impact of trade sanctions on south africa: a social accounting matrix approach

IMPACT OF TRADE SANCTIONS ON SOUTH AFRICA: A SOCIAL ACCOUNTING MATRIX APPROACH

HAIDER ALI KHAN*

This paper discusses a new approach for studying sanctions based on multipliers derived from the Social Accounting Matrix. I apply the methodology to the South African case and discuss output efiects and distributional impacts on factors and householb. The results show that the effects of trade sanctions on the South African economy are significant.

I. INTRODUCTION

Following decades of theoretical and empirical research, the effect of economic sanctions is still a controversial issue. Certainly, one reason is the many variations among types of sanctions and the target countries involved (Hufbauer and Schott, 1985).

However, even when different observers consider a specific type of sanction applied to a particular country, they may draw fundamentally opposite conclusions. The recent debate over imposing economic sanctions on South Africa is a pertinent example. On the one hand, proponents of sanctions- citing what they view as flagrant abuse of human rights of most people in South Africa-advocate imposing sanctions as a necessary corrective measure (Wright, 1987). On the other hand, opponents of sanctions stress what they view as excessive costs of sanctions in terms of losses of output, incomes, and jobs of the very people whose rights such sanctions are presumed to advance. The purpose of this paper is to assess the relative merits of these conflicting policy proposals by analyzing sanctions empirically in the South African context.

I use a somewhat novel framework based on a Social Accounting Matrix (SAM) for modeling and analyzing sanctions empirically. Students who apply the framework to the South African policy debate, might also find the framework of interest in studying sanctions in general.

Generally, at least one serious obstacle to settling the South African policy impasse, and perhaps other cases as well, is the lack of empirical analysis at a sufficiently disaggregated level, (W. J. Richardson’s, 1986, efforts are in the right direction. But, as argued later, his input-output approach is quite

*Graduate School of International Studies, University of Denver, Denver, Colo. I thank Oscar Plaza for valuable research assistance. Any errors are my own responsibility. An earlier version of this paper was presented at the 62nd Annual Western Economic Association International Con- ference, Vancouver, B.C., July 1987, in a session organized by William H. Kaempfer, Univer- sity of Colorado, Boulder.

130 Contemporary Policy Issues Vol. VI, October 1988

K H A N TRADE SANCTIONS ON SOUTH AFRICA 131

limited.) Only recently have some attempts been made to estimate the impacts of sanctions on the South African economy quantitatively.

Porter’s (1979) eight-sector linear programming model pioneered estimating the short-run impact of reducing import and capital flows to South Africa from abroad. The weakness of Porter’s model stems from both the aggregation and the unreliability of parameters derived mainly through guesswork. Stewart (1985) presents an econometric model also including political variables. One conclusion of his model is that curtailing exports will lead to increased black political activism. However, how economic variables affect political activism exogenous to the model is unclear.

The model that Kaempfer and Lowenberg (1986) propose endogenizes aspects of political reactions to investment sanctions within an interest group state framework. However, they note that the a priori results are “complex and ambiguous.” Furthermore, they attempt no empirical estimates of cru- cial parameters even for this highly aggregated model. Kaempfer and Lowen- berg note the relationship between investment sanctions and trade sanctions but fail to analyze the latter.

Recently, Richardson (1986) used the South African economy’s 1975 input-output matrix to analyze the impact of the sanctions on such factors as output, employment, and factor shares. This paper generalizes the open Leontief modeling approach to studying sanctions. Richardson’s method is calculating the Leontief inverse so as to analyze the impact of sanctions. However, the Leontief model leaves out the cumulative impact of household income distribution and consequent household expenditures on output, employment, and other variables that Richardson and others have analyzed. Contrary to such studies, the present approach is based on the SAM for South Africa. In the following section, the methodology used avoids both the high level of aggregation found in Porter’s exercise and the openness of the input- output approach. I derive a set of consistent general equilibrium multipliers and use them to analyze the impact of trade sanctions on output and factoral income distribution.

In section 11, I describe briefly the methodology based on the SAM framework. In section 111, I present some results from the SAM-based multiplier analysis of the South African economy. Summary and conclusions follow in section IV.

