extfieldsofstudy: Medicine Geography

s2fieldsofstudy: Economics Sociology

text:
Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves

The growth rates of COVID-19 across different geographical regions (e.g., states in a nation, countries in a continent) follow different shapes and patterns. The overall summaries at coarser spatial scales that are obtained by simply averaging individual curves (across regions) obscure nuanced variability and blurs the spatial heterogeneity at finer spatial scales. We employ statistical methods to analyze shapes of local COVID-19 growth rate curves and statistically group them into distinct clusters, according to their shapes. Using this information, we derive the so-called elastic averages of curves within these clusters, which correspond to the dominant incidence patterns. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic at two spatial scales: A state-level analysis within the USA and a country-level analysis within Europe during mid-February to mid-May, 2020. Our analyses reveal a few dominant incidence trajectories that characterize transmission dynamics across states in the USA and across countries in Europe. This approach results in broad classifications of spatial areas into different trajectories and adds to the methodological toolkit for guiding public health decision making at different spatial scales.

Introduction
The ongoing pandemic of novel coronavirus disease (COVID-19) that erupted in China in December 2019 has already generated substantial morbidity and mortality impact around the world during the first few months of transmission [10]. As the novel coronavirus continues its march around the world, the daily trajectory of the epidemic in terms of new cases or deaths represents a key tool for epidemiologist and public health scientists to quantify the reproduction number, assess the evolution of the doubling time, and evaluate the impact of social distancing strategies in different parts of the word [11,1]. However, overall epidemic curves, obtained by accumulating or averaging curves over large regions, can hide substantial differences in transmission dynamics that exist at finer spatial scales [6]. These nuanced patterns associated with individual regions may be critical to inform the type and intensity of interventions to bring the epidemics under control [11].
There is a need to better understand the spatial variability in the trajectory of the COVID-19 pandemic in different geographic areas around the world [10]. Indeed, the epidemic curves across different continents or countries may display completely different dynamics at a given time. At a higher level, such dynamics include increasing trends, a leveling off, stationary incidence patterns, and decreasing trends. At a finer level, the growth may be characterized by multiple modes depicting multiple waves of the epidemic [2]. Similarly, the epidemic curves at the subnational level within a country display different dynamics over time. Because the type and intensity of public health interventions are expected to vary across space, classifying and summarizing the spatial-temporal dynamics of the novel coronavirus is key for real-time public health decision making.
Quantitative methods for shape analysis of functional data (see e.g., [12]) help investigate the diversity of the spatial-temporal dynamics of transmission of the COVID-19. They provide an objective framework to characterize the spatial-temporal dynamics of the epidemic in different geographic areas within the same country. This methodology is an important tool in a branch of statistics called Functional Data Analysis. It has been used to steady stock markets in finance, weather patterns in meteorology, growth rates in biology, and speech data in signal processing. In the context of the COVID-19 pandemic, clustering of curves has been used to analyze travel patterns of migrants in China [3]. We demonstrate how this methodology can be used to generate representative epidemic curves at broader spatial scales (national level) to avoid losing information that results from aggregating local epidemic curves at coarser spatial scales. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic at two spatial scales: A state-level analysis within the USA and a country-level analysis within Europe during mid-February to mid-May, 2020. Our analyses reveal characteristic trajectories of the pandemic at the state level in the USA and at the country level in Europe, which provide information that may help guide public health decision making at different spatial scales.

Data
We analyzed daily series of reported COVID-19 cases at two different levels of spatial aggregation: States within the USA and countries in Europe.
• For the USA analysis, we retrieved daily cumulative case count data from the COVID Tracking Project, a volunteer organization dedicated to collecting and publishing data on the spread of COVID-19 in the United States [14]. Data from multiple sources, such as state or district health departments and trusted news reports, are compiled and assessed for data quality to 3 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 2, 2020. . https://doi.org/10.1101/2020.05.25.20112433 doi: medRxiv preprint  report the best available data for each state. Here we use reported daily state -and nationallevel cumulative case counts from February 27th, 2020 to May 21st, 2020.
• For the country-level analysis in Europe, we retrieved the data from World Health Organization: Coronavirus disease (COVID-2019) situation reports on May 23, 2020 [9].

Methodology
We apply quantitative methods to characterize shapes of the epidemic rate curves. The rate curves represent the daily count of new cases of reported COVID-19 cases characterizing the trajectory of the epidemic. While epidemic curves have been analyzed statistically in the past [5], focus on their shapes is a relatively new concept. Why is it interesting to study shapes of these curves? By focusing on shapes, one is more interested in the numbers and relative heights of peaks and valleys in a curve, rather than their precise locations. For instance, bell-shape curves will be deemed similar even if their peaks are located with small shifts and some height differences, and these bell curves will be considered different from curves that are mostly increasing. (This is different from past work, for example [7], where all epidemic curves are bell shaped and are classified into different classes according to their means and spreads.) Similarly, curves with different up and down patterns will also be considered different irrespective of the locations of their crossover points. Fig. 1 illustrates this idea pictorially. In the left panel we see a number of curves that differ only in heights and horizontal shifts. We consider all these curves to have the exact same shape. In the right panel we see curves with different numbers, locations, and relative heights of the modes. These curves are deemed to have different shapes and one can quantify the shape differences using tools from shape analysis. By comparing the shapes of epidemic curves, representing the transmission dynamics in different geographic areas within the same population, we can classify rate curves into groups or clusters that exhibit a similar growth-decay pattern. There are multiple benefits of this approach. This way we can compute national averages that are better reflective of the actual growth patterns of the states. Secondly, and more importantly, it helps label each state in terms of the state of the pandemic. It can also help us discover predominant patterns in epidemic growth, using data across different locations, times, and scales. 4 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 2, 2020. The next question is: How does one quantify and statistically analyze shapes of rate curves? Mathematically, shape is a property that remains unchanged if we rescale axes or translate the curves along axes. In fact, one even allows nonlinear time warping of the time axis, resulting in uneven horizontal shifts of the peaks and valleys, to be a shape-preserving transformation. The invariance of shape to such transformations makes shape analysis a difficult problem. In order to compare and analyze shapes of multiple curves, one has to standardize their domains by scaling axes, normalizing heights, and aligning their peaks and valleys using time warping functions. The resulting curves can then be analyzed for shapes. We employ well-established methodology for shape analysis of functional data described in ref. [12]). This framework provides comprehensive tools for generating statistical summaries and modeling of curves while focusing only on their shapes. Here we apply these tools to trajectory data of the COVID-19 pandemic with the goal of providing sound a framework for sound public health decision making at different spatial scales.
The following sections describe the methodology to analyze and cluster growth rate curves of COVID-19 reported cases. We first pre-process the data to form smooth rate curves for each local unit (a state or a country) over the observation interval. Then, we analyze shapes of these rate curves to compare, cluster and summarize growth rates.

Pre-Processing Steps
We start by listing the pre-processing steps applied to COVID-19 daily count data from each unit individually.
• Time-Differencing: Since the data includes cumulative counts (or total growth) of positive test counts for different states, we first calculate time differences (approximating time derivatives) of the data to reach growth rates. If f i (t) denotes the given cumulative positive 5 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 2, 2020. . https://doi.org/10.1101/2020.05.25.20112433 doi: medRxiv preprint counts for state i at time t, then the per-day growth-rate for that state at time t is given by • Re-Scaling: The scales of growth rates for some states are very different from other states, due to different population counts, densities, and other variables. In order to separate shape of a curve from its scale, we rescale each curve as follows. We compute the total positive tests for a state, i.e. r i = t g i (t), and then we define h i (t) = g i (t)/r i . .
• Smoothing: Next, we smooth these normalized growth rate curves using the smooth function in Matlab. With a slight abuse of notation, we shall call the resulting function h i (t) also. These are the smoothed and normalized growth rate curves, or simply rate curves henceforth.

Clustering of States
The resulting smoothed and normalized rate curves are then used in statistical analyses. There are several possibilities for this analysis, including modeling, testing, prediction [4,7] and classification [8]. A raw averaging of data across all states is bound to smooth over interesting patterns and lose interesting smaller structures. The right panels of Fig. 2 show averaging of the daily counts of states for each dataset. Looking at these average curves, one gets the impression that the growth rate is declining universally. However, this conclusion overlooks the variability in dynamics of growth in different states. It is difficult to justify the use of overall averages as representatives of growth patterns. Since our main goal is to extract predominant patterns of growth rates across different states, we start by clustering them into smaller, homogeneous groups. This clustering is important in that it helps recognize spatial heterogeneity of growth rates across geographical regions.
For the purpose of clustering, we use a simple metric to compare any two curves. For any two rate curves, h i and h j , we compute the norm h i − h j , where the double bars denote the L 2 norm of the difference function, i.e.
To perform clustering of 51 curves into smaller groups, we apply the dendrogram function in Matlab using the "ward" linkage. The number of clusters is decided empirically based on the display of overall clustering results. We elaborate on this further later on in data analysis.

