Why LDC growth rates differ: Measuring “unmeasurable” influences

Vol. 16. No. 11, pp. 1771-129-l. World Developmml, Printed in Great Britain.

53.00 + O.OO 0305-750x/88 0 1988 Pergamon Press plc

1988.

Why LDC Growth Rates Differ: Measuring “Unmeasurable” Influences EBERHARD

SCHOLING

and VINCENZ TIMMERMANN*

University of Hamburg,

West Germany

article is a cross-country statistical analysis of the relationships among social. Summary. -This political, and economic factors in determining growth and development in less developed countries. It uses a path model in which social. political, and economic factors are specified as latent variables. According to the empirical results, almost all the socio-economic variables considered show significant direct and/or indirect growth effects. It is remarkable that this is also true of such factors as climate. ethnic homogeneity, intensity of governmental control, infrastructure. availability of raw materials, all of which are normally not included in traditional econometric growth models.

1. INTRODUCTION It is our aim to contribute to a better understanding of the typical growth and development process in poor economies. In developing countries such a process involves more social changes and is more influenced by social and political factors than in industrialized countries. For this reason we have to consider not only economic variables and relations, but also social and political factors such as government regupolitical stability and socio-political lations, conditions.

Since the mid-1960s there have been a number of efforts to include non-economic factors and to model their effects in growth and development studies. These approaches are data-oriented. They get by without the relatively hard II priori assumptions normally made in economic modeling; the algorithms used are applied directly to the data often without explicitly formulating hypotheses. These approaches drew upon traditional multivariate analysis. The most famous representatives of this kind of approach are Irma Adelman and Cynthia T. Morris, who have used multivariate analysis methods in many investigations to trace the complex process of economic development back to its different social, political and economic deterrninants.l While the disadvantage of traditional econometric models lies in the fact that they largely ignore the influence of socio-political aspects, the disadvantage of the data-oriented approach lies in the fact that it utilizes too little a priori

information. However, there are so many plausible and empirically tested hypotheses about socio-economic determinants of growth and development, that to ignore this knouledge unnecessarily limits the validity of the research results. We believe that the information requirements of the following investigation are best met by the use of a path model with latent variables. Our study proceeds as follows: First the formal-theoretical specification of a path model with latent variables is made clear. Then the construction and empirical estimates of our socio-economic latent variable model are treated. Finally, there is a multiplier analysis for variables of strategic importance for growth and development.

2. THE THEORETICAL

MODEL

In empirical social research there are often phenomena that cannot be observed directly, for example “intelligence” in psychology, “socioeconomic status” in sociology or “political parti-

*The following study was sponsored by the Deutsche Forschungsgemeinschaft. The authors would like fo thank the members of the IDS-Seminar on International Economic Relations in Brighton (18 June, 1986), especially Hans Singer and

Adrian Wood for helpful comments. Thanks are also due to an anonymous referee for helpful suggestions.

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cipation” in political science. In economics latent variables have hardly been utilized even though there are a number of not directly measurable phenomena such as “economic development,” “human capital.” “technical progress.” “international competitiveness” or the “international trade cycle.“’ To specify a path model using latent variables, the latent variables must be represented by manifest variables: the manifest variables act as indicators. A path model with latent variables is defined by two linear equation systems. the “inner model” and the “outer model”:-’ - The inner model describes the core fheory: it contains the relationships between the theoretical (latent) variables. - The outer model describes the rrze~zsltrenrenf theory; it contains the relationships between the observable (manifest) variables and the latent variables they measure. The latent variables of the inner model are called “inner variables,” the manifest variables of the outer model are called “outer variables.” The structural relations of the inner model are given by: r_l=Ijn+I-E+b -;

-

(1)

where n = [vi, . , qhl)’ is the vector 01 endogerious inner variables, : = [qi. . . , &I’ is the vector of exogenous inner variables. 6 = [8,, . . 1 S,,,]’ is the vector of the residuals of the inner relations. Ij (&I x M) is a matrix of coefficient parameters (with zeros in the diagonal) for n, and r (kf X N) is a matrix of coefficient parameters for z. The outer model consists of a system of linear structural relations, in which each outer variable is assumed to be linear in .*its” inner variable: in symbols: x = II, 5 + v_r

(2)

y=H,?+v.v

(3)

where, x = [xi, . . . , x,,]’ is the vector of outer variables associated with the exogenous inner variables, y = Iv,. . . , yv]’ is the vector of outer variables associated with the endogenous inner variables, H,V(P x tV) and H,. (Q x M) are the corresponding regression matrices (“loading matrices”); yr and yY are residual vectors. The loading matrices H,V and H, are constructed so that the “main diagonals” are the loading vectors ?I’;’ (n = 1, . . , N) and JJ’;‘(~ = l,.... M) respectively; all other elements are zero:

J?I ‘IlO

(!

nr = [.

h

c! !,

&”

‘.

.(!

. . . ,-c,(”.I

. A latent variable model of the type described above can be estimated by different methods. The most appropriate one tor our purposes is the technique developed by Herman Wold. the partial least squares (PLS) method. because it gets by on relatively “soft” stochastic assumptions. Wold has described the PLS approach in a series of articles;J he first called it nonlinear partial least squares (NIPALS) and then partial least squares. In PLS the inner variables are estimated as exact linear combinations of “their” outer variables. Accordingly. the model specification (l)-(3) is complemented by the weight model equations:

where C& (N x P) and &2, (M x Q) are weight coefficient matrices. The PLS algorithm proceeds in two stages. Starting with the observed values of the outer variables. the first stage is an iterative procedure to obtain estimates of the parameter matrices for the case &+ and C2,,. and thus estimates values of the inner variables. Using the inner variables estimated in stage one, the second stage involves a non-iterative estimation of the outer model and inner model coefficients using ordinary least squares (OLS) for the coefficients in II,, H,. and TSLS for the coefficients in Ej, r. To determine the estimates of an inner variable in the first stage the investigator has the option of choosing between two modes which in the terminology of Wold are called Mode A and Mode B. Which mode to choose depends on the direction of the hypothetical relation between the inner variable and “its” outer variables. Mode A is chosen if the inner variable can be

WHY

LDC

GROWTH

conceived of as a factor which determines ..its” outer variables. An example for Mode A is the inner variable HEALTH which. interpreted as the health status of the population, determines the level of different health indicators. In Figure la this choice is illustrated by arrows directed outwards. Mode B is chosen, if the outer variables are assumed to influence “their” inner variable, that is, the inner variable is regarded as dependent on the outer variables. An example for Mode B is the inner variable INTERNATIONAL COMPETITIVENESS. In this case the outer variables represent certain influences on the international competitiveness of a nation; this is illustrated by a bundle of arrows directed inwards, as in Figure lb. The computations of PLS estimation are performed by iterations of explicit simple and multiple regressions. This can easily be accomplished within such computer packages as SPSS, SAS. and TROLL. Specialized PLS programs are also available.5 To illustrate the computation of PLS estimation an example is given in Appendix C.

3. THE SPECIFICATION

OF THE MODEL

The arrow scheme in Figure 2 shows the causal structure of the inner model. The inner variables are shown as rectangles. The number in the lower left-hand corner indicates the order of the inner variables. Inner variables are differentiated by

-

m

Outervariables

RATES

DIFFER

1273

their “degree of latency” which is of an order of zero. one or two. Inner variables with only one outer variable are called “inner variables of the those with two or more outer order zero.” variables are the “inner variables of the first order.” and inner variables without outer variables but associated with inner variables of the first order are called “inner variables of the second order.” The number in the lower righthand corner shows how many outer variables are associated with each inner variable. The letters (A, B) indicate the estimation mode. Taking the inner variable “per capita gross domestic product” into account, which is not included in the diagram for the sake of simplicity, the model presented here includes 19 inner variables and 118 associated outer variables.6 Crosssection data from 70 developing countries’ were used for the estimation of this model. Figure 2 illustrates in which way the traditional growth accountancy approach has been augmented here. In our model, the labor growth rate, human capital, political instability, real capital, and, international competitiveness have been introduced as direct determining factors for the growth rate of gross domestic product, whereby international competitiveness and the labor growth rate’ are formulated as exogenous inner variables. As Figure 2 indicates, along with the economic factors in a narrower sense, the specification includes socio-political factors normally considered as constants. To describe the structure of the model and the estimation results, we will proceed by first discussing those submodels directly connected to economic growth; i.e., the formation of human capital and of real capital and the international competitiveness.

u Figure

la.

Dependency strucmre of the measurement model HEALTH (Mode A).

Outervariables

Figure model

.

International competitiveness

1b. Dependency structure of the measurement INTERNATIONAL COMPETITIVENESS (Mode B).

