Abstract:The primary aim of the paper is an analysis of the relationships between growth, innovation and subsidies based on a large fi rm-level data set in the period [2004][2005][2006][2007]. The novelty of the approach lies in linking data from fi nancial statements with data from innovation surveys of the Czech Statistical. Innovation activities of fi rms are modelled as a four stage model (CDM) which allows studying several interrelated questions while controlling for simultaneity and for causality problem. In the fi rst two stages determinants of decision to innovate and consequent innovation investment are separated. In the third stage innovation input (R&D investment) is linked to innovation output, and fi nally, in the fourth stage it is determined how the productivity of fi rm is related to its innovation activities.Our analysis proved that innovation input signifi cantly increases innovation output, with increasing fi rm´s size, however, ceteris paribus, the innovation output is decreasing. This means that bigger fi rms are less effi cient in transforming the innovation input into output. More importantly, our analysis shows that access to subsidies has signifi cant, yet negative infl uence on innovation output. This result may throw a shadow on the effi ciency of supported fi rms and have some implications for competition policy.

Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.

The Czech Republic (or Czechia) is facing the second wave of COVID-19 epidemic, with the rate of growth in the number of confirmed cases (among) the highest in Europe. Learning from the spring first wave, when many countries implemented interventions that effectively stopped national economics (i.e., a form of lockdown), political representations are now unwilling to do that again, at least until really necessary. Therefore, it is necessary to look back and assess efficiency of each of the first wave restrictions, so that interventions can now be more finely tuned. We develop an age-structured model of COVID-19 epidemic, distinguish several types of contact, and divide the population into 206 counties. We calibrate the model by sociological and population movement data and use it to analyze the first wave of COVID-19 epidemic in Czechia, through assessing effects of applied restrictions as well as exploring functionality of alternative intervention schemes that were discussed later. To harness various sources of uncertainty in our input data, we apply the Approximate Bayesian Computation framework. We found that (1) personal protective measures as face masks and increased hygiene are more effective than reducing contacts, (2) delaying the lockdown by four days led to twice more confirmed cases, (3) implementing personal protection and effective testing as early as possible is a priority, and (4) tracing and quarantine or just local lockdowns can effectively compensate for any global lockdown if the numbers of confirmed cases not exceedingly high.

We assess the ability of health insurance plans with gatekeeping restrictions to control the utilization of medical care through their inuence on the choice of the initial provider. Empirical results are based on the individuallevel utilization panel data from 2001-2006 Medical Expenditure Panel Survey. We nd only small dierences between the initial provider chosen by individuals enrolled in gatekeeping and non-gatekeeping plans. This, together with the fact that within gatekeeping plans, 21 percent of patients self-refer to specialists, imply that the intended cost-containment eect of gatekeeping, namely restricting the utilization of specialty care, is surprisingly weak. AbstraktV na²í práci hodnotíme efektivitu plán• zdravotního poji²t¥ní v USA za pouºití tzv. "gatekeeping" restrikcí (t.j. s restrikcí primárního poskytovatele zdravotní pé£e na vybraného v²eobecného léka°e) p°i regulaci poptávky a následné spot°eby zdravotní pé£e. Na²e zji²t¥ní jsou zaloºena na analýze dat z Výb¥rového panelového ²et°ení výdaj• na zdravotní pé£i -Medical Expenditures Panel Survey, provedeném na reprezentativním vzorku americké populace v letech 2001-2006. Výsledky analýzy ukazují, ºe p°i výb¥ru poskytovatele prvotního kontaktu s zdravotní pé£í je mezi poji²t¥nci s a bez "gatekeeping" restrikcí jen malý rozdíl, coº je p°ekvapující zejména vzhledem k explicitnímu zam¥°ení této restrikce. Taktéº jsme zjistili, ºe skoro 21 procent pacient• s "gatekeeping" restrikcí, kte°í by jako prvního m¥li kontaktovat svého v²eobecného léka°e a ºádat jeho doporu£ení, nav²tíví p°ímo specialistu. Z t¥chto zji²t¥ní vyplývá, ºe zamý²lený efekt této restrikce, tedy regulace vyuºívání specialist•, je p°ekvapiv¥ slabý.