Drivers of Inflation Convergence across Countries: The Role of Standard Gravity Variables

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Drivers of Inflation Convergence across Countries: The Role of Standard Gravity Variables

One sentence summary: Having a common currency, a free trade agreement, proximity, a common border, or a colonial relationship between countries increases the probability and decreases the half-life of inflation convergence.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Macroeconomic Dynamics.

The working paper version is available here.

 
Abstract

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.

 

Non-technical Summary

Lifetime welfare costs of inflation are significantly high. Recent studies suggest welfare costs of inflation up to 13% of one-year consumption following a 3% increase in inflation. As year-on-year headline inflation rates are highly different across countries and over time (e.g., Democratic Republic of the Congo having a year-on-year inflation of about 555% in 1994, whereas Equatorial Guinea having a year-on-year inflation of about -38% in 1986), investigating inflation differentials across countries and over time is important to understand the welfare inequality across countries. Accordingly, this paper investigates inflation convergence across bilateral countries to shed light on the relative welfare costs of inflation over time.

The inflation convergence analysis in this paper is achieved by using unit root tests for each country pair. Ten-year rolling windows using monthly data are considered to control for potential structural changes over time. Once inflation convergence is determined for any country pair, the corresponding speed of convergence is estimated based on half-life measures. The key innovation in this paper is to connect the pooled version of inflation convergence results (across country pairs and ten-year windows) to the standard gravity variables in the preceding period that are effective in explaining not only international trade (of goods and services) but also international finance (e.g., bilateral asset holdings).

As country-and-time specific factors are known to be effective in explaining inflation convergence in the literature, they are controlled for during the investigation of this paper to mainly focus on the role of gravity variables on inflation convergence. Moreover, to consider causality through the time dimension, inflation convergence results based on ten-year windows are connected to the standard gravity variables in the preceding year.

The motivation behind considering the standard gravity variables as well as country-and-time specific factors in explaining inflation convergence comes from a simple theoretical model combining the two well-known arbitrage conditions, namely the uncovered interest parity and the relative purchasing power parity. Specifically, it is shown that future (expected) inflation differentials between any two countries depend on the current country-specific nominal interest rates (e.g., reflecting monetary policy, exchange rate regime, or business cycles of countries) as well as the deviations from the relative purchasing power parity that can be captured by the standard gravity variables.

 

The inflation convergence results based on ten-year windows for the monthly period between January 1971 and December 2020 covering 184 countries suggest that certain country pairs have experienced inflation convergence for each and every ten-year window, whereas certain others have not experienced any inflation convergence in any of the ten-year windows. Conditional on having convergence, the results also suggest that there is significant evidence for heterogeneity across country pairs regarding their speed of convergence (based on half-lives).
 

The heterogeneity across country pairs regarding their inflation convergence and the corresponding half-lives is further investigated in secondary analyses by estimating the effects of standard gravity variables in the current year on the inflation convergence and its speed within the next ten years. The corresponding results suggest that robust to the consideration of country-and-time fixed effects, having a common currency, a free trade agreement, proximity, a common border, or a colonial relationship between countries increases the probability of inflation convergence. For the speed of convergence (conditional on convergence), the very same gravity variables are shown to reduce the half-life of inflation convergence across countries. When the effects of alternative gravity variables are compared in terms of their magnitude, the effects of having a common currency are shown to dominate those of others.

 

Regarding policy implications, as inflation convergence across countries is an indicator of welfare improvement, international policies toward having a common currency or a free trade agreement would be beneficial for countries in a significant way. As having a common currency dominates the effects of other gravity variables, policy makers may want to prioritize having common currencies with other countries if they would like to benefit more from welfare-improving inflation convergence with other countries. These implications are robust to the consideration of not only country-and-time specific factors but also certain measurement errors and alternative window lengths used for the investigation of inflation convergence.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Macroeconomic Dynamics.

The working paper version is available here.

