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Brazilian society today faces a serious macroeconomic problem that is the high rate of unemployment. With persistent stagnation of growth, it is concluded that the resumption of jobs will not be something verifiable in the short term, requiring actions that can boost the growth of the product with the improvement of the business environment. The purpose of this paper is to analyze empirically the long-term relationships between aggregate unem ployment, monetary policy instruments, exports and direct investments in the country. For this, the econometric strategy used is the approach by ARDL models with causality test developed by Toda and Yamamoto. The results reveal the importance of monetary policy instruments for the job creation environment where percent increases in the level of national activities, inflation and exports lead to reduction of unemployment and also reveal that the direct investments made in the did not contribute to the generation of workplaces. There is evidence that dynamic shocks in the labor market take 4 - 7 months to return to equilibrium unemployment levels.

High level of unemployment is a problem that affects Brazilian society today. He associated with decrease in disposable income, which turn causes a reduction in aggregate consumption and, depending on the intensity and duration, triggers several others problems in the economy, such as the fall in the domestic product, the increase in welfare expenditures in support of families without income, among other social problems.

It is not surprising that unemployment is a central issue in policy discussions and is often used as an indicator for measuring the labor market situation, performance and health of the economy. It is also a sign of success or failure of economic policies implemented by governments, which is evident in Brazil with the poor economic performance achieved in recent years. In the year 2015, for example, real GDP decreased by −3.8% compared to the previous year and in 2016 showed a result of −3.6%. In 2017, it grew by 1% and remained the same in 2018. In contrast, unemployment showed a vertiginous increase from the year 2015. Against this backdrop, it is concluded that the resumption of jobs will not be easily verifiable in the short term, if actions are needed that can boost the growth of the product while combining monetary and fiscal policies to favor the business environment.

Inflation is another problem that demands the sacrifice of the population, since the monetary policy instrument that seeks to correct this market distortion ends up generating a tradeoff between inflation and unemployment. Future uncertainties play a fundamental role in this process, since, depending on how expectations are anchored, economic cycles may be amplified [

Exports can be a booster in terms of job creation, given that with low aggregate demand in the domestic market, foreign exchange policies favorable to foreign trade can be a solution for the maintenance of existing jobs or for with the need to increase more manpower resources.

The purpose of this paper is to analyze empirically the long-term relationships between aggregate unemployment, monetary policy instruments, exports and direct investments in the country. The data used cover the period from March 2012 to June 2018. To investigate the relationships among macroeconomic variables, the ARDL model with cointegration test [

It is possible to find different theoretical approaches that seek to associate unemployment with certain macroeconomic variables. In Brazil the studies that go deeper into this singular theme of the labor market are centered in three groups. The first group seeks to improve the concept of unemployment and to specify how it should be operationalized empirically. A second group seeks, from the criteria of segmentation by age, sex, race, level of schooling and regionalization, to determine the structure of the unemployment rate; and finally, a last group studies the aggregate unemployment rate in order to find relations with other macroeconomic variables [

For the Brazilian case, there is no consensus about which variables are the most indicated, or more assertively, which compose a better set of variables to understand the dynamics of unemployment and, therefore, there is no agreement on which theory best describes [

Reference [

A smoothed transition regression model (STR) was used by [

Reference [

In terms of the role that exports play in generating employment in the country, the perception is that the increase in exports of products is positive because, even if they are not recognized as a source of aggregate demand generation [

The relationship between direct investment in the country and unemployment is little explored in the literature. Reference [

In unemployment studies, Autoregressive Distributed Lag (ARDL) models have been found in the international literature [

These considerations make the need to identify the dynamics of unemployment and its determinants in Brazil more significant as a result of the changes in the political and economic scenario that occurred in recent years.

^{1}Except for inflation (INFL) and interest rate (RATE) that are in their original form. These already have percentage values. This option is also justified by the fact that INFL, in some months of the series, presented negative values.

