Table 5.C.1: Probit estimation results for other currencies.
the black market for euros
premia w.r.t. Bloomberg premia w.r.t. NBB
(1) (2) (3) (4)
constant -1.605*** -4.684*** -3.590*** -4.449***
(0.221) (1.709) (0.511) (1.537)
7d premium 1.992** 7.294*** 7.624*** 9.445**
(0.981) (2.110) (1.677) (4.679)
foreign reserve assets -1.17×10−3*** 9.07×10−4
(3.26×10−4) (8.98×10−4)
log likelihood -59.046364 -45.384236 -43.853405 -40.122483
observations 198 198 198 198
the black market for Russian rubles
constant -1.638*** -6.324** -4.252*** -6.026
(0.284) (3.170) (0.869) (4.265)
7d premium 3.349*** 7.227*** 7.927*** 7.819**
(0.934) (1.982) (1.966) (3.658)
7d volume -3.75×10−7*** -4.12×10−7** -3.49×10−7*** -3.66×10−7**
(6.28×10−8) (1.59×10−7) (7.33×10−8) (1.76×10−7)
7d range 0.090* 0.115 0.057 0.112
(0.049) (0.304) (0.076) (0.350)
refinancing rate 0.188*** 0.011
(0.044) (0.092)
balance of payments -9.59×10−4*** -1.49×10−4
(2.23×10−4) (2.19×10−2)
foreign reserve assets -1.28×10−3*** 5.31×10−4
(2.70×10−4) (5.81×10−4)
log likelihood -45.676 -35.369 -33.526 -33.008
observations 198 198 198 198
Note: All definitions of variables and standard errors are analogous to Table 5.1, but we use data on the black markets for euros and Russian rubles instead. We have fewer observations than in Table 5.1 because activity in these markets begins later than in the market for US dollars.
Table 5.C.1 shows regressions analogous those in Table 5.1 using data on activity in
the black markets for euros and Russian rubles. Recall that there is less activity in these markets than in the black market for dollars. In contrast to Table 5.1, average premia on the seven preceding days are now the strongest and most robust predictor of upcoming devaluations. The signs of the coefficients on volume and range are the same as in Table 5.1, but they are not always statistically significant. Fundamental variables, which we add in (2) and (4), do. again, not affect the predictive power of black market activity much.
May24 Sep21 Oct12
−20020406080100120
May1 Jul1 Sep1 Nov1
Date (2011)
USD EUR RUB
Black Market Premium with Respect to NBB
Figure 5.C.1: Black market premia in percent with respect to NBB
Appendix 5.D Unit root tests
Table 5.D.1: Testing for stationarity/unit root.
Level statistics p-value
BM BYR USD -1.85 0.35
BM BYR EUR -1.65 0.46
BM BYR RUB -2.25 0.19
NBB BYR USD -0.53 0.88
NBB BYR EUR -0.54 0.88
NBB BYR RUB -0.56 0.88
Bloom BYR USD -0.65 0.86 Bloom BYR EUR -0.78 0.82 Bloom BYR RUB -0.51 0.89
Note: Augmented D-F test.
Details of the LM test proposed by (Lee & Strazicich 2003):
The test proposed by (Lee & Strazicich 2003) is a Lagrange multiplier unit root test with two structural breaks where the break dates are endogenously determined. Their test allows for changes in the level and/or the trend under both the null and alternative hypotheses. We consider the version with changes in both level and trend. Under the null hypothesis, the series has a unit root, and under the alternative, the series is trend stationary. In both cases, there are two changes in the levels and trends. Formally, the null and alternative hypotheses can be written as follows:
H0 : yt=µ0+d1B1t+d2B2t+d3D1t+d4D2t+yt−1+ν1t (5.1) H1 : yt=µ1+γt+d1D1t+d2D2t+d3DT1t+d4DT2t+ν2t. (5.2) whereν1tand ν2tare stationary errors. Let TBj forj = 1,2 denote the time period when a break occurs;Bjt= 1 fort=TBj+ 1,j= 1,2, and 0 otherwise. Djt= 1 fort≥TBj+ 1, j= 1,2, and 0 otherwise. DTjt=t−TBj fort≥TBj+ 1, j= 1,2, and 0 otherwise.