Specialization, Field Distance, and Quality in Economists' Collaborations

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Specialization, Field Distance, and Quality in Economists' Collaborations

One sentence summary: High quality collaboration is more likely to emerge as a result of an interaction between specialists and generalists with overlapping fields of expertise.

The corresponding academic paper by Ali Sina Önder, Sascha Schweitzer and Hakan Yilmazkuday has been accepted for publication at Journal of Informetrics.

The working paper version is available here.

 
Abstract

We analyze economics PhDs' collaborations in peer-reviewed journals from 1990 to 2014 and investigate such collaborations' quality in relation to each co-author's research quality, field and specialization. We find that a greater overlap between co-authors' previous research fields is significantly related to a greater publication success of co-authors' joint work and this is robust to alternative specifications. Co-authors that engage in a distant collaboration are significantly more likely to have a large research overlap, but this significance is lost when co-authors' social networks are accounted for. High quality collaboration is more likely to emerge as a result of an interaction between specialists and generalists with overlapping fields of expertise. Regarding interactions across subfields of economics (interdisciplinarity), it is more likely conducted by co-authors who already have interdisciplinary portfolios, than by co-authors who are specialized or starred in different subfields.

 

Non-technical Summary

Collaboration has become the dominant mode of research production in many disciplines in recent decades. Collaboration may be motivated by career pressures to publish more and better as well as by the need to circumvent a gap of knowledge or expertise. Influence of research collaboration on citation impact is not uniform and varies largely across disciplines. However, most disciplines, including economics, reveal a strong positive correlation between citation counts and the number of co-authors. As far as economics research is concerned, co-authored papers not only have been dominating the publication scenery for several decades now but also are more likely to get accepted for publication and receive more citations than sole author papers.

In this paper, we focus on the outcome (in terms of the journal prestige and citation impact) of economists' collaborations and investigate how similarity and specialization of co-authors' research portfolios are related to the quality of collaboration. Focusing on economists provides a preferable environment for our analysis because research and collaboration in this field still takes place at a very personal level as opposed to laboratory driven research with large research teams as in many of the natural sciences. We use peer-reviewed economics journal articles between 1990 and 2014 of PhD graduates of US and Canadian economics departments whom we refer to as North American PhDs throughout this paper. This particular subset of economists can be controlled for educational background and potential social ties from the graduate school, because the American Economic Association provides full lists of all graduating North American PhDs sorted by their graduate department each year. We know that North American PhDs are a particularly influential group in academic publications: 20% of all EconLit papers, more than 50% of all papers in top general and top field journals, and about 60% of all papers in the so-called top five have at least one North American PhD on board.

Two important features in our study are co-authors' field distance and specialization levels. Co-authors with a very close field distance have publications in similar areas of economics, whereas co-authors with a large field distance have publications in different areas from one another. A concept similar to our field distance is being referred to as cognitive distance in informetrics literature. Authors' specialization levels are calculated as the Herfindahl index of their research portfolios. 

 

 

Our analysis starts with a descriptive part that yields three stylized facts on co-authors' field distance and specialization: 

1. Co-authors have become geographically more distant but much closer in terms of field distance over the last couple of decades.
2. Co-authors whose collaboration reveals better quality have a significantly smaller field distance.
3. Co-authors' specialization levels are little or not related to the overall quality of the collaboration. 

Assuming a two-step process for collaborative research where co-authors search and match in the first stage and the quality of their collaboration is revealed in the second stage, we investigate the statistical significance of relations that are picked in these stylized facts. Our estimations reveal that the field distance between co-authors is negatively and significantly related to the quality of their collaborative output. This relation is robust to how quality is measured as well as whether it is the co-authors' first time collaboration or a subsequent collaboration.

