Institutional reforms now and benefits tomorrow:

How soon is tomorrow?

Pierre-Guillaume Méon a *

Khalid Sekkat a #

Laurent Weill b ¤

Journées de L’AFSE

Clermont-Ferrand, 19 and 20 May 2005

a DULBEA, University of Brussels, CP-140, avenue F.D. Roosevelt 50, 1050 Bruxelles,

Belgium b LARGE, Université Robert Schuman, Institut d’Etudes Politiques, 47 avenue de la Forêt

Noire, 67082 Strasbourg Cedex, France

Abstract: This paper aims to investigate the timing of the impact of changes in the quality of

institutions on macroeconomic efficiency. We do so by applying Battese and Coelli (1995)'s

stochastic frontier model at the aggregate level. We find that changes in the quality of

institutions exert two effects on macroeconomic efficiency, a short-term, i.e. after two to four

years, and a medium-term effect, around eight years. Robustness checks performed with

different measures of the quality of institutions tend to support these results.

Keywords: governance, efficiency, income.

JEL Classification: C21, K49, O1, O4.

* phone : 32-2-650-66-48 ; fax : 32-2-650-38-25 ; e-mail : pgmeon@ulb.ac.be. # phone : 32-2-650-41-39 ; fax : 32-2-650-38-25 ; e-mail : ksekkat@ulb.ac.be. ¤ phone : 33-3-88-41-77-21 ; fax : 33-3-88-41-77-78 ; e-mail : laurent.weill@urs.u-strasbg.fr.

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1. Introduction What is now referred to as the new empirics of growth has not only boosted academic

activity in that field but also provided a flurry of new insights into the determinants of

economic growth. Two consensuses have in particular emerged from that one and half decade

old research agenda.

The first point of consent is that, as Easterly and Levine (2001) put it, “it is not factor

accumulation” that is the main engine of growth but productivity growth. This consensus rests

on the finding of standard growth accounting or more recent income levels accounting that

factor accumulation may only account for about one half of cross country growth or income

level differences. Caselli (2004) shows that this result is particularly robust to various

refinements in the measure of input factors. That finding is also corroborated by the stylised

fact, underlined by Easterly and Levine (2001), that growth is much more erratic than factor

accumulation.

The second consensus pertains to the determinants of differences in growth rates and

incomes. It consists in the recognition of the paramount role of good political institutions in

shaping those differences. This result was obtained as a by-product of Barro (1991)’s seminal

study, and subsequently confirmed by a specific literature initiated by Knack and

Keefer (1995) and Mauro (1995). This second finding is moreover congruent to the previous

one, since it has been found that institutions determine aggregate productivity, by Hall and

Jones (1999), and aggregate productivity growth, by Olson et al. (2000).

However, both consensuses have recently undergone parallel evolutions. Namely,

whereas both strands of research had first concentrated on the long term, new findings and

renewed interest in so far overlooked evidence have led researchers to pay more attention to

the middle term.

As regards growth, several authors (Easterly et al., 1993, Rodrik, 1999, Pritchett,

2000, or Easterly and Levine, 2001) have emphasised the volatility of growth performances

over horizons as short as decades. Attention should accordingly switch to research into the

causes of medium term growth variations. Together with the stylised fact that investment is

much more stable than growth over the same horizon, that finding suggests that it is indeed

the efficiency with which an economy uses its resources that is the cause of growth

fluctuations, even in the medium term.

As far as institutions are concerned, the perception of their role in economic

performance is also turning to a shorter term, although this recent trend is still controversial.

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Thus, since the seminal works of Knack and Keefer (1995) and Mauro (1995), viewing

institutions as a deep structural and inert factor, capable of affecting economic performance

over several decades, had become the rule. Consistent with this view was the recourse to

instrumental variables such as ethnolinguistic fractionalisation or legal origins to investigate

causality. In the extreme, Acemoglu et al. (2001) argued in an influential paper that the

quality of institutions could be inherited from countries’ colonial origins. In other words, the

relevant horizon for thinking about the relationship between institutions and growth was not

decades but centuries.

That view was recently challenged on several grounds by Glaeser et al. (2004). They

first questioned the meaning of the instrumental variables used. A country’s colonial origin is

as likely to have determined its initial stock of human capital as its subsequent institutions.

Second, Dollar and Kraay (2003) also point out that measures of the quality of institutions

exhibit substantial decadal variations. Usual measures of the quality of institutions used in the

literature reflect outcomes of previous decisions made by rulers. Those measures are therefore

policy outcomes that may prove useful in explaining growth variations, a line of research

already followed by Dollar and Kraay (2003), Hausmann et al. (2004), and Giavazzi and

Tabellini (2004).

To discriminate between the two conflicting views of the nature of institutions, further

investigation into the timing of the relationship between changes in the quality of institutions

and changes in growth is warranted. Thus, if the deep view of institutions was correct, then

variations in indices of institutional quality would in fact reflect nothing substantial. Instead,

evidence that those measures are associated with economic outcomes would be hard to

reconcile with the presumed deepness of institutions.

Apart from its scientific appeal, the practical importance of that question cannot be

underestimated. It in fact hinges on the countries’ capacity to improve their economic

situation thanks to well-designed institutional reforms. If the timing of reforms was found

consistent with the deep view of institutions, poor countries would be predestined to remain

poor. Attempts at improving their institutional framework would consequently prove futile.

Moreover, if institutional improvements were found to produce their effects only in the long

run, then no government would ever have an incentive to enter into immediately costly

reforms to secure future benefits. The time horizon of rulers is limited and there is no reason

to expect them to implement policies that would produce their effects after they have left

office, especially if they also result in short term costs.

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Moreover, Fernandez and Drazen (1991) argue that uncertainty in the distribution of

the gains and losses of a welfare-enhancing reform may cause individuals to favour status

quo. Namely, individuals that would support the reform ex post, that is when its effects have

materialised, may oppose it ex ante lest they have to share its cost. The length of the period

between the implementation of reforms and the materialisation of their effects therefore

determines their political sustainability and the viability of the government that considers

those reforms.

