Political Aspects of Unemployment: Brazil's Neoliberal U-Turn
Thesis
1. INCOME DISTRIBUTION PATTERN IN BRAZIL
As A Consequence of Sectorial Shift and Trade
Liberalization:1981-2009
Senior Seminar Thesis
Baris GURSOY
N02627586
2. GURSOY 1
Income Distribution Pattern in Brazil
As A Consequenceof Sectorial Shift and Trade Liberalization: 1981-2009
Brazil is known as one of the greatest economies. According to World Bank Data
Bank, it had the 10th greatest GDP in the world in 2005. Despite this remarkable
achievement, there is the other side of the story that eclipses this achievement. This fact
brings up the great deal of inequality situation in Brazil. World Bank Data shows that Gini
Coefficient for Brazil was 57.4 in the same year, while in another huge economy, for instance
India has the Gini coefficient of 33.4. However, income inequality in Brazil has a decreasing
trend recently. Reasons for this trend could be investigated within economic reforms and the
changes in its economic structure.
After1980’s, Brazil economy has faced many reforms. The cornerstones of these
reforms include “Trade Liberalization” and “Privatization of State Enterprises”. These two
structural change leads to Brazil economy to face competition with the World market.
Moreover, Privatization is expected to eliminate the inefficiency in State Runned-Enterprizes.
Therefore, it was inevitable to face some structural shifts in the employment market after
these drastic changes. For instance, Agricultural sector’s share in the economy decreased as
well as industrial sectors, while service sector was booming. After these structural changes, it
is expected to have respectively fair income distribution, since liberalized market would be
expected operate more efficiently through facing a competitive pressure from the world
market.
Backgroundof Brazil’s Economy
After W.W.II, Brazil implemented a quite short lived liberalized era as a consequence
of the change in the government, after the dictatorial of Vargas was fallen down. However, in
1951, Vargas was reelected in democratically, his government established a strict import
substitution policy. This change in policy was aimed to support infant-industry thesis, so that
the government expected to industrialize rapidly by protecting it against competition in the
world market. In this context, capital movements were controlled as well as trade restrictions.
3. GURSOY 2
Therefore, industry led economic growth was significant during this era. Economic growth
was %7 in average between 1950s. However, Brazil has experienced common disadvantages
of ISI policy as well. Increased import expenditures to satisfy production needs, such as
petroleum, led to a rise in current account deficit. These problems continued to rise until the
military forces interfered the government in 1964 of which called “coup d’ état”. These
political issues combined with the problems occurred by the ISI policy resulted with a
stagnation in Brazil’s economy. For instance, economic growth drastically decreased between
1962 and 1968. Military regime set up some reforms in ISI policy to improve its results by
prevent the problems occurred by that. Substitutions and bureaucratic support to foreign direct
investments as well as an improved exchange system and systematic devaluation of local
currency, cruizero, are some of the reforms that were implemented by the military regime. As
a result of those reforms and the positive atmosphere in the world economy provided
conditions for Brazil economy to grow much faster than it used to be between 1968 and 1973.
In this period, Brazil economy faced a rapid growth with around %11 on average. In fact, this
growth was mostly supported by industrial sector and based on exporting industrial goods. In
this era, Brazil economy benefited its existing physical capital stocks to which were
established back in the ISI policy era. These stocks were enough to compensate that rapidly
growing economy. Because of that, no need emerged to invest more of which an investment
drive, to strengthen the basis of an economy as well as providing a sustainable growth, could
not be executed in this era. Therefore, Brazil had left with a fragile economy, even though
those high rates of growth. Inevitably, this trend has got reversed after oil shock crisis
occurred in 1973. Terms of trade has decreased significantly not in favor of Brazil, as oil
prices as well as other commodities’ that were imported has sky-rocketed. In this case, Brazil
had to finance its current account debt either by barrowing or by liberalizing capital
movements and expect FDI and hot money movements. As a result, Brazil moved on with ISI
policy through restricted capital movements and controlled exchange rates to sustain its
economic growth. As Baer puts forward in his study, the idea was to barrow from
international superficies, so that the conservator economic policy could be sustained in the
short-run, since economic growth was expected to be experienced through ISI policy and
trade surplus would increase. Therefore, loans could be compensated in the long-run. On the
other hand, this policy tragically ended up with needs to printing money to pay out the
barrowings (Baer 1995; 90). Besides that, currency was to be devaluated on the regular basis
to maintain competitive power of Brazil’s industry in the world market. Both of these facts
combined, Brazil economy suffered extremely high inflation rates with declined growth rates
4. GURSOY 3
in 1980s. This is why this era is called as Lost Decay for Brazil as well (Thomas, 2006).
