Counterfactual Values Research Articles

Impact on Local Economy from Zhoushan National New Area

To empirically study the policy impact of a National New Area on the local economy, this paper evaluates the effect of the Zhoushan Archipelago New Area on local GDP growth rate and economic efficiency. Firstly, the economic efficiency of 20 prefectural-level cities in Jiangsu, Zhejiang and Anhui provinces is estimated by data envelopment analysis with input and output data from 1995 to 2015. In selecting some cities except Zhoushan as a control group, we construct some counterfactuals of Zhoushan by a panel data approach. The difference between the actual and counterfactual values for the GDP growth rate and economic efficiency of Zhoushan is calculated and compared to conclude the treatment effect of the National New Area. The research shows that in the first four years, the policy of the National New Area promoted Zhoushan's economic quality by raising its efficiency, but relatively reduced its economic quantity by negatively affecting the local GDP growth rate. Then the policy influence on Zhoushan’s GDP growth rate and economic efficiency gradually disappeared after four years. Combining some other research on our study, we find that the policy effect on the GDP growth rate is related to the level of economic growth in these areas. The policy of the National New Area has less influence on GDP growth if approved in the developed region rather than in the undeveloped region. We think that it is better to set up a National New Area in a relatively undeveloped area.

Indian Buffet process factor model for counterfactual analysis

This article proposes a factor model-based counterfactual analysis. We explicitly estimate the underlying factor structure of the outcome variables and estimate the counterfactual values of the unit subject to an intervention. With the help of the non-parametric Bayesian Indian Buffet Process prior, our approach is capable of exploring heterogeneous factor exposures, and the number of latent factors is endogenously determined in the estimation process. The flexible Markov Chain Monte Carlo algorithm utilizes the maximal intervention-free information provided by the data, whereas the original synthetic control only uses pre-intervention data. The counterfactual values are estimated by simulating from the posterior predictive distribution so that we can integrate out any parameter estimation uncertainty. We also calculate the posterior predictive upper- and lower-quantile bounds for inference. The two applications, namely California’s Tobacco Control Program and the West German Reunification, demonstrate the usefulness of our approach compared to the synthetic control method and the elastic net model. The Monte Carlo simulation study also shows the robustness of our approach with respect to nonlinear dynamics in the underlying factor process.

Financing decisions following negative shocks in the product market: A matrix-completion study of the U.S. pharmaceutical industry

We investigate whether firms adjust their financing policies in response to a negative shock affecting their product market. Focusing on the pharmaceutical industry, we leverage the U.S. Inflation Reduction Act (IRA) of 2022 as an exogenous shock, marking the government's inaugural authority to negotiate drug prices. Using a matrix completion approach, a supervised machine-learning methodology that allows to compare treatment outcomes against predicted counterfactual values in absence of the treatment, our analysis reveals that pharmaceutical firms react to this regulatory intervention by issuing more equity. This finding suggests that firms raise fresh capital to mitigate the adverse impact of IRA on the product market.

Two Decades of Air Pollution Health Risk Assessment: Insights From the Use of WHO's AirQ and AirQ+ Tools.

We evaluated studies that used the World Health Organization's (WHO) AirQ and AirQ+ tools for air pollution (AP) health risk assessment (HRA) and provided best practice suggestions for future assessments. We performed a comprehensive review of studies using WHO's AirQ and AirQ+ tools, searching several databases for relevant articles, reports, and theses from inception to Dec 31, 2022. We identified 286 studies that met our criteria. The studies were conducted in 69 countries, with most (57%) in Iran, followed by Italy and India (∼8% each). We found that many studies inadequately report air pollution exposure data, its quality, and validity. The decisions concerning the analysed population size, health outcomes of interest, baseline incidence, concentration-response functions, relative risk values, and counterfactual values are often not justified, sufficiently. Many studies lack an uncertainty assessment. Our review found a number of common shortcomings in the published assessments. We suggest better practices and urge future studies to focus on the quality of input data, its reporting, and associated uncertainties.