There is research documenting that distant collaborations are related to better quality research compared to same location collaborations. Although the alleged importance of physical proximity between co-authors is sensitive to the nature and technological context of the research in question, and economists might still benefit from positive agglomeration effects that can be offered by large and prestigious departments with significant spillover for their colleagues in certain fields, distant collaborations have already become fairly common among economists. We find that distant and same location collaborations reveal significantly different field distances on average and since field distance is negatively related to research quality, same location collaborations are of less quality, on average. This finding complements the existing literature by providing a possible motive for engaging in distant collaboration, namely, co-authors that engage in a distant collaboration are significantly more likely to have a close field distance, and a close field distance is significantly related to having a high quality outcome for this collaboration.

Our contribution to this line of literature is to show how specialization works for and at the same time against the quality of collaboration. A high specialization level has an indirect positive effect on the quality of collaboration output because more specialized authors are more likely to team up with co-authors that have a very close field distance, and such closeness is related to a high quality of collaboration output. However, once the indirect effect is accounted for, a high specialization level has a direct negative effect on the quality of collaboration output. The total effect of specialization is negative.

The corresponding academic paper by Ali Sina Önder, Sascha Schweitzer and Hakan Yilmazkuday has been accepted for publication at Journal of Informetrics.

The working paper version is available here.

 

Welfare Costs of Shopping Trips

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Welfare Costs of Shopping Trips

One sentence summary: Welfare gains from removing traditional shopping-trip costs is about 4% for the average census block.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at the Annals of Regional Science.

The working paper version is available here.

 
Abstract

Using data on the number of visitors at the store level, this paper investigates the welfare costs of traditional shopping-trips for the U.S. census blocks. The investigation is based on an economic model, where individuals living in census blocks decide on which store to shop from based on the corresponding shopping-trip costs and idiosyncratic benefits. The implications of the model suggest that the welfare gains from removing shopping-trip costs in percentage terms can be measured for each census block as the weighted average of log distance measures between shopping stores and census blocks. The corresponding results show that the welfare gains from removing shopping-trip costs is about 4% for the average census block, with a range between 0.021% and 18% across census blocks that is further connected to their demographic or socioeconomic characteristics. Certain practical policy implications follow regarding how shopping-trip costs can be reduced to achieve higher welfare gains.

 

 

Non-technical Summary

Despite the increasing trend in online shopping, 88.5% of sales in the U.S. is still through traditional shopping, which may be due several reasons including its convenience or urgency of shopping. Since traditional shopping requires leaving home and walking/riding to a shopping store, it results in not only travel costs but also time and opportunity costs. Nevertheless, the literature lacks a quantitative investigation regarding the corresponding welfare costs of traditional shopping.

This paper attempts to measure the welfare costs of traditional shopping trips at the U.S. census block group level. The empirical investigation is based on an economic model, where individuals living in census blocks decide on which store to shop from based on the corresponding shopping costs (increasing with distance to the store) and idiosyncratic benefits. The implications of the model suggest that the welfare gains from removing bilateral shopping costs (that we consider as the welfare costs of traditional shopping in this paper) in percentage terms can be measured for each census block as the weighted average of log current bilateral distance measures between shopping stores and census blocks, where weights are the bilateral probabilities of individuals (living in certain census blocks) shopping at certain stores.

The model is empirically tested by using SafeGraph cellphone location data that provide information on the total number of visitors at the store level, where the census block group of visitors regarding their residence (home) is also given. The estimation results based on about 75 million observations show that the bilateral probabilities of individuals (living in certain census blocks) shopping at certain stores decrease with the corresponding distance measures. Quantitatively, the elasticity of shopping probability from a store with respect to distance is estimated around 0.0767.

The estimated distance effects on the bilateral probabilities of individuals (living in certain census blocks) shopping at certain stores are removed in a counterfactual investigation to measure the welfare costs of traditional shopping. This is achieved for each census block in the data set. The corresponding results show that the welfare gains from removing bilateral shopping costs is about 4% for the average (or median) census block, with a range between 0.021% and 18% across census blocks. The heterogeneity of welfare gains across census blocks is further investigated in a secondary analysis, where it is shown that the welfare costs of traditional shopping increase with cars per capita as census blocks with higher per capita number of cars currently make shopping trips to more distant stores.