In contrast with the importance of that question, we know of no systematic attempt to

answer it. One is accordingly at best left with interpreting the by-products of studies with a

different focus. The aim of this paper is therefore to fill that gap by providing an estimate of

the lag between institutional changes and their impact on economic performance, and more

precisely on the chief component of growth, i.e. productivity.

Apart from addressing an issue that had been so far neglected, the present paper’s

second specificity is to measure productivity thanks to efficiency frontiers. This method was

first applied to aggregate production functions by Moroney and Lovell (1997). Its main aim is

to rank countries by order of ascending relative distance to a common production frontier. The

relationship between aggregate efficiency and institutional quality was recently studied by

Adkins et al. (2002) and Méon and Weill (2005) but in a static context.

This paper’s third specificity is to use a measure of institutional risk that has not yet

received much attention and that was developed in Henisz (2000). This measure allows

graining the evolutions of political risk more finely than previous measures. In particular, it is

an objective measure of political risk, as opposed to subjective measures that are based on

survey data. That index measures the very characteristics of the institutional framework

whereas usual indices tend to reflect the outcomes of that framework as much as the

framework itself. By the same token, Henisz’s index moreover allows capturing the precise

timing of institutional changes, while other measures respond loosely to new institutional

innovations.

The rest of the paper is organised as follows. The next section sketches the theoretical

arguments that allow a first insight into the dynamic relationship between the quality of

institutions and aggregate efficiency. The third section presents our methodology, while the

fourth section describes our data set. The fifth section displays the results of our

computations, and the sixth section concludes.

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2. The timing of the effects of institutional reforms: conceptual framework

Any reform takes time to produce its effects, good or bad, and there is no reason to

believe that institutional reforms are an exception to the rule. Although more controversial

statements happen to be, the literature remains surprisingly silent on the issue of the time

profile of the consequences of institutional reforms.1

In what follows, we therefore try to grasp from the existing literature what can be said

about the speed of the effects of institutional change. However, an inescapable first step in

doing so is to recall the main arguments that underline the general relationship between

institutions and economic performance, without reference to the dynamic aspects of that

relationship.

2.1. Institutions and productivity The general relationship between institutions and efficiency must be traced back to the

impact of institutions on state policies and state behaviour. Namely, inefficiencies may arise

either as a direct consequence of the action of the government, or indirectly if implemented

policies distort the incentives faced by the private sector. First, the government may directly

impact efficiency through the provision of public goods. Thus, there is ground to believe that

an unstable government is less likely to provide efficient public infrastructure than a more

stable one. A first reason is that changes in the executive are usually associated with changes

in priorities, hence policy reversals. Therefore, an investment that was regarded as essential

by a previous administration may be considered useless by its successor, resulting in a credit

cut if not outright abandonment. In the process, a waste of public capital is likely, especially if

sunk costs are involved.

Whereas such sunk costs may be viewed as the price to pay for democracy, although

they are not ruled out in more authoritative regimes, there are other mechanisms that may

result in an inefficient provision of public capital. This point is made forcefully by Tanzi and

Davoodi (1997) who argue that corruption, though it tends to be associated with higher public

investment, results in a lower productivity of public infrastructure. Their line of reasoning

rests on the presumption that corruption produces an incentive for public officials, especially

at the higher levels of the administration, to distort public investment toward investments that

1 To be sure, the timing and speed of reforms, in the context of either transition or developing economies, have fed a heated debate. Nevertheless, that debate has focused on the sequence of reforms rather than on the timing of their effects. The interested reader may refer to Roland (2002) for a recent survey.

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more easily lend themselves to the extraction of bribes. This is the case of larger and more

complex projects, as opposed to efficient ones. The geographic repartition of public

investments may also be distorted, either to please the leaders’ constituency or to reap private

benefits from the evolution of the price of land.2 Those incentives cause the efficiency of new

public investment to be of secondary importance in the eyes of public officials.

Tanzi and Davoodi (1997) further argue that the efficiency of past investments may

also be affected because resources will be diverted from operation and maintenance

expenditures. They even suggest that the deliberate deterioration of existing infrastructures

may be used as a strategy to extort more bribes.

In line with their view, Tanzi and Davoodi (1997) document a negative relationship

between the level of corruption in a country and both the quality of its infrastructure and the

share of operation and maintenance expenditures in its budget. That result is corroborated by

Mauro (1998)’s observation that corrupt countries devote fewer resources to education.

Consequently, if a country’s institutional framework does not limit corruption of public

officials, aggregate efficiency may well be reduced.

Second, the institutional environment also affects efficiency through its impact on the

incentives faced by private agents. The key characteristic of the institutional framework here

lies in the definition of property rights, as emphasized by North (1990). For instance, taxation

reduces the share of their production that accrues to productive units. This not only provides

an incentive to accumulate fewer productive resources, which would not necessarily reduce

efficiency, but also to exploit those resources less intensively, which directly affects

productivity.

Moreover, taxation or expropriation do not have to materialize to produce their effects.

The mere risk of being faced with expropriation suffices to distort incentives. Agents will

accordingly devote resources to securing property rights instead of productive activities. The

securing of property rights may take the guise of the defence of one’s returns to one’s

property, or of seeking to acquire new property rights. This motive is central to Murphy et

al. (1991)’s analysis. They develop a model where talented people must choose between

entrepreneurship and, legal or illegal, rent seeking, according to the returns of both activities.

If property rights are poorly defined, due to a defective institutional environment, then rent

seeking, which is nonetheless a zero sum game, will be more lucrative to the talented,

resulting in an allocation of talent that reduces productivity. To support their theoretical

2 The incentive to cater for one’s constituency or base is not restricted to corrupt regimes. It may also appear useful to leaders of politically or socially unstable polities.

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argument, Murphy et al. (1991) report that growth is positively correlated with the initial

fraction of college enrolment in engineering while it is negatively associated with the initial

fraction of college enrolment in law, in a cross-section of countries. They interpret this

finding as meaning that countries with a higher initial share of would-be rent seekers grow

more slowly.

By the same token, Anderson and Marcouiller (1997) argue that doubtful property

rights may give agents an incentive to specialize in predation instead of production according

to their comparative advantage. Similar arguments can be drawn from the analysis of the

deadweight loss of rent seeking initiated by Krueger (1974). In that literature, rent seeking

causes the economy to lie below its productive frontier, which is exactly what efficiency

scores aim at measuring.