4. HUMAN

CAPITAL

Our starting point for specifying the HUMAN CAPITAL submodel is the supposition that health and education are important determining factors for human capital, and that they both have a-positive influence on economic growth and development. In traditional econometric growth studies the growth effects of health and education are inferred from the estimated contribution of single health and education indicators. This procedure implicitly assumes three relationships: - The relationship between a single measurable indicator, e.g., life expectancy or the

Growth rate

-7

1l/

Health

(Altl6l

A!_-

-

_A

+ l/

Education lA)l16l

d

-

’L *

Oi

Growth rate ofGDP

I*+.1

Political

I

Ill

__, li

f\

Fmancial systenl (AN61

v

WW

to save

Real capital

l/

Ability

li

Absorptive capactry IAN3

-

(AH21

b

T---

l/

WHY

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GROWTH

literacy rate. and a not directly observable variable. i.e.. health or education: - the relationship between health and education. and the endowment with human capital; - the relationship between the endowment with human capital and economic growth. The approach chosen in this study ties human capital, as an inner variable of the second order, in with growth. This is an attempt to model explicitly the three relationships mentioned above. The diagram in Figure 3 shows the assumed causal structure of the HUMAN CAPITAL submodel. There is an indirect relation between HEALTH and EDUCATION, and the inner variable ECONOMIC GROWTH, which is “mediated” through HUMAN CAPITAL. The variable HUMAN CAPITAL is introduced as an inner variable of the second order which is not (directly) associated with any outer variables. The variables HEALTH and EDUCATION are inner variables of the first order: they are modeled in Mode A, relating them to their outer variables as “effectindicators.” The outer model (measurement model) HEALTH contains 16 health indicators from the year 1970 as outer variables which attempt to give a comprehensive picture of the health status of the population. The outer variables named in Table 1 can be divided into three groups. The variables of the first group (l-5) represent basic medical care, those of the second group (G-11) represent the quantity and quality of nutrition, the outer variables of the third group (12-16) show the mortality of the total population as well as of particularly exposed groups. The statistical relationships between the inner variable HEALTH and its 16 outer variables are shown by the loadings listed in Table 1. The numerical estimate for a loading ?c is equivalent to the simple correlation coefficient

ECONOMIC GROWTH

HEALTH

i/ Figure

3. Causal srrucrure of the HUMAN

submodel.

CAPITAL

RATES

Table

Outer

DIFFER

1274

1. Esrimorrd loudings (,Llode AJ of rlreimrr rcrriclblr HE.-\LTH in ifs ourer ruriablrs. 1970 ,i

variable

1. Population 7 ;:

4. i: 7. 8. 9. 10. II. 12. 13. 1-t. IS. 16.

per physician Population per dentist* Population per pharmacist+ Population per nursing person Population per hospital bed Daily per capita calorie supply Daily per capita protein supply Share of animal calories in calorie supply+ Share of animal proteins in protein supp1vt Daily- per capita fat supply Dailv Der catGta calcium supplv Infant ‘mortality rate (under. ‘1 ‘year) Child mortality rate (I-l years) Crude death rate Life expectancy at birth, females Life expectancy at birth. males

Sources: See Appendix * 1975. t1969171.

-0.91 -0.83 -0.68 -0.66 -0.68 0.64 0.45 0.75 0.78 0.6X 0.30 -0.X-l -0.90 -0.9 I 0.95 0.95

A

between an outer variable and the inner variable HEALTH. We see that all loadings have signs and relative sizes in accordance with expectations. Table 2, refers to the measurement model EDUCATION. This inner variable is indirectly observed by 16 outer variables which can be divided into four groups. The first group (l-5) covers school enrollment rates at different levels of the educational system. The variables of the second group (6-8) describe a qualitative aspect of education: they characterize three professions of particular importance for economic development. The third group (9-13) characterizes formal levels of education. Finally, the fourth group (14-16) describes certain results of the education system at a higher level. As shown in Table 2 all loadings have, as expected, a positive sign; their relative sizes also seem plausible. The especially high loadings of the variables 1, 2 and 13 underline the relatively large importance in developing nations of elementary education. As the diagram in Figure 3 shows the inner model specifies an influence of HEALTH on EDUCATION and a corresponding influence of EDUCATION on HEALTH. The causal relationship EDUCATION + HEALTH reflects the fact that a large share of the diseases in developing countries are “avoidable” diseases; i.e., infectious and parasitic diseases as well as complications at birth. There is

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Table

Outer I. 2. 3. 4. 5. 6. 7. 8. 9. 10.

2. Esrimared loadings

DEVELOPblENT

(.Wode A) of rhe inner variable ower varitrbks. 1970

EDUCE

TlOlL’ in ILY

variable

.i

0.93 0.91 0. SY 0.S’) 0.85 0.61 0.81 0.7-l 0.7s 0.81 0.75 0.71 0.92 0.66

Primary school enrollment ratio. female* Primary school enrollment ratio, male’ Secondary school enrollment ratio. female Secondary school enrollment ratio, male Post-secondary school enrollment ratio

Students of agriculture per thousand population Engineering students per thousand population

Students of medicine per thousand population Share of the population with a primary education Share of women with a secondary education 11. Share of men with a secondary education 12. Share of the population with a post-secondary education 13. Adult literacy ratet 14. Number of patents given to nationals IS. Production of books by number of titles per ten thousand population$ 16. Production of textbooks by number of titles per ten thousand population*

Sources: See Appendix * 1960. t 1970-80. $1969171.

= 0.29 EDUCATION (1.90) + 0.21 TRANSPORT (1.41)

0.6-l

A.

reason to suppose that better educated people can better avoid and treat these diseases; for example through personal hygiene or by changing their nutritional habits. Empirical studies which investigate the influence of education on health, on the micro as well as on the macro level, support this conjecture.’ Plausible arguments can also be found for the opposite causal relationship, HEALTH -+ EDUCATION. There is reason to assume that better health leads to better school attendance, more willingness to learn, and. as a result, to a higher level of education and training in the population. This relationship is also supported by empirical analyses on the micro”’ and on the macro” level. We expect the regression coefficients between the inner variables HEALTH, EDUCATION and HUMAN CAPITAL, i.e., for the causal relationships shown in Figure 3, to be positive. The HEALTH and EDUCATION equations of the inner model are: HEALTH

0.69

+ 0.23 URBANIZATION (2.17) & COMMUNICATION

- 0.22 CLllMATE

+ 0.11 GDP p.c.

(1.65)

(0.90) R‘ = 0.82

EDUCATION

= 0.10 HEALTH (3.55)

+ 0.12 URBANIZATION (1.11))

+ 0.25 TRANSPORT

s;

(1.77) COMMUNICATION

+ 0.11 COLONY (1.71)

+ 0.21 ETHNIC

HO~1OGENEITY

(2.69) + 0.05 GDP p.c. (0.29) R’ = 0.88

The variables GDP p.c. (GROSS DOMESTIC PRODUCT PER CAPITA), COLONY and ETHNIC HOMOGENEITY are inner variables of the order zero: that means that each is measured by only one outer variable.‘* The measurement models of the inner variables of the first order, TRANSPORT & COMMUNICATION, URBANIZATION and CLIMATE are shown in Tables 3. 4 and 5. The

WHY

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Table

3. Estimated loadings (Mode COMMUNIC~ TION

Outer

variable

RATES

1277

DIFFER

A) of the inner variable TRANSPORT in ifs outer variables. 1970

0.94 0.72 0.82 0.78 0.86 0.78 0.82 0.68 0.36 0.43 0.77 0.74 0.82 0.45 0.44

I. Telephone connections per thousand population 2. Letters sent and received per thousand population 3. 4. 5. 6. 7. 8. 9. 10. Il. 12. 13. 14. 15.

Domestic telegrams per thousand population Radio receivers per thousand population TV receivers per thousand population Number of daily newspapers per million population Daily newspaper circulation per thousand population Cinema attendance per capita Railway passenger-kilometers per capita Railway net ton-kilometers per capita Air transport passenger-kilometers per capita Air transport net ton-kilometers per capita Vehicles per thousand population Railway density (km/km’) Road density (km/km’)

Sources:

See Appendix

&

A.

