Capital Asset Pricing Model (CAPM)

Capital Asset Pricing Model (CAPM) 1. Introduction Markowtiz (1952) did the ground work for the CAPM (Capital Asset Pricing Model). From the study of the early theories we know that the risk of an underlying security is measured by the standard deviation of its pay off or return. Therefore, for a larger risk we will have higher standard deviation of the respective security return. Markowtiz argued that the standard deviations of security returns for any two securities are not additive if they are combined together unless the returns of those two assets are perfectly positively correlated. He also observed that the standard deviation of security return of a portfolio is less than the sum of the standard deviation of those assets constituted the portfolio. Markowitz developed the efficient frontier of portfolio, the efficient set from where the investors select the portfolio which is most suitable for them. Technically, an investor will hold a mean-variance efficient portfolio which will return the highest pay off to them with a given level of variance. Markowitz’s computation of risk reduction is very rigorous and tedious. Sharpe (1964) developed the single index model which is computationally efficient. He derived a common index where the asset return is related with the common index. This common index can be any variable which has influence on the asset return. We can apply this single index model to the portfolio as well since the expected return of a portfolio is the weighted average of the expected returns of the constituents of the portfolio. When we need to analyze the risk of an individual security, we have to consider the other securities of the portfolio as well. Because, we are interested about the additional risk being added to the portfolio when one addition security is added to the portfolio. Thus the concept of risk share of an individual security to the portfolio is different from the risk of that security itself. An investor faces two kinds of risks. One is called the systematic risk and the other is known as unsystematic risk. Unsystematic risk is a kind of risk which can be minimized or eliminated by increasing the size of the portfolio, namely, by increasing the diversity of the portfolio. The systematic risk is well known as the market risk. Because, it depends on the overall movement of the market and the financial condition of the whole economy. By diversifying the portfolio, we cannot eliminate the systematic risk. Theoretically CAPM offers very commanding predictions about how to measure risk and return relationship. However, the empirical evidence of CAPM is not very encouraging. One may conclude that these failings are rooted in poor construction of the model but once can argue that this failing arises because of the difficulties of building comprehensive and valid test model. The estimation strategy of CAPM is not free from the data-snooping bias. Because of the non-experimental nature of economic theory we cannot avoid this problem. Moreover a lot of investigations already have been done to test the validity of the CAPM. Thus, no attempt has been made in this paper to test the validity of the model. Here in this paper we will critically examine some literatures on CAPM testing. We will begin with understanding the model. We will briefly outline some mathematics required to understand the underlying assumptions of the model. Then we will focus on the single and multi-factor CAPM models to a nalyze the model assumptions and restrictions required to hold these models to be true. 2. The Capital Asset Pricing Model Explained   In 1959 Markowitz introduced the notion of mean-variance efficient portfolio. According to him it is optimal for an investor to hold a mean-variance efficient portfolio. The mean-variance efficient portfolio is a portfolio for an investor where he minimizes the portfolio return, given the expected return and maximizes expected return, given the variance. Later Sharpe (1964) and Lintner (1965b) further developed the work of Markowitz. In their work it has been showed that if the investors’ expectations are homogeneous and when the hold the mean-variance efficient portfolio then in the nonexistence of market friction the market portfolio will be a mean-variance efficient portfolio. There are two basic building blocks to derive the CAPM: one is the capital market line (CML) and the other one is the security market line (SML). In CAPM the securities are priced in a way where the expected risks are compensated by the expected returns. As we will be investigating different form of CAPM in this work it is worthy to review the basic notions of CML and SML. The capital market line (CML) conveys the return of an investor for his portfolio. As we have already mentioned, there is a linear relationship exists between the risk and return on the efficient portfolio that can be written as follows: On the Other hand the SML specifies the return what an individual expects in terms of a risk-free rate and the relative risk of a portfolio. The SML with security i can be represented as follows: Here the Beta is interpreted as the amount of non-diversifiable risk intrinsic in the security relative to the risk of the efficient market portfolio. The utility function of the market agent is either quadratic or normal All the diversifiable risks are eliminated The efficient market portfolio and the risk-free assets dominate the opportunity set of the risky asset. We can use the security market line can be used to test whether the securities are fairly priced. 3. The Logic of the Model: To understand the logic of CAPM, let us consider a portfolio M. To clear the asset market this portfolio must be on the efficient frontier. Thus the underlying concept that is true for minimum variance portfolio, must be true for the market portfolio as well. With the minimum variance condition for portfolio M when there are N risky assets, we can write the minimum variance condition by the following equation: Where is the expected return on the asset i and . The market beta for the asset is derived by dividing the covariance of the market return and individual asset return by the variance of the market return, In the minimum variance condition stands for the expected asset return whose market beta is zero which implies that the asset return is not correlated with the market return. The second term of the equation represents the risk premium. Here the beta measures how sensitive the asset return is with the variation in the market return. Sharpe and Lintner focused on three important implications. They are: 1)the intercept is zero; 2) Beta can completely capture the cross sectional variation of expected access asset return; and, 3)The market risk premium is positive. Sharpe and Lintner in their CAPM model assumed that the pay off from a risky asset is uncorrelated with the market return. In their model the beta becomes zero when the the covariance of a asset return offsets the variance of the other assets’ returns. When the borrowing and lending is risk free and when the asset return is not correlated with the market return then the asset return equals the risk free rate. In the Sharpe-Lintner model the relationship between the asset return and the beta is represented by the following equation: However, this assumption of riskless borrowing and lending is unrealistic. Black (1972) developed a CAPM model where he did not make this extreme assumption. He showed that the mean variance efficient portfolio can be obtained by allowing the short selling of the risky assets. The Black and Sharpe-Lintner model differ in terms of the . Black observed that has to be less than the expected market return which allows the premium for the market beta to be positive. In the Sharpe-Lintner model the expect return was the risk free interest rate. The assumption that Black made about short selling is not realistic either. Because, if there is no risky asset (Sharpe-Lintner version) and if there is unrestricted short selling of the risky asset (Black version) then the efficient portfolio is actually not efficient and there does not exist any relation between market beta and CAPM (Fama and French: 2003). So, the CAPM models are built on some extreme assumptions. To testify the validity of these models researchers have tested the model against the market data. In this paper we will investigate some of those empirical researches. 