KALEIDOSCOPIC DAMPING: OPTIMAL LABOR ADJUSTMENT FOR INTERNATIONAL TRADE INDUCED SECTORAL SHIFTS WITH

APPLICATIONS TO BRAZIL

David Hudgins and Jill BourgeoisDepartment of EconomicsUniversity of Oklahoma

Abstract.

This analysis develops a framework to optimally mitigate the transitional unemployment that results from the sectoral shift induced by temporary losses in international competitiveness. Since globalized trading allows for kaleidoscopic comparative advantage that alternates back and forth between industries and countries, this induces volatility in employment that creates losses when workers are displaced. A stabilization policy could be used to dampen this kaleidoscopic effect so that the sector does not overly downsize in response to the temporary portion of the shifts. We simulate the Brazilian manufacturing sector in order to demonstrate the welfare benefit of a pragmatic kaleidoscopic damping policy.

Keywords: International Trade, Optimal Policy, Sectoral Shift, Brazil

JEL classification: C61, F16, J21, J65, O24, O31,

Contact Author. David Hudgins, Department of Economics, University of Oklahoma, 729 Elm Ave., Hester Hall Rm. 329, Norman, OK, 73019. 405-325-2861 phone, 405-325-5842 fax, e-mail: hudgins@ou.edu

1. Introduction

The path of globalization has generated greater instability in the world’s

international trading patterns. Since every sector faces worldwide competition and global

technology transfer, comparative advantage is determined by a narrow margin that

alternates back and forth between sectors in different countries with knife-edge stability.

Bhagwati (1998, 2007) defined this situation as kaleidoscopic comparative advantage due

to the analogous behavior of the shifting of the patterns in a turning kaleidoscope, and has

argued that it has increased the problem of economic insecurity, and is especially

applicable to farmers in poor countries where there is little institutional support.

Empirical evidence also supports an associated increase in job insecurity for skilled

workers (Aaronson and Sullivan, 1998).

This paper develops a policy that is defined as kaleidoscopic damping. This

policy approach can be channeled though a country’s sectoral tariff rate or through its

various sectoral subsidy programs. It is designed to stabilize the negative effects on

firms’ profits and sectoral worker unemployment that result from adverse productivity

shocks arising from international trading relations. The use of a kaleidoscopic damping

policy provides an optimal partial resistance to the current international competitive

growth gap affecting a given sector. This is effectively a temporary transitional leaning-

against-the-wind policy that is superior to free-trade or ad hoc protection policies. The

policy is designed so that at the end of a negative sectoral employment episode, the tariff

rate will return to a level close to that which prevailed before shock occurred. Under

kaleidoscopic damping, however, a large number of jobs can be saved in the interim

period, creating a welfare increase for firms and workers.

1

To demonstrate the method, we will simulate the benefit of this policy for the

Brazilian manufacturing sector in the early 1990s. One of the reasons for examining the

manufacturing sector is because it is highly exposed to trade. At the beginning of 1990,

the Brazilian manufacturing sector had over 2,727,300 workers, which accounted for

around 22% of the economy’s total employment (Ribeiro, 2003). Then in 1990, under

the newly elected Fernando Collor de Mello, the administration enacted a new

stabilization plan. It was designed to liberalize international trade, increase productivity,

and lessen government restrictions on the private sector. Part of the new Brazilian

administration’s broad trade reform policy involved a decrease in tariff rates. The

manufacturing sector tariff rate was planned to fall from 40.6% in 1990 to 14.1% in 1994

(Pereira, 2006). Brazil entered MERCOSUR (the Southern Common Market) in 1991,

and it later approved the intraregional CET (Common External Tariff) agreement in

1994. It agreed to linearly reduce its average nominal tariff rates until they reached the

level of 12%, which was the level specified by the CET. It also terminated nearly all the

special import programs that caused the legal tariff to differ from the effectively applied

tariffs (Pereira, 2006).

Over the recessionary period from 1990 to 1993, manufacturing employment fell

by about 23%, or about 628,000 jobs, during its worst quarter (Amadeo and Neri, 1998).

Job growth rebounded later in the decade so that the manufacturing sector had a net job

loss of 130,000 jobs, or about 4.77%, over the entire decade, while overall economy-wide

employment grew. Ribeiro (2003) argues that the Brazilian 32.8% manufacturing job

reallocation rate was one of the highest among the world’s developing countries, and that

this is partly due to the trade liberalization policies of the 1990s. Our baseline simulation

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shows that if the authorities would have implemented a kaleidoscopic damping tariff

policy rather than the ad hoc linearized CET tariff plan, the highest quarterly

manufacturing unemployment rate would have been closer to 202,600. Hence, much of

the interim sectoral unemployment could have been eliminated. Under both policies, the

average tariff rate at the end of the period would have been about the same.

2. Problem Formulation

The problem can be laid out as follows. In a given sector or industry j, suppose

that an international trading partner experiences relative gain in comparative advantage

given by dt, where is a function of the difference between the

foreign rate of productivity growth, pr*t, and the home rate of productivity growth, prt.