Table 4. Estimared loadings (Mode A) of the inner variable URBANIZATION

its outer variables, Outer

3i

variable

1. Population density (per square kilometer of total area) 2. Population density (per square kilometer of agricultural 3. Urban population (in cities with over 20.000 inhabitants) percentage of total population 4. Urban population (in cities with over 100.000 inhabitants) percentage of total population 5. Urban population (in the largest city) as a percentage of population

Table 5. Estimated loadings (Mode A) of the inner vari-

able CLIMATE Outer

in its outer variables

variable

ic

1. Dummy variable that equals 1 if the country is located in the tropics 2. Amplitude of the median monthly temperatures Sources:

See Appendix

A.

in

1970

0.88 -0.30

land) as a

0.47 0.50 0.95

as a 0.98 total 0.81

negative loading (Table 5) in the measurement model CLIMATE - specified here as tropical climate - can be explained by the fact that the amplitude of the median monthly temperatures is relatively small in tropical zones. The coefficients in the HEALTH and EDUCATION equations are standardized regression coefficients (beta-coefficients). They are scaleinvariant, and so can be used as a measure of the relative importance of a predictor. As the distribution of the model variables is

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unspecified, we cannot derive significance tests and confidence intervals for the estimated coefficients. The r-values shown in brackets are solely descriptive, and are a heuristic statistic for assessing the coefficients.12 When assessing the inner model coefficients, we are confronted with the question of when a beta-coefficient is sufficiently large to signal an interpretable influence. So far, no generally accepted answer has been found. Noonan and Wold suggest that only beta-coefficients absolutely larger than 0.05 should be considered.” For the purpose of this study. we have chosen an absolute minimum value of 0.10. Looking at the regression coefficients of the variables HEALTH and EDUCATION in the inner model equations, it seems that the hypothesis about a feedback effect of these variables is supported. Both coefficients are positive and lie well above the critical value. The coefficients of the other predictors also meet the expectations of their signs and sizes. The positive influence of the variable URBANIZATION and TRANSPORT 8: COMMUNICATION on HEALTH and EDUCATION may imply that health and education are generally better in urban than in rural areas, and that better transport and communication conditions allow for better servicing of isolated regions with medical care and educational facilities. The negative coefficient of the variable CLIMATE (tropical climate) in the HEALTH equation can be explained by the well-known fact that many life-threatening infectious diseases, such as malaria, yellow fever, or cholera, occur mainly in the tropics because their pathogenic agents and carriers can only survive in warm climates. The positive coefficient of the variable ETHNIC HOMOGENEITY in the EDUCATION equation confirms the conjecture that countries with a greater ethnic and linguistic homogeneity are better able to carry out and carry through education programs. The negative influence of COLONY on EDUCATION - though a cautious interpretation is necessary because of the relatively small coefficient - seems to show that the colonial past of those developing countries that gained independence after 194.5 has been more of a hindrance than an aid in endeavors towards education policies of their own. The relatively low coefficients of GROSS DOMESTIC PRODUCT PER CAPITA in both equations indicate that the high direct influence of per capita income on health and education shown in other studies practically disappears

when as in our case socio-economic variables are added. Up to now. two structural relations of the HUMAN CAPITAL part of the model have been dealt with. The HUMAN CAPITAL equation describes the relationship between HEALTH and EDUCATION on the one hand, and HUMAN CAPITAL on the other: HUMAN

CAPITAL

= 0.44 EDUCATION (157) + 0.53 HEALTH (5.33) R’ = 0.89

Here, HUIMAN CAPITAL is specified as an inner variable of the second order: both EDUCATION and HEALTH are inner variables of the first order. The coefficients meet the expectations of an important positive impact of EDUCATION and HEALTH on HUMAN CAPITAL. In the studies mentioned at the beginning of this section usually only one of the two, either health or education, IS seen as an important influence. Our results show that especially in developing countries both HEALTH ~tld EDUCATION are of great importance for human capital formation. and so also for economic growth, as ive will show further on.

5. REAL

CAPITAL

The accumulation of real capital is already stressed by the classical economists as a particularly important prerequisite for the growth of production and income, and so for the economic development of a country. The formation of real capital in poor countries is limited by the inability to save, to mobilize savings, and to absorb them efficiently. Accordingly, we trace the formation of real capital back to three sources: the ability to save, the establishment of an efficient financial system and the absorptive capacity of an economy. These three “sources” of capital are modeled here as latent variables, i.e., as inner variables. The diagram in Figure 4 shows the assumed causal structure. Between the inner variables of the first order, ABILITY TO SAVE, FINANCIAL SYSTEM and ABSORPTIVE CAPACITY on the one hand, and ECONOMIC GROWTH on the other, an indirect relationship “mediated” through the inner variis supposed, able of the second order REAL CAPITAL.

WHY

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ECONOMIC GROWTH

Figure 4. Card

smrcmre of r/w REAL submodel.

CAPITAL

The inner variable ABILITY TO SAVE is indirectly measured by seven indicators listed in Table 6. The indicators (outer variables) are assumed to influence .-their” inner variable, that is, the variable ABILITY TO SAVE is regarded as dependent on the outer variables (Mode B). The numerical estimates of the weight coefficients (0) are standardized regression coefficients (beta-coefficients). For example, in Table 6 the first coefficient of 0.48 can be interpreted to mean that a 1 standard deviation change in the outer variable GDP p.c. will lead to a 0.48 standard deviation change in the inner variable ABILITY TO SAVE. We see that all coefficients have an absolute value larger than the minimum value of 0.10 suggested above. Important determining factors for the ability to save are the size and the distribution of private income. The ability to save increases, as expected, with increasing per capita income and decreases with increasing income equality. A high population growth rate means that the share of young people is relatively high. According to the life-cycle hypothesis, this is associated with a lower ability to save. This relation between the dependency rate and the ability to save is found in different cross-section studies of developing countries.” However. for the developing countries considered here, the positive Q-value of variable 3 does not confirm this relationship. The O-value of variable 4 indicates that there is a positive association between export growth and ability to save, most likely because the possibilities for self-financing tend to be better the more developed the exporting sector.

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1279

In developing countries the share of direct taxes in the total tax revenue is generally smaller than in industrialized countries. mainly for two reasons: First, the collection of direct taxes calls for a particularly efficient collection system: the taxable income in developing second, countries is so small that in a case of direct taxation very many citizens would not be approached at all; so that the relation between collection costs and tax revenue would be very unfavorable. In view of this, it can be argued that the higher the share of direct taxes in a developing country, the more efficient the tax system should be. and the better the chance that, at a given level of public expenditure, public savings and so the total ability to save can be increased. The weight coefficient of variable 5 points to such a positive relationship. Inflation is often seen as a special form of As long as the government is more “taxation.” free from money illusion than the private sector. inflation leads to a redistribution of real income from the private sector to the public sector. That may result in an increase in the ability to save of the economy concerned. Admittedly, this effect is only to be expected in case of a moderate inflation. As inflation rises more and more, the “stabilizing” effect of money illusion may decline. Thus, a maximum at some rate of inflation may be reached, beyond which the ability to save may be expected to fall. The signs of the tijvalues of variables 6 and 7 in Table 6 do indeed point to such a non-linear relationship. The second inner variable that determines REAL CAPITAL is FINANCIAL SYSTEM. Empirical studies have shown that financial systems of developing and industrialized countries differ greatly from one another.” The financial system of an economy is to be understood as the totality of financial institutions, financial instruments and financial markets. Financial institutions create financial instruments by offering claims against themselves (secondary assets) on the financial market and accepting claims against others (primary assets) in return. In this way they fulfill important functions for economic development.” The introduction of financial instruments allows for a division of labor between savers and investors; in this way the financial system makes intersectoral and interthe interpersonal, temporal transformation of investable funds possible. Banks and other financial institutions act as intermediaries between savers and investors. They give savers the possibility to place even the smallest amount at favorable terms as far as risk

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Table 6. Esrirnawd wrighr coefficients (.Clode 5) of [he ouw variables as~ociared w+rlr rhr inner variablr ABILITY TO SAL’E. 19704U Outer

I. 2. 3. 1. 5, 6. 7.

variable

Gross domestic product per capita (log)* Percentage of income received by lowest 40% of households+ Average annual growth rate of population Average annual growth rate of real exports Direct taxes as a percrnrage of total government rccciptst Average annual rate of inflation Average annual rate of inflation (squared)

Sources: See Appendix * 1970. tk’ears around 1975.

