Conversations with Marvin Kang

Something his friends often tease him about, Marvin has experienced most parts of Singapore’s formal education system, having gone both through both the Junior College and Polytechnic pathways. He completed his undergraduate studies in Business Administration at the National University of Singapore, before pursuing postgraduate studies in Public Policy at the Harvard Kennedy School. Together with a group of fellow enthusiasts, Marvin volunteers with The Apprenticeship Collective, a not-for-profit organisation they quite unwittingly founded that now provides opportunities for youths to try out different professions – and in the process, explore what they might find interesting, meaningful, or purposeful to do in life.

M: My typical workday will be divided into three parts. There’s the part which is more reflective of the title. So, we analyse policies and we look at policies from the past and see some of the policies that have been in place for a number of years. We think through if it’s still something that is applicable in today’s environment. It involves discussions with our team and our senior bosses to see what their thoughts are. Sometimes, because in the government side we work with political officers, we hook on to some ideas there. We also look at the new things we want to implement. That’s the mental kind of engagement.

There’s also what we call “staffing” which is coordinating a lot of meetings. Meetings are where decisions are made, so the civil service does staffing which can be as simple as setting up a meeting towards drafting the notes of the meetings and ensuring that different parties feel like their views are sufficiently captured as well as the follow-up actions. In staffing meetings, we also staff forces like company political holders. I used to be in MOE, for example, so I would go down to the universities and polytechnics quite often. We do a lot of coordination work there.

M: Subject matter expertise. I was from Higher Education in MOE and that was okay because all of us went through the system whether we were from universities, polytechnics or the Institutes of Technical Education (ITE). Then I went to the Land Transport Authority and I had to acquire a whole new set of languages to communicate with the engineers because I am not an engineer by training, to understand why trains fail. It’s an entire industry that I’m not familiar with. Now I’m at the Ministry of Communication and Information dealing with frontier technology which I am not very familiar with.

I think that’s the most challenging part, to know enough of the subject matter to think coherently and logically of what policy you want to support. Normally the first two months on the job, there’s a very steep learning curve. Every night I would read 100 over pages of some new technologies because we don’t know the field. However sometimes not knowing the field is an advantage, you can look at it from an outsider’s point of view and you can look at it more objectively. Not being familiar with the subject matter is double edged because it’s quite exciting when you get to learn something new. It can be very trying but very inspiring as well.

At the work level, it’s a lot more organic than what I’ve learnt. I did my Masters in Public Policy and they break it down for you and teach you about eight steps which is very systematic. However, in reality it’s more organic. So the process is more apparent in longer range policies. For example, working on the manpower issue, we’re thinking about the problem ten years ahead of time. It gives us more time to think through the different steps. For example, we have data. Is the data we have today enough to tell us about the problems? Is the data categorised into the right way to give us insights into the problem? It’s not just statistical. It’s about knowing what matters and what doesn’t. There’s a data part and we do a lot of data and research related matters. There’s this move for more governments to go into data improvement for policy making. (It’s not as simple as my aunty telling me that people can’t find jobs and us coming up with a solution. Inevitably, we will rely on some anecdotal information but it should be more data driven. That’s one step.

The next few steps are more iterative. We come up with a hypothesis, for example: “I think that the university students are not having enough training in coding which makes them unable to take on coding related jobs in the future.” Then I will validate this hypothesis through methods like focus groups or research that others have done. That’s the stage where you have a better idea of your problem statements or the key problems that you are trying to tackle. Then you try to get everyone on the same page. Then we move to coming up with solutions. Is it about giving more subsidies? Is it about information provision?

For example, why do people take so much sugar? Is it because they don’t know how bad sugar is for your body? Is it because sugary things are cheaper than healthy options? We can choose different solutions but we need to first ascertain that Singaporeans are consuming too much sugar or that too many people have diabetes.

We will then structure our diagnostics which is essentially putting review mechanisms to determine whether our policy works. How do we put in the tools to help us say after one year of implementing the policy that: “oh, this didn’t work because the incentive wasn’t enough.”

Subsequent stages are iterative. Maybe you realise that you didn’t craft the problem statement accurately. So, you rework the problem statement, validate it again, implement the strategy and then review it.