Alignment and Averaging of Growth Curves
Once we have clustered states into different groups, we seek to derive an appropriate average or a representative curve for each cluster of states. However, a simple arithmetic averaging of curves is not always the best option. In particular, the curves are not perfectly aligned (in terms of their peaks and valleys) and it is well known that direct statistical summaries of misaligned curves often loose structure due to matching of peaks with valleys and vice versa. The solution is to first align peaks and valleys across all curves, using a time-warping algorithm, and then perform averaging. The problem of alignment of curves using time warping is also called phase-amplitude separation. In this paper we use the specific alignment algorithm introduced in ref. [13] and described in Chapter 6 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Results
The following sections describe the results of our analyses using state-level data for the USA and country-level data for Europe.

State-level analysis for the USA
The normalized rate curves for the period -2/27/2020 to 05/21/2020 -are shown in the top-right panel of Fig. 2. The results for clustering these 51 curves are shown in Fig. 3. The left side of the figure shows a dendrogram plot -a hierarchical clustering of states. It shows that there are four predominant clusters, which we consider for further analyses. We could also choose five clusters instead, but the results do not change significantly. The resulting four groups are shown in different colors in the dendrogram plot. The right side of the figure shows a color coding of the states -each color represents a different cluster. The listing of states according to this clustering is as follows.
• The rate curves for these clustered states are shown in Fig. 4, both before (top) and after (bottom) normalization. Since curves for different states have quite different scales, it is not easy to discern overall shape patterns, even within the same cluster in the top row. After rescaling the curves to the same scale, the general trends in the growth rates become clearer. For example, for states in Cluster 4, the rate first goes up sharply and then comes down sharply. For states in Cluster 3, on the other hand, the rate increases sharply at first but has not come down yet, at least not completely.
Although the shapes of curves within a cluster are quite similar, the averaging of these curves still can lose structure. As mentioned earlier, one needs to align the peaks and valleys of curves before averaging. To further extract typical trends, we use an alignment technique from [12,15] and align rate curves within each cluster. Fig. 5 shows these aligned rate curves in each cluster; the top row shows the unscaled curves while the bottom row shows the scaled curves.
Once the curves are aligned it is easier to visualize the common peaks and valleys (highs and lows) in each group. Figure 6 shows the average rate curves for each cluster. We obtain these curves by aligning and averaging rate curves in each cluster individually. This figure also plots a one standard-deviation band around the mean curve, in order to display the variability in the data. The pandemic growth patterns are very distinct in the four categories. For Cluster 1: the rate 7 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 2, 2020. climbs rapidly and has come down only slightly; for Cluster 2, the rate climbs rapidly and then the growth slows down; for Cluster 3, the rate climbs rapidly and starts coming down slowly; and, for Cluster 4 the rate climbs rapidly and comes down all the way. The rightmost panel of this figure shows all the cluster averages in the same plot, to help visualize their differences.

Country-level analysis in Europe
Now we present clustering and shape analysis of rate curves for 60 European countries. In this case we omit some intermediate results from clustering and alignment steps, and present the final clustering results. A dendrogram-based hierarchical clustering of countries is presented on the left side of Fig. 7. We choose to divide these countries into four clusters, as shown in the figure. The right side of the figure shows a color coding of the countries according to their cluster memberships.  . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

(which was not certified by peer review)
The copyright holder for this preprint this version posted June 2, 2020.  Figure 7: Clustering of European countries according to their normalized growth rate curves. Note that the color palette and the ordering of clusters here is different from that of USA.
The cluster membership of 60 countries is enumerated below: • Once the curves are aligned within their clusters, it is easier to visualize the common peaks and valleys (highs and lows) in each group. Figure 8 shows the average normalized growth curves for each cluster. We obtain these curves by aligning and averaging normalized curves in each cluster individually. The growth patterns are very distinct in the four categories. For cluster 1, the rate climbs rapidly and comes down all the way to the normal levels. For cluster 2, the rate climbs rapidly and starts coming down slowly, while in cluster 3, the rate has only started to come down slowly. In case of cluster 4, the growth rate is still climbing rapidly. The rightmost panel shows all the cluster averages in the same plot.

Discussion
In this paper we employ well-established methods for classifying and synthesizing shapes of growth rate curves underlying the spatial heterogeneity that exists at finer spatial scales. Specifically, we 10 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

(which was not certified by peer review)
The copyright holder for this preprint this version posted June 2, 2020. have applied this methodology to characterize the spatial-temporal dynamics of the pandemic at two different scales of spatial aggregation: Across states within the USA and across nations within Europe. Our analyses reveal a few dominant incidence trajectories that characterize transmission dynamics at the state level in the USA and at the country level in Europe. Overall, our approach reveals broad classifications of spatial areas into different trajectories and adds to the methodological toolkit for guiding public health decision making during epidemic emergencies at different spatial scales.
The main findings are: • The shapes of rates curves (or incidence curves) are different across the spatial units, but they cluster into a few groups with characteristic patterns.
• The resulting broad classifications of the shapes (e.g., rapidly rising, slowly rising, slowly decreasing, and declining and reaching low incidence levels) are clearly visible and very meaningful.
• Computing average growth rates within the clusters is more appropriate than taking raw averages across all spatial areas comprising the population, particularly when the epidemic is comprised by asynchronous outbreaks.
Our state-level analysis indicate that Alaska, Hawaii, Idaho, Louisiana, Montana, and Vermont are the only states that appear to be bringing the COVID-19 epidemic under control (Cluster 4). The other three characteristic patterns that emerge from our analysis paint a grim picture of the COVID-19 epidemic in the USA at a time when most of the states have begun to reopen their economies at least in some way. In comparison, our country-level analysis of the epidemic in Europe reveal that Liechtenstein, Faroe Islands, and Greenland appear to have brought their epidemics under control while countries in Cluster 2 are characterized by a downward trend and appear to be in process of controlling their epidemics. In contrast, the epidemic is following an alarming increasing trend in Sweden, Azerbaijan, Russian Federation, Belarus, Armenia, Poland, Ukraine, Republic of Moldova, Kazakhstan, Kyrgyzstan, and Tajikistan while several other countries (Cluster 3) appear to be reaching an endemic level of the disease.
It is worth noting that the methodology employed here requires little human intervention. The only quantity that may be specified manually is the number of clusters, and that is left as a choice for the end user. Hence, the clusters of spatial units that emerge from the analysis provides an objective classification system of epidemic patterns, which can be used to guide the implementation 11 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 2, 2020.  or relaxation of public health measures as the epidemic emergency evolves over time. For instance, the resulting configuration of clusters for the state-level analysis in the US states using a truncated dataset for an earlier period 02/27/20 -04/25/20 is presented in Fig. 9 where the average rate curves of the four clusters are displayed. In the bottom panel we show the clustering labels for 50 states. Comparing this map with that obtained from the analysis using the entire dataset (Fig. 3), we see that a number of states have changed cluster memberships while many others remain in the same cluster. States along the northeastern, western, and southeastern borders have shifted to the next worst cluster in terms of the higher growth rates. While we have focused our analysis on the time series of confirmed cases across spatial areas, our analysis could be extended to consider the trajectory of the epidemic in terms of the number of reported COVID-19 deaths. Moreover, a limitation of our analysis stems from the fact that testing rates have generally improved across spatial areas during the course of the pandemic, which could have influenced the shapes of the epidemic curves particularly during the early transmission phase. In particular, the process of ramping up testing rates took several weeks in the USA, and accumulating evidence suggests that the epidemic in the USA and Europe likely started much earlier than initially thought.

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COVID -19 INFECTION, THE PANDEMIC OF THE CENTURY- ARE WE PREPARED YET?

