Welfare Costs of Urban Traffic through Retail Prices

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Welfare Costs of Urban Traffic through Retail Prices

One sentence summary: Reducing the time spent in urban traffic by 1 minute would result in a welfare gain of about 1.3% for the average city.

The corresponding paper by Hakan Yilmazkuday is available as a working paper here.

 
Abstract
This paper investigates the welfare costs of urban traffic by considering its implications on good-level prices. Using price data for 40 products from 70 cities (covering 47 countries) for multiple years, the estimation results suggest that the elasticity of good-level prices with respect to the time spent in urban traffic is about 0.5. This elasticity is further connected to the welfare of individuals by using the implications of a simple model. The corresponding investigation shows that reducing the time spent in urban traffic by one minute results in a welfare gain of about 1.3% for the average city, with a range between 0.8% and 2.3% across cities.

Non-technical
The amount of time spent in urban traffic changes significantly across cities. While one-way transportation takes only about 23 minutes in Thessaloniki, Greece, it takes about 64 minutes in Mumbai, India. These differences are important to have an economic comparison across cities, because besides the well-known opportunity costs of time and energy costs that can be considered as direct costs, there are also indirect (off-the-road) costs of urban traffic. For example, based on interviews with managers at distribution centers in the United Kingdom, it has been shown that urban traffic can increase warehousing costs by about 20%. On top of this, it has been shown that unreliability of delivery due to urban traffic has added about another 8% to 11% to the costs of traffic congestion in Netherlands. Similarly, the majority of the companies in Ireland has experienced traffic problems (and thus cost increases) according to the literature.

Against this background, this paper attempts to measure the welfare costs of urban traffic through its effects on good-level retail prices. The main idea is that retail costs can be affected by traffic congestion as in the studies introduced above, and these effects are further reflected in the welfare of individuals through their purchasing power. Accordingly, using the implications of a simple economic model, first, we investigate the effects of urban traffic (measured in minutes for one-way transportation) on good-level prices. The results based on price data from 40 products coming from 70 cities (from 47 countries) show that good-level prices are affected positively and significantly by urban traffic. Regarding the magnitude of this effect, the elasticity of good-level prices with respect to the time spent in urban traffic is estimated about 0.5.

 

The effects of urban traffic on good-level prices are further connected to the welfare of individuals by using the implications of the economic model introduced. In particular, it is shown that changes in individual welfare depend on changes in urban traffic, subject to the elasticity (of good-level prices with respect to the time spent in urban traffic) estimated. Within this picture, we consider two hypothetical changes in urban traffic. To measure the overall welfare costs of urban traffic, the first hypothetical change corresponds to the removal of the overall effects of urban traffic from good-level prices. Although this exercise may not be convenient to have a policy investigation that could only partially reduce the effects of urban traffic, it provides useful information on the overall welfare costs of urban traffic. To have a more convenient policy investigation, a second hypothetical change is defined as reducing the time spent in urban traffic by one minute.

 

The results for the first hypothetical change (based on the price data introduced above) suggest that removing the overall effects of urban traffic on prices would increase welfare by about 1.8 times the current welfare in the average city, with a range between 1.6 times (for Thessaloniki, Greece) and 2.1 times (for Mumbai, India). Hence, there are significant overall effects of urban traffic on consumer welfare through retail prices. 

 

The results for the second hypothetical change show that reducing the time spent in urban traffic by one minute would result in a welfare gain of about 1.3% for the average city, with range between 0.8% (for Mumbai, India) and 2.3% (for Thessaloniki, Greece) across cities.

The corresponding paper by Hakan Yilmazkuday is available as a working paper here.

Welfare Costs of Bilateral Currency Crises: The Role of International Trade

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Welfare Costs of Bilateral Currency Crises: The Role of International Trade

One sentence summary: A single currency crisis can result in welfare reductions through international trade corresponding up to 41% of the costs of autarky.

 

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at International Finance.

Free access to the read-only version is available here.

 

The working paper version is available here.

 
Abstract
This paper shows that bilateral currency crises reduce bilateral trade up to 50% after controlling for the depreciation rate. Using a trade model, these reductions are connected to the welfare costs of currency crises. The results show that a single currency crisis can result in welfare reductions through changes in international trade corresponding to more than 10% (and up to 41%) of the costs of autarky for 23 different currency crisis episodes between 1960 and 2014. These welfare costs are also shown to be greater than the welfare gains from having free trade agreements and using common currencies for 25 different currency crisis episodes.

