V krizi smisla tiči misel

Dear all,

this post will be in English. It is related to some of the fascinating material/data I’m working with for an article with Marilena Vecco and Simeng Chang – the data are based on the Artlinkart data encompassing what should be all the exhibitions of contemporary art in Chinese museums and galleries in the period 1989-2015.

Although still very preliminary and prone to mistakes, here is at least some visual information on the development of this system over the years – the work was done in Pajek software, it is a so-called two-mode (temporal) network, where on the left of its picture are the museums and galleries and on the right the exhibiting artists/artist groups.

I hope it will provide you some pleasure in observing – again, please note the article is still in preliminary phase.

Year 1989:

Year 1990:

Year 1996:

Year 1997:

Year 1998:

Year 2000:

Year 2001:

Year 2002:

Year 2003:

Year 2004:

Year 2005:

Year 2006:

Year 2007:

Year 2008:

Year 2009:

Year 2010:

Year 2011:

Year 2012:

Year 2013:

Year 2014:

Year 2015:

Nekaj začetnih del s področja MIMIC (Multiple Indicators Multiple Causes, t.j. “multipli kazalniki, multipli prediktorji/vzroki”) modelov, literatura se bo verjetno dodajala tudi sprotno. Modele uporabljam pri enem člankov, kjer sem soavtor, mislim pa, da na področju manjka ustrezen pregled dosedaj opravljenih del – v vsakem primeru pa bo tole služilo vsaj za lastno evidenco.

Aigner, D., Schneider, F., & Damayanti G. (1988). Me and my shadow: estimating the size of the US hidden economy from time series data. V: W. A. Barnett, E. R. Berndt in H. White (eds.), Dynamic econometric modeling, Cambridge (Mass.): Cambridge University Press, pp. 224-243.
Dell’Anno, R., & Schneider, F. (2003). The shadow economy of Italy and other OECD countries: What do we know? Journal of Public Finance and Public Choice, 21(2-3), 97-120.
Frey, B.S., & Weck, H. (1983). Estimating the Shadow Economy: A ‘Naive’ Approach. Oxford Economic Papers, 35, 23-44.
Frey, B.S., & Weck-Hannemann, H. (1984). The hidden economy as an “unobserved” variable. European Economic Review, 26(1), 33-53.
Giles, D.E.A. (1999). Measuring the hidden economy: Implications for econometric modelling. The Economic Journal, 109(456), 370-380.
Giles, D.E.A., & Tedds, L.M. (2002). Taxes and the Canadian Underground Economy. Canadian Tax Paper No. 106, Toronto: Canadian Tax Foundation.
Giles, D.E.A., Tedds, L.M., & Werkneh, G. (2002). The Canadian underground and measured economies: Granger causality results. Applied Economics, 34(4), 2347-2352.
Goldberger, A. S. (1972). Maximum Likelihood Estimation of Regressions Containing Unobservable Independent Variables. International Economic Review, XIII, 1-15.
Johnson, S., Kaufmann, D., & Zoido-Lobatón, P. (1998a). Regulatory discretion and the unofficial economy. The American Economic Review, 88(2), 387-392.
Johnson, S., Kaufmann, D., & Zoido-Lobatón, P. (1998b). Corruption, public finances and the unofficial economy. Discussion paper, The World Bank, Washington, D.C.
Joreskog, K., & Goldberger, A.S. (1975). Estimation of a Model with a Multiple Indicators and Multiple Causes of a Single Latent Variable. Journal of American Statistical Association, Vol. 70, 631-639.
Lippert, O., & Walker, M. (eds.) (1997). The Underground Economy: Global Evidences of its Size and Impact. Vancouver: The Frazer Institute.
Mummert, A., & Schneider, F. (2002). The German shadow economy: Parted in a united Germany? Finanzarchiv, 58(3), 286-316.
Posey, C., Roberts, T.L., Lowry, P.B., & Bennett, R.J. (2015). Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modeling Technique: A Tutorial and an Annotated Example. Communications of the Association for Information Systems, 36, Article 11.
Schneider, F. (1994). Can the shadow economy be reduced through major tax reforms? An empirical investigation for Austria. Supplement to Public Finance/ Finances Publiques, 49, 137-152.
Schneider, F. (1997). The shadow economies of Western Europe. Economic Affairs, 17(3), 42-48.
Schneider, F. (2003). The shadow economy. V: C.K. Rowley in F. Schneider (eds.), Encyclopedia of Public Choice, Dordrecht: Kluwer Academic Publishers.
Schneider, F. (2005). Shadow economies around the world: what do we really know? European Journal of Political Economy, 21(3), September, 598-642.
Tanzi, V. (1999). Uses and abuses of estimates of the underground economy. The Economic Journal, 109(456), 338-340.
Thomas, J.J. (1992). Informal Economic Activity. LSE Handbooks in Economics, London: Harvester Wheatsheaf.
Weck, H. (1983). Schattenwirtschaft: Eine Möglichkeit zur Einschränkung der öffentlichen Verwaltung? Eine ökonomische Analyse. Frankfurt/Main: Lang.
Zellner, A. (1970). Estimation of Regression Relationship Containing Unobservable Independent Variables. International Economic Review, 11, 441-54.

