 I'll introduce the panel from left to right, or my left, and start with the two respondents who will be speaking a little bit later in the program. So I've got Linda Calabresi, and she is sitting beside Irene Yuan Sun. And then we are going to be listening very shortly to the two key authors of the study that we're discussing today. We have Dr. Florian Ashafa. He's from the London School of Economics, but we're going to claim here and so as that we taught him everything that he knows, of course, because he does his PhD here. And then a good colleague, Dr. Carlos Boyer, in the Department of Development Studies. So what we have here is a discussion to do with a very, very important issue in the study of Chinese and African relations. It's a field that all of you will be aware is full of accusations and very little analyzed data. So one of the key important elements of what we're going to be discussing this afternoon is the presence of serious investigation based on serious data concentrated in two countries, Ethiopia and Angola, which have multi-sectoral manufacturing and other enterprises. So you're looking at something which is variegated and complex. And looking at the whole question of Chinese firms and employment dynamics in Africa across a range of these variegated factors is something which I don't think has actually been done before to the extent that our two authors have managed to do it. So it's pioneering work, groundbreaking work, very much detailed field work, which caused into question a lot of the assumptions that have been glibly made by people who are either alarmist or uncritically in favor of Chinese activities in Africa. We're going to have a presentation about their findings and then we're going to have two respondents who are very expert in things African and Chinese responding to what the two authors have got to tell us. But I'm going to hand over to the authors now and I think we start with Carlos. Yeah. Thank you very much, Stephen. Thank you for coming. Such a hot day, end of June, not easy to get big crowds. So this is the last dissemination workshop of this four-year project. We have already presented our findings, our main findings in two other workshops in Luanda a few weeks ago and in Addis Ababa last week. So we already get a feel of the reactions and comments on our findings. But before we start, we kick off with the presentation. I just wanted to say a few words just to make sure people understand that a project like this cannot be run by two people. It has been a very complex, challenging project in many ways and it has involved quite a number of people, doing all sorts of different things. And each of these individuals have contributed to what we achieved. I think it's four years ago, not many people we talked to gave us much hope in terms of trying to achieve what we did. So I'm just going to just give you a few minutes of acknowledgements to key individuals. I mean, first of all, usually we want to acknowledge the funding from ESRC and the Defect Economic Growth Research Program which gave the resources that are necessary for this kind of project. And particularly also to ODI, it's been very supportive from the very start in many different ways. Also to our advisory group, we have peer reviewers who've been supporting us over the years and giving us feedback. Three individuals, Qin Kuan Li, Lucy Corking and Pedro Martins, who is now the World Bank. But I mean, this wouldn't be possible without a big team. I'm just going to give you a flavor of who was involved and I mean, the main individual, I cannot name everyone, but I'll try to be as far as possible. Obviously, I mean, Florian is my companion arms, you know, involved in this from the very, very start and he's managed to put up with my demanding micromanagement for too often that we've managed to survive, both of us, you know, without fighting too much. Christina Wolf, she is not here, unfortunately, but she was an important catalyst in the early stages of the project and she contributed a lot to our desk reviews. And there were a number of other colleagues at SOAS like Terry McKinley, who's in the room, Tim Pringle, Dick Lowe, who also contributed to the research in different ways. Certainly this wouldn't be possible with our local partners in Ethiopia, the Ethiopian Economics Association, and in Angola, the Facultade di Economia of Universia Agostinietto, and also Renmin University of Beijing, which cooperated in various ways. From Fekuan, we're lucky to have Fernandez Juanda, who's sitting there at the back, who's also a PhD student at SOAS now and he was a major game changer for what happened in Angola. And important qualitative research provided by Xia Yongtang, who is very well known in the field and also very humous and a fun person to work with. Now, obviously this kind of project with this sort of large-scale surveys that we are going to be reporting on wouldn't be possible with very, very strong field teams. I always say that, I mean, I've been doing this for a number of years in Africa, conducting labor surveys, and the field teams are critical. If you don't have a strong, committed, determined field team, there's no chance of success. We had teams of eight people in each country with two supervisors within those teams and they did an unbelievably fantastic job. It was difficult, it was very challenging because of the obvious problems with access that you would imagine. In that effort, there were some people who particularly helped in the early stages of the process, like Elena Peretnina, but especially I would like to sing about Borja Monreal, who's sitting here. He came to see what we actually found and he was unbelievably useful, especially in those very difficult stages of getting the teams used to the demands of this kind of field work. And I don't want to end without mentioning the most important person in this project. This is Weiwei Chen. She's been there from the very, very start of the project. She did her first Africa trip at the beginning of that project and it wasn't the last, fortunately. And she got basically hooked up with these topics and she couldn't stop going. So she's been involved in, across all stages of the project and I don't think what we did would have been possible without her role. So I'm going to stop there for acknowledgments. I think there are other people, obviously, but I would like to mention our field team individuals, one by one, but all the names are in the reports that you can download and we're going to crack on with the actual content. So we're going to give you a little bit of context about China's engagement in Africa and particularly what ways China's context matters to understand some of these issues. I need to give you some background about the actual project. So I will say something about the research process and the research design because I think that is what sets this project apart in many ways. And then we're going to go straight to some of the main findings. Of course, we cannot cover everything. This was a four year period of research, quite intensive field-based research. So we're going to focus on some of the main findings around job creation and the localization rates, i.e. workforce localization, which is one of the big questions in this field. We say something about labor force characteristics, that was one of our research questions, as I'll mention later, and probably the topic that most people are waiting for is wages, working conditions, and industrial relations in these worksites. And then we held up with some basic conclusions. Just to say that we have already quite a bit of material online in our project websites, which is the SOAS website plus IDCEA. And you will find some background material in working papers, but also some of the emerging reports. We are still writing up and indeed we will have a synthesis report fairly soon, which will incorporate some of the feedback that we're going to get in this workshop, as well as the previous workshop, and from our discussions. And then obviously we will continue writing up until I don't know when. Now, on China's engagement in Africa, there's a number of issues that we need to bear in mind. First of all, part of our objective was to look into what ways different Chinese actors have contributed or are catalysts of processes of structural transformation in Africa. And this is an issue that has been revived now. For the past 40 years, the word structural change has barely been mentioned. And for the first time, you really get people talking about structural change and industrialization in some African context. And it's quite clear that the example of China has been significant, particularly for the aspirations of those African policymakers that see China and other East Asian economies as examples of success in a neoliberal world. So China did grow to become the world's manufacturing hub and still despite changes in the structure of the Chinese economy, this is still the case. But we are moving into a new scenario that is labeled as the new normal, which also implies a different kind of structural change in the Chinese economy. Moving towards a process of economic growth and economic dynamism that is led by innovation and internal demand rather than exports. So that obviously has some implications for the global stage. Certainly what is happening in the Chinese labor market matters as well, for those sectors that are likely to become obsolete for all sorts of reasons. And a big question nowadays, and those who work in on labor issues in China is the paradox or the evidence of wage growth, very rapid wage growth, even faster than productivity growth, particularly in the last 10, 15 years. Clearly this labor market dynamics in China having important effects on certain sectors, particularly light manufacturing would be one of them. And that has led some well-known commentators like Justin Lin to say that where there are 80 million jobs up for grabs in the near future, jobs that will go out from China, they might go to robots of course, but they might also go to actual people somewhere else in the world. So part of that process has to do therefore with this notion of go-out, the go-out strategy which was formalized in 2002 and has been taking speed, especially in the last 10 years. And one of the aspects of this go-out strategy is the globalization of Chinese firms. Chinese firms state on enterprises as well as private firms going global and searching new markets. And certainly Africa, Sub-Saharan Africa is one of those destinations. I think there is an exaggeration sometimes when people focus too much on Africa and actually many of these firms are operating in several other regions and particularly Asia still the primary recipient of FDI. Now this is a picture of the pattern of wage growth as you can see, quite substantial shift especially from the early 2000s which have to do with all sorts of issues. We don't have time to go into the detail. But clearly this is affecting especially those sectors in China that have been operating on the basis of low wages or low wages as a market of competitiveness. So wage growth is gaining pace. Of course productivity is still very, very high so you need labor costs in certain sectors are still fairly competitive on a global scale. But this is clearly changing the game. Now when we talk about Chinese engagement in Africa one needs to, I think the literature now has moved on from sort of early myths. But we do know now that the multiple vectors from trade, trade clearly is one of the key vectors in this growing relationship between China and African countries. Investment, official finance is particularly important to understand the contribution of Chinese institutions to the aspirations of structural change in many African countries. Construction services, which is one of the targets of our research. And of course aid, cooperation and migration. When we look at Chinese firms and who are they, they are different players there. And our main focus is on two sets of players which have been or had had an impact, significant impact on some countries much more than others of course. Infrastructure contractors, a lot of official finance from China is actually going to economic infrastructure a very large proportion of it especially power generation and transport and communication. But also you have these emerging phenomenon still early days of private manufacturing firms moving in groups and in packs to particular destinations. Of course, not many countries have succeeded in attracting these and other firms. And one of our target countries, Ethiopia is a very good example of that and that's why we also focus on that. But they are not the only ones. There's also mining corporations both private and state owned and some of the best research that has been done in recent years especially Lee's work on Zambia is precisely about construction and mining. And of course not forget trade and services which can be found almost anywhere in the African continent. As I say, finance was an important vector in this process and this is the sort of most reliable data we have. This is just for loans. You can see the pace of growth slowing down and it's this kind of dynamics which has given rise to a number of heated debates nowadays on whether this is causing some kind of debt distress. So there's a lot of comment and debate nowadays on the extent to which African countries are falling into a new debt trap as a result of that and of course we can leave that debate for the moment out of this panel. On FDI and what we call construction overseas projects, again, we have, we see the same dynamics of very fast growth. What is interesting about this is very often we tend to pay attention to FDI. We're thinking about investment but actually in quantitative terms the size and the contract revenues of construction prices are far more significant. So, and this is a sector that is primarily dominated by state owned enterprises. So Chinese state owned enterprises have become global competitors in infrastructure construction partly because of the huge market they've enjoyed in China for many, many years and now they are pushed to globalize and they have all the obviously technical capacities to implement all sorts of demanding projects. So Africa is becoming indeed or has become an important market for some of these companies and indeed some of the available evidence would suggest that it's not only important for them but especially for the Africa markets. Nowadays, it is estimated that between 40 and 50% of these infrastructure construction market is accounted for by these Chinese enterprises, as I say, most of which are state owned enterprises. And the sort of dynamics of the trend is towards continuous growth. Partly because the competitors, mainly from Europe, Latin America, are not able, particularly certain African markets to offer the same technical and financial conditions that most Chinese firms tend to offer. So what is happening now, for example, just going back to this, is that some people might be tempted to think that this is essentially on the back of Chinese finance. Actually, not true. The proportion of contracts worn by Chinese firms that is financed by other non-Chinese sources of finance keeps growing and growing. In other words, that more and more Chinese firms are winning bids from projects financed by the European Union, World Bank, and so on. These dynamics do have employment effects. Okay, so we're done with the sort of background, the context, let's go to our main target. They create jobs, at least this is what some people