Count Your Hours: Returns to Education in Poland

We show how significant may be the difference in the estimated returns to education in Poland conditional on the measure of wages used and the estimation approach applied. Combining information from two different Polish surveys from 2005 and taking advantage of the Polish microsimulation model (SIMPL) we demonstrate how different the results can be depending on whether we use net or gross, and monthly or hourly wages, and show how important selection correction is for the conclusion. While there are several papers examining the wage equation in Poland, so far none of them has provided a comprehensive analysis of the effects of using different methods and the issue of selection-correction in the estimation of the wage equation in Poland has not been examined in detail. Annual rates of return to university education for men vary from 6.7% to 9.7% and for women from 8.0% to 13.4% when we compare results using net monthly wages without correcting for labor market selection to those from a selection corrected specification using gross hourly wages. We also demonstrate that simple linear estimation performs relatively well for men in comparison to our preferred selection corrected estimation, while using family demographics as exclusion restrictions seems to be the "second best" in the case of the wage equation estimation for women.returns to education, wage equation, selection models, instrumental variables

Count your hours: returns to education in Poland

We show how significant may be the difference in the estimated returns to education in Poland conditional on the measure of wages used and the estimation approach applied. Combining information from two different Polish surveys from 2005 and taking advantage of the Polish microsimulation model (SIMPL) we demonstrate how different the results can be depending on whether we use net or gross, and monthly or hourly wages, and show how important selection correction is for the conclusion. While there are several papers examining the wage equation in Poland, so far none of them has provided a comprehensive analysis of the effects of using different methods and the issue of selection-correction in the estimation of the wage equation in Poland has not been examined in detail. Annual rates of return to university education for men vary from 6.7% to 9.7% and for women from 8.0% to 13.4% when we compare results using net monthly wages without correcting for labor market selection to those from a selection corrected specification using gross hourly wages. We also demonstrate that simple linear estimation performs relatively well for men in comparison to our preferred selection corrected estimation, while using family demographics as exclusion restrictions seems to be the second best in the case of the wage equation estimation for women

Comrades in the Family? Soviet Communism and Informal Family Insurance

We study the effect of exposure to communism (EC), a political-economic regime based on collectivist planning, on preferences for family supports, which we refer to as 'informal family insurance'. We exploit both cross-country and cohort variation in EC in a large sample of Central and Eastern European countries (CEEC). Against the backdrop that 'communism gives rise to the abolition of the family', we find robust evidence that EC strengthens the preference for family insurance which coexists with a stronger preference for social insurance. We find a six per cent increase in preferences for care to older parents and a four per cent increase in preferences for support to pre-school children and financial support to adult children. These effects are explained by the erosion of both generalized trust and the lower confidence in public institutions, suggesting that (raising uncertainty and adversity during) communism increased the demand for all types of available insurance

How inheriting affects bequest plans

We present and test the idea that bequest planning is linked with the experience of inheriting. We consider “a family tradition of bequeathing” as a channel through which the intention to bequeath is moulded by and is positively correlated with the experience of inheriting. Using data from the Survey on Health, Ageing and Retirement in Europe (SHARE), we find that the experience of inheriting enhances the intention to bequeath, independently of the positive impact of wealth. We also find that the expectation of inheriting has a positive impact on the intention to bequeath, controlling for the expected increase in wealth on account of future inheritances

Long-Lasting Effects of Communist Indoctrination in School: Evidence from Poland

Education can serve skill formation and socialisation goals both of which are conducive to desirable economic outcomes. However, the political manipulation of the school curricula can give rise to indoctrination effects with counterproductive welfare consequences on its pupils. This paper studies the effects of communist indoctrination on human capital accumulation and labour market outcomes in Poland. We document that the reduction of Marxist-Leninist indoctrination in school curriculum after 1954 exerted long-lasting beneficial effects. Unlike in East Germany, the school reform after the fall of communism in Poland had negligible effects on human capital and labour market outcomes. Our results are in contrast, explained by the ideological content of the school curriculum in the Polish education system

CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area

<p><strong>Introduction</strong></p> <p>Data here are for the Greater Warsaw area, Poland originating in the CoMobility project. It contains data relevant to traffic activity, emissions, air quality and related health studies in the area. Files contain road properties along with traffic volume and rushhour delays as well as emissions of NOx, NO2 and PM from road traffic on individual road segment level. Also 500m gridded surface air concentrations are included for PM2.5 and PM10, and for NOx, NO2.</p> <p><strong>Data production</strong></p> <p>Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (<em>Model Transportowy Aglomeracji Warszawskiej </em>in Polish). It was developed based on the 2015 comprehensive travel survey in Warsaw and it is the main strategic transport model for the Greater Warsaw area, revised most recently in 2019. </p> <p>The NERVE model (Grythe et al, 2022), developed by NILU, provides detailed estimates of greenhouse gas and air pollutant emissions specifically from road traffic. Using a bottom-up approach, it combines data from regional traffic model (RTM), vehicle fleet composition, and emission factors from the Handbook Emission Factors for Road Transport (HBEFA). NERVE can be set up to calculate emissions at various levels, including road link, municipality, or national levels. It is a tool researchers and policymakers use this model for environmental assessments, policy decisions, and constructing different emission scenarios. Its high level of detail makes it valuable not only for practical emissions estimation but also as a research tool. Emissions for other sources came from the Central Emission Database by the Environmental Protection - National Research Institute (IEP-NRI) in Poland (Gawuc et al., 2021). The background concentrations were taken from the Copernicus Atmospheric Monitoring Services (CAMS) ensemble forecast for 2019 (Marécal et al., 2015)</p> <p>The EPISODE model (Hamer et al. 2020), developed by NILU, is an Eulerian urban dispersion model designed to address the need for an accurate urban air quality model in support of policy, planning, and air quality management. EPISODE operates as a 3D grid model coupled with numerical weather prediction (NWP) data. It simulates dispersion from point and line sources to receptor points, with a focus on the photochemical production of ozone in urban areas. The model’s CityChem extension enhances its capabilities for complex pollution sources, incorporating numerical chemistry solvers, sub-grid photochemistry, and a simplified street canyon model. EPISODE serves as a valuable tool for understanding and managing air quality in urban environments.</p> <p><strong>Data files</strong></p> <p>The data on road traffic contains 60 084 road links that cover the Greater Warsaw area. The file input is a traffic file from the MTAW model and is processed and formatted with NREVE. The format is an ESRI shapefile with the following road parameters:</p> <p>“<em>DISTANCE</em>” -length of road segment in kilometers.</p> <p>“<em>CAPACITY</em>” -Hourly capacity of the road.</p> <p>“<em>SLOPE</em>” -Vertical gradientor slope of the road (in %)</p> <p>“<em>SPEEDLIM</em>” -Signed speed on the road (kilometers per hour)</p> <p>In addition there are traffic volume parameters;</p> <p>“<em>ADT_LIGHT</em>” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_HEAVY</em>” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_BUSES</em>” – Annual Daily Traffic, public transport buses average 2019.</p> <p>“<em>MRN_delay</em>” – delay during morning rush hour peak (%)</p> <p>“<em>EVE_delay</em>” – delay during evening rush hour peak (%)</p> <p>The files also contain the annual emissions:</p> <p>“<em>EM_NOx</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_ NO2</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_PM</em>” – 2019 annual emissions of NOx (gram).</p> <p>EPISODE output files for atmospheric concentration files are given on NetCDF file format.  Concentrations are given as annual average grid concentration for each of the components. In addition, 42 000  spatially spread out receptor points gives the 2 meter concentrations to allow for surface air concentration levels at individual point locations. Furthermore, these allows for downgridding concentrations to higher resolution.</p> <p>The source contribution files are from EPISODE and gives atmospheric concentration fields for NOx, PM10 and PM2.5 from individual sources. The individual sources are</p> <p><em>“RDU” </em>-Road dust (PM only)</p> <p><em>“EXT”</em> – Exhaust  (PM only)</p> <p><em>“TRA”</em> - Exhaust  (NOx only)</p> <p><em>“IND” </em>– Industry</p> <p><em>“RES”</em> – Residential</p> <p><em>“OTH”</em> – Other (all other sources within the domain combined )</p> <p><em>“BGC”</em> – Background (all sources outside the domain combined )</p> <p> </p&gt