Given that this shift is part of an intermediate or long term trend, the net result will be a

gain in employment in this sector for the foreign trading partner, and a loss in

employment in the home country. The comparative advantage would shift so that some

or all of the home workers in sector j would be forced to leave their jobs, and they would

gravitate to other sectors in which the home country now has comparative advantage.

The opposite would occur in the foreign country, where worker employment would shift

away from other sectors and toward sector j.

Let nt denote the percentage of workers in the industry that have become

displaced or unemployed due to the sectoral shift resulting from the sectoral technology

differential. In standard trade models based on Ricardian or Heckscher-Ohlin derived

comparative advantage, or models based on advantages due to economies of scale, this

poses little problem since all of the displaced workers are assumed to immediately take

3

jobs in other sectors. But in most cases, this is not how the transitional adjustment

actually unfolds. Many of these workers will have a long lag before finding another job.

As the firms in industry j downsize or leave the industry altogether, many of these

workers are forced to migrate away from their desired geographic municipality or region.

Alternatively, they may accept pay cuts, take jobs which do not match their skills, remain

unemployed, or even leave the labor force rather than accept the prospect of moving.

This would lead to nontrivial temporary or long-term unemployment, especially for the

families that suffer as a result. Traca (2005) offers support that increasing kaleidoscopic

trade exposure leads to increases in firm shutdowns that will result in negative effects

manifested in wage negotiations where wage gains are below productivity increases, job

instability, and increased worker displacement.

The Stolper-Samuelson effects that could lead to overall welfare-enhancing trade

benefits will not benefit the losing sector unless the losers are actually compensated.

From a welfare perspective, the kaleidoscopic damping policy discussed above alleviates

three types of problems. First, the unstable nature of comparative advantage means that

an industry may downsize in response to a loss in its global competitiveness, only to find

that shortly after the transitional shrinking, the industry has regained its comparative

advantage due to interim technology development and changes in prices and other

economic fundamentals. In that case, the excess impulse response has resulted in

unnecessary and suboptimal volatility, including distortions between the tradeable and

nontradeable sectors.

It should also be noted that in the absence of damping policies, firms that

maximize shareholder wealth by maximizing the discounted present and future profit

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stream will lay off workers during kaleidoscopic episodes. The firms in the sector have

no incentive to retain workers during the volatile periods, since it is not consistent with

profit maximization. This creates an imbalance, since workers will not maximize their

welfare by being unemployed for the indefinite time while they incur the costs of

temporary unemployment, job search, retraining, and relocation. Very few displaced

workers will be able to avoid these costs, and thus they will not improve their welfare as

a result of the sectoral downsizing. Once the kaleidoscopic episode ends, then these

workers will be forced to suffer repeated exposure to these transitional frictions.

The second objective that damping achieves is to provide an automatic

compensation to the losing sector when its unemployment loss exceeds the target level, as

will be modeled below. This is especially desirable since even if the loss of comparative

advantage is long-term, the transitional problems for workers will still be substantial.

Even though it is economically efficient for there to be a sectoral decline that is in-line

with the productivity loss, this scheme allows for some transition cushioning for firms

and workers that are left behind by the sectoral shift.

Lastly, under a more liberalized trading system, transitional sectoral

unemployment shifts must also be managed due to the effect on the overall economy-

wide unemployment rate. Helpman (2009) shows that under some conditions, when the

unemployment rate is asymmetric across sectors, increased international trade will lower

national consumer welfare and raise the overall unemployment rate by shifting labor

toward the high-unemployment sectors.

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3. Model Derivation

The kaleidoscopic damping method can be modeled within an optimal control

tracking framework that has a mathematical structure somewhat similar to that in Shupp

(1975). The objective is to reduce the job loss in sector j, while balancing this

unemployment gap with the cost of actively using the sectoral tariff rate or direct industry

subsidy as a stabilizing instrument. In order to maximize social welfare, the policymaker

seeks to minimize the loss function given by equation (1) subject to the linearized

stochastic difference equation specified by (2).

(1)

subject to

a 1 , a 2 , b > 0 (2)

The expression in (1) defines a social welfare index as a loss function that results

from three sources. Let denote the sectoral unemployment rate defined as the number

of displaced/unemployed workers from sector j in period t, expressed as a percentage of

the total number of workers that are employed by sector j at the beginning of the planning

horizon in period 1. Let n* represent the exogenously determined optimal target

percentage of workers in sector j that will become displaced due to the increase in foreign

sectoral productivity. This would represent the percentage of workers that are willing

and able to improve their welfare by finding employment in another sector, and possibly

another geographic location, with little transitional problems. Given that at least some of

the productivity change is long-term, there must be some adjustment in order to maintain

a competitive equilibrium in the international markets. However, if more workers are

6

displaced than this amount, then there will be widespread regional recessions and lasting

sectoral unemployment that is accompanied by lower wages. The value of n* would vary

across sectors, countries, and time periods, and would in part depend on the skill levels of

the workers in the sector.

The first term in expression (1) allows the policymaker to exogenously assign a

penalty weight of for any job losses that exceed the targeted level n*. Since the

purpose of the index is to reduce the unemployment to a smaller level than that which is

currently occurring, it assumes that throughout the planning horizon, > n*.