Regarding heterogeneity of welfare gains across demographic or socioeconomic groups, it is depicted that census block groups with a higher share of Asian people would benefit the least from removing shopping costs, whereas those with a higher share of American Indian and Alaska Native people would benefit the most from it. When the relationship between welfare costs of traditional shopping and family income is investigated, it is shown that there is evidence for a hump-shaped relationship between family income and welfare costs. Finally, it is depicted that census block groups with a higher share of an educational attainment of an elementary school diploma would benefit the least from removing shopping costs, and those with a higher share of an educational attainment of a high school diploma would benefit the most from it. Based on the implications of the model used, these results suggesting that certain demographic or socioeconomic groups would benefit less from removing costs of traditional shopping can be explained by such groups currently making shopping trips to relatively close-by stores so that they gain relatively less when shopping costs are removed.

This paper contributes to the literature by measuring bilateral shopping costs between individuals (residing in census blocks) and stores by using the corresponding distance between them, where price faced at the store is also considered; the remaining factors such as quality of service or convenience are captured by idiosyncratic benefits at the store level for each individual. Measuring the corresponding welfare gains from removing shopping costs at the census block level is the key innovation in this paper, where connecting the heterogeneity of welfare gains across census blocks to certain demographic and socioeconomic characteristics is a further contribution.

 

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

 

 

Unequal Welfare Costs of Staying at Home across Socioeconomic and Demographic Groups

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Unequal Welfare Costs of Staying at Home across Socioeconomic and Demographic Groups

One sentence summary: There is evidence for unequal welfare costs of staying at home across socioeconomic and demographic groups.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Journal of Urban Sciences.

The working paper version is available here.

 
Abstract

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.

 

Non-technical Summary

Staying at home is considered as one of the most effective ways to fight against COVID-19. Accordingly, several layers of government around the world have implemented lockdowns to mitigate the spread of COVID-19. Individuals have also reduced their mobility to protect themselves from COVID-19 in a voluntary way. Despite its success in reducing the spread of COVID-19, staying at home has resulted in many individuals having economic and psychological problems. Moreover, as individuals belonging to different socioeconomic and demographic groups have access to different employment, consumption or health-related opportunities, they have stayed at home in different amounts of time during COVID-19, suggesting that they might have been affected differently from COVID-19.

This paper attempts to measure the welfare implications of staying at home across alternative socioeconomic and demographic groups. The investigation is achieved by using the implications of an economic model, where both direct and indirect welfare effects of COVID-19 are considered. Specifically, the direct welfare effects are captured by the standard economic measures, namely the amount of consumption versus the amount of labor supplied, whereas the indirect welfare effects are captured by idiosyncratic benefits of being in another location (outside of home) versus the corresponding costs of mobility. In this context, idiosyncratic benefits of being in another location capture the welfare effects of having social interactions, whereas their absence captures the effects of mental distress, anxiety, worry, disinterest, depression, increased risks of suicide, domestic violence, obesity or poor general health perception. The corresponding costs of mobility capture not only the standard measures of traffic or the opportunity cost of time but also the effects of COVID-19 (e.g., the probability of getting sick) that is essential to measure the welfare effects of COVID-19.

In equilibrium, the model implies that the overall welfare effects of COVID-19 (discussed so far) can be captured by the changes in stay-at-home probabilities of individuals. This implication is used to measure the daily welfare changes in the U.S. at the census block group level. The measurement of mobility is achieved by using SafeGraph cellphone location data for each census block group (220,115 of them) for the daily period between January 1st and December 31st, 2020. The period between January 1st and February 29th, 2020 is considered as the pre-COVID-19 period, whereas the period between March 1st and December 31st, 2020 is considered as the COVID-19 period. The empirical results show that the median census block group has experienced a welfare loss of about 7.1% during the COVID-19 period. The corresponding nationwide welfare costs of COVID-19 (calculated as the weighted average across census block groups) is as much as 6.4%, with a daily average of about 2.1% during the sample period.