4. Literature on CAPM testing There are three relationships between expected return and market beta which is implied by the model. First, the expected returns on all the underlying assets are linearly related to their respective betas. Second, the premium for beta is positive which implies that the expected return on the market portfolio exceeds the expected return on assets. Moreover, the returns of these assets are uncorrelated with the expected return of market portfolio. Third, in the Sharpe-Lintner model we see that the underlying assets which are uncorrelated with the market portfolio have the expected returns which are equal to the risk neutral interest rate. In that model, if we subtract the risk free rate from the expected market return, we get the beta premium. Conventionally the tests of CAPM are based on those three implications mentioned above. 4.1 Tests on Risk Premiums Most of the previous cross-section regression tests primarily focus on the Sharpe-Lintner model’s findings about the concept and the slope term which studies the relationship between expected return and the market beta. In that model they regressed the mean asset returns on the estimated asset betas. The model suggests that the constant term in the cross-section regression stands for the risk free interest rate and the slope term stands for the difference between market interest rate and risk free interest rate. There are some demerits of the study. First of all, the estimated betas for individual assets are imprecise which creates the measurement error when we use them to explain average returns. Secondly, the error term in the regression has some common sources of variation which produces positive correlation among the residuals. Thus the regression has the downward bias in the usual OLS estimate. Blume (1970) and Black, Scholes and Jensen (1972) worked on overcoming the shortcomings of Sharpe-Lintner model. Instead of working on the individual securities they worked on the portfolios. They combined the expected returns and market beta in a same way that if the CAPM can explain the security return, it can also explain portfolio return. As the econometric theory suggests, the estimated beta for diversified portfolios are more accurate than the estimated beta for the individual security. Therefore, if we use the market portfolio in the regression of average return on betas, it lessens the cr itical problem. However, grouping shrinks the range of estimated betas and shrinks the statistical power as well. To tackle this researchers sort securities to create two portfolios. The first one contains securities with the lowest beta and it moves up to the highest beta. We know that when there exists a correlation among the residuals of the regression model, we cannot draw accurate inference from that. Fama and Macbeth (1973) suggested a method to address this inference problem. They ran the regression of returns on beta based on the monthly data rather than estimating a single cross-section regression of the average returns on beta. In this approach the standard error of the means and the time series means can be used to check whether the average premium for beta is positive and whether the return on the asset is equal to the average risk free interest rate. Jensen (1968) noted that Sharpe-Lintner model also implies a time series regression test. According to Sharpe-Lintner model, the average realized CAPM risk premium explains the average value of an asset’s excess return. The intercept term in the regression entails that â€Å"Jensen’s alpha†. The time series regression takes the following form: In early studies we reject Sharpe-Lintner model for CAPM. Although there exists a positive relation between average return and beta, it’s too flat. In Sharpe-Lintner model the intercept stands for the risk free rate and the slope term indicates the expected market return in access of the risk neutral rate. In that regression model the intercept is greater than the risk neutral rate and the coefficient on beta is less than . In Jensen’s study the p value for the thirty years period is 0.02 only which indicates that the null hypothesis is rejected at 5% significance level. The five and ten year sub-period demonstrates the strongest evidence against the restrictions imposed by the model. In past several studies it has been confirmed that the relationship in between average return and beta is too flat (Blume: 1970 and Stambaugh: 1982). With the low betas the constant term in the time series regression of excess asset return on excess market return are positive and it becomes negative for the high betas of the underlying assets. In the Sharpe-Linter model, it has been predicted that portfolios are plotted along a straight line where the intercept equals the risk free rate, , and the slope equals to the expected excess return on the market rate . Fama and French (2004) observed that risk premium for beta (per unit) is lower than the Sharpe-Lintner model and the relationship between asset return and beta is linear. The Black version of CAPM also observes the same where it predicts only the beta premium is positive. 4.2 Testing the ability of market betas of explaining expected returns Both the Sharpe-Lintner and Black model predict that market portfolio is mean-variance efficient. The mean-variance efficiency implies that the difference in market beta explains the difference in expected return of the securities and portfolios. This prediction plays a very important role in testing the validity of the CAPM. In the study by Fama and Macbeth (1973), we can add pre-determined explanatory variables to the month wise cross section regressions of asset return on the market beta. Provided that all the differences in expected return are explained by the betas, the coefficients of any additional variable should not be dependably different from zero. So, in the cross-section analysis the important thing is to carefully choose the additional variable. In this regard we can take the example of the study by Fama and MacBeth (1973). In that work the additional variables are squared betas. These variables have no impact in explaining the average asset return. By using the time series regression we can also test the hypothesis that market betas completely explain expected asset return. As we have already mentioned that in the time series regression analysis, the constant term is the difference between the asset’s average return and the excess return predicted by the Sharpe-Lintner model. We cannot group assets in portfolios where the constant term is dependably different from zero and this applies only the model holds true. For example, for a portfolio, the constant term for a high earning to price ratio and low earning to price ratio should be zero. Therefore, in order to test the hypothesis that betas suffice to explain expected returns, we can estimate the time-series regression for the portfolios and then test the joint hypothesis for the intercepts against zero. In this kind of approach we have to choose the form of the portfolio in a way which will depict any limitation of the CAPM prediction. In past literatures, researchers tend to follow different kinds of tests to see whether the constant term in the time-series