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Proceedings

9:00–9:30
Session 1: Social Network
Chair: Anson Ho
Understanding US cross-county differences in stock market participation: Networks matter Abstract: This presentation exploits the geographic heterogeneity in stock market participation (SMP) rates across US counties and the role information sharing through social network plays in explaining this heterogeneity.
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Using Facebook county-level connectivity data, the US Census Bureau, and the Internal Revenue Services (IRS) information, I find that the traditional determinants of SMP explain the observed cross-county heterogeneity rather well, on average. However, traditional determinants fail to explain SMP heterogeneity across the income distribution. Explanatory power of the empirical model with only the traditional determinants of SMP is particularly low for income-rich households. An empirical model that accounts for the county┬┤s network, namely, when the average SMP rates across the income distribution in the connected counties is included as determinants of SMP, outperforms the traditional framework. SMP rate in the county's network is a particularly important covariate of SMP for the high-income households. This finding highlights the importance of social connectivity in a household┬┤s investment decisions and improves our understanding of nonparticipation among wealthy households.

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Additional information:
Canada21_Laurinaityte.pdf

Nora Laurinaityte
Bank of Lithuania
9:30–10:00 Bitcoin adoption and beliefs in Canada Abstract: There has been a growing discussion on digital currencies in the last few years, particularly Bitcoin. Nevertheless, research studies on Bitcoin adoption and experimentation are limited. In this presentation, we develop a tractable model of Bitcoin experimentation in which agents are uncertain about the quality of the underlying technology and update their beliefs by observing the survival of Bitcoin.
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The model determines how adoption decisions depend on (1) network effects, (2) own learning effects, and (3) social learning effects. We test the theoretical model's findings using unique data from the Bank of Canada's Bitcoin Omnibus Survey for the years 2017 and 2018. After accounting for the endogeneity of beliefs, we find that both network effects and own learning effects have a positive significant impact on Bitcoin adoption, while social learning effects have a negative effect. In particular, a 1-percentage-point increase in the network size increases the probability of adoption by 0.41 to 0.45 percentage points, whereas a 1-percentage-point increase in Bitcoin survival beliefs increases the probability of adoption by 0.43 to 0.55 percentage points. Our results suggest that network effects and individual experimentation were key drivers of Bitcoin adoption in 2017 and 2018.

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Additional information:
Canada21_Voia.pdf

Marcel Voia
University of Orleans
10:10–10:40
Session 2: Economic Inequality
Chair: Murtaza Haider
Joint estimation of employment and unemployment hazard rates with unobserved heterogeneity using the hshaz2s command Abstract: In this presentation, I describe hshaz2s, a new command that estimates two-states proportional hazard rates models with unobserved heterogeneity specific to each of the two modeled states.
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hshaz2s uses the d2 ml method to provide the algebraic expressions of the first- and second-order derivatives of the log-likelihood function to achieve model convergence faster, which takes special relevance for empirical researchers dealing with large longitudinal microdatasets. Results of fitting a discrete time-duration model that jointly estimates the transition rates from employment and unemployment on a sample of workers in the Spanish labor market are presented to show the main features of the hshaz2s command.

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Additional information:
Presentation unavailable

David Troncoso-Ponce
University of Seville
10:40–11:00 Does economic inequality breed murder? An empirical investigation of the relationship between economic inequality and homicide rates in Canadian CMAs: 1981 to 2017 Abstract: National and international research documents a relationship between greater economic inequality and higher homicide rates. However, much of this work uses simple cross-sections at high levels of aggregation rather than longer time series of cities or districts and lacks controls for a more substantial range of confounding factors.
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Using longitudinal Canadian provincial-level data over the period 1981 to 2017, we occasionally find a positive correlation between inequality and homicides rates. However, the relationship between income inequality and homicide rates in Canada reverses to become negative when looking at Canadian Census Metropolitan Areas (CMAs). Moreover, the province-level result between greater inequality and homicide rates also appears to break down once accounting for regional effects. We conclude that much of the literature that finds a relationship between greater economic inequality and homicide rates needs to be reexamined within a longer time and more disaggregated framework.

Contributor:
Livio Di Matteo
Lakehead University
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Additional information:
Canada21_Petrunia.pdf

Robert Petrunia
Lakehead University
11:00–12:00
Session 3: StataCorp presentation
Chair: Murtaza Haider
Custom estimation tables Abstract: In this presentation, I build custom tables from one or more estimation commands. I demonstrate how to add custom labels for significant coefficients and how to make targeted style edits to cells in the table. I conclude with a simple workflow for you to build your own custom tables from estimation commands.