would hope. And there are questions about the nature of these jobs and how they differ from the labor market conditions that we find in this context. When you look at the literature on employment effects, on employment issues associated with Chinese firms in Southern Africa, you have three fairly widespread perceptions, particularly in the early days, if you read the literature from the early 2000s and then much of the media reporting, you get the perception that Chinese firms mostly rely on Chinese expatriate labor, particularly in construction projects. And this has been a life motif for many, many years. In other terms, that localization rates were really, really low. The second type of perception was the working conditions are poor in absolute terms, not just relatively to other companies. And also that as a result of those two, that Chinese firms barely contribute or make a very limited contribution to skill development in these emerging sectors. Of course, these are widespread perceptions, particularly still in the media and when you get questions on these issues, these perceptions keep coming back. This by the fact, I mean, we are not the only ones to do some slightly more long term research. Long term research, I mean, again, I mentioned Qin Quan Li's work on Zambia and when you read that kind of work, people should be already revising some of those perceptions or indeed the work that McKinsey did a few years ago in the service of firms that they did, which debunked some of these myths. However, when we interrogate the evidence that we find on these issues, both from research that has been conducted but also from media reporting, we find some disturbing facts. First of all is the excessive reliance on media reports and all sorts of anecdotal evidence coming from different sources. Secondly, a problem that is actually quite common to many African countries is the sheer fragmentation of official statistics. Generally, official statistics on labor are extremely weak in most African countries and that obviously creates problems for those who want to assess the extent to which working conditions are good, bad, or what. Labor market statistics are either absent or not very reliable or they only cover a very, very small segment of the labor force. Third, there is a lack of large scale quantitative surveys of workers. There is quite a lot of research based on interviews with managers. But of course, interviewed managers and asking managers about working conditions is always going to be slightly problematic. But we didn't really find enough substantial evidence coming from the reports that workers provide on their conditions. From the fact that there are not enough surveys of this kind and indeed actually many African countries lack labor force surveys, which creates problems when we try to compare with average conditions. In most cases, it's not possible to actually compare to an average condition. If I'm asking, what is the average wage in manufacturing sector in Ethiopia, the answer is we don't know. Simply, there's no data about that. But part of the problem with a lot of these narratives is the lack of comparative analysis. So much emphasis and focus has been on Chinese firms that we've forgotten that there are other firms in those markets, in those sectors, which are also operating or have been operating for some time. They also employ people. And they also require some sort of probing and investigation. And generally, we see that in much of the literature with some notable exceptions. Of course, there is an oversimplification of labor relations and labor markets, more generally, in understanding things in very binary terms. There's excessive reliance on binaries. So given that these problems with the evidence, then we try to create something that was obviously a challenger from the start, but which could overcome some of these substantial weaknesses. So what did we do? We tried to first focus on questions that we could try to answer. Of course, not all these questions were easy to answer. So we look at, we focus on national workers, African workers, Ethiopian and Angolan workers in this case. We try to look at what are the employment effects of these and other firms in these particular sectors? And what can we say about the comparative working conditions? We look at what kinds of labor regimes predominate in different sectors and different companies. Can we say something about Chinese firms really being substantially different from the rest? And if they are different, in what ways and why? One of the reasons why we chose to do this analysis in two countries was because our hypothesis was that country context matters a lot. And we chose two countries that for all sorts of reasons are quite different, but also share some few similarities. So we try to look at in what ways the conditions in these countries do shape the way firms configure the labor regimes and the way they recruit labor and the way they treat the workers. And last, but not least, we also are interested beyond these comparisons between firms. We want to know something about who are these workers. There isn't much research done on construction and manufacturing workers in Africa, very, very little. There's quite a bit on agricultural workers, but not, I mean, even on that, there's not a lot. But we wanted to know, what are they emerging? What are the profiles of these people? How young they are, the women, men, what kinds of skills they bring, how differentiated they are as well. Because there's always a tendency towards homogenizing these different categories of workers. So in order to make sense of all these questions and variations, we have worked with an analytical framework that tries to combine three basic levels of analysis. This is what we call it a labor regime configuration framework. Similar variations of these have been used by other researchers, some of whom have been also saw as researchers, by the way. And we try to look at first, what are the characteristics of the national political economy? Are the macro contexts that are important to bear in mind when thinking about what we observe at the micro level? So overall, the balance of class forces in any given context and more broadly, the politics of production that one finds, which includes also an understanding of things like the relative weakness of the trade union movement in these countries or any trends and patterns of, for example, informalization or weakening of the bargaining power of workers in this context. The second level is the sector value chain characteristics. This is important, especially when you're working with different kinds of sectors, construction and manufacturing are very, very different. So in each of these, you will find different types of labor regime configurations. But also, even when you look at the manufacturing sector, you're going to see quite a lot of variation, both within countries, but also between countries. And the contrast between Angola and Ethiopia was particularly productive in the sense that Ethiopia has manufacturing firms which are incorporated in global production networks and therefore subject to the pressures that one finds in some of these global production networks, particularly in textile and garment, whereas that is not the case in Angola, where our target was the building materials manufacturing sector, which grew on the back of a very fast reconstruction program and infrastructure rehabilitation. And the third level is the workplace dynamics, the firm level. So the role, basically, we're looking here at the role encounters between employers, managers, and workers, labor process organization, and wage bargaining over productivity concerns, and so on. So our take on this is that it is only through a combination of these different levels of analysis that we can actually understand the many sources of variation in working conditions and that only focusing on one or two of these aspects is not suitable to really understanding the outcomes, labor outcomes that we observe. So how did we design this research? Definition would be that this is a multi-stage mixed methods program with large and quantitative surveys. What this means is that the primary source of the evidence are these large quantitative surveys. However, this is complemented in many different ways through various different types of integration with careful qualitative research conducted at different stages of the research process. So the stages were between late 2015 to late 2018, so three years of fieldwork, started with literature reviews, database searches globally, but also in particular referring to the cases of Ethiopia and Angola, extensive qualitative and cost data scoping research that took several months. One of the aims of this phase was precisely to gain a, to actually prepare the ground for the large-scale surveys. You can't really do these kinds of surveys unless you prepare the conditions on the ground. You can't show up to interview workers in construction sites and factories that easily. Once that scoping phase was completed, we managed, therefore, to implement this large and sample service of African workers, Angola, and Ethiopia at different stages. But of course, when you have these long-term projects, we also hit by the circumstances of the time in each context. So in Angola, we were affected by the crisis, which followed the drop in the oil price, which had a massive impact on the construction sector at the time we were doing the survey. So that also affected the timing. And in Ethiopia, we were affected by the state of emergency, which was declared in October 2016, which basically prevented any serious fieldwork for a number of months. So that is the reality of these kinds of projects and fieldwork when you try to do the surveys. Then this was followed by firm survey and interview with company managers. And finally, in-depth follow-up qualitative research, which included some tracking work, phone services to track attrition rates for workers, what sort of changes and wages we had observed in the last 18 months. This was at the end of 2018. And also a number of life histories to understand more in-depth the profiles of some of these workers. As I said before, one of the problems with the current evidence is that there is no real comparative analysis. So following our analytical framework, we looked at three layers of comparisons. Of course, for the national context, the country context, we chose Angola and Ethiopia. Angola and Ethiopia are two very, very good examples of Chinese engagement in Africa for all sorts of reasons. But certainly in quantitative terms, these are two of the main destinations of both FDI, but also construction services. In terms of sectors, we try to homogenize as much as possible. So for the construction sector, of course, there was no point in doing all sorts of kinds of construction, no point in doing real estate developments. If you want to reduce the number of variables and confound in factors, you need to be as specific as possible on the sector. So we chose road building, primarily because this was one of the areas of public works, which had higher volume of funding and infrastructure development, but also because this was an area where you could find a good sample of Chinese, other foreign, and local or national companies. In relation to the manufacturing sector, of course, there was not much choice, much more choice in Ethiopia than in Angola. But we chose the sectors that were particularly most relevant for this study, given the size and the employment generated of the previous years. And in order to basically address the big questions that have been occupying people around employment conditions and Chinese firms in Africa, we had to divide our samples into distinct groups of Angola, all Ethiopian, all the foreign and Chinese companies in as systematically as possible for each of these subsectors. So we have this evidence for each of those subsectors, with samples that are actually fairly representative of the leading companies in each of these sectors. So that was the sampling process. So the first step was the selection of firms. When you get, I mean, this is important for a simple reason. Whenever you present these kinds of findings, there's always going to be people who don't like them. And so we always have to defend what we did. And one of the things that often are thrown at researchers that all your sampling is rubbish, or you didn't have enough people, or whatever. So we have to be very clear about what we sample and why. There was no point in having a sort of random representative sample of the whole sector, because these sectors are highly heterogeneous. And what we want is to really compare like with like. So what we did was to choose the top leading firms within each sector. So of course, this means that we are setting the bar high, because we are comparing those Chinese firms to the most established and leading firms within each sector, both national firms, but also global or international firms. So in the case of, let's say, Angola, that would mean that in construction sector, you have some of the biggest contractors, global contractors that operate in Angolan markets. So in fact, our sample actually included all the main players in these sectors, which has implications. So basically, for the sake of clarity, we are not comparing these conditions to any average. This is not the average. This is top benchmark. What you would expect that the conditions in this company is actually better than the average for all sorts of reasons. Within each firm, so once we selected the firms, and we were lucky enough to basically pretty much include all the target firms we had in our initial sampling frames. We had very, very few examples of rejections. So within each firm, again, in order to avoid excess heterogeneity, we just focused on those categories of workers who've received, who've got jobs in these sectors. From our scoping phase, it was quite clear that 80% of more of the jobs created for Angolan Ethiopian workers were in the unskilled or low-skilled and semi-skilled categories. Semi-skilled categories meaning jobs like a machine operator in a road project, or slightly more qualified machine operators in factories. Within those strata of workers, we then randomly selected workers who were present at the work sites or at the workplace at the time of the survey. Of course, we tried to time those surveys at a time where we could maximize the number of workers in place. So that way, we also tried to be as independent as possible in terms of our sampling frames, i.e. not really simply asking companies for lists of workers, which could be biased, but also trying to probe this by finding everyone who was present at work at the time so that we could also capture some temporary workers, not simply permanent workers. You can imagine, actually, this is quite difficult. When you go to, even when you have, when you give an access to work, especially in factories, managers don't like random sampling, OK? For all sorts of, not necessarily because they think that, you know, that might be bad for the company, but also because if you go to a factory, how disruptive that can be in the production process. So it is also a question of how they expect the survey to be disruptive. So all these have to be negotiated. But at least I can guarantee you that there was no one single case without random selection of workers within each of these strata. So this is the sample. So we have amalgamated all so that you get a sense of the size, the scale of the surveys. In terms of