price transmission in agricultural commodity markets?

Research under this theme explores the co-movement, long-run equilibrium relationships, and causal price linkages among geographically separated agricultural markets. These studies analyze how efficiently price signals traverse trade networks and regional markets, affecting market functioning, arbitrage efficiency, and food security outcomes. Understanding spatial price transmission patterns is essential for policy design to reduce market segmentation, improve producer and consumer welfare, and enhance food accessibility in diverse agro-economic settings.

Spatial integration and price transmission among major potato markets in India
by kapil choudhary

2024, The Indian Journal of Agricultural Sciences

Key finding: Applying Johansen co-integration and Granger causality tests on monthly potato wholesale prices from major Indian markets, this study finds robust long-run price integration with evidence of both bidirectional and... Read more
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Price Transmission in Domestic Agricultural Markets: Evidence from Selected Cassava Markets in Ghana
by DR. REBECCA OWUSU

2023

Key finding: Using cointegration and causality tests on Ghanaian cassava market prices, the paper finds no evidence of market integration or causal relations among the selected markets, indicating poor price transmission and market... Read more
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Spatial market integration and price transmission for papaya markets in Ethiopia
by DR. REBECCA OWUSU

2024, Journal of Development and Agricultural Economics

Key finding: Employing Johansen co-integration, vector error correction, and Granger causality analysis on 13 years of Ethiopian papaya prices, this study finds significant long-run market integration among key regional markets. The speed... Read more
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Spatial Market Integration and Price Transmission of Selected Plantain Markets in Ghana
by DR. REBECCA OWUSU

2024

Key finding: Analyzing weekly wholesale prices of plantains from Ghanaian markets through co-integration and vector error correction models, this research documents multiple co-integrating vectors indicative of stable long-run price... Read more
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Testing the Integration in Pulses Market: Empirical Evidence from Major Lentil Wholesale Markets in India
by Alka Rai

2023, IJIRT 159769 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY

Key finding: Employing Johansen's co-integration and Granger causality tests across key Indian lentil wholesale markets, this study finds long-run price linkages despite geographic isolation, with both unidirectional and bidirectional... Read more
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Onion Market Integration: The Case Of Harar, Dire Dawa And Central Markets
by Lemma Zemedu

2022

Key finding: Using correlation, unit root, co-integration, and error correction modeling on Ethiopian onion prices, this paper finds stronger spatial integration between regional markets (Harar and Dire Dawa) than with the central market.... Read more
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3. How do network structures and competitive spatial configurations shape price competition and allocation of costs in geographically bounded markets?

This thematic area encompasses theoretical and methodological studies on spatial competition among firms in networks or bounded regions, focusing on how facility location, cost allocation, access pricing, and consumer transportation costs interact. It also deals with equilibrium existence, stability conditions, and the role of secession-proofness (no incentives for subsets of consumers to break away) in such markets. These issues have implications for designing cost-sharing mechanisms and assessing firms' strategic pricing and location behaviors in spatially segmented markets.

Equilibrium spatial pricing in a multi-dimensional setting
by Shlomo Weber

2016

Key finding: This paper analyzes the problem of locating and pricing public facilities on a plane with uniformly distributed consumers facing linear travel costs. It establishes that stable, secession-proof cost allocations (where no... Read more
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Competitive Location and Pricing on Networks
by Daniel Serra

2015

Key finding: Focusing on firms entering spatial markets where incumbents serve multiple network locations, this study formulates a two-stage game combining location and uniform pricing decisions. It shows, under conditions such as concave... Read more
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Spatial Price Competition: A Network Approach
by gordon mulligan

2021, Geographical Analysis

Key finding: Proposing a general theoretical framework for spatial price competition on transportation networks, this paper models firms as nodes competing over market areas defined on network edges, incorporating consumer uniform demand... Read more
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All papers in Spatial price transmission