Additional information:
Canada21_Pitblado.pdf

Jeff Pitblado
StataCorp
12:30–1:00
Session 4: Survey Methods
Chair: Murtaza Haider
A user-friendly technique for weighting survey data using Stata Abstract: We develop a command that implements the Imbens and Lancaster (1994) and Hellerstein and Imbens (1999) approach to estimating population-weighted regression models for survey data by taking advantage of auxiliary information on moments reflecting the population from which the sample was drawn.
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Typically, the method is employed to improve regression estimates from a modest researcher-conducted survey dataset with selected variables in common with a census or large-scale survey. In a generalized method of moments framework, the method simultaneously minimizes the score functions of the estimator of interest (for example, OLS/logit/probit) while also matching suitable moments (for example, means, squares, and cross-products) for the variables common to the survey of interest and the population. As is the case with traditional approaches to generating survey weights, this addresses selection on observed variables that may arise because of issues such as survey design, attrition, and nonresponse. Using Monte Carlo simulation, we show how this method can sometimes improve estimates compared with unweighted OLS and traditional methods of generating weights. We illustrate this method using crowdsource data on COVID-19. The method is easy to use and is also beneficial.

Contributor:
Arthur Sweetman
McMaster University
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Additional information:
Canada21_Sweetman.pdf

Rabiul Islam
McMaster University
1:00–1:20 Survey calibration via the generalized-method-of-moments Abstract: This presentation aims to create a standardized weighting procedure for our ongoing survey program. We propose generalized method of moments estimators as an alternative covariate balancing procedure to IPF.
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Our goal is to create weights that address three issues. First, the weights should reduce selection bias in each individual survey. Second, the weights should ensure comparability across time of each survey, even if surveys contain different "mixtures" of sample sources. Third, we would like to create separate weights that account for the panel dimension (and therefore attrition) in our April 2020 Survey, where about 1,000 respondents were from past surveys.

Contributors:
Kim Huynh
Gradon Nichols
Bank of Canada
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Additional information:
Presentation unavailable

Heng Chen
Bank of Canada
1:30–1:50
Session 5: Model Selection
Chair: Anson Ho
Forward model selection using AIC or BIC format Abstract: Often, one cannot conduct all possible subsets regression, because there are too many models to consider. The traditional alternative has been forward/stepwise/backward model selection.
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While the sequence of models generated this way is unproblematic, the p-values are not valid, because they do not account for the selection procedure. One solution is to compute AIC for the sequence of models generated and to choose the model with the smallest AIC. My command aic_model_selection facilitates this by performing a regression on a sequence of models, adding one x-variable (in the order specified) at a time. I will give an example related to prices of apartment rentals in Munich.

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Additional information:
Canada21_Schonlau.pdf

Matthias Schonlau
University of Waterloo
1:50–2:20 posw, a Neyman-orthogonal estimator after stepwise covariate selection Abstract: This talk discusses the new posw command, which produces valid inference for causal parameters after using a stepwise method to select which covariates should be included in the model.
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The talk provides a quick introduction to using the lasso and to using the stepwise methods of covariate selection and some tradeoffs between them. It also discusses the authors recommendation to use BIC-based stepwise instead of testing-based stepwise. It also discusses some of the methodology implemented in posw, including some new results in Drukker and Liu (2021).

Contributors:
David Drukker
Sam Houston State University
Di Liu
StataCorp
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Presenter:

Additional information:
Canada21_Drukker.pdf

David Drukker
Sam Houston State University
2:30–3:00
Session 6: COVID-19
Chair: Anson Ho
Payment habits during COVID-19: Evidence from high-frequency transaction data Abstract: We investigate how the COVID-19 pandemic has changed consumers' payment habits in Canada. We rely on high-frequency data on cash withdrawals and debit card transactions from Interac and Canada's ACSS clearing system. We construct daily measures of payment habits reflecting cash usage, average transaction values, and the share of transactions in which the customer or card holder and the acquiring machine like ATM or POS are of the same bank ("on-us").
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Using dummy regressions and local projection models, we assess how these indicators of payment habits have changed with the evolution of the COVID-19 pandemic. We find evidence that consumer behavior adjusted to the pandemic by avoiding frequent trips for cash withdrawals and point-of-sale purchases through higher transaction amounts. Consumers withdrew less cash compared with card payments, which could reflect a reduced use of cash for point-of-sale transactions. Consumers also made relatively more withdrawals from ATMs that are linked to their financial institutions (on-us transactions). Finally, we highlight that estimates of economic activity based on card data alone could be biased if shifts in payment habits are not accounted for. We estimate that debit card payments might have overstated consumer expenditure growth by up to 7pp over the course of the pandemic.