Series k TB1 TB2 Test-stat. λ= (TB1/T, TB2/T) Bloom BYR USD 0 25-May 20-Sep -4.26 (0.2, 0.7) Bloom BYR EUR 0 25-May 19-Sep -4.38 (0.2, 0.7) Bloom BYR RUB 7 23-May 19-Sep -5.14 (0.1, 0.7)
Note: k is the optimal number of lagged first-difference terms included in the unit root test to correct for serial correlation. TB1 and TB2 are the estimated break dates. λis the location of the breaks within the sample for which critical values are determined. Critical values are reported in Table 1 of (Strazicich et al. 2004). ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
Figure 5.D.1 shows that the two endogenously estimated structural breaks coincide with the respective devaluations in the NBB’s and Bloomberg’s official exchange rate data.
300040005000600070008000900010000
Figure 5.D.1: Exchange rates and estimated break points: Dashed vertical lines are estimated break points. Solid vertical lines are the important dates; May 24: first devaluation, September 21: second devaluation, October 12: final devaluation.
Appendix 5.E Tests for Granger causality
Details on detrending:
In order to apply the Granger causality test among exchange rate series, we need to detrend the series. For that, we use the results of the LM-Unit root test. For the series for which we rejected the unit root hypothesis, i.e. NBUSD, NBEUR, NBRUB, BUSD, BEUR and BRUB, we run the following regression:
Yt=m0+m1D1t+m2D2t+m3t+m4DT1t+m5DT2t+ǫt (5.3) The residuals from this regression, ˆεt, are detrended series of these series denoted by NBUSD*, NBEUR*, NBRUB*, BUSD*, BEUR* and BRUB*. The black market ex-change rates, BMUSD, BMEUR and BMRUB, are detrended differently since they are non-stationary. For these series we run the following regression:
∆Yt=m0+m1B1t+m2B2t+m3t+m4D1t+m5D2t+ǫt (5.4) By taking first differences, we deal with the unit roots and by regressing on the structural break dummies, we detrend the stationary first difference series. Again, the residuals are the detrended stationary series denoted by BMUSD*, BMEUR*, BMRUB*. In Figure 5.E.1, we plot the detrended series. For all graphs, the red, short-dashed lines are depen-dent variables from equation 5.3 or 5.4. Hence, for black market exchange rates, the red lines are first differences of the exchange rates, whereas for the other two market the red lines are the actual series. The green, long-dashed lines are the fitted series and the blue, solid lines are the residuals which we use for the following causality analysis.
−600
Figure 5.E.1:Detrended series: The regression equations are given in equations 5.3 and 5.4. The decision on which of these two models is used is made based on the results in Table 5.D.2. For stationary series equations 5.3 is used and for non-stationary series equations 5.4 is used.
Granger causality tests:
Table 5.E.1: Causality among black market exchange rates.
AIC LR
H0 F-Stat p-value k F-Stat p-value k
BMUSD ;BMEUR 17.61 0.00 3 3.75 0.00 17
BMEUR; BMUSD 5.95 0.00 1.26 0.23
BMUSD ;BMRUB 14.21 0.00 5 2.55 0.00 29
BMRUB; BMUSD 0.44 0.82 1.27 0.19
BMRUB; BMEUR 7.82 0.00 2 3.82 0.00 6
BMEUR; BMRUB 13.70 0.00 4.57 0.00
Note: AIC and LR are used to determine the optimal lag length. kis the optimal number of lagged. ; stands for “does not Granger cause.”
AIC LR
H0 F-Stat p-value k F-Stat p-value k
NBUSD; NBEUR 2.04 0.04 9 1.56 0.07 22
NBEUR; NBUSD 2.08 0.03 1.66 0.04
NBUSD; NBRUB 4.38 0.01 2 3.16 0.00 30
NBRUB; NBUSD 9.31 0.00 2.96 0.00
NBRUB ;NBEUR 1.20 0.26 22 0.83 0.48 3
NBEUR ;NBRUB 1.48 0.09 5.31 0.00
Note: AIC and LR are used to determine the optimal lag length. kis the optimal number of lagged. ; stands for “does not Granger cause.”
Table 5.E.3: Causality among Bloomberg exchange rates.
