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The purpose of this article is to analyze the paradox of atrophy of capital flows to African countries, while economic theories predict that such flows of investment should be large enough because of higher returns on capital in countries where its presence is relatively low in production factors. The GMM estimate of a system of two equations for a sample of 25 African countries over the period 2004-2014 gives the following results: the low investment flows are due to the production structures of African economies, which lack efficiency and attractiveness. Also, by improving the structural elements of the economy that render production ineffective, African countries can thus increase their potential to raise larger flows of foreign direct investment.

In line with the principle of declining marginal productivity, so dear to neoclassical theory, capital must migrate from the industrialized countries where it is abundant, to the developing countries where it is scarce and where its remuneration is consequently higher. In this context, low-income countries should not have particular difficulties in financing their development projects. But it is clear that developing countries in general and those in Africa in particular have difficulties in attracting foreign direct investment (FDI): this phenomenon is not new, it is the famous paradox of Lucas [

According to United Nations Conference on Trade and Development (UNCTAD), in 2012, when Africa reached a peak in attractiveness with more than $77 billion in FDI received, this amount accounted for only 4.8% of global flows. This disproportion, to the detriment of Africa in particular, is harmful for the financing of investments in this continent, especially since, as Kose et al. [

Although Lucas’ paradox has been the subject of an abundant literature to date, it has given rise to two main approaches. The first, supported by Lucas [

But despite this fertile literature, some aspects of this paradox remain unexplored. First of all, the works cited above very often consider the sharing of a common technology. In other words, countries face the same local conditions of production of goods and services. However, the shortcomings of productive structures, characteristic of African economies, can affect the ability of economies to effectively combine the factors of production, which can contribute to reducing the productivity of capital and consequently its profitability. It must be said that the poor quality of transport infrastructure can make it difficult or impossible to get products to the consumer. However, the development and regular maintenance of infrastructure in an economy can be a source of reduced transaction and production costs. In this case, the production system is getting closer to efficiency, which favors the attractiveness of this economy. Moreover, as Muchielli [

Therefore, the purpose of this article is to determine the role of the structures of African economies on the attractiveness of foreign direct investment. Specifically, this is on the one hand to determine whether total factor productivity improves the attractiveness of African economies vis-à-vis FDI and, secondly, to evaluate the influence of each component of structural characteristics on total factor productivity. This approach has the advantage of identifying the channel through which structural characteristics affect the attractiveness of economies relative to FDI flows. Indeed, local conditions can affect attractiveness by making productivity. It is then necessary to show, firstly, that attractiveness depends on the overall productivity of the factors and then to determine the factors that affect this productivity.

To achieve this dual objective, this article uses an econometric methodology based on a two-stage model. The first, based on Alfaro et al. [

For the rest of the article, Section 2 makes a statistical analysis of FDI flows. Section 3 presents a review of the literature on the determinants of FDI. Section 4 describes [

FDI flows have steadily increased between 1980 and 1990. In fact, from 208 billion dollars in 1990, the amount of FDI will peak at 1400 billion dollars in 2000. Subsequently, the amount of FDI will is heavily contracted following the bursting of the bubble on new technologies: $ 825 billion in 2001, and $ 566 billion in 2003.

Despite the recovery that followed the year 2003 (

The analysis of the distribution of FDI flows shows that developed economies are the preferred destination for FDI over time, even if their attractiveness has been somewhat eroded. More specifically,

uniform. Since the 1990s, developing countries in Asia have consistently received most FDI to developing economies, while Africa has been the least attractive region.

Africa’s share of world flows has been stable, although the reading of this progression can be analyzed in two phases. A first where flows go from close to 10% to less than 1% in 1980. Then the second phase where the influx of capital rises to stabilize at an average around 3% until 2013. The process of globalization that was developing at that time left Africa behind. The share of African economies in the flows of developing countries is also falling, it has remained below 10%.

