Saturday, December 15, 2018

The Great Trade Collapse: An Evaluation of Competing Stories


The Great Trade Collapse: An Evaluation of Competing Stories


One sentence summary: Retail inventories have contributed the most to the great trade collapse and the corresponding recovery, followed by protectionist policies, intermediate-input trade, and trade finance.

The corresponding working paper by HakanYilmazkuday is available here.

 
Abstract
The reduction in international trade has been more than the reduction in economic activity during the 2008 financial crisis, against the one-to-one relationship between them implied by standard trade models. This so-called the great trade collapse (GTC) has been investigated extensively in the literature resulting in alternative competing stories as potential explanations. By introducing and estimating a DSGE model using eighteen quarterly series from the U.S., including those that represent the competing stories, this paper evaluates the contribution of each story to GTC. The results show that retail inventories have contributed the most to the collapse and the corresponding recovery, followed by protectionist policies, intermediate-input trade, and trade finance. Productivity and demand shocks have played negligible roles.


Non-technical Summary
The reduction in international trade has been more than the reduction in economic activity during the 2008 financial crisis. This observation has been accepted as extraordinary, because its magnitude has been far larger than in previous downturns; accordingly, it has been called as the Great Trade Collapse (GTC, henceforth).

Since the relation between trade and economic activity is one to one in standard trade models (mostly implied by constant elasticity of substitution preferences in gravity-type studies), this collapse in trade has attracted attention in the recent literature, and its causes have been investigated extensively not only because the decline in trade flows relative to overall economic activity is surprisingly high but also because it has important implications for optimal policy response. Accordingly, alternative explanations have been achieved, including the dynamics of inventories, intermediate-input trade, compositional differences between traded goods and GDP, trade finance/credit, declining aggregate demand, or higher trade costs due to protectionist policies. Since most of these papers have competing stories, they have sometimes found conflicting results with each other as well. However, what if there were multiple stories contributing to GTC at the same time? If yes, what was the contribution of each story? In other words, are these stories complements of or substitutes to each other? Based on the literature introduced so far, answering these questions requires a structural estimation of a dynamic trade model with ingredients such as intermediate-input trade, inventories, protectionist policies, trade finance, and financial interactions between countries.

Accordingly, this paper introduces a dynamic stochastic general equilibrium (DSGE) trade model to create a bridge between the literatures of international trade and macroeconomics through investigating trade patterns in a dynamic framework that borrows the stories explaining GTC from the literature introduced above. The model considers individuals, manufacturers and retailers, where the latter two hold inventories of finished goods. There is a monetary authority who decides for the policy rate, although the interest rate faced by individuals (due to intertemporal choices) and manufacturers/retailers (due to financial needs, including trade finance) is subject to the country-specific risk premium. To consider compositional effects, the model distinguishes between traded versus nontraded goods, home versus foreign goods, and durable versus nondurable goods.


The model is estimated by state-of-the-art Bayesian techniques using eighteen series of quarterly data from the U.S., including durable and nondurable imports, durable and nondurable production, services versus overall consumption, prices, inventories, duties, risk premium, and wages. The estimated model is further used to decompose durable and nondurable imports into their components, representing the competing stories of intermediate-input trade, retail inventories, protectionist policies, trade finance, retail productivity shocks, and consumer demand shocks. When overall U.S. imports are considered, the results show that retail inventories have contributed the most to GTC and the corresponding recovery, followed by protectionist policies, intermediate-input trade, and trade finance. The compositional effects within imports are significant: while retail inventories are mostly responsible for the changes in durable imports, intermediate-input trade is responsible for the changes in nondurable imports. In all cases, productivity and demand shocks have played negligible roles.

Saturday, December 8, 2018

Understanding the International Elasticity Puzzle


Understanding the International Elasticity Puzzle


One sentence summary: The macro elasticity in international trade is a weighted average of the macro elasticity in international finance and the corresponding elasticity of substitution across products of foreign source countries.

The corresponding paper by HakanYilmazkuday has been published at Journal of Macroeconomics.

The working paper version is available here.