Furthermore, the impact of risk on private property can affect the efficiency of the

economy because of its effect on trade in goods, capital, and technology. Namely, FDI, and to

a lesser extent trade in goods, are considered to be crucial vectors of international transfers of

technology. They are also very sensitive to institutional risk. This impact of risk that has been

widely documented in the literature, for instance by Wei (2000), therefore implies that

institutional uncertainty may slow down the diffusion of technology, thereby affecting

efficiency. Anderson and Marcouiller (2002) document a similar effect of political risk on

trade in goods. Furthermore, Lambsdorff (1998) observed that institutional deficiencies could

also affect the geographic structure of trade, which opens the door to trade diversion.3

Institutions may consequently draw the economy further away from its production frontier by

preventing it from adequately exploiting its comparative advantage.

Finally, the main channel through which institutions hamper aggregate efficiency may

well be the quality of private investments, either domestic or from abroad. Faced with some

political hazard of taxation or regulatory change, an investor may either choose not to invest

at all, to invest elsewhere, to delay its investment, or to adopt a hedging strategy. As

mentioned above, rent seeking provides such a hedge. However, another possibility,

emphasized by Henisz (2000) is to change the nature of the investment. Namely, relatively

more fungible and short termed assets allow their owner to redirect his/her activity more

easily and swiftly, to avert a change of regulation. Typically, an investor aware of the

likelihood of a change of regulation will avoid investing in long term projects with large sunk 3 This impact of institutions on trade is important for the subsequent analysis because the impact of institutional factors on the volume of trade can be easily controlled for thanks to some measure of openness to trade, which will be done below. Controlling for the inefficient specialisation that results from trade diversion is bound to be much trickier.

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costs. Unfortunately, infrastructure investments and new technologies, that nonetheless

improve aggregate productivity the most, present both features, and are therefore the prime

victims of political risk. On the other hand, countries plagued with political uncertainty will

tend to over-invest in general-purpose standardized technologies, whose productivity is more

limited. The ratio of output to capital will therefore be lower, leading to lower aggregate

efficiency.

All the arguments listed so far all point to an inimical effect of institutional

deficiencies on aggregate efficiency. To be sure, counterarguments exist. Przeworski and

Limongi (1993) for instance recall that Ricardo viewed universal suffrage as threat to

property. Alternatively, some, like Huntington (1968), argue that corruption could enhance

efficiency in some institutional environments by “greasing the wheels” of the bureaucracy.

The sign of the effect of the quality of institutions on efficiency is as a consequence an

empirical matter, but the available evidence, that can for instance be found in Hall and

Jones (1999) or Olson et al. (2000), emphasizes the detrimental impact of defective

institutional frameworks on economic performance in general, and productivity in particular.

This is why the focus of this study is less to assess the sign of that impact as to determine its

speed, whose theoretical explanations are the object of the next subsection.

2.2. The speed of the effects Whereas the arguments mentioned above all suggest that institutions affect productivity,

and that their quality positively affects efficiency, they remain silent on the timing of the

relationship. In other words, although the literature has reached a consensus on the fact that

upgrading institutions should result in greater efficiency, the question of determining how fast

it does remains open. However, it may be possible to grasp a few insights from that literature

by pointing out the various lags that may occur between institutional reforms and their effects

on efficiency.

The common thread of our line of reasoning is that better institutions result in better

policies, that is more efficient public infrastructures and clearer and safer property rights,

which in turn improve the business environment and the incentives of private individuals. In

other words, politics affect policies that eventually affect economic outcomes. There are

therefore at least two sources of lags. First, it takes time for changes in political institutions to

affect policies. Second, it takes time for policies, good or bad, to affect investment and

production decisions. This scenario is backed by the recent finding by Giavazzi and

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Tabellini (2004) that political reforms, democratisation in that instance, tend to lead economic

policy reforms, here economic liberalizations, and that past democratisations increase the

probability of observing an episode of economic liberalizations.4

However, those considerations are of limited help in gauging the impact of political

reforms. To say the least, the fact that reforms produce their effects with a lag does not come

as a surprise. This is where empirical analysis becomes necessary. To our knowledge, no

study has so far focused precisely on the speed of the effects of institutional reforms, but one

may infer an order of magnitude from recent work on the timing of reforms or from studies of

the determinants of growth that have a dynamic dimension.

Thus, Hausmann et al. (2004) study the determinants of eighty episodes of growth

acceleration in sixty countries over 1950-1992. They identify growth accelerations by

focusing on episodes where the growth rate exceeds 3.5 percents, increases by at least 2

percentage points, and over a horizon of 8 years. They subsequently investigate the

determinants of the probability of such an episode. They then observe that a regime change is

significantly associated with a change of political regime in a window of five years. Therefore

one may infer that regime changes take five years to produce their first effect on growth.

With a different method and a slightly different aim, Giavazzi and Tabellini (2004)

obtain a timing of the effects of reforms of a similar order of magnitude, although their results

are sometimes difficult to interpret from the point of view of the present study. From that

point of view, their main finding is that four years is the minimum delay for the consequences

of democratisation to appear on growth, the protection of property rights, and corruption.

Furthermore, democratisation’s payoff is maximized when it follows rather than precedes a

process of trade liberalisation.5

Finally, whereas the two previous studies suggest that reforms may produce their

effects over a time span of two couples of years, Rodrik (1999)’s results underline that longer

horizons should not be ruled out. He studies the determinants of cross-country differences in

the difference in average growth rates between the periods 1960-1975 and 1975-1989, in a

cross-section of countries. His findings confirm the hypothesis that the quality of institutions

is a good predictor of growth rate differences.

Overall, the available evidence therefore suggests that if four years is the lower bound

of the time that institutions take to affect economic outcomes, twenty years or more should no

4 These authors however concede that they cannot rule out that feedback effects may work in both directions. 5 On the other hand, Giavazzi and Tabellini (2004) observe that the effect of democratisation tends to be reversed by foreign trade liberalization when it precedes it.

