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Official websites use. Share sensitive information only on official, secure websites. Email: a. This was expected to lower retail prices through increased competition. Evidence of such price reductions is scarce. We use price data on five popular OTC drugs for all retailers located in Lisbon for three distinct points in time , , and We find no evidence of price reductions following OTC outlet entry. During the last two decades, European countries have extensively reformed their community pharmacy sectors. Throughout this paper, we refer to these as nonpharmacy retailers. Existing literature posits that pharmacies are not used to price competition and do not place competitive constraints on each other Pilorge, ; Stargardt et al. The fact that, at least in urban areas, nonpharmacy retailers charge lower prices than traditional pharmacies Anell, ; OFT, might mechanically lead to lower average prices but provides no evidence of competitive forces. We examine whether facing increased competitive pressure following the entry of a nonpharmacy competitor, who is able to charge lower prices, triggers price decreases by incumbent pharmacies. This is an important question that, to the best of our knowledge, has not yet been fully addressed in the literature. OTC drugs are one of the few product segments for which pharmacies can make their own pricing decisions. Because they are frequently used, we expect consumers to be aware of price differences between retailers Sorensen, By shedding light on how competition takes place in this market, we contribute to inform policy makers on the market dynamics they might expect upon liberalizing OTC medicine distribution. The empirical analysis draws on the Portuguese experience. In Portugal, OTC market liberalization started in late and allowed OTC drugs to be sold outside pharmacies, namely, in supermarkets and outlets. OTC market liberalization reforms similar to the Portuguese one were implemented all over Europe during the last two decades: in , Poland allowed for a limited range of OTC products to be sold outside pharmacies; Denmark, Norway, Italy, Hungary, Sweden, and France adopted similar policies in the following years; Germany and the United Kingdom had already done so during the s. We use price data for five popular OTC drugs across all retailer types traditional pharmacies, supermarkets, and outlets located in Lisbon. The dataset has a panel structure, and each retailer is observed for at most three points in time, the years of , , and We use two alternative baseline measures to define the set of main competitors of a pharmacy. One measure takes as main competitors of a pharmacy its three nearest neighbors selling OTC drugs. Identification comes from the different timing of exposure of incumbent pharmacies to different types of nonpharmacy entrants among their main competitors. We do not find evidence that outlet entry leads to price reductions by pharmacies. We find a fair degree of heterogeneity in price responses across pharmacies operating in areas with different degrees of market concentration with our results being driven by the most isolated pharmacies, who likely enjoyed some degree of market power prior to experiencing entry. We interpret our findings in the context of a model based on Salop with nonpharmacy entrants differing from incumbent pharmacies in their marginal cost and, in particular, supermarkets being more efficient. Our results do not seem to be driven by existing pretreatment trends and survive a battery of robustness checks. When varying the number of nearest neighbors and the radius distance that define the set of main competitors of a pharmacy, we find that the statistical significance of our results falls quickly as we enlarge the set of main competitors of a pharmacy, suggesting competition is fairly localized. The causal interpretation of our findings, however, rests on the assumption that market structure is exogenous so that exposure to nonpharmacy entry is random. We address endogeneity concerns in two ways. We find no evidence of an association between past prices and nonpharmacy entry. We show that OTC liberalization reforms can lower prices via increased competition, though this crucially depends on the ability of entrants to exert competitive pressure on incumbent pharmacies. Our study also contributes to a broader literature within industrial organization on the price effects following the entry of supermarkets and chain stores in general in a market previously composed of small, independent firms, as is the case of traditional pharmacies in Portugal. Bennett and Yin study the entry of a retail pharmacy chain in India on the price of incumbent pharmacies. We contribute to this literature by providing evidence for the OTC drug market. The remainder of this paper is as follows. Section 2 provides institutional background on the Portuguese OTC market and the liberalization process. Section 3 describes the dataset, and Section 4 presents the empirical strategy. Section 5 presents the results, and Section 6 concludes. Traditionally, community pharmacies enjoyed a monopoly for selling both prescription and OTC drugs. Prescription drugs remain available only at traditional pharmacies. The first nonpharmacy retailers entered the OTC market in October In supermarkets, by regulation, OTC drugs are not are freely accessible to customers. Either way, customers wishing to purchase a given OTC drug must request it from the cashier or the employee attending to the dedicated area. Most supermarkets selling OTC drugs in Lisbon belong to either one of the two biggest supermarket chains in Portugal. Nonpharmacy outlets are stores selling