 
Abstract
International trade studies have higher macro elasticity measures compared to international finance studies, which has evoked mixed policy implications regarding the effects of a change in trade costs versus exchange rates on welfare measures. This so-called international elasticity puzzle is investigated in this paper by drawing attention to the alternative strategies that the two literatures use for the aggregation of foreign products in consumer utility functions. Using the implications of having a finite number of foreign countries in nested CES frameworks that are consistent with the two literatures, the discrepancy between the elasticity measures is explained by showing theoretically and confirming empirically that the macro elasticity in international trade is a weighted average of the macro elasticity in international finance and the corresponding elasticity of substitution across products of foreign source countries.


Non-technical Summary
International trade studies have higher macro elasticity measures compared to international finance studies. Since price movements due to policy changes are converted into welfare adjustments through these elasticities, this observation evokes mixed policy implications regarding the effects of trade costs in international trade versus the effects of exchange rates in international finance. Due to these mixed implications on welfare, this observation is called the international elasticity puzzle.

In the literature, international finance studies mostly have a macro elasticity value of about 1.5, while international trade studies mostly have a macro elasticity value of about 5. It is implied that if we directly employ these numbers in a policy analysis, say, in order to investigate the effects of a foreign price change due to tariffs or exchange rates, international trade studies imply quantity changes that are at least three times the international finance studies.

This paper attempts to understand the international elasticity puzzle by drawing attention to the alternative strategies the two literatures have for the aggregation of foreign products in consumer utility functions. In particular, while the majority of international finance models include a unique foreign country (in their two-country frameworks) in order to have an understanding of the macroeconomic developments in the home country, the majority of international trade models include multiple foreign countries in order to investigate the bilateral trade patterns of the home country. Since having alternative numbers of foreign countries is reflected as alternative macro elasticity measures between the two literatures in a nested constant elasticity of substitution (CES) framework, as shown in this paper, the international elasticity puzzle can be understood by paying attention to the alternative ways that foreign products are aggregated in the two literatures.

Regarding the details, when a finite number of goods and foreign countries is considered in nested CES frameworks that are consistent with both literatures, this paper finds alternative expressions for the price elasticity of demand as a function of the macro elasticity measures in the two literatures. In order to investigate the conditions under which the two literatures have the very same policy implications (e.g., regarding changes in trade costs versus exchange rates), this paper equalizes the price elasticity measures between the two literatures. This strategy results in an expression that connects the alternative macro elasticity measures in the two literatures, where good-level details are cancelled out during the equalization of the price elasticity measures. In particular, it is theoretically shown that the macro elasticity in international trade is a weighted average of the macro elasticity in international finance and the elasticity of substitution across products of different foreign source countries, where the weight is shown to depend on the number of foreign countries and home expenditure shares. Therefore, the alternative strategies in the two literatures for the aggregation of foreign products are reflected as alternative macro elasticity measures between the two literatures.



The implications of equalizing the price elasticity of demand measures between the two literatures are also tested empirically. Since this investigation requires data on both domestic and foreign trade, it cannot be achieved by using any international trade data set, where domestic trade data are not recorded. As an alternative, this paper uses the available trade data within the U.S. by considering interstate trade as foreign trade and intrastate trade as domestic trade. The results based on the estimation of macro elasticity measures in both literatures confirm the theoretical solution provided in this paper that the macro elasticity in international trade is a weighted average of the macro elasticity in international finance and the elasticity of substitution across products of different foreign sources. Therefore, the discrepancy between the macro elasticity measures in the two literatures can in fact be understood by paying attention to the alternative ways that foreign products are aggregated in the two literatures.


The corresponding paper by HakanYilmazkuday is available at Journal of Macroeconomics.


Sunday, March 18, 2018

Welfare Gains from Trade in Multi-Sector Models: The Role of Aggregation and Income Elasticities


Welfare Gains from Trade in Multi-Sector Models: The Role of Aggregation and Income Elasticities


One sentence summary: In the calculation of welfare gains from trade, the income elasticity interacts with the trade elasticity that can be estimated in a bilateral trade regression as the coefficient in front of aggregated unit prices instrumented by gravity variables.