companies, total number of companies was 76, 40 in the case of Ethiopia. And in terms of numbers of workers, over 1,500, biggest sample was in Ethiopia with 837 and 682 in the case of Angola. So these are fairly large samples, and we're confident that within the target sectors they're fairly representative of the realities of these sectors. And this was complemented by over 260 qualitative interviews in both countries combined. These were interviews with government officials, with company managers, with trade union leaders, and at different levels, of course, and then also other key informants. What kinds of firms did we find? So as I said before, these were all the leading biggest employers within these target sub-sectors, leading Chinese, leading other foreign, leading national. In the context of construction, we're building mostly central and provincial state-owned enterprises, some of the best known names in the Chinese construction market in both Angola and Ethiopia. In manufacturing, basically only private firms, in the case of Chinese firms, but also in the other cases. And there were actually an interesting number of what we call trans-local firms, particularly in Angola in the building material sector, which is slightly smaller, meaning that this is not, strictly speaking, foreign direct investment. These are individuals with no business in China or elsewhere. Actually, there were a few cases of Portuguese investors who only have a business in Angola. And therefore, they registered a business as an Angolan business. So that's why we call them trans-local. Now, this is a term that actually has been used in the literature. And then other firms usually were part of transnational corporations, except for these trans-local firms, particularly in the case of Angola, where you basically found, especially in the construction sector, quasi-European labor standards. And in terms of the contractual arrangements, the conditions, and so on. In most cases, both in Angola and Ethiopia, one big difference between Chinese firms and the rest is that clearly, Chinese firms had less experience and time in that market. So there were new players, in most cases. We have Datown, the average number of years in the market. And I think in Angola was around 10 or 12 years, in contrast with over 20 for all the companies. So this is some of the work sites or workplaces that we were basically visiting, road construction projects in Angola, building materials factories again in Angola, textile and garment factories in Ethiopia. So as you can see, actually, even visually, there are quite different types of workplaces. And the labor relations are likely to be different. And this is just a basic illustration of our fieldwork. So this is an interview on the road project site. So these are the conditions under which these interviews had to be done. And we have people like Borchai sitting there in the rain. And then up there is our friend Martin Zbota, one of our supervisors. So we're not lying when we say that random sampling was being done. This is what he was doing. He was getting put in the list of all the workers that had listed in the previous censors. And then he was generating random numbers for the different categories of workers on site so that we could do the interview straightaway that day or the day after. Obviously, you must imagine that this couldn't be done over a relaxed period of a few days. Companies would tell you, you want to do the certifying? Just get it done in one day or two days maximum. So it was very, as I say, very, very challenging for our researchers on the ground. Now the findings. OK, enough of background. But I have to say, because it's been four years. So if I don't say it now, I don't know when. Job creation and localization rates. I think the key research question is these localization rates. Because this is what has attracted all the imagination of people. This guy is just employing Chinese labor. And actually, not just Chinese labor, prison labor. That's one of the myths that have been circulated for many, many years. Ludicruz, but still some people buy it. So the common narrative is this many Chinese firms, especially in construction that create limited local employment. And to an extent, this was propelled essentially by media reporting, but also by very few two or three studies, which based on extremely small samples, they just rolled out this narrative. And as a snowball, continuously was recycled. Now, there's two ways of looking at this. One is, OK, look at the official statistics. Are there any official statistics that can tell us something about what's going on? So you could get statistics on Chinese workers. That are registered. Of course, you would imagine that these are likely to be underestimations of the real size of these groups. But this is what we get. And this is work that has been done by the China Africa Research Institute by the way of very good source of data on these issues, not on employment issues generally in China Africa issues. What you can see there is an interesting thing. First of all, yes, 200,000. This is in the whole of Africa, not just Southern Africa. 200,000, less than 200,000, and so on. But not clear trend. And certainly from 2013-14, a downward trend. So declining the absolute number of record registered Chinese workers. Usually, these people are recorded because they are part of the registers of the companies that provide these construction services. So it's not really made up, of course, but they might miss out on other flows of migrants and Chinese workers who might be in other sectors. However, you must note that when we look at these data, there's huge concentration. So actually, a few countries account for a very large proportion of these numbers. So let's take in 2017, Algeria alone accounted for 30% of these total figures. Actually, the number of Chinese workers nowadays in the whole of Southern Africa is much less than what many people assume. But of course, this is not enough to say anything about localization rates. Because we don't know really much about how many jobs have been created overall. So we did a desk review. We tried to do an exhaustive search of all the various case studies. We tried to, obviously, not media reporting, but actual studies that tried to count how many jobs for locals, how many jobs for Chinese, and so on. Look at also some similar type of desk review, which has been done by other researchers, like Jan Hirong and Barry Sotman. And what we got was a database with a weighted average of 85% African workers in the total workforce. Most of the evidence was really in the range of 65% to 99%. So there is actually quite a lot of variation. And you do find some basket cases of individual construction projects where localization rates might be even lower than 50%. I think one of the countries where you can find this is Guinea-Aquatorial. I'm not sure. Borja is not surprised. So that means that there is variation. So you have some basket cases, say Algeria, Angola, and Guinea-Aquatorial, where localization rates are expected to be very, very lower, and much better cases if you opiate Ghana would be examples of that. Also, what this desk review showed is that a lot depends on the sector. So manufacturing sector, you're likely to have much higher localization rates. Also the type of skills needed and the experience of each firm in each country market. That is the other stylized fact that comes out of this desk review is that the longer the company stays in that country, obviously, the faster the pace of growth in localization rates. So one thing is looking at this data now, a very different looking at this data 10 years ago. So there is a clear trend towards greater localization. So what did we find in our own survey? We did our own survey for across firms. Localization rates in Angola were lower than the subs in African average. But even for Angolan standards, actually, a lot of people in Angola were surprised that these rates were actually quite high. Of course, they are lower than other companies. But when you look at the differences and you know what other companies we're talking about, these are actually not huge. And in fact, what really comes out of this is other companies also employ expat labor. So if you take, for example, the case of Angola, it was really hard to find, especially in the construction sector, any company that had to middle to high level any Angolan management workers or even engineers and some skilled labor. So these are non-Chinese firms. But clearly, there is a difference there. But as they say, Angola is the basket case. What we find in Ethiopia is that on average, 90% of localization rates across firms, there was actually less variation there. And almost all low-skilled and semi-skilled workers were basically Ethiopian. So that also shows you that there is something about the country context that matters. Why is that? Because actually, the sample in Angola and the sample in Ethiopia included some same firms. So basically, we had one firm that had operations in both countries. And localization rates were different across these two countries, same company. One reason, of course, we could go on, but one of the reasons is much stricter application of visa regulations in the context of Ethiopia. So the Ethiopian government would be far more stringent in terms of giving work permits to Chinese workers for certain types of occupations. Of course, you can cheat the system in different ways. But in Ethiopia, clearly, this didn't succeed. Whereas in Angola, there was always sort of generally a fairly less effort attitude towards these things. So if projects really needed workers in order to complete projects on time, being under a lot of pressure, then that would be done. So this was actually from Angola. Some years ago, you wouldn't expect an Angolan worker sitting in one of these machines, even driving trucks, for example. So things are also changing there. Nonetheless, for example, in Ethiopia, we did find that a very common complaint across all firms, not just Chinese, was that it was actually quite difficult to replace expert labor in management position, especially the middle level into higher level, even factory floor supervision. Although most of the companies, again, for the same kinds of restrictions, they had to employ Ethiopian workers at these levels. So there were issues around the lack of experience, the lack of practical experience, and to what extent national systems of vocational training and so on or higher education were good enough to provide the labor force that these especially global competitors, suppliers incorporated in global value chains, actually needed for their production processes. So that is something that is actually something we're going to perhaps be looking into in the next phase of our research, looking at these constraints on employing management labor. So this is some of the data on actual real jobs created just for manufacturing jobs in the Ethiopian context, how actually Chinese firms, in absolute terms, contributed quite a lot. So there were the leading creators of jobs in Ethiopia compared to other foreign firms. This is from different sources, including the Ethiopian Investment Commission. But also in public works in Angola, you do find an interesting profile, especially in the times of the crisis, 2016-17, the largest proportion of new jobs, of course, this is new contracts generated were in Chinese firms, which also reflects the fact that a lot of the other firms in the sectors were pretty much operating at a very low intensity and not really employing new workers. So what can we say about the workers that we found? What are the main characteristics and can we find some patterns across different types of firms and sectors? We did find some interesting patterns on some key differences between Chinese firms and others, particularly in Angola. So we can say that Angola, in Angola, Chinese firms were actually operating with a distinct different type of labor force. Younger migrants from poorer provinces of Angola, more likely to be from rural settings and housed by companies in dormitories, much less educated than the others with much less work experience. In fact, a lot of large proportion of the workers we found in Chinese firms had zero relevant experience in those sectors, both in construction and manufacturing. And generally poorer, we did run and would use some socioeconomic basic asset indices and we found some very, very significant differences between these different groups of workers. On the other hand, all the foreign and Angolan firms tended to operate with a far more formal type of labor force, with more experience, longer job tenure in those jobs, residents near the workplaces, paying rents or owning property, and therefore the living costs, particularly for those who were based in Luanda, the living costs were higher and the reservation wage was also much higher. So that also, it's important to note that when we come back to the issue of wages, this is an important determinant of the wages. So you see here the difference when we looked at dormitory labor regimes in manufacturing sector in Angola, very large proportion of workers, you know, nearly 70% of them basically living in dormitories. This is particularly important for those who are based in Luanda. Okay, Luanda is an extremely expensive city. And for all the foreign and Angolan companies, the use of dormitories is much less significant. In Ethiopia, we also found some results in terms of segmentation. Generally, we found that Chinese and other foreign manufacturing firms, particularly those integrated in these global production networks, tended to employ mostly young women with relatively high levels of education for Ethiopian standards in particular, even in those sectors. Whereas workers in Ethiopian manufacturing firms, again, same sectors, leading firms, et cetera, were significantly older and also less educated. I mean, less users of schooling. In the construction sectors, low-skilled workers have very, very little education. So actually there's a big, big difference between some of the new new employee workers in these industrial parts in Ethiopia and the profiles that you find in the construction sector. Whereas the semi-skilled labor force is a labor force with a lot of experience. And in fact, in a very much so after by these companies, these construction companies, because the skills are not very common. So here's some data, so you can see in relation to age, very, very statistically very significant differences between Ethiopian and the rest. So Chinese and other foreign are actually not that different. And of course, this is also reflected in quite different proportions of those who have never, who are single, never married. And also you have evidence on gender-based labor market segmentation, how particular segments of these sectors are very, very female, dominated in the workforce. What you also find both in Angola and Ethiopia is very high rates of migration. Most of these workers across sectors are migrants, internal migrants. And they've been migrating actually for extended periods of time. But this is particularly the case for all workers in manufacturing in Ethiopia and semi-skilled workers in the construction sector. You see that the low-skilled workers in the construction sector typically would be hired locally. You know, these were road construction projects and therefore a lot of these hiring happened at the local level. So obviously this evidence on labor market segmentation is crucial to understand some of the difference