Abstract not available
Bangladesh J Medicine July 2020; 31(2) : 43-45

EDITORIAL
In late December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, the capital of Hubei province and is the seventh-largest city in China, with a population > 11 million. A case control study revealed a novel virus linked with these pneumonia cases. A previously unknown beta corona virus was discovered initially named 2019-nCoV on 12 th January 1 . WHO lately revised the virus name as SARS CoV-2 and named the disease as COVID-19. Although different from both MERS-CoV and SARS-CoV in characteristics, SARS CoV-2 is the seventh member of the family of coronaviruses that infect humans have phylogenetic similarities with SARS CoV and hence the name was changed 1 .
COVID-19 is a zoonotic virus. From phylogenetics analyses undertaken with available full genome sequences, bats appear to be the reservoir of COVID-19 virus 2.3 while the intermediate host was suggested as pangolin. COVID-19 is transmitted via droplets and fomites during close unprotected contact between an infector and infectee 4,5 . Airborne spread has been reported only in context of aerosol generating procedure and very close gatherings with recent report for COVID-19 4,5 but it is not believed to be a major driver of transmission based on available evidence. There has been shedding of virus in stool of infected person reported in few case reports. Human-to-human transmission of the COVID-19 virus is largely occurring in families in china and also observed in all over Europe especially Italy, United kingdom, France, Belgium, iran 1,3 and now in USA with huge transmission with mortality and morbidity. The containment strategy of lockdown leads to such transmission, but it also halts the widely community transmission in a country. Preliminary studies ongoing in Guangdong estimate the secondary attack rate in households ranges from 3-10% 5 .China has a policy of meticulous case and contact identification for COVID-19 and are tracing tens of thousands of contacts a day. Contact follow up is painstaking, with a high percentage of identified close contacts completing medical observation. Between 1% and 5% of contacts were subsequently There has been mutation in SARS CoV-2 virus as early as in January in Wuhan city. The early form of S converts into L form which were associated with extensive community transmission and severity and death. The huge mortality was due to ARDS and the involvement of cardiac arrest as cardiac myocyte ACER2 showed sensitivity to L form. The extensive mitigation effort by China then halts the L form transmission dynamics and both S form and L form are circulating outside of china now. It seems Italy and Iran both have more L form than S form of mutated virus as the transmission and fatality suggest the experience of Wuhan. It is not sure whether any other mutation happens to virus or not at this moment. The mutation can pass through both way of devastation or calmness while the reassortment with other viral gene is also a high possibility of more evolving situation of SARS-coV 2. Currently many other mutation has been identified.
Testing for COVID-19 from routine respiratory disease surveillance systems to explore if COVID-19 is circulating more broadly and undetected in the community is important for any country including Bangladesh. These systems could include RT-PCR testing of COVID-19 virus in influenza-like-illness (ILI) and severe acute respiratory infection (SARI) surveillance systems, as well as testing of results among all visitors to fever clinics. As Bangladesh already declared two hundred thousands of cases and worldwide pandemic is extensively observed from community to community, its prudent for us to explore the CoVID-19 case detection through RT-PCR and suology while continuing regular ILI and SARI surveillance. As COVID-19 is a newly identified pathogen, there is no known pre-existing immunity in humans. Everyone is assumed to be susceptible, although increasing susceptibility to infection depends on risk factors like Diabetes, hypertension, immune suppressed states etc.
That is why aggressive case and contact identification, isolation and management and extreme social distancing, should be implemented to interrupt the chains of transmission nationwide. Of note, the highly clustered nature of local transmission may explain a relatively high R0 (2.5) in the absence of interventions in China and low confirmed case counts with intense quarantine and social distancing measures. The pandemic pyramids explains the scenario perfectly in China indicating the numbers of cases were actually far more than confirmed while the severe cases were all counted as they took service from designed hospital.
Among the Health care worker infections, most were identified early in the outbreak in Wuhan when supplies and experience with the new disease was lower. But later discrete and limited instances of nosocomial outbreak within health care settings and amongst health care workers appear due to extensive availability of Personal protective equipment(PPE). But the scenario of such ideal standard may not be observed in Bangladesh as HCW here are not acquainted with PPE and there is gross lack of availability and quality of such equipment in case of huge public health transmission. The close proximity and contact among people in closed living settings and the potential for environmental contamination are important factors, which could amplify transmission in CoVID 19 infection which all are alarming for Bangladesh. Data on individuals aged 18 years old and under suggest that there is a relatively low attack rate in this age group (2.4% of all reported cases in China). it is not possible yet to determine the extent of infection among children, what role children play in transmission, whether children are less susceptible or if they present differently clinically.
Typical signs and symptoms include: fever (87.9%), dry cough (67.7%), fatigue (38.1%), sputum production (33.4%), shortness of breath (18.6%), sore throat (13.9%), headache (13.6%), myalgia or arthralgia (14.8%), chills (11.4%), nausea or vomiting (5.0%), nasal congestion (4.8%), diarrhea (3.7%), and hemoptysis (0.9%), and conjunctival congestion (0.8%) 6 . People with COVID-19 generally develop signs and symptoms, including mild respiratory symptoms and fever, on an average of 5-6 days after infection (mean incubation period 5-6 days, range 1-14 days). About 80% cases are having mild illness, 15% cases were severe and hence need hospitalization and 5% case need ICU support due to development of ARDS 6,7,8 . The crude death rate in china was 2.4% and more the ages, more was the fatality. The big problem is around 20% cases need hospital care with many ICU support. 8 If the transmission is heavy, then this seemingly less danger disease can give serious toll to the health system and infrastructure of a country. That has been observed in Italy and Iran and also in many developed country in Europe and America who doesn't think beforehand. The same problem will be is an alarm for Bangladesh as our health facility will not cover these intense burdens of hospitalization and ICU care. The prejudice of deadly for old concept is also not always correct as Italy & USA is observing recently severity and fatality in young groups of people. Asymptomatic infection has been reported, but the majority of the relatively rare cases who are asymptomatic on the date of identification/report went on to develop disease. The proportion of truly asymptomatic infections is unclear but appears to be relatively not common and does not appear to be a major driver of transmission. Those with underlying conditions such as older age, hypertension, diabetes, cardiovascular disease, chronic respiratory disease, immunosuppressive agent taker and cancer are the major risk factor that has been observed globally 8 .
Bangladesh is now facing a new viral disease which is nonimmune to all of its citizen and with unpredictable course and future. Up to the date (30.6.2020), Bangladesh is having around 150000 patients with 2000 death and trends is increasing 9 . Bangladesh is in the middle of lockdown now for a month and hospitals are just about to be prepared and facing extra burden due to COVID-19 cases. The lockdown is not perfectly observed, and we had few big upset like garments worker walking for unplanned BGMEA decision, a poor religious burial activity of a leader with thousands of people and again a surge of unplanned opening of offices and industries. The hospital preparedness is completely in disarray with inappropriate infrastructure, poor planning and lack of personal protective equipment, safety issue and lack of coordination with health care worker and administration. We are also far behind of test which has been repeatedly suggested by WHO with poor capacity development and approach and no system at all for capturing pre symptomatic or asymptomatic sates. The primary, secondary and tertiary health care system (public and private) all are in confused states due to lack of uniform command system for pandemic. The contact tracing is now in jeopardy and makes transmission in upload mood and proper isolation (for case) or quarantine (for contact) in home and institutional are not appropriately in placed. The country is in peak of level 4 but the administration, surveillance, case management, research, contact management, mitigation and containment process, community engagement all are in premature stage. Until we cannot engage the community, it will be simply impossible to mitigate the COVID-19 from Bangladesh. We need planning to understand the preparedness for pandemic rather self-belief of that we are prepared.


Elections and Selfishness

Elections affect the division of resources in society and are occasions for political elites to make appeals rooted in voters’ self-interest. Hence, elections may erode altruistic norms and cause people to behave more selfishly. We test this intuition using Dictator Games in a lab-in-the-field experiment involving a sample of more than 1,000 individuals in Kenya and Tanzania. We adopt two approaches. First, we experimentally prime participants to think about the upcoming or most recent elections and find that this priming treatment reduces how much money participants are willing to give to other players. Second, we compare results obtained across lab rounds in Kenya taking place right before the country’s 2013 national elections and one eight months prior and find that selfishness is greater in the lab round more proximate to the election. Our results suggest that elections may affect social behavior in important—and previously unrecognized—ways.<br>