Abstract
Sectoral heterogeneity has been shown to affect country-level welfare gains from trade that can be calculated by sector-specific trade elasticities and home expenditure shares. However, empirical analyses of multi-sector models are restricted to a limited number of countries and sectors, mostly due to the lack of data on sector-specific home expenditure shares. This paper first proposes a solution to this limitation by changing the way that foreign products are aggregated at the destination country. Second, when firm-level productivity differences are controlled for in a bilateral aggregate trade equation, gravity variables are shown to be perfect instruments for aggregated unit prices that enter trade regressions for the estimation of trade elasticities. Third, when the assumption of unitary income elasticity is relaxed, the trade elasticity in the calculation of welfare gains is shown to be replaced by the newly-introduced welfare elasticity, a function of trade and income elasticities. Empirical evidence is shown for the heterogeneity of these elasticity measures across countries.


Non-technical Summary
Welfare gains from trade (measured as costs of autarky) can be expressed by two key parameters, namely the trade elasticity and home expenditure share. When the one-sector environment is extended to a multi-sector one, due to the way that sectors are aggregated at the destination country (i.e., an upper tier aggregation of individual utility across sectors), the two key parameters are required at the sector level in order to calculate welfare gains from trade. Although the trade elasticity can be estimated for pretty much any sector and any country by using the corresponding trade and price/tariff data, home expenditure data are available only for certain aggregation of sectors in certain countries. Accordingly, in order to calculate welfare gains in a multi-sector environment, several studies focusing on the estimation of both parameters (at the sectoral level) have been restricted to a limited number of countries and a limited number of sectors. Since one-sector trade elasticity measures are biased due to sectoral heterogeneity, unbiased welfare gains from trade cannot be calculated for several countries that lack sector-level home expenditure data (although they have all the necessary trade data). Moreover, when the number of countries is limited, the estimated elasticity parameters (especially those that are common across countries) simply cannot represent global trade patterns, which would lead into biased welfare calculations.

Considering a multi-sector framework in order to have unbiased welfare implications, this paper proposes a new aggregation approach that does not require any sector-level home expenditure data. Instead, the overall home expenditure share data are shown to be enough to calculate the welfare gains from trade. This is achieved by using an Armington model, where aggregation at any destination country is achieved across source countries at the upper tier and across sectors at the middle tier; the lower tier aggregates firm-level goods. Having an upper-tier aggregation across source countries corresponds to having a unique trade elasticity measure interacting with a unique home expenditure share in order to calculate welfare gains from trade for any destination country. Accordingly, when the upper-tier elasticities are estimated, welfare gains can be calculated for any country that has data for overall home expenditure share.

On top of solving the problem of not having sector-level home expenditure data, this paper also relaxes the restrictive assumption of having unitary income elasticity in the calculation of welfare gains. In an Armington (1969) framework, it is shown that the standard trade elasticity in the case of unitary income elasticity (which is one minus the elasticity of substitution) is replaced with the country-specific income elasticity minus the elasticity of substitution (that we call welfare elasticity in this paper). It is implied that income effects counteract those of substitution effects when economies open up for trade, because consumers may simply value the utility out of home products different from that of foreign products due to the heterogeneity in income elasticity measures.
The inclusion of income elasticity in welfare gains calculations is achieved by using implicitly additively separable nonhomothetic constant elasticity of substitution (CES) preferences across source countries at the upper tier of individual utility. Such an approach is essential to separately capture the income effects in the utility function, without giving away the standard features of having CES preferences, so that one can easily distinguish between income and substitution effects, even in the calculation of welfare gains.

The model is estimated by using UN Comtrade data at the six digit Harmonized System (HS) level between 1995-2015. The estimation of the country-specific income elasticity and the country-specific elasticity of substitution is achieved by using data on bilateral trade (of imports measured at the destination country) and unit prices. Since the lower tier aggregation of individual utility is achieved across firm-level goods, firm-level productivity differences are carefully connected to the data on unit prices and the corresponding estimation; therefore, although firm-level data are not utilized, firm-level productivity differences are still taken into account at the aggregated level, without making any assumptions on their distribution.