A

and return are concerned. Investors receive loans to finance often risky and long-term expenditures at acceptable conditions. Financial intermediaries save costs for the participants through their extensive knowledge of the market and the service they deliver. The saving in costs and the improvement in quality of financing and investment result in a larger investable surplus and a more efficient allocation.‘” What has been said so far leads to the hypothesis that the development of the financial system and the resulting improvement of financial intermediation promote the formation of real capital and the economic development of a country. The selection of outer variables to represent the inner variable FINANCIAL SYSTEM was based on important performance aspects of the financial organization. In Table 7 the outer variables considered are listed along with their loadings (I?) estimated in Mode A. They can be divided into two groups: (l)-(3) refer to the number of persons employed

in banking and the coverage of the country with banking services; (4)~(6) refer to the offered services. As the numerical estimates in Table 7 show, the loadings of the first group have the expected positive signs. The variables 4 and 5. which show the relation money (M3) to currency and the private credit supply, also have positive loadings. The negative loading in variable 6 may be surprising. It is difficult to say in advance tvhether a higher share of loans to the government represent a more or a less efficient financial system. Our empirical findings seem to show that in developing countries a higher share of loans to the public sector indicates a poorer performance of the financial system. Economic historians have found a similar result for some European countries in the early phase of their industrialization: for example, Austria, Italy and Spain.‘” Along with FINANCIAL SYSTEbf and ABILITY TO SAVE, the ABSORPTIVE CAPACITY is the third independent variable in the REAL CAPITAL equation (see Figure 4).

Estimated loadings (Mode A) of the inner voriable FINANCIAL SYSTEM in ifs outer variables, 1970430

Table

7.

Outer

variable

I. 2. 3. 4. 5. 6.

f

Bank employees per ten thousand population Bank branch offices per ten thousand population Rural bank branch offices per ten thousand rural population* Money (M3) to currency Domestic loans of the banking system per capita Loans to domestic public debtors as percentage of loans to domestic private debtors

Sources: * 1970.

0.4s -0.75 O.‘2 O.S6 0. IS 0.31 -0. I8

See Appendix

A.

0.68 0.67 0.35 0.70 0.53 -0.29

WHY

LDC GROWTH

Absorptive capacity sets a limit to the amount of efficient investment physically possible in an economy. There is extensive literature using this concept to analyze investment opportunities in developing countries.“’ In the 1950s and 1960s the absorptive capacity was primarily discussed in connection with public development aid. More recently the discussion has concentrated more on private capital movements into developing countries. Until now, no agreement has been reached over how to interpret the concept of **absorptive and how to measure its different capacity” aspects.*’ Some important aspects for example. human capital, the financial and physical infrastructure, the political and administrative system, and certain socio-cultural conditions, are already covered elsewhere in this model. Thus we are able to narrow down the range of the concept. Our concept is based on the idea that a higher capacity to absorb investment funds is associated with a greater “balance” among the production sectors of an economy. This is obvious for the extreme cases of an economy with a very onesided production structure on the one hand and of an economy with a highly differentiated structure on the other. We also assume that in the range between these two extremes the level of sectoral “diversification” points to the absorptive capacity of a country. The measurement model ABSORPTIVE CAPACITY is shown in Table 8. The outer variables are the value added shares of the eight listed production sectors. The signs and sizes of

the estimated loadings do not appear unreasonable: There is a positive relation between ABSORPTIVE CAPACITY and a structural change from agriculture to secondary and tertiary sectors. Table 8. Estimated loadirlgs (Mode A) of rhe innrr rnriable ABSORPTIVE CA PACITY in its outer vuriables (GDP-shares), 1975 Outer variable 1. 2. 3. 4. 5. 6. 7. 8.

A

Agriculture Mining Manufacturing Construction Energy Transport & communicarion Trade & banking Government

Sources:

See

Appendix

A.

-0.91 0.37 0.61 0.59 0.60 0.58 0.41 0.33

RATES

1281

DIFFER

This completes the description of the inner variables which have been specified as direct determinants for the real capital formation (see Figure 1). The variable ABILITY TO SAVE, estimated in Mode B. is explained through its outer variables and is exogenous in the inner model. The variables FINANCIAL SYSTEM and ABSORPTIVE CAPACITY, both estimated in Mode A, are endogenous in the inner model and are explained through the following structural equations: FINA.UCIAL= SYSTEXI

0.16 COLONY (1.31) + 0.20 TRANSPORT

& COMXltiFulCATtON

(1.15) - 0.2’) POLITICAL

INSTABILITY

(2.Q) - 0.31 GOVERNMENT

REGULATIONS

(!.%I) + 0.19 AVAILABILITY

OF RAW MAT?ZRIALS

(1.61)

ABSORPTIV’E CAPACITY

R’

= 0.63

R’

= 0.51

= I).60 GDP P.c. (6.18) t 0.45 AVAILABILITY

OF

(3.S) RAW

MATERIALS

+ 0.21 INTERNATIONAL (2.01) COMPETITIVENESS

The inner variables of the order zero COLONY and GROSS DOMESTIC PRODUCT PER CAPITA, as well as those of the first order URBANIZATION, CLIMATE and TRANSPORT 81 COMMUNICATION are already known. The measurement models of the variables POLITICAL INSTABILITY, GOVERNMENT REGULATIONS and the AVAILABILITY OF RAW MATERIALS are shown in Tables 9, 10 and 11.22 The inner variable POLITICAL INSTABILITY (Table 9) is measured by six event indicators which. following the suggestion of Hibbs,13 describe two aspects of political instability: “collective protest” (variables l-3) and “civil war” (variables 4-6).

1252

WORLD

DEVELOPMENT

Table 9. Esrimared loadings (Mode A) of the inner variable POLITICAL IIL’STA BlLlTY in its outer variables (number per million populalion). 1970-77

Table 10. Estimated weight coefficients (,Wode B) of rhe

Outer

Outer

1. 2. 3. 4. 5. 6.

variable

5

Riots Protest demonstrations Political strikes Deaths from political violence Armed attacks Assassinations

Sources:

See Appendix

0.43 0.03 0.43 0.70 0.50 0.45

A.

The loadings show that in developing countries the milder forms of political unrest (“collective protest”) are not associated as closely with political instability as the extremer forms (“civil war”). The inner variable GOVERNMENT REGULATIONS (Table 10) is intended to mirror the intensity of governmental interference in the economic and political sphere. As outer variables we chose an index called “intensity of governmental economic control” and an index ‘*democratization.” To measure the first index we interviewed the members of the committee for the comparison of economic systems and economic development in the “Verein fur Socialpolitik.” These experts were asked for their assessment of the intensity of governmental economic control and were asked to classify a large number of developing countries in three catagories as follows: “mainly free enterprise system,” system, ” “mainly centrally controlled “classification either not possible or not meaningful.“‘4 On the basis of these interviews, index 1 in Table 10 was constructed.” The democracy index is from Vanhanen.‘” This index is based on two empirical political variables: The share of the votes cast for the smaller parties in parliamentary and/or presidential elections (“Competition”) of electoral participation and the degree (“Participation”). The index of democratization is calculated by multiplying the percentages of competition and participation. In this way, for example, a oneparty regime with compulsory electoral participation receives a low index value. The signs and sizes of the estimated weights shown in Table 10, imply that the economic and the political index are both equally important in determining the intensity of government regulations. The next inner variable to be introduced is the AVAILABILITY OF RAW MATERIALS.

ourer variables associated wirh the inner variable GOVER,YMENT REGULATI0.W. 197&79 variable

4

1. Index of the intensity of governmental economic control 2. Index of democratization Sources:

See Appendix

0.62 -0.65

A.

In the literature there are different concepts of the availability of raw materials; this concerns the definition of raw materials as well as that of availability. A very wide definition of raw materials includes all natural resources that are suitable for consumption or production.” We define raw materials in a narrower sense, as mineral resources, including the fossil fuels: crude oil, natural gas and coal. A very wide definition of availability includes identified and not yet identified resources.‘8 We understand availability as the amount of raw materials that are actually at disposal and economically usable.” The outer variables associated with the inner variable AVAILABILITY OF RAW MATERIALS are the per capita stocks listed in Table 11. Classifying the 21 raw materials by use,)” four groups may be distinguished: Energy resources (l-4). iron and steel refiners (5ll), light and non-ferrous metals (12-17), and precious and special metals (lS-21). All indicators are associated, as expected, positively with the inner variable AVAILABILITY OF RAW MATERIALS. They are relatively well represented by their inner variable which explains 61% of their total variance.” The regression coefficients (beta-coefficients) of both the FINANCIAL SYSTEM equation and the ABSORPTIVE CAPACITY equation are, without exception, greater than 0.10. The coefficients of multiple correlation (R’) indicate a satisfactory approximation for a cross-section analysis of developing countries. The positive coefficients of the variables COLONY and TRANSPORT & COMMUNICATION in the FINANCIAL SYSTEM equation show that the colonial past, and as expected, the supply of transport and communication services has helped the formation of an efficient financial system. The financial system seems also to be promoted by a more plentiful AVAILABILITY OF RAW MATERIALS. One reason for this could

WHY LDC GROWTH Table 11. Estimated loadings (Mode A) of rhe inner variable AVAILABILIN OF RAW MATERMLS in irs outer variables (stocks per capira, logarithmic),

years around 1975 Outer variable 1. 2. 3. 4. 5.