According to World Bank Data growth rate in this era was fallen down to %3.3 and inflation
peaked to %340. In the lost decay period, it became crystal clear that some reforms should be
implemented to control inflation and macro-economic stability. In 1980s, three major plans
were enforced; in 1986 “The Cruzado Plan” followed by “The Bresser Plan” in 1986 and
“The Summer Plan” in 1989. However, none of these plans were truly effective until “The
Real Plan” was introduced in 1994. For instance, economic growth has fallen down to %0.8
and inflation was boosted to %1645 between 1990 and 1994 (World Bank). This era is also
spans the changing trade regime of Brazil from restricted ISI policy to more liberalized one.
To achieve price stability in this era, tariffs on imported goods were reduced to the levels that
are foreseen in “Common External Tariff Agreement” (CET). In addition, “Real Plan” was
introduced, which, as its most important feature, allows liberalized capital movements. These
too drastic reforms combined, are considered to be successful, since inflation was cut down to
%9 and economic growth was somewhat recovered to about %3 (Castilho 2012). However,
the key feature of this plan was to change the all characteristic of Brazil economic policy.
After the liberalized capital accounts in 1994, Brazil has faced a significant increase in FDI
and other capital movements. As a result, service sector experienced a boom after 1994 as
well. Meanwhile, industrial sector has been exposed to competition in the world market, as
government protection on industrial sector has been cut off. Therefore, these trade and capital
liberalization reforms resulted to deindustrialization. For instance, employment in industrial
sector in one of the most industrialized city of Brazil, Sao Paulo, decreased from %48.7 to
%32 from 1990 to 1999 (Taylor 2006). On the other side, employment in service sector has an
increasing trend, due to the liberalization. Therefore, it would not be an ambiguous argument
to associate the shift in employment structure with trade liberalization.
Income Inequality in Brazil
Income distribution is a serious problem in Brazil. Gini index shows that there is a
huge gap between richest quarter and the poorest. This gap also had an increasing trend
during “The Lost Decay”. On the other side, a quite significant decrease in income inequality
coincides with the era of liberalized economy. In this era, service sector faces a boom, so that
employment shifts from agriculture and industrial sectors to service sector. This does not only
mean service sector creates more jobs, but it means that productivity in both agriculture and
5. GURSOY 4
industry rises. When this fact is combined with the positive effect of competition pressure
among industrial sector, labour force turns into a more efficient factor of production.
According to World Bank Data, value added production in industrial sector increases,
especially after 2004, while employment share of industry decreases. This means that
industrial sector specializes after it faces the competition from the world market. In fact, it
could be claimed that inefficiency in industry that occurs as a defect of the policies of ISI and
Infant Industry thesis has a decreasing trend. Therefore, wages in industrial sector are
expected to adjust itself in a way that it would contribute through more equal income
distribution in Brazil. In addition to that, employment in industry has been increasing after
2000s. It could be suggested that Brazil becomes more competitive in its industrial sector
against the world market, so that industry attracts more investment through time. This shows
that employment shifts from agriculture to industrial sector. This leads to a more equalized
economy. Therefore, it could be a factor that explain the decreasing inequality.
Data & Estimation
In this study, data is provided from World Bank and The Central Bank of Brazil. By
using those data, a time series regression will be used to support the thesis. Brazil economic
situation that from 1981 to 2009 will be compared to the current expectations based on the
theories related. This step is followed by the interpretation of the regression results. In the
regression, GINI coefficient will be used as the dependent variable, which is widely accepted
as the main indicator of income distribution. Explanatory variables that are used in this study
are employment share of agriculture (EAG), employment share of service sector (ESER),
trade-dummy (TRADE_DUMMY), added value in industrial sector (INDUS_V_ADD) and
GDP growth rate (GDPGROWT). All independent variables, besides EAG, will be examined
to capture their long-term effects on GINI. Therefore, their lagged versions are preferred.