Contributor:
Angelika Welte
Bank of Canada
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Additional information:
Canada21_Dahlhaus.pdf

Tatjana Dahlhaus
Bank of Canada
3:00–3:30 A population-based model for rationing the COVID-19 vaccine Abstract: With the distribution of COVID-19 vaccines, the model presented here may with reproduction based on COVID-19 as an endpoint diagnosis serve to assist the rationing of initially limited supplies of vaccines to those most vulnerable to infection, potentially helping to curb the spread of this disease.
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Background
As COVID-19 vaccines develop, methods for identifying vulnerability within groups to prioritized vaccination remain unestablished. This presentation describes a novel approach based on population-based analysis of viral pneumonia vulnerability, as an example.

Methods
The analysis employed an anonymous, 16-year population dataset (n = 768,460) consisting of International Classification of Diseases (ICD-9) diagnoses, demographics, and dates identifying those with and without viral pneumonia linked to all associated diagnoses (~90 million) for calculation of independent main-class diagnoses (17) odds ratios and proportions of disorders before and after the index viral pneumonia diagnosis. A subsample of those under the age of 1 year in the first year of the dataset was analyzed prospectively with representation of the intensity of the first 50 diagnoses across all diagnoses (~1000), comparing those who did with those who did not develop viral pneumonia.

Results
Females and males had results of differing magnitude. For those with viral pneumonia, the mean number of diagnoses was greater in both the subsample and whole sample, with associated diagnoses arising about four years on average before the viral pneumonia index diagnosis. Within the subsample, compared with those without, the temporal analysis revealed distinct overrepresentation for those with viral pneumonia at visit 1 and over the first 50 visits. Further, those with viral pneumonia had diagnoses not represented in the group without viral pneumonia.

Conclusions
The population-based analysis of temporal hypermorbidity may be a viable and economical approach to identifying viral pneumonia vulnerability. The approach presented here may provide an economical means of identifying vulnerability to COVID-19 in regions where comparable data are available for analysis. Rational approaches may optimize vaccination and help to limit the spread of the disease and to some extent alleviate the health service burden.

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Additional information:
Canada21_Cawthorpe.pptx

David Cawthorpe
University of Calgary
3:30–3:50
Session 7: Health Science
Chair: Anson Ho
Disparities and healthcare utilization among general surgery patients with opioid use disorders Abstract: Introduction: Opioid use disorder is a major public health issue. We investigated the national burden of opioid use disorders among general surgery patients in the USA.
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Methods: We queried the 2017 Nationwide Inpatient Sample (NIS) for patients >18 years admitted for a general surgery procedure, comparing outcomes for those with an underlying opioid use disorder with those with none. Opioid use disorder was categorized as abuse, dependence, or poisoning using ICD-10CM coding. The primary outcome was in-patient mortality; secondary outcomes were costs and length of stay. Results: Among 4,445,253 general surgery patients, 157,816 (3.6%) had an opioid use disorder. GS-opioid patients were younger (53 versus 62 years).

Contributor:
Lisa M. Knowlton
Stanford University
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Additional information:
Canada21_Tennakoon.pdf

Lakshika Tennakoon
Stanford University
4:00–4:30
Chair: Anson Ho
Open panel discussion with Stata developers
StataCorp

Scientific committee


Anson Ho

Anson Ho is an assistant professor in the Real Estate Management Department. His primary research interests include consumer finance, housing, and macroeconomics. Prior to joining Ryerson University in 2020, Anson was a senior economist in the Financial Stability Department at the Bank of Canada (2016–2020) and an assistant professor at Kansas State University (2011–2016). Ho received his PhD in economics from the University of Iowa in 2011.


Laura Rosella

Dr. Laura Rosella is an epidemiologist and associate professor in the Dalla Lana School of Public Health (DLSPH) at the University of Toronto, where she holds a Canada Research Chair in Population Health Analytics. She is the site director for ICES U of T and faculty affiliate at the Vector Institute. In 2020, she was made the inaugural Stephen Family Research Chair in Community Health at the Institute for Better Health, Trillium Health Partners. She leads the Population Health Analytics Lab out of DLSPH, which is focused on using population databases to inform population health and health system planning.


Murtaza Haider

Murtaza Haider is a professor of Data Science and Real Estate Management at Ryerson University. He also serves as the research director of the Urban Analytics Institute. Professor Haider holds an adjunct professorship of engineering at McGill University. In addition, he is a director of Regionomics Inc., a boutique consulting firm specializing in the economics of cities and regions, and holds a masters in transport engineering and a PhD in civil engineering from the University of Toronto.

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