 

The empirical results also provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home due to COVID-19. This heterogeneity is further used to obtain measures of welfare changes for alternative socioeconomic and demographic groups at the national level, where the American Community Survey data on socioeconomic and demographic characteristics (at the census block group level) are used to aggregate across census block groups. The corresponding results based on race/ethnicity show that the average (across days) welfare costs have been experienced by the Asian population, followed by the Hispanic population, the white population, the black population and the native population. The results based on education level suggest that the average (across days) welfare costs have been experienced by the master's degree holders, followed by bachelor's degree holders, doctorate degree holders, elementary school graduates, high school graduates, and middle school graduates. Finally the results also that the average (across days) welfare costs have increased by the income level of individuals.

 

The results can be explained by individuals belonging to different socioeconomic and demographic groups having access to different employment opportunities. Specifically, the heterogeneity in welfare changes due to COVID-19 based on race/ethnicity can be explained by the Hispanic and black populations not being able to work from home compared to the white or Asian populations. This is reflected in welfare calculations of this paper as the white or Asian populations staying at home more compared to the Hispanic and black populations and thus experiencing higher welfare costs of COVID-19. Similarly, the heterogeneity in welfare changes due to COVID-19 based on education or income levels can also be explained by higher-educated or higher-income individuals being able to work from home. This is reflected in welfare calculations of this paper as higher-educated or higher-income individuals staying at home more compared to lower-educated or lower-income individuals and thus having experiencing welfare costs of COVID-19.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Journal of Urban Sciences.

The working paper version is available here.
 

 

 

Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty

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Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty

One sentence summary: The positive effects of mobility on COVID-19 increase with certain demographic or socioeconomic characteristics.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.

The working paper version is available here.

 
Abstract

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.

 

 

Non-technical Summary

The relationship between the spread of COVID-19 and social interactions through mobility is well established. Accordingly, several governments have employed lockdowns to slow down the spread of COVID-19. However, this relationship by itself does not suggest anything related to targeted lockdowns that can be useful when policy makers face trade-offs between health-related concerns and economic slowdown as certain group of people or certain communities can be more vulnerable to the spread of COVID-19.

This paper investigates how the relationship between mobility and the COVID-19 spread changes with demographic or socioeconomic characteristics. The formal investigation is achieved by using daily county-level data from the U.S., where a difference-in-difference approach is employed. The nonlinear relationship between mobility and COVID-19 cases or deaths is 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; accordingly, county-specific factors that are constant over time and day-specific factors that are common across U.S. counties are already 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 that we utilize as threshold variables.
 

Several demographic or socioeconomic characteristics of U.S. counties are considered for investigating the nonlinear relationship between mobility and the COVID-19 spread. These include 45 different variables based on the categories of population characteristics, economic variables, occupations, employment in industries, school attendance, educational attainment, and race. The motivation behind including these potential threshold variables comes from the existing literature, where several studies have shown how the spread of COVID-19 is related to these demographic or socioeconomic characteristics. 

 

The results of the nonlinear investigation 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. Since mobility restrictions to fight against COVID-19 would work better in counties where the positive effects of mobility on COVID-19 cases or deaths are bigger, it is implied that policy makers can consider targeted lockdowns based on the threshold variables identified in this paper.

 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.

The working paper version is available here.

 

Welfare Costs of COVID-19: Evidence from U.S. Counties

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Welfare Costs of COVID-19: Evidence from U.S. Counties

One sentence summary: The average (across days) welfare reduction during COVID-19 is about 11% for the average U.S. county and up to about 46% across counties.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

 
Abstract

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.

 

 

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only numerous casualties but also unprecedented reductions in economic activity. Since both COVID-19 cases and economic activity are positively related to mobility, individuals have faced trade-offs regarding the optimal amount of mobility that they should have. It is implied that investigating the welfare changes due to COVID-19 requires taking into account the mobility of individuals.