Employment share of industrial sector is not used to avoid ambiguous result, because of the
technological unemployment, which is that Industrial sector would be expanding, even though
employment share of industrial sector is decreasing due to the increased productivity as a
result of technological progress. To sum up, econometric model is formed as;
6. GURSOY 5
𝐺𝑖𝑛𝑖 = 𝛽0 + 𝛽1 𝐸𝐴𝐺 + 𝛽2 𝐸𝑆𝐸𝑅(−1) + 𝛽4 𝐺𝐷𝑃𝐺𝑅𝑂𝑊𝑇(−2)
+ 𝛽5 𝑇𝑅𝐴𝐷𝐸_𝐷𝑈𝑀𝑀𝑌 + 𝛽6 ln[𝐼𝑁𝐷𝑈𝑆_𝑉_𝐴𝐷𝐷(−2)]
Where,
EAG : % of Employment in Agriculture to All
ESER : % of Employment in Service Sector to All
GDPGROWT : GDP Growth Ratio %
TRADE_DUMMY : Dummy variable ( 1 after 1994 )
INDUS_V_ADD : Value Added in Industrial Sector
In the model, all variables except GDPGROWT and TRADE_DUMMY were
stationary at their first order of integration. “STA” added to the beginning of integrated
variables. TRADE_DUMMY reflects the trade liberalization in Brazil at 1994. Therefore,
TRADE_DUMMY is 0 before 1994. In the model, Industry Value Added used in their
logarithm forms. After necessary adjustments, sample size has down to 25. Therefore, data
only spans between 1985 and 2009 after adjustments. The regression was tested for normality,
heteroscedasticity, serial correlation and for functional misspecification.
ModelPreview
According to the model, it could be expected that % share of agricultural employment
would have a negative correlation with Gini coefficient. As people move in to the cities and
find a job either in industrial or service sector, they are expected earn more income than they
could have in agricultural sector. However, this migration would create both frictional and
structural type of unemployment in the short-run. In this model, EAG only spans the short-run
effect of this shift. Therefore, it is expected to be negative.
7. GURSOY 6
On the other side, % of Employment in Service Sector (ESER) has an increasing trend
through the time period of this study. It means that demand for labour in service sector is
increasing. Besides, service sector is considered as a labour driven one, unlike industrial
sector where there are labour substituting capital use. In other words, most of the added value
is produced by labour in the service sector. Thus, wages in service sector could be expected to
increase in this period of time. In this manner, more people employed in service sector means
that more people have a better standard of living, which leads to a down-grading effect on
Gini coefficient. In this study, ESER is focused on its long-run effects. Thus, ESER is
expected to have a negative correlation with Gini coefficient. Deinlein’s reasoning could be
used to explain service sector’s effect on Gini.
As more and more people leave the traditional sector and migrate to the modern sector,
the marginal product of labour begins to rise in agriculture, and decrease in industry.
This could create a convergence in wage rates between the sectors and a decrease in
inequality ( Deinlein 2012)
10
20
30
1981 1986 1991 1996 2001 2006
% Employment in Agricultural Sector
8. GURSOY 7
As it is seen, there is a shift through service sector in Brazil. Therefore, it could be
expected that these shifts would adjust overall wages in a more convergent way.
Gdp Growth rate shows the volatility of the economy. Breen’s study shows that more
volatile economy suffers an increase on Gini coefficient. “Our analysis of cross-country data
shows that greater volatility, measured by the standard deviation of the rate of growth of
output, is associated with a higher degree of inequality.” (Breen 2005). In this context, Brazil
growth rate which could be observed with a volatile trend, is expected to affect Gini
coefficient in a negative meaning. Therefore, it could be expected that GDPGROWT and Gini
would have a positive correlation.
40
45
50
55
60
65
1981 1986 1991 1996 2001 2006
% Employment in Service Sector
-6
-4
-2
0
2
4
6
8
10
1981 1986 1991 1996 2001 2006
GDP GROWTH
9. GURSOY 8
Trade-Dummy variable would express the difference before and after 1994, which is
the date trade restrictions have been withdrawn. According to the modified version of
Hecksher-Ohlin Theorem by Paul Samuelson, countries would specialize in their abundant
factor. Therefore, both parties would benefit increase in relative prices of labour and capital.