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be deemed a priori unrealistic. The common drawback of the existing studies however is that

they do not focus on the timing of the effects of reforms. Instead they simply observe an

effect over the time horizon on which they focus, which we can use to make informed

guesses. This does not mean that they isolate the precise duration of the lags that reforms need

to affect economic outcomes. The next section attempts to fill this shortcoming.

3. Measuring and explaining efficiency: Methodology

Our aim here is to measure macroeconomic performance to assess its link with the

quality of institutions. The stochastic frontier approach is applied to measure technical

efficiency at the aggregate level. Technical efficiency measures how close a country’s

production is to what a country’s optimal production would be for using the same bundle of

inputs. Adkins et al. (2002) and Méon and Weill (2005) adopted the same approach to

evaluate the relationship of macroeconomic performance with institutional variables. In

practice, a production frontier is estimated with the stochastic frontier approach, providing a

benchmark for each country regardless of its inputs. Then, the efficiency score is computed by

comparing the optimal output per worker with the effective output per worker.

There are several reasons why macroeconomic performance is better measured using

this approach than more usual performance indicators. First, it provides synthetic measures of

performance. Indeed, unlike basic productivity measures (e.g. per capita income), the

efficiency scores computed with the stochastic frontier approach allow to include several

input dimensions in the evaluation of performances. As a result, the output is not only

compared to the labour stock, but also to the stock of physical capital. Second, it computes

relative measures of performance. Namely, a production frontier is estimated, which allows

the comparison of each country to the best-practice countries. As a result, the efficiency score

assesses how close a country’s production is to what a country’s optimal production would be

for using the same bundle of inputs. It then directly provides a relative measure of

performance. Third, whereas total factor productivity measures assess performance by the

whole residual from the production frontier for each country, stochastic frontier approach

allows to disentangle the distance to the production frontier between an inefficiency term and

a random error, taking exogenous events into account.

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To study the link between efficiency and quality of political institutions, we use the one-

stage approach suggested by Battese and Coelli (1995), according to which the stochastic

frontier model includes a production frontier and also an equation in which inefficiencies are

specified as a function of explanatory variables. This approach is widely used in studies on

the determinants of technical efficiency at the aggregate level.

Therefore, our stochastic frontier model includes two equations. The first equation is the

specification of the production frontier. We assume a constant returns-to-scale Cobb-Douglas

production technology6, which we write as:

ln (Y/L)it = α0 + α1 ln (K/L)it + vit − uit (1)

where i = 1,…, 55 indexes countries, t = 1970,…, 1997 indexes years. (Y/L) and (K/L)

are respectively output per worker, and capital per worker.

vit is a random disturbance, reflecting luck or measurement errors. It is assumed to have

a normal distribution with zero mean and variance σv². uit is an inefficiency term, capturing

technical inefficiencies. It is a one-sided component with variance σu². As is common in the

literature, we assume a half-normal distribution for the inefficiency term.

The second equation is the specification of inefficiencies as:

uit =δ zit + Wit (2)

where uit is the inefficiency, zit is a p×1 vector of p explanatory variables, δ is a 1×p

vector of parameters to be estimated, Wit the random variable defined by the truncation of the

normal distribution with mean zero and variance σ ² (σ ² = σu² + σv²).

We use the Frontier software version 4.1 by Coelli (1996) to perform the maximum

likelihood estimation of the stochastic frontier model.

4. Data

We use two sets of data: macroeconomic data, and measures of the quality of

governance, which must be described in turn.

6 When Hall and Jones (1999) estimate aggregate productivity in a related cross-country study, they find that results obtained with a Cobb-Douglas production function are very similar to the results obtained when the production function is not restricted to that specification. We adopt constant returns-to-scale because, as Moroney and Lovell (1997, p.1086) put it, “at the economy-wide level, constant returns-to-scale is virtually compelling”.

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4.1. Macroeconomic data

Data on real output per worker as well as those on the size of the labor force are taken

from the World Bank Indicators database. The data on real capital per worker are those used

by Nehru and Dhareshwar (1994). They were complemented after 1990 by applying the

perpetual inventory method on real investment figures from World Bank. As those data are

provided in local currency, and our computations require comparisons of output and input

levels, we expressed them in US dollars by using the annual average exchange rate provided

by the Macro time series database of the World Bank.

We moreover introduce control variables, accounting for ethnic fractionalization,

latitude, and openness to trade, proxied by the ratio of trade to GDP. These data were also

obtained from the Macro time series database of the World Bank.

4.2. Institutional data

As our aim is to measure the lag between political reforms and their effects, we had to

restrict our analysis to data that vary over time. This is why we used the political constraints

indices constructed by Witold Henisz, that can be downloaded freely on his website, as the

workhorse of our analysis.

In addition to allowing year to year variations of the quality of institutions, these indices

exhibit several features that make them particularly relevant for the purpose of this study.

First, the political constraints indices are available in two versions. The first one, called

Polcon3 takes three points of veto power into account, whereas the second one, Polcon5,

considers five points of veto power. Both indices are measures of institutional risk and

increase when risk decreases. Using them alternatively will allow an easy robustness check.

We will however focus primarily on Polcon5 that is more comprehensive.

Second, and chiefly, the political constraint indices consist in objective measures of

political risk. Namely, unlike other measures of political risk they are not based on survey

data. Instead, they summarize information about the number of independent branches of the

government that may veto a change of regulation, and distribution of preferences across and

within those branches, according to a method described in Henisz (2000). The objective

nature of Henisz’s indices allows them to instantaneously reflect institutional changes,

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whereas subjective indices based on surveys only incorporate new information with a lag and

in a very imprecise manner. Accordingly those indices authorize a precise evaluation of the

timing of institutional changes, which is crucial for the present study.

To provide an additional robustness check, we will also use the International Country

risk Guide index, henceforth ICRG. This index, which increases with perceived political risk,

is based on subjective assessments of the political risk associated with a particular country. It

is therefore a broad but imprecise measure of the quality of institutions. Moreover, it may not

react immediately to changes in a country’s institutional environment. However, it has been

widely used in empirical studies, such as Knack and Keefer (1995), and therefore provides a

benchmark to weigh our results in light of those of the rest of the literature.

