A To What Extent Do Governance Indicators Reveal Institutional Obstacles to Development?

The use of indicators meant to describe the quality of institutions to make a judgement about whether institutions in a country are more or less favourable to economic growth, and more generally to development, is justified by the theoretical arguments surveyed in the preceding chapter and, supposedly, by empirical evidence. The latter is statistically fragile, however, and not without ambiguity. Precautions should thus be taken in using those indicators as a tool to identify institutional strengths and weaknesses. This is even more necessary as the indicators themselves provide descriptions of the quality of institutions that do not exhibit the precision required by a diagnostic.

Empirical evidence points to a strong correlation between institutional indicators and the level of GDP per capita across countries. The problem is that this correlation is consistent with causality going both ways: from better institutions to faster growth and from growth to better institutions. Instrumental variables that are assumed to be correlated with institutional quality but not with the level of development or past growth are used to control for this problem.Footnote ¹ This procedure tends to confirm that institutions affect economic growth, or the contemporaneous level of income per capita, among developing countries. However, the exogeneity of these instruments with respect to economic development is often debatable. On the other hand, the cross-country relationship between institutional indicators and the average rate of GDP per capita growth over ten- or twenty-year periods of time is weaker. Moreover, when other country characteristics are introduced to control for other exogenous factors that may condition growth it turns out that the effect of institutional indicators and their statistical significance tends to vary with the set of controls being used, which does not suggest a robust relationship.

These issues are thoroughly discussed in a recent survey by Stephen Durlauf (Reference Durlauf, Baland, Bourguignon, Platteau and Verdier2020) of the imposing cross-country growth and institution literature of the past twenty years.Footnote ² Its main conclusion is that, if there is no doubt about the influence of the quality of institutions in general on economic development, the exact channels for such an influence are essentially ambiguous. As an example, consider the following three well-known studies: Acemoglu et al. (Reference Acemoglu, Johnson and Robinson2001) provided evidence on a cross-section of countries that the protection of property rights delivered by a country’s institutions matters for development; likewise, Mauro (Reference Mauro1995) found with another instrument that corruption significantly and negatively affects growth; whereas Dawson (Reference Dawson2003) applied Granger causationFootnote ³ methodology on a panel of countries to show that the degree of economic freedom influences economic development. Those three studies show that, indeed, on average across countries, the quality of institutions matters for economic growth, but they do not say much about what institutions matter, and by what mechanisms these relationships are obtained. There are many different types of corruption – high-level politicians or civil servants siphoning away public money, taxpayers bribing tax authority personnel, the petty corruption of police officers – with a priori differentiated effects on economic efficiency and growth. A lack of protection of property rights may be due to corruption, to a weak judicial system, or to predatory rulers, while a lack of economic freedom may be due to over-regulation but also to excessive taxation or weak property rights. Surely the fact that significant relationships are found in those three studies, which are very representative of the empirical institution–development literature, means that the quality of ‘some’ institutions affects growth and development. Yet no one would accept an analyst making a diagnosis about what is wrong or right in a country’s institutions concerning economic growth based on those sole relationships.

As can be seen from the previous examples, the difficulty is as much in providing evidence of a causal relationship as in identifying what the institutional indicators used in cross-country analyses of the institution–development relationship stand for. To a large extent, this ambiguity results from the fact that these indicators most often describe the consequences of something being wrong in the way in which institutions function but not what is wrong. In other words, they point to symptoms rather than dysfunctions. Corruption is a case in point. It can always be described as the consequence of a judicial system that is unable to enforce the law – for instance, due to a lack of resources or (honest) personnel – but it may also be the consequence of loopholes in the law or in the regulatory framework that create rent-seeking situations, or of a lack of transparency of operations in the public sector. Yet the information gathered from experts relates to their perceptions of corruption, rather than the relative importance of the institutional causes behind it. In other cases, indicators rely on a set of very precise questions that are then aggregated into a single index. Yet the field covered by these questions is often incomplete. For instance, the ‘Rule of Law’ indicator in the Global Integrity Index relies exclusively on questions about the independence of the judiciary from political influence and the transparency of judgements, but no information is gathered on the time it takes to clear a case, the degree of corruption of judges and judicial officers, or their level of competence. By contrast, other indicators rely on long lists of questions covering various, often heterogeneous subfields.

