The Spatial Effect on The Provincial Wage Increasing In Indonesia

The Spatial Effect on The Provincial Wage Increasing In Indonesia

(Data Analysis of Sakernas 2008-2010)

Ida Budiarty

ABSTRACT

The purpose of this study is to analyze the spatial effect of wage increases. Generally, wage increases in one region affect the rates in nearby places. In spatial econometrics, this shows that there is an altitudinal influence in increasing wage rates. In this study, a spatial regression model is used. The data used was obtained from the 2008-2010 Sakernas panel. The results showed that the spatial effect was significant, meaning it influences the wage increases of several provinces in Indonesia. This finding shows the need for caution when determining the value of wages. In case the increase affect wages in neighbouring provinces without being accompanied by increased productivity, production processes may become inefficient, negate added value, reduce regional competitiveness, and in turn, reduce employment expansion.

Keywords: spatial influence, wage rates, productivity, competitiveness, and expansion of employment opportunities.

INTRODUCTION

Increased wages occur due to more significant minimum wages (MW) in the region. Research in developed countries shows that MW is significant in increasing the wages of workers, especially for low-wage young workers in the formal sector.  Additionally, it has changed the wage distribution of workers (Gramlich, 1976; Neumark, Schiwetzer, and Wascher, 2004). In developing countries, MW also proved to be significant in increasing the wages and incomes of workers in the formal and informal sectors (Lemos, 2004; Fajnzylber, 2000). Increased wages in individual provinces can be a driving factor for improved remunerations in other regions that are geographically near. This is especially the case where the average wages in the province are relatively low with an abundant supply of labor. In econometrics, this indicates a spatial influence on the increase in the wage level. In general, the influence occurs since there is a spatial dependency or heterogeneity. The difference lies in the dependency structure related to location, the economy in general, or spatial webspace. The presence of spatial influences causes the correlation between cross-sectional units not to be equal to zero but follows specific spatial orders. Economic, spatial linkages occur when E (εit, εjt) for certain t and i j and their covariance is not equal to zero but corresponds to certain neighborhood relations (Anselin et al., 2008).LITERATURE REVIEW

According to Elhorstet al. (2011), there are 3 types of spatial interaction effects. This includes 1) the effect of exogenous interactions which occurs in case the value of observations in region i affects region j; 2) the effect of endogenous interactions in case the independent factors in region i affect the value of observations in region j; and 3) the effect on error term where the shock occurring in region i affects the economy in region j. Based on the influence of spatial interactions, there are types of models, including the lag model or the Spatial Autoregressive (SAR) model and the spatial error model (SEM). These two models were developed by Buettner (1999) and Longhi (2006) in estimating the wage curve model in East Germany. In the case of wage equations affecting payments in certain areas Anselin, Buettner (1999) stated that the presence of the dependent lag spatial variable in the wage equation model becomes significant. The study of spatial interaction begins with quantifying the structure into the weighing matrix of inter-provincial linkages called the W matrix, which is in with the established neighbor relations. For analysis, the weighing matrix used is transformed into a standardized matrix. It is assumed that the closest area has the same proportion of influence on a province. The magnitude of a province’s influence on other provinces can be easily identified. This criterion was widely used by previous researchers (Getis and Aldstadt, 2004). The presence of spatial influence variables in the model is not carried out a priori but will be tested.

Buettner (1999) tested the specification of the wage curve in East Germany 1987-1994. There are two types of spatial dependencies investigated, first-order autoregression in error, and lag dependent variables. From a series of tests conducted, the conclusion was the same as Elhorst et al., showing that estimates using ML provide the best results. Also, Longhi et al. (2006) stated that there are indications of monopsonistic competition in the East German local labor market. This competition causes employers to develop incentives and opportunities to reduce or increase wages at times of high or low unemployment, and this affects the wage curve. The method used by Longhi et al. is the Two-Stage Least Square (2SLS) estimation based on the Hausman endogeneity test results of the unemployment as an endogenous variable. High unemployment rates in neighboring areas are likely to fill local job openings and reduce the outflow of local unemployment (Burgess and Profit, 2001).