While Asia grew from 22 billion inflows in 1990, to a peak of more than 430 billion US dollars in 2011, an increase of 408 billion, the African rose from 2 billion US dollars to 57 billions of dollars over the same period, an increase of 55 billion, which corresponds to only 13.48% of the increase of flows towards Asia. It must be said that this volume appears relatively small, compared to the performance of Asia and given the context of globalization.

This limited influx of investment in developing countries in general (

Two main explanatory approaches emerge to explain the “Lucas paradox”. The first approach concerns differences in the fundamentals of economies. More specifically, these are factors affecting total factor productivity, such as the heterogeneity of factors of production and the institutional structure [Lucas (1990) [

The second approach, advocated mainly by Reinhart and Rogoff (2004) [

With regard to human capital, Lucas (1990) [

of the factors and the externalities it generates eliminates this productivity gap. Indeed, human capital can make a significant contribution to the ability to adapt and implement new, more productive technologies. Similarly, in the context of the use of existing technologies, capital productivity may also depend on human capital in the broad sense, that is, not only literacy and other aspects of education, but also the industrial experience. However, for Darreau and Pigalle (2008) [

North (1991) [

Also, the results of authors such as Alfaro et al. (2003) [

But for Azemar et al. (2013) [

The main explanations for the imperfection approach in international capital markets are information asymmetry and sovereign risk [Reinhart (2004) [

The work of Ahearne et al. (2004) [

African countries do not have a well-developed financial system that can play an effective intermediary role between domestic and foreign private creditors on the one hand, and domestic private enterprises on the other, resulting in significant external financing premium. Mendoza et al. (2009) [

Odedokun (2003) [

The risk associated with the host country is also an important factor. Indeed, the risk is deemed to increase the cost of investments and uncertainty: even if the private return is expected to be high, a large amount of uncertainty surrounding the expropriation can be unacceptable. Whether for Gertler and Rogoff (1990) [

Odedokun (2003) [

Nevertheless, information frictions are only one explanation among others, not necessarily the most convincing. Because, the works evoked until then have not taken very much into account the difficulties relating to the appropriation of the new technologies and the process of production by the developing economies. These difficulties of ownership can lead to technologies being accessible to African economies being less productive than those of creditor countries. Indeed, according to Eichengreen (2003) [

At the same time, the productive structures of African economies are reputed to be failing. First, ICT development in Africa is still embryonic with an ICT Development Index (IDI) of 2.48, while Europe ranks first with 7.35. In addition, the poor quality of transport infrastructure is garish: only 29% of roads are asphalted compared to 44% for middle-income countries. In Central Africa, less than 15% of the road network is asphalted. Finally, new high-efficiency technologies sometimes involve large-scale production. In such cases, the small size of the economies, the persistence of trade barriers, as well as the infrastructural deficiencies mentioned above may limit the scope of the market and thus hinder efficient production.

In total, the structural factors of an economy can affect the efficiency of the productive system and reduce the profitability of investments. This will ultimately lead to a reorientation of the destination of investments. It is then necessary to build an evaluative approach of the role of the structures of the economies on their attractiveness vis-à-vis FDI.

The methodological approach adopted is presented in three stages: first, the presentation of the empirical model; then the justification of the choice of the estimation method and finally the presentation of the data.

The work on the explanation of the Lucas paradox is generally based on a Cobb-Douglas production function, assumed to have constant returns. In addition, the assumption of free movement of factors of production is retained. In this context, considering two countries producing the same good, the decrease in the marginal productivity of capital and the direction of corresponding investment flows can be approached from Equations (1) and (2).

Y t = A t F ( K t , L t ) = A t K t α L t 1 − α , F ′ ( . ) > 0 , F ″ ( . ) > 0 , F ( 0 ) = 0 (1)

where Y is production and A is the productivity parameter, K and L are respectively capital and labor and α, the share of capital in income.