Coal Brown coal Crude oil Uranium Iron 6. Chromium 7. Cobalt 8. Manganese 9. Molybdenum 10. Nickel 11. Tungsten 12. Copper 13. Lead 14. Zinc 15. Tin 16. Antimony 17. Lithium 18. Silver 19. Zircon 20. Tantalum 21. Ilmenite

f 0.91 0.88 0.54 0.82 0.82

0.87 0.62 0.91 0.41 0.68 0.79 0.73 O.S6 0.91 0.53 0.80 0.77 0.82 0.78 0.59 0.83

1’83

RATES DIFFER

higher the level of economic development is no matter how it is measured - the greater the sectoral balance in the production structure.33 Figure 5 summarizes the measurement models of the three inner variables ABILITY TO SAVE. FINANCIAL SYSTEM and ABSORPTIVE CAPACITY which are the direct determinants of the inner variable (second order) REAL CAPtTAL. The corresponding inner relation is estimated as: REAL CAPITAL

= 0.43 ABILITY TO SAVE (5.06) + 0.40 FINANCIAL (4.69)

SYSTEM

+ 0.33 ABSORPTIVE (3.86) CAPACITY R’ = 0.75

The results confirm the thesis formulated above: ABILITY TO SAVE, FINANCIAL SYSTEM and ABSORPTIVE CAPACITY, each have a strong positive influence on the accumulation of REAL CAPITAL.

Sources: See Appendix A. be that a larger existence of natural resources generally means that we are dealing with a more

internationally oriented economy, especially with regard to financial intermediation. Political instability can hardly be expected to promote the efficiency of the financial system. The negative sign of the variable POLITICAL INSTABILITY confirms this expectation. The negative coefficient of the variable GOVERNMENT REGULATIONS points to the fact that a high intensity of governmental interference in the economic and political sphere tends to disturb the performance of the financial system in developing countries. The ABSORPTIVE CAPACITY equation reveals the positive impacts of the AVAILABILITY OF RAW MATERIALS and INTERNATIONAL COMPETITIVENESS on the capacity of a structurally more balanced economy to allocate investment funds more efficiently. This capacity, is shown to be higher, the more plentiful and manifold the natural resources are in a country,32 and the more successful a country is on foreign markets. However, the most important impact on ABSORPTIVE CAPACITY, as modeled here, is that of per capita income. This confirms a relationship already observed in earlier cross-section studies: The

6. INTERNATIONAL

COMPETITIVENESS

The variable INTERNATIONAL COMPETITIVENESS models the ability of an economy to hold its own in world trade, using the advantages offered by the international division of labor and attaining the gains from trade. In the literature, the market shares achieved in certain goods and/or on certain markets as well as export and import quotas are used as a measure of the international competitiveness of a country.3’ Here, in contrast to that, international competitiveness is introduced as an ex ante concept. Achieved market shares and export quotas can be lost; for example, if certain goods for which one has had a competitive advantage for a long time are now produced and sold by firms in other countries, or simply if markets generally stagnate or shrink. For this reason, we must detect those factors which are essential if a nation is to adjust to ‘changing world market conditions. A whole range of causal factors discussed in the literature could be used to model international competitiveness in the sense aimed at here.” The most important of these are the given natural conditions of a country, its geographical

128-r

u’ORLD

Per

DE’..ELOPME?;T

capm income

lncomedistribution

Inflation

rate

Bank branch offices per inhabitant

per rural inhabitant Money

(M3) to currency

Public to private domestic loans

Manufacturing

Construction

Government

Figure5. Outern&els (meastuement models) of the inner variables ABILITY and ABSORPTIVE

CA PACtTY.

TO SAVE,

FINA NCfA L SYSTEM,

WHY

LDC

GROWTH

location and endowment with raw materials. the socio-political stability, the infrastructure in the sectors of health. education. energy. traffic and communication. and special economic factors. The given natural conditions, the institutional regulations and the infrastructure have all been covered in this model by specific inner variables. For this reason, the inner variable INTERNATIONAL COMPETITIVENESS will be made up of economic factors in a narrower sense. In the measurement model the construct INTERNATIONAL COMPETITIVENESS is determined by nine outer variables (.llode B), listed in Table 12. The variables 1-3 stand for functional disturbances in different areas of an economy that are identifiable on the commodity markets and the labor market. The negative signs of the weights of these variables may point to the fact that a high inflation rate. high wages (adjusted for productivity) and rapid changes in the internal terms of trade impair the ability of an economy to adapt to changing world market conditions. The variables 4-6 cover different aspects of innovative activity within an economy. Their effect on international competitiveness is, as expected, positive. The variables 7-9, representing the size of the domestic market and its attractiveness for domestic and foreign producers, also have a positive effect on international competitiveness. This seems to confirm the thesis, well known in foreign trade theory, that a large domestic market is particularly favorable for the development, and later the export, of new products and/or more efficient production methods. After the exposition of the inner variables that are the direct determinants of economic growth

RATES

DIFFER

1%

(see Figure 2). we shall now turn to the responding (inner) growth equation: GROWTH RATE OF GDP

= 0.12 GROWTH (1.21) + 0.36 HUMAN (3.52) + 0.41 REAL (4.X)

RATE

CAPITAL

+ 0.3-l iNTERNATIONAL (3.65) COhlPETITIC’ENESS - 0.11 POLITICAL (2.47)

A total of 70% of the variation in the GROWTH RATE of GDP in the period 197&80 is explained by the five independent variables, indicating a reasonably good approximation of the cross-section country data. The coefficients are all more than 0.10, the critical value suggested above. and have the expected signs. REAL CAPITAL proves to be the most important determinant. This strongly supports the notion. that even after the inclusion of social and political factors, real capital formation still is of central importance for the economic growth of developing countries. As regards the influence of labor. it should be noted that HUMAN CAPITAL - which may be interpreted as the qualitative component of labor - plays a much greater role than the quantitative increase of the GROWTH RATE of LABOR. very plausible for developing This seems

variable

0

1. Average annual rate of inflation 2. Changes in the internal terms of trade (industry/agriculture) 3. Wage rate adiusted for oroductivitv’ 4. Scientists and-engineers as a perce’ntage of the labor force; 5. Expenditure for research and development as a pecentage of the gross domestic productS 6. Annual number of patents per million people 7. Total population (log.) 8. Change in the ratio of labor productivities (industry/agriculture) 9. Foreign direct investments per capita

* 1970. tYears SYears

around around

Appendix 1970. 1975.

A

INSTABILITY R’ = 0.70

12. Esrimared weighr coefficients (Mode B) of the outer variables associared with the inner variable I.\‘TERNATIOiVAL COMPETITIVENESS. 197c-80

Sources: See

OF LABOR

CAPITAL

Table

Outer

cor-

-0.74 -0.36 -0.40 0.31 0.20 0.44 0.31 0.80 0.16

WORLD

1286

DEVELOPMENT

j = (! -

countries with a relatively unlimited supply of labor. Along with labor and capital in a wider sense, INTERNATIONAL COMPETITIVENESS is a kind of dispositive factor which seems to make important contributions to economic growth. As expected, POLITICAL INSTABILITY has a negative impact on economic growth: strikes, riots and political violence have often so disrupted developing countries that this negative relationship is immediately plausible.

13. Direct and total effects on HUMAN

Independent variables

Dependent variables

1. CLIMATE 2. ETHNIC HOMOGENEITY 3. COLONY 4. GOVERNMENT REGULATION 5. POLITICAL INSTABILITY 6. URBANIZATION 7. TRANSPORT & COMMUNICATION 8. RAW MATERIALS 9. INTERNATIONAL COMPETITIVENESS 10. GROWTH RATE OF LABOR 11. HEALTH 12. EDUCATION 13. HUMAN CAPITAL 14. ABILI-IY TO SAVE 15. FINANCIAL SYSTEM 16. ABSORPTIVE CAPACITY 17. REAL CAPITAL

direct

fi.

