The study of the factors that affect new firm birth is a topic of interest to many parties, both scholars and policy makers. Literature has investigated regional variation in firms birth rate focusing on demographic factors (population growth), entrepreneurial environment characteristics (industrial specialization, industrial intensity, R&D), financial and economic development of the area (credit market, income growth rate, unemployment), socio-economic characteristics (immigration, social capital, human capital) physical and social infrastructure (regional diversity and creativity). In this paper, we investigate whether the criminal shape of a territory can affect new firms formation. Precisely, we focus the attention of two specific criminal behaviors, both of them influencing the capability of a firm to raise money to set up a firm. First, usury crimes, that are an alternative channel to legal credit market in providing capital to new firms. Second, tax evasion that has a contrasting effect on the ability of a territory to generate new firms. On the one hand, tax compliant firms in a territory where tax evasion is widespread afford unfair competition; in this sense, "honest" entrepreneurs should be discouraged from setting up new business. On the other hand, since tax evasion can be chosen as a source of self-financing to firms, "less honest" entrepreneurs might have a higher 1 A previous version of this paper has been presented at XIII EBES Conference (Istanbul, 5-7 June 2014) ♣ University of Perugia, Department of Economics. ♠ University of Rome Tor Vergata, Department of Economics. ♦ University of Naples Parthenope, Department of Economic and Legal Studies (corresponding author. Email: bruno.chiarini@uniparthenope.it). ♥ University of Naples Parthenope, Department of Economic and Legal Studies and Cesifo. 2 incentive to start their business in areas characterized by high tax evasion. As we will see, our results suggest that this latter effect prevails. JEL classifications: C33, H26, K42, M13

Attraverso una metodologia di misurazione del riciclaggio di denaro sporco basata su un modello teorico che utilizza serie storiche di dati macroeconomici e possibile costruirne una tecnicamente consistente con il comportamento ottimale di imprese e consumatori. La metodologia viene applicata a tre realta economiche: Italia,Area euro e Stati Uniti per le quali si evidenzia una incidenza sul Pil, rispettivamente, del 12, del 19 e del 13%. Per gli Stati Uniti, il fenomeno osservato sembra mostrare una variabilita piu pronunciata rispetto al Pil e appare in crescita nelle fasi espansive e in diminuzione nelle fasi recessive del ciclo. Per Italia e Area euro non sembra valere la stessa tendenza.Al contrario, in Europa il miglioramento delle condizioni economiche sembra rallentare l’attivita di riciclaggio.

This paper explores the ability of a class of two-sector dynamic general equilibrium models to generate equilibrium time series for Money Laundering (Ml), through numerical simulations.The paper adopts this approach for the Italian, Us and the Eu-15 economies.The simulations show that Ml accounts for 19 % of Gdp in the Eu-15 economy, while it accounts for 13 % in the Us and 12% in Italy. Moreover, the Ml simulated for the Eu-15 is less volatile and negatively correlated with respect to Gdp, while the correlation is positive for the Us economy.

The global dimension of the current financial crisis and the speed of contagion make necessary a new national and international regulatory framework. New rules to overcome recent failures must not be disruptive, stopping financial innovation necessary to increase the spectrum of financial and lending solutions.

This paper explores the ability of a class of two-sector dynamic general equilibrium models to generate equilibrium time series for Money Laundering (ML), through numerical simulations in accordance with the works of Ingram, Kocherlakota and Savin (1997), Busato, Chiarini and Di Maro (2006), and Argentiero, Bagella and Busato (2008). The paper adopts this approach for the US and the EU-15 economies. The simulations show that ML accounts for 19 percent of GDP in the EU-15 economy, while it accounts for 13 percent in the US economy over the sample 2000:01-2007:04. Moreover, the ML simulated for the EU-15 is less volatile (relative standard deviation to GDP is 0.288 compared to a figure of almost 0.4 for the US economy), and negatively correlated with respect to GDP. The latter statistic is positive for the US economy.

Starting from August 2007, the FED intervened by injecting liquidity in the inter-banking market and reducing interest rates. Day after day, the financial markets register negative trends and rallies. This is not due to events which are particularly related to the market itself. This appeared in the days when there were government interventions, when everybody expected a positive sign in the financial market but a negative sign occurred. Sometimes, this is due to the intensity of actions taken by the governments. The markets always expect appropriate interventions (in terms of intensity). Looking at these market reactions (in unexpected signs) after each government action, we can suppose that policy makers underestimate the intensity of this crisis. The capacity of making enforcement on the system should avoid underlining the side of governance rules which will never be precise. Being able to count on an active control of the market dealers, broadly speaking is a way of giving active confidence to individual/institutional agents who decide the allocations of saving in the financial market. There is no such confidence at the moment, if one focuses only on the definitions of new rules. If one starts from existing rules and does continuous monitoring so that they are applied adequately at crucial moments, then one could reduce the possibility of facing new exceeding volatilities of banking securities in the stock market. This work is focused on understanding how governance as well as central banks’ policy impact on the crisis, as well as possible future scenarios.