II. A NEW METHOD FOR ANALYZING TRADE SANCTIONS

One can trace the origins of social accounting back as far as 1681 to the work of Gregory King. However, the modern SAM emerged quite recently. During the past 15 years, use of this new economy-wide framework both for gathering data and as a conceptual tool has been refined steadily. The SAM was developed originally by researchers at Cambridge and was refined further through the works of Stone, Pyatt, Round, Thorbecke, and myself. Stone

132 CONTEMPORARY POLICY ISSUES

(1986) provides a useful summary of the recent state of the art. The following discussion is written in as non-technical a manner as possible so as to make it understandable across disciplines.

Perhaps the best way to understand a SAM is visualizing it as a snapshot of the entire economy at a certain point in time. As such, the SAM summarizes the interrelations among the productive activities, value added going to the factors of production (land, labor, capital, etc.), household income, saving, government expenditures and revenues, and balance of payments. All are organized in a matrix format as a single-entry bookkeeping system in which the columns and rows designate expenditures and receipts, respectively. Table 1 shows the basic format of the SAM.

The advantages of using a data system in a SAM framework are con- siderable. First, the amount of data gathered in a large SAM is enormous. A 50 x 50 SAM-about average-gives 2,500 data points summarizing the economic activities and their outcomes for the given time period. More importantly, the constraint expendituredreceipts, which translate into column- sums/row-sums, means that the SAM data set is consistent. An appropriate SAM thus ensures consistency during later empirical analysis.

To conduct such an analysis, the SAM accounts are divided into endogenous and exogenous categories. Typically, the production activities, households, and factors can be considered endogenous and everything else exogenous. (The former are considered endogenous in that they are to be determined by economic activities in the exogenous accounts.) One can com- pute accounting and fixed-price multipliers so that the effects of any change in an exogenous account on an endogenous one translate into a multiplier of the initial change. Thus, an increase in government expenditures may lead to an actual change in output of three times that amount.' (See figure 1.)

The interesting aspect of the SAM is that incorporating households in terms of socio-economic characteristics such as race, ethnicity, region, gender, occupation, and income allows researchers to observe the effects of any exogenous change on these different categories of households. This is illustrated in a study of Indonesia in which the SAM consists of 44 different categories of households divided by gender, occupation, region, ownership of assets, and income (Khan and Thorbecke, 1988).

Fortunately, the Central Economic Advisory Service (CEAS) (Sentrale Ekonomiese Adviesdiens-SEA) of the Office of the President of the Republic of South Africa just recently completed the first SAM ever prepared for South Africa. The data base corresponds to the year 1978-recent enough to yield meaningful answers to policy questions. The CEAS has published both an income SAM and a regional SAM.

1. Methods have been developed for decomposing these multipliers and tracing the network of influences in the economy through structural path analysis. See Pyatt and Round (1979) and Khan and Thorbecke (1988).

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FIGURE 1 Simplified Interrelationship Among Principal S A M Accounts

(Production Activities, Factors, and Institutions)*

Production Activities T6.3 \ “‘r,. T1.6

Institutions Factors, Factoral < Income Including

Distribution T2.2

Distribution Household Income T2.1

*T represents the corresponding matrix in the simplified S A M appearing in table 1. Thus, for example, Ti.6 refers to the matrix appearing at the intersection of row 1 (account 1) (i.e., “factors”) and column 6 (account 6) (i.e., “production activities”).

T1.6:

Tz.1: T2.2: Transfers between households. T6.z: Consumption.

Source: Khan and Thorbecke, Macroeconomic Effect and Difusion of Alternative Tech- nologies Within a Social Accounting Matrix Framework: The Case of Indonesia, Gower, Lon- don, 1988.

Income flow to the factors. Value added going to the household.

T6.6: Inputs to prodUCtiOII.

Essentially, the income SAM describes the circular process in which production activities generate household incomes and household expenditures, which generate the demand for output. Other related variables such as government spending, imports and exports, and transfers are linked to this core process wherever necessary.