This study covers the period from March 2012 to June 2018, a period in which countercyclical efforts related to the subprime crisis of 2008 began to lose strength in the country and shortly afterwards a process of political exhaustion of the current government began, culminating in a serious political-institutional crisis that results in the impeachment of the president of Brazil. From 2012 also, the Brazilian Institute of Geography and Statistics (IBGE) modified the metrics of data calculation of unemployment and, therefore, this investigation starts from this change. ^{1}. The series used follow as described below:

1) Unemployment (lnUN)—The unemployment rate used is that provided by the IBGE through the national survey for a sample of households (Series 24369 of the website of the Central Bank of Brazil—BCB).

2) IBC-Br (lnPROD)—The Central Bank of Brazil (IBC-Br) Activity Index with seasonal adjustment is adopted as proxy for GDP. The index incorporates the trajectory of variables considered as proxies for the performance of all sectors of the economy. Available in BCB Series 24364. The trajectory of Brazilian economic activity can be seen in

3) Inflation (INFL)—The broad consumer price index (IPCA) is used as the proxy for national inflation, since the index incorporates the cost of living of households with wages ranging from one to forty minimum wages. BCB Series 433.

4) Interest rate (lnRATE)—The rediscount rate defined by the Central Bank (Selic interest rate) accumulated in the month, annualized, serves as a parameter of the interest rate in the country. BCB Series 4189.

5) Exchange rate shocks (lnEXCH)—The BCB Series 3697 and 3698, dollar values for sale and purchase, respectively are used. The shock in the change was captured from the mean of the results of both series.

6) Direct Investment in the Country (lnINV)—Direct investment in Brazil is divided into two main instruments: equity participation and intercompany operations. The first considers the inflows of resources in currency or assets related to the acquisition, underwriting or increase of capital stock of companies in the country. The intercompany operations include the granting of credits by subsidiaries or affiliates abroad to their parent companies in Brazil accounted for as investments. The series used is the 22885 BCB.

7) Export (lnEXP)—National export data are made available by the Ministry of Foreign Trade and Services (MDIC), compiled from Simplified Export Declarations (DSE) and are reported in kilograms of all monthly records of foreign trade in goods.

The bound test [

objective is to verify the existence of a long-term relationship between the variables, and it is not necessary to analyze the order of integration of these variables, since the variables have a minor integration order of what two. Therefore, all series can be integrated of order 0 [I(0)], of order 1 [I(1)] or mutually cointegrated. It is estimated the cointegration test based on the dynamics of the Error Correction Model (ECM) to confirm the existence of a long-term relationship between the variables of interest using the F-statistic. According to [

Δ Y t = β 0 + ∑ i = n β j Δ y t − j + ∑ i = 0 n δ j Δ x t − j + φ y t − 1 + φ x t − 1 + ε t (1)

where ΔY_{t} represents the first difference; β_{0} is the term of the constant; β_{j} and δ_{j}, are the short-run parameters; φ, represents long-term parameters and ε_{t} is a white noise process, N (0, σ2), identically, independently distributed.

The analysis of the relationship between the variables of this study occurs through the estimation of three distinct ARDL models. The model 1 described in Equation (2) suggests the hypothesis that the volume of national economic activity, inflation, interest rate and exchange rate shocks have an influence on the determination of the level of Brazilian unemployment. Equation (3) presents model 2, where it is assumed that product, inflation and direct investments are related to unemployment, and finally, in model 3, the variables used are product, exchange rate shocks and export are inserted, according to Equation (4).

Model 1

Δ ln U N t = β 0 + ∑ j = 1 n β j Δ ln U N t − j + ∑ j = 0 n δ j Δ ln P R O D t − j + ∑ j = 0 n ψ j Δ I N F L t − j + ∑ j = 0 n ∂ j Δ ln R A T E t − j + ∑ j = 0 n τ j Δ ln E X C H t − j + β 1 ln U N t − 1 + β 2 ln P R O D t − 1 + β 3 I N F L t − 1 + β 4 ln R A T E t − 1 + β 5 ln E X C H t − 1 + ε t (2)

Model 2

Δ ln D E S t = β 0 + ∑ j = 1 n β j Δ ln U N t − j + ∑ j = 0 n δ j Δ ln P R O D t − j + ∑ j = 0 n ψ j Δ I N F L t − j + ∑ j = 0 m θ j Δ ln I N V t − j + β 6 ln U N t − 1 + β 7 ln P R O D t − 1 + β 8 I N F L t − 1 + β 9 ln I N V t − 1 + ε t (3)