Table 1: Summary statistics on variables and efficiency scores

Variable Mean Standard Deviation

Minimum Maximum

Output per worker 9,697.02 12,029.21 308,27 41,255.39

Capital per worker 29,155.00 38,323.69 613,50 135,520.61

Polcon3 0.2606 0.1817 0 0.6433

Polcon5 0.3946 0.3022 0 0.8843

ICRG 62.18 16.26 32.95 92.23

Latitude 25.56 17.49 0.23 60.21

Trade 58.03 23.85 11.68 122.57

Ethnic Frac. 40.85 30.32 0 90

Overall, our data allow us to study the period 1970-1997. Our sample consists of 55

countries for the Polcon3 and Polcon5 indices, and 59 countries for the ICRG index. The

sample features both developed and developing countries. Descriptive statistics are displayed

in table 1 for the sample of 55 countries, with the exception of ICRG for the sample of 59

countries.

5. Results This section displays the results of our estimations. All tests were run in turn with the

three available measures of institutional risk. The results are all displayed in tables 2a to 4b.

However, to avoid repetitions, we focus on estimations involving Polcon5, that is the most

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precise index, in our comments. The rest of the estimations will be commented upon more

briefly, as a robustness check.

Within each table, the first three lines exhibit the coefficients of the estimated

production frontier, whereas the lower part of the table is devoted to the coefficients of the

equation in which inefficiency is explained. It must be stressed that it is inefficiency that is

explained in the second equation, and that a minus sign consequently indicates that an

increase in the explanatory variable implies a reduction in inefficiency, in other words a rise

in efficiency.

We display two parameters obtained in the estimation of efficiency scores. The estimate

of sigma is the sum of the variance of the inefficiency term and of the variance of the random

error. The estimate of gamma is the share of the variance of the inefficiency term in the total

variance of the residual, i.e. sigma. Namely, a greater value of gamma means higher

inefficiencies, and consequently the greater value of gamma observed in all estimations means

that the larger share of the residual relative to the production frontier is due to the inefficiency

term. Therefore, the higher gamma, the more legitimate the recourse to efficiency scores.

Before commenting upon the impact of governance on efficiency, it is noteworthy that

the coefficients of the production function are fairly stable from one estimation to another,

even when the measure of institutional quality changes. Moreover, they are of a magnitude

similar to those reported in the literature, as e.g. in Cavalcanti Ferreira et al. (2004).

Furthermore, control variables are in general intuitively signed and significant.

Accordingly, efficiency tends to increase with latitude and decrease with ethnic

fractionalisation, two measures that have been used as proxies for the quality of institutions in

the literature. This finding deserves attention as it suggests that, since our other measures of

institutions are also significant, there may be an impact of deep factors that is distinct from

that of the quality of institutions as measured by our variables. This lends support to Glaeser

et al. (2004)’s view of institutions, as a policy outcome rather than an iron-clad structural

characteristic of the economy. The only surprising coefficient is the one that affects openness,

which is positive. This is surprising, as it implies that openness should adversely affect

efficiency. We have no ready explanation for this stylised fact.

The results of the estimations with Polcon5 are displayed in tables 2a and 2b. We test

the influence of governance on efficiency by investigating the presence of several lags in the

institutional variable. We perform 13 estimations: from no lag to a 12 year-lag. For each

additional lag, we had to drop one year from the sample, by construction. For instance, when

we consider only Governance in t, the period under scrutiny is 1970-1997. However when we

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consider simultaneously Governance in t and in t-1, we are constrained to using period 1971-

1997.

15

Table 2a: Results with lagged Polcon5 (t to t-5)

Until t Until t-1 Until t-2 Until t-3 Until t-4 Until t-5 Intercept -0.443** (–26.11) -0.451** (–32.32) -0.462* (–29.14) -0.472** (–29.79) -0.477** (–37.94) -0.487** (–39.61) Log (K/L) 0.855** (184.78) 0.856** (214.35) 0.858** (187.98) 0.859** (186.22) 0.859** (235.63) 0.861** (287.98) Intercept -1.962** (-3.36) -1.817** (–18.43) -1.669** (–4.21) -1.592** (–13.59) -1.618** (–13.23) -1.508** (–7.26) Openness 0.021** (4.15) 0.020** (26.65) 0.019** (4.84) 0.019** (16.60) 0.019** (22.08) 0.019** (12.67) Latitude -0.020** (-4.01) -0.016** (–9.38) -0.013** (–3.91) -0.013** (–7.97) -0.013** (–4.76) -0.014** (–5.30) Ethnic Fraction. 0.009** (5.17) 0.009** (11.63) 0.008** (7.17) 0.007** (9.90) 0.007** (7.85) 0.006** (5.88) Governance in t -2.716** (-3.74) -0.190 (–1.12) 0.261 (1.39) 0.221 (1.17) 0.186 (0.65) 0.404 (0.23) Governance in t-1 -0.254** (–13.57) -0.413 (–1.57) 0.042 (0.16) 0.035 (0.12) 0.068 (0.24) Governance in t-2 -0.256** (–4.71) -1.007** (–3.50) -0.588 (–1.91) -0.570* (–1.73) Governance in t-3 -1.949* (–9.19) -0.681* (–1.92) -0.031 (–0.08) Governance in t-4 -1.755** (–8.25) -0.546 (–1.29)Governance in t-5 -1.756** (–8.22) Sigma 0.309 (5.16) 0.281 (21.71) 0.252 (6.75) 0.241 (18.87) 0.247 (12.84) 0.237 (7.60) Gamma 0.866 (32.98) 0.858 (67.19) 0.850 (35.95) 0.848 (72.31) 0.860 (60.40) 0.859 (39.17) Log-likelihood -65.551 -34.891 -7.659 14.091 36.688 58.841Number of iterations 24 24 24 29 119 102Number of observations 1540 1485 1430 1375 1320 1265Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

16

Table 2b: Results with lagged Polcon5 index (t-6 to t-12)

