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Using monthly headline inflation data covering 184 countries for the period between January 1971 and December 2020, this paper investigates the role of standard gravity variables on inflation convergence across country pairs. The convergence analysis by unit root tests is based on ten-year rolling windows to control for potential structural changes over time, whereas the corresponding results are connected to the standard gravity variables in the preceding year to investigate the drivers of inflation convergence and its speed. Regarding the existence of inflation convergence, empirical results show that having a common currency, a free trade agreement, proximity, a common border, or a colonial relationship between countries increases the probability of inflation convergence. Regarding the speed of inflation convergence, the very same gravity variables are shown to reduce the half-life of convergence. In both cases, the effects of having a common currency are shown to dominate those of other gravity variables.

Gravity variables such as distance, adjacency, colony, free trade agreements or language are used to capture the effects of trade costs in empirical studies. By using actual data on trade costs, this paper decomposes the overall effects of such variables on trade into those through three gravity channels: duties/tariffs (DC), transportation-costs (TC), and dyadic-preferences (PC). When PC is ignored as is typical in existing studies in the literature, it is shown that nearly all gravity effects are due to distance, 29 percent through DC and 71 percent through TC. The tables turn as the additional channel of PC is introduced and shown to dominate other channels, with adjacency contributing about 45 percent, distance about 32 percent, colony about 14 percent, free trade agreements about 7 percent, and language about 2 percent. It is implied that gravity variables mainly capture the effects of demand shifters rather than supply shifters (as implied by the existing literature). The results are further connected to several existing discussions in the literature, such as welfare gains from trade and the distance puzzle.

Using daily data on the coronavirus disease 2019 (COVID-19) cases from China and the rest of the world (ROW), this paper investigates the corresponding effects on the global economic activity measured by the Baltic Exchange Dry Index (BDI). The empirical results based on a structural vector autoregression including crude oil prices (COP) suggest that a weekly increase of 1,000 in daily COVID-19 cases in China results in about 7% of a cumulative reduction in BDI after three months, whereas a weekly increase of 1,000 in daily COVID-19 cases in the rest of the world results in about 0.4% of a cumulative reduction in COP. The historical decomposition results further suggest that the oil price plunge starting from March 2020 is mostly explained by the corresponding OPEC disagreement.

PurposeThis paper investigates nonlinearities in the relationship between mobility and COVID-19 cases or deaths based on demographic or socioeconomic characteristics, with a special focus on income and poverty.Design/methodology/approachThe formal analysis is achieved by using county-level daily data from the US, where a difference-in-difference design is employed. Nonlinearities in the relationship between mobility and COVID-19 cases or deaths are investigated by regressing weekly percentage changes in COVID-19 cases or deaths on mobility measures, where county fixed effects and daily fixed effects are controlled for. The main innovation is achieved by distinguishing between the coefficients in front of mobility measures across US counties based on their demographic or socioeconomic characteristics.FindingsThe results suggest that the positive effects of mobility on COVID-19 cases increase with poverty, per capita income, commuting time or population, whereas they decrease with health insurance or grandparents responsible for grandchildren.Originality/valueImportant policy implications follow regarding where mobility restrictions would work better to fight against COVID-19 through targeted lockdowns.

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The formal investigation is achieved by using a difference-in-difference design, where county-specific and time-specific factors are controlled for. The estimation results suggest that 1,000 additional COVID-19 cases (deaths) result in about $20 ($501) of a reduction in housing prices. When counties are split into those which can versus which cannot afford housing and transportation costs, the negative effects of COVID-19 cases or deaths on housing prices are significant only in counties which cannot. Similarly, when counties are split into those which can versus which cannot access to a variety of jobs, the negative effects of COVID-19 cases or deaths on housing prices are significantly higher in counties which cannot. Important policy implications regarding affordability, accessibility and equity across U.S. counties follow.

This paper investigates the drivers of global trade at the six-digit product level. The identification is achieved first by estimating the log-linear product-level bilateral trade implications of a...

This paper investigates the effects of inflation on per capita income growth for 36 developed and developing countries by using structural vector autoregression models that are robust to the consideration of endogeneity by construction. The results show evidence for heterogeneity of such effects across countries that are shown to be further connected to the strength of their institutions. While the effects of inflation on growth are negative and significant in countries with stronger institutions, they are positive and significant in countries with weaker institutions.

This paper investigates the effects of the coronavirus disease 2019 (COVID-19) cases in the US on the S&P 500 Index using daily data covering the period between 21st January, 2020 and 10th August, ...