AIC LR
H0 F-Stat p-value k F-Stat p-value k
BUSD ;BEUR 0.19 0.99 22 0.19 0.99 22
BEUR; BUSD 1.75 0.03 1.75 0.03
BUSD ;BRUB 0.37 0.99 29 0.37 0.99 29
BRUB; BUSD 25.81 0.00 25.81 0.00
BRUB; BEUR 0.36 0.99 28 0.38 0.99 27
BEUR; BRUB 2.86 0.00 2.86 0.00
Note: AIC and LR are used to determine the optimal lag length. kis the optimal number of lagged. ; stands for “does not Granger cause.”
Table 5.E.4:Causality among black market and Bloomberg exchange rates for all currencies
LR
H0 F-Stat p-value k
Black Market USD ; Bloomberg USD 2.36 0.00 28 Bloomberg USD ; Black Market USD 0.97 0.52
Black Market EUR ; Bloomberg EUR 1.60 0.04 28 Bloomberg EUR ; Black Market EUR 2.12 0.00
Black Market RUB ; Bloomberg RUB 3.05 0.00 24 Bloomberg RUB ; Black Market RUB 1.19 0.26
Note: AIC and LR are used to determine the optimal lag length. k is the optimal number of lagged. ; stands for “does not Granger cause.”
Appendix 5.F Black market exchange rates across the country
Tables 5.F.1 to 5.F.2 show the distribution of implied volumes of the quotes across the six largest cities in Belarus. The black market for foreign exchange was thickest in Minsk.
The share of volume in Minsk is higher for dollar trades than for euros and Russian rubles.
Euros are traded more in cities in the west of the country like Brest, Russian rubles are traded more in cities in the east of Belarus like Gomel. As mentioned earlier, we dropped ads posted by corporations. Few ads were posted by corporations but these had high volumes. If we had included them, more than 99% of the volume would have been for ads where trades were supposed to take place in Minsk.
Table 5.F.1: Distribution of adds across cities.
city pop (2010) no. of quotes percent no. of quotes/pop
Minsk 1,834,200 123,552 89.3938 0.0674
Gomel 484,300 2,787 2.0163 0.0058
Brest 310,800 3,035 2.1958 0.0098
Grodno 328,000 2,155 1.5591 0.0066
Vitebsk 348,800 2,078 1.5034 0.0060
Mogilev 354,000 1,521 1.1004 0.0043
. . .
Belarus 9,503,807 138,221 100.00 0.0145 Note: All three currencies; entire period until January 25, 2012.
Table 5.F.2: Implied volume of quotes by currencies across cities.
Implied volume of quotes
city pop (2010) USD EUR RUB
(million USD) (million EUR) (million RUB)
Minsk 1,834,200 310.781 24.431 1,075.554
Gomel 484,300 3.505 0.392 44.494
Brest 310,800 4.112 0.687 13.221
Grodno 328,000 2.330 0.254 7.295
Vitebsk 348,800 2.996 0.546 47.690
Mogilev 354,000 2.009 0.158 13.751
. . .
Belarus 9,503,807 330.863 27.668 1,248.665
Note: Entire period until January 25, 2012.
In the following, we analyze the black market exchange rates for USD for six big cities.
The structure is similar to the Appendices 5.D and 5.E. First, we test for unit root using the ADF test. Then, to take care of structural breaks we apply the LM unit root test proposed by (Lee & Strazicich 2003). Lastly, using the detrended series, we apply the Granger causality test to find causal relationships between the series in these cities.
Table 5.F.3: Augmented D-F test results in big cities.
Minsk Gomel Brest Grodno Vitebsk Mogilev
statistics -1.72 -1.98 -1.67 -2.01 -1.91 -1.79
p-value 0.42 0.29 0.45 0.28 0.32 0.38
Note: Augmented D-F test results for stationarity/unit root of BM USD in big cities.
According to the results in Table 5.F.3, the unit root hypothesis cannot be rejected for all cities. However, due to the possible structural breaks, we apply LM unit root tests to test for unit roots with structural breaks against trend stationary processes. The results for all six cities are given in Table 5.F.4.