Where does all this leave us concerning the institutional diagnostic of a specific country? Mostly to the fact that institutional indicators only provide a measure of the overall quality of institutions and, at best, some more detailed information on the strengths of various types of symptoms that may point to specific institutional flaws. It must be clear, on the other hand, that the measurement precision of these indicators is limited. In the Corruption Perceptions Index published by Transparency International, for instance, accounting for a reasonable degree of measurement error, it is not possible to say whether Kenya, ranked 124, significantly differs from Madagascar, ranked 149, or Egypt, ranked 117. When benchmarking a country relative to others, the lesson is that not too much meaning must be attributed to a country ranking 10 or 15 slots ahead or behind another. Attention should focus on those institutional domains where indicators show substantial differences.

A last remark is of importance when using global rankings of institutional indicators as an input into the institutional diagnostic of a country. It is that the correlation observed across countries between indicators and development outcomes applies to the ‘average country’, not to all single countries, far from it. In other words, it is not because the Risk of Expropriation of Foreign Investment published by Political Risk Services is shown to affect negatively the level of GDP in a cross-section of countriesFootnote ⁴ that a specific country where this risk is perceived to be high will necessarily underperform. There is some strong idiosyncrasy behind statistically significant cross-country relationship, which cannot be ignored when analysing a single country.

B Benchmarking Low-Income and Lower-Middle-Income Countries According to Institutional Indicators

The first question to ask when using institutional indicators to benchmark a country against others is what indicator to use. As mentioned earlier, many indicators are available, and even when they are supposed to cover the same institutional domain it turns out that they may substantially differ in some cases. Aggregating indicators covering close domains is a way of extracting from their diversity more robust differences across countries. This is the approach taken by the authors of the widely used Worldwide Governance Indicators (WGI).Footnote ⁵ An alternative approach, based on the extensive set of institutional and governance indicators stored in the Quality of Government (QoG) databaseFootnote ⁶ and focused on developing countries only, was also used in the IDP case studies. They are briefly described in turn.

The WGI indicators cover six broad domains: (i) rule of law; (ii) voice and accountability; (iii) control of corruption; (iv) government effectiveness; (v) political stability; and (vi) regulatory quality. Each aggregate indicator results from a statistical procedure that involves extracting from the large number of individual indicators which seem to fit the domain under analysis a ‘common factor’ in the way these various indicators rank countries. Practically speaking, this is done through looking for a linear combination of individual indicators whose average correlation, so to speak, with each indicator is the closest.Footnote ⁷ This common factor is then taken as the aggregate indicator which best describes the quality of institutions in the domain being considered.

The WGI methodology for defining aggregate indicators regroups individual indicators available in various sources according to the six institutional domains listed above on an a priori basis. An alternative approach consists of being agnostic about these domains and regrouping individual indicators according to their proximity in the way they rank countries. The number of groupings is decided a priori, and a ‘cluster analysis’ procedure determines which indicators enter each group. In other words, each group comprises a set of indicators that are highly correlated to each other in the way they rank countries, while this common ranking is made to differ as much as possible across groups. Then a common factor is identified in each group that summarises the way indicators in that group ranks countries. As the clustering is the result of a purely statistical procedure operated on the whole set of individual indicators, it is not clear whether they should be conceptually close to each other. Yet it turns out to be the case that they are close, suggesting that available individual indicators from a host of different sources tend to describe the functioning of institutions within a country according to a small number of key dimensions.

In the application of this methodology to some of the IDP case studies, the QoG database was restricted to developing and emerging countries so as to avoid aggregate indicators being mostly based on differences between advanced and less advanced economies, which may be a problem of the WGI indicators. Even though the dataset comprises more than 2,000 indicators, coming from practically all sources of institutional/governance indicators available, only those that were available for all countries in the sample were kept. They numbered 350, which were then clustered in six groups or ‘categories’.Footnote ⁸ Upon inspection of the indicators they comprised, the six categories were identified as:

1. a. democratisation;

2. b. human rights;

3. c. administrative capacity;

4. d. control of corruption and rule of law;

5. e. conflict and violence; and

6. f. competitiveness.