Non-technical Summary
The negative effects of having a currency crisis at the country level are well known. These country-level currency crises are mostly identified by using the depreciation of the nominal exchange rate of a country with respect to a vehicle currency such as the U.S. dollar. However, international trade patterns are determined over bilateral exchange rates, since both exporters and importers solve their optimization problems based on their home currencies due to their costs and/or income being subject to these currencies. Therefore, the negative effects of currency crises on international trade can be at the bilateral level, especially when bilateral currency transactions are interrupted due to a crisis.

This paper investigates the possibility that bilateral currency crises (defined over bilateral nominal exchange rates) can affect bilateral imports. This is achieved by using the implications of a trade model, where bilateral currency crises are accepted as additional trade costs due to potential increases in transaction costs. Accordingly, bilateral imports are shown to depend on bilateral currency crises after controlling for the depreciation rate of the importer country's currency with respect to the exporter country's currency. This implication is tested empirically by using bilateral trade data from 66 countries covering the annual period between 1960-2014. The empirical results suggest that having a bilateral currency crisis can reduce international trade up to 50%, depending on the severity of the crisis.

These negative effects of bilateral currency crises are further connected to the corresponding welfare costs by using the implications of the trade model. It is shown that these welfare costs can be measured as the weighted average of the negative effects of bilateral currency crises on international trade, where weights are bilateral import shares. The corresponding empirical results suggest that the welfare costs of a single bilateral currency crisis are up to 2.5% (for Costa Rica in 1982). In order to put these welfare costs into context, they are further compared to the costs of autarky and the welfare gains from having free trade agreements and common currencies. The results show that the welfare costs of a single bilateral currency crisis correspond to more than 10% of the costs of autarky for 23 different episodes, up to 41% (for Angola in 1991). These costs are also shown to be more than the welfare gains from having free trade agreements and common currencies (at the time of the crisis) for 25 different episodes.

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at International Finance.

Free access to the read-only version is available here.

 

The working paper version is available here.

Gains from Trade: Does Sectoral Heterogeneity Matter?

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Gains from Trade: Does Sectoral Heterogeneity Matter?

One sentence summary: Sectoral heterogeneity does not always lead to an increase in the gains from trade, which is consistent with the theory.

The corresponding paper by Rahul Giri, Kei-Mu Yi and Hakan Yilmazkuday has been accepted for publication at Journal of International Economics.

 

The NBER working paper version is available here.

 
Abstract 
This paper assesses the quantitative importance of including sectoral heterogeneity in computing the gains from trade. Our theoretical framework has sectoral heterogeneity along five dimensions, including the elasticity of trade to trade costs. We estimate the sectoral trade elasticity with the simulated method of moments estimator and micro price data. Our estimates range from 2.97 to 8.94. Our benchmark model is calibrated to 21 OECD countries and 20 sectors. We remove one or two sources of sectoral heterogeneity at a time and compare the gains from trade to the benchmark model. We also compare an aggregate model with a single elasticity to the benchmark model. Our main result from these counterfactual exercises is that sectoral heterogeneity does not always lead to an increase in the gains from trade, which is consistent with the theory.

Non-technical Summary
Estimating the gains from international trade is one of the oldest and most important issues in economics. In recent years, owing to the development of easily accessible sectoral, bilateral trade and output data, as well as input-output tables, on the one hand, and tractable multi-sector, multi-country general equilibrium trade models on the other hand, there has been a surge in research quantifying the gains from trade. In many of these studies, there is a presumption that increased sectoral heterogeneity leads to higher gains from trade. This presumption is natural; in a simple multi-sector model in which the only source of heterogeneity across sectors is the initial sectoral trade shares, the multi-sector setting will always yield greater gains from trade, owing to Jensen's inequality, than the aggregate version of this model (with the same parameters).

 

However, there are many sources of sectoral heterogeneity in a typical multi-sector trade model. Trade elasticities, value-added shares of gross output, input-output linkages, and final demand shares, in addition to initial trade shares (driven by fundamental productivity and trade costs) can all vary across sectors. The gains from trade are a non-linear function of these parameters and variables; ultimately, whether sectoral heterogeneity yields greater gains depends on whether, for example, sectors with high initial trade shares are also sectors with low value-added shares of gross output. The goal of this paper is to quantitatively evaluate how sectoral heterogeneity affects the gains from trade in a systematic, comprehensive, and structurally consistent way.