Based on this background, this paper investigates the welfare costs of COVID-19 by considering the interaction between COVID-19 cases, economic activity and mobility of individuals. A multi-region model is introduced to motivate the empirical investigation, where individuals optimally decide on their mobility that further determines their current consumption and future COVID-19 cases. The parameters and unknown variables of the model are estimated by using daily U.S. county-level data on consumption, employment, mobility and COVID-19 cases.

The estimation results confirm that economic activity (measured by either consumption or employment) increases with mobility of individuals. The estimation results also confirm the positive relationship between mobility and COVID-19 cases. These results are robust to the consideration of county-specific factors that are constant over time and time-varying nationwide factors that are common across counties.
 

The implications of the model are further used to investigate welfare costs of COVID-19 and its components based on economic activity and COVID-19 cases. The corresponding model implications suggest evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average U.S. county. 

 

When welfare costs are decomposed into those due to each model component, it is shown that COVID-19 cases contribute the most to welfare reductions in early months of COVID-19, whereas they have similar contributions with consumption/employment starting from about May 2020. Mobility contributes negatively to welfare in a steady way during the sample period, whereas other factors have been more effective in early months of COVID-19. In terms of the contribution of each welfare component as an average across days, increases in COVID-19 cases reduce welfare by about 6.7% for the average county (up to 14.2% across counties), whereas consumption reductions contribute to welfare costs by about 3.7% for the average county (up to 43.2% across counties). The contribution of mobility (with respect to other factors) is much more on average (across days) during the sample period.

 

 

 

The empirical results of this paper also provide 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. These results are robust to the consideration of alternative data sets as well as alternative parameter values considered in the model.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

  

COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

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COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

One sentence summary: The effects of COVID-19 cases on housing prices are negative and significant after controlling for other factors.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.

 
Abstract

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only a health crisis through its direct effects but also an economic one through its indirect effects. These indirect effects are reflected in housing prices within the U.S. in an unequal way across counties, where housing prices have increased by about $1,408 on average across counties on a monthly basis (between February 2020 and August, 2021), with a range between $1,979 of a reduction and $14,963 of an increase. Within this context, what is the contribution of COVID-19 cases on this heterogeneity representing unequal changes in housing prices across U.S. counties? The answer to this equation depends on several channels that affect the housing market at the local (U.S. county) level.

This paper investigates this heterogeneity representing unequal changes in housing prices across U.S. counties due to COVID-19 cases. The formal investigation is achieved by using a two-way fixed effects panel regression, where county-specific and time-specific factors are controlled for. Several mobility measures of individuals are also considered as control variables as they not only represent the overall economic activity at the U.S. county level over time but also the developments in the housing sector at the U.S. county level over time due to staying at home as a housing-demand shifter.

The benchmark empirical results show evidence for negative and significant effects of COVID-19 cases on housing prices, and they have been confirmed by several robustness checks based on permutation tests, exclusion tests, or interactions with other variables. 

 

When the channels of causality are further investigated, poverty is shown to be an important factor. Specifically, the U.S. counties with higher rates of poverty have experienced more reductions in housing prices due to COVID-19, whereas those with lower rates of poverty have experience almost no changes (or sometimes increases) in housing prices. Therefore, there is evidence for unequal effects of COVID-19 on housing prices across U.S. counties due to poverty differences.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.

 

 

 

Drivers of Global Trade: A Product-Level Investigation

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Drivers of Global Trade: A Product-Level Investigation

One sentence summary: Supply-side factors, capturing production and exporting costs in source countries, are responsible for about 85% of changes in global trade between 1995 and 2018.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Economic Journal.

Working paper version is available here.

Abstract
This paper investigates the drivers of global trade at the six-digit product level by using the implications of a model that are consistent with a large class of trade models. The drivers of global trade at the product level are identified first by estimating the product-level bilateral trade implications of the model and second by aggregating the fitted estimation results across bilateral countries using Taylor series. The empirical results suggest that supply-side effects (capturing production or exporting costs in source countries) contribute to changes in global trade more than six times the demand-side effects (capturing economic activity or preferences in destination countries) and more than ten times the effects of bilateral trade costs (capturing bilateral protectionism measures). Several product-level implications follow.