It is interesting that some of these categories very much overlap with the WGI domains – that is, ‘democratisation’ and ‘voice and accountability’; ‘administrative capacity’ and ‘government effectiveness’; and ‘control of corruption and rule of law’, corresponding to the two domains with the same name in WGI. Yet the overlap is far from perfect since the ‘human rights’ and ‘competitiveness’ categories have no clear counterparts among the WGI domains, even though the primary sources used to define the latter include datasets oriented towards competitiveness or human rights issues.

With these institutional indicators at hand, a second issue is which comparator countries are to be included in the benchmarking. Clearly, it does not make sense to compare the institutional quality of low- or lower-middle-income countries to countries that are much more advanced in the economic development process, have the resources to maintain better institutions, and whose population demands better-performing institutions. The comparison must allow for income differences, but within a reasonable range of variation.

Two sets of comparator countries were used in the IDP case studies. The first one comprises neighbour countries, with the justification that these may share with the country under analysis a common geo-physical context and, depending on the region, some common cultural or historical references – such as, for instance, the same past coloniser. Lack of significant differences within this set of countries may then reflect the strong influence of this context, as well as some homogeneity in terms of living standards. By contrast, variations across countries could mean either that the geo-physical and cultural context are not major determinants of the institutional features of countries in that region, or that the region is rather heterogeneous with respect to these geographical and historical characteristics. The significance of a country differing from comparators may be stronger in the second case.

The second set of comparator countries consists of countries which were at the same income level as the country under analysis two or three decades ago but that have managed to grow substantially faster since then. The question then is whether some institutional domains were of a better quality in the latter when growth accelerated, compared to the country being studied. The difficulty is that institutional indicators rarely go back as far as two decades or more. The comparison can only be performed on relatively recent years, and, in some cases, there is no possibility of going back in time.Footnote ⁹

Examples of the kind of benchmarking based on the WGI and QoG-based indicators are given in Figures 2.1 and 2.2 for Tanzania. The radar chart in Figure 2.1 compares that country with its neighbours in 2019. The WGI indicators range from −2.5 (worst institutional quality) to 2.5 (best), and the standard deviation of the six indicators – within the sample of developing and emerging countries – is around 0.5. The radar chart is thus constructed in such a way that the difference in graduations along all axes is precisely around one standard deviation, which allows us to pass a judgement on the significance of differences between countries. On the other hand, country scores tend to concentrate on the negative part of their interval of definition, which means, unsurprisingly, that governance and institutional quality in this set of low- or lower-middle-income countries are below the world medianFootnote ¹⁰.

Figure 2.1

Comparing Tanzania and neighbour countries according to the WGI indicators, 2018

Figure 2.2

Comparing Tanzania and neighbour countries according to the QoG-based indicators, 2018Footnote ¹¹

Looking at the figure from the point of view of Tanzania, the conclusion is undoubtedly that there is no difference when compared to the bulk of its neighbour countries, except Rwanda and Burundi, since differences with other countries never exceed one standard deviation. When restricting the comparison to these countries, one would tend to conclude, as suggested earlier, that Tanzania shares with them common geographical, demographic, and historical factors that lead to comparable institutional quality features. On the other hand, except for the rule of law in Mozambique, scores tend to be similar across the six institutional domains, so that no particular domain can be singled out for special attention later in the diagnostic exercise.

The chart may also be looked at from the point of view of other countries. If a diagnostic had to be established for Rwanda, for instance, this benchmarking exercise would have led to the conclusion that Rwanda tends to perform better than other countries in the region, except for a very low score in ‘voice and accountability’: that is, the democratic functioning and transparency of the government’s action. This is a valuable clue for an institutional diagnostic. Likewise, Burundi is shown to perform worse than other countries in the region – and as a matter of fact very badly in absolute terms, but a little less badly for ‘regulatory quality’ – whereas Mozambique would be comparable to other countries if it were not for the ‘rule of law’ domain.