At the end, abundant factor would benefit from trade liberalization. In other words, an
increase in trade-openness would decrease the inequality as it is suggested by Paul
Samuelson. In case of Brazil, abundant factor which is unskilled labour would experience an
increasing trend in their wages. (Samuelson 1948, Deinlein 2012). Therefore, Trade-Dummy
variable’s sign is expected to be negative.
Value added in industrial sector might be expected as negatively correlated to income
inequality. Sharpe et al. shows that there is a relation between productivity of labour and
wages. Growth in real wages is expected to determine by productivity of labour (Sharpe et al
2008). Since, value added in industry increases while employment share of industrial sector
has a downward trend, it could easily be assumed that marginal productivity of labour in
industrial sector is rising as well as their wages.
0
2
4
6
8
10
12
14
1981 1986 1991 1996 2001 2006
Log of Industry Value Added
10. GURSOY 9
Empirical Results
Final estimation results show a significant relationship for all explanatory variables
that are used in the model. Estimation results are as following:
DependentVariable:STAGINI
Method: LeastSquares
Sample (adjusted):1985 2009
Included observations:25 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C -0.148347 0.452300 -0.327983 0.7465
STA_EAG -0.934564 0.222124 -4.207394 0.0005
GDPGROWT(-2) 0.298486 0.099233 3.007924 0.0072
STA_ESER(-1) -0.945207 0.320497 -2.949195 0.0082
TRADE_DUMMY -1.466013 0.541646 -2.706590 0.0140
STA_LN_INDUS_V_ADD(-2) -0.911321 0.367269 -2.481346 0.0226
R-squared 0.705347 Mean dependentvar -0.147600
Adjusted R-squared 0.627806 S.D. dependentvar 2.088915
S.E. of regression 1.274398 Akaike info criterion 3.528389
Sum squared resid 30.85774 Schwarz criterion 3.820919
Log likelihood -38.10486 Hannan-Quinn criter. 3.609524
F-statistic 9.096509 Durbin-Watson stat 1.880443
Prob(F-statistic) 0.000151
“STA” is used for variables with stationaryin D(1)
𝐺𝑖𝑛𝑖 = −0.15 − 0.93𝐸𝐴𝐺 − 0.945𝐸𝑆𝐸𝑅(−1) + 0.3𝐺𝐷𝑃𝐺𝑅𝑂𝑊𝑇(−2)− 0.3 𝑇𝑅𝐴𝐷𝐸_𝐷𝑈𝑀𝑀𝑌
− 0.911ln[𝐼𝑁𝐷𝑈𝑆_𝑉_𝐴𝐷𝐷(−2)]
When the results are observed, it could be seen that all variables related with the same
way as they expected. Moreover, F-Statistic shows that equation as a whole is significant.
According to Durbin-Watson statistics, there is no enough proof to claim that the regression
has autocorrelation problem in %5 significance level (Du = 1.77; k = 5, n = 25). Therefore,
data shows that residuals are randomly distributed around zero. In addition to all, R-Square is
11. GURSOY 10
% 70 which provides that the regression is quite functional in case of its explanatory power.
Besides, it could be easily supported that all the explanatory variables are significant in %5
level. Moreover, Jareuq-Bera test shows that distribution is normal in %5 significance level
with test probability of 0.73. Cross product included White test is used to test
homoscedasticity. According to test results, there is no homoscedasticity in %5 significance
level (Prob. Chi-Square [19] = 0.18 ). Also, stability tests show that all residuals lie inside the
%5 significance area. In conclusion, there would be no inconvenience to interpret the results.
Data shows that a 1 percent less employment in agricultural sector is expected to
increase Gini coefficient, in the short-run, roughly by 0.88 points. When the actual trend in
employment share of agricultural sector is observed it could be seen that it decreased from
almost 30 percent in 1981 to 17 percent in 2009. So, one could argue that migration to urban
areas for seeking a job in a sector besides agricultural would be a huge mistake for people,
since they would be worse-off in short-time. This argument may easily be answered by
claiming that this negative effect of shifting away from agriculture is composed as frictional.
In other words, people would be worse-off until just they find another job in another sector,
such as service or industry.
