The actual transition might be smoother due to multiple rounds of bargaining that are not collected in the observational data. The final- price markup has non-zero density below 0. The average age of the respondents is approximately 34 years.
Ages in the overall sample range from 20 years to 77 years. Since each respon- dent had to review 3 images of buyers, the sample contains transaction-level observations. The first two columns suggest that women are often quoted lower prices from the sellers as compared to men.
This is consistent with findings from the observational data. On average, the women are first-asked a price of about Rs.
Another large difference in two demographic groups is between foreigners and Indians. Foreigners are asked a first price of Rs. People above 40 years of age also tend to be asked a price Rs. An increase in appearance from 1 working-class to 2 middle-class increases the ask prices by more than Rs.
Similarly, an increase in appearance from 2 middle-class to 3 upper- class increases the first-ask prices by about Rs. These differences are consistent with those seen in the observational data. Mean first-ask prices across demographic groups are shown in Figure 5. These observed differences across multiple demographic groups motivate my use of multivariate regressions.
The marginal cost of the good is fixed at Rs. A large proportion of sellers prefer Rs. The prices range from to As expected, sellers prefer round numbers such as , , , as their WTA. Sellers get only 2 rounds to bargain with a buyer; so, the lowest-ask price is a hypothetical third ask price elicited from the seller after the buyer has decided not to purchase the good.
As bargaining progresses, the histograms show a leftward shift. This model will be extended in section 4. Figure 9 plots the probability of a purchase with the final price. Since the buyers have a fixed prior probability of entering the store and willingness to pay, the cumulative density function is a piece-wise constant decreasing function.
Let Fsig be the first-ask price of seller s to buyer i for good g. I abbreviate sig as t since a seller s, buyer i, and good g together constitute a transaction t. The variable incomei , collected as a categorical variable, is the income level on a scale of , where 3 is the highest of the buyer.
Does bargaining help drive down the price? Let Psig be the final price of seller s to buyer i for good g. I can abbreviate sig as t for transaction. I define Zt as a proportion of the marginal cost c, termed the final price markup. To control for this, I use the second and third structural models which include the first-ask price Ft or first-ask markup Mt. In this section, I present a profit maximization model of a seller who price discriminates using buyer observables. I then test how these models compare to a counterfactual profit-maximizing uniform price model.
Imperfect price discrimination case only first-ask A one-ask price discrimination model abstracts from bargaining and allows the seller to quote a single take-it-or-leave-it price to the buyer. Zt is the final price markup as defined previously.
I will estimate this using a logit model as detailed below. This time, the seller has two attempts to bargain with a buyer to sell his good. Note: Since the observational dataset is small at observations, I use bootstrap sampling with replacement to calculate the expected profits in all 4 cases.
In order to maximize the objective function for a given resample in the case of imperfect price discrimination, I use a gradient ascent based optimization function. Lt is the lowest price the seller is willing to take from the buyer in the image after both his bids have been rejected.
I call this the lowest-ask price. Ft , St , and Lt are the sellers first-ask, second-ask, and lowest-ask prices respectively. Et represents the bargaining intensity, which is quantified as the number of rounds of bargaining that took place on a scale of To control for this, I add dummy variables for demographic types, which capture fixed effects within each buyer type.
Does this model of price discrimination increase profits? Uniform pricing case Similar to Graddy and Hall , I use the prior probabilities of the 7 buyers entering the store frequenting the market from the observational data. Appearance is an indicator vari- able for perceived affluence.
The first regression does not include the interaction between is foreigner and appearance. I find that appearance has a strong effect on the extent of price discrimination, and sellers raise prices by Females are also quoted prices that are lower by about This result is different than that of List , in which females and males tend to receive similar bids and pay similar prices for the same good conditional on the execution of the purchase.
As expected, foreigners are quoted prices that are on average After including the interaction term between is foreigner and appearance to capture affluent-looking foreigners, the effect of foreigner on the first-ask markup decreases to 9. The effect of appearance falls by about 5 percentage points from I find that, controlling for the other observables, foreigners are quoted a price that is about Rs. Ceteris paribus, females and people older than 40 years of age also tend to be asked lower prices, especially in the first round.
This translates to females being offered prices that are lower by about 10 - 15 percentage points, which is consistent with the observational data. However, the result that older people are asked lower prices is not supported by results from the observational study. This suggests that the price discrimination observed in the field is a consequence of statistical discrimination - the belief that different demographic groups have different distribution of reservation prices - rather than animosity against cer- tain demographic groups.
This result is consistent with the findings of List Appearance is measured on a scale of , where 3 is the most affluent-looking. A 1 point increase in appearance represents a jump from working-class to middle-class or middle-class to upper-class in perceived affluence. For a 1 point increase in appearance, there is approximately a Rs. In the case for the variable above40, the difference also loses its statistical significance, which implies that sellers do not continue price discriminating based on age as bargaining continues.