Table 5.F.4: Two-break minimum LM unit root tests for BM USD series in big cities
Series k TB1, TB2 Test-statistics λ= (TB1/T, TB2/T) Minsk 8 23.06, 16.08 -3.7654 (0.30,0.56)
Gomel 3 22.05, 12.08 -4.7128 (0.18,0.70) Brest 0 24.05, 10.08 -4.0067 (0.16,0.58) Grodno 8 20.05, 15.08 -4.1678 (0.17,0.72) Vitebsk 8 24.05, 15.08 -4.3336 (0.18,0.72) Mogilev 8 17.05, 10.08 -6.5550∗∗ (0.14,0.73)
Note: k is the optimal number of lagged first-difference terms included in the unit root test to correct for serial correlation. TB1 and TB2 are the estimated break dates. λis the location of the breaks within the sample for which critical values are determined. Critical values are reported in Table 1 of (Strazicich et al. 2004). ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
Results of two-break minimum LM unit root tests for BM USD series in big cities are given in Table 5.F.4. For the five biggest series, we cannot reject the unit root hypothesis at all meaningful significance levels. For the series for Mogilev, the unit root hypothesis is rejected at a 5% significance level. The plots of these series, along with estimated break dates, are given in Figure 5.F.1.
3,000
Figure 5.F.1: Exchange rates and estimated break points: Vertical, red dashed lines are estimated break points.
Based on these results, we detrend these series by taking the first difference and estimating as in Equation 5.4.
-800
Figure 5.F.2: Detrended series. The Regression equations are given in Equation 5.4.
LR AIC
H0 F-Stat p-value k F-Stat p-value k
Minsk; Gomel 4.86 0.00 29 7.55 0.00 16
Gomel ; Minsk 1.83 0.02 3.78 0.00
Minsk; Brest 2.25 0.00 21 8.12 0.00 5
Brest ; Minsk 2.11 0.01 2.22 0.06
Minsk ;Grodno 3.19 0.00 27 3.19 0.00 27
Grodno ; Minsk 1.15 0.32 1.15 0.32
Minsk; Vitebsk 2.33 0.00 22 2.33 0.00 22
Vitebsk; Minsk 1.33 0.18 1.33 0.18
Minsk; Mogilev 4.68 0.00 15 3.46 0.00 21
Mogilev ; Minsk 1.43 0.15 1.46 0.12
Note: LR, AIC and SIC are used to determine the optimal lag length. k is the optimal number of lagged. ;stands for “does not Granger cause.”
Table 5.F.5 indicates that we always reject the hypothesis that the black market exchange rates for Minsk do not Granger cause the exchange rate in smaller cities. For the second and third biggest cities, Gomel and Brest, there is statistical evidence that the causality works in both directions. However, for the other three cities, there is no statistical evidence for causality from smaller cities to Minsk. Unfortunately, we cannot repeat this analysis for other currencies, since disaggregation of the data by cities would lead to time series with too many missing observations.
Aghion, P., Dewatripont, M., Hoxby, C., Mas-Colell, A. & Sapir, A. (2010), ‘The governance and performance of universities: evidence from europe and the us’, Economic Policy 25, 7–59.
Akgiray, V., Aydogan, K., Booth, G. & Hatem, J. (1989), ‘A causal analysis of black and official exchange rates: The turkish case’,Weltwirtschaftliches Archiv 125, 337–345.
Alchian, A. A. & Demsetz, H. (1972), ‘Production , information costs, and economic organization’, American Economic Review 62(5), 777–95.
Arrow, K. J., Bernheim, B. D., Feldstein, M. S., McFadden, D. L., Poterba, J. M.
& Solow, R. M. (2011), ‘100 years of theAmerican Economic Review: The top 20 articles’,American Economic Review 101(1), 1–8.
Arrow, K. J., Chenery, H., Minhas, B. & Solow, R. M. (1961), ‘Capital-labor sub-stitution and economic efficiency’, The Review of Economics and Statistics 43(3), 225–250.
Azoulay, P., Graff Zivin, J. S. & Wang, J. (2010), ‘Superstar extinction’, The Quar-terly Journal of Economics125(2), 549–589.
Backes-Gellner, U. & Schlinghoff, A. (2004), Careers, incentives, and publication patterns of us and german (business) economists, Working paper, University of Zurich.
Backes-Gellner, U. & Schlinghoff, A. (2008), ‘Monetary rewards and faculty be-haviour: How economic incentives drive publish or perish’, Southern Manage-ment Association Proceedings pp. 725–730.
