 So hi everyone and welcome to this webinar. It's the first in our new series of webinars think wider webinar series, new perspectives on domestic revenue mobilization. So what the webinar series aims to do is facilitate a discussion on how domestic revenue mobilization can be supported in developing countries when the global south. The monthly webinars cover a range of topics related to the development of tax systems, development of non tax domestic revenue mobilization as well as political institutions. The series is aimed for policy and development cooperation professionals, as well as researchers who are interested in hearing the latest insights from you and you why does research on domestic revenue mobilization. The program seeks to help improve developing countries tax systems and to strengthen their domestic capacities for revenue mobilization. So first of its kind is going to be a webinar tax effort revisited how much tax can low income countries expect to collect. It's going to be a presentation on recent research on the subject, as well as the panel discussion involving two panelists, the presentation is going to be done by Kyle McNabb, who is our friend and former colleague. Currently a development economist who works with the overseas development Institute in London is a tax policy analyst for the tax dev program, and he's based in Kampala. As part of the panel we're going to have a colleague here at wider Michael Dankwa, with a development economist and research fellow at UNWider. Michael maintains primary research interests in economic development, especially in sub-Saharan Africa, primarily focusing on issues such as inclusive growth, informality and productivity growth amongst others. And also in attendance with the panel is Joyce Truma, who is the founder and director of Salima Research Consultancy. Prior to that Joyce was a lecturer in development economics for 12 years at the Midlands State University in Zimbabwe. And main research interests are in tax policy and tax administration in developing countries. I've been a chair of this session, and I'm Abraham Stagem, myself a research associate at UNWider, focusing primarily on taxation in developing countries as well. A few housekeeping rules, the webinar is being recorded, and it's going to be available, I think a couple of days or weeks from now on YouTube for people who want to revisit and you know watch the presentation. Many people in attendance, we can't raise our hands to ask questions so please post your questions in the chat. For questions which might be a bit technical or people are interested in the technical or econometric aspects of the questions of the paper, please refer to the working paper. I think Eva is going to include the link to the working paper, as well as the link to the current GRD update. I don't think there is much more which we have to say so enjoy the webinar and Kyle, the floor is yours. Can you see the presentation on my screen. Yes, I can. Perfect. I'll assume everyone else can as well. So, thank you for the introduction. Abrams, as everyone said my name is Kyle McNabb. I'm a researcher with the ODI and I'm based in Kampala, Uganda. And so we're going to be presenting today. I have a paper between myself, Michael and Abrams. We started a couple of years ago and published at the end of last year called tax effort revisited near estimates from the government revenue data set. I should probably start by disappointing everyone, because the question posed in the title of the webinar was how much tax can low income countries collect to expect to collect. I'm not sure that we're going to give a definitive answer to that during the course of the presentation. But we will be tackling issues around how you think about measuring that and why we think the work that we've done here makes a step in the right direction and toward better estimating tax effort and tax potential in in low and middle income countries. So, in terms of the roadmap for the next 20 or so minutes, we'll talk a little bit about the background and context of why this is perhaps important or timely and briefly review some literature and that has gone before us. Talk a little bit and sort of high level terms about the estimation that we do before talking about the results and importantly the implications and limitations of the of the study. Okay, so in terms of the context for this, I'm sure most of you in the audience are very well aware that average tax to GDP ratios in low income countries are much lower than those in high income countries are our most recent estimates from the government revenue data set, which is maintained that you and your wider suggests that on average, low income countries collect around 12% of GDP and tax revenue, compared to around 22% in high income countries. When you include social security contributions and the average in high income countries in recent years stands at around 29% of GDP. And there's almost no difference for low income countries when you account for mandatory social security contributions. It's quite a gap. And there's also a very pressing need for low and middle income countries to to raise revenue in order to not only recover from the economic fallout of the pandemic. But also to tackle the current cost of living crises. But also there's this overarching agenda of sustainable development goals which were hoped to be attained by the end of this decade and recent research from the IMF, Dora Benedict and others has estimated that on average, both public and private sector is about 14% of GDP per year per country in spending to meet the sustainable development goals by the end of the decade. That is, I've said on the screen that's a non trivial amount, perhaps I'm underselling how much that really is in terms of of what's currently being collected in many low income countries. So there is this sort of overarching focus still on how can developing countries work to collect more tax revenue to fund those pressing development needs, which brings us to the idea of tax effort. For