Following the literature, the aggregation across sectors is achieved by a Cobb-Douglas aggregation. Due to the tiers of aggregation introduced earlier, this corresponds to having a weighted average of log unit destination prices in the bilateral aggregate trade estimation, where the weights are simply the expenditure weights of sectors. Nevertheless, since firm-level productivity measures are carried over to the bilateral aggregate trade estimation as residuals, aggregated log unit prices become endogenous. By using the implications of the model, bilateral trade costs measured by standard gravity variables are shown to be strong instruments for aggregated unit prices in a Two-Stage Least Squares (TSLS) estimation. In this bilateral aggregate trade estimation, the coefficient in front of aggregated unit prices represents the country-specific trade elasticity (i.e., one minus the elasticity of substitution), while the coefficient in front of log total imports (due to having non-unitary income elasticity) represents the country-specific income elasticity; these estimates are further used to construct country-specific welfare elasticity estimates.

The corresponding welfare elasticity estimates have a median value of -4.3 across countries with a range between -6.0 and -2.7 that are highly consistent with trade elasticity estimates in the literature. Although the estimates are comparable, the methodology introduced in this paper in a multi-sector framework is much easier to implement, and it allows us to calculate welfare gains in pretty much all countries that have trade data. In order to show the contribution of this paper in a clear way, the obtained welfare elasticity estimates are further compared to the common (across countries) trade elasticity estimate of about -3.8 that is obtained by the very same data set. It is shown that the difference between country-specific welfare elasticity estimates and common trade elasticity estimate is the key to understand the heterogeneity of welfare gains across countries for a given measure of home expenditure share. This heterogeneity is further connected to the trade patterns and per capita income of countries by using the implications of the model.
 






Friday, March 16, 2018

Domestic vs. International Welfare Gains from Trade


Domestic vs. International Welfare Gains from Trade


One sentence summary: Domestic trade contributes about 91 percent to overall welfare gains from trade.

Abstract
Using varieties of a rich model that considers sectoral heterogeneity and input-output linkages, this paper shows that the overall welfare gains of a region within a country can be decomposed into domestic versus international welfare gains from trade. Empirical results based on state-level data from the U.S. suggest that about 91 percent of the overall welfare gains of a state are due to domestic trade with other states, on average across alternative model specifications, with a range between 72 percent and 99 percent across states. When national-level data are used for the U.S., international welfare gains are shown to be almost identical to the those obtained by the aggregation of state-level results, suggesting that one can use the implications of a region-level analysis to have national-level results based on welfare gains from trade. We use this implication to propose an approximation to measure the domestic welfare gains from trade when domestic trade data are not available. Accordingly, using the implications of the model introduced, a Dispersion of Economic Activity Index (DEAI) is introduced that depends on internal distance and elasticity measures. It is empirically shown that DEAI can capture domestic welfare gains from trade within the U.S. when standard internal distance and elasticity measures in the literature are employed. Important policy suggestions follow.


Non-technical Summary
Domestic trade of a typical state in the U.S. is about five times its international trade, where about three quarters of this domestic trade is achieved with other states. It is implied that a typical state is about 20 percent open to international trade, while it is about 60 percent open to domestic trade. Since welfare gains from trade are known to be directly connected to such openness measures for a vast variety of models, the greater part of the welfare gains are implied to be through domestic trade. Nevertheless, since domestic trade data are not available for the majority of the countries, the existing literature has mostly focused on international welfare gains from trade that represent only a small portion of overall welfare gains.

Within this picture, this paper introduces a rich model considering sectoral heterogeneity as well as input-output linkages, where the unit of investigation is set as regions representing U.S. states. As standard in the literature, the corresponding welfare gains from trade are shown to be a function of expenditure shares and model parameters, where changes in expenditure shares are used to capture the changes in welfare in case of a hypothetical change in trade costs. The corresponding literature has focused on the hypothetical case of an autarky in the context of international trade. This paper follows this literature by having the same definition of international autarky while calculating the international welfare gains from trade.

