Chapter 1 discusses the econometric pitfalls associated with the use of patent production functions to study the invention process. It then goes on to argue that a sound understanding of the invention process necessarily requires an understanding of the propensity to patent. The empirical analysis carried out in Chapter 1 seeks to explain the proportion of inventions patented – a potential metric for the propensity to patent – from an international sample of manufacturing firms. <p><p>Chapter 2 proposes a methodology to filter out the noise induced by varying patent practices in the R&D-patent relationship. The methodology explicitly decomposes the patent-to-R&D ratio into its components of productivity and propensity. It is then applied to a novel data set of priority patent applications in four countries and six industries.<p><p>Chapter 3 takes stock of the literature on the role of fees in patent systems while Chapter 4 presents estimates of the price elasticity of demand for patents at the trilateral offices (that is, in the U.S. Japan and Europe). The estimation of dynamic panel data models of patent applications suggests that the long-term price elasticity is about -0.30.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

This study estimates the price and income elasticity coefficients of electricity demand in the mining sector of South Africa for the period ranging from April 2006 to March 2019. A time varying parameter (TVP) model with the Kalman filter is applied to monitor the evolution of the elasticity estimates. The TVP model can provide a robust estimation of elasticities and can detect any outliers and structural breaks. The results indicate that income and price elasticity coefficients of electricity demand are lower than unit. The income elasticity of demand has a positive sign and it is statistically significant. This indicates that mining production – used as a proxy for mining income – is a significant determinant of electricity consumption in the mining sector. In its final state income elasticity is estimated at 0.15 per cent. On the contrary, price does not play a significant role in explaining electricity demand. In fact, the price elasticity coefficient was found to be positive which is contrary to normal economic convention. This lack of response is attributed mainly to the mining sector’s inability to respond, rather than an unwillingness to do so. A fixed coefficient model in a form of Ordinary Least Squares (OLS) is used as a benchmark model to estimate average price and income elasticity coefficients for the period. The results of the OLS regression model confirm that price does not play a significant role in explaining electricity consumption in the mining sector. An average price elasticity coefficient of -0.007 has been estimated. Income elasticity was estimated at 0.11 for the period under review. The CUSUM of squares test indicate that parameters of the model are unstable. The Chow test confirms 2009 as a breakpoint in the data series. This means that elasticity coefficients of electricity demand in the mining sector are time variant. Thus the OLS results cannot be relied upon for inference purposes. The Kalman filter results are superior. This study cautions policy makers not to interpret the seeming lack of response to price changes as an indication that further prices increases could be implemented without hampering electricity consumption in the sector. Furthermore, it recommends that the electricity pricing policy should take into account both the negative impacts of rapid price increases and the need to invest in long-term electricity infrastructure in order to improve the security of energy supply. A long term electricity price path should be introduced in order to provide certainty and predictability in the price trajectory. This would allow all sectors of the economy sufficient time and space to make investment and operational decisions that would have the least adverse effects on economic growth and job creation. / Economics / M. Com. (Economics)

Indiana University-Purdue University Indianapolis (IUPUI) / Accurate estimation of alcohol price elasticity is important for policy analysis – e.g.., determining optimal taxes and projecting revenues generated from proposed tax changes. Several approaches to specifying and estimating the price elasticity of demand for alcohol can be found in the literature. There are two keys to policy-relevant specification and estimation of alcohol price elasticity. First, the underlying demand model should take account of alcohol consumption decisions at the extensive margin – i.e., individuals' decisions to drink or not – because the price of alcohol may impact the drinking initiation decision and one's decision to drink is likely to be structurally different from how much they drink if they decide to do so (the intensive margin). Secondly, the modeling of alcohol demand elasticity should yield both theoretical and empirical results that are causally interpretable. The elasticity estimates obtained from the existing two-part model takes into account the extensive margin, but are not causally interpretable. The elasticity estimates obtained using aggregate-level models, however, are causally interpretable, but do not explicitly take into account the extensive margin. There currently exists no specification and estimation method for alcohol price elasticity that both accommodates the extensive margin and is causally interpretable. I explore additional sources of bias in the extant approaches to elasticity specification and estimation: 1) the use of logged (vs. nominal) alcohol prices; and 2) implementation of unnecessarily restrictive assumptions underlying the conventional two-part model. I propose a new approach to elasticity specification and estimation that covers the two key requirements for policy relevance and remedies all such biases. I find evidence of substantial divergence between the new and extant methods using both simulated and the real data. Such differences are profound when placed in the context of alcohol tax revenue generation.