cosmetics, baby care products, vitamins, and supplements, among others. OTC drugs represented a natural expansion of their product range. Outlets can be either independently owned or part of small chains of two or three stores. Nonpharmacy retailers wishing to enter the Portuguese OTC market must apply for a license at the National Authority of Medicines and Health Products Infarmed and satisfy specific requirements related to drug storage, qualification of personnel, etc. Application by supermarket and outlet chains is done individually by each store belonging to the chain as opposed to one license application for all stores belonging to the chain. The entry of supermarkets and outlets in the OTC market took place quickly following market liberalization. In the first quarter of , there were over nonpharmacies in Portugal, and by the end of , there were about 1, The volume share of OTC drugs in the total outpatient pharmaceutical market was The corresponding value share was Our data consist of the prices of five popular OTC drugs charged by all pharmacies, supermarkets, and outlets located in the municipality of Lisbon for three different points in time, the years of , , and In , these five drugs accounted for More importantly, they are available at all retailers. We then carried out two additional rounds of data collection, in and Infarmed keeps an online, updated list of all active retailers that are licensed to sell OTC drugs. We examined these lists before each data collection round and identified the active retailers and their exact locations. We collected price data for and between December and February and between February and April , respectively. When we carried out the data collection in and , we purchased the drugs at retailers whose staff refused to disclose prices. In these two periods, we observe prices for all retailers located in Lisbon. We use the latitude and longitude coordinates of each retailer to identify its main competitors at each time period. We also construct indicators for retailer type traditional pharmacy, supermarket, or outlet and the parish where each retailer is located. Finally, we have data from the Portuguese census on the population living in the census block where each retailer is located. We follow retailers over the three time periods for which we have data. Our dataset is unbalanced because there are retailers entering and exiting the market between each data collection round. The number of supermarkets selling OTC drugs in our dataset increased over time, from one in to 25 in The number of outlets selling OTC drugs raised from eight in to 25 in and then slightly declined to 21 in The number of traditional pharmacies has been declining over time, from in to in We now highlight a few patterns present in our data. The average prices of the drugs under analysis increased over time, as did their variance. This might be due to economies of scale in the distribution chain of supermarkets, more efficient practices regarding stock management and logistics, and stronger bargaining position when engaging in price negotiations with suppliers because of larger quantities purchased. All of these result, cumulatively, in lower marginal costs, leading to lower equilibrium prices for supermarkets. Outlets are either independent stores or part of very small chains, which might imply that they face wholesale prices similar to those faced by traditional pharmacies. We use a DID strategy to assess the price effects following the entry of nonpharmacy retailers. Nonpharmacy entry started before our first round of data collection. However, nonpharmacy entry took place gradually, meaning that each pharmacy experiences entry of different types of nonpharmacies among its main competitors at different points in time. This is our source of identification. We start by defining the set of main competitors of pharmacy i. One way to define the main competitors of a pharmacy is to consider its N nearest neighbors in terms of walking distance as main competitors. Another way to define the main competitors of pharmacy i is to consider all retailers located within a radius R centered around i as main competitors. We use these two alternative definitions of main competitors throughout our analysis. Prior to treatment, its set of main competitors consists only of traditional pharmacies. Because supermarkets and outlets charge different prices, they might exert different levels of competitive pressure on incumbent pharmacies and generate different price effects. Additionally, for each type of treatment, we distinguish three treatment cohorts, c , according to treatment timing. In total, there are six treatment groups corresponding to two types of treatment and three treatment cohorts. The control group is composed of pharmacies who never face nonpharmacies among their main competitors. We estimate the price differences between each treatment group and the control group at each of our sample years. The regression counterpart of these differences is as follows:. In Equation 1 , i indexes the pharmacy, t indexes time in years, k indexes the drug, and c indexes the treatment cohort. The dependent variable is the natural logarithm of the price charged by pharmacy i , for drug k in year t. S U P E R i c and O U T L E T i c are vectors of indicators for each of the three cohorts that experienced the entry of a supermarket and outlet, respectively, among their main competitors. For example, S U P E R i 2 is a binary indicator taking value 1 in case pharmacy i experienced the entry of a supermarket among its main competitors between and the second treatment cohort and value 0 otherwise. Similarly, O U T L E T i 3 is a binary indicator taking value 1 if pharmacy