those of you that aren't aware or didn't haven't really engaged with literature in this say, or engaged with this literature tax effort is essentially the ratio of actual tax collected to potential tax collected. We're reasonably good at, at understanding how much taxes currently actually collected in most countries. We're less good at understanding what's the potential amount of tax that could be collected and that's where different modeling approaches have come up with different, different ideas and results. This is a big challenge and one that we want that we tackle in this work. Why is this important and tax effort figures and results from tax effort studies over the last decade or two. And, you know, often appear in sort of donor work advisory advisory reports civil society work and can sometimes be seen or misconstrued as realistic targets or benchmarks. To which governments should be aiming or or expectations over what governments might currently be collecting and have often been used as evidence to encourage developing country governments to enhance the tax collection or to collect more tax. And so in this world that tax effort figures are used in this in this sort of way by donors civil society and others. We felt that it was important that they were estimated as accurately as possible, and thus any sort of advice that finds the desk of a policymaker is grounded in fairly realistic expectations, regarding tax revenue mobilization. We came at this with sort of a lens that with a concern over the, the previous tax effort estimates that existed and we're being used for those purposes. Particularly, we saw some bias in the estimation methods and perhaps a lack of attention to detail in how those scores were produced. And that's something that we try to tackle and we say a little bit more about later. And, importantly, we absolutely don't reinvent the wheel here, but we do revisit existing findings. We employ new wider data sources, make some enhancements on the methodology, and we hope that we've been able to better estimate tax effort. Or more closely estimate the true value of tax efforts, according to these modeling approaches for a bigger sample of countries than has been done before, and for longer time periods. I probably already hinted in the direction that tax effort estimates to come with several very important limitations which I will discuss at the end. So please wait for for that before before drawing any conclusions. Because of the sort of literature in this field, there's a fairly rich literature of economists trying to estimate the determinants of tax ratio across countries, and since at least the 1950s perhaps someone had thought about it or written about it prior to that. And that's the earliest sort of record we were able to find. Additionally, this involved when econometrics came into the writing some regression of the tax ratio on the left hand side of an equation on a measure of development such as GDP per capita, a measure of exposure to international trade sometimes called openness, some proxy for the structure of the economy, usually, how big is the agricultural sector how big is the manufacturing sector. Whether or not a country is resource rich or resource dependent. And later the literature increasingly attempts to understand the sort of mediating role of demographic and socio economic or governance factors like things like are more urbanized countries better at collecting tax are more educated companies countries excuse me better at collecting tax. And what about the role of democracy perceptions of corruption, etc, etc. So, there's quite a rich literature of those sorts of regressions and if you sort of engage the literature you know that a lot of authors have have looked at the topic and in order to move from those from those papers to understanding tax effort what some authors did. was to was to calculate a tax effort score based on actual tax revenue divided by the predicted value from that regression. And that very simple equation essentially means that a tax effort score could be greater than one equal to one or less than one depending on where the country lay in in respect to the regression line from the oil less regression. And this is fine in theory and you know, then thinking about what does that actually mean. We're not convinced and I'm personally not convinced that I read that to tell a policymaker that his or her revenue effort is 1.5 is a particularly salient thing. That might suggest or that might be interpreted as a country is collecting revenue beyond its means. And that's absolutely not the case. And all that says is that in the group of countries that the estimation has been carried out on that country might be doing better relative to the other ones in that equation or in that regression sorry. So understanding tax effort and tax potential according to ordinary least squares estimations and don't provide particularly salient or met or meaningful results. More recent studies over the last sort of 10 to 15 years have moved to thinking about estimating tax effort according to what's called a stochastic frontier analysis. And broadly, this sort of has its roots about, yeah, around about 2010 by authors at the IMF for not yet so and casino and has notably been replicated by authors at the IGC, Langford and Olin Berg and more recently. In 2019 by Moeje and Cebude. This approach essentially models tax collection according to a production function and estimates a kind of what's known as a tax frontier which gives a theater represents the theoretical maximum amount of tax a country could collect, given the inputs in the model. And, and so a tax effort score of one would mean that a country is at the tax frontier or the theoretical maximum amount and anything less than one. And, or the distance between tax effort and that tax frontier or tax potential. This is essentially caused by two things in the model and a random error and an inefficiency term the inefficiency term is what we're trying to get at and isolate in tax effort studies we