Impact evaluation using Difference-in-Differences

Fredriksson, Anders; Oliveira, Gustavo Magalhães de

doi:10.1108/rausp-05-2019-0112

Cited by 133 publications

(67 citation statements)

References 38 publications

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“…For each station, the idea is first to compute the time trends in turnstile entries and coronavirus incidence, and then assesses whether there is a relation between the two trends across different Jeffrey E. Harris 4/9/2020 subway stations (Fredriksson and Oliviera 2019). Unfortunately, there is a serious problem with this extraordinarily popular method of doing policy analysis (Bertrand, Duflo, and Mullainathan 2004).…”

Section: Diversity Of Covid-19 Incidence By New York City Zip Codementioning

confidence: 99%

The Subways Seeded the Massive Coronavirus Epidemic in New York City

Harris

2020

SSRN Journal

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The opinions expressed here are solely those of the author and do not represent the views of the Massachusetts Institute of Technology, Eisner Health, the National Bureau of Economic Research, or any other individual or organization. The author has received no direct or indirect remuneration for this article, and has no conflicts of interest to declare. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Diversity Of Covid-19 Incidence By New York City Zip Codementioning

confidence: 99%

The Subways Seeded the Massive Coronavirus Epidemic in New York City

Harris

2020

SSRN Journal

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“…The DID model determined the effect of a specific treatment (e.g., large-scale program implementation) by comparing the alteration in outcomes over time between a population which joined the program (i.e., the treatment group) and a population that did not (i.e., the control group) [102]. DID uses longitudinal data of control and treatment groups to gain a suitable counterfactual to analyse a causal effect [103]. The study used Eq (2) to calculate the DID-based model.…”

Section: Discussionmentioning

confidence: 99%

Impacts of community forestry on forest condition: Evidence from Sri Lanka’s intermediate zone

2020

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Sri Lanka's community forestry (CF) program emerged in the early 1980s following a global trend to conserve forest resources and provide benefits to the local community. However, very little is known about the effect of CF on forest resources. We assess the impacts of CF on forest conditions of semi-mixed evergreen forest in the intermediate zone of Sri Lanka using the before-after control-impact method. The study examines tree density, regeneration, woody species diversity, and evidence of disturbance as parameters to analyze the impact of the CF program. Data are analyzed using the difference in differences approach. The results show that the CF program has increased seedling and sapling density to a significant degree and reduced human disturbances. A major contribution of the CF program is that it was found to reduce invasive species and forest fires. The program reduced the amount of invasive species up to six times less than previous. The findings revealed that the impact of CF on forests may vary depending on pre-existing forest conditions, length of period to implement, perception, and decisions by local people. Community understanding and decision-making, in tandem with government policy, will weigh heavily on its future effectiveness.

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“…A naive comparison of post implementation search trends across states under different price-gouging regulation would mistake variation driven by unobserved preexisting differences as variation attributable to differences in price-gouging regulation (Fredriksson and de Oliveira 2019 ; Lechner 2011 ). For instance, if average households have more members in states with preexisting regulation which causes a larger proportion of searches for hand sanitizer, the naive approach would end up attributing this increase in relative search volumes to the difference in regulation.…”

Section: Methodsmentioning

confidence: 99%

Learning to Hoard: The Effects of Preexisting and Surprise Price-Gouging Regulation During the COVID-19 Pandemic

Chakraborti

Roberts

2021

J Consum Policy

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Theory suggests anticipation of shortages stemming from price regulation can motivate households to stock up more and thereby aggravate the regulation-induced shortage. We test this theory on online shopping searches for two typically store-bought staples: hand sanitizer and toilet paper. Combining (i) interstate variation in type of price-gouging regulation—preexisting versus surprise versus none, (ii) their temporally staggered implementation, and (iii) the demand surges for hand sanitizer and toilet paper during the COVID-19 pandemic facilitates identifying the impacts of different price-gouging regulation on consumer searches. Our results are consistent with price-gouging regulation–driven anticipatory hoarding. Difference-in-differences estimates reveal that states with preexisting-regulation experience the largest increases in post-implementation search proportions for both products. Accounting for potential endogeneity of implementation using a nearest-neighbor matching strategy reveals states that make surprise announcements of new regulation during the pandemic also experience larger increases in post-activation hand sanitizer search proportions than states without any such policy, but smaller increases than what preexisting-law states experience. These results corroborate the theoretical predictions about consequences of regulation-induced anticipation of shortages and inform the current policy debate surrounding impacts of price-gouging laws. Fundamentally, our results indicate behavioural responses to policy evolve as experience reveals the effects of the policy, and this evolution might influence the welfare consequences of the policy. Supplementary Information The online version contains supplementary material available at 10.1007/s10603-021-09493-1.

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Impact evaluation using Difference-in-Differences

Cited by 133 publications

References 38 publications

The Subways Seeded the Massive Coronavirus Epidemic in New York City

The Subways Seeded the Massive Coronavirus Epidemic in New York City

Impacts of community forestry on forest condition: Evidence from Sri Lanka’s intermediate zone

Learning to Hoard: The Effects of Preexisting and Surprise Price-Gouging Regulation During the COVID-19 Pandemic

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