Nekaj novosti – zadnja številka Journal of Econometrics, šest prispevkov, ki so predstavljeni tukaj, z nekaj dodanimi razmisleki o uporabi na področju kulturne ekonomike.

1) Tim Bollerslev, Andrew J. Patton, Rogier Quaedvlieg: “Exploiting the errors: A simple approach for improved volatility forecasting” (članek v celoti najdete tukaj, podobno kot članka pod 2 in 4 bi ga bilo koristno uporabiti pri ekonometrični analizi umetnostnih trgov).

2) Xin Jin, John M. Maheu: “Bayesian semiparametric modeling of realized covariance matrices” (članek v celoti najdete tukaj).

3) Li Gan, Qi Li: “Efficiency of thin and thick markets” (članek v celoti najdete tukaj, fantastično bi bilo search and matching modele uporabiti tudi pri analizi trgov dela v umetnosti, verjetno v kratkem spišem kaj na to temo).

4) Aurore Delaigle, Alexander Meister, Jeroen Rombouts: “Root-T consistent density estimation in GARCH models” (članka v celoti ne najdete na spletu, je pa tam dostopna večina prispevkov prvopodpisane avtorice).

5) H. Peter Boswijk, Giuseppe Cavaliere, Anders Rahbek, A.M. Robert Taylor: “Inference on co-integration parameters in heteroskedastic vector autoregressions” (članek v celoti najdete tukaj, v kulturni ekonomiki bi ga bilo zanimivo uporabiti pri inferenci v razmerju zaposlenosti in javnega financiranja kulture).

6) Seojeong Lee: “Asymptotic refinements of a misspecification-robust bootstrap for GEL estimators” (članek v celoti najdete tukaj, na probleme napačne specifikacije GMM modelov naletite kar pogosto pri uporabi denimo sistemskih GMM cenilk, s katerimi se v kulturni ekonomiki srečate pri analizi dejavnikov, ki vplivajo na javne proračune za kulturo – glej denimo Srakar in Tóth 2013;2014;2015; ali denimo pri ex-post preverjanju ekonomskih učinkov, glej denimo Slabe-Erker in Srakar 2015).

… je odlično in preprosto statistično programsko orodje, ki si ga vsak lahko prosto naloži tule.

Nekaj več o orodju v zadnjem prispevku Davea Gilesa.

O novi knjigi Imbensa in Rubina lahko preberete tale zapis na blogih Svetovne banke.

Najbolj in najmanj učinkoviti javni zavodi na področju kulture glede na pridobivanje sredstev na trgu oz. “iz drugih virov” (vstopnine, članarine, sponzorstva, donacije, itd.).

V analizo so bili vključeni le tisti zavodi, ki jih redno financira Ministrstvo za kulturo.

“Dear Mr. Srakar!
We are glad to inform you that your paper was included in the program of the II Int. Conference ‘Modern Econometric Tools and Applications’, EC2015.”

Na srečo je bilo takšnih sporočil v preteklih mesecih v izobilju. Je pa to prva čisto ekonometrična konferenca, na katero sem bil sprejet. Čeprav zaradi udeležbe na neki drugi, v italijanski Ferrari na temo Public Policies in Financial Crisis, sem ne bom šel, vseeno koristno potrdilo, da delam dobro in da je treba po tej poti tudi naprej.