i experienced the entry of an outlet among its main competitors between and the third treatment cohort. Their estimates convey the price impact of nonpharmacy entry on incumbent pharmacies and their dynamics over time. Our empirical design is as flexible as possible, given that we only have data for three time points in time. Pharmacies experiencing supermarket and outlet entry after are observed twice prior to treatment, in and in Because these are price differences prior to treatment, the statistical significance of these estimates is informative about the plausibility of the parallel trend assumption. Additionally, pharmacies experiencing supermarket and outlet entry between and are observed twice after treatment, in and in Comparing these two pairs of estimates allows us to assess the persistence of the price effects induced by nonpharmacy entry. When taking our model to the data, we select specific values of N and R. In this case, the treatments consist on the entry of a supermarket or outlet in the set of three nearest neighbors before , between and , or between and We vary our choices of N and R in robustness checks. We cluster standard errors at the pharmacy level to account for serial correlation in pharmacy pricing decisions. Because our main interest is on the effects on the pricing of incumbent pharmacies, we estimate our baseline model among pharmacies only. Throughout most of our analysis, we focus on samples in which all treatment and control groups are mutually exclusive. Thus, the number of pharmacies used in the estimation and the number of pharmacies in the treatment and control groups vary with the definition of main competitors. Each pharmacy belongs to the same group throughout all time periods in which it is observed. However, the number of pharmacies in each group can vary over time because of market entry and exit. The increase in the number of pharmacies in the control group between and reflects the missing price data for , as discussed in Section 3. Note : The table shows the number of pharmacies included in the baseline estimation samples per treatment group and year for our two alternative definitions of main competitors. In the first three columns, the main competitors of a pharmacy are defined as its three nearest neighbors. The lower number of pharmacies in the control group in is a consequence of missing price data for that year, as discussed in Section 3. Within a definition of main competitors, we focus on a sample of pharmacies for which all the treatment groups and the control group are mutually exclusive. One concern is that pharmacies in the control group and those that eventually face nonpharmacy entry are already somewhat different prior to treatment. In Table 2 , we compare the pretreatment means of our main variables for pharmacies in the control group and those treated after We do this for our two alternative measures of main competitors. We exclude pharmacies who experienced nonpharmacy entry before , as for these we have no pretreatment observations. Pharmacies that eventually experience nonpharmacy entry charge lower prices for some of the drugs under analysis in , and they tend to be located in areas with higher population. This motivates the estimation of Equation 1 on a matched sample of pharmacies Section 4. Note : The table shows the mean of several variables of interest across pharmacies in the control and treatment groups for our two alternative measures of main competitors. For each panel, the first column reports averages across pharmacies belonging to the control group. The second column reports averages across pharmacies that were not yet treated in but will eventually face the entry of a nonpharmacy among their three nearest competitors, thus grouping together pharmacies facing the entry of a supermarket or an outlet either between and or between and Pharmacies already treated in are not accounted for in this table because they are not observed prior to treatment. Entry is expected to have stronger effects in areas where market structure is more concentrated, that is, closer to a monopoly. We assess this hypothesis by estimating Equation 1 among the most and the least spatially isolated pharmacies, alternatively. We define the most spatially isolated pharmacies on the basis of information for In the case of our nearest neighbors measure of main competitors, the most least spatially isolated pharmacies are those whose walking time in minutes to their third nearest competitor is above below the sample median in We mitigate this concern by restricting the control group to pharmacies whose main competitors are in the control group themselves. This robustness check is informative about whether our choice for the set of main competitors and our definitions of control and treatment groups are adequate. The maps of the market structure of the OTC market in Lisbon in Appendix S2 show that some retailers exited the market during our study period. Most of these were pharmacies. In robustness checks, we address pharmacy exit in several ways. First, we estimate Equation 1 on a balanced panel of pharmacies. Second, we estimate Equation 1 among pharmacies whose main competitors do not exit the market. Third, we assess whether experiencing the entry of a nonpharmacy retailer makes pharmacies more likely to exit the market in the future. Specifically, we estimate a logit model whose dependent variable is a binary indicator taking value 1 in case pharmacy i exits the market before the next round of data collection and value 0 otherwise. If the estimates corresponding to the treatment group indicators are not statistically different from zero, then experiencing entry of a supermarket or outlet does not systematically