To evaluate the consistency of using the WGI indicators, Figure 2.2 shows the same benchmarking exercise but now based on the aggregate developing countries’ indicators built based on the QoG database using cluster analysis. Roughly speaking, the same proximity among the comparator countries, except for Rwanda and Burundi, is observed and most country profiles exhibit the same regularity, with scores comparable across institutional domains, although somewhat less so than with the WGI indicators. The salient features are: (i) the superiority of Rwanda in all domains except ‘civil society and voice’, and an impressive advantage in ‘competitiveness’; and (ii) the inferior performance of Burundi, in all areas but competitiveness and democracy and accountability, where it is similar to all other countries. Mozambique’s chart also departs from the mean shape in the ‘competitiveness’ dimension.

A comparison is now made between Tanzania and countries which, although at a roughly comparable level of GDP per capita in the late 1980s, grew so much faster since then that they have reached an income level double or more that of Tanzania, on average. These are essentially Asian countries: Bangladesh, Cambodia, Lao People’s Democratic Republic, and Vietnam. The comparison is made using both 2019 and 2005 WGI indicators, with 2005 being the year when the income gap was roughly half what it is today (see Figure 2.3). Unfortunately, it is not possible to go back much before 2005 because indicators lack precision, due to the fact that fewer observations are available.

Figure 2.3

Benchmarking of Tanzania with respect to fast-growing Asian countries: WGI indicators, 2005 (top figure) and 2018 (bottom figure)

Two lessons may be drawn from this new benchmarking. First, back in 2005, Tanzania’s institutions did not seem to be worse than those of Bangladesh, Cambodia, and Lao People’s Democratic Republic. On the contrary, Tanzania was surpassing these three countries in almost all institutional domains. It also compared well with Vietnam, except for ‘voice and accountability’, where Tanzania prevailed, and for ‘political stability’, where the situation was the opposite. The view that faster-growing developing countries are endowed with institutions of better quality is thus unwarranted when looking at this particular case. The second lesson stems from the rather strong improvement observed in several dimensions among some of Tanzania’s outperformers. This is clearly the case of Bangladesh, Cambodia, and Lao People’s Democratic Republic, and to a lesser extent Vietnam, but not of Tanzania. It is interesting that Tanzania’s development outperformers saw the quality of their institutions improving while growing substantially faster. An obvious hypothesis in the case of Tanzania would thus be that the lack of progress on the institutional front may have delayed progress on economic development.

Other examples taken from the IDP case studies could further illustrate the use that can be made from the comparison of institutional indicators over time and across countries. For instance, a striking example of worsening institutions is Mozambique, whose WGI indicators scored close to the average of the sample countries at a comparable level of income per capita in 2005, and then drastically worsened in practically all domains after 2010 (see Figure 2.4).

Figure 2.4

The worsening of institutions/governance in Mozambique, 2005–2019

This example confirms an important fact to be taken into account when establishing an institutional diagnostic: the quality of institutions, as gauged by institutional indicators like the WGI or the QoG-based indicators, may vary considerably over time, most often following political changes. In other words, it would be wrong to consider that the institutional framework of an economy or, more exactly, the way it is used, has some degree of permanence. Observing an institutional weakness at a point of time may thus result from a real flaw in the institutional framework being temporarily ignored by the power in place. In other words, the law may be flawed, or it may be temporarily disobeyed. The distinction is clearly important.

Overall, aggregate institutional or governance indicators like the WGI indicators, or those indicators obtained by aggregating in a different way those individual, more focused, indicators available in the QoG database, are useful instruments for starting an institutional diagnostic. It is true that the aggregation procedure introduces some imprecision into the description of the quality of institutions, but it is not clear that one would get a better idea of this by considering the numerous and highly diverse individual indicators available, especially because their precision and mutual consistency is often uncertain. The above-noted congruence between the two sets of aggregate indicators is thus reassuring.