want to understand that in efficiency term as best as possible and disentangle that from any sort of random or stochastic error within the modeling process. And that's where we think we've made a bit of a contribution in the modeling side of things and I should sort of say that Michael Danka who's on the panel and will hopefully say a few words afterwards is the, the expert in that side of the, of the paper and will hopefully give a few more technical details if required. But, but in order to illustrate why we think our approach has been a slight improvement on previous papers and let me show your results from four different types of estimation of this stochastic tax frontier. So four different approaches are on the screen there's a pooled model, a random effects model, the batase and coeli model and there's a true random effects model so previous literature has focused largely on numbers to, and especially number three the batase coeli model. So the case, the key question is which of these models is actually best. And if you look at the results of what the tax effort scores look like after estimation. And there's four histograms on the screen. And so these simply show the distribution of the tax effort scores which again are at a maximum of one. So on the x axis runs from zero to one. So according to the pooled random effects batase coeli and true random effects procedures from top left to bottom right. And you can see that the estimation according to true random effects approach stands out, and it's a lot more rightward skewed so on average, the tax effort scores are higher than under other modeling assumptions. And there's also a much tighter variance in the estimation of the scores. And you can see that the, for example, just to the left of the true random effects histogram the batase coeli specification. Has a reasonably normal distribution with the sort of, you know, average of a round maybe not point four, which is significantly lower. And then you see under random effects and the pooled model and the top two pains of the, of the top two histograms, and you see a massive spread in the distribution of tax effort scores. So we set about to try and understand why the true random effects model stood out like this. And what we uncovered was that this model is actually much better able to disentangle those two terms I spoke about five minutes ago. Remember I said the distance from the tax frontier to where a country is at a given point in time is a mixture of a random error or what's written here as unobserved time invariant heterogeneity and inefficiency. The models aren't able to disentangle these two to the same extent, and that's what ends up happening is that part of what's actually a random error ends up being attributed to inefficiency so you end up with a lower tax effort score and a higher degree of inefficiency. We go back to the graphs or the histogram sorry you see that under the other modeling approaches the tax effort scores are on average, lower. And that's the substantive limitation of the taste coeli and random effects models which had previously been published and used and sort of made their way into some, you know donor advice or policy thinking. And it does have implications for their interpretation and for their use. In terms of what our results actually looked like I've tried to give a snapshot on the screen without going into too much by new detail but we find that the average tax effort scored globally is about 0.84. The potential which is simply simply the current average tax collect tax GDP ratio globally divided by 0.84. It's about 20.9% of GDP for the year 2019, which represents about an average increase of about three and a quarter percent of GDP across countries. Now an average across countries perhaps isn't the most salient measure to look at. In terms of comparison with previous work. And the IGC study in 2016 found an average tax effort score of 64 or 0.64 and the replication a few years ago find it to be 0.47. And that's saying that on average countries are collecting less than half of their tax potential. And that's on the right hand side, the short table on the screen you see that across across specifically across regional regions as defined by the World Bank those scores differ somewhat so you see that in perhaps Europe, Central Asia, Latin America, the Caribbean, those tax effort scores are an average, you know, 88 87% whilst in areas like Sub-Saharan Africa or East Asia and Pacific, they're closer to 0.8 or 80%. So there is, there is a bit of heterogeneity across regions. So we've compared our scores against a database called the collecting taxes data set or the collecting taxes database which is maintained by US aid and I think at least every year or every two years and US aid re estimates tax effort scores along the lines of some of the previous work and under some of the same previous work that we've discussed. And so what this on the screen shows you again is that again we see a much a much tighter distribution of our scores on the X axis. And so from left to right you see a much less of a spread and the estimates from the collecting taxes data set have a much have a much wider spread between zero up to up to one. So again on average versus vis-a-vis this other data set that has done tax effort score estimation, the approach we take ends up with a more conservative estimate of tax effort scores in almost every case. Okay, so let me move to talking about the limitations and some concluding remarks of work like this. Essentially our main I think takeaway is that recent estimates of tax tax effort have in many cases we think being substantial under estimates and primarily primarily this is due to the methodology employed. We highlighted that the different modeling assumptions and give you vastly different tax effort scores. And you end up with with a massive variation in scores across countries and ultimately where these sorts of scores have entered policy dialogues. It's really potentially misleading to tell a policymaker that his or her