regression is zero. However, it is very debatable to conclude about the best small sample properties of the test. Gibbons, Shanken and Ross (1989) came up with an F-test for the constant term that has the exact-small sample properties and which is asymptotically efficient as well. For the tangency portfolio, this F-test builds an entrant by combining the market proxy and the average value of an asset’s excess return. Then we can test if the efficient set and the risk free asset is superior to that one obtained by combining the market proxy and risk free asset alone. From the study of Gibbons, Ross, and Shanken (1989) we can also test whether market betas are sufficient enough to explain the expected returns. The statistical test what is conventionally done is if the explanatory variables can identify the returns which are not explained by the market betas. We can use the market proxy and the left hand side of the regression we can construct a test to see if the market proxy lies on the minimum variance frontier. All these early tests really do not test the CAPM. These tests actually tested if market proxy is efficient which can be constructed from it and the left hand side of the time series regression used in the statistical test. Its noteworthy here that the left hand side of the time series regression does not include all marketable assets and it is really very difficult to get the market portfolio data (Roll, 1977). So, many researchers concluded that the prospect of testing the validity of CAPM is not very encouraging. From the early literatures, we can conclude that the market betas are sufficient enough to explain expected returns which we see from the Black version of CAPM. That model also predicts that the respective risk premium for beta is positive also holds true. But at the same time the prediction made by Sharpe and Lintner that the risk premium beta is derived from subtracting the risk free interest rate from the expected return is rejected. The attractive part of the black model is, it is easily tractable and very appealing for empirical testing. 4.3 Recent Tests on CAPM Recent investigations started in the late 1970s have also challenged the success of the Black version of the CAPM. In recent empirical literatures we see that there are other sources are variation in expected returns which do not have any significant impact on the market betas. In this regard Basu’s (1977) work is very significant. He shows that if we sort the stocks according to earning-price ratios, then the future returns on high earning-price ratios are significantly higher than the return in CAPM. Instead of sorting the stocks by E/P, if we sort it by market capitalization then the mean returns on small stocks are higher than the one in CAPM (Banz, 1981) and if we do the same by book-to-market equity ratios then the set of stocks with higher ratio gives higher average return (Statman and Rosenberg, 1980). The ratios have been used in the above mentioned literatures associate the stock prices which involves the information about expected returns which are not captured by the market betas. The price of the stock does not solely depend on the cash flows, rather it depends on the present discounted value of the cash flow. So, the different kind of ratios discussed above play a crucial role in analyzing the CAPM. In line with this Fama and French (1992) empirically analyzed the failure of the CAPM and concluded that the above mentioned ratios have impact on stock return which is provided by the betas. In a time series regression analysis they concluded the same thing. They also observed that the relationship between the average return and the beta is even flatter after the sample periods on which early CAPM studies were done. Chan, Hamao, and Lakonishok (1991) observed a strong significant relationship between book-to-market equity and asset return for Japanese data which is consistent wit h the findings of Fama and French (1992) implies that the contradictions of the CAPM associated with price ratios are not sample specific. 5. Efficient Set of Mathematics The mathematics of mean-variance efficient set is known as the efficient set of mathematics. To test the validity of the CAPM, one of the most important parts is to test the mean-variance efficiency of the model. Thus, it is very important to understand the underlying mathematics of the model. Here, we will discuss some of the useful results of it (Roll, 1977). Here we assume that there are N risky assets with a mean vector ÃŽ ¼ and a covariance matrix ÃŽ ©. In addition we also assume that the covariance matrix is of full rank. is vector of the portfolio weight. This portfolio has the average return; and variance. Portfolio p is the minimum variance portfolio with the mean return if its portfolio weight vector is the solution to the following constrained optimization: We solve this minimization problem by setting the Lagrangian function. Let’s define the following: The efficient frontier can be generated from any two minimum variance portfolios. Let us assume that p and r be any two minimum variance portfolio. The covariance of these two portfolios is as follows: For a global minimum-variance portfolio g we have the following: The covariance of the asset return of the global minimum portfolio g and any other portfolio as defined as a is as follows: For a multiple regression of the return of an asset or portfolio on any minimum variance portfolio except the global minimum variance portfolio and underlying zero-beta portfolio we have the following: The above mentioned result deserves some more attention. Here we will prove the result. As . The result is obvious. So, we just need to show that and . Let us assume that r be the minimum variance portfolio with expected return . From the minimization problem we can write the following: Portfolio a can be expressed as a combination of portfolio r and an arbitrage portfolio which is composed of portfolio a minus portfolio . The return of is expressed as: Since , the expected return of is zero. Because, as mentioned earlier that it is an arbitrage portfolio with an expected return of zero, for a minimum variance portfolio q. We have the following minimization problem: The solution to the optimization problem is c=0. Any other solution will contradict q from being the minimum variance. Since, , thus taking the derivative gives the following expression: Setting the derivative equal to zero and by substituting in the solution c=0 gives: Thus the return of is uncorrelated with the return of all other minimum variance portfolio. Another important assumption of the CAPM is if the market portfolio is the tangency portfolio then the intercept of the excess return market model is zero. Here we will prove the result. Let us consider the following model with the IID assumptions of the error term: Now by taking the unconditional expectation we get, As we have showed above, the weight vector of the market portfolio is, Using this weight vector, we can calculate the covariance matrix of asset and portfolio returns, the expected excess return and the variance of the market return, Combining these results provide, Now, by combining the expression for beta and the expression for the expected excess return give, Therefore, the immediate result is 6. Single-factor CAP In practice, to check the validity of the CAPM we test the SML. Although CAPM is a single period ex-ante model, we rely on the realised returns. The reason being the ex ante returns are unobservable. So, the question which becomes so obvious to ask is: does the past security return conform to the theoretical CAPM? We need to estimate the security characteristic line (SCL) in order to investigate the beta. Here the SCL considers the excess return on a specific security j to the excess return on some efficient market index at time t. The