cause pharmacies to exit the market. Our estimates from Equation 1 can only be interpreted as causal if entry and location decisions of nonpharmacies are exogenous. The decision to open a supermarket or outlet in a given location is plausibly unrelated to pharmacy market structure, as OTC drugs are a small subset of their product range. However, it is more difficult to defend the exogeneity assumption when not all retailers belonging to a given chain apply for a license to sell OTC drugs. These shocks are difficult to measure at the small geographic level we are working with. In an attempt to mitigate this concern, we combine propensity score matching with our DID design Heckman et al. The underlying intuition is that by matching treated and untreated pharmacies on their propensity score, that is, on their probability of being treated, we make treated and control groups more similar in terms of the observables used in the estimation of the propensity score. Thus, treatment should be random, conditional on those observables. We estimate the propensity score as a function of the levels of competitive pressure and demand faced by each pharmacy prior to experiencing nonpharmacy entry. We then use the estimated propensity scores to build a matched sample of pharmacies using single neighbor matching. Finally, we estimate Equation 1 in this matched sample. Another potential threat is that, in addition to pharmacies adjusting their prices in the presence of a nonpharmacy, nonpharmacies make location decisions on the basis of prices charged by existing pharmacies in the area. That is, nonpharmacy entrants select where to enter the market on the basis of past prices in the area. For example, entrants might choose to enter in areas where prices are higher as there they could potentially only slightly undercut the incumbents and make higher profits. Then in year t , entry is realized and observed, and all players make their pricing decisions for that year taking entry as given. We have no information on retailers that did not enter the market. Thus, we use the fact that we observe entry in certain locations but not in others. The equation taken to the data is as follows:. Although we do not observe this probability, we observe whether a pharmacy experienced nonpharmacy entry at a given point in time, entry it. Thus, entry it is a binary indicator taking value 1 in case pharmacy i experienced the entry of a supermarket or outlet among its main competitors in year t and value 0 otherwise. Because we take lags of price, the model is estimated using the years and only, and the lags are taken with respect to the previous period for which we have data. We estimate separate models for the probability of experiencing entry of a supermarket or an outlet and for our two definitions of main competitors. Table 3 shows our main results. The results are broadly similar across the two definitions of main competitors. The effects are insignificant for pharmacies experiencing entry of a supermarket between and In Columns 1 and 3, the main competitors of pharmacy i are its three nearest neighbors. The first two columns estimate the model in the original sample. The last two columns estimate the model on a matched sample of treated and control pharmacies matching was done using single neighbor matching on propensity scores. We disregard the groups that were treated already in in the matching, as for those we do not observe a pretreatment period. Standard errors are shown in parenthesis. In Columns 1 and 2, standard errors are clustered at the pharmacy level. The entry of an outlet among the main competitors of a pharmacy is not associated with price reductions. The finding that incumbent pharmacies react differently to supermarket and outlet entry is consistent with a model in the spirit of Salop , where competition is localized and nonpharmacy entrants can have a cost advantage or cost disadvantage relative to traditional pharmacies. We outline such a model in Appendix S1. In our model, the extent to which pharmacies lower prices after experiencing nonpharmacy entry depends on two distinct forces. On the one hand, there is now a closer competitor that creates downward pressure on incumbent prices. On the other hand, because of the localized nature of competition, incumbents may face a softer or tougher rival at the margin. In case of a more efficient entrant, both these forces go in the direction of lowering pharmacy prices closer and more efficient rival. In case of a less efficient entrant, the two forces work in opposite directions, and the total impact on pharmacy prices is ambiguous. In the last two rows of Table 3 , we compare the prices charged in by pharmacies that experience nonpharmacy entry only after with those charged by pharmacies in the control group. The lack of statistical significance of these estimates supports the plausibility of the common trend assumption, but their magnitude is sometimes not too different from our main effects. Figure 5. These plots do not suggest different trends across groups, though we would need a longer panel to make a stronger claim regarding this matter. In the last two columns of Table 3 , we report the results from estimating Equation 1 on a matched sample of treated and control pharmacies, with matching done using single neighbor matching on propensity scores. The size of the matched sample is considerably smaller than the size of the baseline sample. Although the results obtained with the matched sample go in the same direction as the ones obtained with the baseline sample, some statistical significance is lost. Table 3. Our baseline results are robust to estimating the model on a balanced panel of pharmacies, including all