that we find in working with conditions and wages. So we're not really talking about exactly the same labor forces. I mean, this couldn't happen by design. This is something we had to find on the ground. So let me summarize some of the main picture, the picture that we got. So what we find is that for some categories of workers, we do find that wages are slightly lower in Chinese firms, you know, 18, sorry, eight, 12% or 15%, depending on 20% sometimes. But for other categories, we find no statistically significant differences. So there is quite a substantial variation there that needs to be explained. And of course, this is just basic comparisons. We're not taking anything into account and then we will come back to this point later on. When we look at non-wage working conditions, including a range of fringe benefits, like social security, paid sick leave, health assistance, these tend to be better in non-Chinese firms. Again, these will be partly related to the different labor forces that we mentioned before. And this is especially the case in Angola. But this is, again, because of different types of workers. Then workforce is very a lot between the sectors by origin. So these comparisons need to take these aspects into account. I will come back to this issue of work-related accidents. But one strike in finding was actually Chinese firms were generally doing better in terms of occupational hazard. So the frequency of reported accidents or injuries as a result of workplace accidents was lower or very similar in Chinese firms. For working hours, actually, we did find some differences and they were not really that significant, especially in Angola, a little bit more in Ethiopia. Especially we looked at the manufacturing sector. And the other big difference we found was especially for Angola is much more likelihood of Chinese firms to offer accommodation and food. Obviously, this is related to the point that I made before of the preference for these dormitory labor regime, migrant labor regime, that was applied in the context of Angola, which means that social wage may be higher in Chinese firms in Angola for certain categories of workers. On training, there was, again, a very mixed picture. In Angola, basically what we captured was formal type of training. And of course, what we find because of the nature of the firms, the other non-Chinese firms in these sectors, which have much more established systems of training, especially for induction processes by Human Resources Department. So that was captured partly by these evidence. But it was only through qualitative evidence that we captured the extent to which Chinese firms, and of course, all the firms also engage in all sorts of informal mechanisms of training and skill development. This is actually the norm in many of these sectors, whether it's construction or manufacturing. But particularly, we found that it was in manufacturing where this kind of on-the-job training was critical for the kinds of skills that these workers were getting. So these are some of the descriptive differences. You see there, essentially, the confidence intervals. So were we likely to find or confident of finding the average wages. So for certain categories of workers, indeed, the wages in Chinese firms tend to be on the lower end. But as you can see from these figures, this is for Angola, also quite a lot of variation for each of these different categories. And for Ethiopia, the same kind of pattern, some variation, two categories where the differences are more significant and two categories where they are not significant. So just by looking at these very simple statistical comparisons, quite a bit of variation, less variation across among Chinese firms. But it's hard to conclude whether, overall, and by large, there is any systematic and statistically significant difference in wages. So when you find these, obviously, and also considering the different characteristics of these different workforces across different types of firms, also different sectors, means that there may be many other factors that underpin some of these variations. And this is what we will try to show now, and Florian is going to take over. Thank you. Sorry to interrupt. Thank you. Hello, everybody. So up to now, we've looked at essentially a descriptive analysis that is an analysis that doesn't take into account the confounding factors and the contributing factors that can help us explain the difference in wages that we see across different sectors, across different companies, and across different company origins, in particular. So to go further and to look at what is actually driving these differences, we conducted some more formal statistical analysis. In particular, we conducted some pretty standard OLS wage regressions. Here, we ran regressions on the log of monthly wages. And where regressions in this context allow us to do two things. In particular, they allow us to look at the partial effects of different variables. That is, excuse me, in how far the different variables contribute to the differences in wages that we observe. And in particular, they allow us to look at in how far the Chinese origin, or whether a company is Chinese, plays an important role once we take other variables into account. So in our regressions, we control for individual level characteristics, such as age, gender, migration status, education, work experience, the socioeconomic status of the individual, a job tenure, that is the amount of time somebody has spent in the job, and of course, the skill level of the respondent. We look at firm level characteristics, in particular, of course, the company origin, but also the company size. And in Ethiopia, also, whether or not the company is located in an industrial park. And I'll explain why that's important in a minute. And then, of course, we control for sector level characteristics. That is the sector of operation of the company. In Angola, we also control for sample bias, because, as Carlos has explained, the workforces we found in some of these companies were structurally different, and those structural differences were related to the timing of the surveys, which in part, overlapped with an economic crisis in Angola that followed the oil price crash, which meant that some companies were operating with essentially only their core labor forces, so with skeleton crews, while other companies were running their full labor forces and therefore had a much higher contingent of temporary workers in their sites. So what did the regressions tell us? If we look at Angola first, we can see that a number of variables play an important role in explaining differences in wages. In particular, the biggest difference is whether workers are semi-skilled or not. As we already saw in the descriptive analysis, semi-skilled workers have a high wage premium. The tenure and the current job is important, as is previous experience in the relevant sector, so in construction or in manufacturing. The firm's size is important, bigger firms tend to pay better, but the most important variable really is the socioeconomic status of workers, and that again relates to the sampling issues that I've just explained. So core workers with longer experience do tend to be able to command better wages, and so we did also add control variables to control for the sampling bias. What we found was in our preferred specifications, including all of these control variables and with appropriately clustered standard errors, we find that company origin is no longer a statistically significant predictor of wage differences. That is, what explains differences in wages are the characteristics of the labor force and the company, but not the national origin of the company. Similarly in Ethiopia. In Ethiopia, the most important variables that contribute to differences in wages were again a semi-skilled wage premium, working in the construction rather than the manufacturing sector as the semi-skilled construction labor force is the best paid in our sample. Previous work experience in the construction or manufacturing sector does help raise wages for workers, and in particular, once we take into account whether or not a company is located in an industrial park, we find here again that the national origin of the company is no longer a statistically significant predictor of wage differences. Now, why are industrial parks important? And in fact, we find a large difference in wages paid to workers in industrial parks and wages paid to workers outside of industrial parks. Wages in industrial parks are 19% lower. Now, in part, that is due to simple locational effects, whether or not you're close to Addis Ababa, where wages are obviously higher because living costs are higher, but the more important effect has to do with global value chains. The companies that are present in Ethiopian industrial parks are the ones that are operating in the most sophisticated global production networks, ones that produce relatively high quality goods for sale in the European and American markets. And companies that operate as suppliers in these global production networks are subject to cost pressures by the lead companies that organize these networks that what you might call simple exporters that export lower quality goods to simpler markets are not subject to. And that is a big reason behind the lower wages in industrial parks, as is the particular nature of industrial parks as controlled spaces of wage setting and labor control, which I will come back to in a minute. But once again, to reiterate, taking all of these variables into account, the regression analysis shows that company origin, that is whether or not a company is Chinese or not, is no longer a statistically significant predictor of wage differences. Wage differences are driven by other characteristics. Sure, if it's a short one. It is, if you want to, in the Q&A, we can have a look at the actual output tables of the regression if you want to, and then we can go through variable by variable. But if you don't mind, we'll keep it for then. Okay, great. One claim that is often made in particular in the media is that these wages are poverty wages. Now, that can have many meanings, but the most common interpretation of poverty is to compare wages to international poverty lines. The World Bank uses different poverty lines for low-income and middle-income countries, and these poverty lines are expressed in so-called purchasing power parity international dollars. So to compare wages to international poverty lines in purchasing power parity, we have to look at what the actual poverty lines are for low-income countries. A monthly poverty line is $58 in purchasing power parity terms. Per month. Sorry, per month. What did I say? No, just two. Okay, per month. Yes, per month. In middle-income countries, they adopt a higher poverty line, which is 96 international dollars in purchasing power parity terms. So what we did is we converted the wages that we got in our survey from local currency into purchasing power parity terms, and because these are relatively poor countries, if you convert wages into purchasing power parity terms, you get a much higher number than if you convert them at market rates into just U.S. dollars, yeah? So if you convert in purchasing power parity terms, which is what you have to do to be able to compare to poverty lines, we find that no workers in our sample earned wages that were below the poverty line either for low-income countries or for middle-income countries. So in Ethiopia and purchasing power parity terms, low-skilled manufacturing workers earned a minimum of $130, that at market rates, that's about $53 U.S. dollars, and low-skilled construction workers earned a minimum of $160, again in PPP terms. In Angola, wages were higher in purchasing power parity terms, and low-skilled workers there earned about $300. The lowest-paid workers we had in Angola, temporary construction workers, earned about $244 in purchasing power parity terms. So in terms of comparing these wages to international poverty lines, no, these are not poverty wages. However, international poverty lines are, I would say, not the best measures of deprivation, and in particular they are supposed to measure extreme poverty. So we don't think this is a good way of assessing whether or not people who are, unless we forget, in full-time employment are actually poor or not. So what we also did is we compared wages to living costs. And here we find that while wages are consistently above international poverty lines, people do struggle to make ends meet. So we collected a lot of detailed information on the real expenses that people have on the monthly level and on the types of food and commodities they own and they consume. What we found was in Ethiopia, for instance, 56% of our entire sample spent more than half of their monthly income on food, which is a very, very high rate of food expenditure. If you look at Ethiopian manufacturing, for instance, only 27% of low-skilled workers report that their wages were enough to cover their monthly expenditures, while amongst the better-paid semi-skilled workers, 41% of people reported that their wages were enough to cover their monthly expenditures. So what this suggests is wages are above poverty lines, but these are certainly not living wages. Wages are still, though. Carlos, would you like to say something about that? Yeah, I mean, and I'm going to what you find is an interesting paradox, which is that the lowest-paid workers, and that's partly because of the characteristics that I've mentioned before, were actually managing to save more money than those who were earning higher salaries. So when you compare the actual monthly expenditures to the wages you see those categories for especially for low-skilled workers in Angolan firms versus Chinese firms, you do see that the proportion of expenditures cash for monthly expenditures of wages was much lower than the others. Of course, this has different types of interpretation. At that point, number one, it is partly due to the dormitory labor regime, so a lot of these works, the last portion of these workers were not spending anything on accommodation and food, which particularly in the Rwandan context are extremely high expenses. Also, obviously, because they live in dormitories, they tend to have a much more frugal lifestyle than the other workers on much significantly higher wages working in factories in Rwanda. So what you could see is that for especially manufacturing workers in Angolan factories where the best paid, they were the ones who didn't actually manage to save any money. But it is also true that they were more likely to have other sources of income to complement these wages. Thanks, Carlos. So what can improve these wages? Following Eric Ollenwright and Beverly Silva, we can conceive of wages as being dependent on the associational power of workers on the one hand that is on the strength of workers' organizations and on the other hand on the structural power of workers, an important aspect of which is marketplace bargaining power, which is in part again driven by the scarcity of skills that people own. So we would expect skill development, the upskilling of the labor force on the one hand and the strengthening of workers' own organizations on the other hand and of collective bargaining processes to improve wages. So how did Angola and Ethiopia fare in those terms? So if we look first at training, we see that there were relatively high rates of training provision across some parts of the sample. But we do see that, for instance, Chinese companies provide less formal