Several lessons can be drawn from the few aforementioned examples shown above. They can be summarised in the following way:

1. a. Not much is to be learned from the absolute level of aggregate institutional indicators when concentrating on low- and lower-middle-income countries. For these countries, scores tend to be low, thus reflecting the causal relationship running from the level of development to the quality of institutions.

2. b. When considering a single country, the possible asymmetry between scores in various institutional domains is of special interest since it suggests directions for further scrutiny of the functioning of institutions.

3. c. Benchmarking country A in relation to a group G of other countries requires distinguishing outliers. Comparisons between country A and median countries or against outliers have different meanings.

4. d. The quality of institutions or the use made of indicators may change over time, which points to the need to distinguish between permanent and transitory elements in the diagnostic to be established. Note that this has implications for the analysis of the empirical relationship between institutions and development. If the quality of institutions changes over time, it is difficult to relate it in a causal way to development indicators over a long period.

This section on indicators has relied on aggregate institutional indicators defined for a range of general domains and based on specialised individual indicators – most often based on the opinion of experts. This opinion may differ from the perception that insiders may have of the institutions in their own country, and most importantly on the practical implications of their possible dysfunctions. Surveying their views and identifying the weaknesses they point to is the objective of the second mechanical approach to an institutional diagnostic. The way it was implemented in the IDP is detailed below.

A Using Opinion Polls: the case of the Afrobarometer

Opinion polls are conducted more or less regularly in most countries, including low-income countries. Their goal is to get a picture of: (i) individual well-being – income, health, life satisfaction; (ii) opinions on major current policy and political issues; and (iii) the most common appraisal of the functioning of society, including local communities and national institutions. Polls may be conducted for profit, or they may be implemented by non-profit organisations, like the Afrobarometer in Africa. Given the multidimensional scope of these surveys, however, they comprise few questions on institutions or governance per se.

As a way of experimenting with existing opinion surveys, this section makes use of a harmonised opinion poll run at certain time intervals in a rather large set of African countries – the Afrobarometer, nicely subtitled ‘A pan-African series of national public attitude surveys on democracy, governance, and society’. It is now in its ninth edition, covering the years 2019–2020, but country surveys have not yet been put together in a single database, as was done in the previous rounds. The rest of this section thus uses Wave 8, taken between 2016 and 2018, depending on the country, and covering some 34 countries in the region.

The questionnaire used in the Afrobarometer is common to all countries. It is rather long, since the codebook comprises some 350 variables, among which 80 questions are about the respondent’s evaluation of the country’s governance. They include the degree of democratisation, the efficiency of the government in providing services, the areas which the respondents see as the most problematic, their perception of corruption and their trust in the main actors in society (president, government, parliament, military, courts etc.).

Since it would have been too cumbersome to deal with all of these questions one by one, the same methodology as the one described above to aggregate individual indicators has been followed. Namely, five areas were predefined, closely mimicking the WGI and QoG synthetic indicators in the preceding section. Average question scores for each area were then summarised by their principal component. This yielded an aggregate indicator with a mean of zero and a unit standard deviation across the thirty-four countries present in the eighth wave of the Afrobarometer. Because of non-response in those categories that comprised a relatively small number of original questions, the category meant to represent ‘regulatory quality’ had to be dropped.

Figure 2.5 shows the results obtained with this procedure for a few countries. As before, attention to each country’s institutional profile should focus on two features: (i) the shape of the radar line (i.e., whether it is regular, implying comparable scores on its different branches, or asymmetric); and (ii) how it compares to other countries, keeping in mind that the zero line stands for the mean across all African countries – with no implication whatsoever regarding how African countries perform in comparison to other regions.