Cassava Markets Integration Analysis in the Central region of Ghana
by Richard K Bannor
The benefits of cassava production must be reflected in efficiency of the markets as in most Africa countries and Ghana, cassava cultivated by small scale farmers are often sold either in the nearby markets or the farm gate. That is to... more
The study first examined each price series for evidence of non-stationarity in order to confirm that co-integration approach. At level 0, Kasoa, Swedru and Bawjiase markets cassava wholesale prices were not stationary but Ajumako and Cape Coast prices were stationary. DF-GLS test, Augmented Dickey Fuller (A DF) and Philips-Perron (PP) showed similar results as indicated in Table 2.
A test for a suitable lag length to be included in the co-integration test was performed, because the results of co- integration tests can be quite sensitive to this Hafer and Sheehan, 1991 and Hai etal., 2004). The number of lags was selected by applying five different multivariate lag selection criteria: the Akaike information criterion (AIC), the Hannan-Quin information criterion (HQIC), and the Schwarz’s Bayesian information criterion (SBIC), FPE and LR. A vector autoregression (VA R) on the differenced series was conducted and lag length of the model with the least AIC, HQIC and SBIC values chosen as the appropriate lag length to be included in the cointegration test. The results of co-integration test as given in the Table 1 indicate the right lag length for the analysis was 5 lags.
source: Author s Computation At rank 0: Ho: There is no co-integration between the various market prices H1: There is co-integration between the various market prices. NB: We accept null hypothesis when trace statistics or max statistics is less than the 5% Critical value at rank 0. At rank 1,3: Ho: There is (1) or (3) co-integration of the various markets at rank 1 or 3 H1: There is no 1 or 3 co-integration of the markets at rank 1 or 3. NB: We accept null hypothesis when trace statistics or max statistics is less than the 5% Critical value at rank 1 or 3
The co-integration tests results as shown in the Table 4 indicates three co-integration between two markets pairs such as Ajumako- Kasoa, Swedru-Cape Coast and Ajumak- Swedru at Rank Order 1. This means that, the prices of the two markets move closely together in the long run although in the short run they
ource: Author's computation |: No short run causality running from market A to B [: Short run causality running from A to B or market A causes price changes in market 1B: Reject null hypothesis when the p value is > 5%
JUULILE, AULNUL 5 UUIIVPULALLUEE Langrange Multiplier Test; Ho: No autocorrelation; H1: There is autocorrelation Jarque Bera Test: Ho: Residuals are normally distributed; H1: Residuals are not normally distributed NB: Reject null hypothesis when p value is less than 5% Table 7: Diagnostic checking of the VAR and VEC models
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SPATIAL MARKET INTEGRATION AND PRICE TRANSMISSION OF SELECTED PLANTAIN MARKETS IN GHANA
by Rebecca Owusu
Spatial price analysis is important in explaining market performance and the degree of integration. This study therefore provides an econometric analysis of plantain markets integration. Using co integration analyses and vector error... more
If all the variables are integrated of the order! (I (1)), then they are stationary in the first difference. Therefore all the if all the variables are integrated of the order! (I (1)), then they are stationary in the first difference. Therefore all the summations in the equation above are also stationary. In addition, if the variables are co integrated, the ECM term linear combination of the variables represented in parenthesis) will also be stationary. The a, coefficients represent he short run effects and m, coefficients represent the stationary long run impacts of the right hand side variables. [he parameter mp measures the rate of adjustment of the short run deviations towards the long run equilibrium. This value theoretically lies between 0 and 1. Zero signifies no adjustments and 1 indicates an instantaneous adjustment. A value between 0 and 1 indicates that any deviations will have gradual adjustment to the long run equilibrium
or otherwise. The model indicates that the null cannot be rejected for all the price series as the absolute value of the
Source: authors construct Table 2: Result of J ohansen’s Multiple Co integration Test The trace test ind icates three co integration equations at the 0.05 alpha level. Upon establishing that the markets were co integrated, the vector error correction model was applied to study the transmission among the markets. This was to analyse t he short run dynamics of the effect of plantain prices in Techiman after establishing that co integration exist among the markets since according to Gujarati (2004), if two variables X and Y are co integrated, then the relations hip between the two can be expressed using the error correction mechanism. The result of the VECM is presented in Table 3. The trace test indicates three co integration equations at the 0.05 alpha level. Upon establishing that the markets integration has been used as a measure of market efficiency.
Standard errors in () & P values in [], P=0.05 Source: Authors construct
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Onion Market Integration: The Case Of Harar, Dire Dawa And Central Markets
by Lemma Zemedu