country is collecting 30% of its potential when in actual fact it's probably collecting 80, 85% of its potential are two very different messages. So we hope that the work that we've done here is in some way convincing and can lead to a more conservative sort of use of these scores, but a more responsible use as well. We also find that the the other methods and that had previously been employed are very sensitive to outliers in the inputs to the model. And if you read the paper we give a few sort of examples of those where we think that that's something at play. In terms of a few more limitations and we've covered tax efforts in this presentation and for a lot of countries, a more salient measure to focus on is actually revenue effort. There are a number of resource rich countries which barely collect any tax whatsoever but do collect a lot of government revenue via royalties or other non taxes. And so if you look at some countries that are very, very rich according to because of natural resources they might have a very low tax effort according to our estimations but of course they're collecting a lot of revenue elsewhere from non tax instruments. There's one limitation to looking at tax effort scores. We, we will be publishing I think, both revenue and tax effort scores at some point according to this methodology. A major limitation as well is that tax effort scores are backward looking and they don't predict how much tax revenue country X could collect tomorrow or next year in the next five years. So on a set of inputs, and at in the past for which we have data so you know, given the size of a certain economy, how open it is to international trade the structure of its economy, how urbanized it is, and how well do we think it's doing or how well does the model compare to its potential. Another key limitation is that tax effort scores likely means something quite different in developing countries to high income countries. I think it's probably reasonably fair to say that most low income countries would like to collect some more tax revenue or would like to collect as much tax revenue as possible. But at some point along the development path, the, the choice of how much tax to collect becomes more of a political decision, which is reflective of society's preferences over some appropriate level of government spending. So think about whether whether the particular party in power is more fiscally conservative or fiscally liberal. And that may lead to differences in the, in the amount of tax collected so there is a point at which tax effort scores do mean something different for different groups of countries. And that's an important thing to bear in mind. We definitely don't suggest that anyone should be relying on tax effort scores to guide strategy or donor strategy or make judgments of tax collection in developing countries, or recommendations they are very high level indicators. And we think they're useful part of a toolkit for assessing tax performance or tax potential in a country definitely shouldn't be solely relied upon. There are many other indicators that can be useful guides as to where tax tax collection performance, maybe enhanced going forward for example a lot of countries are not under undertaking tax expenditure reporting. A lot of countries have undertaken that gap analysis for example so there are, there are a lot of different modeling techniques in, in the toolkit that can be employed concurrently to get a picture of, of tax collection in a given country. So I hope we can sort of stress that point, but, but definitely in a world where tax effort scores are being used and we hope that the, the, the, the modest methodological improvements we've made leads to a more realistic expectation on the side of tax effort. Ultimately, though, the bottom point is probably the most important one. A very narrow focus on revenue targeting, whether it's informed by tax to GDP ratio targets or tax effort scores distracts from wider efforts to develop more equitable broad based unfair taxation. Arguably, these are more important targets and goals in the long run in the domestic resource mobilization arena so I'd, I'd probably leave it with that thought and just to sort of wrap up finally. I think that we've estimated in this paper and we have committed to updating them manually and publishing them alongside the government revenue data set on the wider website and the first set of those estimates will be published online within a couple of weeks alongside the working paper here. So I will leave it there and pass back to Abrams. Thank you. Thank you very much, Kyle. It's been a very good presentation. And so far we've learned a lot so we'll move to the panel discussion would start with Joyce Joyce please share your reflections, after which would move to Michael. So Joyce, please. I think while waiting for Joyce to sort out a computer or something. Michael, I think you can begin. Yeah, I think I can take that up. Well, Joyce rise to the subject. So many, many things Abrams and many, many things. Now, for the, you know, for the presentation as well. I will just add some few bits to what Kyle has, you know, said I have three, three minor points here, which I would just elaborate. I think the first thing is that we have a very wide data coverage using the DRD and the other data sources. This is very, you know, important, and we would have to stress that so in all, we have a full sample of about 161 countries, and then a data spanning from 1980 or the way up to 2019, which they would also date as well. These things are quite significant in there. We would have to take notes of that. Then the other thing that Kyle talked about, he talked about the global average for the tax effort, for the tax effort scores, all right, which is about 0.84. I think the main point is that the, the, you know, the global tax average masks, a lot of the details. What we have, we, you know, also have a time series of the tax effort scores for all these over 161 countries from 1980 or the way to 2019 as well. And