This model predicts that capital should migrate freely from rich to poor countries until marginal returns are equal between these two groups of countries.

Therefore, for countries i and j,

A t f ′ ( k i t ) = r t = A t f ′ ( k j t ) (2)

Lucas (1990) [

This gap between theoretical profitability and actual profitability of capital could be explained by the fact that the structural features of economies hinder the adoption and/or efficient use of technology, which results in lower capital productivity and, in turn, fine, to a limited attractiveness. Prescott (1998) [

So for two countries i and j, the equalization of the yield is given by:

A i t f ′ ( k i t ) = r t = A j t f ′ ( k j t ) A v e c k i t ≠ k j t (3)

In order to achieve this dual objective, this article uses an econometric methodology based on a two-stage model. The first, based on the Alfaro et al (2003) [

This choice is justified by the fact that the intrinsic structural factors of an economy can constitute obstacles to FDI flows, passing through the TFP. In the case of African countries, characterized by small economies, the low ICT use and the shortcomings of the transport network, by reducing the efficiency of the productive combination, are likely to reduce the profitability of the investments compared to the yield found in continents that have more efficient structures. In the case of Africa, this implies, for a rational investor, a reorientation of the destination of his investments. This leads to the specification below:

T F P i t = γ i + δ 1 Q T I i t + δ 2 S I Z i t + δ 3 I c t i t + δ 4 O P E i t + δ 5 C a p i t + δ 6 I N V i t + ϵ i t (4)

L F D I i t = α i + β 1 T F P i t + δ 2 N R E S i t + β 3 H C i t + β 4 R P R i t + β 5 F D i t + β 6 G R O i t + β 7 I N F i t + ε i t (5)

The data used come from four main sources: the World Development Indicators (WDI), the Penn World

It should be emphasized that the use of a regression model with a temporal dimension requires the need to ensure that each series involved in the modeling has good properties (stationarity), in order to guarantee unbiased inference.

In panel data, there are two generations of unit root tests in the literature (Dickey-Fuller, 1979) [

From the results, most of the variables are stationary in level for both types of tests. Thus, even if there is interindividual dependence (Pesaran test, 2003) [

Since the theoretical model does not make any assumption about individual independence, we note the stationarity resulting from the Pesaran test (2003)

Variables | Signification | Measure |
---|---|---|

TFP | Total Factors Productivity | TFP level knowing that the United States is worth 1 (Penn World |

LFDI | Foreign Direct Investment | Logarithm of incoming FDI flows (Base WDI), transformed by the formula: FDI 2 = FDI + | min ( FDI ) | 2 + 1 (In order to make them strictly positive) |

QTI | Quality of Transport Infrastructures | Synthetic Index of the African Infrastructure Development Index (AIDI) |

SIZ | Market size | Average income per capita (WDI basis) |

ICT | ICT use | Percentage of people subscribed to mobile telephony (WDI base) |

OPE | Opening of the economy | Measured by the report (X + I)/GDP |

CAP | Capital structure | Measured by the ratio of physical capital stock to GDP |

NRES | Natural resources | Share of natural resources in GDP (WDI basis) |

HC | Human capital | Average number of years of study (Penn World Table) |

RPR | Respect of property rights | WGI synthetic index |

FD | Financial development | Credit volume as a percentage of GDP based on WDI |

GRO | Rate of growth | GDP growth rate (WDI basis) |

INF | Rate of inflation | Rate of inflation ( WDI basis) |

INV | Rate of investissement | Gross fixed capital formation as a percentage of GDP (WDI basis) |

Source: Authors.

[

The analysis of the correlation matrix in the Appendix shows that the variables TFP and LCAP are correlated and strongly correlated with the variable LFDI (r = 0.52 and r = 0.80). Similarly, the FD variable is correlated to FDI (r = 0.46). In addition, the variables HC, QTI, LCAP and SIZ are strongly correlated with the variable TFP. The variables QTI, SIZ and FD are strongly correlated with the variable HC. We also noted a strong correlation between SIZ and FD on the one hand, LCAP and FD on the other hand. It then appears a problem of multicolinearity between the explanatory variables that should be corrected from an appropriate estimation method.