The 1978 income SAM for South Africa contains 24 separate productive activities. Clearly, enough detail exists here on the production side. The value added generated in these productive activities is distributed among land- owners, capitalists, and 40 occupation-by-race groupings. The realism may

K H A N TRADE SANCTIONS ON SOUTH AFRICA 135

be unintended, but the classification captures the nature of the apartheid regime by indicating the determinations of many occupational categories by racial factors. Finally, seven groups of households exist within each of the four racial groups, and these are stratified by income. Therefore, both racial and economic stratifications are embodied here.

The starting point for analyzing sanctions based on this SAM is recogniz- ing the exogenous, or external, nature of these policies. Trade sanctions typically consist of boycotts of exportables and/or embargoes on importables. These measures either deny certain inputs to the producers or deny final products to the consumers in South Africa. They also bar South Africa’s com- modities from the world market. Refusing to buy South African products translates simply into decreasing expenditures along certain columns of the SAM. One should note that the SAM multipliers capture not only the direct reductions of expenditures and their effects on a given sector but the indirect effects as well. For example, boycotting South African gold will result in declining output and employment not only in that particular sector but in many others as well-e.g., trade and finance. One reason is that households and companies receiving their incomes from this sector will have less to spend on goods from other sectors, and this will depress the demand for these goods and services. Section 111 illustrates the approach outlined above for the South African case.

111. OUTPUT AND DISTRIBUTIONAL IMPACTS OF TRADE SANCTIONS

This section examines the direct and indirect effects of imposing trade sanctions on the South African economy. Many question the assumption that sanctions will be 100 percent effective in an open economy such as that of South Africa, which engages in multilateral trade. It is hazardous to guess just how effectively sanctions can be imposed. Below, I circumvent the problem of estimating quantitatively the relative effectiveness-or ineffectivenessaf sanction parameter E. This enables readers to create different scenarios as E is allowed to vary between 0 (no effect) and 1 (100 percent effective sanctions).

A. Ourput Effects of Sanctions Imposing sanctions on South African export items will reduce output not

only for the exportables but for other sectors as well. The indirect effects operate through two distinct channels. The first channel through which a decline in export demand affects the output of other sectors is via the demand for intermediate inputs. For example, as the output of gold declines, so will the demand for fuels, drilling equipment, trucks, and even packaging materials. The second channel of influence operates through the changes in both levels and distribution of income. As output declines in a specific sector, incomes will fall absolutely. More importantly, incomes will fall dif-


ferently among different classes and socio-economic groups. This will lead to both a decline in the overall demand and a shift in the demand structure. As a result, a decline in gold output will likely be reflected in falling output in a sector as remote as tobacco.

The total output multiplier for the South African economy is close to 3 . This means that if sanctions lead to a decline of $1 million of exports, then output will decline by $3 million. Of course, this will not be distributed even- ly across sectors. To gauge the impact of various sectors on output, one must examine the sectoral multipliers. For agriculture, the own multiplier is 1.120, meaning that $1 million sanctions on agriculture will lead to a $1.12 million decline in agricultural output. One can make similar interpretations regard- ing the output multipliers in other key sectors such as gold (1.00029), mining (1.018), and food (1.305). All of these sectors will affect others indirect- ly. For example, the above-mentioned $1 million sanctions against gold exports will lead to a $40,000 decline in agricultural output and a $13,000 fall in mining output. These output reductions naturally will be accompanied by unemployment. 2

B. Implications for Capital and Labor Sanctions will lead to more than just an absolute decline in output and

employment. They also will result in a different allocation of the value added between capital and labor on the one hand and of the various types of labor on the other. For the sake of brevity, I consider a decline of export demand by $1 million for four prominent sectors-agriculture, gold, mining, and food.

If each sector is considered separately, the capitalists will lose $676,670 in agriculture, $665,030 in gold, $608,150 in mining, and $555,360 in food (see table 2). White workers lose relatively more than do the other groups but, in absolute terms, these losses are miniscule compared to those of the mainly white and Afrikaner capitalists. To illustrate this, consider the white professionals and laborers in two key sectors: agriculture and gold. In our experiment, these two groups in the agricultural sector will lose $24,658 and $25,591, respectively. The same two groups in the gold sector will lose $29,587 and $72,167, respectively.