  

Non-technical Summary

Global merchandise trade has increased by more than 6 trillion U.S. dollars between 1995-2018. This increase is mostly accounted for by products such as machinery/electrical (with a contribution of about 26%), mineral products (with a contribution of about 18%), chemicals and allied industries (with a contribution of about 11%), and transportation (with a contribution of about 11%). Across broad economic categories, trade of intermediate inputs account for about 66% of this increase, whereas trade of capital goods and intermediate inputs account for about 18% and 16%, respectively. Although these statistics provide useful information on products or categories that drive the global trade, policy making requires knowledge on the economic forces that are responsible for the contribution of these products or categories.

This paper investigates the economic drivers of global trade by using six-digit product level data covering the years 1995-2018. These economic drivers are identified by using the implications of a large class of trade models, where bilateral trade between any two countries depend on source prices, bilateral trade iceberg costs, and a measure of economic activity at the destination country. Based on this motivation, a simple trade model is introduced of which implications are used for decomposing the changes in global trade into those due to supply-side factors (capturing source prices and thus production or exporting costs in source countries), demand-side factors (capturing economic activity or preferences in destination countries), and bilateral trade costs (capturing bilateral protectionism measures).

The knowledge of the decomposition of changes in global trade is important especially countries focusing on export-led growth, because if supply-side factors are effective in explaining changes in global trade, source countries may want to invest more into their production technologies, infrastructure, financial depth, operational costs of exporting, costs related to entering foreign markets, or modifying their products for individual foreign markets. In contrast, if demand-side factors are effective, source countries may want to invest in removing information barriers (e.g., through advertising their products) to affect preferences of destination countries. Finally, if bilateral trade costs are effective, source countries may want to get involved in negotiations to reduce trade barriers (e.g., through free trade agreements).

Regarding the methodology, the decomposition of changes in global trade is achieved first by estimating the product-level bilateral trade implications of a trade model and second by aggregating the fitted estimation results across bilateral countries using Taylor series to obtain global product-level measures. This methodology results in identifying the contribution of supply-side factors, demand-side factors and bilateral trade costs to changes in product-level global trade between 1995 and 2018. The corresponding results suggest that supply-side effects have contributed to changes in global trade by about 85%, followed by demand-side effects with a contribution of about 13% and by bilateral trade costs with a contribution of about 8%. The corresponding contribution of residuals by only about -6% capturing unexplained part of the data by the model implications or approximation due to using Taylor series further supports the investigation.

 

Across products, supply-side effects explain cumulative changes in product-level global trade between 47% (for Textiles) and 97% (for Chemicals & Allied Industries). In comparison, demand-side effects explain cumulative changes in product-level  global trade between 3% (for Stone/Glass) and 45% (for Animal & Animal Products). Finally, bilateral trade costs contribute to product-level global trade between 3% (for Chemicals & Allied Industries or Wood & Wood Products) and 24% (for Textiles). Across broad economic categories, supply-side factors contribute to global trade between 62% (for consumption goods) and 89% (for intermediate goods), demand-side factors contribute to global trade between 8% (for intermediate goods) and 29% (for consumption goods), and bilateral trade costs contribute to global trade between 7% (for intermediate goods) and 13% (for consumption goods).

 

Since supply-side factors are shown to be the main drivers of global trade, it is implied that rather than purely focusing on reducing bilateral trade costs through trade negotiations, one additional way for source countries to increase their exports is to reduce their production costs, say, by investing more into technology, infrastructure, or financial depth, while another way is to reduce their export-related costs such as operational costs of exporting, costs related to entering foreign markets or modifying their products for individual foreign markets. 

 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Economic Journal.

 

Working paper version is available here.