_

REAL

CAPITAL total

CAPITAL

and GROWTH

REAL

CAPITAL

direct

total

-0.26 0.27

-0.01

-0.08

0.40

0.07 -0.16 -0.12 0.05

0.33 0.07

0.12 0.25

RATE

GROu’TH direct

0.02

-0.2’

0.80 0.67 -

0.36 0.43 0.40 0.33 -

0.47 0.40 0.33 -

OF GDP RATE total -0.10 0.10 0.00 -0.12 -0.27 0.17 0.17 0.13

0.34 0.12 0.53 0.44 -

6

multiplier matrices (I - B)-’ 1 and (I contain the total effects. Since in PLS all inner variables are standardized to unit variance the elements of the two multiplier matrices can be interpreted like beta-coefficients, that is, they can be used to make statements about the relative importance of an independent variab!e. The elements of column n of the (I - B)-’ r matrix indicate the total effects of the exogenous variables rj,,, (m variable &, on all endogenous 1 M) of the inner model. Accordingly, t=he ‘elements of column m’ of the (I - B)-’ matrix indicate the total effects of the_ endogenous variable $,,‘, (resp. its residual S,,,‘.) on the endogenous variables r&_,*(m > m’). The multiplier anz$sis is based on 10 inner structural equations. According to these estimates, almost all the socio-economic variables considered here show significant direct and/or indirect growth effects. It is remarkable that this is also true of such factors as climate, ethnic homogeneity, the intensity of government control, the infrastructure and the availability of raw materials, all of which are normally ignored in traditional econometric models of economic development. As shown by the path diagram in Figure 2, the indirect growth effects are given by two sorts of paths: the paths over HUMAN CAPITAL and those over REAL CAPITAL.

CAPITAL,

HUMAN

gy

l3)-’

The system of inner equations introduced in Section 4, describes the structural form of the inner model. The coefficients of a structural equation provide information about the direct effects. In this section we concentrate on the coefficients of the reduced-form equations of the inner model, measuring the total (i.e., direct and indirect) effects, in order to investigate the impacts of a ceteris paribus change in single variables on important goal yariables. If the estimated coefficient matrix B is not singular then the system of estimated inner structural relations

Table

(1 -

The

7. MULTIPLIERS

can be solved for the endogenous variables The result is the reduced form:36

,,-’ f $ +

0.41

0.37 0.12 0.29 0.24 0.36 0.26 0.16 0.13 0.41

WHY

LDC

GROWTH

While the indirect growth effects of most variables considered here are mediated either by HUMAN CAPITAL or by REAL CAPITAL the indirect effects of the variable COLONY are mediated by both of them. The colonial past affects economic growth though not very strongly - negatively through the formation of human capital and positively through the formation of real capital. The direct (negative) growth effect of POLITICAL INSTABILITY has already been pointed out. The corresponding total effect shows that this hindrance of growth is intensified through indirect effects. Along with the strategic importance of human and real capital formation and their determining factors (HEALTH, EDUCATION. ABILITY

RATES

DIFFER

1257

TO SAVE, FINANCIAL SYSTEM, ABSORPTIVE CAPACITY) for development, the growth promoting role of the infrastructure has a particularly strong indirect effect on economic growth via the formation of human capital; but obviously there is also an effect via the formation of real capital. With the socio-economic path model developed here, we have attempted to utilize methods and concepts frequently proven successful in other social sciences. Our results show that it may also be advisable to utilize the latent variables approach in the economic development field for acquiring more insights into the growth process of underdeveloped countries.

NOTES 1.

See Adelman

and

Morris

(1965,

1965.

1973).

2. For first attempts of the authors to formulate the “international trade cycle” and “economic development” as latent variables see Scholing and Timmermann (1977) and Scholing (1982). 3. For a general overview estimation of latent variable and Everitt (1984). (1966,

of the construction and the models, see Bentler (1983)

1.

See Wold

1973. 1975.

1977.

5.

See, e.g..

6.

See Appendix

A for sources

7.

See Appendix

B for the countries

13. For the derivation of significance tests in the PLSModel and for the properties of PLS-Estimators. see Dijkstra (1951. 1983). 14. See Noonan and Wold (1980, p. 11). To test the stability, of the estimation results we performed several sensitivity tests by deleting part of the data. These tests suggest that the differences among the various submodel estimates are typically very small. so that the interpretation of the results would not change. For the results of the test runs, please contact the authors.

1982). IS.

Lohm(iller

(1981) and Scholing of outer

variables.

included.

8. The inclusion of labor as an endogenous inner variable in a demographic sub-model considerably increases the extent of the total model. For sociodemographic models see, e.g., Correa (1975); Wheeler (1980); and Moreland (1984). 9. For a survey of empirical studies about the influence of education on health, see Cochrane (1980) and the literature mentioned therein. 10. See International (1979). 11.

See Wheeler

Development

Research

See Leff (1969,

1980).

(1983).

Center

(1980).

12. The outer variables are: the per capita gross domestic product of the year 1970; a dummy variable that equals 1 if the developing country was still a colony in 1945; an index that measures ethnic and linguistic homogeneity (see Appendix A).

16. See Cameron

Goldsmith (1972).

17. For (1982).

a more

(1969);

detailed

Cameron

account

18. The arguments presented Gurley and Shaw (1960).

et al. (1967);

see Timmermann

here

are

based

19. See chapters by R. Cameron, R. Rudolph, Cohen, and G. Tortella in Cameron (1972). 20.

See, e.g.,

21. See. (1983).

e.g.,

Adler

(1965) and Guillaumont

Stevens

(1971)

or

Geis

and

22. The measurement model for the inner INTERNATIONAL COMPETITIVENESS treated in the following section. 23.

See Hibbs

24. Of answered.

on

J. S.

(1971). Hartig

variable will be

(1973).

99 committee members written to, We would like to thank all participants

68 for

138X

WORLD

DEVELOPMENT

their cooperation and alho for suggestions and criticism rendered on the questionnaire. 25. For more about the questtonnairr. the answers and the construction of the index. see Scholing and Timmermann (1987).

37. Seven of them. the equations for HEALTH. EDUCATION, HUhlAN CAPITAL. FINANCIAL SYSTEM. ABSOREIVE CAPACITI.. REAL CAPITAL. GROWTH RATE OF GDP. \\
26.

tiRBASIZXTION

See Vanhanen

(19S1).

p. 65.

= -0.20

CLlhlATE

(3.M) 27.

See Pethig

(1979.

2s.

See Bender

29.

See Schmidt

30.

See Gocht

p. 189) and Siebert

(1983). + 0.31 ETHNIC

(1976). and

HO!.lOGENEIT\I

(5.26) Kruszona

+ 0.54 GDP p.c

(1982)

(Y. 13) (197-l) R’ = 0 hh 31. The percentage is equivalent to the “Communality” which show how closely an inner variable is tied (linearly) to its outer variables. The communality is the arithmetic mean of the squared loadings.

TRANSPORT

Rr

= 0.12 RAW hlATERI.iLS

COhl,MUNICATION

(4.98) + 0 17 GDP p.‘. (5.66)

32. For more about the relationship between the availability of raw materials and absorptive capacity, see Mews (1985). 33. See Timmermann er (11. (1978. mann and Scholing (198-l). 34. See, e.g., Banerji (1974); 35.

1980) and Timmer-

Balassa (1965); Richardson and Jtirgenscn (1975).

See Schelbert-Syfrig

and Inderbitzin

(1971);

= 03

POLITICAL INSTABILITt

-

(lY82)

GOLERNMENI-

REGCLATIONS

(2.17) 0.3, ABlLIn

TO SAVE

(2.75) 36. For estimated

more about the reduced form of a PLSlatent variable model see Knepel (1981).

REFERENCES Adclman. I.. and C. T. Morris. “A bctor analysis of the interrelationship between social and political variables and per capita gross national product.” T/w Qunrrer1.v Jowrd of Erononrics. Vol. 79 (1965). Adelman. I.. and C. T. Morris, “An econometric model of socio-economic and political change in underdeveloped countries.” The Americun Economic Review. Vol. 58 (1968). Adelman, I., and C. T. Morris, Ecotloruic Growdz wd Social Eguiw in Derelouitlr Co~rries fstanford. CA: Standfdrd Universit): P‘;ess. 1972). Adler, J. H.. Absorptive Cqxzciy: The Coucepr o/~ci Irs Derernziruuzrs (Washington. DC: 1965). Balnssa. B., “Trade liberalisation and revealed comparative advantage.” i~ltrnciw~ler SCllOOl of Eco,wmic affd Social Srudies. Vol. 33 (lY65). Banerji. R.. “Export performance of 1~5s developed countries: A constant-market-share analysis.” We/r~cirrschafdiche~ Archive, Vol. I 10 (1974). Bender. F.. “Metall-RohstoffvorrHtr aus throretischer und uirtschaftlicher Sicht.” in Bsihefte der Zeitschrift Konju,lkrltr~olilik( Die Versorgrure tier Weirwirtschnfr mif Rohsroff&z. No. 73 (Berltn: 1976).

Bentler. P. kl.. “Simultaneous equation systems as moment structure models with an introduction to o/ Ecorzornetrics. latent variable models.” Jwrnnl

Vol.

22 (1983).