Until t-6 Until t-7 Until t-8 Until t-9 Until t-10 Until t-11 Until t-12 Intercept -0.498 (–38.96) -0.512** (33.72) -0.533** (–28.15) -0.544** (–30.02) -0.552** (–3.16) -0.575** (–29.39) -0.579** (–24.09) Log (K/L) 0.863** (244.07) 0.866** (209.18) 0.869** (228.61) 0.872** (192.63) 0.873** (183.33) 0.877** (174.09) 0.878** (162.78)Intercept -1.454** (–6.01) -1.387** (–6.14) -1.213** (–4.30) -1.120** (–3.80) -1.194** (–5.48) -1.035** (–3.73) -1.028** (–4.06)Openness 0.018** (13.78) 0.018** (11.88) 0.017** (5.43) 0.016** (4.91) 0.017** (11.61) 0.016** (5.01) 0.016** (5.72) Latitude -0.013** (–5.12) -0.012** (–3.28) -0.009** (–3.60) -0.010** (–3.64) -0.012** (–4.51) -0.010** (–3.96) -0.009** (–4.37)Ethnic Fraction. 0.007** (4.95) 0.006** (4.68) 0.005** (5.78) 0.004** (5.02) 0.004** (3.89) 0.004** (4.22) 0.003** (3.52) Governance in t 0.634 (0.34) -0.245 (–0.12) -0.049 (–0.24) -0.122 (–0.54) -0.169 (-0.75) -0.176 (-0.82) -0.141 (-0.67) Governance in t-1 -0.053 (–0.18) 0.053 (0.17) -0.018 (–0.05) 0.110 (0.30) 0.036 (0.11) -0.007 (-0.02) -0.024 (-0.07) Governance in t-2 -0.500 (–1.28) -0.601* (–1.73) -0.375 (–0.99) -0.764* (–1.79) -0.613 (-1.57) -0.434 (-0.90) -0.746 (-0.02) Governance in t-3 -0.051 (–0.10) 0.013 (0.03) -0.184 (–0.35) 0.387 (0.71) -0.072 (-0.14) -0.162 (-0.25) 0.124 (0.20) Governance in t-4 0.037 (0.07) 0.019 (0.04) 0.121 (0.20) -0.273 (–0.48) 0.155 (0.30) 0.058 (0.09) 0.052 (0.08) Governance in t-5 -0.714 (–1.46) -0.167 (–0.30) -0.303 (–0.59) -0.280 (–0.53) -0.465 (-0.87) -0.282 (-0.43) -0.650 (-0.92) Governance in t-6 -1.574** (–5.40) -0.881 (–1.03) -0.285 (–0.40) -0.149 (–0.21) -0.345 (-0.44) -0.552 (-0.75) -0.382 (-0.49) Governance in t-7 -1.165** (–2.23) -0.289 (–0.44) -0.070 (–0.11) 0.028 (0.04) -0.165 (-0.22) -0.061 (-0.08) Governance in t-8 -1.231** (–2.87) -0.578 (–0.72) 0.063 (0.09) 0.217 (0.31) -0.104 (-0.12) Governance in t-9 -0.825 (–1.62) -1.076* (-1.92) -0.484 (-0.66) -0.296 (-0.39) Governance in t-10 -0.219 (-0.55) -0.576 (-0.77) -0.146 (-0.18)Governance in t-11 0.053 (0.13) -0.246 (-0.30)Governance in t-12 0.068 (0.12) Sigma 0.229 (7.40) 0.219 (6.50) 0.182 (7.90) 0.175 (7.10) 0.194 (5.98) 0.165 (6.95) 0.168 (9.13) Gamma 0.860 (34.70) 0.858 (31.14) 0.837 (34.14) 0.837 (36.66) 0.863 (30.475) 0.843 (30.38) 0.859 (44.29) Log-likelihood 73.396 90.458 106.992 123.708 138.503 153.475 169.015Number of iterations 180 57 28 32 152 30 23Number of observations 1210 1155 1100 1045 990 935 880Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

17

Table 3a: Results with lagged Polcon3 index (t to t-5)

Until t Until t-1 Until t-2 Until t-3 Until t-4 Until t-5 Intercept -0.429** (-19.28) -0.443** (-20.56) -0.454** (-21.17) -0.466* (-24.88) -0.478** (-23.44) -0.491** (-25.97) Log (K/L) 0.856** (178.20) 0.858** (175.78) 0.860** (176.12) 0.862** (185.89) 0.864** (180.14) 0.866** (192.59) Intercept -1.869** (-2.06) -1.909** (-2.00) -1.927** (-1.99) -1.991** (-2.38) -2.027** (-1.99) -1.963* (-1.91) Openness 0.016** (2.75) 0.017** (2.61) 0.017** (2.56) 0.017** (3.11) 0.018** (2.48) 0.018** (2.43) Latitude -0.020** (-2.56) -0.019** (-2.54) -0.019** (-2.51) -0.019** (-2.40) -0.020** (-2.10) -0.020** (-2.22) Ethnic Fraction. 0.010** (2.97) 0.010** (2.76) 0.010** (2.81) 0.010** (3.35) 0.009** (2.99) 0.008** (2.79) Governance in t -2.972** (-2.36) -1.176 (-1.45) -0.830 (-0.99) -1.054 (-1.35) -1.200 (-1.16) -1.238 (-1.05) Governance in t-1 -2.121** (-2.11) -0.641 (-0.63) -0.131 (-0.11) -0.279 (-0.18) -0.413 (-0.35) Governance in t-2 -2.056** (-2.47) -0.758 (-0.82) -0.122 (-0.14) -0.318 (-0.27) Governance in t-3 -1.858* (-1.79) -0.522 (-0.50) -0.322 (0.23) Governance in t-4 -1.932** (-2.59) -0.393 (-0.30)Governance in t-5 -2.221** (-2.59) Sigma 0.354 (3.03) 0.355 (2.93) 0.359 (2.82) 0.372 (3.12) 0.384 (2.70) 0.379 (2.67) Gamma 0.888 (24.68) 0.892 (24.82) 0.896 (24.87) 0.904 (28.55) 0.911 (27.90) 0.914 (29.14) Log-likelihood -89.916 -63.345 -40.456 -19.181 3.908 26.521Number of iterations 35 39 38 38 43 44Number of observations 1540 1485 1430 1375 1320 1265Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