The position of the black workers obviously is an important issue. Paradoxically, black workers’ low economic status makes the impact of sanctions much less pronounced. This is true not only for the large pool of un- employed but also for the economically active workers. In the latter case,

2. The following subsection discusses the impact of sanctions on wages and salaries, inter alia. Given sectoral average wage figures, computing the resulting employment changes is easy. Such information was not available to me when I wrote this paper. However, it should be possible, in principle, to obtain average wage figures.

KHAN. TRADE SANCTIONS ON SOUTH AFRICA 137

TABLE 2 Impact of Sanctions on Capital and Various

Racial-Ethnic-Occupational Categories of Labor (Fixed Price Multipliers) Showing Changes in Factor Incomes for Unit Changes in Demand

Category

Capital White

Professional White Collar Farm Worker Laborers Not Classified Elsewhere

White Total Colored


Colored Total Asian


Asian Total Black

Professional White Collar Farm Worker La borers

Black Total

-

0.676673

0.024658 0.065618 0.01 5 164 0.025591 0.000872 0.130721

0.000872 0.0447 80 0.02 1092 0.009226 0.000235 0.036205

0.00048 1 0.007202 0.000657 0.002655 0.000054 0.01 1049

0 .OO 1682 0.013339 0.080979 0.001514 0.127570

Other Agriculture Gold Mining

- 0.665033

0.029587 0.048139 0.000848 0.072167 0.000951 0.15 1692

0.000608 0.002891 0.0008 09 0.0063 19 0.000226 0.010853

0.0003 72 0.00239 1 0.000028 0.001582 0.000051 0.004424

0.003085 0.014758 0.003337 0.001616 0.178350

0.608 149

0.03 4502 0.076849 0.000769 0.069096 0.001748 0.152964

0.001534 0.005461 0.000767 0.013872 0.000584 0.022218

0.000799 0.004 1 95 0.000026 0.002714 0.000098 0.007832

0.003787 0.01 9495 0.003100 0.003323 0.13 95 75

Food

0.555363

0.035627 0.107489 0.007953 0.049727 0.001295 0.202091

0.001286 0.007553 0.01 1679 0.02 1 107 0.0005 45 0.03333 1

0.000955 0.0075 00 0.0003 83 0.00641 0 0.000098 0.015354

0.002291 0.020675 0.041712 0.003322 0.134839

Note: The CEAS data have 10 categories of employment for each of the four racial groups. I have simplified such data by reducing these to 5 categories. I combined the CEAS categories Professional, Technical, and Other Professional into the single category Professional, and combined the CEAS categories Administrative, Clerical, Sales, and Services into the heading White Collar. The other three CEAS categories-Farm Worker, Laborers, and Not Classified E l s e w h e r e are not combined.

the multipliers appear higher for some black groups. However, one must consider the fact that black workers outnumber white workers more than five to one. Thus, in the previous example, black laborers will suffer a loss of $29,86O+xceeding by some $4,000 the loss suffered by white workers. If one adjusts for the number of workers in each group, then a black worker will lose only 23.3 percent of what a white worker loses, in per capita terms.


TABLE 3 Impact of Sanctions on Household Income Distribution

Agriculture Gold Mining Food

0.113424 0.0004980 0.015830 0.147857 White Bottom 20%

0.390890 0.0015844 0.043139 0.511430 Top 20% 0.21 8315 0.0008740 0.017429 0.287201 Colored Bottom 20%

0.477710 0.0018900 0.044195 0.647930 Top 20% 0.211112 0.0008120 0.016605 0.289574 Asian Bottom 20%

0.551290 0.0021570 0.046913 0.759600 Top 20% 0.254929 0.0009330 0.017695 0.348455 Black Bottom 20%

0.290940 0.0014490 0.025325 0.372800 Top 20%

0.291350 0.0013250 0.042377 0.377140 21-80%

0.496330 0.0022720 0.045962 0.652820 21-808

0.537030 0.0023 I20 0.047749 0.734430 2 1-80%

0.596580 0.0029890 0.048978 0.794880 21-80%

Source: Author’s calculations from the CEAS SAM.