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.

Exchange rate pass-through (ERPT) into prices and into income loss are shown to be enough to calculate ERPT into welfare loss using implications of a simple model. These ERPT measures are estimated at the good level using a unique micro-price data set from Turkey, and they are combined with income-group-specific expenditure shares at the good level to obtain aggregate-level ERPT measures for alternative income groups. An exchange rate shock resulting in a real depreciation of 1% is shown to decrease welfare by about 0.80% for the average-income consumer, while this estimate ranges between 0.73% and 0.83% for consumers in the lowest- and highest-income quintiles, respectively, suggesting evidence for redistributive effects of an exchange rate shock. Using micro prices has further resulted in showing that traded, nondurable, flexible-price, or income-elastic goods contribute more to ERPT into welfare loss for the average-income consumer, suggesting important policy implications for filtering out the noise in the measurement of aggregate-level prices.

Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the early coronavirus disease 2019 (COVID-19) era. Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. The estimation results based on common factors across zip codes show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has increased up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period.

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 ...

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 dynamic stochastic general equilibrium model using 18 quarterly series from the USA, 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.

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.

Abtract Using daily U S county-level data on consumption, employment, mobility and the coronavirus disease 2019 (COVID-19) cases, this paper investigates the welfare costs of COVID-19 The investigation is achieved by using implications of a model, where there is a trade-off between consumption and COVID-19 cases that are both determined by the optimal mobility decision of individuals The empirical results show evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average county There is also evidence for heterogeneous welfare costs across U S counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020 that are further connected to the socioeconomic characteristics of the U S counties This article is protected by copyright All rights reserved

Using daily county-level travel data within the United States, this paper investigates the welfare costs of travel reductions due to coronavirus disease 2019 (COVID-19) for the period between 20 January and 5 September 2020. Welfare of individuals (related to their travel) is measured by their inter-county and intra-county travel, where travel costs are measured by the corresponding distance measures. Important transport policy implications follow regarding how policymakers can act to mitigate welfare costs of travel reductions without worsening the COVID-19 spread. © 2021 The Authors. Regional Science Policy & Practice © 2021 Regional Science Association International

This paper investigates (crude) oil price pass-through into gasoline spot and gasoline retail prices in the U.S. due to the effects of coronavirus disease 2019 (COVID-19). The investigation uses daily data in a structural vector autoregression framework. Oil price pass-through is measured as the cumulative impulse response of gasoline spot or gasoline retail prices divided by the cumulative impulse response of oil prices. The results suggest evidence for complete pass-through of oil prices into gasoline spot prices, whereas the corresponding pass-through into gasoline retail prices is about 29% in the long run.

Using daily census block group level data from the U.S., this paper investigates the welfare costs of staying at home due to COVID-19 across socioeconomic and demographic groups. The investigation is based on an economic model of which implications suggest that the welfare costs of staying at home increase with the stay-at-home probabilities of individuals. The empirical results provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home. This heterogeneity is further used to obtain measures of welfare changes for different socioeconomic and demographic groups at the national level.

This paper estimates the exchange rate pass-through (ERPT) by using good-level daily data on wholesale prices of imported agricultural products, where the identification is achieved by using daily data on the domestic inflation rate. The results of standard empirical analyses are in line with existing studies that employ lower frequencies of data by showing evidence for incomplete daily ERPT of about 5 percent. The key innovation is achieved when nonlinearities in ERPT are considered, where ERPT is doubled to about 10 percent when daily nominal exchange rate changes are above 0.55 percent, daily frequencies of price change are above 3.12 percent, and storage life of a product is above 10 weeks. Important policy implications follow.

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.

This article investigates whether consumer search behaviour differs across zip codes within the U.S. As an application, daily gasoline price data covering virtually all gas stations within the U.S. are employed to estimate the distribution of search costs in each zip code. The results show that there are significant differences across zip codes regarding the expected number of searches achieved before consumers purchase gasoline. In order to have a systematic explanation, such differences are further connected to geographic, demographic, and economic conditions of the zip codes in a secondary analysis. The corresponding results imply several strategies for gas stations in order to maximize profits/markups; suggestions follow for policy makers and regulators to reduce redistributive effects of information barriers across locations.

