Minimum wages in Australia: an analysis of the impact on small and medium sized businesses

A survey of 1800 small and medium sized businesses is used to shed light on the number of workers covered by minimum wage legislation in Australia. Estimates are obtained and reported of the employment effects of changing the way in which minimum wages are set in Australia.Minimum wage; small and medium enterprises; random survey

The Definition, Dating and Duration of Cycles

The ultimate objective of this paper is to discuss the duration of business cycles and the related issue of the probability of recession. To reach that objective it is necessary to first agree on a definition of business cycles. It is also necessary to agree on how to define the key features of business cycles and to agree on the rules for identifying and dating these key features. Although not strictly necessary for this paper, it is also helpful to discuss why we seek to identify and date key features of the business cycles.Business cycle; recession; business cycle dating; probability of recession

Using turning point information to study economic dynamics

Procedures are developed to compute the proportion of turning points located in the sample path of time series data. It is shown that the proportion of turning points can be directly related to the data generating process. Methods for estimating model parameters are developed using counts of turning points. It is shown that the proposed method has the advantages of tractability and robustness. The later feature arises as it does not require that any of the moments of the series Y(t) exist. Tests of model specification are developed using these counts of turning points. These tests are applied to several models one including the issue of whether GDP is better modelled as trend stationary or difference stationary. Monte carlo results are presented for both the estimation and testing proceduresKeywords: Business cycle, turning points, elliptically symmetric distribution

Detecting and forecasting business cycle turning points

The R word has begun to appear in the media again bringing with it three technical questions viz, How will we know we are in recession? How will we know when it has ended? And How can we forecast its onset and ending? This paper does not provide answers to these questions rather it focuses on the technical issues that we need to resolve in order to provide good answers to these questions. The paper has three significant findings. First, the business cycle states obtained by the BBQ algorithm are complex statistical processes and it is not possible to write down an exact likelihood function for them. Second, for the classical and acceleration cycles it is possible to obtain a reasonably simple approximation to the BBQ algorithm that may permit one to write down a likelihood function. Third, when evaluating these algorithms there is a large di¤erence between the results using US GDP as compared to UK GDP or simulated data from models fit to US GDP. Specifically, turning points are much easier to detect in US GDP than in other series. One needs to take this into account when using US based research on detecting and forecasting business cycle turning points.Business cycle; turning points; forecasting; peak; trough

Identifying and measuring the economic effects of unfair dismissal laws

Theory cannot provide an unambiguous prediction regarding the economic effects of employment protection laws. Such laws confer benefits on employees and shift the labour supply curve to the right. But they also impose costs on business and therefore shift the labour demand curve to the left. The net effect on employment is ambiguous and depends on the magnitudes of the costs and benefits as well as the elasticities of labour supply and labour demand. The net effect on welfare is also ambiguous. However, since many businesses did not provide protection against unfair dismissal in the 1990s one can argue that that indicates that the costs exceed the benefits and thus the unfair dismissal laws introduced in 1993 reduced employment and welfare. Reduced form models that use employment or unemployment as the dependent variable are useless for identifying and measuring the economic effects of unfair dismissal laws. Structural models or survey based evidence is required to answer these questions. Evidence from a survey of 1800 businesses establishes that unfair dismissal laws impose significant costs on businesses and cause them to make major changes to the way in which they hire and fire workers. An estimated lower bound on these costs is $296 per full time employee. This is a lower bound in part because some business said unfair dismissal laws raised their costs but were unable to quantify by how much. Also, the opportunity cost in terms of lost productivity of continuing to employ those workers whose performance is unsatisfactory is excluded from the calculation above. Evidence from the 1990 and 1995 AWIRS survey shows that dismissal rates for cause declined from 4.4 per cent to 2.1 per cent and may have declined even further in the past decade. This suggests that the lost productivity from retaining unsatisfactory employees is likely to be high. The AWIRS data shows that in 1990 small and medium sized businesses were more likely than larger business to dismiss employees for cause. The 1995 AWIRS survey shows that small and medium sized businesses made much larger adjustments to their firing practices and thus shouldered more of the burden of these laws. Discussion of unfair dismissal laws has ignored the fact that these laws increase the risk born by businesses. Small business is unable to pool this risk and so it poses a much greater cost for such businesses. This feature may also explain why small businesses reported that they spent more on complying with and reducing their exposure to unfair dismissal laws. These considerations suggest that the Government's policy of exempting businesses with fewer than 100 employees from the unfair dismissal laws will most likely not cause major resource allocation costs. But a better policy would be abolish the unfair dismissal laws. Using a labour demand elasticity of 0.7 percent I estimate that the existing unfair dismissal laws reduce employment by at least 0.46 per cent (about 46,000 employees). Freyens and Oslington question my findings. There are two mistakes in their paper that account for their position. First they underestimate by a factor of 10 the probability that a worker is dismissed for cause. Second, they exclude the costs incurred by business in avoiding exposure to the law and focus only on the cost of complying with the law. Both their paper and my paper can be criticised for underestimating costs because we exclude the foregone productivity that arises where businesses retain some employees whose performance is unsatisfactory and who would have been dismissed under an employ-at-will regime.Employment termination laws; unfair dismissal

FuelWatch: evidence-based-policy or policy based evidence?