The main contribution of this paper is achieved by considering an additional/alternative hypothetical case of autarky, namely domestic autarky, which is useful to calculate the domestic welfare gains from trade. In particular, domestic autarky is defined as the case in which a region still imports products internationally, but the domestic trade with other regions of the same country is shut down in this hypothetical case. It is shown that the overall percentage welfare gains from trade is the summation of domestic and international welfare gains from trade.


Based on the significant difference between international and domestic openness measures of states in the U.S., the corresponding welfare analysis shows that about 91 percent of the overall percentage welfare gains of a state are due to domestic trade with other states, on average across alternative model specifications, with a range between 72 percent and 99 percent across states.


When the same investigation is replicated at the U.S. level, it is shown that the international welfare gains from trade measures are almost identical to the measures obtained by the state-level analysis. Therefore, one can use the implications of a state-level analysis to have U.S. level results based on welfare gains from trade. We combine this result with other implications of the model in order to propose an approximation for domestic welfare gains from trade when domestic trade data are not available. Accordingly, we introduce a Dispersion of Economic Activity Index (DEAI) that can be calculated by using domestic distance measures (that can easily be obtained within a country) as well as other parameters such as trade elasticity and distance elasticity of trade costs. It is empirically shown that the proposed DEAI can capture the effects of domestic welfare gains from trade within the U.S. when great circle distance measures (that are calculated using latitudes and longitudes of states) are employed together with the elasticity measures borrowed from the literature.

Overall, this paper contributes to the existing literature by showing that (i) domestic welfare gains are much higher than international welfare gains from trade and (ii) when domestic trade data are not available, domestic welfare gains from trade can be approximated by a dispersion of economic activity index (DEAI). Regarding policy implications, the calculated DEAI measures can be compared with the standard measures of international welfare gains from trade in order to evaluate policies toward integrating the regions of a country with the rest of the world.




Thursday, March 15, 2018

Spatial Dispersion of Retail Margins: Evidence from Turkish Agricultural Prices


Spatial Dispersion of Retail Margins: Evidence from Turkish Agricultural Prices


One sentence summary: The farmer share of Turkish grocery prices is only about 16 percent, corresponding to about 84 percent of a distribution share (77 percent of retail margins and 7 percent of transportation costs).

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at Agricultural Economics.

The working paper version is available here.

Abstract
The farmer share of retail prices is shown to be about 16 percent, corresponding to about 84 percent of a distribution share, on average across agricultural products and regions within Turkey. The share of transportation costs in retail prices is only about 7 percent, while the share of retail margins is about 77 percent of retail prices. The dispersion of retail prices across regions is shown to be mostly due to local wages and variable markups, while the contribution of traded-input prices is relatively small. Accordingly, the high dispersion of farmer prices across locations is not reflected in the dispersion of retail prices due to the high contribution of retail margins. These retail margins are also shown to account for about one third of the consumer welfare dispersion across regions and more than half of the consumer welfare dispersion across products.


Non-technical Summary
The portion of agricultural retail prices received by farmers, the so-called farmer share, is about 15 percent across countries. Accordingly, the distribution share consisting of transportation costs and retail margins constitute the bigger portion of retail prices. The decomposition of this distribution share into its components is important to understand the welfare and policy implications for both consumers and farmers. For example, if the distribution share is high due to transportation costs, the optimal policy to improve welfare would be to reduce them through investments on infrastructure or subsidies on transportation-related costs, while if the distribution share is high due to retail margins, they can be subject to effective price regulations that can increase consumer welfare due to lower prices and increase farmer welfare due to higher sales. Since retail margins increase with the market share, the effects of retail margins (and thus the corresponding price regulation) may even be higher in regions with higher market power. Accordingly, it is essential to have a decomposition of the distribution share both an average and across regions to achieve optimal policies.