We employ a model that embodies these forms of sectoral heterogeneity. Our calibrated model has 20 sectors and 21 countries, and we estimate and calibrate the parameters to match key features of the sectoral production, trade, expenditure and micro-price data. One of the main contributions of our paper is that we estimate the elasticity of trade with respect to trade costs for each of 19 traded sectors using the simulated method of moments (SMM). This methodology builds on the method-of-moments estimation methodology with micro price-level data, by correcting the bias from a small sample of price observations. To our knowledge, this is the first application of the SMM estimator to estimate the trade elasticity at the sector level. We use the Eurostat surveys of retail prices, which covers 12 OECD countries and 19 three-digit ISIC traded good sectors for 1990.

Our sectoral trade elasticity estimates range from 2.97 to 8.94; the median is 4.38. We also estimate the sectoral trade elasticities with the original method-of-moments method and the minimum, maximum, and median elasticities are 4.26, 35.55, and 10.29. So, our SMM estimates are clearly lower, as earlier studies have shown in their papers incorporating a one-sector framework. In addition, the “bias” is larger the smaller the sample size. For example, ISIC 352, Other chemicals, has a sample size of 4, while ISIC 311, Food products, has a sample size of 343. Our SMM estimates are similar across these two industries, 3.75 and 3.57, respectively, but the method-of-moments estimates are 11.93 and 4.28, respectively. These estimates are used in our calibrated model.

We calibrate the other parameters to match their data counterparts and/or to be consistent with sectoral outputs and trade flows. With our calibrated model, we compute the gains from trade by comparing the welfare in our benchmark equilibrium relative to welfare in a counterfactual autarky equilibrium. Our benchmark calibrated model delivers gains from trade ranging from 0.40 percent in Japan to 8.33 percent in Ireland. The median gain in going from autarky to the calibrated equilibrium is 3.96 percent (Mexico). We also decompose the gains into the trade effect and sectoral linkage effect and find that the former is considerably larger than the latter. 

We then conduct two sets of counterfactual exercises to assess the role of sectoral heterogeneity. We focus on five sources of heterogeneity in the gains from trade equation: sectoral trade elasticities, value-added gross output ratios, final demand shares, input-output linkages, and initial trade shares. In the first set of exercises, which we think of as “inspect the mechanism” exercises, we eliminate one or two sources of sectoral heterogeneity at a time. For each source of sectoral heterogeneity, we substitute a parameter (or variable) that is common across all sectors. For example, we replace the estimated sectoral trade elasticities with a single elasticity common to all sectors. We compute the gains from trade and compare these gains to those from the benchmark model.

When we eliminate one source of heterogeneity at a time, we find that in all cases the gains from trade are little or moderately changed relative to the benchmark model. That is, when we replace our estimated sectoral trade elasticities with the median estimate (4.38), the sectoral value-added shares with the average value-added share, the sectoral final demand share with the average final demand share, the sectoral intermediate use requirements with an average intermediate use requirements, or the initial sectoral trade shares with a common average initial share, the median gains from trade are within 10 or 20 percent of the benchmark gains. Our results for removing two sources of heterogeneity are similar, as for the most part, the difference in the gains from removing two sources of heterogeneity (relative to the benchmark model) is a sum of each difference in gain from removing one source of heterogeneity. In one final exercise, we remove all heterogeneity associated with intermediate goods and sectoral linkages by considering a value-added only model. We find, as other research has shown, that the gains from a value-added only model are less than one half that of the benchmark model. Overall, we find that most sources of sectoral heterogeneity lead to slight or moderate additional gains from trade, and some sources lead to less.

 

 

In the second set of exercises, we compare the welfare gains in our benchmark model to our aggregate model. The aggregate model has just one tradable sector; all heterogeneity across tradable sectors is eliminated. We also estimate the aggregate trade elasticity with the SMM methodology; we obtain a value of 2.37. Owing in part to this low estimate, we find that the gains from trade in the aggregate model are moderately (about one-third) larger than in the benchmark model. That is, when we compare our benchmark model with its estimated sectoral trade elasticities and sectoral heterogeneity on several other dimensions to our aggregate model with its estimated aggregate trade elasticity and no sectoral heterogeneity across tradable sectors, it is the aggregate model with greater gains from trade. Further investigation shows that the low estimated aggregate elasticity plays a key role. It is important to reiterate that both sets of elasticities are estimated in a model-consistent way.

To understand better all of our results, we conduct a Monte Carlo-type exercise in which we simulate prices and trade shares from our calibrated benchmark model. We then aggregate across sectors, and ask: “suppose this data were generated from an aggregate model. What would be the implied aggregate trade elasticity?” We find that the estimated aggregate elasticity from this exercise is about 2.65, which is only slightly larger than our actual estimated aggregate elasticity. In other words, our benchmark model generates data that would be consistent with a low aggregate elasticity in an aggregate model.

