scholarly journals Sticky Wages: Evidence from Quarterly Microeconomic Data

2012 ◽  
Vol 4 (3) ◽  
pp. 1-32 ◽  
Author(s):  
Hervé Le Bihan ◽  
Jérémi Montornès ◽  
Thomas Heckel
Keyword(s):  
Sample Selection ◽  
Econometric Analysis ◽  
Selection Model ◽  
Time Dependent ◽  
Nominal Wage ◽  
Sample Period ◽  
Wage Setting ◽  
Wage Stickiness ◽  
State Dependent ◽  
Sticky Wages

Using an original micro-dataset from France, we investigate nominal wage stickiness. Nominal wage changes are found to occur at a quarterly frequency of around 38 percent over our sample period, and to be to a large extent staggered across establishments, and very synchronized within establishments. We carry out an econometric analysis of wage changes based on a two-threshold sample selection model. Our results are that the timing of wage adjustments is time-dependent as opposed to state-dependent, there is evidence of predetermination in wage changes, and both backward and forward-looking behavior is relevant in wage setting. (JEL E24, E52, J31)

scholarly journals Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

2020 ◽  
Author(s):  
David Benatia ◽  
Raphael Godefroy ◽  
Joshua Lewis
Keyword(s):  
Public Health ◽  
United States ◽  
Sample Selection ◽  
The United States ◽  
Underlying Disease ◽  
Selection Model ◽  
Sample Period ◽  
Infection Rates ◽  
Public Health Response ◽  
Sample Selection Model

SummaryBackgroundPublic health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7.MethodsWe adapted a sample selection model that corrects for non-random testing to estimate population infection rates. The methodology compares how the observed positive case rate vary with changes in the size of the tested population, and applies this gradient to infer total population infection rates. Model identification requires that variation in testing rates be uncorrelated with changes in underlying disease prevalence. To this end, we relied on data on day-to-day changes in completed tests across U.S. states for the period March 31 to April 7, which were primarily influenced by immediate supply-side constraints. We used this methodology to construct predicted infection rates across each state over the sample period. We also assessed the sensitivity of the results to controls for state-specific daily trends in infection rates.ResultsThe median population infection rate over the period March 31 to April 7 was 0.9% (IQR 0.64 1.77). The three states with the highest prevalence over the sample period were New York (8.5%), New Jersey (7.6%), and Louisiana (6.7%). Estimates from mod-els that control for state-specific daily trends in infection rates were virtually identical to the baseline findings. The estimates imply a nationwide average of 12 population infections per diagnosed case. We found a negative bivariate relationship (corr. = -0.51) between total per capita state testing and the ratio of population infections per diagnosed case.InterpretationThe effectiveness of the public health response to the coronavirus pandemic will depend on timely information on infection rates across different regions. With increasingly available high frequency data on COVID-19 testing, our methodology could be used to estimate population infection rates for a range of countries and subnational districts. In the United States, we found widespread undiagnosed COVID-19 infection. Expansion of rapid diagnostic and serological testing will be critical in preventing recurrent unobserved community transmission and identifying the large numbers individuals who may have some level of viral immunity.FundingSocial Sciences and Humanities Research Council.

Time-dependent or state-dependent wage-setting? Evidence from periods of macroeconomic instability

2016 ◽  
Vol 78 ◽  
pp. 50-66 ◽  
Author(s):  
Jósef Sigurdsson ◽  
Rannveig Sigurdardottir
Keyword(s):  
Time Dependent ◽  
Wage Setting ◽  
State Dependent ◽  
Macroeconomic Instability

scholarly journals PRICE SETTING IN LITHUANIA: MORE EVIDENCE FROM THE SURVEY OF FIRMS

Ekonomika ◽  
2011 ◽  
Vol 90 (4) ◽  
pp. 76-99
Author(s):  
Ernestas Virbickas
Keyword(s):  
Data Analysis ◽  
Ad Hoc ◽  
Time Dependent ◽  
Price Adjustment ◽  
Price Setting ◽  
Wage Setting ◽  
State Dependent ◽  
The Cost ◽  
Cost Factors ◽  
State Dependent Pricing

The article examines price setting in Lithuania, based on the ad hoc survey “On Price and Wage Setting” of the Bank of Lithuania. The study extends the survey data analysis presented in Virbickas (2009). The article points to the incidence of both time-dependent and state-dependent price reviewing policies used by the firms under study, though the price reviewing practices appear to be somewhat tilted to the state-dependent pricing. Analysis provides evidence on the reasons for the upward and downward stickiness of prices. Delayed price adjustment is found to be related to the price adjustment rather than the price reviewing stage. The most momentous explanations for not adjusting prices upwards or downwards rest on the cost-based pricing and the explicit contracts. The study finds an asymmetric influence of some of the price factors. In particular, the cost factors are found to be decisive in invoking a price increase rather than a price decrease.

scholarly journals A Flexible Sample Selection Model: A Gtl-Copula Approach

2012 ◽  
Author(s):  
Takuya Hasebe ◽  
Wim Vijverberg
Keyword(s):  
Sample Selection ◽  
Selection Model ◽  
Sample Selection Model ◽  
Copula Approach

scholarly journals A Sample Selection Model with Skew-normal Distribution

2015 ◽  
Vol 43 (1) ◽  
pp. 172-190 ◽  
Author(s):  
Emmanuel O. Ogundimu ◽  
Jane L. Hutton
Keyword(s):  
Normal Distribution ◽  
Sample Selection ◽  
Selection Model ◽  
Skew Normal Distribution ◽  
Sample Selection Model ◽  
Skew Normal

The Job Satisfaction Determinants using Sample Selection Model

2021 ◽  
Vol 23 (1) ◽  
pp. 115-124
Author(s):  
Jangsik Cho
Keyword(s):  
Job Satisfaction ◽  
Sample Selection ◽  
Selection Model ◽  
Sample Selection Model
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