This paper achieves such decomposition by using retail- and farm-level micro price data on agricultural products across Turkish regions. In order to have an empirical motivation and implications for welfare, a simple model is introduced, where the economic environment consists of regions inhabited by individuals who consume local retail goods and retailers who purchase traded-inputs from producers subject to transportation costs. Since we empirically focus on individual products of green groceries, producers correspond to farmers who produce homogenous goods. Accordingly, each retailer searches for the minimum price across farmers at the product level, subject to transportation costs. Once transportation costs and source farms are identified through estimations based on price data obtained from both farmers and retailers, traded-input prices are determined for retailers. By further introducing a structure on local retail costs through the model, the retail prices are decomposed into farmer prices and distribution costs (consisting of transportation costs and retail margins).

The empirical results show that the farm share is about 16 percent of retail prices on average across agricultural goods and regions, corresponding to about 84 percent of a distribution share. The share of transportation costs in retail prices is only about 7 percent, while retail margins (defined as the ratio of retail to traded-input prices including transportation costs) are about 4.47, implying that about 77 percent of retail prices are accounted for by the retail sector. This result corresponds to slightly higher transportation costs within Turkey compared to similar costs in the U.S. of about 4 percent for food products. This may be surprising, because the U.S. is a much more spatially dispersed economy (due its land size) and thus one may expect lower transportation costs within Turkey. Nevertheless, since transportation costs between farmers and retailer highly depend on fuel prices, the difference in transportation costs of Turkey and the U.S. can easily be attributed to ratio of fuel prices in Turkey to those in the U.S. which has an average of about 2.6 between 1994 and 2011 according to World Development Indicators.

 


When a comparison is achieved across regions, the dispersion of retail prices is mostly due to local wages and variable markups (95 percent), while the contribution of traded-input prices is relatively small (5 percent). When we further investigate the dispersion of traded-input prices across locations, the contribution of transportation costs dominate by 95 percent, while that of source prices is only 5 percent. It is implied that the high dispersion of farmer prices across locations is not reflected in the dispersion of retail prices due to factors such as local input costs, variable markups, and transportation costs. It is also shown that retail margins are dispersed across regions at the product level. The implications of the model suggest that the dispersion of retail margins (across regions) is explained 52 percent by traded-input prices, and 48 percent by local wages and variable markups. Since the dispersion of traded-input prices is mostly due to transportation costs, it is implied that final consumers face different retail margins across locations due to all of transportation costs, local wages and variable markups.

Finally, using the implications of the model for consumer welfare, on average across individual products, about 30 percent of the consumer welfare dispersion is explained by retail margins across Turkish locations, while another 70 percent is explained by differences in either real economic sizes of regions or traded-input prices. On the other hand, within the same location, retail margins contribute by about 60 percent to the consumer welfare dispersion across products. Hence, the retail margin (that can be mostly explained by local wages and variable markups) is one of the key variables in understanding the dispersion of consumer welfare across regions.

The corresponding working paper by Hakan Yilmazkuday is available here.

Wednesday, March 14, 2018

Redistributive Effects of Gasoline Prices


Redistributive Effects of Gasoline Prices


One sentence summary: There are significant redistributive effects of gasoline price changes among U.S. consumers, where the main determinant is shown to be the consumer income.

The corresponding paper by Demet Yilmazkuday and Hakan Yilmazkuday has been accepted for publication at Networks and Spatial Economics.


Abstract
Consumers face significantly different gasoline prices across gas stations. Using gasoline price data obtained from 98,753 gas stations within the U.S., it is shown that such differences can be explained by a model utilizing the gasoline demand of consumers depending on their income and commuting distance/time, where the pricing strategies of both gas stations and refiners are taken into account. The corresponding welfare analysis shows that there are significant redistributive effects of gasoline price changes among consumers where the main determinant is shown to be the consumer income; e.g., welfare costs of an increase in gasoline prices are found to be higher for lower income consumers.


Non-technical Summary
Gasoline prices have significant effects on an economy, because higher energy prices can slow economic growth and affect individual welfare in many ways. As one example, gasoline prices have increased before any historical economic downturn in the U.S. As another example, consider the survey reported by Bankrate.com in May 2012, which depicts that, from the end of December 2011 through mid-April 2012, the price of regular gas rose from a national average of $3.30 per gallon to $3.94 (an increase about 19%), and, as a result, 59% of consumers cut back on nonessential spending on things such as vacations and dining out, only because of gasoline price changes. These macroeconomic examples provide an average picture of the gasoline price effects, but is the magnitude of these effects the same across consumers? The answer to this question is essential to understand the redistributive effects of gasoline prices, especially when gasoline prices differ across consumers.