then, yes, there are some countries, particularly in the Sub-Saharan Africa that have low tax scores as well, you know, below 70% in this case. So, so we have a time series which is very detailed that others can really, you know, engage with. One thing that also struck me when we did this work is that there is a lot of variation in tax effort scores within countries. When you look at the time series for particular countries, they're not just starting now, you see a lot of variation in there, and I think that is key as well. It would be great to understand what really, what really explains that within countries. Is it some type of legislation? Is it some donor effect playing out? What is really going on there? And I think that once we put these things out there, you know, really, researchers can get into detail and then look at what is, what is really explaining this, you know, variation within these countries. Let me end by talking about the policy to kids. I mean, I think that the tax effort scores are very, very important, you know, a spot of the tax policy to kids, for countries, especially looking at individual countries here. And here, I think it's very important to look at the gap between the potential and that of the actual tax, what we call the tax, what we call the effort. Actually, it's the inverse of that, that would give you the tax effort. If the gap is high, all right, in this case, if you have a low tax effort, then yes, there is a need to actually push tax authorities to see how best they could collect more taxes. But that is when your gap is high and the tax effort is low. But what if the gap is low and your tax effort is high? Do you still push tax authorities to go on and to collect taxes? So what one may do in this case is to actually find ways to actually push the tax frontier up. In this case, it's about looking at how to expand or how to increase the tax support potential of that particular country. So I think these are things that we would have to take note of. And so trying to get as much as possible, some good estimates of the tax effort scores is very good here. So what really happens is that if there is that low gap and that high tax effort scores, then they need to be a complete shift here from the normal rate rhetoric of aggressive taxation and aggression. Clearly, if this is high, then how do we push up the tax frontier? So that is what you call the thinking that must go into this when trying to look at the tax effort scores for countries. Let me end here and I think we can come back and talk. Many thanks. Thanks Michael. Joyce, you can come on now. Thank you for the presentations. My apologies, I came in a bit late, but I was able to capture quite a few things and also from the data that I reviewed. I think for me, the major things that we highlighted by Kyle, one of the most important things for me is that at the end of the day when we do research, the purpose is for us to have recommendations or to cite the implications of what we have done in terms of research. In terms of the work that was done here, there are quite a number of things that are quite pleasing in my perspective from a research perspective, largely because at the end of the day we find people making decisions in terms of policymaking, guided by what we would have found in terms of research. I think the idea of using a stochastic frontier analysis model can be should be applauded, because it gives us a better picture, especially on the aspect of disentangling the random effect as well as the inefficiencies. I can cite an example and say the moment you're able to understand whether your inefficiencies are for the short term or they are coming from the long term, you are able to tell what kind of policies you should be using. I think moving away from the normal methods that were used or the usual methods that were used in using this kind of model, the SFA, you find that a better picture and new closer to reality was a time for sure we've seen scenarios where random effects have been counted as inefficiencies and that is not the issue at hand. Then the other issue that I also want to highlight is one of the major important aspects coming from the research in terms of implications. There is the issue that if we are referring to text effort studies that have been done now as being the required looking. Then it also means for researchers, there's needs for the research community to think about forward looking methods that allow us to have a better picture of what the future has as opposed to what we've seen from the past in terms of looking at it based on our report. But nonetheless, it doesn't take away the beauty that comes with being able to make those estimations, especially using the methods that have been cited here. One of the most important aspects also for me was that there are scenarios where even for instance if I look at the African context, the account is where if you look at the text effort, they are actually high and there's a low gap. But when you look at policymaking, you find that the recommendations that are almost put out each other at the time are the same recommendations as if you have low effort and a high gap. So I think from the research there's a lot that researchers can take back in terms of if you're going to look at individual countries but at some point you can have revenue authorities within specific countries looking for solutions. So the research comes with a lot of insight that when we come at country level, implement those methods and try to see whether we can improve on whatever has been made. And at the same time for sure, whatever recommendations have been made in the path for specific countries, do they go ahead in hand with what comes out from using the SFP for specific countries. So for my analysis, I was just trying to check for country levels, how many countries have really tried to use the stochastic frontier, especially in developing countries at individual level. You'll find that it's quite rare, because whilst we took off 10 to 15 years, you'll