Alternatively, if < n*, then there is no need for the use of a damping policy.

The first term in equation (2) reflects the situation where the total percentage of

workers who have left sector j in the next period, , increases with the percentage of

displaced workers in the present period, , and increases with the size of current

relative domestic productivity lag function . The percentage of

sectoral worker displacement decreases as the rate of tariff protection increases, due

to the change in the resulting higher sectoral price and the inflated internal terms of trade

ratio. The error term, , is assumed to be normally distributed with a mean of 0,

serially uncorrelated, and have a constant variance.

The second term in equation (1) results from the policymaker using a tariff as a

policy control variable. Let represent the ad valorem tariff rate (or the ad valorem

equivalent for a specific tariff) in period t. If the tariff level exceeds the optimum tariff

target level s*, then a penalty is assigned with a weight of r. In order to keep the

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employment losses in check, this will require that > s* over the planning horizon.

Alternatively, the policymaker could use a direct industry subsidy to aid in worker

retraining and firm investment, rather than a protective tariff. In this case, would

represent the subsidy level or rate, and s* is the optimum long-term subsidy target value.

Bhagwati (2007) argues that automatic stabilizers are the best way to deal with

the downsides of trade pattern shifts, and that adjustment subsidy assistance, such as job

retraining programs, are superior to protection through higher tariff rates. The U.S. Trade

Adjustment Assistance (TAA) program provides an additional year of unemployment

compensation for workers who are displaced as result of import-induced plant closures or

overseas plant relocations to any country that receives preferential trade with the United

States. However, due to developing countries’ lack of resources to provide subsidized

assistance, Bhagwati further argues that, although the subsidy programs should be

implemented domestically, the funding for the subsidies should be financed externally by

corporate exporters and the World Bank (Bhagwati, 2007).

Implementing this type of arrangement would necessitate the use of some form of

policy guidance framework in order to be economically and politically viable. The

scheme provided here provides a pragmatic approach that could be used to convince the

parties that the system is operational. It could be placed under the regulation of the WTO

within one of its initiatives, such as its Aid-for-Trade program (WTO website). Bown

and McCulloch (2004) note that although WTO/GATT rules on safeguards allow

temporary protection of sectors experiencing serious injury due to fairly traded imports,

the distinction between injury due to fair and unfair trade is not sharply drawn. A

flexible policy whose general form is predetermined could be jointly implemented by the

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country’s policymakers in conjunction with the oversight of the WTO. This would

alleviate much of the problem of trying to distinguish between unemployment losses due

to fair and unfair trade. Instead, it would allow the tariffs and/or subsidies to deviate

from their baseline values as soon as a sector begins to experience an unemployment

displacement above the pre-selected target level. Since the damping policy is constructed

to be temporary with a predetermined time horizon, it already conforms to the WTO

“sunset clause” that sets time limits on all safeguard actions. Also, this framework does

not require government policymakers to immediately distinguish between sectors that are

experiencing a temporary shock versus those that are suffering a permanent decline. If

the loss in sectoral comparative advantage persists beyond the planning horizon, then the

tariffs/subsidies will be phased out according to the long-term realignment.

Although using tariff manipulation would have some different effects than the

subsidy approach, the model has a simplicity advantage since it assumes that either policy

effect can be captured by the linear equation given in (2) where the value of the

coefficient b measures the translation of the level of the tariff or subsidy on sectoral

unemployment in the relevant range of interest. Although the coefficients , , and b

in the linearized equation (2) will change over the long-run, they are assumed to be time-

invariant during the short-run transitional planning period.

The last term in expression (1) assigns a penalty with a weight of at the end of

the transitional planning horizon if the expected level of externally induced sectoral

unemployment exceeds the targeted level. If firms expect that the foreign productivity

gains will lead to further market penetration that will necessitate layoffs, then they will

shrink domestic operations and continue to shrink future employment, or at least curtail

9

planned expansion. If workers expect a continuing economic decline in the sector, then

they will negotiate bargains that lead to declining wages, further layoffs, and other

continuing problems. These problems include the inefficiency distortions induced by

improper labor allocation due to transitional barriers associated with workers’ sectoral

and geographic mobility (Aaronson and Sullivan, 1998, and Traca, 2005). This could

also lead to an increase in the wage-skill gap, since workers who are limited by barriers

to mobility are more willing to accept lower wages in exchange for extra job security

than are the more mobile highly-skilled workers (Traca, 2005). Thus, the policies over

the planning horizon should leave the expected level of sectoral employment close to the

targeted level. This last term is also positive since the employment losses are designed to

be only partially dampened.

Assume that expectations are formed by an ARIMA(0, 1, 1) process as given by

equation (3).

0 < < 1 (3)

This is identical to the simple exponential smoothing model, as expressed in equation (4).

0 < < 1 (4)

Equation (4) can also be equivalently written as

(5)

Substituting equation (5) into equation (1) shows that the last term becomes

(6)

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Using equation (6), the first and last terms in equation (1) can be combined to yield

(7)

Let be defined by

(8)

Then, equations (7) and (8) can be substituted into equation (1) so that the new

expression becomes

(9)

This expression provides the basis for which the policymaker can form the actual

welfare index to set the tariff rate in each period. However, the monotonic

transformation to a quadratic form provides a version that has more desirable

mathematical properties, and allows for a varying weight on the final state. The time-

variant quadratic form of the welfare performance index is given by equation (10).