Gender is not correlated with income. The results of the OLS regression are given in Table 7. The results, given in Table 8, are similar to those found in the previous section. Similarly, females are willing to pay about Rs. On the other hand, age does not seem to have a large effect on WTP.
Older people tend to have a WTP that is about Rs. This effect persists until the final transaction price, but decreases in statistical significance. These characteristics may not be correlated with income. The results are given in Table This is the reason why the number of observations N varies across the last 3 columns.
Observational Table 12 shows the results of the three OLS regressions of the final price markup Zt on bargaining intensity Et. As expected, bargaining intensity is negatively correlated with the final price markup. Table 10 column 1 shows that a 1 point increase in bargaining intensity decreases the final price markup by 18 percentage points. In column 2 , after controlling for first-ask markup Mt to rule out anchoring effects, I find that the magnitude of this effect increases by 1 percentage point.
In column 3 , I regress the final price markup from the first-ask price ZFt on bargaining intensity. The bargaining intensity has a weaker effect on this measure; a 1 point increase in Et decreases the final price below the first-ask price by about 9 percentage points.
However, our result might be an overestimate of the true effect of bargaining since the bargaining intensity is decided by the observer ex-post the completion of the transaction. Survey Table 13 shows the results of the OLS regression of the difference between the first-ask Ft and final transaction price Zt , termed as the ask markup, on the bargaining intensity variable.
After including fixed effects, I find that bargaining still lowers the price below the first-ask by about Rs. This supports our findings from the observational study, which suggests that bargaining has a strong downward influence on the price markup above the first-ask price. The prices are also defined as percentages of marginal cost. So, a price of 1. Table 14 shows the expected prices, probabilities of purchase, and profits under the four estimated models.
The first ask price of the two-ask price discrimination model Imperfect Ft , St is about 1. Additionally, the second-ask price of the two- ask model is about the same as the first-ask price of the one-ask model. This makes sense, since at the last attempt in any price discriminating model, the seller wants to quote a price to maximize profits as well as the probability of purchase to ensure that the sale is completed.
The OLS model has the highest expected probability of purchase at 0. A potential explanation is that since sellers have two attempts to convince a buyer, they quote a high first bid which has a lower probability of success to maximize their profits and then return to a baseline lower price that increases the probability that the good is purchased. Mean, standard deviation, and confidence interval are calculated using bootstrap sampling.
The calculation is given in Table This model of price discrimination extracts all consumer surplus and maximizes producer surplus. The seller has two attempts to sell the good to the customer. The estimated final prices based on these transaction prices are calculated using an OLS regression of final price Zt on the observables xi and ask price.
I then try to maximize the profit objective function in the model section using a gradient ascent algorithm. Table 17 shows the variables used to calculate the profits. As expected, the first-ask prices Fi to the different demographic groups are higher than their corresponding second-ask prices Si. Under this model, we obtain expected profits of Rs. With a marginal cost of Rs. This is consistent with our result from the observational study but larger in magnitude.
One potential explanation might be that sellers in the observational study did not reveal their true marginal cost. Another explanation is that sellers might face lower profit percentages on certain categories of goods such as leather goods and jewellery. This heterogeneity in profits across goods might lower the average profit percentage in the observational setting. Thus, our findings suggest that price discrimination raises profits as compared to a profit- maximizing uniform price model.
Using observational data collected from an informal market, I find that sellers price discriminate primarily based on observable charac- teristics of gender, appearance, and race. Males, foreigners, and affluent-looking individuals tend to pay higher prices as opposed to their counterparts.
The survey data confirms that this is a consequence of statistical discrimination rather than taste-based discrimination. However, buyers can lower the price through bargaining, which has a strong downward effect on the final price markup. These results are consistent with the those from the survey experiment; the difference being that the effects from the survey are larger in magnitude.
This paper contributes to a small but growing body of research on first-degree price dis- crimination. The study takes advantage of the unique dynamics of this informal market to explore economic phenomena in a naturally-occurring marketplace.
However, the models in this paper present a simplification of the multidimensional nature of the informal market. On the seller side, I omit dimensions such as quantity sold, inventory management and het- erogeneity in quality of goods. Another limitation of the model is that it does not capture existing relationships be- tween buyers and sellers, which might have downstream effects on bargaining intensity and the extent of price manipulation.
Further research should explore these aspects of informal markets. Price discrimination also incurs costs that are difficult to measure. In terms of welfare, price discrimination transfers surplus from the consumer to the producer and increases to- tal welfare. In comparison, a single price model often prices these consumers out of the market. However, flexible pricing increases search costs for buyers and may decrease demand. Price discrimination across homogeneous goods can intensify competition between sellers in the marketplace.