Given the literature review and the descriptive analyzes above, the estimation method chosen is that of generalized moments (GMM) on a dynamic panel. Indeed, the literature suggests the possibility for past values of an endogenous variable to influence the dynamics of future values of the explained variable. Baltagi (2012) [

L F D I i t = δ F D I i t − 1 + X i t β + u i t , i = 1 , ⋯ , N ; t = 1 , ⋯ , T (6)

where δ is a constant, β the vector K × 1 of the coefficients corresponding to K explanatory variables; X i t the matrix whose columns represent the explanatory variables of the model; u i t = μ i + ε i t is the error term of a compound error model: μ i ~ I I D ( 0 , σ μ 2 ) is the country-specific error term i and ε i t ~ I I D ( 0 , σ ε 2 ) the term random error. The two terms of error are for each of them independent and identically distributed, and independent of each other.

Two sources of autocorrelation of errors are identified: autocorrelation due to the presence of delayed values of the dependent variable and the consequence of the presence of individual specific effects ( μ i ), characterizing the heterogeneity between countries. One can also have problem of homoskedasticity of the errors. Unlike other methods capable of solving the aforementioned problems, the GMM method is more suitable for small panels, whose temporal dimension is small compared to the number of individuals. It is an extension of the simple instrumental variable estimation method in which one can have delayed values of endogenous variables as instruments. There are two variants of estimation by this method:

- The estimation of the first difference model with instrumental variables of Arellano and Bond (1991) [

Δ L F D I i t = δ Δ L F D I i t − 1 + Δ X i t β + ε i t (7)

- The estimation of a system of two equations of Blundell and Bond (1998) [

{ Δ L F D I i t = δ Δ L F D I i t − 1 + Δ X i t β + Δ ε i t ( 8 ) L F D I i t = δ L F D I i t − 1 + X i t β + μ i + ε i t (9)

The GMM estimate in system amounts to making a GMM estimate for a single equation on a database consisting of an online concatenation of the differentiated values of the variables on each individual (above) and the initial database (in below). The instruments in the first difference equations are expressed in level and the equations in level are expressed in first difference.

As the methodological approach suggests, the presentation of the results will also be done in two stages: first, the results of the first stage of the model, which links the volume of FDI received to overall factor productivity; secondly, those of the second stage, relating to the impact of the structures of the economies on the total productivity of factors. However, the estimation of the model is done by distinguishing the case of all the countries in the sample from that of the Central and West Africa region. The results of the Maghreb and Southern and Eastern Africa regions are not presented because they do not have a sufficiently large number of observations to ensure the econometric validity of the estimates.

FDI | Africa | Central and West Africa |
---|---|---|

Constant | 23.202*** (0.000) | 22.423*** (0.000) |

Total Factors Productivity | 5.463** (0.031) | 3.551*** (0.000) |

Human Capital | −4.322*** (0.006) | −2.845** (0.012) |

D_ICT use | 0.591** (0.012) | 0.346 (0.020) |

Inflation rate | 0.192 (0.037) | 0.054* (0.184) |

Observations | 250 | 150 |

Number of instruments | 9 | 9 |

AR(1) | 0.102 | 0.219 |

AR(2) | 0.360 | 0.919 |

Sargan test | 2.03 | 8.12 |

Probability test of Sargan | 0.730 | 0.087 |

Hansen test | 2.94 | 5.34 |

Probability test of Hansen | 0.568 | 0.254 |

Source: the authors. Note: The p-values are indicated in parentheses; *, **, and *** indicate significance at the confidence level of 10%, 5%, and 1% respectively.

residues shows that country-specific residues and the term random error are normal.