Barnett, A. H., Ault, R. W. & Kaserman, D. L. (1988), ‘The rising incidence of co-authorship in economics: Further evidence’, The Review of Economics and Statistics 70(3), 539–43.
science’, Recherches Economiques de Louvain forthcoming.
Bergstrom, T. C. (2001), ‘Free labour for costly journals?’, Journal of Economic Perspectives 15(4), 183–198.
Blank, R. M. (1991), ‘The effects of double-blind versus single-blind reviewing: Ex-perimental evidence from the american economic review’, American Economic Review 81(5), 1041–67.
Booth, G. & Mustafa, C. (1991), ‘Long-run dynamics of black and official exchange rates’,Journal of International Money and Finance 10(3), 392 – 405.
Bornmann, L. & Daniel, H.-D. (2008), ‘The effectiveness of the peer review process:
Inter-referee agreement and predictive validity of manuscript refereeing at ange-wandte chemie’, Angewandte Chemie International Edition47(38), 7173–7178.
Boschini, A. & Sj¨ogren, A. (2007), ‘Is team formation gender neutral? evidence from coauthorship patterns’, Journal of Labor Economics 25(2), 325–365.
Cacioppo, J. T. & Berntson, G. G. (2005),Social Neuroscience: Key Readings, New York: Psychology Press.
Cacioppo, J. T., Visser, P. S. & Pickett, C. L. (2006), Social Neuroscience: People Thinking about Thinking People, The MIT Press.
Caporale, G. M. & Cerrato, M. (2008), ‘Black market and official exchange rates:
Long-run equilibrium and short-run dynamics’, Review of International Eco-nomics 16(3), 401–412.
Cardoso, A. R., Guimarandaes, P. & Zimmermann, K. F. (2010), ‘Trends in economic research: An international perspective’, Kyklos 63(4), 479–494.
Cattell, R. B. (1963), ‘Theory of fluid and crystallized intelligence: A critical exper-iment’, Journal of Educational Psychology 54(1), 1–22.
Combes, P.-P. & Linnemer, L. (2003), ‘Where are the economists who publish? pub-lication concentration and rankings in europe based on cumulative pubpub-lications’, Journal of the European Economic Association 1(6), 1250–1308.
Combes, P.-P. & Linnemer, L. (2010), Inferring missing citations: A quantitative multi-criteria ranking of all journals in economic, Working paper, GREQAM Marseille.
the effects of publication lags on life cycle research productivity in economics, Nber working papers, National Bureau of Economic Research, Inc.
Coup´e, T. (2010), Peer review versus citations - an analysis of best paper prizes, Discussion Papers 35, Kyiv School of Economics.
Dasgupta, P. & David, P. A. (1994), ‘Toward a new economics of science’, Research Policy 23(5), 487–521.
Dawson, J. W., Millsaps, S. W. & Strazicich, M. C. (2007), ‘Trend breaks and non-stationarity in the yugoslav black market for dollars, 1974–1987’, Applied Economics39(1), 43–51.
De Long, J. B. & Lang, K. (1992), ‘Are all economic hypotheses false?’, Journal of Political Economy100(6), 1257–72.
De Solla Price, D. J. & Beaver, D. D. (1966), ‘Collaboration in an invisible college’, American Psychologist21(11), 1011–1018.
Demougin, D. & Fabel, O. (2004), ‘Autonomie der universit¨at und professional-isierung ihrer leitungstrukturen’, ifo Schnelldienst57(16), 6–9.
Demougin, D. & Fabel, O. (2006), ‘Autonomie der universit¨at und profes-sionalisierung ihrer leitungstrukturen’, Zeitschrift f¨ur betriebswirtschaftliche Forschung 54(6), 172–191.
Ding, S. & Kovtun, D. (2010), Republic of belarus: Selected issues, Imf country report no. 10/16, International Monetary Fund.
Dornbusch, R., Dantas, D. V., Pechman, C., de Rezende Rocha, R. & Sim˜oes, D. (1983), ‘The black market for dollars in brazil’, The Quarterly Journal of Economics98(1), 25–40.
Ductor, L. (2011), Does co-authorship lead to higher academic productivity?, Work-ing paper, Universidad de Alicante.
Ellison, G. (2002), ‘The slowdown of the economics publishing process’, Journal of Political Economy110(5), 947–993.