Model 3

Δ ln U N t = β 0 + ∑ j = 1 n Δ ln U N t − j + ∑ j = 0 n δ j Δ ln P R O D t − j + ∑ j = 0 n ψ j Δ I N F L t − j + ∑ j = 0 n τ j Δ ln E X C H t − j + + ∑ j = 0 m ω j Δ ln E X P t − j + β 11 ln U N t − 1 + β 12 ln P R O D t − 1 + β 13 I N F L t − 1 + β 14 ln E X C H t − 1 + β 15 ln E X P t − 1 + ε t (4)

Aprimary condition for the ARDL cointegration test is that the series used be integrated I(0), I(1) or mutually cointegrated. If the series are I(2) it becomes impossible to use this approach. In order to verify this condition, stationarity tests were performed, according to Enders [

Series | ADF | PP | Order | ||
---|---|---|---|---|---|

Level | 1^{st} Difference | Level | 1^{st} Difference | ||

lnUN | [−3.8899] | - | [1.1147] | [−3.4960] | I(1) |

(−3.1686) | - | (−1.6139) | (−1.6149) | ||

lnPROD | [−0.5049] | [−9.6764] | [−2.3498] | [−9.6900] | I(1) |

(−1.6139) | (−1.6139) | (−3.1624) | (−1.6139) | ||

INF | [−3.4872] | - | [−3.5433] | - | I(0) |

(−2.5786) | - | (−2.5786) | - | ||

RATE | [−2.7472] | - | [−0.6880] | [−3.3025] | I(1) |

(−2.5889) | - | (−1.6139) | (−1.6139) | ||

lnEXCH | [1.1540] | [−5.2833] | [1.6275] | [−5.1152] | I(1) |

(−1.6139) | (−1.6139) | −(1.6139) | (−1.6139) | ||

lnINV | [−7.5418] | - | [−7.5309] | - | I(0) |

(−2.5882) | - | (−2.5882) | - | ||

lnEXP | [−5.3670] | - | [−6.5840] | - | I(0) |

(−3.1639) | - | (−3.1624) | - |

Note: [ ] denotes T-statistic values; ( ) denotes the critical value. Source: Author’s calculation.

The use of autoregressive models is aimed at finding dynamic relationships between time series of a system, more precisely the response of endogenous variables to shocks in their innovations. The reduced form of VAR can be presented as Equation (5).

y = c + ∑ i = 1 k Γ i y t − i + η x t + ε t (5)

where y_{t} is the vector composed of p endogenous variables of the system y_{t} = [y_{1}, t, ..., y_{p}, t], c is the vector of constants, Γ_{i} is the coefficient matrix of the lags of the endogenous variables, k is the number of lags in the system, x_{t} is the vector of exogenous variables, η represents the matrix of exogenous variables and ε_{t} the error term.

The effects can be measured, starting from a shock in the innovation of the temporal process, through the impulse-response function. The OLS estimation for the autoregressive vector coefficients necessarily need to be stationary and, if any series of the analyzed model presents any unit root, the inference is not valid in the VAR representation. So that do not get caught up in this impediment, [

The test [

With this approach, the causality test can be applied to a non-stationary series with the possibility of determining the direction of causality between the analyzed variables. The assumption in this case is that if the maximum order of series integration (d_{max}) is added to the Granger model, it can be applied to the non-stationary series level and provide valid estimates [_{max}) comprised by unemployment and independent variables, according to Equation (6) and Equation (7) below:

X t = ω + ∑ i = 1 m θ i X t − 1 + ∑ m + 1 m + d max θ i X t − 1 + ∑ i = 1 m δ i Y t − 1 + ∑ m + 1 m + d max δ i Y t − 1 + v 1 t (6)

Y t = ψ + ∑ i = 1 m φ i Y t − 1 + ∑ m + 1 m + d max φ i Y t − 1 + ∑ i = 1 m β i X t − 1 ∑ m + 1 m + d max β i X t − 1 + v 2 t (7)

where X is unemployment, Y is the dependent variable. ω, θ, δ, ψ, φ and β are parameters of the model and d_{max} is the maximum order of integration that occurs in the system; ν_{1t} ~ N (0, Σv_{1}) and ν_{2t} ~ N (0, Σv_{2}) are the residuals of the model and Σv_{1} and Σv_{2} are the covariance matrices of ν_{1t} and ν_{2t}, respectively. The null hypothesis of non-causality can be expressed as H0: δ_{i} = 0, ∀ i = 1, 2, ..., m.