Experience from the United Kingdom and the United States suggests that expert evidence is often reshaped and repackaged by governments so that it supports existing policy rather than informing policy decisions. The Australian government based its decision to introduce FuelWatch on evidence in the form of econometric work by the ACCC. This paper asks two questions about that decision. First, was the policy shaped by the economet- ric evidence or was the government�s presentation of the evidence shaped by the pre-determined policy? Second, is the econometric evidence sufficiently robust as to support the FuelWatch policy? I find that some of the facts suggest that evidence was reshaped and repackaged to support the FuelWatch policy. I also find that the ACCC analysis was not robust. Specifically, they study the nominal retail margin when economic theory suggests that analysis should focus on the real retail margin to producers. Using data digitized from a graph in the ACCC report I redo the econometric analysis and find that the evidence no longer unambiguously supports the FuelWatch policy. The ACCC claim that their analysis is robust because it has been subject to scrutiny within the ACCC and by Treasury but such claims of robustness cannot be verified because they refuse to release the data for public scrutiny. Publication of data and analysis underpinning government decisions and independent review of econometric work provides a more credible evidence base for future policy decisions.FuelWatch; Evidence-based policy; Petrol pricing;Time series econometrics; Structural breaks;Hypothesis testing; HACC standard errors

FoolWatch - Further Discussion of Econometric Analysis Undertaken By ACCC

Using data supplied by InformedSources I find additional flaws in the ACCC analysis of FuelWatch. First, the drop in petrol prices that is so visually convincing in the ACCC chart S1 is in fact an artifact of the method of data construction and can be attributed primarily to increases in prices in Adelaide and Melbourne --- events that had nothing to do with Western Australia or FuelWatch. Second, redoing the analysis using Sydney as the point of reference and adding prices in other cities as explanatory variables lead to results that contradict ACCC findings. First I find that the two best models that I estimate yield the conclusion that Fuelwatch either increased petrol prices in Western Australia by a small amount or had no effect. Third, I find that the entry of Woolworths and Coles into the Western Australian market had the effect of reducing unleaded petrol prices Perth by about 2.67 per cent relative to Sydney.Petrol Pricing; Fuelwatch; Econometrics and public policy; Evidence based policy

Applying shape and phase restrictions in generalized dynamic categorical models of the business cycle

To match the NBER business cycle features it is necessary to employ Generalised dynamic categorical (GDC) models that impose certain phase restrictions and permit multiple indexes. Theory suggests additional shape restrictions in the form of monotonicity and boundedness of certain transition probabilities. Maximum likelihood and constraint weighted bootstrap estimators are developed to impose these restrictions. In the application these estimators generate improved estimates of how the probability of recession varies with the yield spread.Generalized dynamic categorical model, Business cycle; binary variable, Markov process, probit model, yield curve

Estimating baseline real business cycle models of the Australian economy

This paper is concerned with the issues that arise in building a small Dynamic Stochastic General Equilibrium (DSGE) model of the Australian economy. Our ultimate objective is to build a model that can be used to study long run economic growth and the business cycle. We agree with Cooley and Prescot�s (1995) view that these are phenomena to be studied jointly rather than separately. Adopting this view has several implications for what constitutes the essential components of our a model. We see these as being: a major role for a persistent technology shock in driving economic activity; and consistency with a version of the Ramsey-Cass-Koopmans (RCK) exogenous growth model. Without the former it is not possible to generate realistic business cycle features; demand shocks alone are insuffcient see Harding and Pagan (2007). The RCK exogenous growth model remains the simplest model available to encompass the salient features of economic growth which is why we rate it as essential. We also take the methodological stance that it is desirable to obtain a satisfactory baseline model before adding other desirable features such as: money; openness to international trade, capital flows, and immigration; and price and wage stickiness. In short we see small real business cycle (RBC) models as the natural starting point for our work.Real business cycle; stochastic growth model; technology shock; persistence

The Econometric Analysis of Constructed Binary Time Series. Working paper #1

Macroeconometric and financial researchers often use secondary or constructed binary random variables that differ in terms of their statistical properties from the primary random variables used in microeconometric studies. One important difference between primary and secondary binary variables is that while the former are, in many instances, independently distributed (i.d.) the later are rarely i.d. We show how popular rules for constructing binary states determine the degree and nature of the dependence in those states. When using constructed binary variables as regressands a common mistake is to ignore the dependence by using a probit model. We present an alternative non-parametric method that allows for dependence and apply that method to the issue of using the yield spread to predict recessions.Business cycle; binary variable, Markov chain, probit model, yield curve