retailer types, including pharmacies that are in multiple treatment groups, restricting the sample to pharmacies whose main competitors are in the control group themselves and restricting the sample to pharmacies whose main competitors do not exit the market Tables 3. We vary the values of N and R for the definitions of main competitors in Appendix S4. The findings from that exercise convey the fact that competition in the OTC market is very localized. For example, increasing N from 3 to 5 shows very few statistically significant price effects following nonpharmacy entry. Similarly, when enlarging the radius within which main competitors are located from to or m, most of the price effects vanish see Table 4. Experiencing the entry of a nonpharmacy retailer does not seem to cause pharmacies to exit the market before the next round of data collection Table 3. There are two panels. The top panel takes the main competitors of pharmacy i as its three nearest neighbors. The second row tests whether it depends on the lagged prices of pharmacy i relative to the average bundle price in the city of Lisbon. Recall that our estimation sample differs according to how we define the set of main competitors of pharmacy i , so that a different number of observations is used to obtain each estimate shown on the table. Standard errors shown in parenthesis are clustered at the pharmacy level. We do this separately by year and by type of nonpharmacy entrant. If entry is in any way related to current or past prices, then these plots should convey a nonrandom relationship. In particular, if entry occurred in locations that were potentially more profitable because they had higher prices, then pharmacies in the highest price deciles would experience the largest shares of entry by nonpharmacies. We find no such pattern Figures 5. A similar analysis using deciles of resident population instead of price deciles yields again no clear pattern Figures 5. We use unique OTC price data at the retailer level for the city of Lisbon to examine the effects of nonpharmacy entry on the prices of incumbent pharmacies. We show that nonpharmacy entry can be successful at fostering competition and lowering prices charged by pharmacies. However, the extent to which this occurs depends crucially on the type nonpharmacy entrant and, particularly, on their ability to exert competitive pressure on incumbent pharmacies. This means that supermarkets have a greater ability to exert competitive pressure on pharmacies than outlets. Our baseline results reflect those differences. Furthermore, although incumbent pharmacies lower their prices as a response to supermarket entry, they do not lower prices enough so as to match the prices charged by supermarkets. This findings are in line with predictions from a model in the Salop tradition with nonpharmacy entrants differing from incumbents in their marginal cost. Our results are specific to retailers operating in the municipality of Lisbon and to the set of drugs and time periods we analyze. They might not generalize to other settings. In particular, price reductions may not occur in rural areas, where entry of supermarkets takes place on a smaller scale. Nevertheless, our study contributes to a deeper understanding of how competition takes place in retail pharmaceutical OTC markets. Moura A, Pita Barros P. Health Economics. In Table 2, the euro sign was previously omitted and has been added in this version. In Table 4, the missing text has been added in column 1 row 3. This inability may be associated with either the development of close professional relationships among pharmacists or their use to the noncompetitive environment in place prior to market liberalization. These predictions pointed in very different directions, and the real impact of the reform was never assessed. Traditional pharmacies in Portugal are independently owned because of existing ownership restrictions that limit the number of pharmacies that an agent can own. Ownership restrictions are common and seek to ensure a certain degree of market competition. We cannot completely rule out that pharmacies adopted strategies other than pricing to prevent nonpharmacy entry. Nevertheless, the fact that nonpharmacy entry took off quickly after liberalization, combined with pharmacies not being used to operate in a competitive environment, leaves less scope for such strategic behavior. Throughout the paper, entry in the OTC market refers to the moment at which a retailer is granted a license to sell OTC drugs. After , Infarmed stopped releasing sales data by commercial designation, so we do not have more recent figures. Supermarkets and outlets typically carry a smaller selection of OTC drugs than pharmacies. Portuguese municipalities are composed of smaller areas called parishes. The number and geographic borders of the Lisbon parishes were revised in According to the revised version, which we use in our analysis, there are 24 parishes in Lisbon. This accounts for physical barriers that might cause two nearby retailers not to be regarded as competitors by consumers, for example. We use indicator variables for facing nonpharmacy entry, as opposed to measures of the general level of competitive pressure faced by a pharmacy. This is because we are specifically interested on the additional competitive pressure originating from the entry of different types of retailers, supermarkets, and outlets. Our main interest is not on the general level of competitive pressure originating from a higher density of traditional pharmacies in an area, which has been assessed in previous literature see, for example, Pilorge, Because our data contain the universe of retailers operating in Lisbon, this assumption seems less