The results in

The estimates of the models follow the ARDL construction (a, b, c, d, e), where “a” is defined as the number of lags of the dependent variable, while the others, b, c, d, e, refer to the number of lags of the variables used sequenced. The Adjusted R^{2} criterion was used for the automatic lag determinations with Newey-West matrix to correct possible correlation effects of the error terms in the constructed regressions. Usually the shocks applied to the macroeconomic variables do not have immediate repercussions on the real economy, so the maximum number of 10 lags for each parameter was used in this study with the order of the variables following the one described previously for each model.

The stability of the regression parameters were verified by the CUSUM and CUSUMSQ tests proposed by [

The verification of the existence of long-term relationship among the variables occurs from the cointegration test. It is possible to verify that, in all the proposed

Null hyphotesis | Terms of lag (k + d) | Chi^{2} | P-value |
---|---|---|---|

lnPROD does not Granger causes lnUN | 3 + 1 | 9.0550** | 0.0286 |

lnUN does not Granger causes lnPROD | 3 + 1 | 5.4605 | 0.1410 |

INFL does not Granger causes lnUN | 6 + 1 | 26.2257*** | 0.0002 |

lnUN does not Granger causes INFL | 6 + 1 | 3.2543 | 0.7763 |

RATE does not Granger causes lnUN | 6 + 1 | 10.2024 | 0.1164 |

lnUN does not Granger causes RATE | 6 + 1 | 5.2491 | 0.5123 |

lnINV does not Granger causes lnUN | 3 + 1 | 4.4030 | 0.2211 |

lnUN does not Granger causes lnINV | 3 + 1 | 3.4898 | 0.3221 |

lnEXP does not Granger causes lnUN | 7 + 1 | 16.7315** | 0.0192 |

lnUN does not Granger causes lnEXP | 7 + 1 | 37.2497*** | 0.0000 |

lnEXCH does not Granger causes lnUN | 3 + 1 | 4.4030 | 0.2211 |

lnUN does not Granger causes lnEXCH | 3 + 1 | 3.4898 | 0.3221 |

Note: Indicates * significant at 10%; ** significant at 5%; *** significant at 1%. Source: Author’s calculation.

models, the results of the cointegration tests indicated that it is possible to have a short or long term relationship between the variables. The F-statistic in all models are above the upper boundaries I(1), thus rejecting the null hypothesis at 1% that there is no cointegration between the variables, as reported in

All models presented adequate residuals, with absence of serial autocorrelation and heteroskedasticity (

In

Parameters | Model 1 ARDL (10, 9, 0, 10, 5) | Model 2 ARDL (10, 5, 7, 7) | Model 3 ARDL (10, 5, 7, 5, 10) |
---|---|---|---|

F-statistic | 13.44 | 7.84 | 7.35 |

Inferior limit I(0) | 3.74 | 4.29 | 3.74 |

Superior limit I(1) | 5.06 | 5.61 | 5.06 |

Selection criteria | R^{2} Adjusted | R^{2} Adjusted | R^{2} Adjusted |

Source: Author’s computation based on survey data.