SCL can be written as follows: Here is the constant term which represents the asset return (constant) and is an estimated value of . We use this estimated value as an explanatory variable in the following cross-sectional regression: Conventionally this regression is used to test for a positive risk return trade off. The coefficient of is significantly different from zero and is assumed to be positive in order to hold the CAPM to be true. This also represents the market price of risk. When we test the validity of CAPM we test if is true estimate of . We also test whether the model specification of CAPM is correct. The CAPM is single period model and they do not have any time dimension into the model. So, it is important to assume that the returns are IID and jointly multivariate normal. The CAPM is very useful in predicting stock return. We also assume that investors can borrow and lend at a risk free rate. In the Black version of CAPM we assume that zero-beta portfolio is unobservable and thus becomes an unknown parameter. In the Black model the unconstrained model is the real-return market model. Here we also have the IID assumptions and the joint normality return. Many early studies (e.g. Lintner, 1965; Douglas, 1969) on CAPM focused on individual security returns. The empirical results are off-putting. Miler and Scholes (1972) found some statistical setback faced when using individual securities in analyzing the validity of the CAPM. Although, some of the studies have overcome the problems by using portfolio returns. In the study by Black,Jensen and Scholes (1972) on New York stock exchange data, portfolios had been formed and reported a linear relationship between the beta and average excess portfolio return. The intercept approaches to be negative (Positive) for the beta greater than one (less than one). Thus a zero beta version was developed of the CAPM model. The model was developed in a model where the intercept term is allowed to take different values in different period. Fama and Mcbeth (1973) extended the work of Black et al (1972). They showed the evidence of a larger intercept than the risk neutral rate. They also found that a linea r relationship exists between the average returns and the beta. It has also been observed that this linear relation becomes stronger when we work with a dataset for a long period. However, other subsequent studies provide weak empirical evidence of this zero beta version. We have mixed findings about the asset return and beta relationship based on the past empirical research. If the portfolio used as a market proxy is inefficient then the single factor CAPM is rejected. This is also true if the proxy portfolio is inefficient by a little margin (Roll: 1977, Ross: 1977). Moreover, there exists survivorship bias in the data used in testing the validity of CAPM (Sloan, 1995). Bos and Newbold (1984) observed that beta is not stable for a period of time. Moreover, there are issues with the model specifications too. Amihud, Christen and Mendelson (1993) observed that there are errors in variables and these errors have impact on the conclusion of the empirical research. We experience less favourable evidence for CAPM in the late 1970s in the so called anomalies literature. We can think the anomalies as the farm characteristics which can be used to group assets in order to have a high ex post Sharpe ratio relative to the ratio of the market proxy for the tangency portfolio. These characteristics provide explanatory power for the cross-section of the average mean returns beyond the beta of the CAPM which is a contradiction to the prediction of CAPM. We have already mentioned that the early anomalies include the size effect and P/E ratio as we have already mentioned. Basu (1977) observed that the portfolio formed on the basis of P/E ratio is more efficient than the portfolio formed according to the mean-variance efficiency. With a lower P/E firms have higher sample average return and with high P/E ratio have lower mean return than would be the case if the market portfolio is mean-variance efficient. On the other hand the size effect shows that low market capitalization firms have higher sample return than would be expected if the market portfolio was mean-variance efficient. Fama and French (1992,1993) observed that beta cannot explain the difference between the portfolio formed based on ratio of book value of equity to the market value of equity. Firm has higher average return for higher book market ratio than originally predicted by the CAPM. However, these results signal economically deviations from CAPM. In these anomalies literatures, there are hardly any motivations to study the farm characteristics. Thus there is a possibility of overstating the evidence against the CAPM since there are sample selection bias problem in estimating the model and also there is a problem of data snooping bias. This a kind of bias refers to the biases in drawing the statistical inference that arises from data to conduct subsequent research with the same or related kind of data. Sample selection bias is rooted if we exclude certain sample of stocks from our analysis. Sloan (1995) argued that data requirements for the study of book market ratios lead to failing stocks be ing excluded which results the survivorship bias. Despite an ample amount of evidences against CAPM, it is still being widely used in finance. There is also the controversy exists about how we should interpret the evidence against the CAPM. Some researchers often argue that CAPM should be replaced with multifactor model with different sources of risks. In the following section we will analyze the multifactor model. 7. Multifactor Models So far we have not talked anything about the cross sectional variation. In many studies we have found that market data alone cannot explain the cross sectional variation in average security returns. In the analysis of CAPM, some variables like, ratio of book-to-market value, price-earning ratio, macroeconomic variables, etc are treated as the fundamental variables. The presence of these variables account for the cross-sectional variation in expected returns. Theoretical arguments also signal that more than one factor are required. Fama and French (1995), in their study showed that the difference between the return of small stock and big stock portfolio (SMB) and the difference between high and low book-to-market stock portfolio (HML) become useful factor in cross sectional analysis of the equity returns. Chung, Johnson and Schill (2001) found that the SMB and HML become statistically insignificant if higher order co-moments are included in the cross sectional portfolio return analysis. We can infer from here that the SMB and HML can be considered as good proxies for the higher order co-moments. Ferson and Harvey (1999) made a point that many econometric model specifications are rejected because they have the tendency of ignoring conditioning information. Now we will show one of the very important results of the multifactor model. Let us consider a regression of portfolio on the returns of any set of portfolios from which the entire minimum variance boundary can be generated. We will show that the intercept of this regression will be zero and that factor regression coefficients for any asset will sum to unity. Let the number of the portfolios in the set be K and is the (Kx1) vector of time period t of asset returns. For any value of the constant ÃŽ ¼, there exists a combination of portfolio and assets. Let us consider ÃŽ ¼ be the global minimum variance portfolio and we denote the portfolio as op. Corresponding to op is minimum variance portfolio p which is uncorrelated with the return of op. As long as p and op are efficient portfolios in terms of the minimum variance their returns are the linear combinations of the elements of , where and are (Kx1) vectors of portfolio weights. As p and op are minimum variance portfolio their returns are linear combinations of the elements of , Then for the K portfolios we have, By rearranging, we get the following, Substituting this value into ÃŽ ¼ returns the following: Now let us consider a multivariate regression of N assets on K factor portfolios, where a