Figure 2.5

Comparison of selected African countries, based on aggregated indicators elaborated on the basis of the Afrobarometer: Round 8, 2016–2018

Only two countries in the small sample represented in Figure 2.5 exhibit a regular pattern, meaning that there is no specific institutional domain with a noticeable weakness. These two countries are Senegal and Tanzania, although the rule of law indicator is particularly strong in Tanzania relative to other indicators, and relative to all African countries in the sample. All other countries show a bias in at least one or two domains. Thus, it is not surprising to see that ‘political stability’ and ‘absence of conflict and violence’ are weak in Kenya (remember the post-election killings in 2017) and Mozambique (on account of the resurgence of the Frelimo–Renamo conflict). This feature may not necessarily be considered as an institutional weakness per se, but it is a strong determinant of the context in which institutions function. More interesting from a diagnostic point of view is Ghana’s comparatively weak score on the front of corruption control, which contrasts with quite good scores along all other institutional dimensions. Benin also fares rather badly on the corruption axis, but also on government effectiveness, whereas Uganda fails on corruption and political stability. Finally, Malawi fails in regard to the opinion of the population on both government effectiveness in delivering services and democracy, that is, voice and accountability. If a diagnostic were to be conducted in these last four countries, the Afrobarometer would thus suggest clear directions of investigation.

Despite differences among them, it can be noted that the countries appearing in Figure 2.5 tend to do better than the average African country, since few scores are below zero (which is the mean for the whole sample of countries in every dimension). Equally noticeable is the similarity between the relative scores of countries in Figure 2.5 and comparisons made earlier using the WGI indicators or the indicators constructed from the QoG database. For instance, Tanzania tends to dominate its neighbour countries, as was roughly the case in Figures 2.1 and 2.2, when excluding Rwanda.Footnote ¹² The similarity is not perfect, however. The ‘voice and accountability’ score appears to be low in Figure 2.1, based on WGI 2019, whereas it is relatively strong in Figure 2.5, based on the Afrobarometer. Interestingly, this difference likely reflects objective changes that took place between 2017 (the year the Afrobarometer survey was undertaken) and 2019, in regard to the freedom of the press and other media in Tanzania.Footnote ¹³ Overall, it is interesting that a public opinion survey like the Afrobarometer delivers a message about institutional strengths and weaknesses in Africa that is similar to aggregate expert-based indicators.

Even though the discrepancy in the ‘voice and accountability’ score may have an objective explanation relating to changes in the control of the media by the executive in Tanzania, it raises several issues. First, it is another example of the kind of noticeable change that may take place during a short time span in the evaluation of institutional quality. Second, it may suggest that public opinion is more volatile than that of experts, or that the various factors that should be taken into account in the evaluation of the strength of (democratic) institutions covered under the heading ‘voice and accountability’ are not given the same weight by citizens and experts. Third, and more fundamentally, the questions asked in opinion surveys like the Afrobarometer bear upon limited aspects of institutions.

Public opinion surveys provide information on individual attitudes and perceptions that seem far away from the institution–development relationship but may nevertheless be of some indirect importance for development. Questions about people’s views on basic principles like democracy or justice, about their own moral values, about their degree of trust not only in formal institutions, which are accounted for in the above indicators, but also in relatives and neighbours, surely matter for the way a society – and therefore its economy – functions. Because they were not directly related to the way institutions work, they have not been included in the set of questions used to build the indicators analysed above. Of course, this should not prevent us from considering some of them, especially trust in others, if they appear relevant for a deeper exploration of the way specific sectors of a country’s economy work.Footnote ¹⁴

B The Country Institutional Surveys (CIS)

Overcoming the limitations of opinion surveys in dealing with such specific issues as the role of national institutions in economic development requires two things: (i) restricting the sample to people with some direct experience in dealing with institutions, or with good knowledge about the way they work; and (ii) orienting the questionnaire towards the institution–development relationship while substantially broadening it to cover the full range of relevant institutional dimensions. The IDP has developed such a surveying tool, whose characteristics are now described, before showing the use that can be made of it.

C Asking Key Informants

The last group of people to be approached for their personal insights into the role of the nature and quality of institutions in their country are those persons who exercise significant responsibilities as politicians in power or in active opposition, top bureaucrats, high-level academics, and personalities of the civil society. Numerous such key informants were interviewed as part of the initial exploratory phase of every IDP case study. In this essentially methodological chapter, the point is to summarise what was learned from them about each country, as this is fully reported and then developed in each case study. The main purpose of this section is to reflect on the way these interviews were conducted and, possibly, on some common features in the opinions expressed by the key informants across countries.