Contradictory Results: Reassessing the Impact of Loan‐to‐Value Restrictions on House Price Inflation

AbstractUsing Bayesian Structural Time Series analysis, this study examines the causal impact of loan‐to‐value (LTV) restrictions imposed by the Reserve Bank of New Zealand in October 2013. By incorporating state‐space components, such as local linear trend, seasonality and regression, counterfactual values of house price indices are predicted. Surprisingly, the study reveals that the implementation of LTV restrictions had no significant effect on national house price indices, contradicting prior Central Bank studies that reported a nearly 3 percentage‐point decrease in housing cost inflation. This contradictory evidence challenges existing perceptions of the effectiveness of LTV restrictions in curbing house price inflation.

Linking the Perceived Stress Scale to the PROMIS Psychological Stress Experiences-Pediatric in adolescents.

The Perceived Stress Scale (PSS) and PROMIS Psychological Stress Experiences-Pediatric (PPSE) are two self-report instruments that have been used with adolescents to measure subjective feelings of stress. Stress is a critical construct to evaluate, as chronic stress can result in negative long-term consequences for an individual's physical and mental health. While the PSS is more commonly used in research and clinical practice, use of the PPSE is becoming more widespread. This increased usage necessitates a way to compare the measures, for example, when changing which instrument is used in practice or when combining data from the two measures in pooled analysis. After assessing linking assumptions, we used equipercentile linking to score-link the two measures on the raw score and T-score metrics for both longer forms (10 PSS items; 8 PPSE items) and shorter forms (4 PSS or PPSE items), yielding crosswalk tables (Open Science Framework: https://osf.io/9pzyk/) to convert scores on each measure to the metric of the other. Raw scores between measures (.670 ≤ r ≤ .794) were not sufficiently correlated for score linking. Therefore, linked scores were treated as predictions, rather than counterfactual values on the alternative measure. Further, confirmatory factor analyses revealed that this low correlation was primarily due to the low reliability and multidimensionality of the PSS, and the derived linking functions were found to have minimal bias. Recommendations for the use of these conversion tables by researchers and practitioners are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Don’t Predict Counterfactual Values, Predict Expected Values Instead

Counterfactual Regret Minimization algorithms are the most popular way of estimating the Nash Equilibrium in imperfect-information zero-sum games. In particular, DeepStack -- the state-of-the-art Poker bot -- employs the so-called Deep Counterfactual Value Network (DCVN) to learn the Counterfactual Values (CFVs) associated with various states in the game. Each CFV is a multiplication of two factors: (1) the probability that the opponent would reach a given state in a game, which can be explicitly calculated from the input data, and (2) the expected value (EV) of a payoff in that state, which is a complex function of the input data, hard to calculate. In this paper, we propose a simple yet powerful modification to the CFVs estimation process, which consists in utilizing a deep neural network to estimate only the EV factor of CFV. This new target setting significantly simplifies the learning problem and leads to much more accurate CFVs estimation. A direct comparison, in terms of CFVs prediction losses, shows a significant prediction accuracy improvement of the proposed approach (DEVN) over the original DCVN formulation (relatively by 9.18-15.70% when using card abstraction, and by 3.37-8.39% without card abstraction, depending on a particular setting). Furthermore, the application of DEVN improves the theoretical lower bound of the error by 29.05-31.83% compared to the DCVN pipeline when card abstraction is applied. Additionally, DEVN is able to achieve the goal using significantly smaller, and faster to infer, networks. While the proposed modification may seem to be of a rather technical nature, it, in fact, presents a fundamentally different approach to the overall process of learning and estimating CFVs, since the distributions of the training signals differ significantly between DCVN and DEVN. The former estimates CFVs, which are biased by the probability of reaching a given game state, while training the latter relies on a direct EV estimation, regardless of the state probability. In effect, the learning signal of DEVN presents a better estimation of the true value of a given state, thus allowing more accurate CFVs estimation.