find that most people have not got to the level of actually understanding what you mean by disintending the random infectious noise inefficiencies. So the paper comes with a lot of wealth in terms of what you can take back as researchers and also when you try to dismissify what you mean by what do you mean they are under effects they are inefficiencies. For the donor community, I think it becomes also very clear because normally do you have questions where people are not quite understanding. What do you mean by random effects? But when you talk about these inefficiencies, even today when you call it is when you talk about looking at the short-shand looking at the long run, it becomes very clear to say maybe you should do certain activities in the short-shand to avoid these problems in the long run. So that's the beauty of using the stochastic frontier analysis. Then also, I wanted to highlight that also one of the important aspects from the paper was that besides just looking at tech's effort, the idea of also embracing the use of revenue effort is something that we need to take back. We need to take back. No money at revenue. Our countries that have got data resources are rich. I'll give examples of areas close to me. And Gwana is an example. You can talk about it. You'll find that there's not much effort that has been put towards grasping what can be done with countries that have got low tech's effort, but they do have a lot of resources, but those are not accounted for. So I think also for the donor community, it's also good that when people broaden their minds to going to look at issues like revenue effort, you get a better picture, because when people are states, for instance, we are unable to fight for that because we have low tech's But when a country's resources, which they can find that is a problem. So the beauty of what Kyle was saying was that we are able to broaden our minds to say we don't stop at Texas alone. There are other points of revenue that we can also look at. From a research perspective, I think there's a lot that we, including myself can take away from this research and put forward in terms of maybe expanding what has been done. You also maybe looking closer at different countries for each and every country, trying to use the same methods, but trying to find out if I'm doing for another country, what is likely to happen, what insights are we going to get. So I believe that it gives us a new way of thinking and I think it is important because as we have revisited the question, we might come up with different answers, what they predicted may be in the past may not be true, because the estimates we're using may not actually giving us the right to turn around. So I think for me, these are some of the important issues that I found from the start. Otherwise, thank you. Thank you very much for your remarks, Joyce. They're very accurate. And for those who have read the paper, you know, you find that she's able to pinpoint some inconsistencies across previous findings, which is what motivated our use of the people motivated us to read the paper to begin with. So now the floor is open to a Q&A session. I was going to ask the first question, but that's no longer the case. We have a question in the chat from Olu Akincube. So it goes thus, any sort of consideration for regional differences or spatial heterogeneity. For example, the tax effort in Southern Africa differs substantially from what obtained in West Africa and other African regions, just same as tax capacity is just not even hence lumping SSA countries together as a unit seriously impacts the results. So I think the question is about, you know, breaking countries down in terms of geographical region, and also possibly in terms of their level of development. So what can we say about that, Kyle? Thanks. Thanks for the question. It's a really good question. In the modeling, we haven't done a lot to account for spatial heterogeneity along those lines, but I do agree with what you're saying like obviously tax effort and tax institutions in different parts of, for example, the continent of Africa do differ strongly. We do, maybe I should just apologize for the way in which the results were presented because I did show the regional averages. So we just break down the tax effort scores at a country by country level. So you can check where Botswana is vis-a-vis Uganda vis-a-vis Ghana. So we do do that. And I'm not sure that in the modeling we sort of put in any sort of geographic control variables in there, but it might be something that might be useful to look at. Thanks. Now, now a question for myself. We know that there's a difference between revenue effort and tax effort, the former being influenced by the availability of natural resources and also receipts of foreign aid. What kind of policy implications might there be in circumstances where the revenue effort differs fundamentally from the tax effort, you know. So in Nigeria, where they have a lot of natural resources, it's not entirely unusual to assume that revenue effort and tax effort are going to differ fundamentally. Likewise, in other countries that receive a lot of foreign aid, for example, Afghanistan or Tanzania, it's fair to assume that revenue effort and tax effort are going to differ fundamentally. So are there any important, you know, policy implications for that? Thanks. In terms of important policy implications, I'm not entirely sure. Obviously you see these discrepancies for the reasons that you've discussed, perhaps natural resource wealth or a large dependence on foreign aid. I suppose ultimately it depends on expectations. Let's say the revenue effort is quite good, but the tax effort might be estimated to be quite poor in the country because of large natural resource wealth. It may be the case that one might want to think about how sustainable is it to rely on revenues from that resource. Is it likely to be a resource that can fund development in the country for many years to come? Or should that country be looking to move away to alter