Series | Estimation of ARDL Models | ||
---|---|---|---|

Model 1 ARDL (10, 9, 0, 10, 5) | Model 2 ARDL (10, 5, 7, 7) | Model 3 ARDL (10, 5, 7, 5, 10) | |

lnPROD | −5.9157*** | −7.8351*** | −7.6499*** |

INFL | −0.0790*** | −0.1935* | −0.0607 |

RATE | 0.0177*** | - | - |

lnEXCH | 0.2945*** | - | 0.5462*** |

lnINV | - | 0.1531** | - |

lnEXP | - | - | −0.6788* |

R^{2} | 0.9993 | 0.9986 | 0.9994 |

R^{2} Adjusted | 0.9984 | 0.9974 | 0.9984 |

F-statistic | 1104.699 | 790.784 | 1000.312 |

Prob(F-statistic) | 0.0000 | 0.0000 | 0.0000 |

Jarque-Bera | 0.8931 | 0.7538 | 0.0931 |

LM test | 0.0719 | 0.3556 | 0.3695 |

Heteroskedasticity(BPG) | 0.4404 | 0.6894 | 0.3862 |

Short-term Correction Mechanism | |||

CointEq(−1) | −0.2908*** | −0.2601*** | −0.1638*** |

Note: Coefficients in bold. Indicates *significant at 10%; **significant at 5%; ***significant at 1%. Source: Author’s computation based on survey data.

reveals that the monetary policy instrument affects the level of unemployment, where the increase in interest rate contributes to rise of unemployment in the long term. Same situation when analyzed the exchange rate shock variable, which also positively impacts on unemployment. Model 2 revealed evidence that, in the period analyzed, direct investments in companies in the country were positively related to unemployment.

As opposed to what occurred in the periods prior to the subprime crisis, and why not to say, before the institutional crisis that occurred after the presidential elections in Brazil in 2014, a period in which foreign investments were linked to the long-term engagement of companies under the [

In Model 3, the signs found were those expected with a statistically significant exchange rate shock at 1% and positively related to unemployment. The results indicate that increases in the volume of exports contribute to the reduction of the unemployment rate.

Even with long-term relationships, it is possible that short-term shocks significantly alter these relationships. In this case, in order for the cointegration to continue, a mechanism for correcting these shocks is necessary in order to return to long-term dynamics. In

For all cases, the Error Correction Mechanism presented a negative and statistically significant result. This condition guarantees that there will be convergence of models, indirectly indicating a significant relationship for the long term. The coefficients found were −0.16, −0.26 to −0.29, for each situation analyzed. These results reveal the speed of adjustment in each model for return to initial equilibrium. A result denotes that the unemployment rate takes around 7 months to return to its long-term equilibrium in the first model and approximately 4 months in the other two models.

This paper aimed to demonstrate empirical evidence of the long-term relationship between unemployment and macroeconomic variables that influence it. Evidence from the causality test indicates that national product, inflation, and exports cause the level of unemployment in Brazil. These results were corroborated with the analysis of the ARDL models of cointegration.

For the long term, the national product, inflation and exports are negatively related to unemployment, indicating that percentage increases in these variables cause a reduction in the level of unemployment. There’s evidence that the increase in the interest rate, shocks in the exchange rate and an increase in the direct investments madecontribute to the increase of unemployment in Brazil. In the latter case, the importance of the monetary policy instrument for improving the economic environment and generating job opportunities is revealed. As for direct investments made in Brazil, it can be understood that in the analyzed period, they were not necessarily aimed at expanding productive capacity with more job creation, instead, they may have been directed towards the formation of working capital or up to gross fixed capital formation to which, once productivity levels have been reached, a reverse process of downsizing of labor begins. If there are short-term dynamic shocks in which the labor market changes to the level of unemployment, it is possible that the adjustment time to the long-term dynamics takes from 4 to 7 months to return the levels of equilibrium unemployment, depending on the model analyzed.

It is not the purpose of this work to exhaust the debate on the subject, however, it is hoped that it can contribute to the literature since it shows the long-term relationships of unemployment and the determinants that compose it. Knowing their behavior, it is possible to target specific macroeconomic policies combined that can contribute to the resumption of employment levels in Brazil.

The authors thank the unidentified reviewer for the comments made to a preliminary version of the article, exempting him from any responsibility for any remaining errors.

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

de Lima, F.R.S. and Marques, J.B. (2019) Macroeconomic Determinants of Unemployment in Brazil: An ARDL Approach. Modern Economy, 10, 1744-1758. https://doi.org/10.4236/me.2019.107112