(10)

This allows for a variable level of the actual sectoral employment gap at the end of the

planning horizon that will be optimized. The policymaker’s problem is to minimize

expression (10), subject to the time-invariant linear difference equation given by (2). The

solution can be approached by using Pontryagin’s minimum principle along with the

Hamiltonian given by equation (11), where represents the shadow cost.

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(11)

; (12)

(13)

Combine (2), (12), and (13) to get

(14)

(15)

where the boundary conditions are n(1) = and . Using a solution

method similar to those used in Chow (1977) and Kendrick (1981) allows for the

following tractable representation for a closed-form feedback control.

(16)

The optimal affine policy control rule is given by (16), where the feedback coefficient

matrices are defined in (17) and (18), subject to the Riccati difference equations in (19)

and (20).

(17)

(18)

(19)

12

(20)

In order to employ the above procedure empirically, another facet should be

addressed. The target values for the sectoral job loss and the tariff/subsidy targets are

assumed to be exogenous for this paper, and must be defined when using the procedure

with data. The tariff/subsidy target is likely to be defined politically, based on current

trade agreements and current domestic policies. Of course, there is generally an

economic rationale behind the negotiations and concessions resulting in the trade

agreements. In this case, the policymaker is only maximizing transitional welfare within

the fixed parameter targets for the fixed short-run time horizon. Alternatively, these

tariff/subsidy targets, as well as the unemployment targets, can be approximated based on

econometric estimates of the optimal tariff/subsidy that would be consistent with long-

term sectoral trends and industry forecasts.

4. Empirical Application: Manufacturing in Brazil

The methodology described above can be utilized by any economy, but is

especially applicable for developing countries. This section applies the model to the job

loss that occurred in the manufacturing sector in Brazil due to the liberalization of trade

in the 1990’s. During the period of 1990 to 1998, there was massive decrease in jobs in

the Brazilian manufacturing sector. Brazil, like other Latin American developing

economies, has used various ad hoc tariff policies, rather than following an optimally

constructed rule, such as the one that we are proposing.

The new Brazilian stabilization plan of 1990 was designed to linearly decrease

tariff rates as part of a broad effort to liberalize trade and loosen restrictions on the

13

private sector. The average tariff rates for the economy were planned to decrease from

over 40% in 1990 down to a target of 12% by the time that the CET (common external

tariff) agreement was to go into effect. In 1991, the CET was negotiated by Brazil,

Argentina, Paraguay, and Uruguay. The objective of the CET for Brazil was to reduce

the ad hoc tariff rate fluctuations, improve the management of tariff policy, and ensure

commitment to the tariff aspect of the trade liberalization program (Pereira, 2006). The

stabilization plan led to a recessionary period from 1990 – 1993, where manufacturing

unemployment reached 23% (Amadeo and Neri, 1998). Brazil then implemented the

Real Plan in 1994 which was designed to stabilize the currency and curb inflation. The

appreciated currency, decreased tariff rates, and increased imports led to an increase in

the trade deficit. This trade deficit increase led to a “tariff dance” where a series of

changes in the tariff structure were introduced (Pereira, 2006).

When analyzing the data from this period covering most of the 1990’s, Ribeiro

(2003) found that the effect of tariffs was to “chill” the labor market, and limit excess job

reallocation. This is exactly what the proposed procedure aims to achieve. The use of

the damping procedure seeks to define a stabilizing policy that aligns the short-term

transitional adjustments with the long term sectoral employment trend, without excessive

and inefficient job reallocation. The use of this optimal policy rule would allow for a

balance that provides the flexibility to adjust to labor market conditions with tempered

discipline without forcing policymakers into an arbitrarily constructed linear tariff

reduction such as the overly restrictive CET agreement. It would also avoid the relapse

into the ad hoc reactionary tariff policies that led to a destabilizing tariff dance binge,

14

which policymakers will likely revert to when dealing with the continual shocks that

developing economies face under a kaleidoscopic liberalized global trading system.

The following analysis applies the procedure defined above using quarterly data

for Brazil during the period 1990 – 1993, with data compiled from Amadeo and Neri

(1998), IBGE (1985 – 1995), Pereira (2006), and Ribeiro et al. (2003). This period offers

at least three aspects that make it a kaleidoscopic sectoral unemployment episode

desirable for exposition. First, it is clear that the changes in the labor market of the

manufacturing sector were driven by external influences from international markets and

competitive effects. Second, there was a tariff policy that should have been optimally

constructed, but which was not. Thirdly, and most importantly, labor productivity was

actually increasing over this period, and the manufacturing jobs returned in large

numbers in the late 1990s following the sectoral shift that decreased the employment in

the manufacturing sector. It is thus evident that any economy in this situation would

suffer an unnecessary welfare loss if allowed to operate under a liberalized trade policy

that leads to kaleidoscopic short-term shifts that are not warranted by the long-term

structure of the economy.