its revenue mix more toward domestic taxation or taxation of employment or domestic activity? Which ultimately in the long run has implications for designing a fair and equitable tax system and for tax morale as well, which in the long run can help to boost collections. So I don't think there's one size fits all answer to your question, but that's just some thoughts on the case of resource rich countries. Which means in trying to develop a toolkit for each country, would it be logical to assume that countries with those significant resources or foreign aid, is it more interesting to look at the revenue effort or the tax effort in your opinion? I will sit firmly in the fence and say it depends. Because some countries that might be resource rich might rely on certain tax instruments to collect revenue from that resource whilst other countries might rely on royalties or non tax payments to collect revenue from that resource so it's entirely down to the fiscal system in place in each country and through the work we've done in the GRD we've seen massive discrep disparities between countries that are perhaps under some definitions equally rich in natural resources but some might collect a lot of non tax revenues and some might collect a lot of tax revenues from that resource. And so it ultimately comes down to the way in which a fiscal policies applied country to country. Thank you. Thank you. Which now brings me to my second question. I mean, based on the analysis of tax effort indices. Under which circumstances can the, you know, tax frontier or tax potential be pushed forward, because in, as Michael described you may have a situation where you have a no tax gap. And then there are high tax effort scores which means that tax reform to improve tax effort is not, you know, particularly useful, which now brings us to a situation where the entire tax frontier has to be pushed. And I'm thinking this is more like a remark anyway it's going to be based on the you know structure of the economy right informality maybe high trade maybe low organization maybe low, which is what is making the you know tax frontier to be low. But for the frontier to be pushed is going to depend on most exclusively on the political settlement in each country. What kinds of incentives, you know, I mean if you were policymaker in the country, what do you think are the kinds of incentives which can be brought forward for the policymakers to push the tax frontier forward. Let me first try to answer that, and I might pass to Michael afterward, if, if he has more to add. You mean you sort of you sort of pick up on something that both, both Michael and Joyce raised that in many, many relatively low income countries we see a tax effort score that's actually relatively good or quite close to one right. And that's saying that given the inputs in the model country X is actually doing okay or is exerting a reasonably good tax effort. And I think that's an important message to come out of our work. And I think that's a very intuitive method as well if you look at the inputs that go into the model for somewhere like say Uganda where I live GDP per capita is quite low it's quite high we have a large share of agriculture and GDP we're not particularly urbanized. And so, given that our tax ratio is in the region of 13 or 14%. To me, given the low value of many of the inputs that's a reasonable score to come out but if we put in inputs to a model that say, you know, economically this country is doing quite poorly then the tax effort scores is 0.3. So those two things square with one another and that's what we were seeing from previous work. And my, my sense then is how do you then push the frontier forward if you're almost at it. That comes through improvements in, as you say the input variables, such as the level of development the structure of the economy and how that translates into actionable policy advice is maybe difficult. You can't go to a country and say please find natural resources, it will help your tax effort in future or push your tax frontier, you can't do that but, but there may be. There may be work to be done to trying to unpack the, the effect of each of those individual input variables on where the tax effort score is relative to the frontier. It's suggesting, you know, okay, if you rebalance your economy, two points away from agriculture toward manufacturing or something we could maybe make predictions in a forward looking model as Joyce was mentioning about what that might mean for tax effort and tax revenue collection down the line. But I might be speculating there. Michael, do you have anything to add on that one. Well, I think that technically, I mean, what one can do is to, you know, improve the, you know, inputs that go into the terminology, the attacks for frontier. So, so these things will need to be looked at in a way that we can, what do you call it, improve that. So that's, that's what I can say from the, from the technical side. But if you look at Sub-Saharan Africa, many of the, many of the, the countries have natural resources, so also, and then, you know, fuels as well. I mean, clearly, there is that excessive focus on the exploitation of these natural resources, unless focused on the governance aspect of this, unless focused on adding value to these, you know, what do you call it, you know, natural resources as well. So, so yes, it's, it's, it's a bit indirect, but for them to improve their inputs that goes into pushing the attacks frontier. These are some of the things that they need to be thinking about. Michael, thanks Kyle. We have another question. This is probably taking us to a different kind of literature on the formality of firms. So it is from Ethio, De Gefe, and Nulo. So it's a question for Kyle and Joyce. For countries that have very large underground economies, how can they bring those informal, you know, informal sector firms into the formal sector to boost revenue mobilization. And this might take us into theories of formalization or empiric. So anybody can respond to the