training. As Carlos has explained, this is partly driven by the types of labor forces that were engaged at the time of the survey. While, for instance, in the manufacturing sector, we see that especially Angolan firms are very good at providing training to their workers. This is only formalized training as well. So this doesn't capture all of the skill transfer that happens through learning by doing and other types of under job training. This is just participation in formalized training programs. In Ethiopia, we looked at all forms of training. And here we see that the differences aren't driven as they are in Angola so much by the types of workers that work for companies of different origin, but they're very much driven by the sector in which companies work. So we see in the construction sector, rates of training are very low, hovering around 20%, while in the manufacturing sector, rates of training are uniformly very high across both skill levels and across all company origins. So there are very few differences in the Ethiopian manufacturing sector between Chinese, other foreign, and Ethiopian companies in terms of the training that they provide. So we have hopes that at least in the manufacturing sector, we will see upskilling as we go forward. Some of these international companies, especially some of those operating in industrial parks, are very new enterprises. The first Ethiopian industrial park opened only in 2012 and they're still coming on online right now. I think the last one opened this year, last year. Jostian will correct me later if I'm wrong. So there is still scope for improvement, we hope, in terms of the upskilling of the labor force. What about unionization and workplace conflicts? So in Ethiopia, for instance, we see that unionization rates are low in manufacturing, but even lower in the construction sector where unions are almost entirely absent. In the construction sector, this is probably related to the difficulties in unionizing transient labor forces in remote areas which is where the lower skilled part of the labor force is being recruited, while high skilled workers in those labor forces do have a lot of marketplace bargaining power and consequently do command quite high wages. If we look at manufacturing in Ethiopia, for instance, we see that the main difference is not between Chinese and other companies, but between all foreign firms and Ethiopian firms. And that difference, in turn, is largely driven by whether or not a company is present in an industrial park. Industrial parks are enclosed spaces, which of course makes it much easier for companies to resist the formation of workplace unions. So we see this very clearly in the statistics. So in Ethiopia, only 13% of workers working in industrial parks report having a workplace union, while outside of parks, 56% of workers do. The absence of formal workplace organization does not mean the absence of industrial conflicts. Strikes are incredibly common in Ethiopia and were almost universal in our sample of companies. Because of the limited nature of formal workplace organizing, these strikes tend to be wildcat in nature. So if we look in a little bit more detail at the distribution of, so this is showing the percentage of workers who report having a trade union in their workplace in Ethiopia. As I just said, we see that the rates in the construction sector are extremely low in the manufacturing sector amongst unskilled workers, which is the largest part of the sample. We see that there are a few differences between Chinese and other foreign companies, but that both of those, so all foreign companies together, are very different to Ethiopian enterprises. Ethiopian enterprises are so much older, which gives unions a lot more time to establish themselves in these companies. As I said, quite a lot of the Chinese and other foreign companies haven't been operating in Ethiopia for a very long time, and they are themselves acclimatizing, if you want to call it that, to the national legal context as well. Amongst the semi-skilled workers, the pattern is less clear. There are differences, and we see that clearly the Chinese companies have the lowest rate of unionization. Turning to similar statistics for Angola, here we have data on reported strikes, which is the black bar, on the presence of collective bargaining, and on the presence of a trade union at work. We see that in other foreign and Angolan companies, actually more than half have implemented collective bargaining procedures of some kind, whereas in Chinese companies, it's about 35%. And we also see again here a marked difference in the amount of trade unions that are available at the workspace. While we see, while we, yeah, sorry, please. Just to leave a comment on this to give context. One of the reasons you might be surprised by the other foreign and Angolan companies having very high rates, the reality of these sectors is that the rates on average are extremely low. So actually these other foreign and Angolan companies are very unrepresentative of these sectors. And what really happens is very simple. Trade unions in Angola are extremely weak, and they can only manage to target the top leading companies in these sectors. And that's what they've been doing for some time. So the reality of the unions, and this is what came out of the interviews with them, is that because these other foreign and Angolan firms have been in this market for quite a long time, so they've already been present for many, many years, and they've managed to incorporate the trade unions into these firms, even if it's not 100%, as you can see from the picture. But the Chinese firms, most of them arrived in around 2004 or five, and trade unions were basically unable to persuade these Chinese firms to actually have union presence. So that probably explains some of these differences. But generally, the overall rates of unionization in sectors are typically lower than 10%, 15%. Yeah, thank you. So one of the main policy conclusions of our report is that if we are to see sustained increases in the wages and working conditions in these sectors, we really need to be pushing for greater unionization in workplaces and for labor market institutions such as sector level bargaining and collective bargaining, both in the company and ideally at the sector level. There are, of course, understandably a lot of clashes at the workplace. The question is, are these clashes driven by cultural differences, or are these clashes driven by the standard antagonisms between capital and labor that you would expect to find in any workplace? Now, there is some evidence, both from our survey and from the qualitative data, that cultural issues of work culture in particular, but also language issues, do exacerbate some of the conflicts that we find. Communication becomes more difficult if there's barely a shared language and lack of appreciation of what is expected in terms of work culture or what is expected in terms of an employee do exacerbate some of the conflicts that we see. However, for instance, in Ethiopia, we see that the differences, again, really not between Chinese companies and other companies, but between all foreign companies as a group, so other foreign and Chinese companies and Ethiopian companies, on the other hand. Once again, we think that is driven in part because of course communication is easier in Ethiopian companies that have entirely Ethiopian management, who of course all speak the relevant languages and therefore find it easy to communicate and also understand the local cultural context, but also these companies, again, do not operate in the same markets. They do not operate in the same value chains as these international and Chinese companies do, and they are subject to much less pressures by international, by the lead companies that organize these global production networks, and therefore their management takes a much more lax approach to the organization of the labor process than companies that operate in these very low-margin global production networks can afford to do if they want to maintain in profitable operation. And we do see paradoxical contrasts between the two different countries. So in Ethiopia, there were large differences between firms of different ownership types, again, driven by whether or not a company was foreign, rather than driven by whether or not a company was Chinese. However, in Angola, we saw that company origin is not actually a good predictor of conflictual relations between managers and workers at all, and in fact, the overall rates of reported abuse, harassment, and other types of conflict in Angola were much lower than in Ethiopia. Coming finally to the conclusions of our talk, before we conclude, I think it's fair to give you a couple of methodological caveats that we feel you should be bearing in mind when interpreting the findings of our study. So this is us being honest about the inherent weaknesses that come with every type of research project. In the company survey, but not the worker survey, the response rates for firms were lower amongst non-Chinese companies, in particular in Angola, and that may affect some of the estimates of localization rates, because we have higher non-response rates among them in non-Chinese companies. In Angola, as both Carlos and I have explained, the sampling of workers in non-Chinese companies in particular was affected by an ongoing economic crisis, which led to the predominance of a core, more permanent labor force in construction sites. This was not the case in Chinese companies that tended to be operating with the full contingent of labor force that obviously included many more temporary workers than either Angola or other foreign companies. In Ethiopia, it's important to bear in mind that, as I just said, the Ethiopian manufacturing sector is growing and is quite recent in origin in many parts, so some of the workers were sampled from newly established factories that had only been in operation for a brief period of time. And this affected mainly the companies in industrial parks, which were exclusively Chinese and other foreign, whereas the Ethiopian companies in our sample had existed for much longer, in some cases, 70 years in the case of some of the oldest Ethiopian manufacturing firms. So please do bear these things in mind when you hopefully go and read our detailed company reports and then also the synthesis report once it's out. What can we conclude from this research? Well, the first big conclusion, as I think Carlos made clear during the first part of the presentation, is that we need more research and we need better research. This is a highly polarized and evidence-scarce environment, and so we feel there is a need for careful mixed-message research to engage with some of the predominant questions in this field. However, doing this type of research, in particular, at scale and doing it comparatively, is extremely demanding, not least in terms of effort and resources, and that's why there's still too little of it. So our first plea is for more research to really comparatively look at these things at scale. Secondly, if we compare conditions for workers in Chinese firms and non-Chinese firms, we find that overall the results are mixed. Once again, please bear in mind that what we looked at was not sector averages, but really the top performers in these sectors. We really wanted to compare like with like. If we compare like with like in the way that we did, we find that it doesn't seem to be a clear pattern across countries, across sectors, and there are multiple confounding factors. So we really need to combine the macro context, the sectoral dynamics, and firm level attributes if we want to get a full explanation. So what matters? Workforce characteristics, as I think we've shown, the type of sector or market in which countries operate, in particular, for instance, global production networks, the country context, and specific micro-level, local labor regime configurations, which again are partly determined by the networked nature of production in contemporary capitalism, are much better explanations for working conditions than the national flag of the company. That was our presentation. We very much look forward to our discussion. Thank you so much for your attention. We really appreciate it. Well, that was a tour de force. I'm sure that you would all agree that that was extremely stimulating and informative. We're going to have reactions from two discussants. Leading off, there's going to be Linda Calabresi. She works for the Overseas Development Institute. She has a wealth of experience in East Africa in both the public and the private sectors, but also speaks fluent Mandarin. So she's very well-placed to make the first set of discussant comments. As you said, a very long research project, very large-scale, impressive number of results and findings that you have collected. And there's a lot of value-reeling looking at the comparative study in the way of the comparative, can you hear me? And then Carlos and Florian for the presentation. I think it was extremely interesting, very rich in detail, a lot of depth. I really, really want to praise the value of this work that you have done. As you said, a very long research project, very large-scale, impressive number of results and findings that you have collected. And there's a lot of value-reeling, looking at the comparative study in the way, at the comparative analysis in the way you have done. And most of all, there's a lot of empirical evidence that the rest of researchers can now draw from rather than relying on sort of smaller-scale studies. So really, I think there's a lot of interesting points here. Personally, one thing that I also found very useful is how well you separated the sort of, the infrastructure and manufacturing side, showing really the differences between these two sectors and also showing that when we talk about infrastructure companies, very often we talk about contractors. I think there's a lot of confusion very often when people talk about Chinese investments and not understanding the sort of, where does the finance come from? And what you explained is these are really companies undertaking services and operations which I think is really useful. I have a few reflections based on what you just presented. The first one is really interesting that you're focused on localization rates. I think this is definitely something that's very highly debated in the literature around China-Africa issues. And one thing that always strikes me with this is, of course, this is really important, but the job creation itself is also extremely important. If you think about countries in Africa, especially the East African ones where they work, they really have very high job creation targets that they set for themselves. So a country like Rwanda, for example, they want to create 200,000 new jobs, off-farm jobs every year, right? And that's huge. So regardless of whether these Chinese companies or other companies employ 60% or 90% of domestic workers, still the job creation effects are really, really good and very important for this country. But the high localization rates that you find them, that you find to be very similar among Chinese and other foreign and domestic companies, these I think are very sort of natural in a way, right? Because they make business sense. Companies think about what makes business sense for them. And hiring and employing foreign workers, for which you have to pay accommodation, for which you have to pay expert salaries and work for them and so on, may not always make business sense. So really I