Estimating the macroeconomic effects of Japan’s expansionary monetary policy under Quantitative and Qualitative Monetary Easing during 2013–2020

This paper estimates the macroeconomic effects of the Bank of Japan’s expansionary monetary policies since the introduction of Quantitative and Qualitative Monetary Easing (QQE) using the Bank of Japan’s large-scale macroeconomic model, the Quarterly Japanese Economic Model. We consider counterfactual paths of major financial variables, such as real interest rates, constructing hypothetical scenarios where the QQE and subsequent easing measures had not been introduced. We then conduct counterfactual simulations to examine how Japan’s macroeconomic variables, such as real GDP and CPI, would have evolved under those hypothetical scenarios. In these settings, we estimate the policy effects on the macroeconomic variables as the difference between actual values and the counterfactual values. The estimation results demonstrate that, on average, during the period from the introduction of QQE to the July–September quarter of 2020, the policy effect on the level of real GDP is between around 0.9 and 1.3 percent, and on the year-on-year rate of change in the CPI (all items less fresh food and energy) is between around 0.6 and 0.7 percentage points.

A novel indicator of selection in utero.

Selection in utero predicts that population stressors raise the standard for how quickly fetuses must grow to avoid spontaneous abortion. Tests of this prediction must use indirect indicators of fetal loss in birth cohorts because vital statistics systems typically register fetal deaths at the 20th week of gestation or later, well after most have occurred. We argue that tests of selection in utero would make greater progress if researchers adopted an indicator of selection against slow-growing fetuses that followed from theory, allowed sex-specific tests and used readily available data. We propose such an indicator and assess its validity as a dependent variable by comparing its values among monthly birth cohorts before, and during, the first 10 months of the COVID-19 pandemic in Sweden. We apply Box-Jenkins methods to 50 pre-pandemic birth cohorts (i.e., December 2016 through January 2020) and use the resulting transfer functions to predict counterfactual values in our suggested indicator for selection for ten subsequent birth cohorts beginning in February 2020. We then plot all 60 residual values as well as their 95% detection interval. If birth cohorts in gestation at the onset of the pandemic lost more slow-growing fetuses than expected from history, more than one of the last 10 (i.e. pandemic-exposed) residuals would fall below the detection interval. Four of the last 10 residuals of our indicator for males and for females fell below the 95% detection interval. Consistent with selection in utero, Swedish birth cohorts in gestation at the outset of the COVID-19 pandemic included fewer than expected infants who grew slowly in utero.

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research

BackgroundInterrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment.ResultsThe effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is based on the ITS model-based fitted values and the predicted counterfactual (the exposed period had the intervention not occurred) values. A user-friendly R package to fit an ITS and estimate the effect size was developed and accompanies this paper. To illustrate, we implemented a nation population-based ITS study from January 2001 to May 2021 covering the all-cause mortality of Israel (n = 9,350 thousand) to quantify the effect size of Covid-19 exposure on mortality rates. In the period unexposed to the Covid-19 pandemic, the mortality rate decreased over time and was expected to continue decreasing had Covid-19 not occurred. In contrast, the period exposed to the Covid-19 pandemic was associated with an increased all-cause mortality rate (relative risk = 1.11, 95% CI = 1.04, 1.18, P < 0.001).ConclusionFor the first time, the effect size in ITS: was quantified, can be estimated by end-users with an R package we developed, and was demonstrated with data showing an increase in mortality following the Covid-19 pandemic. ITS effect size reporting can assist public health policy makers in assessing the magnitude of the entire intervention effect using a single, readily understood measure.

Mediating Role of Fine Particles Abatement on Pediatric Respiratory Health During COVID-19 Stay-at-Home Order in San Diego County, California.

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID-19 stay-at-home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay-at-home order, we quantified the specific contribution of fine-scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n=72,333) at the ZIP-code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015-2020) and ZIP-code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay-at-home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay-at-home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4-year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay-at-home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits.

Early human judgment forecasts of human monkeypox, May 2022

Early human judgment forecasts of human monkeypox, May 2022

The misalignments in Chinese real effective exchange rate from 1994 to 2020: A counterfactual analysis

Using a novel panel data method proposed by Hisao, Ching and Wan (2012), we empirically evaluate the misalignments between the actual and counterfactual values of Chinese real effective exchange rate (REER, CPI-based) from 1994 to 2020, where “counterfactual” refers to the counterfactual scenarios that some major economic events had not happened. These events include China's accession to WTO from December 2001, the reforms of exchange rate regime in July 2005, June 2010 and August 2015, China's economic stimulus package in November 2008, and the US-China trade dispute started from March 2018. We calculate the counterfactual values for both real and nominal effective exchange rates (REER and NEER) in order to examine the channels of the misalignments. We find that the reform of China's exchange rate policy in July 2005 and August 2015 increased Renminbi's trade competitiveness in real term compare to foreign currencies, while other events decreased it or had no effects. The misalignments in REER are caused by the changes in both NEER and China's CPI.