Public Administration Dichotomy

RESOURCE MATERIAL SERIES No. 56 COMMUNITY POLICING IN THE CONTEXT OF SINGAPORE Jarmal Singh* I. INTRODUCTION Singapore has grown in many ways over the last 40 years to become a city-state that enjoys a high level of economic growth, political stability and most importantly, a sense of safety and security. It was not a smooth journey, as the country experienced its turbulent periods in the 1950s and the 1960s, characterised by problems of political instability, communist insurgency, secret societies, unemployment and communal riots. Today, Singapore’s crime rate is low by international standards and has declined successively for 9 years from 1989 till 1997. Crime rate has only edged up slightly by 5. 2% in 1998 when the entire South East Asia region plunged into financial and economic crisis. The rise was mostly attributed to theft of handphones and cash cards, and immigration offences. Amidst the rapid modernisation of society, much of the transformation from the old crime-ridden town to a safe city today can be attributed to the Singaporean government’s tough stance towards criminals and criminality in the form of strict laws and heavy penalties. Apart from the strict laws and rigorous enforcement, the improvement of the social and economic situation helped to control crime. Over the past 15 years, the Singapore Police Force (SPF) has made two significant innovations in the area of policing in light of social and economic changes. The first being the introduction * Deputy Director Operations, Police Headquarters, Singapore Police Force, Republic of Singapore. of a community-based policing strategy through the Neighbourhood Police Post (NPP) system in 1983 and the shift towards community-focused policing through the creation of Neighbourhood Police Centres (NPCs) in 1997. Prior to 1983, policing strategies were reactive in nature. Police services were dispensed centrally, mainly through the 8 police stations existing at the time. Each police station served a very large area and these areas were patrolled by cars. Impacted by urbanisation, social and economic transformations, the police were faced with rising crime and a loss of public contact and support. The SPF then realised the importance of fostering closer police-community relations in an effort to prevent crime. By re-orienting a patrol strategy that was skewed towards the motorised mode to one that was community oriented and emphasised foot patrol, it is felt that the police could create a heightened sense of presence and visibility to deter crimes. The Neighbouhood Police Post (NPP) system, adapted from the Japanese Koban System, was introduced in 1983. Eight NPPs were set up as a pilot in a constituency, with a view to assess the impact and success of the system in Singapore’s environment. The trial was a success. By 1993, the entire set of 91 NPPs was set up throughout the island. This was accompanied by falling crime and increased sense of safety and security amongst the public. However, in view of rising expectations of both the public and police officers, and the need to address new challenges arising 126 RESOURCE MATERIAL SERIES No. 56 IV. SPF’S ADOPTION OF THE COMMUNITY POLICING STRATEGY The key strategic driver for the SPF’s adoption of the community-oriented policing model was to establish and leverage community support for our own law enforcement policies and strategies in the face of a changing operating environment. The key changes in the operating environment were as follows. A. Increasing Crime Trend (1974 to 1983) Firstly, although the crime rate in Singapore was low by world standards, there was a disturbing trend of an overall increase in all types of crime (except violent property crimes) for the period 1974 to 1983. Analysis revealed that 70% of such crimes were house-breaking, theft of vehicles, and robbery which could have been prevented. The promotion of community-oriented policing would help the SPF to battle crime, as improved policepublic relationships should result in a higher level of crime prevention awareness and greater public co-operation with the police. B. Change in Population Distribution Secondly, the population distribution was changing. In the 1960s, before the creation of the Housing Development Board (HDB), the bulk of Singapore’s population was concentrated in the centre of the city. However, as a result of the HDB success in providing low cost public housing, about 84% of Singapore’s population shifted from the city area to new towns and estates all over the island. These new public housing estates comprised mainly of high rise buildings and these lessened the opportunities for the public to interact with the police. The SPF could no longer simply rely on the 8 divisional police stations for efficient and speedy service. There was a pressing need to develop a new, efficient response system that could cope with the changing operating environment and at the same time, offer opportunities for meaningful police-public interaction in a densely populated, urban environment. This spurred the SPF to develop the decentralised system of the NPP to serve the needs of the urban population better. C. Rising Public Expectations Thirdly, the general population was increasingly affluent and educated. This raised two challenges for the SPF. One was to meet rising public expectations of the police service. The other was stiff competition from the public and private sectors for quality recruits. The nature and prospects of police work was deemed by the younger generation as comparatively unappealing. Community-oriented policing addressed these two challenges by raising the quality of police services through attracting better quality officers seeking job challenge and satisfaction in the enhanced job scope. D. Learning From the Japanese Koban System At this stage, the SPF was in search of a successful community policing model. This was found in the form of the Japanese Koban (or police post) system. In Japan, the high crime clearance rate of 60% was due to the trust and co-operation that the public had with the police. Statistics also indicated that 80% of the Japanese public readily provided information to help the police arrest offenders. The desire to learn from the Japanese experience and to emulate the success of the Koben system led to the institutionalisation of community policing as the new policing strategy and philosophy of the SPF. 28 112TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS V. THE NEIGHBOURHOOD POLICE POST (NPP) SYSTEM A. Study Team In November 1981, a team of Japanese experts was invited to help implement the Koban System in Singapore. The Koban system was to be adapted for use in the Singaporean environment, with varying social settings and attitudes of the populace. With the recommendation of the Study Te a m t o e s t a b l i s h o n e N P P p e r constituency, 8 NPPs were first established on 1 June 1983 in the 8 onstituencies in one police division. The second phase of implementation began with the setting up of several NPPs in two other police divisions. Positive public response to the NPP system lead to the acceleration of the final implementation phase ending in December 1994. There are now 91 NPPs throughout the island. One division was merged with neighbouring divisions so that manpower saved could be deployed to the frontline, to meet the needs of the new system. B. Purpose of the NPP system The NPP system was implemented in Singapore with the