feel like, at least in my experience and in my research, it always sounds like companies bring these foreign workers only when they have to, only when they have no other resources, only when they're told that they need to finish this project or complete this role within the next six months and therefore they really need workers who can hit the ground running and immediately start working in the country. One thing that I should also mention is that I work with a number of projects funded by the DFIDSRC growth research program. And it's very interesting to find that some of your conclusions are actually in line and converge with some of these other projects. So specifically, one project by the China Institute of Research Institute on skills transfer, technology transfer by Chinese firms operating in Africa also finds that there's a lot of training provided and a lot of sort of skills transfer provided by the Chinese companies in ways that, as you say, may not always be the formal training activities, the sort of classroom type training. But there's a lot of variation. There's a lot of learning on the job, learning by doing shadowing and so on, so forth. So I think these results are very much in line. Another research project under this sort of funding stream is one that's conducted by the International Institute for Environmental Development and they look at Chinese investment in natural resources in African countries. And one of the very interesting things that they found comparing Chinese and other foreign companies in African settings is that the most striking differences are not between Chinese companies and everyone else. The most striking differences are between the new entrants in the market and the older companies. So the older, more established companies, such as, for example, in the mining sector that South Africans or from other countries behave in a different way compared to the new entrants, which may be, in this case, the Chinese very often, but also the Indian companies that have just started operating in the sector. So I think this is sort of an interesting comparative perspective. Getting back to your work and to your conclusions, I think your study is extremely interesting and has any good research project answers a million questions and raises another million questions, which I now have, so I'm not sure whether some of these you may be able to answer based on your data and based on information that you have and some of these will require another four year research project or maybe another two or three. I think it's very interesting when you describe the labor market segmentation, when you describe how Chinese companies in the Angola set things specifically seem to hire younger migrant workers, for example. And I was wondering if you found any sort of reason or explanation for that, is there any network of workers, for example, that call each other if I'm hired by this company, then I call my friends and my relatives in my village or is there any other way through which this sort of segmentation continues is perfectly effective. And another one that I find very interesting, especially because I find it so counterintuitive actually, is the issue around lower pays in the industrial parts, lower wages in the industrial parts. You mentioned, for example, that one of the issues is around these companies being embedded in global value chains and global production networks. And if I think about it, that is very counterintuitive to me. Because I think about a company that supplies H&M or GAP or ZARA. These are companies that really need to answer to their stakeholders and that really pay a lot of attention to the consumers in Europe here and US markets. So why does this not emerge? Is this because there's not set minimum wage that they need to answer to? What is the reason? I think this is very, very counterintuitive based on my knowledge in other settings. One thing this is linked to, for example, is also around the subcontracting issue. So when a company works in supplying a global production network, normally there's a lot of control over what this company does. But when there's a surge or when there's an order that this company cannot fulfill, a lot of the work is very often sort of subcontracted to other companies. And that's when you lose sight of what are the working conditions in these other companies. But the one that's normally the first year subcontractor, you would find a sort of generally better wage on working conditions. So I wonder if you have any ideas about that. One thing I had a question on is around turnover as well, which I think at least in the work I've done in manufacturing, there's a lot of turnover. This is something that companies buy every day, but they don't seem to have a clear answer for. So they say, you know, my work is always me. I cannot hold, like, they never stay for more than a month, but then no one really has an answer or a solution on how to deal with it. And in general, I think with the premise that my work really focuses on firms and companies more than on labor markets, I think it would be very interesting to understand a bit more again about these companies. Are they exporters? To which markets do they export? Do they subcontract the work to other companies? Who are the suppliers? Who are the contractors? These may be, again, things that you don't really have information on, but I think it's very interesting. Another thing that I also really found very interesting is around the dormitory regime. For some workers. One thing that I think we can think about is how this changes the real wage of workers, right? So if you are provided accommodation, you're provided meals, it means it's a worker, actually. Your real wage remains high. So even if your nominal wage doesn't increase, in fact, if you're given the services, your real wage is higher. And there are multiple benefits to this, of course, maintaining the sort of salary is competitive for companies that want to invest in these countries, but also creating jobs. Because building these dormitories, it's something that's done by the construction companies in the country. So again, there's sort of multiple positive effects to this. And one final point that I wanted to raise is, sorry, because I'm on the eye, because I'm sorry, work on policy issues, I love to. And I think the interesting part for me is what are the implications of policy So you say that localization rates are actually very similar. And wages are also taking all factors into account. Wages are also very similar across different types of companies, which I think is great and shows the value of having foreign investment as a catalytic force for development or for economic development. One thing that I think a lot of governments are starting to ask about is, OK, we got this foreign direct investment at the moment. So the jobs that we have in the country, they increase our exports. That's fantastic. But how do we make it so that a larger part of the value stays in the country? So how do we build more linkages, first of all, with the domestic companies that exist? But also how do we maintain more of the activities, more of the decision making, and more of the value in our country? And I think this is one where one needs to sort of think carefully about the value of foreign investment versus domestic investment as well. So of course, the value of foreign investment is also on the sort of skills transfer and tax transfer and so on. But it's also interesting, I think, to think about, how can you use them to build more and more of the linkages with your local economy so that the development sort of is more sustainable in the longer term? Thank you very much. Thank you very much, Linda. We're going to have a comment from our second discussant, who's Irene Yuan Sun, who is with the Center for Global Development. She comes to us for multiple graduations from Harvard, but from McKinsey's in particular, where she was the lead person who compiled the major database on Chinese firms operating in Africa. And her own book on the subject of Chinese investment in Africa was highly praised by the Financial Times as one of the great business books of the year, of its publication. So we await your words. Thank you so much for having me. It's an honor to be here. And first and foremost, I just need to congratulate the whole team for truly a tremendous accomplishment. Having been in your shoes, at least somewhat, running large-scale surveys about Chinese firms, and you guys even went beyond that and did other firms, national firms, and so forth, this stuff is tremendously hard. I mean, I know what you guys have been through, being yelled at people, being under the sun, having timelines that just blow up in your face, having cost pressures, having difficult logistics. I mean, you guys have really gone to the ends of the earth to get us this data. And so congratulations on this tremendous accomplishment. We in the field and I hope in African countries, everybody in global development genuinely appreciates the effort that has gone into this. I particularly appreciate this study and the way that it was intelligently designed, because it really filled a really key gap in the field that I and others have been responsible for a long time, which is that we keep talking about Chinese firms. And we responded to media reports about all this crazy stuff like prison labor. And so we go and look at the Chinese firms and we don't do so in a way that is comparable to the other firms that are also operating. And so after the McKinsey report where we interviewed more than 1,000 Chinese companies operating across Sub-Saharan Africa, we had all these statistics about Chinese companies and some of the smarter observers would ask us, okay, but what's the comparable statistics for African companies or for Western companies? And we're like, we don't know, we didn't get that. And of course there's only so much that any particular study can do, but you really filled this critical gap, which is that now for the first time we can really compare across different kinds of firms operating in the same market, operating in the same industries. And the other critical gap is that we have now worker perspectives and statistically reliable worker perspectives, which is again one of these biases that's always been present in the field. You go and you just bang on the door so hard just to get an hour of the manager's time. That already is a very, very difficult thing to do to gain that trust, but you went way above and beyond that and gain the trust enough to then go, you know, disrupt production for a day. You went and interviewed a statistically representative sample of their workers. And so you fill these two really critical gaps in the literature. I have a couple of reflections that sort of go beyond the very careful findings that you just presented. And then I have a couple of suggestions for future directions. So the reflections, and I'll put these a little more sharply than perhaps your careful wording and analysis will be entirely comfortable with. But my first reflection is that this work to me supports the notion that really there is very little that's exceptional about Chinese firms or the nationality of any firms, right? And so if I had a my way, we would just talk about firms according to industry, according to the environment that they are working in, including the country environment and the dynamics there, the global value chains, or the domestic or local markets that they're serving, right? It's clear that these are the explanatory factors that determine firms' behaviors, the way they treat workers, the wages that they pay, their labor relations, their productivity. And there's very little sort of, you know, this whole China, is it a Chinese firm that's pretty much a red herring at this point, right? And so I think policymakers, the media, even workers themselves, right? And the workers themselves probably know this better than anybody, right? It's more about the skills that you have, the industry that's available for you to work in, and the competitiveness of your country, right? That's the stuff we should all be talking about and trying to improve, rather than, you know, do I attract firms from this nationality or not, right? Like instead we should be like, okay, what's the sector that you should be attracting, right? What's the kind of the scale of industry you should be attracting? And then find the countries where those investors exist. The second reflection is a bit related, which is that I think for too long, this whole area has been talked about in terms of as if it were static. And in fact, if you, you know, one common thread that runs across all of the, many of the descriptions that you've had is that this is incredibly dynamic, right? You see this in the fact that localization rates for the workforce actually are hugely dependent on how long the company has been in that market. You see that companies are, you know, thinking about evolving their wage regimes in response to retention issues. You see them, there's other sort of anecdotes and qualitative things in the literature that shows that, you know, Chinese firms overseas, for example, get more comfortable with things like trade unions, if they've been operating for longer, and for other firms as well, right? Like what non-African firm wouldn't be the first to say that they learned a lot and changed a bunch of their practices after being in Africa for a long time, right? And so I think we have to really think about this as a dynamic process and again, stop the impulse to essentialize firms as they are in these snapshots and realize that, you know, it's an evolving market and people change and firms change over time. Now, the couple of future directions that I want to suggest, I think one is that, probably the most important one is, I think this study should serve as a huge wake-up call to African policymakers in particular. There is so much important content and policy recommendation linkages in this, this work is incredibly rich with suggestions for future policy directions. A couple that come to mind immediately is number one in terms of worker welfare, particularly the fact that there's no minimum wage in Ethiopia, that has got to be a huge, huge area of translation of these findings that workers are spending more than 50% of their wages on food, that things like dormitories, that leads immediately to a potential policy option that could be costed out. In terms of the minimum wage, having some sort of anchor wage where if you look at the business models of firms and what profits they're making, could you set a minimum wage that still allows them to be profitable and competitive but ensures that workers are not spending 50% of their wages on food? So I think that's one clear area where some targeted work could really translate into some really effective policy making. Another one is this whole area of training, right? The fact that the foreign investors in a country, in some of these countries are dismissive of the, and don't care about the educational qualifications that a worker comes from, right? That that is a non-factor is just that should make, I think everybody stop and pause and reflect on how to improve education so that it's genuinely useful for things like job creation and employment, right? And of course that's not the only value of education. I would be the last person to say that we should be educated just to find a job but in these markets in particular being able to have a livelihood on the back of the education that you've gotten I think it should