The results of the estimations make it possible to retain 4 significant variables (TFP, HC, d_ICT, INF) capable of explaining the increase of FDI flows in Africa. Total factor productivity is the most favorable factor for increasing net FDI flows in Africa and the West and Central Africa sub region, which confirms the intuition of Prescott (1998) [

However, increasing the human capital of a unit reduces net inflows of FDI by an average of 4.3%. This result, although in contradiction with most of the work (Cleeve, 2008) [

Differences in results between the global sample and that of Central and West Africa appear mainly on the role of ICT and inflation. On the sample in general, the increase in the number of ICT users in a country causes FDI inflows to grow by almost 0.60%. In other words, the increase in the number of ICT users is accompanied by an increase in inward FDI flows compared to the previous year. This confirms Lee’s (2016) [

In contrast, the role of macroeconomic stability, approximated by the rate of inflation, is a determinant of the attractiveness of FDI in West and Central Africa, but not in Africa in general. This can be explained by the fact that this variable plays a marginal role in the other regions. The results of Mhlanga et al. (2010) [

To determine the factors that may explain changes in total factor productivity, we start with an estimate of the time averages. In addition to the correlation analysis, the inter-country estimate shows that, on average and over the study period, the change in total factor productivity is mainly due to the capital structure, the quality of transport infrastructure and the size of the economy. An increase in total factor productivity in Africa in general, all things being equal, stems from the growth of at least one of these variables. Thus, total factor productivity influences FDI inflows to Africa in a global way, through these factors (

For West and Central Africa, the estimation results show that: if we omit the likely effects of past values of total factor productivity and country-specific characteristics, the capital structure and the size of the market factors that may explain a possible change in total factor productivity. Indeed, these two causes of autocorrelation make it necessary to estimate the model for the countries of Central and West Africa from the least generalized squares. The result is that an increase in total factor productivity results from a growth in the capital structure or the size of the economy.

These two steps show that structural factors that are specific to an economy may be obstacles to FDI flows to Africa through TFP. Indeed, the small size of the African economies and the shortcomings of the transport network, by reducing the efficiency of the productive combination, are likely to reduce the effective profitability of the investments, compared to the yields observed on the continents with more efficient structures. Reducing the return on investment in Africa means that a rational investor will have to reorient the destination of his investments, which helps to explain Lucas’ paradox.

TFP | Africa | Central and West Africa |
---|---|---|

Constant | −0.372 (0.225) | −0.0161* (0.0895) |

Ln(Capital structure) | 0.061** (0.035) | 0.0243** (0.042) |

Quality of Transport Infrastructure | 0.059* (0.036) | |

Market size | 0.00005*** (0.004) | 0.0001*** (0.000) |

Openess | −3.239 (0.378) | 0.0683 (0.200) |

ICT use | −0.0291 (0.476) | |

Investment | 0.001 (0.874) | −0.001 (0.161) |

R^{2} | 0.7643 | |

Wald chi^{2} | 54.73 | |

Prob chi^{2} | 0.000 |

Source: the authors. Note: The p-values are indicated in parentheses; *, **, and *** indicate significance at the confidence level of 10%, 5%, and 1% respectively.

The purpose of this article was to determine the role of the structures of African economies on the attractiveness of foreign direct investment, i.e. specifically, on the one hand, to measure the impact of the total productivity of foreign direct investment factors on the attractiveness of African economies through FDI, and on the other hand, to assess the influence of each component of the structural characteristics on total factor productivity. The application of the generalized method of moments on a database of WDI, AIDI, PWT, WGI, made up of 25 African countries, gives some significant results.

It appears that both for Africa in general and for Central and West Africa, the Lucas paradox is explained by the fundamentals of economies that affect the ability of the economy to effectively combine its factors of production. In other words, the overall factor productivity is determined by the size of the economies, the capital structure and the quality of transport infrastructure for African economies in general. The first two factors are also significant for the economies of Central and West Africa.