These fairness concerns can constrain the profit-seeking behavior of the sellers. In conclusion, my results have important implications for welfare, fairness, and competition. Amir, Ofra, David G. Rand, and Yaakov Kobi Gal. Arnold, Michael A. Ayres, Ian, and Peter Siegelman.
Two field experiments. Evidence and Expert Forecasts. Geertz, Clifford. Graddy, Kathryn. Graddy, Kathryn, and George Hall. Kahneman, Daniel, Jack L.
Knetsch, and Richard Thaler. Norton, Emmanuel Saez, and Stefanie Stantcheva. Evidence from Randomized Survey Experiments. List, John A. Worker Demographics in Amazon Mechanical Turk. Full cost plus pricing is a price-setting method under which you add together the direct material cost, direct labor cost, selling and administrative costs, and overhead costs for a product, and add to it a markup percentage to create a profit margin in order to derive the price of the product.
Incremental Cost Pricing It is the method of pricing a product based on incremental cost. In this type of pricing, the selling price of a product is determined by the variable cost, and not kept according to the overall cost of creating the product. Incremental cost is the cost of creating additional products from the same setup i. This method is used only when the fixed overhead is being absorbed by existing product sales. The advantage of incremental cost pricing is that it can be used to launch a new product with low cost so that it is readily accepted in the market, and also to open up a new customer base by reducing the price of an existing product.
Marginal Cost Pricing Marginal-cost pricing, in economics, the practice of setting the price of a product to equal the extra cost of producing an extra unit of output. By this policy, a producer charges, for each product unit sold, only the addition to total cost resulting from materials and direct labour. Businesses often set prices close to marginal cost during periods of poor sales. If, for example, an item has a marginal cost of Rs.
The business would choose this approach because the incremental profit of 10 paise from the transaction is better than no sale at all. Target Pricing It is a refined variant of cost-plus pricing. The only difference between these two method is about setting the mark-up. The target pricing considers the derived reasonable rate of return on initial investment made by the firm in setting the mark-up.
The rate of return is a relationship between the profits and suitably defined measure of invested capital. The profit target may be different for different firms and in different phases of business cycle. Going Rate Pricing Going rate pricing is when a business sets the price of their product or service based on the market price. This pricing strategy is often used to price similar products, like commodities or generic items, that have little variation in design and function.
A going rate pricing strategy is most often used to price products or services that are homogenous and don't vary in design. Businesses that choose a going rate pricing strategy often set their prices based on the leader of the market. Since competitor prices tend to be similar, it's challenging to differentiate your product or service from the competition.
Sealed Bid Pricing Sealed bid pricing is the process of offering to buy or sell products at prices designated in sealed bids. Companies must submit their bids by a certain time.
The bids are later reviewed all at once, and the most desirable one is chosen. Sealed bids can occur on either the supplier or the buyer side. Via sealed bids, oil companies bid on tracts of land for potential drilling purposes, and the highest bidder is awarded the right to drill on the land. Similarly, consumers sometimes bid on lots to build houses.
The highest bidder gets the lot. On the supplier side, contractors often bid on different jobs and the lowest bidder is awarded the job. The government often makes purchases based on sealed bids. Dual Pricing. Dual pricing is the practice of setting different prices in different markets for the same product or service.
This tactic may be used by a business for a variety of reasons, but it is most often an aggressive move to take market share away from competitors. Dual pricing may be demand-based. For example, an airline may offer one price to an early customer and another, higher price to someone booking at the last minute. Additionally, businesses in many developing nations that rely on tourism employ dual pricing strategies.
Local residents get lower prices for goods and services while tourists pay more. In simple terms, price lining is a process of grouping similar offerings under different price brackets — each varying slightly by the quality features, or attributes on offer.
These brackets usually tend to start low and go higher in price. Cyclical Pricing refers to appropriate pricing strategy at different stages of Business Cycle. Contraction comprises of the first two phases and the last two phases constitute expansion. Likewise during prosperity, high price is charged. LOSS LEADER Loss leader pricing is a marketing strategy that involves selecting one or more retail products to be sold below cost — at a loss to the retailer — in order to get customers in the door.
The loss leaders are the products being sold at such low prices as an enticement to buyers to step foot in the store. This firm is usually the one having the lowest production costs, and so is in a position to undercut the prices charged by any competitor who attempts to set its prices lower than the price point of the price leader.
Competitors could charge higher prices than the price leader, but this would likely result in reduced market share, unless competitors could sufficiently differentiate their products. Transfer pricing is the setting of the price for goods and services sold between controlled or related legal entities within an enterprise. For example, if a subsidiary company sells goods to a parent company, the cost of those goods paid by the parent to the subsidiary is the transfer price.
Setting Pricing Policy 1. Selecting the pricing objective 2. Determining demand 3. Estimating costs 4. Selecting a pricing method 6.
Selecting final price. Open navigation menu. Close suggestions Search Search. User Settings. Skip carousel. Carousel Previous.
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