Tole je v osnovi povzetek članka, ki je bil sprejet.

Revenue Efficiency of Slovenian Public Institutions in the Field of Culture: A Comparison of Estimators

In Slovenia, the reform of the public sector in culture has been widely debated in the past years. Yet, so far no study has been made to empirically estimate whether the institutions are efficient and where are the key empirical problems in their efficiency. Our study addresses this void by using data envelopment analysis (DEA) approach to estimate the revenue efficiency of public institutions in the field of culture. To this end, we use and compare four estimators of DEA and their empirical properties: the “regular” DEA estimator; the naïve bootstrap estimator; the commonly used two-step smoothed bootstrap correction for stochastic analyses following Simar and Wilson; and, finally, the recently proposed Zervopoulos correction. Our results show that although revenue efficiency of public institutions in culture in Slovenia was dropping in past few years, there is no statistical evidence that they were indeed revenue-inefficient. We identify the key institutions that were most revenue-efficient and revenue-inefficient and, finally, explore the characteristics most influencing the revenue efficiency and, using latent class approach, discern two clearly distinct groups of institutions in the analysis and determine their differences. We conclude by policy reflection of the findings and suggestions for future research.

V zadnjih dneh je kot šok (vendar bolj za tiste, ki stanja niso poznali) udarila novica, da se slovenski proračun za kulturo znižuje še za okrog 20 milijonov EUR. Pustimo zdaj politiko tega, ker bo ta rez pustil globoke posledica za področje kulture, morda celo odnesel ministrico.

Prispevek tega zapisa k debati o slovenskem proračunu za kulturo pa je spodnji graf, uporabljen je bil Hodrick-Prescottov (HP) filter, z parametrom lambda=6.25. Ključno sporočilo grafa je verjetno, da so rezi v kulturo postali nekaj običajnega in po letu 2013 niso več zgolj posledica gospodarske krize. Vprašanje torej ni več, kako izničiti učinke krize, pač pa kako obrniti trend. Slednjega ne podpirajo podatki drugih držav – v času začetka krize in v sedanjem času so nekatere države doživele ostre reze (zanimivo: Velika Britanija, ter seveda države PIIGS – Italija, Španija, Irska, Portugalska), nekatere pa manjše oz. sploh nikakršnih in še kar rastejo (zlasti skandinavske države, tudi Belgija, Francija, v zadnjem času celo Madžarska in Bolgarija, pa Malta, Avstrija, itd.). Govorim o podatkih za sektor »Cultural services«, založništvo in predvajanje radijskih in TV programov nista vključena. Tudi za vse omenjene države smo ponovili postopek – HP filter za generalno in centralno raven javnih financ v kulturi, morda kdaj dodam še vse te rezultate.

Rešitev je torej verjetno res v resnih reformah, ki bi popravile kondicijo sistema in dvignile trendno raven strukturnega proračuna. Tistim, ki vas makroekonometrija zanima bolje, pa zelo priporočam odličen nedaven članek Phillipsa in Jina, kjer sta pokazala, da HP filter ni odporen proti problemom nestacionarnosti časovnih vrst.

Tole bo v kratkem krenilo v objavo.

Health of elderly precarious workers: econometric evidence from SHARE

Andrej Srakar
Institute for Economic Research, Ljubljana and Faculty of Economics, University of Ljubljana, Slovenia, andrej.srakar@ier.si

Abstract

In the article we present the results of SHARE wave 4 dataset’ based analysis about the elderly precarious workers and their health situation. Although the topic of precarious work (not the least among the elderly people) is becoming ever more important in Europe, there has been very little empirical and econometric evidence on the issue. We try to remedy for this void by presenting a detailed econometric analysis to study the main question: »Are elderly precarious workers really discriminated in terms of worse health as compared to the elderly employed people?« We firstly present some basic descriptive statistics and bivariate analysis results and tests, followed by econometric results using finite mixture models to appropriately model the heterogeneity among precarious workers. Our results show that, contrary to the expectations, the health of elderly self-employed workers is generally in no way inferior to the health of elderly employees. Problems in health of the precarious workers emerge only when the analysis changes focus to those who are neither employed nor self-employed, while engaged in paid work (»real« precarious people). There are visible differences in the health status of employees and »real« precarious workers in almost all the indicators and in the vast majority of the 16 countries included. Nevertheless, our analysis points to a large heterogeneity among precarious workers which fall into two broad groups which we label »precarious workers for money reasons« and »precarious workers because of »active ageing« reasons«, with clearly visible differences among the two groups in income and health. We also study the effects of social exclusion on health status of precarious workers controlling for apparent endogeneity in the model. We conclude by policy implications of the analysis and paths for future research.