When I solve a problem! When I find a panel of protein markers that can actually be used for patient diagnostics, I think that is really rewarding. I have my own share of different kinds of pleasures that I get from my work. For example, when I publish a paper or when I get a grant, it feels great because it means that I can sustain my team for a longer time period and have exciting science projects to do. Having said that, I think that the biggest joy that I get out of this is, of course, the product. When I see that my analysis of a big data leads to seeing 5 or 6 genes that are really critical in explaining the mechanisms of disease onset, and they can immediately be used in a clinic, I think that is the biggest payoff.

Yet, most of the time, it fails! You face setbacks and failures in research. I think being able to deal with failure is the most important thing in any career. A lot of people take it very personally. Even my friends.

Though you would probably get numb to the failures, there are projects that you will value more. And if those fail, of course it feels like a dead end. Should I stop there? I obviously have better options going out to the industry to get paid better for a much smaller amount of work. But I think you should not really care so much about money.

If you are too smart, usually, you do not stay in academia. Surprisingly, being a professor is actually not the smartest decision you can make in your life. You should really love what you are doing because there is no extraordinary financial payoff, your life will be full of failures, and your friends will be making more money, having a much easier and relaxed life, while you are in a lab doing stuff that don’t work.

M: I have to give you the government answer and say it depends. For some things, they are in their early days and you have to be more experimental. Due to the nature of those projects we do pilot tests. For example, Punggol North is being developed into a digital area and of course the vision is for the whole country but we do have to try it on a smaller scale first. For pilot tests, we are prepared that some things might not work out the way we want it to and we are ready to make changes on the spot.

On the other hand, there are bread-and-butter policies that have been done over the years and you don’t expect them to fail too much.

M: it differs from divisions. For example, when I was in MOE I was the team leader for SIT and my colleague was the team leader for NUS. So, I don’t think about NUS policies but at the same time there are university wide policies like admission criteria. So, that cuts across all universities so we discuss that together. But there are things that SIT champion like more industry engagement, which is not the mandate of NUS, for example, so I deal with that aspect on my own. Whereas when I was in LTA, it was a very big team and we would join the engineers, the planners, the civil engineers.

M: Those that are related to partnerships and collaborations are increasingly important when you work in the government. The type of policies that we push out these days cut across a lot of agencies. For example, when I was working with SIT, my focus was moving SIT to Punggol. Building a campus is a lot like building a town and in towns you need roads, but who is going to pay for the roads? Does MOE pay because the school is there? Does LTA pay since they build roads? Does HDB pay because it’s a public utility? A lot of these issues need to be worked on across various agencies to reach a decision. In each agency, everyone has their own budget, responsibilities and KPI. What I’ve learnt is how to bring everyone together. It sounds very simple but if you think about it from an agency’s point of view, each agency has its own budget and if they use this amount of money, it means they can’t do something else and that something else might be really good too. So, the skill of building partnerships is so important, increasingly so in policy making. I call this “influence-related skills.”

M: Their attitude and passion for the job. Passion sounds very fluffy huh? When the younger students ask me if they should be policy makers, I ask them: are you angry with anything in Singapore? To me, that’s a really good test of your passion. I had this Secondary 2 student who said: I’m very angry that there are no sheltered walkways for people with disabilities. It’s very unfair. Can the government do something about it? I replied: Why don’t you do something about it? It’s about having that spark of injustice. If you feel that something is wrong and that you need to do something about it, you tend to be able to take the day-to-day duties a lot better.

Another aspect, for jobs in general, is that you have to be very willing to learn new things. Many typical jobs don’t actually require people to know industry-specific skills. As long as you have a background understanding, you can come in. I actually don’t think it is very essential to think about skills to build up before you enter a particular industry.

There are more specific jobs than policy making that require you to possess specific skills. If you need to do a coding related job and you cannot code, you will need to learn how to code through school or otherwise. For policy making, there are no specific technical skills that are needed.