Consider the following figure where each circle represents the location of a gas station. The colors of the circles represent the prices in U.S. dollars per gallon. Price intervals represent the intervals corresponding to the first, second, third, fourth and fifth 20th percentile of average of daily gasoline prices obtained from 98,753 gas stations between September 8th and September 14th, 2014. As is evident, while the gasoline prices are more expensive in the Northeast and the West (including Alaska and Hawaii), they are relatively cheaper in the Southeast.





To better understand the magnitude of gasoline price differences across consumers, consider a typical day (of September 14th, 2014) when the retail-level gasoline price difference between any two gas stations within the U.S. was as high as $2.28 per gallon of regular gas. If you think that this price dispersion was due to differences in state-taxes per gallon, which ranged between 42.75 cents (for New York) and 8 cents (for Georgia) in 2014, you are only partially right, because, for a typical day (of September 14th, 2014), the price difference between any two gas stations within any given state of the U.S. was as high as $1.68 (for the state of Massachusetts) followed by $0.99 (for the state of New York). Therefore, a detailed analysis is required to understand gasoline price dispersion at the gas-station level, which is the key to the investigation of the redistributive effects of gasoline price changes.

This paper achieves such an investigation by modeling the gasoline consumption of individuals and the pricing strategy of gas stations and refiners. The optimization in the model results in the gasoline demand of consumers depending on their income and commuting requirements as well as the price of gasoline. Gas stations take this demand into consideration while maximizing their profits, which results in a linear gasoline price expression due to having Leontief production functions. Refiners take into account the demand coming from gas stations to maximize their own profits. When the behavior of all agents in the model are combined, a final expression for gasoline prices is obtained at the gas station level, which depends on the income and commuting behavior of consumers as well as refiner-related costs.

Using data on gas-station level gasoline prices, zip-code level income and zip-code level commuting within the U.S., the implications of the model are estimated. The results show that most of the variation of gasoline prices (across gas stations) is explained by the proposed model. As a supplementary result, the average (across gas stations) markup per gallon is estimated about 16 cents, which is consistent with the surveys achieved by independent organizations.

After showing that the implications of the proposed model are consistent with gasoline price data, together with other supplementary data, we move to the welfare analysis to investigate the redistributive effects of gasoline price changes across consumers within the U.S.. The implications of the model combined with the results coming from the empirical investigation suggest that 1 percent of an increase in gasoline prices can lead to a reduction in consumer utility ranging between 0.08 percent and 2.76 percent (with an average of 0.82 percent) within the U.S.. Therefore, there are in fact significant redistributive effects of gasoline price changes. When the sources of these redistributive effects are further investigated, it is shown that consumer income is the main determinant; i.e., welfare costs related to a gasoline price increase are higher for lower-income consumers. It is implied that, in order to minimize the redistributive welfare effects of gasoline price changes, special policies should be conducted for lower-income consumers, especially when gasoline prices increase significantly.

Although gasoline prices can be affected by income, commuting distance/time, oil prices, and refiner costs according to the proposed model, they can also be affected by local or national taxes that have not been modeled here (nevertheless, they have been controlled for in the empirical investigation). Therefore, a change in any of these variables would change gasoline prices, and, thus, any policy conducted on such variables would result in redistributive welfare effects among consumers according to the analysis, above. Accordingly, one policy suggestion would be to provide gasoline tax cuts for neighborhoods with lower-income consumers. Providing tax reimbursements for lower-income consumers depending on their gasoline consumption and/or the gasoline (or oil) price changes over the preceding year can also be considered. Another one would be to promote/subsidize fuel-efficient cars for lower-income consumers that would effectively reduce the share of gasoline in their expenditure. Even though the formal investigation of such suggestions is out of the scope of this paper, future research can focus on the public policy implications of a more local analysis based on the insights of this study.

The working paper version is available here.