Keywords: precarious workers, older people, self-employed, health indicators, finite mixture models, endogeneity

JEL: I10, I18, I14, C36, C46

Spodnja slika pove več kot besede. Kar smo oz. sem odkril je to, da nekoliko čudne rezultate, ki jih dobite, ko vzamete starejše (od 50 let) prekarne delavce v splošnem in primerjate njihovo zdravstveno stanje s stanjem starejših zaposlenih, razloži heterogenost v populaciji. Na eni strani imate revne, zdravstveno prikrajšane starejše “prekarce”, ki sem ji sam v analizi dal ime »precarious workers for money reasons«; na drugi strani pa dohodkovno ne posebej zamejene in tudi zdravstveno čisto nič prikrajšane “prekarce”, ki jih imenujem »precarious workers because of »active ageing« reasons«. Na spodnji sliki sta prikazani obe distribuciji, uporabili smo modele “končnega mešanja” (angl. finite mixture), na x osi je število kroničnih bolezni. Jasno je vidna prva skupina v modrem in druga v rdečem. Zanimivo je še to, da je druga skupina bistveno bolj skoncentrirana in ne kaže kakšnih koli posebnih težav z zdravjem, pri prvi je slika precej drugačna.

Članek je bil v prvi obliki predstavljen pred nekaj tedni tukajle.

Nekaj videov s področja ekonometrije, najdete jih tudi tukaj, urejeni so po letih.

2015
Econometrics of Matching (Econometrics Journal Special Session)

Chaired by Jaap Abbring, Tilburg University

Estimating Transfer Frictions in the Marriage Market:
By Alfred Galichon; Sciences Po
Presented by: Alfred Galichon, Sciences Po

Multidimensional Skills, Sorting and Human Dimension Calculation
By Jeremy Lise, UCL and IFS and
Fabien Postel-Vinay, UCL, IFS and SciencePo
Presented by Jeremy Lise. UCL and IFS

2014
Large Dimensional Models

Monday 7th April 2014 Including: Dynamic Factors and Volatilities in Panel Data by Matteo Barigozzi, London School of Economics and Marc Hallin, ECARES, Universite Libre de Bruxelles and ORFE, Princeton University and Large Panel Test of Factor Pricing Models by Jianqing Fan, Princeton University with Yuan Liao and Jiawei Yao.

2013
Heterogeneity

Chaired by Richard Smith, University of Cambridge
Speakers: Yuichi Kitamura, Yale and Stephane Bonhomme, CEMFI

Discussant: Lars Nesheim, UCL

2012
(RES 2012 Annual Conference Cambridge – Special Session=

Econometrics of Forecasting

Raffaella Giacomini (UCL) : Economic Theory and Forecasting
Slide Pages: 1 – 18
Timing: 49sec – 35min

Siem Jan Koopman (Free University Amsterdam) : Likelihood-Based Dynamic Factor Analysis for Measurement and Forecasting
Slide Pages: 19 – 24
Timing: 35min 37sec – 73min

Brendan McCabe (University of Liverpool) : Discussion
Slide Pages: 24 – 25
Timing: 73min 14sec – 84min 37sec

2011
(RES 2011 Annual Conference Royal Holloway – Special Session)

Nonparametric Identification: Current Issues and Problems

Rosa L Matzkin (UCLA) : Identification in nonseparable models using shape restrictions
Slides: 35 – 155
Timing: 14min – 47min

Andrew Chesher (UCL) : Identification with multidimensional heterogeneity
Slides: 119 – 183
Timing: 48min – 1h 26min

Victor Chernozhukov (MIT) : Discussion
Slides: 186 – 202
Timing: 1h 27min – 1h 40min