This paper implements a methodology that exploits firms and households’ optimality conditions to measure money laundering for the Italian economy. This approach, first implemented by Ingram et al. (J Monet Econ 40:435–436, 1997) to the household production sector, and by Busato et al. (Using theory for measurement: an analysis of the behaviour of underground economy working paper, Aarhus University, 2006) for measuring the underground economy, allows to generate high frequency time-series for money laundering using a theoretical two-sector dynamic general equilibrium model calibrated over the sample 1981:01–2001:04. The analysis of the generated series suggests two main results. First, money laundering accounts for approximately 12 percent of aggregate GDP; second, money laundering is more volatile than aggregate GDP and it is negatively correlated with it.

Abstract The paper investigates the dynamics and determinants of the earning forecast bias in two (US and Eurozone) stock samples matched by size and industry affiliation. Evidence is found that the European bias is significantly higher in absolute terms, irrespective of the year and the distance from the release date, with the exception of the 1997–2000 period in which US stocks are more optimistically valued. Cross-market differences persist when they are regressed, in a panel GMM estimate, on various controls such as the number of individual forecasts and their standard deviation for any considered stock, with the latter being significantly lower in the US market. Finally, it is observed that a convergence process is at work in both markets, with the bias becoming progressively lower as the release date gets closer.

Firm-specific and aggregate shocks generate reassessment of investors and analysts expectations on earnings forecasts and on the fundamental value of equities. In this article, we evaluate the effects of this combined reaction on the implied equity risk premium extracted from a standard two-stage dividend discount (DD) model. If investors and analysts revisions coincide, and in absence of measurement errors in the DD formula, the observed shocks should not have any significant impact on prices and Implied Equity Risk Premium (IEPR). On the contrary, in an analysis based on data for all S&P 500 COMPOSITE INDEX constituents from 1990 to 2003, we observe substantial overreaction of investors to both downward and upward firm-specific forecast revisions, plus overreaction to changes in GDP and to the announcements of the Consumer and Business Confidence indicator. We also observe that positive overreaction to upward earning forecast revisions and GDP changes falls after the stock bubble burst, while overreaction to upward forecast revision and to announcements of the Consumer Confidence Index looses significance after the 9/11 terrorist attack. These findings are broadly consistent with the hypothesis of reduced participation of uninformed (noise) traders to financial markets after these two shocks. Notes 1 The approach we follow is the same adopted by Damodaran (Citation1999, Citation2000a, Citationb) and Claus and Thomas (Citation2001) who estimate the post-1985 equity premium for the US market, by using I/B/E/S earning forecasts and a risk-free rate, proxied by long-term government bond returns. 2 Since data on dividend growth forecasts are not available, the usual approach is to proxy this variable with I/B/E/S earning growth forecasts, under the assumption of a constant dividend/earning ratio (Claus and Thomas, Citation2001; Adriani et al., 2000; Bagella et al., Citation2004). 3 Results on this point are omitted for reasons of space and are available from the authors upon request. 4 The ISM Business Confidence Index is based on data compiled from monthly replies to questions asked of purchasing and supply executives in over 400 industrial companies. Membership of the Business Survey Committee is diversified by Standard Industrial Classification (SIC) category, based on each industry's contribution to Gross Domestic Product (GDP). Twenty industries from various US geographical areas are represented on the committee. The 20 manufacturing Standard Industry Classification codes are: Food; Tobacco; Textiles; Apparel; Wood Products; Furniture; Paper; Printing Publishing; Chemicals; Petroleum; Rubber Plastic Products; Leather; Glass, Stone, Aggregate; Primary Metals; Fabricated Metals; Industrial Commercial Equipment Computers; Electronic Components Equipment; Transportation Equipment; Instruments Photographic Equipment and Miscellaneous (a preponderance of jewellery, toys, sporting goods, musical instruments). Survey responses reflect the change, if any, in the current month compared to the previous month. For each of the indicators measured (New Orders, Backlog of Orders, New Export Orders, Imports, Production, Supplier Deliveries, Inventories, Customers’ Inventories, Employment, and Prices), this report shows the percentage reporting each response, the net difference between the number of responses in the positive economic direction (higher, better, and slower for Supplier Deliveries) and the negative economic direction (lower, worse and faster for Supplier Deliveries), and the diffusion index. Responses are raw data and are never changed. The diffusion index includes the percent of positive responses plus one-half of those responding the same (considered positive). The resulting single index number is then seasonally adjusted to allow for the effects of repetitive intra-year variations resulting primarily from normal differences in weather conditions, various institutional arrangements, and differences attributable to nonmoveable holidays. All seasonal adjustment factors are supplied by the US Department of Commerce and are subject annually to relatively minor changes when conditions warrant them. The PMI is a composite index based on the seasonally adjusted diffusion