Bnnkirlg (ln(i Eco,zorwc DevelopLessorrs of Hisrory (New ‘t-ark: 1972). Cameron, R. et (11.. Bankitlg it1 rhe Early Srnges of I~ltlftsrriccli,-rrtiotr. A .Sr~iy ill Corrrprc~fir~e Hisroy (New York: 1967). Cameron,

ment.

Cochrane.

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Some

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ton, DC: World Bank. 19%)). Correa. H.. Populruion. Henlrh. h’urrrriort utld Developmem (Toronto: 1975). Dijkstra. T. K.. “Latent variables in linear stochastic models. Reflections on maximum likelthood and partial least squares methods.” PhD dissertation roningen: 1’ISl). Diikstra. T. K., “Some comments on maximum likelihood and partial least squares methods.” Jownnl of Ecotzomefrics. Vol. 22 (19S3). Everitt. B. S.. An Introtltrcfiou 10 Larer~r Variable Modelr (London: 19X-l).

iG

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LDC

GROWTH

and P. Hartig. Messkonzepte des Kupiral Bedarfs and der Absorprionsftihigkeir in Enuvicklungsliindern (Berlin: 1983). Gocht. W. (Ed.). lfandbuch der Mcurllmiirkre (Berlin: Geis. H. G..

197-l). Goldsmith.

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W..

Financiul Structure und Der,elop-

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1969). Guillaumont. P.. L’Absorpfion du Cupirnl (Paris: 1971). Gurley. J. G.. and E. S. Shaw. bfone,v in a Theory of Finnnce (Washington. DC: 1960). iclass Political Violence: A CrossHibbs. D. A.. lvutional Cuusul Annlysis (New York: 1973). International Development Research Center. ,\fol-

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DIFFER

I%9

suchung unter Verwendung der Hauptkomponentenund Transformationsanalyse,” Kyklos. Vol. 30

(1977).

Scholing. E.. and V. Timmermann, ‘-Ein soziodkonomisches Latent Variablen Model1 zur Analyse von Wachstum und Entwicklung in unterentwickelten Volkswirtschaften.” in V. Timmermann (Ed.),

W’uchsrum und Produkrionsstrukrur in Enrwicklungsysliindern (Bern: Peter Lang, 1987). Siebert. H.. iikonomische Theorien ncuiirlicher Ressourcen (Tubineen: 1983). W. J. ,- Capiial Absorptive Crrpcrciry in Stevens. Derelopins Counrries (Leiden: 197 1). Timmermann. V.. ..Zur volkswirtschaftlichen Bedeutung von Verhnderungen innerhalb der Finanzintermediation,” Die Ersre Osrerreichische SpnrCusse. IVirtschuffsanulvsen. No. 4 ( 1982). Timmermann. V. er rrl.. E&e Inpur-Ourpru Unrer-

nutrition (md Lnrer Developmem: A Summcrry Review (Ottawa: IDRC. 1979). Jiirgensen, H.. Die Weubewerbsfiihigkeir der Sch~reiz, Vorsrudie (Hamburg: 1975). Knepel. H., So:ioBk&ornische Indikatormodelle zur Arbeifsrnnrkfclnn/y.~e (Frankfurt a. M.: 1YSI). Leff. N. H.. “Dependency rates and savings rates.” The American Economic Re\,iew,. Vol. 59 ( 196’)).

suchung :unr S~rukrtrrw~trndel in Enrwicklung.slrindern (Hamburg: 1978). Timmermann. V. rr al.. Wirr.schafrliche Enrwieklung und Anderung der Protfllklio,rsstrukrur ( Hamburg:

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1930). Timmermann. V., and E. Scholing. “Forecasting production structures in developing countries.” in Deutsche Forschungsgemeinschaft (Ed.). Recenr

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Weinheim. 1984). Vanhanen. T., “The emergence of democracy: A comparative study of II9 states. 1950-79.” Unpublished manuscript quoted by G. T. Kurian. The IVew Book of World Rankings (New York: 19%). p. 65. Wheeler. D.. “Human resource development and economic growth in developing countries: A simultaneous model.” World Bonk Staff W’orking Paper No. JO7 (Washington. DC: World Bank, 1980). Wheeler. D.. “Basic needs fulfillment and economic growth: A simultaneous model.” lournul of Developmem Economics, Vol. 7 (1980). estimation by iterative least Wold. H.. “Nonlinear squares procedures,” in F. N. David (Ed.). Research Przpers in Su7risric.s (London: 1966). Weld. H., “Nonlinear iterative partial least squares (NIPALS) modelling: Some current development.” in P. R. Krishnaiah (Ed.), Muhivariare .Annlysis II (New York: 1973). Wold. H.. “Soft modelling by latent variables: The non-linear iterative partial least squares (NIPALS) approach.” in J. Gani (Ed.). Perspecri\*es in Probnbilily and Srurisrics (London: 1975). Wold, H.. “Open path models with latent variables: The NIPALS approach,” in H. Albach. E. Helmstadter and R. Henn (Eds.), Quanrifarire Wirrschufrsforschungc Fesrschriff fur W. Krelle (Tubingen: 1977). Wold, H., “Soft modelling. The basic design and some extensions.” in K. G. Joreskog and H. Wold (Eds.). Svsrems Under Direct Observation. Part II (Amsterdam: 1982).

der Wehbergbauproduktion und der Wehvorriite mineralischer Rohsrojfe (Hannover: Bundesanstalt fur Geowissenschaften und Rohstoffe. 1982). Scholing. E.. “Zur mehrdimensionalen Messung des wirtschaftlichen Entwicklungsstandes,” Kyklos. Vol. 35 (1982). Scholing. E.. ILVPLS-Programm, Programmdokumemation. Sozialokonomisches Seminar der Universitat Hamburg (Hamburg: 1983). Scholing, E., and V. Timmermann. Landerund Branchenkonjunkturverbund Empirische Unter-

1290

WORLD

APPENDIX

DEVELOPMENT

A: VARIABLES

AND

SOURCES

HEALTH Population Population

per physician. 1970 per dentist. 1975

World Bank. World Tub/es (19X-t). WHO, World Heultlt Smtistics (various years).

Population

per pharmacist.

Ibid.

1975

Population per nursing person, 1970 Population oer hosoital bed. 1970 Daily per capita calorie supply. 1970 Daily per capita protein supply, 1970 Share of animal calories in calorie supply, Share of animal proteins in protein supply, Daily per capita fat supply, 1969170 Daily per capita calcium supply, 1969/71 Infant mortality rate (under I year). 1970 Child mortality rate (l-4 years). 1970 Crude death rate, 1970 Life expectancy at birth (females), 1970 Life expectancy at birth (males), 1970

World

Bank.

World Tubles (198-I).

Ibid. ibid. Ibid. l969/71 1969/7l

FAO. Production years).

Yearbook

(various

Ibid. Ibid. Ibid. World

Bank.

World Tub/es (19X-1).

Bank,

World Tubles (198-I).

Ibid. ibid. ibid. Ibid.

EDUCATION Primary school enrollment ratio (female), 1960 Primary school enrollment ratio (male), 1960 Secondary school enrollment ratio (female), 1960 Secondary school enrollment ratio (male), 1960 Post-secondary school enrollment ratio, 1970

World

Ibid. Ibid. Ibid. UNESCO. Sutristicnl Yerrrbook (various years).

Students of agriculture per thousand population, 1970 Engineering students per thousand population, i970 Students of medicine per thousand population, 1970 Share of the population with a primary education, 1970

ibid. ibid. Ibid. UNESCO, Statistics of Edttcarional Attainmertt ctnd Illiteracy (various years).

Share of women with a secondary education, 1970 Share of men with a secondary education, 1970 Share of the population with a post-secondary education. 1970 Adult literacy rate, 1970430 Number of patents given to nationals. 1970 Production of books by number of titles per ten thousand population, 1969/71 Production of textbooks by number of titles per ten thousand population. 1969/71

TRANSPORT Telephone

AND

connections

per thousand

population,

1970

Railway net ton-kilometers per capita, 1970 Air transport passenger-kilometers per capita, 1970 Air transport net ton-kilometers per capita. 1970 Vehicles per thousand population, 1970 Railway density (km/km’), 1970 density

(km/km’).

World Bank. War/d Tub/es (1984). WIPO. lrtdttsrriul Property. 1971 UN. Statisticttl Yearbook (various years).

Ibid.

COMMUNICATION

Letters sent and received per thousand population, 1970 Domestic telegrams per thousand population, 1970 Radios per thousand population, 1970 Televisions per thousand population, 1970 Number of daily newspapers per million population. 1970 Daily newspaper circulation per thousand population, 1970 Cinema attendance per capita, 1970 Railway passenger-kilometers per capita, 1970

Road

Ibid. Ibid. Ibid.