Introduction
Elections are moments of intense competition for control of the government and its resources. They tend to polarize electorates, deepen social divisions and generate a discourse revolving around material benefits and voters' self-interest. A natural question, then, is whether elections might erode social norms about altruism and generate a permissive environment for selfish behavior.
The plausibility of the connection between elections and selfishness is especially strong in East Africa, a region in which elections are viewed as occasions for choosing which group will control the country's resources-for determining whose turn it is to "eat" (Wrong, 2009;Branch, Cheeseman and Gardner, 2010)-and where politicians often engage in (or are assumed to engage in) blatantly clientelistic behavior (Mueller, 2008;Vicente and Wantchekon, 2009;Kramon, 2018). We draw on data from two countries from this region, Kenya and Tanzania, both places where politics is strongly associated with rent extraction and clientelism, and where voters often perceive political candidates as self-serving and corrupt. By emphasizing individual opportunism, elections in these countries may erode the social norm on altruism and render selfish choices more acceptable. Perceptions of group favoritism may also cause citizens, without even having to be reminded so by politicians, to view elections as moments for deciding which community will benefit from state resources, thus reinforcing the tendency to view distributive decisions in selfish terms.
To examine the hypothesis that elections promote selfishness, we employ a lab-in-the-field Dictator Game, which provides a standard measure of participants' altruism towards others (with selfishness understood as being the inverse of altruism). We test the impact of elections by experimentally priming more than 1,000 lab participants in Kenya and Tanzania to think about the upcoming or most recent electoral contest. The experimental prime provides cleanly identified evidence on how the situational salience of elections affects selfishness. While this election prime is subtle, it leads to a decline in Dictator Game transfers of 3 percent in our pooled sample (p=0.031), controlling for participant characteristics.
While the priming experiment indicates a causal effect of the election prime on selfishness, one may be concerned about how closely the priming treatment relates to exposure to actual elections. To address this concern, we evaluate the external validity of our findings beyond our priming experiment by comparing results obtained across lab rounds in Kenya that took place close to and more distant from the country's 2013 national election. We find that Dictator Game transfers dropped from 42.6 percent of the endowment in the sessions held 7-8 months before the election to 36.6 percent in the sessions held 1-2 months before the election (p=0.0003).
Taken together, these results suggest that elections affect selfishness. Whereas prior work has examined the effects of altruism or selfishness on turnout and electoral behavior (Riker and Ordeshook, 1968;Fowler, 2006;Jankowski, 2007), researchers have yet to document the impacts of elections on altruism/selfishness. Outside the context of electoral politics, our paper relates to an emerging literature that examines how preferences -which are commonly assumed to be exogenously determined -are endogenously affected by prominent events. For instance, Voors, Nillesen, Verwimp, Bulte, Lensink and Van Soest (2012) examine how exposure to conflict affects social, time and risk preferences in Burundi, while Fisman, Jakiela and Kariv (2015) argue that subjects exposed to an economic recession exhibit greater selfishness. Again though, the impact of elections on social preferences has not been previously examined. Our findings therefore highlight a novel source of changes in social preferences, with potentially important implications for our understanding of how exposure to electoral competition may affect social, economic, and political outcomes that extend far beyond the election itself.

Setting and Sample
We recruited 1,018 participants to play Dictator Games at experimental laboratories in Nairobi, Kenya and Dar es Salaam, Tanzania. 1 In Nairobi, participants attended one of two lab rounds held at the Busara Center for Behavioral Economics: one held in July-August 2012, seven to eight months prior to Kenya's 2013 elections (N=302) and one held in January-February 2013, one to two months before these elections (N=300). We refer to the former lab round as the non-election round and to the latter as the election round. None of our respondents had participated in previous lab sessions at the Busara Center, and all of them participated only once in our study. In Dar es Salaam, we set up our own lab to conduct a single lab round in November-December 2012, two years after Tanzania's 2010 national election and three years prior to its 2015 election (N=416). In both cities, the samples were recruited from low-income neighborhoods and are broadly representative of the cities' largest ethnic groups.
In each round of the Dictator Game, we give participants an endowment of KES 50 or TZS 1000 (circa USD 0.60), roughly equal to an hours' wage for workers in these locations, of which they decide how much to give away to a randomly selected participant in the lab about whom they were given no information. Online Appendix B provides the detailed lab protocols.

Experimental Priming
Measuring the causal impact of elections on altruism requires random variation in lab participants' exposure to elections. We provide this via experimental priming. We pool the 1,018 participants from both the Kenya and Tanzania labs and divide them into two groups: a control group (N=559) and an election prime treatment group (N=459). Both groups are given a short, five question on-screen quiz immediately before playing the Dictator Game. In the control group, we ask neutral questions such as "How often do you ride a matatu/daladala every week?"; "What cell phone provider do you use the most?"; "In your opinion, what is the most popular soda drink?" In the treatment group, we ask a mix of neutral and election-oriented questions. The election prime questions are: "In your opinion, what share of the population voted in the last national election?"; "How many political candidates are (were) running for the Presidency (in 2010)?"; "How many political candidates are (were) running for the office of MP in your constituency (in 2010)?" Our treatment and control groups are balanced on observables (see Online Appendix Table A.1).
To transparently estimate the average treatment effect of our priming treatment, we employ standard OLS regres- sions. More precisely, our baseline specification is where is respondent 's Dictator Game transfer, and is an indicator variable for whether this respondent was in the priming treatment group. Hence, 1 estimates the average treatment effect of the prime, namely the difference in average generosity in the priming versus the control group. We estimate this effect both unconditionally as well as conditional on a vector of control variables .
In additional specifications, we augment our estimation with indicator variables for the Kenya election lab round and the Tanzania lab round. Each time, the coefficients on these indicator variables estimate the difference in average generosity for the respective lab round relative to the Kenya non-election round, conditional on respondents' treatment status and the control variables. Finally, we also estimate heterogeneous effects of the priming treatment by interacting the lab round indicators with the indicator. Throughout this entire analysis, OLS regressions provide a transparent, straightforward and flexible estimation approach. We show our main findings in Table 1 and provide the full estimation output in Online Appendix Table A.2. Pooling the two lab rounds in Kenya and the lab round in Tanzania, we find that the election prime leads to a reduction in the share of the endowment transferred to the other player, i.e. an increase in selfishness. Not controlling for any covariates, Dictator Game transfers fall by 2.7 percentage points, which is an economically meaningful decline of 6.6 percent (column 1 in Table 1, p=0.052). This effect is robust to adding controls for gender, age, education and a score on a Raven (2008) test for cognitive ability administered during the lab (column 2, p=0.031); to allowing for different base levels of generosity in the Tanzanian and the two Kenyan lab rounds (column 3); and to permitting the control variables to have different effects across lab rounds (column 4). In this most exhaustive specification for estimating the average treatment effect, the election prime reduces generosity by 3.12 percentage points (p=0.024). Given the subtlety of our priming treatment-consisting of variation in just three purely informative questions asked to participants prior to playing the Dictator Game-we view these results as likely representing a lower bound on the effects of elections on selfishness. The pattern of heterogeneity in the estimated treatment effect across lab rounds is interesting (columns 5-6). 2 First, we find that the estimated effect of the election prime is close to zero in the Kenya election round. This may be because elections are already salient to participants attending the election round sessions, regardless of the experimental treatment: when the election campaign has primed subjects already, the marginal impact of the experimental priming may be weak to non-existent. This is in line with the intuition in Druckman and Leeper (2012), who argue for a decreasing marginal impact of priming for subjects who are "pre-treated." Second, comparing across countries, we find no statistically significant difference between the election prime in Kenya and Tanzania. If anything, the effect of elections is stronger in Tanzania, as suggested by the 6.2 percentage point decline in average generosity in that country.

Cross-Round Comparison
While experimentally priming lab participants to think about elections is advantageous from a causal inference standpoint, it is not identical to exposing them to the mobilization, political appeals, and social pressures of an actual election campaign. To corroborate the external validity of our experimental results, we leverage the fact that we conducted our lab rounds in Kenya during two periods: one 1-2 months before the 2013 national elections (the election round), and one 7-8 months prior (the non-election round). This allows us to compare how participants play the (2) (3) (4) (5) (6) (7) (8) Election Round -6.02 * * * -6.60 * * * -6.44 * * * -6.09 * * * -6.15 * * * -6.14 * * * -6.05 * * * -6.56 * * * (1. Dictator Game when elections are imminent and when they are in the more distant future. 3 To establish that elections are indeed more salient to participants during the election round, we start by documenting that participants who attended these lab sessions were more exposed to the coming electoral contest. As shown in Panel A of Table 2, participants in the election round were significantly more likely than participants in the non-election round to say that they had attended a campaign rally (62 percent vs. 24 percent), received cash from a politician (27 percent vs. 12 percent), or received some other non-cash gift such as a T-shirt, food, or alcohol (13 percent vs. 8 percent). Election-related themes were also much more prevalent in the media during the election round. An analysis of major Kenyan media outlets aggregated by KenyaMOJA.com reveals that the terms "election," "political parties," and "vote" were mentioned much more frequently during the January-February 2013 period (455 times) than during the July-August 2012 period (206 times).
The increased salience of elections during the election round is associated with a significant decline in generosity. As shown in Table 3, Dictator Game transfers are 6.02 percentage points lower during the election round (column 1, p=0.0003). This difference is strongly robust to controlling for background characteristics such as gender, education, age, cognitive ability, number of children, and number of years living in Nairobi (columns 2-7). 4 When we control for all these characteristics, generosity declines by 6.6 percentage points in the election round (column 7, p=0.0002)-an effect size similar to the 8 percentage point decline in generosity before and during the Great Recession, as reported in Fisman et al. (2015). 5 Notes: The first and second column show average Dictator Game transfers (in percentage terms) in the non-election and election round, respectively. The third column shows the estimated difference between the two, with standard errors in parentheses. The Raven's test score measures cognitive ability (Raven, 2008). P-values: * < 0.10, * * < 0.05, * * * < 0.01.
This decline in generosity is found in all observable subgroups of the sample. As shown in Table 4, we find a substantial and significant increase in selfishness in the election round, regardless of whether participants are female or male, young or old, high-or low-educated, have high or low cognitive ability, have few or many children, and are long-time residents of Nairobi or not. The results are also robust to limiting the sample to participants in the no-prime group rather than pooling across all participants, as we do in our main analyses. In fact, in the no-prime subsample we tend to find effect sizes that are -if anything -slightly larger (see Online Appendix Tables A.8 and A.9).
A concern with the cross-round comparison is that environmental factors that vary across the two lab rounds might offer alternative explanations for the differences we find in selfishness. As shown in Panel B of Table 2, the macroeconomic situation in Nairobi improved modestly between the non-election and election rounds. GDP growth ticked up from 4 percent to 5.2 percent, and inflation decreased from 6.1 percent to 4.5 percent. Commodity prices, meanwhile, were largely unchanged (see Online Appendix Figure A.1). Insofar as improving economic conditions are likely to be associated with a decrease in selfish behavior (Fisman et al., 2015), these macroeconomic trends would bias against our finding of increased selfishness in the election round. 6 election round (see Online Appendix Table A.7). 6 Ideally, we would control for participants' individual economic conditions, but the data on this dimension has too many missing values.