indices for five of the indicators with varying weights: New Orders 30%; Production 25%; Employment 20%; Supplier Deliveries 15% and Inventories 10%. Diffusion indices have the properties of leading indicators and are convenient summary measures showing the prevailing direction of change and the scope of change. A PMI reading above 50% indicates that the manufacturing economy is generally expanding; below 50% that it is generally declining. A PMI over 42.9%, over a period of time, indicates that the overall economy, or GDP, is generally expanding; below 42.9%, it is generally declining. The distance from 50% or 42.9% is indicative of the strength of the expansion or decline. With some of the indicators within this report, ISM has indicated the departure point between expansion and decline of comparable government series, as determined by regression analysis. Responses to Buying Policy reflect the percent reporting the current month's lead time, the approximate weighted number of days ahead for which commitments are made for Production Materials, Capital Expenditures and Maintenance, Repair, and Operating (MRO) Supplies, expressed as hand-to-mouth (five days), 30 days, 60 days, 90 days, 6 months (180 days), a year or more (360 days), and the weighted average number of days. These responses are raw data, never revised and not seasonally adjusted since there is no significant seasonal pattern. The data presented herein is obtained from a survey of manufacturing supply managers based on information they have collected within their respective organizations. ISM makes no representation, other than that stated within this release, regarding the individual company data collection procedures. Use of the data is in the public domain and should be compared to all other economic data sources when used in decision making. 5 The monthly Survey of Consumers Confidence Index (UMich) is an ongoing nationally representative survey based on approximately 500 telephone interviews with adult men and women living in households in the coterminous United States (48 States plus the District of Columbia). The Index of Consumer Sentiment (ICS) is derived from the following five questions: (Equation1) ‘We are interested in how people are getting along financially these days. Would you say that you (and your family living there) are better off or worse off financially than you were a year ago?’; (Equation2) ‘Now looking ahead–do you think that a year from now you (and your family living there) will be better off financially, or worse off, or just about the same as now?’; (Equation3) ‘Now turning to business conditions in the country as a whole–do you think that during the next twelve months we’ll have good times financially, or bad times, or what?’; (4) ‘Looking ahead, which would you say is more likely–that in the country as a whole we’ll have continuous good times during the next five years or so, or that we will have periods of widespread unemployment or depression, or what?’; (5) ‘About the big things people buy for their homes–such as furniture, a refrigerator, stove, television, and things like that. Generally speaking, do you think now is a good or bad time for people to buy major household items?’. 6 This finding is consistent with the hypothesis that market investors overweight some information with respect to other (Kahneman and Tversky, 1982) or ignore some sources of information (Ou and Penman, Citation1989, Bernard and Thomas, Citation1990) which is instead considered useful to predict earnings for analysts. 7 To provide another example of possible measurement bias, our results on the effects of upward and downward consensus forecast revisions might be driven by delays in registering analysts' changes in the I/B/E/S database. In such case, and if revisions are positively auto correlated, market overreaction is in reality an anticipation of current (nonregistered) or future revisions. The structural breaks observed after the bubble burst and the 9/11 would contradict this hypothesis unless we assume that the shock has reduced positive autocorrelation in analysts' revision forecasts. 8The hypothesis uninformed migration has been applied by several authors to the analysis of the effects of the creation of derivatives markets on the volatility of the underlying asset to explain the observed significant reduction in conditional volatility (Gammil and Perold, 1989; Choi and Subrahmanyam, Citation1993, Gorton and Pennacchi Citation1993; Kumar et al . Citation1995).

The paper contributes to the literature on monetary policy and asset prices by investigating the linkages between the Implied Equity Risk Premium (IERP), the Federal fund rate, inflation and the output gap in the US between 1990 and 2003. Preliminary estimation of forward-looking Taylor rules provides the benchmark for the analysis. Improving upon this standard specification, our choice of the Vector Error Correction model makes it possible to separately analyse long-run adjustment via the cointegration space and short-run smoothing through impulse response functions in a multivariate environment. The main result of the paper is to find that monetary authorities in the US react to changes in stock market risk perception, easing monetary conditions when IERP increases and tightening them when IERP falls down. This is consistent with what most of the empirical literature finds and indicates that stock market considerations have played a role in the Fed reaction function until the recent past. PRELIMINARY VERSION, Please do not quote J.E.L. Classification: C32, C51, F21 1 The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. The authors thank Leonardo Becchetti, Gerald P. Dwyer, Fabrizio Mattesini and all UniTV-Finance Group for helpful comments and Daniele Di Giulio for his assistance in the course of this research. Any remaining errors are the authors’ responsibility. The usual disclaimer applies.