This paper investigates the shifting of sales taxes to consumers through retail prices in the short run. Retail data on gasoline prices are used at the station level within the U.S., including observations from all fifty states and the District of Columbia. A difference-in-differences approach is employed to identify the short-run effects of the changes in state taxes as of January 1st, 2015, when five states have increased their gasoline sales taxes, while five other states have decreased theirs. States experiencing such changes in sales taxes (between December 31st, 2014 and January 1st, 2015) are analyzed as the treatment group of a natural policy experiment, where the control group consists of states with no changes in their sales taxes. The results show that both sales-tax increases and decreases are under-shifted to consumer prices, although the under-shifting of sales-tax decreases is much higher (i.e., the asymmetric incidence of sales taxes). The pass-through measures also differ significantly across states, showing the importance of having a nationwide analysis. The results are robust to the consideration of retailer characteristics, wholesale prices, retail brand effects and hourly price changes within each day.

We analyze and document various qualities of peer-reviewed journal publications listed in the EconLit database between 1980 and 2014. At least one author of the publication had to graduate from a North American economics PhD program between 1970 and 2009. We find that the share of single-author papers diminishes over time, and North American PhDs engage more in coauthored publications. While two-author papers are published on average more than three-author papers during 1980-1999, this switches after 2000. We also find that author teams containing at least one author who is a graduate of a top thirty department publish significantly more compared with other author teams. All-female and mixed-gender author teams publish significantly less compared with all-male author teams between 1980-1999, but we find no significant difference after 2000. The shares in the different fields in total publications show little variation in three decades. While male authors are over-represented in micro and macroeconomics, female authors are over-represented in labor and development economics. Some fields, such as microeconomics, econometrics, and experimental economics, are published in high quality journals, whereas macroeconomics, public economics, industrial organization, finance, health and urban economics, and development economics are published in journals that have lower quality weights than the average journal. Labor economics and economic history are published significantly better after 2000.

This paper investigates the (crude) oil price pass-through into gasoline spot and gasoline retail prices in the U.S. due to the effects of coronavirus disease 2019 (COVID-19). The investigation is achieved by using daily data in a structural vector autoregression framework. The oil price pass-through is measured as the cumulative impulse response of gasoline spot or gasoline retail prices divided by the cumulative impulse response of oil prices, both following a percentage change in total number of the U.S. COVID-19 cases. The results suggest evidence for complete pass-through of oil prices into gasoline spot prices, whereas the corresponding pass-through into gasoline retail prices is about 29 percent in the long run.

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.

Daily Google mobility data covering 130 countries over the period between February 15th, 2020 and May 2nd, 2020 suggest that less mobility is associated with lower COVID-19 cases and deaths. This observation is formally tested by using a difference-in-difference design, where country-fixed effects, day-fixed effects as well as the country-specific timing of the 100th COVID-19 case are controlled for. The results suggest that 1% of a weekly increase in being at residential places leads into about 70 less weekly COVID-19 cases and about 7 less weekly COVID-19 deaths, whereas 1% of a weekly decrease in visits to transit stations leads into about 33 less weekly COVID-19 cases and about 4 less weekly COVID-19 deaths, on average across countries. Similarly, 1% of a weekly reduction in visits to retail & recreation results in about 25 less weekly COVID-19 cases and about 3 less weekly COVID-19 deaths, or 1% of a weekly reduction in visits to workplaces results in about 18 less weekly COVID-19 cases and about 2 less weekly COVID-19 deaths.

This paper investigates the effects of the coronavirus disease 2019 (COVID-19) cases in the U.S. on the S&P 500 Index using daily data covering the period between January 21st, 2020 and August 10th, 2021. The investigation is achieved by using a structural vector autoregression model, where a measure of the global economic activity and the spread between 10-year treasury constant maturity and the federal funds rate are also included. The empirical results suggest that having 1% of an increase in cumulative daily COVID-19 cases in the U.S. results in about 0.01% of a cumulative reduction in the S&P 500 Index after one day and about 0.03% of a reduction after one week. Historical decomposition of the S&P 500 Index further suggests that the negative effects of COVID-19 cases in the U.S. on the S&P 500 Index have been mostly observed during March 2020.

This paper investigates inflation convergence among European countries by using sector-level data for the period between 1997:M1 and 2019:M12. Panel unit root tests at the country-sector level are conducted by using moving windows, which is useful to analyze changes in inflation convergence and the corresponding speed of convergence over time. The results suggest evidence for inflation convergence for the majority of sectors within Europe, although disruptions have been experienced by certain countries, especially during the 2008 financial crisis. Regarding the speed of inflation convergence, the average half-life across European countries has decreased from about 15 months to about 8 months during the sample period. Important sector-level implications follow for European Union (EU) candidate countries and non-euro EU member countries regarding the Maastricht Treaty.