following objectives: †¢ To i m p r o v e p o l i c e – c o m m u n i t y relations in Singapore; †¢ To prevent and suppress crime through the co-operation of and support from the community; and †¢ To project a better police image and win the confidence of the public in the police with more community-oriented services. C. Infrastructure / Location NPPs are the most familiar police contacts of the community within their neighbourhood. They are kept small to be personal, but big enough to make an impact in the community. The area of coverage of each NPP is based largely on the political boundaries. The average population covered by each NPP is about 35,000. Being the most familiar police contacts, the NPPs have been located where their services will be demanded most. Factors such as the number of households and population are taken into consideration in setting up and locating NPPs, to reach out to the widest possible section of the population. D. Oganisational Structure A NPP is manned by a team of about 12 29 112TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS in crime prevention. Another effect of the NPP system is the decentralisation of police functions from the divisional level to neighbourhood level. The police at the frontline are more empowered with greater autonomy and discretion to discharge their duties. This has expedited police response to crime incidents and improved quality service. The decentralisation principle has also enabled the police to penetrate deeper into the society and thereby effectively pre-empt criminogenic conditions. It is possible for the police to tap valuable information about the people and the conditions that are causing criminality. Community policing has shifted its emphasis from car patrols to foot and bicycle patrolling. Together with house visits, this has brought the police very much closer to the community. The increased visibility of the police in the neighbourhoods has helped to deter crimes and eliminate the general fear of crime in society. This is also noted in the decreasing crime rates each year. With community policing, the public has also become more aware of its role and responsibility in crime prevention and detection. The number of public-assisted arrests had steadily increased from 33. 6% in 1992 to 34. 8% in 1993 and 36. 1% in 1994. That is about 1/3 of the total arrests made! The close co-operation is not only reflective of the high level of public spiritedness but also the evolving partnership between the public and the police in crime busting. The SPF has strengthened the trust of the public in the police. Two surveys conducted in 1987 and 1991 confirmed that: †¢ NPPs have created more and closer contact with the public; †¢ NPP system has increased the confidence of the general public in the police; †¢ NPP system has had a positive impact on the image of the police. Community policing and the NPP system has brought the police services to the community, helped Singapore achieve a low crime environment, and enabled SPF’s to reach out to the public. There is ample evidence that the community policing strategy and the NPP system in Singapore is a success. The 1996 Global Competitiveness Report ranks Singapore the safest city in the world in terms of its resident’s confidence that their person and property are protected. VII. BUILDING ON THE COMMUNITY POLICING STRATEGY The NPP based system of community policing has served the SPF well for 14 years from 1983 to 1997. In 1996, the SPF initiated a review of its operational strategy. The review was driven by the realisation that the future would not be built through perfecting the past, no matter how successful it had been. More importantly, the SPF must ensure that the success of the NPP system itself does not become a limiting force that stifles growth and innovation in meeting new policing challenges and public needs. As the SPF enters the new millennium, being the sole provider of policing services, the organisation owes its fellow citizens to further improve the already low-crime environment and strive to make Singapore even safer than it already is. Policing must be done smarter and more effectively than before. It is also clear in the SPF’s vision to become a strong service organisation by 31 RESOURCE MATERIAL SERIES No. 56 making continuous improvement to work processes to meet rising public expectations. At the same time, the SPF wants to offer every officer within the organisation a more enriching job scope and experience that stretches the officer’s abilities and helps maximise their potential. Since the implementation of the NPP system, the SPF has retained its traditional, rea ctive policing capabilities (namely, fast response patrols and investigation teams) in its 7 land divisions. Only the 91 NPPs that report to these police divisions have evolved along the direction of developing community-based policing capabilities. Community-based policing has also been refined incrementally over the past 14 years. One recent innovation is the doctrine of ‘problem-solving’, under which NPPs initiate the resolution of certain community law and order problems that arise from simple systemic causes. The problem-solving approach makes community policing more effective because it is proactive and pools the whole community together in a concerted effort to ensure a safe, peaceful and cohesive society. An example of a good problem-solving case is that by Hong Kah South NPP. The NPP used to receive many complaints about coffee-shop patrons consuming beer, after midnight, even when the coffee-shops in question were closed. Residents complained about the incessant noise pullution, littering, vandalism (often vulgar words written on walls), urinating in public places, and fighting. The coffeeshop patrons’ unruly behaviour caused sleep deprivation to the residents, dirtied the neighbourhood and created a sense of public unease (especially to young ladies returning home late at night). Aware that repeated one-off responses to such incidents is not an operationally effective solution to the problem, Hong Kah South NPP adopted the following solution. A. Co-operation with Coffee-shop Owners Mindful of the need to maintain good rapport and act in partnership with community resources to resolve local community problems, Hong Kah South NPP officers sought the co-operation of the owners of the problematic coffee-shops in the following areas: (1) Stop the sale of beer and start preparations to close half-an-hour before the end of the stipulated licensing time, so that ample time is given to the patrons to finish their drinks. 2) Stack up the chairs and chain them up, before closing the coffee-shop. (3) Remove all empty bottles from the tables to prevent their use as weapons. B. Dialogue Sessions with Coffeeshop Owners Based on the recommendation of Hong Kah South NPP, regular dialogue sessions with the coffee-shop owners are conducted at the Division Headquarters. Besides crime prevention advice and police recommendations, there is also sharing of good practices between the coffee-shop owners. This is effective in exerting peer group pressure on the owners of problematic coffee-shops to follow the good example set by their counterparts. The above measures were successful in curbing the problem. Residents now enjoy restful nights, the neighbourhood is pristine in appearance, and a sense of peace and security prevails. 