Measuring the effects of underlying external productivity shocks involves

capturing the effect of two entangled processes that act with opposing forces on sectoral

employment. Although productivity gains improve competitiveness and lead to sectoral

expansion in the long-run, they can have an opposite effect in the short run, as shown in

Hudgins and Shuai (2006). Clearly, this short-run negative impact of productivity gains

was a big factor for Brazil during this 1990s episode since the sectoral response to the

productivity increase was mostly to release workers, rather than to expand output

15

(Amadeo and Neri, 1998, Ribeiro et al., 2003). One of the reasons for these layoffs was

that despite the large productivity increase, the exchange rate induced an even larger

increase in manufacturing wages, which caused the unit labor costs to increase (Amadeo

and Neri, 1998). Thus, there was a short-term loss in the comparative advantage of the

manufacturing sector.

The extent to which firms restructure their employment levels and the scale of

production in response to these shocks also depends on both the average level and the

variability of productivity and the unit labor cost. Firms operating in an environment

with relatively more volatile unit labor costs are more likely to adopt a flexible structure

that allows for larger employment fluctuations than are firms with a stable cost structure.

Taken together, the comparative advantage shock variable for sector j can be written as a

function that is determined by the

gap in foreign and home productivity growth ( ), the gap between foreign and

home unit labor costs ( ), the standard deviations of productivity ( ) and unit

labor costs ( ), and the import and export penetration of the industry ( ).

The quarterly index for this analysis was computed as a quarterly moving

average of the following expression:

(21)

where = = .5, is the standard deviation of the difference between unit labor

costs and productivity for year k, is the sample mean difference between unit

16

labor costs and productivity, so that the coefficient of variation for quarter t in year k is

given by . The term is the maximum

difference between the quarterly unit labor costs and labor productivity indices over the

entire period. Manufacturing productivity in the U.S. was used as a proxy for the

measurement of , since it was Brazil’s primary export partner.

Using a least squares regression procedure, the estimated version for the

difference equation (2) is given by equation (22), where the standard errors of the

coefficients are given in parentheses.

(22)

The variable is defined as the number of jobs lost in the Brazilian manufacturing

sector expressed as a percentage of the total manufacturing jobs at the end of 1989, which

is the beginning of the period. Thus, is the current sectoral unemployment rate that is

due to the sectoral shift. The current quarterly average tariff rate for the Brazilian

manufacturing sector is given by .

Based on these estimation results, simulations were run for the period of 17

quarters beginning at the first quarter of 1990, and ending after the first quarter of 1994.

Table 1 and Table 2 show the calculated variable paths for the optimal tariff policy based

on the welfare performance index derived above using three different sets of parameters.

The tables compare these paths to the simulated ad hoc CET policy that was designed to

produce a linearized decline in the tariff rate during the liberalization of trade in the early

1990s. Since the CET was aiming to achieve an average tariff rate of 12% by the end of

17

the four-year period, the optimal target tariff rate was set at s* = 0.12. As stated above,

this is an example where the tariff target and the time horizon are determined

exogenously by political agreement, and must be treated as exogenous.

At the end of the decade of the 1990s, the manufacturing job loss rate returned to

4.77% when compared to the employment level at the beginning of the decade, so the

optimal target level for the unemployment rate was set at n* = 0.0447. Given an initial

manufacturing employment level of about 2,727,300 workers, this is a total loss of about

130,000 jobs due to the long run sectoral shift in comparative advantage. It must be

pointed out that this figure would have been unknown to the policymaker at the

beginning of the planning horizon. Thus, the policymakers would have been forced to

assess the current trends in the domestic and foreign sectoral unit labor costs, and project

the estimated long-term trend in sectoral output and unemployment. Even if the target

trend manufacturing sector unemployment rate estimate were considerably different

than .0447, however, the procedure outlined here would still work the same. All that is

required for welfare improvement is the general location of the target sectoral

unemployment rate, which would be somewhat lower than the sectoral displacement in

the absence of any policy measures.

The base case for the simulated optimal policy uses the parameters = 20, =

200, = 100, and r = 1. Although the model has been simulated by the authors for a

variety of values for a sensitivity analysis, only three representative cases are presented in

this paper in order to balance between clarity and length considerations. The values for

the welfare index weighting parameters can also be chosen endogenously, but such a

discussion is far outside the scope of the paper, and it is beyond what is required for

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illustrative demonstration of the damping method. Moreover, most control applications

in both engineering and economics employ exogenous weights in the welfare index,

which allows the flexibility for policymakers to access a more robust set of trajectories.

The results for the sectoral manufacturing unemployment rate and the tariff rate

are shown in Figure 1, while the CET ad hoc tariff rates and simulated sectoral rates are

shown in Figure 2. The initial optimal tariff rate is 44.2% as compared to the ad hoc

tariff rate of 40.6%. As shown in Figure 1, the optimally determined tariff rate increases

to a maximum of almost 60% in quarter 5, and then begins to continually decrease

throughout the period. The resulting optimal sectoral unemployment rate never rises

above 7.5%, and is reduced to 6.2% at the end of the planning horizon. This means that

the worst quarterly manufacture job loss is about 202,600 jobs. Conversely, the CET ad

hoc policy shown in Figure 2 produces an almost continually rising sectoral

unemployment rate that grows to a maximum of 23.95% and has an ending value that is

still 23.59%, which translates into about 650,000 jobs. The value of the welfare

performance index under the optimal policy is only J = 1.239, while it increases to J =

8.11 under the CET ad hoc policy. The optimal tariff policy thus avoids a job loss of

around 450,000 jobs that occurred under the CET ad hoc policy.