question. Kyle, Michael, Joyce, you know, I think this, this is a big question. I work on, you know, what do you call the issues of informality. So this is the biggest question we know how that's fine. How do you transition informal firms, be it those in the upper tier, or those in the lower tier to formality, you know, what do you call it, what do you call it employment. I mean, the big issue is that, yes, you can transition them to formal employment into the formal sector, but it's more of a long term thing, you know. Yes, you can do that, but this is in the long term. So there's much focus on what can be done in the short to medium trying to find ways to boost their levels of productivity and on and on. But the whole effort to formalization has not worked. There's been a lot for and these are there's actually been what is called the formalization rate in our versa. So people moving to formality. And then after a year or two years for a back. And so this these are the bigger questions we need to look into and find ways of, you know, I've mentioned that. All right. Thanks, Michael. Before we wrap up, Joyce, do you have any comments? In terms of the informal sector, they are direct ways or indirect ways just as Michael is highlighted. We can talk about formalization, but indirect, indirect methods, for instance, in Zimbabwe they introduced a mobile transaction text which is about 2% which has boosted revenue in Zimbabwe by a notable magnitude. So the, I think for policymakers, it depends on the situation that they are facing. The first reason that we need to understand is that when people in the informal sector, they are not there by choice mostly the effectors which could be within the policy environment, which makes it difficult for them to formalize so the idea of formalization is good but just like Michael is highlighted, it may take time. So what do we do in the short term? Normally I believe that indirect methods would be the ones that would work. And if you look also at the world that we're living in now, most of the things that become digitalized, including people operating in the informal sector, they are also using these platforms. So from a policy perspective, there's need for government to, most governments to review taxation and digitalization. I believe in most developing countries, governments are still struggling to come up with ways of texting the digital economy. So those are some of the ways in which we can try to text the informal sector or maybe they have policies that provide incentives just like I was saying, the incentives that can be given to financial technology companies to embrace more and more people in the sector. That way, then government is able to then text just like I was giving the example of the mobile text. So I think for us the options that are close to being realistic for now are to use indirect methods as opposed to trying to find direct methods of formalizing the informal sector because why they are not by choice, normally because of the environment that we live in. That's the contribution that I wanted to make. Thank you. Alright, thank you, Joyce. We have a question from James McKeon. So Ghana recently introduced an electronic transaction tax in an effort to increase the tax frontiers. The results have been abysmal. Apart from tax collection efficiency, which appears not to be the reason for the poor outcome of the taxation effort, what other factors may account for low taxation under such circumstances. I mean, James, what springs to mind is, you know, maybe the tax was introduced without the government first being a price of the local economic context, right. If you introduce an electronic transaction tax when most of the transactions are carried out in cash, then you really can't expect to receive as much money from the tax as you would normally do if there were electronic transactions. Those are my two sets. So, Kyle, Michael, please. I'm not sure how much I have to add to what Abrams has said. So the question saying tax collection efficiency appears not to be the reason for outcome of tax effort, what other factors may account. I mean, I mean, at least from our own work, if you're referring to our own modeling, it's essentially the inputs that go into the model that sort of determine the level of tax effort. Currently, we've modeled tax collection as a function of those. But as Abrams has said, sometimes there's policy that's poorly designed and poorly implemented. I wouldn't, I wouldn't be so entirely sure that tax collection or tax administrative efficiency isn't always at least part of the reason why tax collection can be poor. It's actually quite difficult to control for and the sort of work that we're doing indicators of administrative efficiency across countries aren't always easy to come by. Hopefully that at least in part answers the question. Okay. It's past our time so you would have to bring this to a close. Thank you everyone for joining. Thank you very much Kyle for taking all the time to give this presentation. Personally, I've learned a lot and I'm guessing the other people in audience have learned as well. We thank the panelists, Michael Joyce for participating and engaging and sharing their reflections. We thank our staff that you and you wider for helping make this possible so ever who has been relentlessly sharing the links in the chat and Utah, who, you know, put this up together. For future purposes, there is a GRD network so you may want to join the mailing list. It provides the latest news on GRD, and it keeps you updated on all things GRD. This is the first, as I said the first of a series of webinars, looking at new perspectives into domestic revenue mobilization so the next one is going to be on the 18th of October. And at the same time, titled World Energy, looking ahead to COP 27. So it's going to be advertised and announced a couple of weeks from now. Please register and so you can attend, and I'm sure it's going to be also engaging as this one so thank you all for joining and you know see you some other time.