132 112TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS VIII. NEW OPERATING ENVIRONMENT The SPF is expected to work in an increasingly complex environment, with forces that affect community safety and security arising from a multiplicity of causes. These complex social ills are characterised by seemingly intractable problems such as juvenile delinquency, spousal violence, the link between substance abuse and property crime, or the law and order problems posed by foreigners working in Singapore. The SPF’s traditional tool of effective enforcement can no longer, by itself, adequately address these challenges. The doctrine of problem-solving, which is targeted at simple systemic causes, and analysed and solved at the level of the NPP, is also impotent in the face of higher-order social dysfunction. In addition, the SPF must continue to meet the rising public expectations of its service standards, as well as its ability to enhance safety and security. A recent survey, commissioned by the Service Improvement Unit (SIU), revealed that one area where the SPF failed to provide high levels of satisfaction, but which was highly important to the public, was the ability of the police to help solve the problems that have been brought to the attention of the NPPs. In October 1997, the SPF made another significant and bold move to re-design the NPP system in a bid to strengthen its community policing approach, in light of the changing environment and factors. A system that can carry the strengths of the previous NPP system, like tapping on local knowledge to solve crimes, and at the same time can enable the SPF to grow and address key policing needs, ensures its continued relevance. The Neighbourhood Policing Centres (NPCs) system has been created to enhance the community policing approach by: †¢ Strengthening the SPF’s front-line operating system; †¢ Building a strong service organisation; †¢ Increasing community involvement and responsibility for its own safety and security; and †¢ Optimising the value contributed by each police officer to the policing process. IX. NEIGHBOURHOOD POLICE CENTRES A. Changes to NPP System In order to deliver decentralised, flexible, integrated and community-focused capabilities, the existing structure of land divisions and NPPs will be modified. At the centre of the new operating system is the Neighbourhood Police Centre (NPC). The existing seven police land division will be reconfigured into six policing regions. Reporting to each of these regional commands will be the NPCs. NPCs will be the sole vehicle for front line policing to ensure the community’s safety and security. In the redesigned system, NPCs will be accountable for the total outcome of policing in the community. A short summary of the key differences between the NPP system and the NPC system is given in the below figure. 133 RESOURCE MATERIAL SERIES No. 56 NPP System †¢ Community policing post with limited services †¢ Disparity in workload due to electoral based boundaries †¢ General services to attract residents and establish points of contact †¢ Low value tasks and narrow job scope †¢ Compartmentalisation of services with many officers each performing a separate task †¢ Lower priority on proactive work †¢ Community-based policing NPC System †¢ One-stop total policing centre provding the full range of policing services †¢ More efficient pooling of manpower resources to serve the community †¢ Focused on services that are critical to safety and security †¢ High value, board job challenge, better quality officers †¢ Integrated service process with one NPC officer handling the entire service process †¢ Dedicated resources for proactive work †¢ Community-focused policing B. One-Stop Total Policing Centre Today, the areas policed by NPPs vary significantly. Some NPPs have jurisdiction over areas with 2,000 households, while other NPPs serve up to 5,000 households. NPCs will however serve areas of similar residential population sizes. On average, each NPC will serve about 100,000 residents. The size of each NPC is kept to between 100-120 officers, with administrative overheads, such as personnel and logistical support borne by the Regional Command Headquarters. It also ensures that the NPCs are not so large as to present a cold and impersonal image to the public. A total of 32 NPCs will be created by the year 2001 to serve an indigenous population of 3. 2 million in Singapore. NPCs, as centres of total policing, have a variety of policing options, ranging from reactive patrols and investigations, to proactive policing activities. These 32 NPCs islandwide will be supplemented by at least another 66 NPPs, with each NPC managing between 1 to 4 NPPs. With the creation of NPCs, the current 91 NPPs would be re-distributed to achieve a balance of easy accessibility to police counter services for the public and an optimal number of NPPs to be deployed in the NPC system. The emergence of the NPC as the sole vehicle for the provision of policing services means that NPPs cease to be sub-units of a larger police unit. The officers manning each NPP will come from the NPC itself. NPPs therefore represent service points only, with the deployment of patrol, investigative and pro-active policing resources based on the overall needs of the entire NPC area of operations. Each of the six policing regions will have a dedicated Regional Command Headquarters to oversee police operations. Six Regional Commands will replace the existing seven police divisions. These Regional Commands will comprise the command and support elements for the region. They will also include specialist investigation units, focusing on investigations into serious crimes and other investigations likely to lead to prosecution in court. Each Regional Command will house a NPC to serve as the frontline service point for public interaction. 1 A DGP is a development plan that defines and builds a particular township. In the plan, each town’s boundaries are clearly mapped out. 134 112TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS C. Relationship to Development Guide Plans (DGPs) Unlike the current NPP boundaries which are linked to political constituencies that may change after a General Election, NPC boundaries are based on Urban R e d e v e l o p m e n t A u t h o r i t y ’s ( U R A ) Development Guide Plan1 (DGP) areas for the following reasons: (a) The DGP areas, being geographicallybased, provide additional leverage by creating a sense of territorial ownership for the community. This will enhance the community’s identity. (b) Each DGP area also has an expressed vision, which further reinforces a coherent identity for the communities living within that area. (c) DGP areas are permanent, unlike electoral boundaries. Over time, distinct identities can emerge for different communities, without being interrupted by changes in constituency boundaries. Each NPC operates out of police facilities sited within its area of operation. Facilities for the NPCs could be co-located with other community agencies. A single edifice, representing all the community agencies for that area, will tity for the comm the sense of community identity and permanence. As NPCs or NPPs are the means through which the SPF engages in community-focused policing, they can easily blend into a building that encompasses community clubs, community libraries, and service points for other community agencies. Most NPCs would be sited at 0. 0heart of 0. 