Discussion
To the best of our knowledge, we are the first to document the impact of elections on selfishness. Our findings suggest that increasing the situational salience of elections makes people less generous to others. Our evidence is based on random experimental variation in priming conditions in Kenya and Tanzania, and is corroborated by the comparison across the two Kenyan lab rounds that differ in their temporal distance to elections.
While our findings are suggestive about the selfishness-inducing effects of elections per se, they are also no doubt rooted in factors specific to the context we study -specifically, the strong association of politics and elections in Kenya and Tanzania with clientelism, rent extraction, and corruption (D'Arcy and Cornell, 2016;Gray, 2015). Indeed, the majority of Afrobarometer respondents in Kenya believe that most government officials are corrupt, and in Tanzania, more than 90 percent believe that at least some of them are corrupt (Afrobarometer Data, 2011/2013). To us, it seems plausible that this strong association of politics and elections with personal rent extraction contributes to the erosion of the social norm on altruism as elections approach. Krupka and Weber (2009) show that the salience of this social norm is context dependent and malleable to priming. From this perspective, elections may then bring about a "scarcity mindset" (Shah, Shafir and Mullainathan, 2015), which induces more selfish behavior.
An alternative explanation is that the lower transfers in the Dictator Game at election time are not driven by a general increase in selfishness, but instead by increased ethnic polarization and an associated decline in generosity toward noncoethnics (Bates, 1983;Eifert, Miguel and Posner, 2010;Hjort, 2014). While we do not have data on coethnic giving in Tanzania, we can test this hypothesis with additional data available from Kenya. After the "anonymous" Dictator Game that we employ in the analyses presented in this paper, we also implemented a "coethnic" Dictator Game in which respondents were given cues about the "home town" of the recipient (See Online Appendix B.2). 7 Specifically, participants were given three pieces of background information about the other player with which they were paired: their education, age, and home town. Piloting prior to the study confirmed that the vast majority of participants could correctly identify the intended ethnic backgrounds of their partners based on the home towns ascribed to them. We included information about education and age to obscure the ethnic focus of this lab game.
Our results indicate that exposure to elections also reduces generosity to coethnics. First, in the cross-round analysis, generosity toward coethnics declines strongly and significantly in the election round (see Online Appendix Tables A.10 and A.11). Second, the election prime is also associated with lower generosity to coethnics (Appendix Table  A.3), particularly in the non-election round (columns 5-6). However, this result is not statistically significant (p = 0.12, 0.15), in part because the absence of the Tanzania round leads to a loss of statistical power. In sum, we conclude that an amplified coethnic bias in altruism is not the main driver of our findings. 8 Instead, the results strongly indicate an increase in general, indiscriminate selfishness.
While we do not find that people exhibit coethnic bias in their generosity, ethnic divisions do matter in a different way. Specifically, in Kenya the decline in generosity associated with elections is concentrated among those ethnic groups who lost the previous national election in 2007 (the Luo, Luhya and Kisii), while the winners of that election (the Kikuyu) exhibit no decline in generosity associated with elections. This pattern holds for both the cross-round comparison and for the priming treatment (see Online Appendix Tables A.4 and A.12), although we are somewhat underpowered to properly examine heterogeneous responses to the election prime. Importantly, the losing ethnic groups also reduce their generosity toward coethnics when exposed to elections, which again shows that our findings are not driven by a coethnic bias in altruism (see Online Appendix Tables A.5 and A.13). Instead, the overall decline in generosity for the losers of the election may be driven by resentment about missing out on material benefits associated with group favoritism by the election winners.
A final alternative argument is that it is not elections per se but the competitive atmosphere induced by elections that erodes altruism, as in Fehr and Schmidt (1999). The strong differences in the degree of electoral competition in Kenya and Tanzania allow us to shed light on this hypothesis. Since the creation of the multi-party system in Tanzania, a single party (Chama Cha Mapinduzi) has won all five elections and has ruled uninterruptedly. In Kenya by contrast, government power has shifted hands several times. If competitiveness is what drives the decline in altruism, we would expect a stronger impact of elections on selfishness in Kenya than in Tanzania. Yet we find the opposite: if anything, 7 In Kenya, "home town" is a strong indicator of a person's ethnicity. 8 Interestingly, Berge et al. (2020) document an absence of coethnic bias in altruism for lab participants in Nairobi, even close to the 2013 general election. Their finding confirms evidence on lack of ethnic bias in dictator games in East Africa from both Habyarimana, Humphreys, Posner and Weinstein (2007) and Blum, Hazlett and Posner (Forthcoming). This combined evidence therefore discredits an explanation for our current findings based on ethnic polarization. the effect of elections on selfishness is stronger in Tanzania than in Kenya (see Table 1). Hence, the competitive aspect of elections does not appear critical for explaining our findings.
Since alternative explanations do not appear convincing, we view the link between elections and clientelism as the most plausible explaination for our findings. Admittedly though, our research design does not allow us to directly test this mechanism, so further research on this topic is clearly warranted. If confirmed by follow-up research, our findings may have more general implications for understanding how elections shape societal outcomes -including beyond East Africa. For instance, the increase in self-interest may encourage "pocketbook voting" based on personal economic considerations rather than "sociotropic voting" (Fiorina, 1981;Kinder and Kiewiet, 1981;Healy, Persson and Snowberg, 2017;Morton and Ou, 2019). More particularly in environments with widespread clientelism, there may be a negative feedback loop between clientelism, an augmented focus on self-interest during elections, and self-serving choices by voters. From this perspective, our findings may help explain why rent extraction remains rampant in young democracies (Keefer, 2005), and why democratization does not necessarily help in battling entrenched corruption (Sun and Johnston, 2009).  Notes: For the first two columns, the table shows average values, with standard deviations in brackets, for the control and the priming treatment group respectively. The third column estimates the difference between the two averages and has standard errors in parentheses. None of the differences are statistically significant. The Raven's score for cognitive ability is demeaned and measured in standard deviation units. Data are pooled from the Tanzania round and the Kenya non-election and election round. Data are pooled from the Tanzania round and the Kenya non-election and election rounds. The coefficient for "Election Prime in Tanzania" sums the first two coefficients in the column. The coefficient for "Election Prime in Kenya Election Round" sums the first and third two coefficients in the column. Standard errors in parentheses. P-values: p < 0.10, ** p < 0.05, *** p < 0.01. Kisii respondents together as the omitted category, since they are long political allies and their home region is geographically proximate. Compared to the data in Table 3, we therefore drop the Kamba respondents from the sample since they switched political sides between the 2007 to the 2013 election. Standard errors, in parentheses, are clustered at the participant level. P-values: p < 0.10, ** p < 0.05, *** p < 0.01. Kisii respondents together as the omitted category, since they are long political allies and their home region is geographically proximate. Compared to the data in Table 3, we therefore drop the Kamba respondents from the sample since they switched political sides between the 2007 to the 2013 election. Standard errors, in parentheses, are clustered at the participant level. P-values: p < 0.10, ** p < 0.05, *** p < 0.01. Notes: Data are pooled from the Kenya non-election and election round only. The table shows average values, with standard deviations in brackets, for the full sample, the nonelection and election round samples. The final column estimates the difference between the two rounds and has standard errors in parentheses. The Raven's score for cognitive ability is demeaned and measured in standard deviation units. P-values: p < 0.10, ** p < 0.05, *** p < 0.01. Notes: The first and second column show average Dictator Game transfers (in percentage terms) in the non-election and election round, respectively. The third column shows the estimated difference between the two, with standard errors in parentheses. Compared to Table 4, data here is restricted to the control group only, which was not primed to elections. The Raven's test score measures cognitive ability (Raven 2008). P-values: * p < 0.10, * * p < 0.05, * * * p < 0.01. (2) (3) (4) (5) (6) (7) (8) Election Round -5.37 * * * -5.87 * * * -6.62 * * * -5.54 * * * -5.99 * * * -5.57 * * * -5.17 * * * -6.51 * * * (1. P-values: * p < 0.10, * * p < 0.05, * * * p < 0.01.