Figure 1

About here

Figure 2

About here

The reduction in the sectoral employment rate in Figure 1 comes with the social

cost of higher tariff rates. The second optimal policy presented in Table 1 shows the

19

optimal path when the relative cost of deviating from the optimal target tariff rate is

increased from r =1 to r = 8. In this scenario, the optimal tariff rate path begins with a

rate that is only 35.09%, which then grows to a maximum of 41.12% in quarter 7 before

continually decreasing to reach a final value of 19.91% at the end of the period. The

largest value of the unemployment loss reaches 16%, which would be a total job loss of

about 436,000 jobs. This figure is still much smaller than the job loss under the CET ad

hoc policy, and the unemployment rates for the other quarters are consistently lower than

those under the ad hoc policy.

Both of the previous optimal policy scenarios still allow for a substantial interim

rate of job loss in the manufacturing sector. If the policymakers wish to reduce this

transitional sectoral unemployment shift, then they will increase the relative weight of the

discrepancy between this current job loss rate and the target unemployment level of n*

= .0477. The framework allows the policymaker to bring the sectoral unemployment rate

arbitrarily close to the target level with an arbitrary speed, by increasing , , and

relative to r. Table 2 compares the base parameter simulations to the optimal path

when the index parameters are = 50, = 500, = 1,000, and r = 1. In this

case, the tariff rate is allowed to reach a maximum of 65.68% in quarter 5 before

declining to eventually end at a final value of 26.5%. Although the tariff rate does not

come near to its target value of 12%, the unemployment level only reaches a maximum of

6.14% in quarter 6. This represents a sectoral job loss of about 167,000 jobs, which is a

loss of only 37,000 more jobs than the target job reduction of 130,000. The

unemployment rate achieves low values throughout the planning horizon, and ends the

period at a value of only 4.97%, leaving it very close to the targeted value of 4.77%. In

20

this scenario, the expected value of the welfare index is J = 1.3371, which is much lower

than the index value of J = 32.6 under the ad hoc policy.

Table 1

About here

Table 2

About here

5. Conclusion

This analysis has derived an optimization policy framework that allows for

welfare improvement over both ad hoc policies and rigid tariff rate restrictions when

dealing with sectoral shocks that are primarily induced by the external changes leading to

a temporary loss of comparative advantage. The methods used above are flexible enough

to be employed in practice to achieve gains compared to the current tariff and subsidy

structures in developing countries.

The estimation and simulation results for Brazil are meant to primarily be

illustrative with respect to the policy mechanism, rather than an attempt to model the

entire effects of the Brazilian stabilization plan or the other macroeconomic policy

variables that had an influence on the economy during this period in the 1990s. The

methods developed here are not meant to imply that the overarching movement to

liberalize trade in Brazil or other Latin American countries during the period was not

warranted. However, they do suggest that the optimally designed policy would have

been a better policy for moving toward more liberalized trade than the ad hoc policies

that were actually employed. The end result of a low and more stable long-term average

21

tariff rate could have been achieved where the economy suffered much less interim

sectoral job losses.

The demonstrated benefits of kaleidoscopic damping also suggest that

incremental improvements to current trade policies could be practically implemented.

The current trade agreements in the WTO, regional Preferential Trade Agreements

(PTAs), and ideas such as safeguards under Most-Favored Nation (MFN) Trade status,

should be written in terms of target tariff rates with allowances for temporary sectoral

variations in the rates based on short-term fluctuations that lead to excessive

unemployment. Rather than specifying a fixed nominal tariff rate, quota, or subsidy level

in the agreement, the tariff and subsidy values should be allowed to be manipulated by

the policymakers in the country experiencing sectoral problems. By employing an

automatically determined feedback policy scheme that monitors sectoral shift, the

policymakers can adjust the tariff and subsidy levels based on the current empirical data.

The framework should be announced so that the policies that will be triggered will be

expected and transparent. There will be continual updating of the data estimates, and the

private sector may be provided only with the general form of the welfare index and the

general neighborhood of its parameters. Nonetheless, workers and employers can then

make a decision based on a more stable environment and a more predictable policy

response to employment, wage, and profit fluctuations. This is a pragmatic approach for

dealing with important transitional issues that are not generally emphasized in

international trade theory and policy.

Although the above analysis focused on tariffs, the optimal design for subsidies

and domestic tax policies aimed at reducing sectoral employment disturbances could be

22

similarly handled. The level of fluctuation in the subsidy rates is likely to be much more

volatile than with the tariff structure. The stabilization aspect of subsidies is especially

important for Brazil, which has been using these policies in an ad hoc fashion through the

present day, rather than designing them in a focused manner based on a well-defined

objective welfare index of the type presented here.