be a policy goal. And so this notion of training, I very much liked Linda's point about the increasing attention to the notion of non-formal training, you know, in a lot of professions that is how people get good at their jobs. And so some way to study that, to emphasize that too to encourage that in policy making I think would be very warranted. And then my other suggested future direction, I'm glad to hear that you're working on managerial level employment as a follow-on to this work. I think in general there, if there could be a clearer sort of even if it were descriptive or conceptual linkage to business model, right? And I think your industrial park data point is a really suggestive illustration of this, right? That business model may be one of the biggest determining factors driving a lot of the outcomes that we see. And so can we get deeper on what is the impact of business model on these outcomes? And so, you know, is it whether the firms are primarily serving domestic market in their countries or versus exporting? Is it the niche versus mass market product type that's really driving things? Is it the contract versus, you know, ongoing investment divide? Is it, you know, the margin of the products? You know, and my clients at McKinsey, there is a, you felt it as a consultant. There was a big difference between being at a high margin consumer packaged goods client versus a low margin retailer, right? The whole workplace culture was different. And so, you know, can we pick up a few of these major dimensions and even just start hypothesizing and data gathering? We're looking at your existing data and looking at some of these connections. And then how can we get some of the rapid indicators? Because I come back to, again, my congratulations to you for this huge seminal accomplishment. And I think also realize that you all need vacations. You all need to take a break. You know, and we can't kind of be in a world where it takes four years of this giant team to get two countries worth of data, you know, and then like this is a heroic, herculean accomplishment, right? So is there some way that we can imagine having more rapid indicators, maybe more, you know, light touch things that policymakers themselves or multilateral institutions, international organizations can do just to keep, you know, almost like a pulse survey on some of these things so that we can make sure that the impact is being created, the policymaking direction is being guided by closer to real-time data and doesn't take, you know, the heroic, you know, it doesn't take heroism in order to see some of these pieces of progress actually being made in the future. So thank you for having me, and congratulations again. Well, ladies and gentlemen, we're almost out of time. We've probably got just enough, however, for one or two questions from the floor if people have got some burning questions. Well, I think that not everyone can stay here until six. But let's start with a couple of questions. Yes, up here. But not as much in this. So I was wondering if that is a Chinese kind of input. I don't want to say that, but... And why the dormitory regime is there and it's not another... And I agree, yes, it helps, but it's also, as far as I'm told, a controlled institution. So it's also to be tricked, I guess, differently from... I would take a bracket of three questions. So gentlemen, at the back there. You have to speak up, please. Given the findings you had, how do you account for them? Okay, and gentlemen here. Yes, please. She, in opinion, is an economic strategy you mentioned. You might be speaking to the mental community, but how does this compare China and Africa? How does China work in Africa compared to the Belgium-Molden Union? Do you think there's anything in that between the two? We can. Is that enough for you to make a first response? Maybe one more. Okay. Well, thanks very much, both of you. I'm great, Quadra, there's lots of interesting findings. A couple of things that you didn't mention and made a very good reason why you went to bring them out. One is the question of what... I mean, you talk about ownership in terms of nationality, but what about ownership in terms of state and private companies, where you're able to look at those at all? And other issues that sometimes are, potentially more casualisation in Chinese companies or not, it's the case of my tree, but again, I think that's another, and obviously there are problems with saying in the case of Angola about looking at that big... because of the timing and so on, but again, it would be interesting to know whether there were any significant differences. And I wonder about... I'm slightly confused about industrial parks and especially economic zones. I mean, does China or other trade zones in other parts of the world, do you think of them as being for the export market? But several of the ones in Africa seem to be more for the domestic market, so I wonder... I mean, you'd seem to be saying that in Ethiopia, the industrial parks were integrated into local production networks, but I know some of the big Chinese firms operating in Ethiopia are exporting, but are there also firms in these industrial parks producing the Ethiopian market and can that be separated out? And I suppose... Well, then, I was kind of picking up on OE's comments. One of my, I agree with you that we need to get away from this kind of methodological nationalism that attributes everything to national origins. I wonder whether you're actually prepared to throw out the baby with the bathwater and say that actually all the firms are exactly the same wherever they come from. It seems to me that there may still be some elements of what you do need to take into account differences between firms from different origins, not just Chinese, of course, but other places as well. Carlos, Florian. Okay, let me just start with... Thank you very much for your reflections and especially the future directions. And of course, we really appreciate that you seem to like what you've seen so far, we've been in waste for years of our time. Oh, here. You hear me so fast. Okay, so, yeah, I mean, let me start from the question I think has come up in two or three instances of dormitory labor regime. I mean, this is a term that has been used in the Chinese context, so that's why we used it. I mean, you can start from the notion of a migrant labor regime first, and then the dormitory part is an element, an aspect of it, which is not necessarily applied in all contexts. So there are reasons in the Angolan context why, and indeed, we did enough quality research to probe with Chinese managers, for example, what was underpinning these sort of choices. So there is a narrative from Chinese management, which sort of makes sense. It doesn't mean that that kind of discourse really reflects the facts. What's really interesting is, so a number of Chinese farmers, this is especially in construction, but also in factories in Luanda. Partly because of the very limited experience in the market and problems they faced in the early days with precisely labor turnover. Another common complaint, again, as you can imagine, you've talked to managers, they also tend to essentialize culture. So they created, in a way in their minds, the stereotypes of extremely unreliable Luanda workers because of absenteeism, lateness, always with excuses, having five, six different fathers and mothers and all the rest. And that sort of stereotypes, because it's part of the manager's perceptions, was affecting in a way the business, and the business model was based on these time pressures, completing projects. Actually, that is one element which we need to take into account. Chinese firms are treated differently in Angolan market and construction market compared to other firms. And this is something that we are exploring with a little bit further research. So they were subject to different kinds of pressures. So for them, the obvious solution was to try to exert more labor control by basically housing the labor force and trying to make sure that it wasn't, and it wasn't just a question of, let's pay lower wages as a result, because we do find other companies that do not apply this kind of regime, paying higher wages, they opt for a different solution. But what is interesting in Angolan context is that they also borrowed a common stereotype in Angola that workers from certain parts of the country, i.e. from the center south, Wambolubango, et cetera, are highly disciplined, hardworking, reliable workers. They borrowed these perception, they sort of packaged it in a way that almost every manager was repeating the same thing. So they didn't just create a dormitory labor regime, they also selected the workers from certain parts of Angola because they thought these guys are far more reliable, they're less likely to leave, they're less likely to go back to the villages and visit the families and never come back. And anyway, so there's a long story to this, I mean, I'm not gonna bore you with all the details, but there is a long history of, the colonial forced labor system in Angola which particularly affected people from these areas, the legacies of the war which particularly affected people from these areas, both MPLA and UNITA, the two warring parties, were dominated in the rank and file by people, soldiers originated from these areas and so on. So there's plenty of evidence that suggests that, a lot of these labor force segmentation has very strong, deep rooted historical roots. And what many of the Chinese firms basically did was exploiting these historical legacies to build their own more reliable labor force and in a way that paid off. And it's true that companies using that system, certainly according to what they report, they managed to really address issues of labor, high labor turnover as well as improve their productivity in many ways. So that's probably the sort of background story to this. Now the complication is that, so then might we expect then Chinese firms to export these dormant labor regimes everywhere? Obviously not because we didn't find it in Ethiopia. There was no one instance of this in Ethiopia. So then the question is, why not in Ethiopia? Well partly because these kinds of stereotypes were not in place, point number one. So there was no structural reasons to think that there were some preferred labor force pools in Ethiopia that companies could tap on top in order to improve their reliability of the labor force. But also quite importantly, what we know from our qualitative research is actually the Ethiopian government is very reluctant to encourage or even allow companies to go down the route of dormitory labor regimes because they do associate these with systems of labor control they don't like. So part of the reason is also an implicit discouragement from the Ethiopian government. And if there's one thing that you can certainly conclude from this and other research, particularly for Chinese state companies and going back to your point, is that they do listen to the governments. They do take the government seriously. So they will adapt whatever they do, the business models, the labor regimes and so on to whatever circumstances of each context and particularly to their relations with the government because their business model, of course, are capitalist enterprises. But that's not, they're not just capitalist enterprises, they have other goals. I mean, if there's one lesson from Qin Quan Li's work on Zambia, it's precisely that the varieties of capital matters state and enterprises do have logics that are not the same as the others. So they will pay attention to that, which is, in a way, the implication of that, and going back to your point on policy implications, is that clearly there's a lot of scope for African policy makers to exploit these potential leverage. That a lot of what the working conditions, I mean, that would be one of our conclusions as well. A lot of the variation in working conditions that we find have to do with what African policy makers decide and do. We show that with the localization rates, a significant factor underpinning these differences between Angola and Ethiopia is very different attitudes of these governments towards questions of labor. In the Angolan context, again, if you wanna go back and also, okay, we found that localization rates were not as low as we suspected, but okay, they're still much lower than Ethiopia. Why? But one explanation of this is very simply that in the early days, especially the first five, 10 years, the Angolan government was primarily interested in building and rebuilding things, roads, et cetera, at high speed and delivering on the brick. They were not so interested in labor. And it's actually quite difficult to find any instance on any even discourse where there is a significant mention of labor or hiring Angolan workers, et cetera, or working conditions in any of these. So that also makes a big difference. So because that wasn't a priority, Chinese firms, again, we know from the research we did, and even when we were doing research, this was quite visible. You just needed to visit the project sites. Chinese firms were subject to far more pressure in terms of completion rate, speed, et cetera, than the top Angolan and other transnational firms in the same sectors. Particularly the Angolan firms seem to be working in a different segment, not really competing with the Chinese firms. So they were working in parallel worlds. So that obviously, that kind of dynamic, market dynamics has an impact on these labor regimes, recruitment, decisions about whether to, so if you think about Chinese firms at that time, subject to these pressures, et cetera, they say, well, I mean, these guys don't really care about whether we employ 80 or 70% of the labor. They're actually asking us to finish these in 18 months. We can only do these if we bring a number of carpenters, et cetera, from China. Otherwise, there's no way we can achieve this. So from that, I mean, one of, we have actually briefed, it is in Portuguese still, but we will bring in English. One of our policy recommendations in Angolan context is precisely that large-scale infrastructure projects or the kinds that we visited, should have labor criteria in their contracts and so on, which would mean a revision of the standards for completion, I mean, completion rates in terms of timeframe, but also what they expect to pay, okay? So budget, I mean, the financial proposals, but also the times of these proposals will have to be adapted to criteria about two things. First, increasing the localization rates. So if you want to hire more Angolan workers, but secondly, encouraging or sort of incentivizing the systematic training of these workers, not just the one-off training, but systematic training of these workers leading to higher labor retention and so on. This can only happen if a project that they will normally complete within 18 months is given 24 or even 36 months, okay? So that is something that the Angolan government should be able, should be prepared to accept in order to improve working conditions. So I'll leave the question on the industrial parts to Florian. If you don't mind, I'll have some view, but clearly you can blame the companies, the global production networks, but I think, you know, Irene has mentioned this, not having a national or sector level minimum wage has been a major issue in Ethiopia, okay? To the point that, I think when you ask, you know, how wages were set in certain spaces like certain industrial parks, which were considered flagship and so on, then you start getting some