18

Table 3b: Results with lagged Polcon3 (t-6 to t-12)

Until t-6 Until t-7 Until t-8 Until t-9 Until t-10 Until t-11 Until t-12 Intercept -0.505**(-26.73) -0.522**(–32.18) -0.535**(–38.63) -0.551**(–33.49) -0.560**(–27.92) -0.573**(–30.87) -0.586**(–33.19) Log (K/L) 0.869**(184.24) 0.872**(190.14) 0.874**(218.28) 0.878**(217.87) 0.880**(182.62) 0.883**(187.33) 0.886** (209.60)Intercept -1.987* (-1.77) -1.996** (–2.18) -1.958** (–4.46) -1.849** (–2.12) -1.639* (–1.82) -154.80* (–1.85) -1.450 (–1.66) Openness 0.018** (2.24) 0.019** (3.09) 0.019** (8.88) 0.019** (3.01) 0.018** (2.47) 0.018** (2.48) 0.017** (2.47) Latitude -0.021** (-2.41) -0.021** (–4.70) -0.020** (–9.50) -0.019** (–3.53) -0.018** (–2.03) -0.016** (–2.42) -0.014** (–2.00) Ethnic Fraction. 0.008** (2.79) 0.007** (2.68) 0.006** (3.41) 0.005** (2.17) 0.004** (2.45) 0.003** (2.20) 0.002 (1.24) Governance in t -1.441 (-1.22) -1.903** (-2.38) -1.990** (-3.68) -1.946** (-2.75) -2.075* (-1.74) -2.003* (-1.70) -1.786 (-1.49)

Governance in t−1 -0.378 (-0.23) -0.228 (-0.40) -0.583 (-0.70) -0.580 (-0.63) -0.377 (-0.37) -0.712 (-0.45) -0.828 (-0.52)

Governance in t−2 -0.396 (-0.29) -0.422 (-0.47) -0.111 (-0.14) -0.952 (-1.09) -0.680 (-0.57) -0.559 (-0.37) -1.100 (-1.17)

Governance in t−3 0.214 (0.13) 0.189 (0.26) 0.289 (0.32) 0.902 (1.00) -0.039 (-0.03) 0.243 (0.16) 0.505 (0.36)

Governance in t−4 0.428 (0.33) 0.351 (0.43) 0.028 (0.03) 0.108 (0.13) 0.703 (0.38) 0.060 (0.03) 0.305 (0.18)

Governance in t−5 -1.397 (-0.80) -0.693 (-0.71) -0.933 (-1.43) -1.163 (-1.54) -1.271 (-1.06) -1.380 (-1.14) -1.851* (-1.76)

Governance in t−6 -1.511 (-1.06) -0.723 (-0.59) 0.313 (0.37) -0.084 (-0.09) -0.043 (-0.03) -0.019 (-0.01) -0.312 (-0.25)

Governance in t−7 -0.140** (-2.04) 0.205 (0.24) 1.344* (1.66) 0.935 (0.73) 1.084 (0.70) 1.136 (0.96)

Governance in t−8 -2.179** (-3.73) -1.301 (-1.44) -0.138 (-0.09) -0.512 (-0.43) -0.376 (-0.24)

Governance in t−9 -1.331* (-1.68) -1.120 (-0.82) 0.168 (0.12) -0.190 (-0.13)

Governance in t−10 -0.529 (-0.59) -0.104 (-0.58) 0.157 (0.17)

Governance in t−11 0.085 (0.07) -0.405 (-0.26)

Governance in t−12 0.265 (0.27)

Sigma 0.386 (2.43) 0.386 (2.96) 0.376 (5.78) 0.364 (2.93) 0.340 (2.51) 0.329 (2.55) 0.312 (2.32) Gamma 0.918 (27.54) 0.921 (32.04) 0.923 (63.90) 0.925 (33.50) 0.925 (32.73) 0.928 (32.54) 0.930 (31.12) Log-likelihood 42.372 60.96 80.00 99.39 116.70 135.98 154.41Number of iterations 44 35 48 37 45 45 49Number of observations 1210 1155 1100 1045 990 935 880Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

19

Table 4a: Results with lagged ICRG index (t to t-5) Until t Until t-1 Until t-2 Until t-3 Until t-4 Until t-5 Intercept -0.500** (–20.20) -0.504** (-20.20) -0.504** (–20.57) -0.507** (–19.76) -0.514** (–18.82) -0.525** (–16.94) Log (K/L) 0.879** (168.95) 0.881** (169.08) 0.882** (166.42) 0.883** (158.70) 0.885** (151.15) 0.886** (133.77) Intercept 0.149 (0.68) 0.108 (0.50) 0.104 (0.49) 0.076 (0.35) 0.036 (0.15) 0.048 (0.18) Openness 0.002** (2.01) 0.002** (1.97) 0.001* (1.86) 0.001 (1.63) 0.001 (1.51) 0.001 (1.59) Latitude −0.011** (–2.93) -0.009** (-2.82) -0.008** (–2.44) -0.008** (–2.14) -0.007** (–2.01) -0.006 (–1.62)

Ethnic Fraction. 0.006** (2.97) 0.006** (2.97) 0.005** (2.82) 0.005** (2.66) 0.005** (2.45) 0.005** (2.29) Governance in t -0.010** (–2.77) 0.001 (0.21) -0.004 (–0.57) -0.301E-3 (–0.04) -0.001 (–0.16) -0.009 (–1.04)

Governance in t−1 -0.010 (–1.47) 0.007 (0.60) -0.003 (–0.26) 0.002 (0.19) 0.011 (0.80)

Governance in t−2 -0.010 (–1.41) 0.011 (0.86) 0.158E-3 (0.012) 0.006 (0.37)

Governance in t−3 -0.014 (-1.52) 0.005 (0.34) -0.018 (–0.99)

Governance in t−4 -0.012 (–1.09) 0.029 (1.42)

Governance in t−5 -0.025* (–1.82)