appropriate in our case. This clustering option is common when defining markets around a focal retailer see Hosken et al. This is also in line with the recommendations of Abadie et al. This does not affect the significance of our results Table 3. Pharmacies experiencing nonpharmacy entry at several points in time or experiencing both supermarket and outlet entry are disregarded from most of our analysis. Our results are unchanged if we include pharmacies that experienced multiple treatments Table 3. Table 4. There can be variation in competition both from entry and from exit of OTC retailers that compose the set of main competitors of a pharmacy i. We are interested in variation originating from entry of nonpharmacies, not exit of pharmacies. In order to isolate the former, we focus on a subsample of pharmacies whose main competitors do not exit the market. Therefore, any variation in competition comes from entry of a new retailer. In the particular case of supermarket chains, OTC drugs seem to correspond to a small share of total sales. Assuming stores are symmetric, on average, OTC drugs amount to 1. Specifically, demand is measured as population living in the census block where the pharmacy is located, as of Competitive pressure is measured by the average walking time, in minutes, to the three closest competitors as of when defining the main competitors of a pharmacy as its three nearest neighbors. Because both measures of demand and competitive pressure are continuous, we categorize them into quintiles and use the categorized variables for the matching. We disregard the groups that were treated already in in the matching, as for those we do not observe a pretreatment period level of competitive pressure. In Appendix S6, we provide additional technical details on the propensity score matching procedure. As an alternative matching algorithm, we use local linear regression to build the matched sample. The results are shown in Appendix S3. We acknowledge that this is a relatively coarse measure of prices in a geographical area, because we are only considering five OTC drugs, and these five particular OTC drugs might poorly represent the prices of all goods sold by the pharmacies in that area. But this is the best measure we have given the available information. Exit of traditional pharmacies cannot be directly linked to the liberalization of the OTC market, as the share of OTC drugs on total pharmacy revenue is probably too small to produce such an impact. Instead, it is more likely a consequence of the overall economic environment and the squeezing of pharmacy margins on prescription drugs Barros, This is consistent with the figures in Table 1 , showing that the vast majority of the pharmacies who exited the market were in the control group. In Table 7. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Health Econ. Find articles by Ana Moura. Find articles by Pedro Pita Barros. Open in a new tab. Testing for mean differences between groups of pharmacies at baseline Click here for additional data file. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Main competitors: three nearest neighbors.

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But is there a relationship between the increase in crime in the Region and drug addiction? The first thing to check is whether crime really has increased. Therefore, it is confirmed that there is indeed an increase in crime in the Region. It should also be noted that last year, the police authorities registered 6, crimes in the Autonomous Region of Madeira, which corresponds to an increase of Therefore, it is also affirmative that cases of drug addiction have increased in the Region. Is there a relationship between these two factors? The reality is that this statement becomes inaccurate. It is true that crime has increased and it is also true that the number of hospitalizations for drug addiction has risen in recent years, but nothing confirms this correlation. Where there is drugs there is crime, of course and unless stamped on and I pick the word stamped, it will increase. But if the authorities like to sweep it under the carpet how is it we have the statistics printed above. The answer must be yes, there was no problems in Madeira some years ago when we had huge problems in the U. K we felt so secure in Madeira. Very sad for the Madeiran people. Any increase in any crime is bad and a single rape is a horrific offense that should be punished with decades in prison. Do they release actual crime statistics? A better indicator of crime would be the actual number of incidents. Since Madeira has a huge amount of visitors, crime stats per capita may not paint an accurate picture either. One would hope no one was turning a blind eye, from the ports to police checks to dealing on the streets! On July 1, , Portugal became the first country in the world to decriminalize all drugs, including meth and heroin. The law made drug possession for personal use legally prohibited, while drug trafficking remains a criminal offense. Not true! Madeira is super safe. The problem rape starts with foreigners who come here and do like in their countries. Venezuelans and Brasilians only do this. We know of them but tourists not so dont talk with them when you see them and you are safe. Dont say it is not safe because of people here! Is the growth of crime in Madeira related to the increase in drug addicts? Madeira News. Like this: Like Loading This iOS nt good news. Type to search or hit ESC to close. See all results. Username Password Remember Me Lost your password? Forgotten Password Cancel. Register For This Site A password will be e-mailed to you. Username E-mail. Remember Me Lost your password?

Porto Santo buying MDMA pills