In 2022, food prices increased rapidly, prompting this investigation into how the price shock has impacted consumer behavior. Previous studies have found that price shocks affect low- income households with children the most. A switch to more calorie-dense food and a decreased consumption of snacks. This study aims to estimate the price elasticity of various food products and to identify how the elasticities differ among various types of households. The elasticities are calculated using OLS regression on receipt data from Coop. Results show that household variables, including income level, presence of children, shopping location, and organic product preferences, significantly impact the price elasticity of food products. Low-income households without children, living outside major cities and their suburbs, have a higher price elasticity, meaning these shoppers are more sensitive to price changes. The same tendencies were observed for all products even though the exact parameters could only be proven for a third of the products. The findings have important implications for understanding how price changes affect consumer behavior and can inform food policy decisions. / Under 2022 ökade livsmedelspriserna kraftigt, vilket inspirerade denna undersökning av hur prischocken har påverkat konsumenternas beteende. Tidigare studier visar att prischocker har störst påverkan på låginkomsthushåll med barn. Även en övergång till mer kaloririk mat och en minskad konsumption av snacks har observerats. Syftet med denna studie är att uppskatta priselasticiteten för olika livsmedel och identifiera hur elasticiteten skiljer sig åt mellan olika typer av hushåll. Elasticiteterna beräknas med hjälp av en OLS-regression på kvittodata från Coop. Resultaten visar att hushållsvariabler, inklusive inkomstnivå, barn i hushållet, varans inköpsplats och val av ekologiska produkter, påverkar priselasticiteten för livsmedel markant. Hushåll med låg inkomst utan barn, som bor utanför större städer och deras förorter, har en högre priselasticitet, vilket innebär att dessa kunder är mer priskänsliga. Samma tendenser observerades för alla produkter även om de exakta parametrarna endast kunde bevisas för en tredjedel av produkterna. Resultaten har viktiga implikationer för förståelsen av hur prisförändringar påverkar konsumentbeteenden och kan även informera livsmedelspolitiska beslut.

This master's thesis seeks to determine the price elasticity of the Swedish housing supply through a vector error-correction model. The elasticities are estimated on a municipal, county and national level using data for the period 1992-2021. Furthermore, this study aims to find key determinants to explain the regional variations of housing supply elasticities. Overall, the results obtained indicate that the price elasticity of housing supply remains relatively inelastic regardless of the level. Only a small quantity of municipalities exhibited an elastic housing supply, those were Härjedalen, Krokom and Östra Göinge. The supply elasticity was estimated to be 0.074 on a national level. On a county level the elasticities varied between -0.107 and 0.461. The responsiveness of housing supply was found to be determined by the size of population and by land use regulations such as building permits, which is in line with previous empirical studies. Neither population growth, population density, house price or local tax rate were found to be significantly correlated with the obtained supply elasticities. The results imply that the housing supply elasticity varies substantially across counties and municipalities. Moreover, the study shows that the responsiveness of housing supply to fluctuations in price is low, which can serve as a potential indicator for future development of housing prices. / Denna masteruppsats syftar till att bestämma priselasticiteten för det svenska bostadsutbudet genom en vektor error-correction model. Elasticiteterna beräknades på kommunal-, läns- och riksnivå med hjälp av data för perioden 1992-2021. Därutöver syftar denna studie till att hitta bestämningsfaktorer för att förklara de regionala variationerna i bostadsutbudets priselasticitet. Sammantaget visade de erhållna resultaten att utbudselasticiteterna till övervägande del är oelastiska oavsett nivå. Några enstaka kommuner visade på ett elastiskt bostadsutbud, däribland Härjedalen, Krokom och Östra Göinge. Utbudselasticiteten uppskattades till 0.074 på nationell nivå. På länsnivå varierade priselasticiteterna för bostadsutbudet mellan -0.107 och 0.461. Vidare har responsiviteten hos bostadsutbudet visat sig bestämmas av befolkningsmängd och av reglering av markanvändning såsom bygglov, vilket är i linje med tidigare empiriska studier. Varken befolkningstillväxt, befolkningstäthet, bostadspris eller lokal skattesats har visat sig vara signifikant korrelerade med de erhållna utbudselasticiteterna. Resultaten tyder på att bostadsutbudets elasticitet varierar kraftigt mellan län och kommuner. Dessutom visar studien att bostadsutbudets responsivitet till följd av prisfluktuationer är lågt, vilket kan fungera som en potentiell indikator för den framtida utvecklingen av bostadspriser.