Ellison, G. (2011), ‘Is peer review in decline?’, Economic Inquiry 49(3), 625–657.
Engel, C. & West, K. D. (2005), ‘Exchange rates and fundamentals’, Journal of Political Economy113(3), 485–517.
interpretation von ranglisten der forschungsaktivit¨aten deutscher betrieb-swirtschaftlicher fachbereiche’, Die Betriebswirtschaft - DBW59(2), 196–204.
Fabel, O., Hein, M. & Hofmeister, R. (2008), ‘Research productivity in business eco-nomics: An investigation of austrian, german and swiss universities’, German Economic Review 9, 506–531.
Fafchamps, M., Goyal, S. & van der Leij, M. J. (2010), ‘Matching and network effects’, Journal of the European Economic Association 8(1), 203–231.
Galenson, D. W. (2006),Old Masters and Young Geniuses: The Two Life Cycles of Artistic Creativity, Princeton University Press.
Galenson, D. W. & Weinberg, B. A. (2000), ‘Age and the quality of work: The case of modern american painters’, Journal of Political Economy 108(4), 761–777.
Galenson, D. W. & Weinberg, B. A. (2001), ‘Creating modern art: The changing careers of painters in france from impressionism to cubism’,American Economic Review 91(4), 1063–1071.
Gans, J. S. & Shepherd, G. B. (1994), ‘How are the mighty fallen: Rejected classic articles by leading economists’, Journal of Economic Perspectives8(1), 165–79.
Glynn, J., Perera, N. & Verma, R. (2007), ‘Unit root tests and structural breaks: a survey with applications’,Faculty of Commerce-Papers p. 455.
Gourieroux, C., Monfort, A. & Trognon, A. (1984), ‘Pseudo maximum likelihood methods: Applications to poisson models’,Econometrica 52(3), 701–20.
Granger, C. W. J. (1969), ‘Investigating causal relations by econometric models and cross-spectral methods’, Econometrica 37(3), pp. 424–438.
Griliches, Z. (1969), ‘Capital-skill complementarity’, The Review of Economics and Statistics 51(4), 465–68.
Gwartney, J., Lawson, R. & Easterly, W. (2006), Economic freedom of the world: 2006 annual report, Technical report, Vancouver: Fraser Institute, www.freetheworld.com.
Hamermesh, D. S. (1994), ‘Facts and myths about refereeing’,Journal of Economic Perspectives 8(1), 153–63.
Hamermesh, D. S. & Oster, S. M. (2002), ‘Tools or toys? the impact of high
tech-quality and quantity in academe’, Economic Inquiry 50(1), 1–16.
Hamermesh, D. S. & Schmidt, P. (2003), ‘The determinants of econometric society fellows elections’,Econometrica 71(1), 399–407.
Hennig-Thurau, T., Walsh, G. & Schrader, U. (2004), ‘Vhb-jourqual: A survey-based ranking of business-administration journals’, Zeitschrift f¨ur betrieb-swirtschaftliche Forschung 56(8), 520–545.
Hirsch, J. E. (2005), ‘An index to quantify an individual’s scientific research output’, Proceedings of the National Academy of Sciences 102(46), 16569–16572.
Hofmeister, R. & Krapf, M. (2011), ‘How do editors select papers, and how good are they at doing it?’,The B.E. Journal of Economic Analysis & Policy 11(1), 64.
Hofmeister, R. & Ursprung, H. W. (2008), ‘Das handelsblatt ¨Okonomen-ranking 2007: Eine kritische beurteilung’,Perspektiven der Wirtschaftspolitik9(3), 254–
266.
Holmstrom, B. & Milgrom, P. (1991), ‘Multitask principal-agent analyses: Incentive contracts, asset ownership, and job design’, Journal of Law, Economics and Organization7(0), 24–52.
Horn, J. L. & Cattell, R. B. (1966), ‘Refinement and test of the theory of fluid and crystallized general intelligences’, Journal of Educational Psychology 57(5), 253–270.
Husain, A. & Arora, V. (2012), Staff report for the 2012 article iv consultation and second post-program monitoring discussions, Imf country report no. 12/113, International Monetary Fund.
Ickes, B. W. & Ofer, G. (2006), ‘The political economy of structural change in russia’, European Journal of Political Economy22(2), 409–434.