Therefore, to improve the attractiveness of African countries for FDI, the recommendations go in the direction of building better transport and communication infrastructure, which would reduce transaction costs, because a reconciliation of companies with their customers and their suppliers. Moreover, the promotion of sub-regional integration and the development of cross-border transport infrastructures can prove to be a saving one, since it would lead to an increase in the size of the markets, and thus to an improvement of the attractiveness, investors being in constant search of outlets.

African economies need to invest more to improve their production structures to enhance the production efficiency and attract more FDI. The priority sectors identified are transport infrastructure and ICT use. In addition, it is good to encourage the development of free trade zones to increase the size of the markets accessible by multinational firms.

The authors declare no conflicts of interest regarding the publication of this paper.

Bidiasse, H., Mbanjo Kitimbi, M. and Ndong Ntah, M. (2019) The Role of Productive Structures in the Attractiveness of African Economies: A “Lucas Paradox” Approach. Modern Economy, 10, 1613-1632. https://doi.org/10.4236/me.2019.106106

Central and Western Africa | Southern and Eastern Africa | North Africa |
---|---|---|

Benin | Botswana | Egypt |

Burkina Faso | Kenya | Morocco |

Burundi | Mozambique | |

Central African Republic | Mauritius | |

Congo | Namibia | |

Ivory Coast | Tanzania | |

Cameroon | South Africa | |

Gabon | Zambia | |

Mauritania | ||

Niger | ||

Nigeria | ||

Rwanda | ||

Senegal | ||

Sierra Leone | ||

Togo |

Tests | Levin et Lin (2002) | Pesaran (2002) | ||||
---|---|---|---|---|---|---|

Variables | P-value | Type of specification | Order of integration | P-value | Type of specification | Order of integration |