EXPLORING HETEROGENEITY IN SHRIMP FARMERS’ PREFERENCES FOR THE CONTRACTS OF PRODUCING CERTIFIED ORGANIC SHRIMP

Certified organic shrimp, Farmers’ stated preference, Choice experiments, Contract design, Heterogeneous preferences ABSTRACT This research applied choice experiments to explore farmers’ preferences for hypothetical contracts of producing certified organic shrimp. The contracts were described by four attributes, including premium for certified organic shrimp, premium-payment delay, selling place and the provision of quality seed. Conditional logit and mixed logit models were used to analyze the data which were collected among shrimp farmers in Vietnam. The results indicate that farmers prefer the contract with a higher premium; however, premium-payment delay decreases farmers’ utility and the probability of choosing the contract. Besides the premium, the provision of quality seed is an important attribute that attracts farmers to choose a contract. Almost farmers prefer to sell their shrimp from farms. The results also show heterogeneity in farmers’ preferences for the contract’s attributes and their willingness to accept these attributes. These findings should be carefully considered in building reasonable and attractive contracts for shrimp farmers.

INTRODUCTION
The increasing demand of consumers on safe, quality-rich and environmentally friendly food, which accompanies the establishment of associated standards, created market barriers, on one hand, new opportunities for the aquaculture supply chain on the other hand. To satisfy consumer demands, the aquaculture industry has been changing its pattern and restructuring its supply chain to meet such standards. Especially, improving vertical coordination in the supply chain is very important to ensure the stable supply of quality products.
Contract farming is the most widely used method of vertical coordination (Rehber, 2007). To take the advantages of adopting standards and supplying the certified products such as better market access, improving competitiveness, and increasing customer trust (Trifković, 2014), processing companies integrate farmers into the value chain through the contract to have a stable supply source of such products. To attract farmers to participate in the contract of producing such products, the company should build reasonable contracts which provide proper benefit for farmers to adopt this sustainable practice over other alternatives. To build reasonable contracts, analysis of farmers' preferences for the contract's attributes is an important step. Successful build and implementation of the contracts not only tighter cooperation between the company and farmers to supply such quality products for the consumers, but also improve economic outcomes for the producers.
Recently, to support policy-makers to build good contract from the farmers' point of view, choice experiment (CE) method has been used to explore farmers' preferences for the contracts (Abebe et al., 2013;Blandon et al., 2009;Broch et al., 2013;Christensen et al., 2011;Espinosa-Goded et al., 2010;Gelaw et al., 2016;Lemeilleur et al., 2020;Ochieng et al., 2017;Schipmann and Qaim, 2011;Van den Broeck et al., 2017;Vassalos et al., 2016;Villanueva et al., 2017). However, there are limited studies research on the contract focusing on sustainable certification. To our knowledge, the are two studies applied CE to explore farmers' preferences for fair-trade contracting in Benin ( Van den Broeck et al., 2017) and eco-certified contract for coffee products in Brazil (Lemeilleur et al., 2020). Moreover, no study applied CE to analyse farmers' preferences for contracts of producing quality and environmentally friendly products in the field of aquaculture. Filling this gap, in this research we apply CE to explore farmers' preferences for the contract of producing certified organic shrimp in Vietnam.
Vietnam is one of the largest producers and exporters of shrimp in the world (FAO, 2019). Recently, to prevent the degradation of mangrove forest and mangroves' ecosystem services due to the expansion and intensification of the shrimp culture system, the Vietnamese government has promoted a model of integrated mangrove-shrimp culture. This is a special kind of extensive shrimp culture model, which is mainly dependent on ecological services supplied by nature, and especially maintains a certain proportion of mangroves in the shrimp farms according to local regulations. However, this policy of protecting forests through regulations has some disadvantages such as lack of economic incentives for forest conservation. Thus, together with this regulation, a potential solution for mangroves' protection through economic mechanism has been implemented recently. In 2000, a certified organic shrimp production project was started in the Ca Mau province to preserve the mangroves and promote responsible aquaculture production through the certification of organic shrimp (GIZ, 2014). The first project, established by the Vietnam Association of Seafood Exporters and Producers and Ca Mau Department of Fisheries, is funded by the Swiss Import Promotion Program (SIPPO). Organic shrimp are cultured following Naturland standards, certified by the Institute for Market Ecology, Switzerland, and bought by the Ca Mau Frozen Seafood Processing Import Export Corporation (CAMIMEX) for export to Switzerland. In 2009, a second project was implemented also funded by SIPPO. In this project, certified organic shrimp are purchased and export by Nam Can Sea-product Import Export Join Stock Company (SEANAMICO) (Ha et al., 2012). In 2013, the International Union for the Conservation of Nature and the Netherlands Development Organization started a third project, funded by BMUB, the German Ministry of Environment, Natural Resources, Building, and Nuclear Safety (IUCN, 2016). In this project, Minh Phu Seafood Corporation buys and exports certified organic shrimp. Being certified as organic shrimp farmers, they can receive a premium price for their products. However, this benefit is judged to be inconsistent with the contribution of people in the supply chain of quality products. After export, the price premium of the organic shrimp-the price is 20% higher than that of normal shrimp-is distributed along the value chain. Farmers were paid 5% and 15% premium by CAMIMEX and SENAMICO respectively. However, farm-gate prices of certified organic shrimp paid by the companies were lower than the prices of non-certified shrimp; this was applied to discourage mixing no-certified with certified organic shrimp. Thus, farmers received "the same (or a little more) from certified organic shrimp compared to non-certified products" (Ha et al., 2012). Also, farmers had to wait several months to receive the premium. Because of these problems, many farmers withdrew from the program (Ha et al., 2012). In the third project, farmers are directly paid 3,000 VND/kg (0.13 USD/kg-approximately 1% of the shrimp price) as the price premium per kilogram of organic shrimp by the Minh Phu Seafood Corporation. In addition, farmers also received 500,000 VND for owning one hectare of mangrove and buy quality shrimp seed at a lower price from Minh Phu Seafood Corporation. In total, the added value for shrimp is 2.5% of the shrimp price (IUCN, 2016). Although there are certain problems in benefit-sharing mechanisms, certified organic shrimp production projects are beneficial to a part of shrimp farmers and contribute to the protection of mangrove forests and mangrove ecosystems. Thus, this sustainable practice is encouraged to be expanded to other coastal provinces.
To successfully carry out the project expanded to other localities, it is important to pay attention to building a suitable contract with appropriate distribution of benefits for farmers to attract their participation and long-term cooperation with the company. Therefore, this paper makes an ex-ante evaluation of farmers' preferences for contracts with a processing company to produce certified organic shrimp. The findings of the paper will provide useful information for the company to design reasonable contracts as well as for the (local) government to create a better institutional environment for promoting this sustainable production model.

Study area and data collection
This study was carried out in the Vietnamese part of the Mekong Delta, which is located in the southern part of Vietnam. With an area of approximately 40,000 square kilometers, the Vietnamese Mekong Delta is the largest shrimp production region in the whole country. In 2016, the shrimp yield of this region was 535,522 tons, accounting for 82% of the county's total (General statistics office of Vietnam, 2016). Mekong Delta also has the largest remaining mangrove forests in Vietnam, accounting for 70% of Vietnam's total. Among the 14 provinces in the Mekong Delta, Ca Mau has the largest mangrove forest area, followed by Tra Vinh (Tran and Ha, 2008). The certified organic shrimp projects have been implemented in Ca Mau province. To serve the plan of expanding the project to other coastal provinces, Tra Vinh province is selected to survey farmers' preferences for the contract of producing certified organic shrimp. 220 integrated mangrove-shrimp farmers were face-to-face interviews from August to September in 2017. Of these 220 farmers, 121 farmers (55.00%) intend to adopt certified organic shrimp practice; thus these 121 farmers were asked their preference for the hypothetical contract of producing certified organic shrimp. However, one respondent could understand and answer CE question because of low education. Thus, 120 respondents' responses to CE questions were analysed. Louviere and Hensher (1982) and Louviere and Woodworth (1983). It has been widely applied in many disciplines such as transportation, marketing, and environmental economics.