Ioffe, G. & Yarashevich, V. (2011), ‘Debating belarus: An economy in comparative perspective’, Eurasian Geography and Economics52(6), 750–779.
Jones, B. F. (2009), ‘The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder?’, Review of Economic Studies 76(1), 283–
317.
Jones, B. F. (2010a), ‘Age and great invention’, The Review of Economics and Statistics 92(1), 1–14.
S. Stern, eds, ‘Innovation Policy and the Economy’, Vol. 11, NBER, pp. 103–
131.
Jones, B. F. & Weinberg, B. A. (2012), ‘Age dynamics in scientific creativity’, Pro-ceedings of the National Academy of Sciencesforthcoming.
Kalaitzidakis, P., Mamuneas, T. P. & Stengos, T. (2003), ‘Rankings of academic journals and institutions in economics’, Journal of the European Economic As-sociation1(6), 1346–1366.
Kalaitzidakis, P., Mamuneas, T. P. & Stengos, T. (2011), ‘An updated ranking of academic journals in economics’,Canadian Journal of Economics44(4), 1525–
1538.
Kim, E. H., Morse, A. & Zingales, L. (2009), ‘Are elite universities losing their competitive edge?’, Journal of Financial Economics93(3), 353–381.
Korosteleva, J. (2007), ‘Maximizing seigniorage and inflation tax: The case of be-larus’,Eastern European Economics 45(3), 33–50.
Korosteleva, J. (2011), ‘The global recession and the belarusian economy: Revealing cracks in the model’, Journal of Communist Studies and Transition Politics 27(3-4), 632–653.
Laband, D. N. (1987), ‘A qualitative test of journal discrimination against women’, Eastern Economic Journal13(2), 149–153.
Laband, D. N. & Piette, M. J. (1994), ‘Favoritism versus search for good papers:
Empirical evidence regarding the behavior of journal editors’,Journal of Polit-ical Economy 102(1), 194–203.
Laband, D. N. & Piette, M. J. (1995), ‘Team production in economics: division of labor or mentoring?’, Labour Economics 2(1), 33–40.
Laband, D. N. & Tollison, R. D. (2000), ‘Intellectual collaboration’, Journal of Political Economy108(3), 632–661.
Laband, D. N. & Tollison, R. D. (2003), ‘Dry holes in economic research’, Kyklos 56(2), 161–173.
Laband, D. N. & Tollison, R. D. (2006), ‘Alphabetized coauthorship’, Applied Eco-nomics 38(14), 1649–1653.
with two structural breaks’,The Review of Economics and Statistics85(4), pp.
1082–1089.
Lotka, A. J. (1926), ‘The frequency distribution of scientific productivity’, Journal of the Washington Academy of Sciences15(12), 317–324.
Malmendier, U. & Tate, G. (2009), ‘Superstar ceos’, The Quarterly Journal of Eco-nomics 124(4), 1593–1638.
Mangematin, V. & Baden-Fuller, C. (2008), ‘Global contests in the production of business knowledge: Regional centres and individual business schools’, Long Range Planning41(1), 117–139.
McAfee, R. P. (2010), ‘Edifying editing’, The American Economist 55(1), 1–8.
McDowell, J. M. (1982), ‘Obsolescence of knowledge and career publication pro-files: Some evidence of differences among fields in costs of interrupted careers’, American Economic Review 72(4), 752–68.
McDowell, J. M. & Melvin, M. (1983), ‘The determinants of co-authorship: An analysis of the economics literature’, The Review of Economics and Statistics 65(1), 155–60.
McDowell, J. M., Singell, L. D. & Stater, M. (2006), ‘Two to tango? gender differ-ences in the decisions to publish and coauthor’, Economic Inquiry44(1), 153–
168.
Meese, R. A. & Rogoff, K. (1983a), ‘Empirical exchange rate models of the seventies:
Do they fit out of sample?’, Journal of International Economics14(1-2), 3–24.
Meese, R. & Rogoff, K. (1983b), The out-of-sample failure of empirical exchange rate models: Sampling error or misspecification?, in ‘Exchange Rates and
Meese, R. & Rogoff, K. (1983b), The out-of-sample failure of empirical exchange rate models: Sampling error or misspecification?, in ‘Exchange Rates and