0residential area in photing geilities commonmay other 135 12TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS The shift to a community-focused model fundamentally alters the SPF’s operating strategy. Successful policing will no longer be measured solely in terms of falling crime rates. As the focus shifts from the police to the community, what matters to the community, for example, its level of safety and security, will determine the success or otherwise of the police mission. Arising from this, it will no longer be appropriate for formulaic approaches to be applied uniformly across the different communities. Police units operating in different communities must develop customised solutions tailored to meet the needs of, and which take into account the constraints on, each community. The need for specific, custom-made and workable solutions will redefine the required levels of operational competence. These demands will fuel the need to develop, at the organisational level, learning competencies so that each customised solution enhances the quality of the future solutions. An operating strategy that is focused on the community also entails a skillful management of relationships between the police and the individuals, grassroots organisations and volunteer groups that form part of the community. Structurally, the SPF will be empowered and decentralised, so that at the front-line, police officers can ‘broker’ for total solutions in response to community law and order concerns. Such an approach also has other benefits, in particular, by providing opportunities for individual participation in improving the quality of life in their community, so that emotional bonds to the community are strengthened. It therefore forms an inportant element in the overall Singapore 21 vision to build a civil society with strong community ties and active citizenry. It is also an integral part of the Government’s strategy to gradually lower the community’s level of dependency on the authorities and to engender shared responsibility for social problems and their solutions. This is done through a new initiative known as the Community Safety and Security Programme (CSSP). A CSSP is an action programme jointly drawn up by the grassroots leaders, residents and the police. It consists of the profile of the community and its needs, the key agencies involved and the action plans to tackle the community problems affecting the safety and security of the neighbourhood. Through CSSPs, the NPC system aims to shift the community’s mindset from what the police are doing about safety and security to what we can do together. One CSSP is crafted for each precinct / constituency and each differs from another, since different communities in different neighbourhoods might not share the same concerns. The CSSP aims to get the residents more involved and be responsible in taking actions to address the safety and security concerns affecting their neighbourhoods. The driving belief is that each citizen can make a difference to society. G. Optimising the Contribution of Each Police Officer The SPF currently has a regular component of nearly 8,000 officers and 1,000 civilian staff. With falling birth rates, the size of new cohorts joining the workforce grows smaller each year. As an organisation competing within a limited pool of labour resources, the SPF must be attractive and challenging to bring in the best talents. Whilst the SPF may face the challenge of attracting the best talent to join the organisation, it has to contend with retaining officers over the medium to long term. 137 RESOURCE MATERIAL SERIES No. 6 SPF Commissioner, Khoo Boon Hui, during one of his dialogue sessions with NPC officers said â€Å"†¦ That is why not only do we have to take care of those who are with us, but to get the best people to join us †¦ one of the main motivations of why we are going into the NPC system, †¦ is to bring our officers to match the expectations of the job, and to be proud of what we do. ’’ Through the NPC system, the SPF is able to redesign its front-line jobs so that it can continue to recruit quality manpower from each graduating cohort. At the same time, in line with the national policy of continuous upgrading, the SPF also needs to upgrade the qualifications of its officers after they have entered the service. Redesigning jobs to create viable and attractive front-line careers in the NPC system will serve to enhance the credibility of the SPF as an institution, and instill trust in the ability of the police to carry out their tasks effectively. The educational profile of police officers serving the community must be kept high to match the broad rise in educational qualification of the society. This will enable the SPF to meet new job demands, greater challenges and the expectations of the public. H. Organisational Structure In order to fulfil its role as being community-focused, and responsive and flexible to the needs of the community, police officers in the NPC operate in an empowered and self-directed fashion. They work as a team, rather than as individual officers. The basic unit in the NPC is a group of three front-line officers, led by one group leader. This group of four officers is selfdirected, and undertake the entire range of policing responsibilities of the NPC. Each group is also responsible for nurturing and building a network of community relationships within a precinct of about 1,200 – 1,500 households. As this group is deployed as an operational entity, it will facilitate the process of team learning, a key lever of change and innovation in the redesigned system. An average of 5-6 such groups form one team, which is led by a team leader and one assistant team leader. Each NPC have four teams, reporting to an NPC commander. Each NPC comprise, on average, 100 regular officers. With another 15 full-time national servicemen attached to the NPP, the total strength of the NPC will be about 115 officers. X. NPC IMPLEMENTATION PHASES As the NPC system is a major change for the entire the SPF. It involves major re-organisation. Many of these changes will take some time to implement. A phased implementation approach has been adopted: Pilot Phase Oct 1997 1st pilot: Queenstown NPC created in Central West Region. Apr 1998 Another 3 NPCs, namely Bukit Timah NPC, Bukit Merah West NPC and Jurong East NPC were created in same region. Phase I Jun 1999 6 NPCs in West Region to be created. Phase II Jun 2000 4 NPCs to be created in North, North-Eastern and East Regions. 138 112TH INTERNATIONAL TRAINING COURSE VISITING EXPERTS’ PAPERS Phase III Jun 2001 SPF believes that its policing changes are set in the right direction. Our success will depend on our willingness to learn and change the way we operate. Our commitm ent and passion to learn collectively as a team, from the top police chief to the front-line officer, may turn out to be the most significant success factor in our journey into the new policing environment presented by the next millennium. 8 NPCs to be created in Central-Western Region. In Commissioner Khoo’s words, â€Å"A pilot is not to test whether the system will succeed or not. It is a pilot in the sense that we allow officers more leeway to improve the system, to share experiences, resolve operational problems and tell us what is wrong. ’’ XI. PRELIMINARY NPC’S RESULTS A recent review of the pilot phase implementation has shown positive results and all the NPC objectives are largely met. A large majority of the officers felt that they had stronger working relationships with their team mates, between teams, supervisors and key officers. 74% of the officers were confident of their abilities in performing the various aspects of NPC duties (information technology systems, procedures and investigative duties). 81% of the officers rated the facilities in the NPC to be much better than their previous workplace (NPPs and Land Divisions). Compared to one year before the NPC was set up, one-third of the residents in the precincts felt that the safety in the neighbourhood now was â€Å"much better†. Two-thirds of the residents felt that the police had at least made some improvements to increase security in the neighbourhood with the creation of NPCs. XII. CONCLUSION The community policing framework in Singapore has been shifted to uplift the professionalism in front-line jobs and getting the community more involved in safety and security matters. The re-design of the NPP system builds on the achievements the SPF has made over the past few years – community policing, empowerment and quality service. The 139

Why Everybody Is Talking About Civil Rights Research Paper...The Simple Truth Revealed

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