The identity of the key informants varied across case studies, but the choice was made at the outset not to interview high-powered members of the current executive – that is, presidents, vice-presidents, or prime ministers. This choice reflects not so much the difficulty of approaching them, but the concern that their opinion would necessarily be biased, partisan, or too much influenced by current challenges. In this category of informants, interviewees were most often personalities who were in this kind of position in the past and could thus have developed deeper insights into institutional constraints on development-oriented action when they were in charge, as well as today.

Different formats were used to gather the opinions of these particularly knowledgeable persons: seminars, open-ended conversations, or a predetermined set of questions. With retrospect, the latter formula proved the most effective. Yet it requires already having some good intuitions regarding the most relevant issues, so as to avoid losing time on commonalities. From this point of view, the Mozambique experience, with a set of well-chosen questions in each interview, delivered particularly interesting indications.

Several common problems were noticeable among these interviews, which often limited what could be learned from the interlocutors. The first one is that the very concepts of ‘institution’ and of their role in development were uneasy to convey to the interlocutors. For instance, the view that corruption per se is only the symptom of ill-functioning institutions, the issue being not only the detection and then the punishment of corruption but the circumstances that create the possibility of extracting rents, was not always uniformly shared. Respondents were often satisfied to cite corruption as the main source of problems in the way their country operates and is governed, rather than identifying its deep causes and, possibly, how it could be remedied. A second related common problem was the tendency of key informants to rely on an ideal normative framework without much relevance to the analysis of institutional problems in their country and solutions to them. For instance, the view was often expressed that the reason something does not work well is because it departs from mostly theoretical norms, like a full-fledged democracy with perfect transparency, effective checks and balances on the executive, and complete separation of the executive, the legislative, and the judiciary. Using such a norm as a reference to think about reforms is fine but illusory, since it misses key political economy constraints that precisely explain the persistence of weak institutions and their consequences. The difficulty is that political or political economy issues are still too sensitive for people who have been closely involved in them, whereas opposition members are generally biased, and people who are not directly involved in politics do not always realise the nature of these constraints. A third difficulty experienced during the interviews was the tendency for the conversation to focus exclusively on current public concerns or concerns which left their mark on the minds of interviewees, rather than on what they thought may be key persistent institutional weaknesses in their country.

Another interesting observation that results from these interviews is the similarity across interlocutors and across countries regarding the institutional fields cited as possible sources of hindrance to the process of development. Beyond corruption, practically all informants touched upon the de facto functioning of the political system and the judiciary, and the excessive centralisation of power and public decision making. Yet the link was not always drawn with the pace and structure of economic development. Closer to the issue of development, issues like a dysfunctional civil service, limited state capacity, the lack of coordination between public entities, or the management of land issues, were also almost unanimously cited. If the symptoms are clear and were widely shared, however, their causes were rarely discussed and the remedies proposed were not always realistic, often boiling down to wishful thinking: for example, ‘eradicate corruption’, ‘decentralise decisions’, and ‘have parliament play its role’.

Being what it is, the whole exercise is nevertheless of utmost interest, not only because it allows us to establish a kind of ranking of the most serious symptoms of institutional weaknesses as seen by informed players, and to sometimes have a glimpse into the political-economic factors behind them, but also, and most importantly, because these weaknesses were usually depicted and discussed in a particular sectoral context, be it a specific ministry, local government, the education sector, tax collection, or banking regulation. At a later stage, this observation of institutional dysfunctions within specific economic or social contexts is what may allow for a better understanding of their causes and possible remedies. In that sense, the direct contact with present or past high-level decision makers or observers sheds a different light on institutions than the general description of the quality of institutions and governance that is obtained from the experts consulted in the construction of international indicators, from opinion surveys, and even from the lower-tier decision makers polled in the CIS.