In 2009, some fifteen years after the period analyzed in the simulation above, the

manufacturing sector in Brazil again experienced the largest sectoral decrease in

employment, mostly due to a decline in production stemming from shocks in export

industries (ILO, 2010). The authorities would have ideally looked to the above approach

for policy guidance. The 2009 Brazilian stimulus package provided an expansion of

unemployment benefits for workers laid-off in sectors that had experienced higher

numbers of layoffs than in the preceding months (ILO, 2010). Without an optimal

response damping mechanism, the policies have continued to be scattered and

inconsistent.

Such ad hoc approaches also undermine the attempt to achieve a more liberalized

trading system with a strong underlying market mechanism. For example, after sectoral

automobile employment fell by 7.2% over a six month period in 2009, the Brazilian

government required automakers to halt job layoffs in order to continue to receive tax

breaks (Kazan, 2009). This demonstrates lack of strength in the market-based policy

response that requires government intervention to force the market participants to

respond to current policies that should be able to generate the desired behavior on their

own accord. Given the likelihood of the persistence of these cyclic sectoral variations in

Brazil and other developing countries, policymakers can improve welfare by using

23

kaleidoscopic damping in the formulation of subsidy and tariff structures in order to

design a clear objective to guide the policies in terms of both thrust and magnitude.

REFERENCES

Aaronson, D. and D. Sullivan. (1998) The Decline of Job Security in the 1990’s: Displacement, Anxiety and Their Effect on Wage Growth, Economic Perspectives, Federal Reserve Bank of Chicago. (http://www.chicagofed.org/digital_assets/publications/economic_perspectives/1998/ep1Q98_b.pdf)

Amadeo, E. and M. Neri. (1998) Opening, Stabilisation and the Sectoral and Skill Structures of Manufacturing Employment in Brazil, Chapter 11 in Employment and Training Papers, Geneva, Switzerland International Labour Office. (http://www.ilo.org/wcmsp5/groups/public/@ed_emp/documents/publication/wcms_120243.pdf)

Bhagwati, J.N. (1998) A New Epoch, Chapter 1 in, A Stream of Windows. MIT Press, Cambridge, MA.

Bhagwati, J.N. (2007) In Defense of Globalization: with a New Afterword. New York, NY, Oxford University Press.

Bown, C. and McCulloch. (2004) The TWO Agreement on Safeguards: An Empirical Analysis of Discriminatory Impact, published in M. Plummer (ed.), Empirical Methods in International Trade. Edward Elgar, Cheltenham, UK, 145 – 169.http://people.brandeis.edu/~rmccullo/wp/Maxfest1003.pdf

Chow, G. (1975) Analysis and Control of Dynamic Economic Systems. New York, NY, John Wiley and Sons.

Helpman, E. and O. Itskhoki. (2009). Labor market rigidities, trade and unemployment: a dynamic model. (http://www.economics.harvard.edu/faculty/helpman/files/LaborMarketRigidities_Dynamic.pdf)

Hudgins, D. and J. Shuai. (2006) Optimal Monetary Policy Rules for Averting Productivity Induced Jobless Recoveries, Review of Applied Economics, 2 (2), 141-149.

24

IBGE, Monthly Employment Survey, National Institute of Geography and Statistic, Sao Paulo, 1985 – 1995.

IBGE, Monthly Industrial Survey, National Institute of Geography and Statistic, Sao Paulo, 1985 – 1995.

ILO, G20 Statistical Update. (2010) Meeting of Labour and Employment Ministers, Washington, D.C.(http://www.dol.gov/ilab/media/events/G20_ministersmeeting/G20-brazil-stats.pdf) .

Kazan,A. (2009) Job Losses in Brazil’s Auto Industry Persist…But Production Improves, The Idle Strategist. April 6, 2009.(http://theidleist.typepad.com/the_idleist/2009/04/job-losses-in-brazils-auto-industry-persistbut-production-improves.html).

Kendrick, D. (1981) Stochastic Control for Econometric Models, New York, NY, McGraw Hill.

Pereira, L.V. (2006) Brazil Trade Liberalization Program, 122 – 138, (http://www.unctad.info/upload/TAB/docs/TechCooperation/brazil_study.pdf ).

Ribeiro, E.P., et al. (2003) Trade Liberalization, the Exchange Rate and Job Flows in Brazil, Inter-American Development Bank Research Network – 11th Round, Instituto de Pesquisa Economica Aplicada (IPEA), Brazil, (http://www.iadb.org/res/files/RIBEIRO%20et%20al.pdf ).

Shupp, F. R. (1975) Optimal Policy Rules for a Temporary Incomes Policy, Review of Economic Studies 43(2), 249-259.

Traca, D. A. (2005) Labor Markets and Kaleidoscopic Comparative Advantage, Review of International Economics 13(3), 431-444. (http://www.bportugal.pt/en-US/BdP%20Publications%20Research/WP200004.pdf ).