Hunger relief: A natural experiment from additional SNAP benefits during the COVID-19 pandemic

BackgroundCOVID-19 has directly affected millions of people. Others have been indirectly affected; for example, there has been a startling increase in hunger brought about by the pandemic. Many countries have sought to relieve this problem through public policy. This research examines the effectiveness of enhanced Supplemental Nutrition Assistance Program (SNAP) benefits in the U.S. to alleviate hunger.MethodsUsing a biweekly cross-sectional survey and corresponding population weights from the U.S. Census Bureau, we estimate the effects of enhanced SNAP benefits on hunger in the U.S. as measured by food insufficiency. We use a Bayesian structural time series analysis to predict counterfactual values of food insufficiency. We supplement these findings by examining the effect of enhanced SNAP benefits on observed visits to a food pantry network in a midsized U.S. city.FindingsOur primary finding estimates that nationwide a total 850,000 (95% credible interval 0·24–1·46 million) instances of food insufficiency were prevented per week by the 15 percent increase in SNAP benefits enacted in January 2021. Secondarily, we find similar effects associated with SNAP benefit increases and local food pantry visits. Specifically, enhanced SNAP benefits resulted in fewer visits to the food pantry network than were predicted in the counterfactual model.InterpretationThese results not only indicate that the policies enacted to mitigate hunger caused by the COVID-19 pandemic helped, but also quantifies how much these benefits helped on a national scale. As a result, policymakers can use this data to benchmark future policy actions at scale.FundingNone.

Evaluating the impact of the COVID-19 pandemic on cancer screening in a central Canadian province

We evaluated the impact of COVID-19 on cancer screening in Manitoba, Canada using an interrupted time series (ITS) design and data from Manitoba's population-based, organized cancer screening programs from April 2020 to August 2021. In June 2020 (breast screening was suspended during April and May 2020), there was a 54% decrease between the predicted (i.e., observed data produced from regression models) and expected (i.e., counterfactual values produced for the COVID-19 period by assuming COVID-19 did not occur) number of screening mammograms (ratio = 0.46, 95% Confidence Interval (CI) 0.28–0.64). By December 2020, there was no significant difference between predicted and expected number of screening mammograms (ratio = 0.95, 95% CI 0.80–1.10). In April 2020, there was an 83% decrease in the number of Pap tests (ratio = 0.17, 95% CI 0.04–0.30). By January 2021, there was no significant difference between predicted and expected number of Pap tests (ratio = 0.93, 95% CI 0.81–1.06). In April 2020, there was an 81% decrease in the number of screening program fecal occult blood tests (FOBTs) (ratio = 0.19, 95% CI 0.0–0.44). By September 2020, there was no significant difference between predicted and expected number of FOBTs (ratio = 0.95, 95% CI 0.65–1.24). The estimated cumulative deficit (i.e., backlog) from April 2020 to August 2021 was 17,370 screening mammograms, 22,086 Pap tests, and 5253 screening program FOBTs. Overall, screening programs adapted quickly to the COVID-19 pandemic. Additional strategies may be needed to address remaining backlogs.

POSB421 A Framework for Expert Elicitation Related to Counterfactual Values: A Case Study of Elicitation to Inform Subgroup Analyses in Clinical Trials.

Subgroup analyses are important components of precision medicine, but typically lack power to distinguish between false negatives and false positives.Hence, prior plausibility, elicited from experts is an important criterion for their credibility. There is little guidance on how this should be elicited, leaving decision-makers to judge plausibility of subgroup effects after trial results have been published. Additionally, counterfactuals are typically unobservable, outside clinical trials, for subgroup effects.

Graphing and Measuring COVID-19’s First Wave Impact on the Bolivian Economy: Facing the Unknown.