2015

The objective of this study was to analyze the integration of onion market among the spatially separated secondary market to onion producers and the central market. Pearson correlation coefficient, unit root test, Co-integration test and... more
Journal of Economics and Sustainable Development ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.5, No.27, 2014 LVEUP INCE BELLO KT ULLUTE LATLULY SES Co-integration and error correction models, introduced by Engle and Granger (1987) were used to determine the market integration which was applied to the price series of sample markets. To do these STATA software was used. The data used in this research were monthly onion price of three markets: Addis Ababa, Dire Dawa and Harar. The data used for this study was five years monthly price data obtained from CSA. The first step was to determine whether onion price data were stationary at their levels. If they were not, other investigations should be undertaken to determine their order of integration by differencing the price series until they reach their stationarity.
Unit root tests for level A non-stationary series is integrated of order I (0), while a stationary series is integrated of the order I (1). The examination of market integration was done in three markets: Addis Ababa, Dire Dawa and Harar. All price series were tested, and the results were as table follows.
SEE SSS Wp IS ES 0 The results of the unit root test show that prices were stationary at first difference in Harar and Dire dawa. This shows that the order of integration of Harar and Dire Dawa monthly prices is one, I (1). Thus, one can test for market integration between Harar and Dire Dawa onion markets. Since the price series for Addis Ababa was stationary process at its level, there was no need to have co-integration test as we know that they do not co- integrate with Harar and Dire Dawa markets. In other words Addis Ababa was stationary at level. Table 3. First difference unit root test
The Engle and Granger test uses a standard OLS estimation for the long run relationship between the pair market prices. Table 22 indicates the OLS co-integration estimate. When the price in Dire Dawa rises by 1%, there is a corresponding long run increase in the Harar price level by 1.019%. Table 4. OLS co-inteevration test a JUGICALe sistiiiedit dt 170 sistiilicallt lOVel, Source: own calculation, 2012 In order to conclude that the price series are co-integrated, the residuals from the OLS estimation have to obey stationarity. To confirm any stable equilibrium relationship between two prices, stationary test is conducted on the residual. The DF/ADF test is used to test for a longer co-integration relationship in the residual of the pair Harar and Dire Dawa markets. The co-integration tests on residual summarized in Table 23 confirm the
existence of co-integration between the different regional markets. The result leads to conclude that Harar onion market has been integrated with Dire Dawa market. Thus, the test result supports the prediction under expectation that the prices are co-integrated. So, the markets are spatially integrated. Table 54. Residual regression test on unit root
Co-integration is not a sufficient condition for full market integration or it does not imply full market integration. Since the series show long run relationship, Error correction model is applied to investigate further on short run relationship between markets. Error correction model is necessary to test for the full market integration. This is achieved through estimating by regressing the change in price series of current market price on current and lagged values of the change in price series on the first lagged of the residuals from the long run regression. Table 6. Error correction model *** and * indicate the statistical significance at the 1% and 10% levels of significance respectively. Source: own calculation, 2012 The result showed that the conditions for error correction were fulfilled; as the computed result of o < 0. This i the speed with which the system approaches its equilibrium depends on the magnitude of the estimate o, th closer it is to -1 the faster the system approaches its equilibrium. That is, o = -1 means there is full integration while o = 0 means no integration. But, the computed result showed that o = -0.258 which is far from -1. So, it i concluded that there is a slower adjustment of the system towards the equilibrium after a shock. This is the spee: of price adjustment measuring the rate at which the price difference in period t-1 is corrected to achieve pric equilibrium between the market pair. That is, the result of error correction shows that the coefficient of th lagged error term e,.; was found to be negative. This means there is a short run effect of Harar price on that o Dire Dawa prices. But, the hypothesis for existence of short run/full market integration was rejected because th calculated value (16.34) exceeds the tabulated value at F (9, 46) which is 2.89. Solomon (2004) and Rahima (2006) also obtained a comparable result of no integration between regional an central market as well as no short run and full market integration between markets on cattle and pepper price Sixty nine percent changes in price of Harar market is due to the current price change in Dire Dawa. The spee: at which the price approaches equilibrium depends on the magnitude of o which is equal to -0.258 between th pair market. Negative sign indicated that the adjustment is towards the equilibrium. That is 25.8% disequilibriun were adjusted at two months and full adjustment requires four month time period (Full adjustment requires ON Ney ET fy, LS 2 OE! OME Ce On Se ey AE . See mL S a E ) ene en tee Cee co kOe OMe SAO: Serena ame Es: UME Rane eerste Ree a nCere mm S
Price series of onion in months collected from CSA which covers a period (2006-2011): Harar (HA), Dire Dawa (DD) and Addis Ababa (AA) markets.
Source: CSA Journal of Economics and Sustainable Development ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.5, No.27, 2014
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Spatial Market Integration and Price Transmission of Selected Plantain Markets in Ghana
by Rebecca Owusu

2012

Spatial price analysis is important in explaining market performance and the degree of integration. This study therefore provides an econometric analysis of plantain markets integration. Using co integration analyses and vector error... more
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