Secondly, data on employment in service sector compromises the expectations, so that
as 1 percent increase in service sector employment is roughly expected to downward the Gini
coefficient by 0.95 points in the long-run. Actual data of employment in service sector has an
increasing trend in the time period of this study. As the graph interprets the upward trend of
service sector, it could be assumed that service sector has a significant role in decreasing
income inequality in Brazil. Moreover, this growth in service sector is also related to reforms
of trade and capital movement liberalization. By taking this into account, effects of
relationship between sectorial shift and liberalization reforms on income inequality could be
clarified on this finding.
According to data, annual Gdp growth rate causes a negative effect on income
inequality in the long-run. 1 point increase in the growth rate of economy is expected to cause
a 0.30 points increase in the Gini coefficient in the long-run. This effect could be explained by
business cycles. In other words, a boom in the economy is expected to increase income
inequality in the long-run, because a bust is followed after a boom. When the Gdp growth
above is examined, it could be seen that Brazil had a quite fluctuating growth cycles over the
period of 1981 and 2009. Since gdp growth rate is used as 2 terms lagged form in the
regression, it catches the business cycle effects on the results.
12. GURSOY 11
Trade openness data shows that it has a impact on Gini coefficient by 1.46. Therefore,
trade-openness is expected to have a positive impact on inequality. This result is compatible
with Hecksher-Ohlin Trade theory’s modified version by Samuelson.
In the regression, it is expected that 1 percent increase in industry value added causes
0.67 point decrease in Gini coefficient. This result explains that as industrial sector becomes
more productive or improves through producing more value added goods, people would
better-off. Since marginal productivity of labour in industrial sector gets higher as well as
their wages. As a result, wealth of worker class would be expected to enhance.
Conclusion
All Results gathered show that Brazil has benefited by trade liberalization as well as
directly also in-directly. As trade liberalization is implemented, economic sectors adjust to the
competitive conditions of world market. Therefore, Brazil economy faces a remarkable
change in its economy through 1994. In this study, examined effects of those sectorial shifts
implement that income inequality is decreasing. Even though, inequality is still quite a
problem in Brazil today with Gini index of 55 in 2009, one could expect that it will go down
in the future.
13. GURSOY 12
Bibliography
Baer, Werner. The Brazilian economy growth and development. 5th ed. Westport, Conn.:
Praeger, 2001. Print.
Breen, Richard, and Cecilia Garcia-Penalosa. "Income Inequality And Macroeconomic
Volatility: An Empirical Investigation." Review Of Development Economics 9.3
(2005): 380-398. EconLit. Web.
Calva, Luis Felipe, and Nora Lustig. "Markets, The State and The Dynamics of Inequality in
Brazil." Declining inequality in Latin America: a decade of progress?. New York:
United Nations Development Programme ;, 2010. 134-175. Print.
Carneiro, Francisco Galrão, and Jorge Saba Arbache. "Assessing the impacts of trade on
poverty and inequality." Applied Economics Letters 10.15 (2003): 989-994. Print.
Castilho, Marta, Marta Menéndez, and Aude Sztulman. "Trade Liberalization, Inequality, and
Poverty in Brazilian States." World Development 40.4 (2012): 821-835. Print.
Deinlein, J. Michael. "The Dichtonmy of Devleopment in Brazil: A Test of Kuznets' U Curve
Hypothesis." Indian Journal of Economics & Business 11.2 (2012): 485-501. Print.
Green, Francis, Andy Dickerson, and Jorge Saba Arbache. "A Picture of Wage Inequality and
the Allocation of Labor Through a Period of Trade Liberalization: The Case of
Brazil." World Development 29.11 (2001): 1923-1939. Print
Sharpe, Andrew, Jean-François Arsenault, and Peter Harrison. The relationship between
labour productivity and real wage growth in Canada and OECD countries. Ottawa:
Centre For The Study Of Living Standards, 2008. Print.
14. GURSOY 13
Taylor, Lance. External liberalization in Asia, post-socialist Europe, and Brazil. Oxford:
Oxford University Press, 2006. Print.
Thomas, Vinod. From inside Brazil developments in a land of contrasts. Washington, D.C.:
Stanford Economics and Finance :, 2006. Print.