WTO. Official Website. http://www.wto.org/

25

Figure 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160.000.100.200.300.400.500.600.70

Optimal Tariff Policy

n = unemployment rate s = tariff rate

Quarter

Rate

Figure 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160.000.100.200.300.400.500.600.70

CET ad hoc Tariff Reduction

n = unemployment rate s = tariff rate

Quarter

Rate

26

Table 1

Optimal Tariff Policy Optimal Tariff PolicyCET ad hocTariff Policy

n* = 0.0477 q1 = 20 q1 = 20 q1 = 20s* = 0.1200 q2 = 200 q2 = 200 q2 = 200

a1 = 0.9400 q T + 1 = 100 q T + 1 = 100 q T + 1 = 100a2 = 0.0677 r = 1 r = 8 r = 1

b = 0.0706 = 0.30 = 0.30 = 0.30Quarte

r d t q t n t s t q t n t s t n t s t

1 0.5503 20.28490.020

00.442

0 20.28490.020

00.350

9 0.0200 0.4060

2 0.6225 20.40690.024

80.497

6 20.40690.031

30.368

8 0.0291 0.3822

3 0.7813 20.58130.030

30.548

5 20.58130.045

50.385

1 0.0441 0.3584

4 0.8610 20.83050.042

70.583

6 20.83050.068

40.398

0 0.0708 0.3346

5 0.8674 21.18640.057

20.598

1 21.18640.094

50.406

4 0.1029 0.3108

6 0.7133 21.69490.070

20.591

8 21.69490.118

90.410

2 0.1347 0.2934

7 0.6791 22.42120.072

50.580

0 22.42120.131

10.411

2 0.1554 0.2759

8 0.6396 23.45890.073

20.564

4 23.45890.140

10.409

4 0.1738 0.2585

9 0.6637 24.94130.072

20.546

7 24.94130.146

10.404

8 0.1897 0.2410

10 0.6006 27.05890.074

20.520

0 27.05890.153

70.396

0 0.2072 0.2267

11 0.5815 30.08420.073

70.486

6 30.08420.157

20.382

7 0.2204 0.2124

12 0.4900 34.40600.074

30.441

2 34.40600.160

10.363

2 0.2326 0.1981

13 0.3992 40.58000.071

80.388

1 40.58000.158

00.336

7 0.2388 0.1838

14 0.3241 49.40000.067

10.332

9 49.40000.151

80.302

2 0.2395 0.1707

15 0.3186 62.00000.061

50.281

8 62.00000.143

20.258

2 0.2359 0.1577

16 0.3193 80.00000.059

50.220

9 80.00000.138

00.199

1 0.2331 0.1446

27

17 100.00000.062

0 100.00000.137

3 0.2314

J minimum =1.239

0 J minimum =6.421

7 Jminimum = 8.11

Table 2

Optimal Tariff Policy Optimal Tariff PolicyCET ad hocTariff Policy

n* = 0.0477 q1 = 20 q1 = 50 q1 = 50s* = 0.1200 q2 = 200 q2 = 500 q2 = 500

a1 = 0.9400 q T + 1 = 100 q T + 1 = 1,000 q T + 1 = 1000a2 = 0.0677 r = 1 r = 1 r = 1

b = 0.0706 = 0.30 = 0.30 = 0.30Quarte

r d t q t n t s t q t n t s t n t s t

1 0.5503 20.2849 0.0200 0.4420 50.71 0.02000.407

3 0.0200 0.4060

2 0.6225 20.4069 0.0248 0.4976 51.02 0.02730.503

7 0.0291 0.3822

3 0.7813 20.5813 0.0303 0.5485 51.45 0.03220.587

9 0.0441 0.3584

4 0.8610 20.8305 0.0427 0.5836 52.08 0.04170.641

3 0.0708 0.3346

5 0.8674 21.1864 0.0572 0.5981 52.97 0.05220.656

8 0.1029 0.3108

6 0.7133 21.6949 0.0702 0.5918 54.24 0.06140.635

2 0.1347 0.2934

7 0.6791 22.4212 0.0725 0.5800 56.05 0.06110.611

5 0.1554 0.2759

28

8 0.6396 23.4589 0.0732 0.5644 58.65 0.06020.587

5 0.1738 0.2585

9 0.6637 24.9413 0.0722 0.5467 62.35 0.05840.566

9 0.1897 0.2410

10 0.6006 27.0589 0.0742 0.5200 67.65 0.05980.533

8 0.2072 0.2267

11 0.5815 30.0842 0.0737 0.4866 75.21 0.05920.495

1 0.2204 0.2124

12 0.4900 34.4060 0.0743 0.4412 86.02 0.06000.439

1 0.2326 0.1981

13 0.3992 40.5800 0.0718 0.3881 101.45 0.05860.376

2 0.2388 0.1838

14 0.3241 49.4000 0.0671 0.3329 123.50 0.05550.319

5 0.2395 0.1707

15 0.3186 62.0000 0.0615 0.2818 155.00 0.05160.286

6 0.2359 0.1577

16 0.3193 80.0000 0.0595 0.2209 200.00 0.04980.265

0 0.2331 0.144617 100.0000 0.0620 1,000.00 0.0497 0.2314

J minimum = 1.2390 J minimum =1.337

1 Jminimum = 32.6

29