TFP | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

LFDI | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

QTI | 0.00 | lag (2) | I (0) | 0.07 | lag (1) | I (1)* |

d_QTI | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

SIZ | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

ICT | 0.99 | trend | I (2)* | 1.00 | Trend | I (1)* |

d_ICT | 0.99 | trend | I (1)* | 0.00 | Trend | I (0) |

OPE | 1.00 | Trend | I (1)* | 0.00 | Trend | I (0) |

d_OPE | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

LCAP | 0.00 | lag (2) | I (1)* | 0.00 | lag (1) | I (0) |

d_LCAP | 0.00 | Trend | I (0) | 0.00 | lag (1) | I (0) |

NRES | 0.00 | lag (2) | I (0) | 0.01 | lag (1) | I (0) |

HC | 0.00 | trend et lag (3) | I (0) | 0.00 | lag (1) | I (0) |

RPR | 0.00 | Trend | I (0) | 0.00 | lag (1) | I (0) |

FD | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

GRO | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

INF | 0.00 | lag (2) | I (0) | 0.00 | lag (1) | I (0) |

INV | 0.00 | lag (2) | I (0) | 0.00 | Trend | I (0) |

LFDI | TFP | HC | D_ICT | OPE | D_OPE | INS | |
---|---|---|---|---|---|---|---|

LFDI | 1 | ||||||

TFP | 0.5245* | 1 | |||||

HC | 0.2911* | 0.6742* | 1 | ||||

D_ICT | 0.2667* | 0.2499* | 0.3548* | 1 | |||

OPE | 0.0014 | 0.2076* | 0.3847* | 0.1434* | 1 | ||

D_OPE | −0.0272 | −0.0224 | −0.0347 | −0.1215 | 0.1098 | 1 | |

INS | 0.0417 | 0.0202 | 0.2531* | 0.0891 | 0.2424* | 0.0157 | 1 |

LCAP | 0.8041* | 0.5215* | 0.4220* | 0.3820* | −0.0842 | −0.0776 | 0.0472 |

INV | 0.0921 | 0.0101 | 0.0342 | 0.0073 | 0.3895* | 0.1114 | 0.1992* |

QTI | 0.2715* | 0.5881* | 0.5361* | 0.1468* | 0.0184 | −0.0075 | 0.1104 |

SIZ | 0.2815* | 0.6933* | 0.8089* | 0.4157* | 0.3079* | 0.0031 | 0.3323* |

INF | 0.1264* | 0.1031 | 0.0356 | −0.0784 | −0.1078 | −0.0262 | −0.0243 |

NRES | 0.0281 | 0.1496* | −0.1179 | −0.0794 | 0.0667 | 0.0338 | −0.2947* |

DEV | 0.4627* | 0.3950* | 0.5298* | 0.3366* | 0.1315 | 0.0233 | 0.3764* |

LCAP | INV | QTI | SIZ | INF | NRES | DEV | |

LCAP | 1 | ||||||

INV | −0.0200 | 1 | |||||

QTI | 0.2609* | −0.0404 | 1 | ||||

SIZ | 0.3506* | 0.1232* | 0.4066* | 1 | |||

INF | 0.0580 | −0.0661 | 0.0715 | −0.0307 | 1 | ||

NRES | −0.1629* | 0.1548* | −0.2152* | 0.0144 | 0.1428* | 1 | |

DEV | 0.5476* | 0.0561 | 0.3718* | 0.5779* | −0.0820 | −0.2647* | 1 |

LFDI | TFP | HC | D_ICT | D_OPE | INS | LCAP | |
---|---|---|---|---|---|---|---|

LFDI | 1 | ||||||

TFP | 0.5392* | 1 | |||||

HC | 0.2603* | 0.6534* | 1 | ||||

D_ICT | 0.2187* | 0.2444* | 0.3590* | 1 | |||

D_OPE | −0.1090 | −0.0618 | 0.0098 | −0.1988* | 1 | ||

INS | 0.0570 | 0.0124 | 0.0969 | 0.0698 | 0.0537 | 1 | |

LCAP | 0.8274* | 0.5222* | 0.3033* | 0.3454* | −0.1587 | 0.1541* | 1 |

INV | 0.0228 | 0.1386 | 0.1748* | −0.0612 | 0.1599 | 0.2502* | −0.0609 |

QTI | −0.0980 | 0.0894 | 0.4101* | 0.0560 | 0.0738 | 0.2164* | −0.0210 |

SIZ | 0.2615* | 0.7734* | 0.8651* | 0.4238* | −0.0017 | 0.1084 | 0.3134* |

INF | 0.2125* | 0.1221 | 0.0302 | −0.0527 | −0.0208 | −0.0390 | 0.0696 |

NRES | 0.2152* | 0.4444* | 0.1571* | 0.0513 | 0.0089 | −0.2051* | −0.0259 |
---|---|---|---|---|---|---|---|

DEV | 0.1957* | 0.1078 | 0.2588* | 0.1260 | −0.0265 | 0.3313* | 0.2759* |

INV | QTI | SIZ | INF | NRES | DEV | ||

INV | 1 | ||||||

QTI | 0.2160* | 1 | |||||

SIZ | 0.2260* | 0.3011* | 1 | ||||

INF | −0.0252 | 0.0658 | 0.0359 | 1 | |||

NRES | 0.2655* | −0.2037* | 0.2954* | 0.1637* | 1 | ||

DEV | 0.3907* | 0.3615* | 0.0937 | −0.0976 | 0.0190 | 1 |

Chi 2 | Prob (Chi 2) | |
---|---|---|

Joint test for Normality on e | 4.13 | 0.1266 |

Joint test of Normality on u | 1.64 | 0.4397 |

Chi 2 | Prob (Chi 2) | |
---|---|---|

Joint test for Normality on e | 5.63 | 0.0598 |

Joint test of Normality on u | 0.07 | 0.9652 |