M: There’s none actually. I’m one of the few at entry level who has public policy training, but it’s really not required. In fact, there isn’t even a bias towards social science students for example. When I look around my department, we have engineers, social sciences, sciences, humanities… There is no specialised degree needed. My colleague who came in after poly is doing her job just as well as anyone else.

I would add that to consider a career in policy making, you shouldn’t be numerically-averse. It’s very important to look at your data when crafting policies. It’s about not being averse to numbers rather than having a skill. For example, when people ask me if excel skills are useful, sure they are but we have Google at our fingertips! We just need to ask the right questions. I don’t remember all the formulas off the bat but if you are not fearful of looking it up on Google or reading guides, it will be fine. These are things that will help you ease into the jobs.

M: I think my business and public policy education was very useful in giving me frameworks to think about problems. It’s not for me to follow from A to F. For example, the policy formulation process I mentioned earlier was adapted from what I learnt in the past. When I was studying business in poly, I did marketing and that taught me the ideas of target audience and target audience segmentation. Now when I think about policies, I’ll think about who we are really reaching out to. For example, you can’t just tell me we’re reaching out to the aged. That’s a huge group. Within the 65-80 age range, it’s another big group. Within that group, you have people who are educated, uneducated, who understand English, who do not understand English… I think about these things very intuitively as a result of my education. I wouldn’t discount my years of education but some soft skills have to be picked up at work.

M: Since I was pretty young. My family is in business and I’m the only one in the government. When I was young I was very involved in the business which is when I realised I wasn’t that interested in making a lot of money. It was fun to think of ways to make a lot of money but I wasn’t particularly interested in making the money. That helped me cut away half the possibilities which pointed me towards the government sector.

M: Apart from the last part, yes actually. In LTA, the working hours are very hectic and I was working on the Thomson East Coast Line tender. We were given 7 months to produce the proposal and the time pressure was immense, with people working crazy hours. Every time they read reports on social media on how “the MRT sucks”, they feel quite sad. My colleagues do question what they are doing, since no one seems to appreciate their efforts. 

However, regarding your last sentence about the people not seeing the good in the policies – I would disagree. How good a policy is is subjective. Sometimes people say the policy isn’t good but they are not who the policy targets. For example, people questioned how worthwhile it was to be spending so much money for the Pioneer Generation but they don’t know about the issues connected to the older generation. There’s always the problem of them getting older as well, and that is the challenge of the civil service. A lot of the people who join the civil service are people who could be drawing higher salaries in the private sector which brings me back to my point of passion and wanting to do something for society.

M: I generally like to talk to people, so I do not have that much of an issue with it. I take it as part and parcel that people may not always understand what you say, and you may have to repeat it again. There are always miscommunications between person to person; we see it even in our personal relationships and this is the same with communicating with the citizens. As long as you are in a people-oriented field, this is what you would face, so you should not be discouraged by this issue.

The idea of stakeholders changes depending on what policy I was working on. When I was working on trains, my stakeholders were mostly the operators; we had to think about how to ensure the train operators know what we are really going for, especially since our policies are created for them so the people would benefit. When I was in MOE, my stakeholders were young people. I handled feedback as well and had to tackle each of them. While doing so, it is sometimes easy to get frustrated when both sides are each seeing things from only their own point of view. For instance, we receive emails from students who were unsuccessful in their university applications, but it is simply not feasible to give everyone who writes in a university place.

M: Yes! This problem is not unique to policy-makers; things to do with data analytics face the same threat. Some companies are already looking at how things like analysing data can be done by technology, and the government is looking at it as well. Today, we have this Chatbot called “Ask Jamie”, which is actually Artificial Intelligence (AI), where we teach the bot what some of the things that the public frequently asks are, and how to react to those queries. Over time, with more and more data points, and as they talk to more and more Singaporeans, the bot will improve the way it responds and handle queries.

However, I suspect that the more strategic levels of policy-making will not be so easily replaced. On the other hand, things that are repetitive and mechanical in nature, such as crunching data, will be replaced easily. For instance, the procedures that we need to carry out each time we open an Excel spreadsheet is fixed and the task can easily be done by technology. Hence, the answer to this question is yes. The work that we do here at MCI involves thinking about the people who will be replaced as technology advances further.