Choice experiments CE was initially developed by
In this paper, we apply this method to explore farmers' preferences for hypothetical contracts to adopt certified organic shrimp production.
Suppose farmer i has preferences for a set of K alternative contracts. Each contract is described as a bundle of attributes and attributes' levels.
The farmer's utility derived from contract j is Uij and can be divided into two components: deterministic component Vij and error component eij: Farmer evaluates each contract by its utility, compares them and then chooses the one with maximum utility. Hence the probability of the farmer choosing contract j is equal to the probability that the utility of contract j is greater than (or equal to) the utility of contract k: Rearrange above equation: The error terms are assumed to be independently and identically distributed and follow a Type 1 extreme value distribution.
Under these assumptions, the probability of the farmer choosing contract j out of the set of K contracts is estimated with Conditional Logit Model (CL): Where are observed variables that related to the alternative and farmer, is a vector of coefficients of these variables for farmer i. This model requires the satisfaction of "Independence from Irrelevant Alternatives" (IIA) assumption.
Mixed logit (ML) is an advanced model that relaxes the assumption of IIA, allow for random taste variation and correlation in unobserved factors over time (Train, 2002). According to the author, the formula of ML model is expressed as follows, In this model, varies over farmers in the population with density ( ). Therefore, it can reveal the preference heterogeneity among respondents from the analysed results.
ML can be also used to estimate individual-level parameters (Train, 2002). The mean β in the subpopulation of people who would choose j when facing choice alternatives described by x is ………………..…….. (6) where ( | , ) is the probability of conditional on knowing ̅ can be estimated through simulation (Train, 2002). This procedure includes taking draws of β from the population density f(β|θ) and then calculating the weighted average of these draws. The simulated subpopulation mean is

Willingness to pay/willingness to accept
Willingness to pay (WTP) or Willingness to accept (WTA) is obtained as the ratio of an attribute parameter to the monetary parameter -the premium parameter in this paper.
The above formula is used to estimate WTP/WTA in CL and ML1. In ML1, the coefficients of all attributes are treated as random coefficients and normally distributed, except the premium. Treating the coefficient of premium as a fixed coefficient has some advantages such as the coefficient has the right sign and the distribution of WTP/WTA for the attributes has the same distribution of the attribute coefficients (Hole, 2008).
However, significant preference heterogeneity in terms of cost should not be ignored. In ML2, the premium is treated as a random coefficient and has a normal distribution. In this case, there are two alternative practices in deriving WTP/WTA (Hensher et al., 2006). The first one is that WTP/WTA can be estimated through simulation. This procedure involves taking the ratio of an attribute parameter and the price parameter which are drawn from the estimated population distribution, repeating this for many draws to obtain an average WTP/WTA. The second one is the estimation of WTP/WTA from an individual-based parameter. An Individual's WTP/WTA is calculated by taking the ratio of the conditional mean of an attribute parameter to the conditional mean of the cost parameter (of that individual). Hensher et al. (2006) conclude that the second practice provides a lower variance of WTP than the first practice and it hence reduces the unreasonable WTP. Besides, Grisolia and Willis (2011) use this practice and confirm its advantages such as allowing to address heterogeneity in the sample and overcoming the problems of interval estimation. Therefore, the second practice is adopted in this paper to calculate WTP/WTA.

Design of the choice experiments
The important attributes of contracts (Table 1) were decided after carefully evaluating the reports and related studies on the implementation of the Naturland organic shrimp production project in Ca Mau province, discussing with experts and making a pilot survey. The chosen attributes for the contract alternatives include: Price premium: This is an important factor that affects farmers' utility when they consider choosing a contract. Farmers prefer contract schemes that offer a higher selling price or price premium (Gelaw et al., 2016;Lemeilleur et al., 2020;Ochieng et al., 2017;Schipmann and Qaim, 2011;Van den Broeck et al., 2017). In our case study, if signing a contract to produce certified organic shrimp, farmers have to comply with the standards of producing organic products. Therefore, a proper premium should be used as compensation for farmers' compliance. We suggest four levels of premia to explore farmers' preferences; these were: 3,000; 10,000; 20,000; and 30,000 VND/kg (this works out to 0.13; 0.43; 0.86; and 1.29 USD/kg and is approximately 1%, 3%, 7%, and 10%, respectively, of the average price of normal shrimp). The lowest level is equal to the current premium of certified organic shrimp paid by a company in Ca Mau province. We expected that a higher premium would increase the farmers' utility and the probability of choosing the contract alternative.
Premium-payment delay: payment delay is one of the problems of contract farming (Glover, 1987;Singh, 2002). This is perceived to be a source of risk for small-scale farmers, and hence decreases their utility and probability of contracting (Blandon et al., 2009;Ochieng et al., 2017). In Ca Mau, certified organic shrimp farmers received premium after selling their shrimp from three to four months because of the delay in exporting shrimp to Europe and late payment of downstream actors in the chain (Ha et al., 2012). While the price premium may increase farmers' utility, the delay in paying it may lose farmers' utility. Therefore, this factor is considered as an attribute in a designed contract to evaluate farmers' preferences. We expected that farmers prefer to receive the premium as soon as possible.
Selling place: Transportation cost is one of the main considerations when farmers choose the selling place for their products. Farmers prefer to sell their products at the farm gate rather than transport them to the market because of the transportation cost (Blandon et al., 2009;Ochieng et al., 2017). In this study, we chose two levels for this attribute, at the farm or company, and expected that farmers prefer selling at the farm. Also, we want to estimate the appropriate compensation for farmers to transport their shrimp to sell at the company because there are some advantages of selling at the company. First, farmers could receive a higher premium. Currently, in Ca Mau, the collectors who buy certified organic shrimp also receive a commission from the company. If farmers sell shrimp directly to the company, the commission could be transfer to shrimp farmers. Second, excluding small collectors in the value chain could reduce the number of steps of storing shrimp with ice; this keeps the good quality of shrimp.
Seed provision: Besides the premium, the provision of quality seed is a significant benefit of contracting. The provision of quality seed, an important input, as a part of the farming contract increases the probability of farmers' participation in the farming contract (Abebe et al., 2013;Schipmann and Qaim, 2011;Van den Broeck et al., 2017). Because shrimp production is a highrisk farming activity, quality seed which has a high survivor rate and strong resistance of disease is a major consideration of shrimp farmers. Moreover, joining in the certified organic shrimp production project requires farmers to use quality seed. There are 2 levels of these attributes, including seed provision by the company and no seed provision (farmers buy seed by themselves). We expected that farmers prefer seed provision from the company. The farmers will receive premium immediately, three-month delay, or six-month delay 3 levels: 0, 3 or 6 month(s)

Selling place
The farmers will sell their products for the company at their farm (through collectors), or bring their products to the company to sell 2 levels: at farm, at company

Seed provision
The company will sell quality seed to farmers 2 levels: no, yes

Premium
The price premium (per kilogram) of organic shrimp compared to normal shrimp 4 levels: 3,000; 10,000; 20,000; 30,000 VND/kg Note: 1 USD = 23,271 VND (date: February 6 th , 2020) In this study, we just focus on the contract's attributes which can be negotiated by farmers, not consider the attribute related to the standards of producing certified organic shrimp. Also, certification cost represents the transaction cost of the contract and has a negative impact on growers' utility (Vassalos et al., 2016), however, we do not consider this cost as a contract attribute in our study because it is now supported by the Non-Governmental Organization.
The combination of all attributes and attribute levels is 48 choice sets. A fractional factorial design was used to reduce the choice sets (Hensher et al., 2005), and finally, 16 choice sets were chosen in the questionnaire. The 16 choice sets were blocked into four segments, so each respondent received four choice sets. Each choice set had 3 options including 2 contract alternatives and status quo options (no-contract). An example of a choice set is shown in figure 1. Table 2 presents the socio-economic description of the surveyed shrimp farmers. The average age of farmers is 50.283±10.593 years and 20% of them are female. The average of farmers' education is 7.067±3.225 year, with nearly half of them at the secondary level. We used both CL and ML (ML1 and ML2) to analyse the data and then compare the results. The descriptive statistic of the independent variables used in the CL and ML models are shown in Table  3. In ML1, we treat the coefficients of 3M delay, 6M delay, Selling place, and Seed provision as random coefficients with the assumption that they are normally distributed, and the coefficient of Premium as a fixed coefficient. In ML2, the coefficients of all variables are treated as random coefficients and normally distributed.  (Table 4), the ML model is preferred to the CL model in terms of its higher value of the log-likelihood function, and lower value of AIC and BIC. Because the superior of the ML model is demonstrated, we focus our interpretation on the results of the ML models. All the coefficients of the models are statistically significant and have the expected sign.