Sigma 0.176 (3.78) 0.160 (3.95) 0.147 (4.02) 0.138 (3.76) 0.132 (3.63) 0.129 (3.08) Gamma 0.957 (80.58) 0.958 (81.94) 0.958 (79.38) 0.957 (70.55) 0.954 (63.27) 0.947 (49.12) Log-likelihood 149.52 194.372 134.761 123.895 110.028 96.516Number of iterations 22 23 24 24 24 25Number of observations 649 590 531 472 413 354Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

20

Table 4b: Results with lagged ICRG index (t-6 to t-10) Until t-6 Until t-7 Until t-8 Until t-9 Until t-10 Intercept -0.537** (–14.40) -0.548** (–12.85) -0.546** (–10.86) -0.538** (–8.82) -0.538** (–6.18) Log (K/L) 0.885** (116.38) 0.885** (99.71) 0.883** (80.96) 0.881** (64.40) 0.880** (44.49) Intercept 0.014 (0.04) 0.082 (0.23) 0.250 (0.61) 0.764 (1.47) 0.894 (1.10) Openness 0.002 (1.54) 0.002 (1.61) 0.002 (1.61) 0.002 (1.38) 0.002 (0.78) Latitude -0.005 (–1.29) -0.004 (–0.72) -0.004 (–0.68) -0.004 (–0.54) -0.006 (–0.63) Ethnic Fraction. 0.005** (1.99) 0.006* (1.85) 0.005 (1.58) 0.004 (1.23) 0.003 (0.71) Governance in t -0.006 (–0.58) -0.018 (–1.33) -0.018 (–1.24) -0.043* (–1.69) -0.030 (–0.83)

Governance in t−1 -0.002 (–0.13) 0.016 (0.90) 0.002 (0.09) 0.031 (1.20) 0.004 (0.09)

Governance in t−2 0.016 (0.97) -0.002 (–0.13) 0.022 (1.00) 0.004 (0.20) 0.031 (1.04)

Governance in t−3 -0.013 (–0.67) -0.001 (–0.06) -0.035 (–1.26) -0.012 (–0.45) -0.052 (–1.22)

Governance in t−4 0.008 (0.39) 0.020 (0.71) 0.045 (1.37) 0.004 (0.14) 0.060 (1.04)

Governance in t−5 0.015 (0.72) -0.020 (–0.67) -0.011 (–0.43) 0.014 (0.49) -0.068 (–1.24)

Governance in t−6 -0.026 (–1.59) 0.314 (1.11) -0.004 (–0.14) 0.024 (0.65) 0.078 (1.50)

Governance in t−7 -0.036* (–1.70) 0.018 (0.64) -0.055 (–1.26) -0.037 (–0.57)

Governance in t−8 -0.032 (–1.39) 0.060 (1.46) -0.004 (–0.07)

Governance in t−9 -0.043 (–1.58) 0.036 (0.80)

Governance in t−10 -0.033 (–1.08)

Sigma 0.132 (2.62) 0.132 (2.34) 0.127 (2.18) 0.099 (2.22) 0.071 (1.94) Gamma 0.937 (37.45) 0.924 (27.21) 0.912 (21.48) 0.885 (14.413) 0.835 (7.742) Log-likelihood 79.535 62.933 45.092 32.326 197.712Number of iterations 27 28 31 29 32Number of observations 295 236 177 118 59Absolute t-statistics are displayed in parentheses under the coefficient estimates. *, ** denote an estimate significantly different from 0 at the 10% or 5% level.

21

The results of tables 2a and 2b are striking. They show that whenever several lagged

values of Polcon5 are introduced in the estimation, it is always the oldest measure of the

quality of institutions that is significant, until the ninth lag is introduced. This result may

imply that the full effect of changes in political risk is observed after nine years.

However, when additional lags are introduced, another remarkable result appears.

Thus, the oldest value of Polcon5 remains significant but the two-year lagged value of that

index also becomes significant. This remains true until eleven years of lag are introduced. A

possible interpretation of this result is that the impact of changes in institutions produce their

effects in two stages. A first limited effect is felt after two years whereas the bulk of the

impact is produced after a nine to ten year period. Beyond, the effect of changes in institutions

becomes blurred. This may explain why Dollar and Kraay (2003) only observe a limited

impact of decadal changes in the quality of institutions on decadal changes in growth rates.

Their horizon may be either too short or too long.

The results obtained with the Polcon3 index are similar to those obtained with the

Polcon5 index. Here we observe that changes in institutions produce their effects after one

year and after a lag of ten years.

As regards the ICRG index, the results remain broadly consistent with our previous

results, although they are more mixed. For instance the significant lagged value of the index is

either the closest to or the furthest from the period where efficiency is measured. This

confirms the idea of a twofold effect of institutional reforms, the institutional index is not

always significant. This may however not be considered too disappointing, as the ICRG

measure of the quality of institutions is only available for a limited sample of countries, which

restrains our sample to fewer observations.

Another different between the estimations performed with the ICRG index and those

performed with the Polcon indices is that the perceived time lag seems smaller with the

former. This finding is quite consistent with each other, when one recalls the way those

indices are constructed. To be precise, the Polcon indices are objective measures that change

immediately after each institutional change. On the contrary, the ICRG index is a subjective

measure based on survey data. It therefore reflects institutional changes with a lag that

correspond to the time it takes survey respondents to start modifying their responses.

Consequently, the same reform producing the same effects at the same time will be recorded

more quickly in Polcon indices than in the ICRG index. The perceived speed of the effects

will therefore look slightly higher with the subjective measure than with the objective one.

This is exactly what our estimations suggest.

22

6. Concluding comments This paper has investigated the relationship between the quality of institutions and

aggregate productivity, thanks to an efficiency frontier analysis. Like in the rest of the

literature, it is found that better institutions result in greater efficiency. In complement to

existing work, the reported estimations provide estimates of the speed of that effect. It is thus

found that improvements in institutional quality result in a first significant rise in efficiency

after a lag of approximately two years. However, another significant and more sizeable effect

is observed around eight years. Those results are found to be robust to the use of three

different measures of institutional quality.

The present paper’s findings must only be viewed as a first investigation of the timing of

the economic consequences of institutional change. The analysis can still be extended in a

number of ways. In particular, the panel dimension of our data set allows for a test of the

causality of the relationship. This opens an avenue for further research.

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