The Bolivian monthly index of economic activity along with ARMA models are used in an attempt to graph and measure the impact of COVID-19’s pandemic on the Bolivian economy. The accumulated difference between the observed and counterfactual values, show an overall 12.6% loss of economic activity in the 10 months from February to November 2020 of the first COVID-19 wave, with a tilted W-shape short-run recovery just before the beginning of the second wave in December 2020. Breakdown into the twelve Bolivian economic sectors show wide heterogeneity in depth of impact and speeds of recovery during the same period.

Measuring Korea’s Industry-level Productivity Change Due to Tariff Cuts using a CGE Model

Purpose - This study examined the effect of tariff cuts on productivity in Korea s manufacturing industries and the effect of initial productivity level before tariff cuts on productivity improvement after tariff cuts. We also attempted to identify whether import-driven or export-driven factors are more important for productivity improvement, especially in low productivity industries. Design/methodology - Since tariff reduction is a policy decision that can affect cross-industry, its impact is spread across all industries beyond the scope of a single firm through the input and output network of industry structure. Accordingly, we proposed a new method to measure the change in productivity to reflect the impact of tariff cuts across industries. Through an Armington CGE analysis, changes in endogenous variables can be directly measured after the exogenous shock of tariff reduction, and the amount of movements in productivity triggered by tariff cuts can also be calculated. We can thus assess the effectiveness of exogenous policy, such as tariff cuts, through the difference between the benchmark and counterfactual values of endogenous variables. Findings - This study confirmed that tariff reduction positively affected productivity improvement in Korea’s manufacturing industries. It also confirmed that productivity gains occur in Korea’s leading export industries. Finally, greater productivity gains were recorded in the group with additional high-export-share or high-import-share conditions for low productivity industries. These results are, in a limited sense, consistent with the existing studies that emphasize the importance of exports and imports on productivity improvement, especially for low productivity industries. Originality/value - The results of our experiments are different from those of non-CGE studies, which measure the industry-level change in productivity with dummy coefficients, in terms of directly calculating the amount of change in productivity. In addition, we propose that the Armington CGE model is more appropriate than the Melitz CGE model to directly measure the productivity after tariff cuts. This is because the Melitz CGE model assumes the given specific productivity density, which does not change after an overall drop of tariffs. To the best of our knowledge, this approach to directly calculating productivity by reflecting the impact of tariff reduction across industries through CGE analysis, is unprecedented in this literature.

Sex-specific deaths in Norway and Sweden during the COVID-19 pandemic: Did mandates make a difference?

To determine whether differences between Norway's and Sweden's attempts to contain SARS-CoV-2 infection coincided with detectably different changes in their all-cause mortality sex ratios. Measuring temporal variation in the all-cause mortality sex ratio before and during the pandemic in populations exposed to different constraints on risky behavior would allow us to better anticipate changes in the ratio and to better understand its association with infection control strategies. I apply time Box-Jenkins modeling to 262 months of pre-pandemic mortality sex ratios to arrive at counterfactual values of 10 intra-pandemic ratios. I compare counterfactual to observed values to determine if intra-pandemic ratios differed detectably from those expected as well as whether the Norwegian and Swedish differences varied from each other. The male to female mortality sex ratio in both Norway and Sweden increased during the pandemic. I, however, find no evidence that the increase differed between the two countries despite their different COVID-19 containment strategies. Societal expectations of who will die during the COVID-19 pandemic will likely be wrong if they assume pre-pandemic mortality sex ratios because the intra-pandemic ratios appear, at least in Norway and Sweden, detectably higher. The contribution of differences in policies to reduce risky behavior to those higher ratios appears, however, small.

Does the euro increase the complexity of exported goods? The case of Estonia

ABSTRACTThe goal of this article is to assess the impact of the euro’s adoption on the complexity of goods in Estonian exports. That policy decision may result in the specialization of production of either more or less sophisticated goods, depending on the country’s technological advancement and factor endowment. At the same time, intensified foreign direct investment (FDI) flows may enhance the engagement of a country in international production chains with ambiguous consequences for export complexity. We applied the Synthetic Control Method to compare the observed post-adoption levels of export complexity in Estonia with the counterfactual values of Estonia remaining outside of the Eurozone.