1970

UN, Statistical Yearbook years).

(various

Ibid. Ibid. Ibid. Ibid. Ibid. Ibid. World Bank. World Tub/es (1984). UN. Statisricctl Yearbook (various years).

Ibid. Ibid. Ibid. Ibid. Bank. Transporrariott. Sector Working Paper ( 1972). Ibid. World

WHY

LDC GROWTH

RATES

DIFFER

1291

URBANIZATION

Population density (per km’ of total area). 1970 Population density (per km’ of agricultural land). 1970 Urban population (in cities with over 20,000 inhabitants) as a percentage of total population. 1970 Urban population (in cities with over 100.000 inhabitants) as a percentage of total population. 1970 Urban population (in the largest city) as a percentage of total population. 1970

World Bank. World

Tables

(1984).

Tables

(1984).

Ibid.

World Bank, World

World Bank. Urbanization Working Ibid.

Puper

Sector

( 1972).

CLIMATE

Dummy variable that equals I if the country is located in the tropics Amplitude of the median monthly temperature

G. Hesse. Die Entstehrmg lisierter Voikswirtschaften

(Tiibingen: ABILITY

1982).

TO SAVE

Gross domestic product per capita (logarithmic),

1970

World Bank, World

Percentage of income received by lowest 40% of households, years around 1975

Aveiage

indttstria-

annual growth rate of population.

Bank Atlas (1972). C. L. Taylor and D. A. Jodice. World Handbook of Political and Social Indicators, vol. I. 3rd edition (New

York: 1983). World Bank, World

197&80

Bank

Atlas

(1983).

Average annual Direct taxes as a 1975 Average annual Average annual FINANCIAL

growth rate of real exports, 1970-80 percentage of total government receipts, years around rate of inflation 1970-80 rate of inflation (squared).

1970-80

Ibid.

SYSTEM

Bank employees per ten thousand population.

ILO, Yearbook of Labor Statistics (1983). The Bankers Almanac and Year Book

1970-80

Bank branch offices per ten thousand population,

World Bank, World Tab/es (1984). UN, Statistical Yearbook (various years). World Bank, World Tables (1984).

1970-80

(various years). Rural bank branch offices per ten thousand rural population, Money (M3) to currency (percentage change). 1970-80

1970

Domestic loans of the banking system per capita (percentage change). 1970-80 Loans to domestic public debtors as a percentage of loans to domestic private debtors (percentage change), 1970-80 ABSORPTIVE

IMF, International Financial Statistics, Yearbook (various years). I MF. International Financial Statistics. Supplement on Money. Supplement Series No. 5 (various years). Ibid. Ibid.

CAPACITY

Agriculture (GDP-share). 1975 Mining (GDP-share), 1975 Manufacturing (GDP-share), 1975 Construction (GDP-share), 1975 Enernv (GDP-share). 1975 Tran&o;t & comm&ication (GDP-share), Trade and banking (GDP-share). 1975 Government (GDP-share), 1975 POLITICAL

Ibid.

World Bank. World

1975

Tables

(1984).

Ibid. Ibid. Ibid. Ibid. Ibid. Ibid. Ibid.

INSTABILITY

Riots (number per million population),

1970-77

C. Taylor and D. A. Jodice. World Handbook Indicators,

York: Protest demonstations (number per million population). 1970-77 Political strikes (number per million population), 1970-77

Ibid. Ibid.

of Political

and Social

Vol. 2. 3rd edition (New 1983).

WORLD

1292

DEVELOPMENT

Deaths from political violence (number per million population). Armed attacks (number per million population), 1970-77 Assassinations (number per million population), 1970-77

GO.VERNMENT

1970-77

Ibid. Ibid. Ibid.

REGULATIONS

Index

of the intensity

of governmental

Index

of democratization

economic

control.

l97G-79

E. Scholing und V. Timmermann (1987). G. T. Kurian. The New Book of

World Runkings (New York: AVAILABILITY Coal,

brown

Crude

OF RAW

Uranium, tungsten, tantalum.

MATERIALS

coal (stocks per capita,

oil (stocks

per capita,

logarithmic),

logarithmic),

years around

years around

1975

1975

iron, chromium, cobalt, manganese, molybdenum. nickel, copper, lead, zinc, tin, antimony, lithium, silver, zircon. ilmenite (stocks per capita. logarithmic), years around 1975

INTERNATIONAL

Total population (logarithmic), 1970 Change in the ratio of labor productivities (industry/agriculture), 19F80 Foretgn direct investments per capita, 1970-80

ETHNIC

W. Peters and H.

D. Schilling.

Coul

Resources (New York: 1978). F. Mayer. Pelro-Aflus (Braunschweig: 1982). H. Schmidt und M. Kruszona

(1982).

COMPETITIVENESS

Average annual rate of inflation, 197&80 Changes in the internal terms of trade (industry/agriculture), 1970-80 Wage rate adjusted for productivity, 1970 Scientists and engineers as a percentage of the labor force. years around 1970 Expenditure for research and development as a percentage of GDP. years around 1975 Annual number of patents per million population. 197&8O

Index

1984).

World

Bank,

World

Tables (1984).

Ibid. Ibid. UNESCO. Srarisrical Yearbook (various years).

Ibid. Industrial

WIPO. years).

Properly

(various

World Bank Arlas (1971). Bank, World Tables (198-I).

World

Balance of Payments Statisrics, Yearbook (various years).

IMF.

HOMOGENEITY

of ethnic

and linguistic

homogeneity.

T. Kurian. The New Book of World Rankings (New York: 1984).

1960-65

G.

COLONY Dummy

variable

that equals

GROSS DOMESTIC Gross

domestic

GROWTH Growth

in 1915

(logarithmic),

1970

World

Bank Atlas (1972).

World

Bank,

OF LABOR aged

15-65,

197&80

World

Tables (1981).

OF GDP

rate of the gross domestic

APPENDIX

I. 2. 3. 4. 5. 6. 7. 8.

RATE

was still a colony

PER CAPITA

per capita

rate of the population

GROWTH Growth

PRODUCT

product

RATE

1 if a country

B: COUNTRIES,

Trinidad and Tobago Hong Kong Venezuela Iraq Uruguay Argentina Iran South Africa

product,

LISTED

1970-80

IN DESCENDING PER CAPITA

ORDER (1970)

9. 10. Il. 12. 13. 14. 15. 16.

OF GROSS

Chile Mexico Brazil Algeria Panama Costa Rica Malaysia Korea, Republic

of

DOMESTIC

PRODUCT

WHY

-17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42 43.

LDC

GROWTH

44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70.

Syria Tunisia Paraguay Ecuador Colombia Dominican Republic Ivory Coast Guatemala Jamaica Nigeria Peru Congo, People’s Republic Morocco Papua New Guinea Nicaragua Philippines Thailand Cameroon El Salvador Rhodesia Egypt Bolivia Honduras Liberia Senegal Indonesia Kenya

APPENDIX

C: PLS ALGORITHM

RATES

Ghana Togo Sudan Madagascar Niger Benin Central African Pakistan Uganda Guinea Tanzania Sierra Leone Sri Lanka Haiti Somalia India Mozambique Zaire Upper Volta Burundi Rwanda Mali Burma Ethiopia Nepal Chad Afghanistan

FOR A SIMPLE PATH MODEL

The PLS iteration procedure can be illustrated with an example involving a simple path model with two exogenous inner variables (A, and AZ) estimated by Mode B and two endogenous inner variables (As and A.,) estimated by Mode A. The arrow scheme of this model is shown in Figure 6.

i;,

Republic

WITH FOUR INNER

Regress A3 on xi,

A2=

I293

DIFFER

VARIABLES

x2. x3jointly:

wni xi +

e,

Construct

Regress A.u = - A> + A, on x4, x3 jointly:

Construct

Figure

6.

variable model with four variables and two inner relations. Latent

Regress y,, y,. ys on AZ3 = A2 + A> separately: inner yi = w, AL3 + e,,

i=3.4,5.

Construct

Inifialize Normalize xi, x2. x2, x4. xs. y,, y2, yz. y, and ys to variance unity. Set

A, =x,

+x2+x2.

Az=Y,+Yz.

Loop

Al=x,+xs. A,=yz+yA+ys

Regressy, and y2 on A ,Zf = i\, - A2 + & separately: yi = wi,_,A 124+ e,,. Construct

i= 1.2.

WORLD

1293

DEVELOPMENT

Finish Regress A4 on AZ, A2 and AZ on A,. AZ for structural parameters of the inner model. Normalize A,, A;. A3 and A, to variance unity

If A, is not equal to A, (j = Otherwise,

I. 2.3.4)

Loop again.

the