Overall, we conclude from our “inspect the mechanism” counterfactual, our benchmark vs. aggregate model counterfactuals, and our Monte Carlo exercise that increased sectoral heterogeneity does not necessarily imply larger gains from trade. This should not be a surprise, because it is just as the theory implies. The formula for the gains from trade shows clearly that whether sectoral heterogeneity per se leads to greater gains depends on two sets of interactions. One is the interaction of the sectoral trade elasticity, initial trade share, final demand share, and value-added share of gross output. The second is the input-output linkages along with the relative prices of inputs. Our results also show that overall, the interactions “cancel” to a large degree. A second conclusion is that model-consistent elasticity estimates should be used no matter the level of aggregation.

The main difference between our results and the previous research is that we use model- consistent trade elasticity estimates of both our benchmark model and our aggregate model. By contrast, much of the previous research uses an average of the sectoral elasticities as a stand-in for the aggregate elasticity. As our work, and previous research, have shown, an appropriate estimated aggregate elasticity is likely to be less than an average of sectoral elasticities. With a lower elasticity, all else equal, there will be greater gains from trade.

The corresponding paper by Rahul Giri, Kei-Mu Yi and Hakan Yilmazkuday has been accepted for publication at Journal of International Economics.

 

The NBER working paper version is available here.

Welfare Implications of Solving the Distance Puzzle: Global Evidence from the Last Two Centuries

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Welfare Implications of Solving the Distance Puzzle: Global Evidence from the Last Two Centuries

One sentence summary: The distance puzzle corresponds to about 81% of a cumulative welfare loss in the world, whereas solving it corresponds to about 58% of a welfare gain.

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at Journal of International Trade and Economic Development.

 

The working paper version is available here.

 
Abstract

This paper theoretically shows that changes in the distance elasticity of trade can be connected to welfare changes that depend on bilateral distance measures and expenditure shares of countries. Empirical results based on international and domestic trade data from the last two centuries show that the negative effects of distance on trade have increased over time when zero trade observations are ignored in inconsistent OLS estimations, confirming the distance puzzle in the literature. The corresponding welfare implications suggest that the world economy has experienced a cumulative welfare loss (about 81%) due to this puzzle in the last two centuries. When the puzzle is solved by considering zero trade observations in PPML estimations, the tables turn such that there are significant welfare gains from trade (about 58%) during the same period due to the decreasing negative effects of distance on trade over time. Welfare gains from further reductions in the negative effects of distance are investigated as well, suggesting significant potential gains from trade in the future.

 
Non-technical Summary
The negative effects of distance on trade are shown to increase over time in standard gravity regressions, which is against the expectations due to decreasing costs of transportation and communication. The so-called "distance puzzle" has been investigated extensively in the literature, where several explanations have been offered, including information barriers, augmented trade barriers, the role of nontradables, marginal costs of transportation, the composition of trade, zero-trade observations, trading propensities of entrants, domestic versus international integration of markets or nonhomothetic preferences. Nevertheless, none of the studies in the literature have investigated the welfare implications of the distance puzzle before and after it is solved.

This paper focuses on the welfare implications of the distance puzzle by considering the implications of a standard trade model. Theoretically, it is shown that changes in the distance elasticity of trade can be connected to the changes in the welfare gains from trade by using bilateral distance measures and bilateral expenditure shares across countries. When trade implications of this model are estimated in a log-linear Ordinary Least Squares (OLS) regression (where zero-trade observations are ignored by construction), the distance puzzle is confirmed, and it is shown that the world economy has a cumulative welfare loss (about 81%) due to this puzzle in the last two centuries. When zero-trade observations are included in a Pseudo-Poisson Maximum Likelihood (PPML) regression, the distance puzzle is solved, and it shown that the world economy has a cumulative welfare gains from trade (about 58%) due to reductions in the negative effects of distance on trade over time. Whereas 47% of these welfare gains are through international trade, 11% of them are through domestic trade.

The implications of the model have also been used to measure the potential future gains from trade. This has been achieved by considering a hypothetical case in which the effects of distance have been set to zero, both within and across countries. The corresponding results have shown that the potential future gains from domestic trade are about 79%, whereas those from international trade are about 197%, suggesting that there is much more to be done to reduce the negative effects of distance on trade.

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at Journal of International Trade and Economic Development.

 

The working paper version is available here.