Farmers' preferences for the contract's attributes
Premium: The coefficient of Premium is positive, indicating that the contracts with a higher premium increase farmers' utility and the probability of choosing a contract. This result supports the findings of previous researches (Gelaw et al., 2016;Lemeilleur et al., 2020;Ochieng et al., 2017;Schipmann and Qaim, 2011;Van den Broeck et al., 2017).
Premium-payment delay: 3M delay and 6M delay reduce farmers' utility and increase their probability of not participating in the contract. This result is in line with Blandon et al. (2009) and Ochieng et al. (2017), who argued that payment delay decreases the probability of contracting.
Selling place: A majority of farmers prefer to sell shrimp at the farm. This finding is in line with the findings of previous researches (Blandon et al., 2009;Ochieng et al., 2017). This could be explained as follows. First, for the integrated mangrove-shrimp culture model, farmers harvest shrimp many times per crop. They harvest shrimp nearly every tide (two tides per month), with several days per tide. Because the shrimp quantity harvested per day is low, farmers prefer to sell shrimp at the farm for convenience. Second, bringing shrimp to the company causes transportation cost and time cost.
Seed provision: The coefficient of Seed provision is positive, indicating that the contracts with the provision of quality seed increase farmers' utility and the probability of choosing a contract. This result is in line with the findings of previous researches (Abebe et al., 2013;Schipmann and Qaim, 2011;Van den Broeck et al., 2017). Moreover, seed provision is more important in the case of low premium and the negative effect of premium-payment delay on farmer's utility. Source: Authors' estimates ***, **, and * indicate significance level at 1%, 5%, and 10% levels, respectively Standard deviations of all the random coefficients in ML1 and ML2 are statistically significant, suggesting the presence of preference heterogeneity among respondents around the mean. Moreover, the estimated means and standard deviations of these coefficients also provide information on the proportion of farmers that places a positive value and negative value on the attributes (Train, 2002). Based on the estimated means (-2.549) and standard deviations (2.466) of the coefficient of 3M delay in ML1, we can know that 84.9 percent of the distribution of this coefficient is below zero and 15.1 percent above. This implies that 84.9 percent of farmers prefer immediate payment, while the other 15.1 percent accept the delayed payment. A similar calculation for 6M delay, 94.2 percent of farmers prefers immediate payment, while the other 5.8 percent accept the delayed payment. 59.2 percent of farmers are estimated to prefer to sell shrimp at the farm, while the other 40.8 want to sell shrimp at the company. 76.3 percent of farmers prefer seed provision, while the other 23.7 percent did not. These estimated numbers are very similar to those in ML2. The standard deviation of the coefficient of Premium in ML2 is statistically significant, suggesting the presence of heterogeneity in farmers' preferences for this attribute. A majority of farmers prefer higher premiums while a small part of farmers can accept the low premium. This can be explained that maybe premium is not the most important factor for some farmers to join in the contract of producing certified organic shrimp. They maybe care more other benefits such as the provision of quality seed and the technical assistants. Because ML2 reflects the heterogeneity in farmers' preferences for premium, it is preferred to ML1. ML2 is also better than ML1 in terms of the log-likelihood, AIC and BIC.

Willingness to accept for contract's attributes
The willingness to accept the contract attributes and the 95% confident interval of WTA are shown in Table 5. WTA in ML1 and ML2 are slightly higher than CL. Because ML1 and ML2 are better than CL, the WTA results in ML1 and ML2 are chosen to interpret. In ML2, individual parameters are issued to calculate WTA in the sample. Follow Grisolia and Willis (2011), we dropped one respondent, whose coefficient of the premium is close to zero, to obtain a more reliable distribution of WTA [because a price coefficient close to zero would generate unreasonable WTA (Grisolia and Willis, 2011)]. The distribution of WTA for all attributes is shown in figure 2. It is clear from the figure that there is heterogeneity in WTA for the attributes, including some farmers willing to pay much and less than the average.  Premium-payment delay: Table 5 shows that farmers need to receive (on average) 29,609 and 31,031 VND/kg higher for certified organic shrimp price to compensate for the utility loss associated with 3M delay in paying a premium in the ML1 and ML2 respectively. The research results also show the heterogeneity in farmers' preferences and WTA for this delay (Figure 2). A small part of farmers strongly hates the delay and wants a much higher compensation, while some others can accept the delay of about 3 months. If the premium-payment delay is 6 months, farmers need to receive 44,977 and 46,295 VND/kg higher for certified organic shrimp price in the ML1 and ML2 respectively. The results also show the heterogeneity in farmers' preferences and WTA for 6M delay, including some farmers who strongly hate the delay also have very high WTA for it. These farmers can easily refuse to join the contract due to this payment delay. Thus the premium should be paid as soon as possible to attract more farmers to participate in the contract. If the company cannot pay the premium immediately, the delayed payment should be within 3 months. This is because the quality seed provision could help to compensate for the loss of farmers' utility caused by the delay (WTP for seed provision is nearly equal WTA for a three-month delay in paying the premium), even with the lowest premium level (suppose selling shrimp at the farm). However, if the delay is above three months, the quality seed provision could not compensate for the loss of farmers' utility due to the delay. WTA for the six-month delay is 15,000 VND/kg higher than WTA for the 3-month delay; this means that 5,000 VND/kg is the average WTA for the onemonth delay (after the third month of the delay). Thus after the third month of the delay, the company should increase 5,000 VND/kg of certified organic shrimp per one-month delay.
Selling place: The price of organic shrimp should be increased 7,264 and 7,844 VND/kg to encourage farmers to transport their products to sell at the company in the ML1 and ML2 respectively. The farmers' WTA for selling shrimp at the company are also heterogeneous. This heterogeneity occurs due to the different location of shrimp farms that leads to the differences in transportation cost and time cost, even some farms located in a remote area with bad road condition. In this situation, farmers should cooperate through farmers' group or cooperative to together transport shrimp to the company. This may help to reduce transportation costs with a big amount of shrimp to be transported to the company. Besides, cooperative can help to protect farmers' benefits from joining in the contract of producing certified organic shrimp. The establish of farmers' group or cooperative should be discussed more in future researches. For farmers who have farms in remote areas and cannot directly bring shrimp to the company, the company can help them reduce the distance, time and cost of transportation by laying some collection points in these areas. In these ways, certified organic shrimp can be transferred from farmers to the company without small collectors. This can help to keep the better quality of certified organic shrimp because of reducing the number of steps of storing shrimp with ice.
Seed provision: Farmers are willing to accept the lower price of certified organic shrimp at an average of 27,923 and 29,616 VND/kg to receive the quality seed in the ML1 and ML2 respectively; this is the farmers' willingness to pay to receive the quality seed from the company instead of buying seed by themselves. The results also show the heterogeneity in farmers' preferences and WTA for Seed provision, including some farmers are willing to pay a very high amount to receive quality seed from the company. This indicates that the provision of quality seed is very important for them because it can help to improve the productivity of shrimp production and thus increase shrimp farm's profit. This also explains why some farmers do not care much about the level of premium because high productivity may affect profits much higher than the premium. However, a small part of farmers wants to choose the shrimp seed by themselves. Due to the heterogeneity in farmers' preferences for seed provision, the company can make flexible contracts with farmers according to their preferences. The company should provide quality seed for the section of farmers who need it while letting the other section freely buy seed as long as its quality is good.
Finally, as mentioned, we did not consider the certification cost as a contract attribute to analyze farmers' preference for it. In the future, if the NGOs do not support this cost any more, it will become a significant barrier of contracting to produce certified organic shrimp. This should be carefully assessed in future researches.

CONCLUSIONS
This paper analyses the heterogeneity in farmers' preferences for the contract of producing certified organic shrimp and in their WTA for the contract's attributes by applying CE approach. The hypothetical contracts consist of four attributes which are price premium, premium-payment delay, selling the place and the provision of quality seed. The survey was conducted in the Vietnamese part of the Mekong Delta.
The results show that farmers prefer the contract which offers a higher premium. However, premium-payment delay reduces farmers' utility. Further, we found that there is heterogeneity in farmers' preferences for the premium and premium-payment delay. Another important finding is that almost farmers prefer seed provision. Quality seed could help to improve productivity and increase profit. This attribute is more important in the case of low premium and the negative effect of premium-payment delay on farmer's utility. Farmers' preferences for seed provision are also heterogeneous. A part of farmers strongly prefers to receive quality seed from the company, while a small other part wants to buy seed by themselves. Regarding selling place of certified organic shrimp, a group of farmers (about 59 percent) prefers selling their shrimp at the farm over at the company while the other group does not. And the WTA for bringing the certified organic shrimp to sell at the company also differs among farmers in the first group.
Based on the above finding, we suggest that the company should carefully consider contract attributes such as proper premium, seed provision and a suitable place to collect shrimp from farmers to build contracts. On the other hand, a premium-payment delay should be reduced as short as possible, especially try to control it within three months. In addition, a flexible contract should be made to suit the farmers' heterogeneous preferences. Our findings provide useful information for the company to design reasonable contracts with farmers to produce certified organic shrimp as well as for the (local) government to create a better institutional environment for promoting this sustainable production model. Our findings may also be useful for contract design in the aquaculture sector of other developing countries where many companies also want to contract with farmers to supply quality-rich, safe and environmentally friendly products to the international market.
Funding: This study is funded in part by the Can Tho University Improvement Project VN14-P6, supported by a Japanese ODA loan.