Daily data at the U.S. county level suggest that coronavirus disease 2019 (COVID-19) cases and deaths are lower in counties where a higher share of people have stayed in the same county (or travelled less to other counties). This observation is tested formally by using a difference-in-difference design controlling for county-fixed effects and time-fixed effects, where weekly changes in COVID-19 cases or deaths are regressed on weekly changes in the share of people who have stayed in the same county during the previous 14 days. A counterfactual analysis based on the formal estimation results suggests that staying in the same county has the potential of reducing total weekly COVID-19 cases and deaths in the U.S. as much as by 139,503 and by 23,445, respectively.

This paper shows that daily Google trends can be used as an alternative to conventional U.S. data (with alternative frequencies) on unemployment, interest rates, inflation and coronavirus disease 2019 (COVID-19). This information is used to investigate the effects of COVID-19 and the corresponding monetary policy on the U.S. unemployment, both nationally and across U.S. states, by using a structural vector autoregression model. Historical decomposition analyses show that the U.S. unemployment is mostly explained by COVID-19, whereas the contribution of monetary policy is almost none. An investigation based on the U.S. states further suggests that COVID-19 and the corresponding monetary policy conducted based on nationwide economic developments have resulted in unequal changes in state-level unemployment rates, suggesting evidence for distributive effects of national monetary policy.

Using daily U.S. county-level data on consumption, employment, mobility and the coronavirus disease 2019 (COVID-19) cases, this paper investigates the welfare costs of COVID-19. The investigation is achieved by using implications of a model, where there is a trade-off between consumption and COVID-19 cases that are both determined by the optimal mobility decision of individuals. The empirical results show evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average county. There is also evidence for heterogeneous welfare costs across U.S. counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020 that are further connected to the socioeconomic characteristics of the U.S. counties.

This paper analyzes whether social distancing experienced by alternative demographic groups within the U.S. has been different amid COVID‐19. The formal investigation is achieved by using daily state‐level mobility data from the U.S. covering information on the demographic categories of income, education and race/ethnicity. The results show that social distancing has been experienced more by higher‐income, higher‐educated or Asian people after the declaration of National Emergency on March 13th, 2020. Since alternative demographic groups were subject to alternative employment opportunities during this period (e.g., due to being able to work from home), it is implied that COVID‐19 has redistributive effects that require demographic‐group specific policies.

This paper investigates nonlinearities in the relationship between mobility and COVID-19 cases or deaths. The formal analysis is achieved by using county-level daily data from the U.S., where a difference-in-difference design is employed. Nonlinearities in the relationship between mobility and COVID-19 cases or deaths are investigated by regressing weekly percentage changes in COVID-19 cases or deaths on mobility measures, where county fixed effects and daily fixed effects are controlled for. The main innovation is achieved by distinguishing between the coefficients in front of mobility measures across U.S. counties based on their demographic or socioeconomic characteristics. The results suggest that the positive effects of mobility on COVID-19 cases or deaths increase with population, per capita income, or commuting time as well as with having certain occupations, working in certain industries, attending certain schools, or having certain educational attainments. Important policy implications follow regarding where mobility restrictions would work better to fight against COVID-19 through targeted lockdowns.

Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the coronavirus disease 2019 (COVID-19). Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. Whereas raw data for the median zip code suggest that spending on all sectors (except for grocery) has decreased amid COVID-19, the estimation results based on common factors across zip codes rather show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has relatively increased up to 21%, while its expenditure share has relatively increased by up to 16% compared to the pre-COVID-19 period.

We study time-series fluctuations in the United States housing market from 2010 to 2019 using the Gordon growth model. We apply a vector autoregressive model (VAR) with fixed coefficients to measure expectations at each point in time. Our results show that, using zip code level data, we are able to explain the broad movements in housing volatility with higher prediction power compared to previous studies. Using variance decomposition analysis, we find that the housing premium is the main driver of housing market fluctuations. Motivated by previous studies and using impulse response functions, we show how different components of the housing market respond over time to a shock in the interest rate in regions with different levels of income or demographics. Our findings suggest that the impact of monetary policy is bigger in the U.S. housing market when households have less income, more female members, more African Americans, or less well-educated members; a combination of these demographics and lower income in households results in a bigger impact of monetary policy in housing market, due to the necessity of housing for these families.