In the gold sector, black miners as a group will lose $155 ,582aore than twice the collective loss of white miners. Given the greater number of black laborers, however, a black laborer will lose at most 50 percent of what a white laborer must sacrifice, in per capita terms. In absolute terms, this still will lead to a decline in living standards. But relative to the losses of white workers and capitalists, the losses of black workers may not be considered too harsh. Furthermore, the historically higher level of white consumption may contribute to a feeling of greater deprivation as whites are forced to curtail their current and future consumption, especially that of consumer durables such as houses and automobiles.

C . Implications for the Household Income Distribution

Table 3 shows the relevant multipliers for capturing the impact of sanctions on the household income distribution. The CEAS SAM breaks down households by racial groups into four categories: white, colored, Asian, and black. Each ethnic group is further disaggregated into five household groups according to income level. The top income recipients in each group are further separated into three strata. For purposes of this study, however, I wanted a simpler categorization corresponding to social class. Accordingly, I revised the household classification so as to correspond to upper-, middle-, and lower-income groups within each racial category.

Table 3 indicates that upper-income white households will lose the most from sanctions. Particularly heavy losses occur in agriculture and food. This is consistent with the finding that white capitalists and white professionals- especially managerial professionals-will lose more than will other white

KHAN TRADE SANCTIONS ON SOUTH AFRICA 139

groups. By contrast, the upper-income black group-i.e., the top 20 percent of black income recipients-will lose less than will the numerically greater middle-income black group. A striking fact, however, is that the loss of the top 20 percent of blacks will be little more than the loss of the bottom 20 percent of blacks. Thus, sanctions will create relatively more inequality among white households than they will among black ones.

Another interesting fact emerges from table 3: In the upper-income households, the Asian and colored groups will lose more in absolute terms than will either of the corresponding white or black households. This may complicate the task of predicting these groups’ responses to sanctions. In terms of relative income inequality, the gap between the top and bottom 20 percent of the Asian and colored groups remains lower than that of the whites and higher than that of the blacks. This is consistent with the fact that the Asian and colored groups in South Africa occupy an intermediate stratum between the rich whites at the top and the poor blacks at the bottom of the socio-economic hierarchy under apartheid.

A final note on the effectiveness of sanctions is in order. As mentioned earlier, sanctions may not be 100 percent effective. In fact, they quite likely are less than 50 percent effective. To take this into account, we introduce a sanctions effectiveness parameter, E, which varies between 0 and 1 and measures the extent to which sanctions are effective. In the previous analysis, the decline in aggregate demand must be adjusted downward if E=l. Thus, if E=0.5, then the $1 million decline used in the previous analysis will reduce to $500,000. One must note, however, that all relative magnitudes will remain unaffected.

IV. CONCLUSIONS

This paper has presented a novel approach and some new results for analyzing trade sanctions. Based on the impacts of sanctions on output and (factorial) income distribution, the effects of sanctions apparently are indeed complex. No defense of sanctions that ignores the real costs in terms of losses in both output and employment can withstand the charge of being too simplistic. On the other hand, no overwhelming economic reason exists for one to expect trade sanctions to fail inevitably. The analysis of this paper indicates that so long as excess capacity characterizes the South African economy, capital will suffer preponderantly. One consequence may be an increased conflict among different segments of capital (Khan, 1986, 1987). In any case, the cost of maintaining the apartheid regime will increase significantly due to sanctions.

More generally, the conceptual scheme that this paper presents can be applied to any episode of trade sanctions and thus should be valuable as a tool for future analysis. Furthermore, one may extend the present approach in several ways. First, one may decompose the multipliers in various ways


so as to study the effects of sanctions in more detail. A particularly attractive alternative, in this respect, is the structural path analysis, which allows one to explore all the paths of influence from the imposition of sanctions to the affected sectors or groups. Finally, one may build models to explore both relative price changes and relative quantity changes. Such models will provide insights to the impact not only of trade sanctions but also of dis- investment on the South African economy.

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Kaempfer, W. H., and A. D. Lowenberg, “A Model of the Political Economy of International Investment Sanctions: The Case of South Africa,” Kyklos, 1986, 39, 377-396.

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impact of trade sanctions on south africa: a social accounting matrix approach

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