answers. Actually, the initial levels at which wages were set were far too low in relation to the ecosystem that workers were going to find around these industrial parks. That was the other part, I mean, we haven't talked about this, but there's certain industrial parks in Ethiopia where everything was done at high speed. I mean, if you visit one of some of these parks, the conditions are great, it's certainly not sweatshops. The big problem is what surrounds these parks. What surrounds these parks is towns that simply did not have the basic conditions to house to attract a very significant migrant labor force. There are some parks in which managers didn't know that the labor force was coming from 50 to 100 kilometers distance because they thought that there was enough labor pool within that area to be employed in the park. And again, we could talk at length, but this was also related to the fact that industrial parks and the way they've been set up and managed, and especially the labor recruitment systems have important political dynamics associated. Okay, where the parks are cited and how labor is recruited and from where. So that is part of the issue. So why perceptions? Why these perceptions? I mean, I think there are two reasons. One is the lack of evidence, obviously. And second, I'm afraid to say there has been a very strong anti-Chinese bias and a lot of media reporting on these issues from the early days. And that was reflected not only in terms of how some of these stories were circulated, but also how they were recycled. We suffer from these as well. I mean, well, I'm not going to go into the details, but our first experience of media reporting in Ethiopia right after the workshop last week was atrocious, was appalling, to the point that the reporter basically said exactly the opposite that we said in the workshop. So whether that is incompetence, just incompetence, or a combination of incompetence and some kind of unconscious bias, that's for you to sort of reflect. But that's pretty much, I think, for a reason. So just to raise this point on, yes, of course, SOAs, we look at that and we take Qin Guangli's work on these biases of capital seriously. The only problem, one of the main problems we have is that is a sector bias, that there's almost perfect correlation between state-owned enterprises and the sector. So 100% of all the companies in the road building were state-owned, but we could still manage, in terms of from the quality of the research, to see, to corroborate, some of the findings from Qin Guangli's in terms of some of these rationales not being uniform across. So varieties of state versus private capital, and even within the private sector, private Chinese firms, there's also a lot of variation, depending on the particular sectors, if they're exporters or not, true in some of the industrial parks, some of the companies were producing for the domestic market, and many were also exporting, so there are differences as well. And also, if Chinese firms, private firms come from different origins, I mean, there are ways to do research on this. And that also matters, origins within China, and the networks of firms and sectors that within which they operate also affect their business models and therefore the labor regime. So these issues are in our... So I would say that on the methodological nationalism, I mean, we tried, we had to respond to this question. I mean, these were a basic research question, so we had to distinguish Chinese versus others and so on. So we are guilty of this methodological nationalism. It was partly also to engage with this debate. Of course, if you take these varieties of capital seriously, there will be elements of the origin that matter, but it's impossible from that to infer that national origin is a major determinant. Our conclusion is basically that it is not. But once you, as you increase the level of granularity, you look more in-depth within certain sectors, yes, you will find differences. For example, obviously the dormitory labor regime is far less likely to be found in Angolan or in Brazilian or Portuguese company in Angola. They just don't have that kind of habit of managing or controlling the labor force. Just to follow on from that, I think it's important what Irene said, that these are dynamic situations. They change over time. So even where you observe origins by nationality, the question is, do these persist over time? One thing where you quite... And it's important to remember that if you imagine who are the foreigners who come to countries like Ethiopia, like Angola to run construction sites, to run production facilities there, these are people with technical training. They are production managers. They are good at organizing workforces, at organizing production sites. They often have extremely limited knowledge of the circumstances or culture in which they're going to be operating before they actually arrive. And it's a learning process for them as well. So in some of the qualitative work we've done, you did see, for instance, with companies that had not been in the country for very long, their views, for instance, about the role and utility of trade unions were very much colored by the role that trade unions play in their own political, in the political economies of their own countries. And it then takes them a while to understand that what they find in Ethiopia or in Angola might be a very different situation. And just because something has to label trade union, it doesn't operate in the same way that they are used to from their own national context. And then you do see adaptation over time. So I think that the dynamism over time is an important aspect here. Going back to the industrial parks, and I think I'll say something about industrial parks and about turnover at the same time. So why did we find lower wages in industrial parks? So like I said, part of the effect is a locational effect. So in part, a lot of the companies that were outside of the industrial parks were in Addis Ababa, where living costs are, of course, the highest in all of Ethiopia. And so you'd expect that, you know, of course, people's reservation wages are much higher. Generally, companies can't afford to pay such low wages, rents and these kind of things are much higher in Addis Ababa. So in part, we're capturing a little bit of a locational effect there, but we ran various different specifications and it's not driven by the locational effect. It really is driven by the fact that companies in these industrial parks have a different business model, as you were saying. And that is driven by their participation in sophisticated global production networks. It is absolutely true that we would expect these companies to be paying more. You know, they are especially in textiles and in leather products. The lead companies in these production networks, customer facing companies, you have business human rights and all these kind of things that mean that, you know, these companies should be facing much greater scrutiny of whether and how they comply with certain minimum standards. So the first thing to say is that, as Carlos just said, these are incredibly modern production facilities that have modern safety systems and these kind of things. So these industrial parks, the physical conditions in these factories are much better than physical conditions in older factories, simply because the factories are modern and they were built to a modern standard. They are well lit, they are well ventilated and they have things like sprinkler systems and fire safety systems. So we're not talking about sweatshops in any way, shape or form here. And then I think what came out quite strongly in the qualitative work was that companies don't really care about the wage level per se. They care about unit labor costs. A lot of these companies are quite new. So they were still in the process of, I mean, they had set up their production, but they were really still in the process of learning how to run a company effectively within these new national contexts that they didn't necessarily know an awful lot about before they actually arrived there. So what we heard again and again in our management interviews was, yes, at the moment, productivity is very low. We do expect productivity to get much higher, you know? So some companies might have been at something like 20% of the productivity that you could expect to have in China, but they were expecting to get to 70 or 80 over a period of two or three years' time. And what they all said was that wages aren't the most important factor here. So we heard several times people say to us, you know, we could double wages tomorrow. That's not the problem. The problem is if I double wages tomorrow, I have to double them again next year and I can't afford to do that. So they were taking a strategic approach to setting wages, whereby they were unwilling to set wages far in advance of increases in productivity in case they don't reach the productivity levels that they were projecting, you know? All with a view to maintaining unit labor costs. However, as Carlos said, their initial pegs for setting wages were much too low. And we know that not just because we looked at people's living costs and compared it to that. So I mean, to give you a concrete example, an Ethiopian manufacturing workers after their living costs were subtracted were able to save on average 0.8% of their monthly salary. I mean, that's living hand to mouth, basically, right? I think the best indicator for the fact that wages were set too low is that people voted with their feet. So a lot of these companies came. So what is the predominant narrative about Ethiopia? It has a huge population, over 100 million now by the latest estimates. It's one of the poorest countries in the world. So if you're an international firm, you would expect that, you know, people would be running your doors in as soon as you open the factory gates. What we actually found was at the time where we were conducting research, the most flagship industrial parks in Ethiopia, housing the best companies were having recruitment issues. They couldn't get enough workers to come and apply for the jobs that they wanted to hire because word had gotten out that it's difficult to live off the wages that these companies were offering. And as Carlos has already said, that's not simply a question of the absolute wage level that these companies were offering, but also a question of the lack of support infrastructure and urban ecosystems, if you want to call them that, that surround these parks, which meant that, for instance, living costs for workers were much higher than had been originally projected both by government and by companies. If you factor into that, the limited representation of workers in decision-making processes through low rates of unionization and very low rates of collective bargaining arrangements, then there wasn't really a mechanism for correcting these things immediately in the short term. What we've seen more recently is that, what you see now in these industrial parks is that there is a huge spread developing in terms of wages. So you see spreads of about a third between the highest and lowest-paying company wages is what we found in our follow-up research and in recent qualitative work. And the best-performing companies in terms of pay, so the companies that pay the highest, they've actually managed to stop the turnover issue from becoming an important predicament for them because turnover is bad for their productivity. You train workers, they start working for you, if they leave after three months, you have to train a new worker and that person doesn't know anything about your production line, so your productivity will never rise. So these companies basically realized, look, if we want to get to the productivity levels that we're imagining, then we have to raise wages now to allow people to have a secure livelihood and then in that way, we can retain them here in our labor forces. But you do also see people who essentially freeride on this better reputation that some of the international companies have established for being relatively high-wage employers by going the sort of the low road to profitability within a sector that internationally is already known for essentially being the low road to profitability. So I think that's probably what explains our findings with respect to that. Differences in terms of labor control, what you see is quite interesting. So what's happening in Ethiopia at the moment is that a lot of the confusion about where wages should be sitting is partly related to the fact that there isn't a minimum wage in Ethiopia. Legislation is just being passed that we'll for the first time set up a wage-setting council and that is supposed to be a tripartite mechanism that will then set and update a minimum wage. However, a minimum wage in and of itself doesn't do anything unless you set it at an appropriate level. And if you set the minimum wage too low, then it's not going to be useful. So we're hoping that minimum wages will be set at sectoral level because if you set a minimum wage for the national economy of Ethiopia, where many people are extremely poor, then it's not simply not going to impact workers who work in these kind of modern production environments because the national minimum wage would have to be set quite low, basically. But the other aspect of this legislation, which clearly shows the impact of a concerted employer lobbying, is that it's going to become possible to lay off people much more quickly for infringements of workplace discipline. So I think it's something like a kind of four strikes in your out system for being late at work. I think if you're on the fifth day, if you miss five days in a six-month period, then you can be fired without compensation. So these kind of mechanisms are now being put into the Ethiopian labor law as part of, because basically employers have at least have the perception that their mechanisms that they've tried, that they know from other contexts of labor control have actually failed and they haven't quite figured out how to adapt to the national context of Ethiopia and the cultural context of Ethiopia to find labor control mechanisms that actually work. Ladies and gentlemen, I'm going to suggest that we continue the discussion informally at the reception. We're going to have a reception. Where is it, Carlos? Yeah, so we're going to, what's it called? The Paul Webley wing under the glass roof. And we're going to have, I mean, another important member of the team is our artist, Davides Kalinge, who we deployed to our field sites to take high quality photographs. So you will see an exhibition of selection of these photographs in the Senate House building with some catering offered. I think, is it the catering there or not yet? The exhibition is not there yet. The exhibition is ready. The exhibition is ready. That's when I left. So the exhibition is ready, though. So maybe another five, 10 minutes, I guess, the catering should be there. Well, maybe we can convene outside of KLT and then we can all go together. Yes, yeah, we can all go together to the exhibition area. Why do we do that? But before we convene outside and go together as a unit to the exhibition, Marina, I think that as chairman, I should once again thank everybody, Linda and Irene and Florian and Carlos. It is a gargantuan work that you two have undertaken. Four more years, four more years. Yes. I mean, hard labor for the rest of your lives, I think. But look, I think we should join together and congratulate them in the time-honoured fashion.