METHODS

The research data is in the form of Sakernas trial panel, which was first conducted by BPS in 2008-2010. The data matching process is based on some information about individual characteristics such as variables of residence, gender, age, level of education completed, and other respondents’ characteristics. The results showed that individual information did not all end in 2010. Therefore, in order to obtain valid information, the Sakernas data set for the period 2008-2010 was broken into two parts, the period 2008-2009 and 2009-2010. The total number of research samples was 266,000 respondents. The research model is a modification of the Neumark, Sweitzer, and Wacher (2004), abbreviated as NSW. However, the difference is in the measurement of the value of the minimum and worker wages as well as the addition of spatial interaction in the model. Δwpit-1W symbolizes the spatial interaction variable. The dependent spatial lag effect is used since there are indications of changes in wages of certain regions affecting the value of remunerations in the labor market of neighboring regions, and the structural model is as follows.

where wi is the individual wage level i, Mwp is the provincial minimum wage value p. Vector X ’is an individual characteristic variable such as the level of education of workers, age, employment, and gender, while W is a weighted spatial matrix. The data ratio of individual workers’ wages to the minimum wage interacts with all variables Rj (j = 1,2, … n).

RESULTS

Given the nature of random data from large populations, estimates are also made for the OLS-RE random-effects model. Hausman test results show the OLS_FE model as a more efficient estimator with a Chi2 value of 249.73. So that in subsequent estimates a fixed effect might always be added to the model. The presence of the lag dependent variable (LagU8.W) proved significant with Wald statistical value of 94.54 and showed a negative influence on a reasonably strong magnitude. This is following the findings of Longhi et al. (2006).

Hsiao in Buettner (1999) explains the addition of fixed effects in the dynamic wage regression model provide estimation bias since lag levels correlate with error rates. Therefore, it requires the use of the generalized method of moments (GMM) instrument variable estimates. The coefficient of spatial influence of LagU8.W is 0.165. This means that the increase in wages in a province in 2008-2009 reached Rp16,484.968 if the value in the surrounding area reaches Rp100,000. In the 2009-2010 period, endogeneity testing of the regressors was not proven with an F value of 0.008. In this regard, a projection of maximum likelihood (ML) was conducted as an efficient estimator (Elhorst et al., 2011). The presence of the dependent spatial lag influence in the estimation of maximum likelihood is increasingly confirmed by the results of the likelihood-ratio test, which proved significant with a test value of 8.20. Additionally, the spatial effect of LagU9.W has a negative impact on a coefficient value of 00.026. This finding indicates a decrease in wage changes in individual provinces of Rp2,607.570 in 2009-2010 in case the wage increase in the surrounding area amounted to Rp100,000. The negative effect was due to the economic situation in 2008-2009. Indonesia experienced a decline in economic growth in the fourth quarter at the end of 2008, and there were restrictions on minimum wage increases in 2009.

note:  *** p<0,01, ** p<0,05, * p<0,1

DISCUSSION

Spatial influence proved significant in increasing provincial wages. The provinces of Papua and Banten need to determine wages in their area carefully due to the spatial influence of the two regions. Wages rise without being followed by increased productivity, eliminates the region’s added value. Based on the average spatial influence in Indonesia, 57.58 percent of provinces whose wage increases are lower than the average improved in 2009-2010. The increase in wages for each province is different. Moreover, in the period 2009-2010, 43.60 percent of the provinces experienced an increase in wages due to the spatial effect from the previous year. This increase will certainly bring changes in the wage distribution density (Motellбn et al., 2011). The wage has network properties through an overflow (diffusion) spatial advantage. Fast wage growth in a region encourages slow wage improvement in the surrounding area. The more developed regions give more impetus to the less developed places but not vice versa. Wages also have a positive spillover effect in an area, and therefore the relative wage mobility might be faster.

CONCLUSION

This study examined the significance of the presence of spatial influence in wage changes in certain provinces. In the NSW model (2004), a new variable was added, which is the spatial dependent lag. The addition of spatial variables is not conducted a priori but through testing. Estimation results show that there is a spatial effect on wage changes in several provinces in Indonesia.

REFERENCES

Anselin, L, Julie Le Gallo dan Hubert Jayet. 2008. “Spatial Panel Econmetrics” dalam The Econometric of Panel Data, L. Matyas, P.Sevetre (eds). Springer_Verlag Berlin Heidelberg.

Aswicahyono, Haryo. 2014. “Kebijakan Ketenagakerjaan Indonesia; Pentingnya Aspirasi Semua Pihak” dalam Untuk Indonesia 2014-2019: Agenda Ekonomi, Damuri, Yose Rizal (eds). Centre for Strategic and International Studies (CSIS).

Buettner, Theiss. 1999. “The Effect of Unemployment, Aggregate Wages, and Spatial Contiguity on Local Wages: an Investigation with German District Level Data”. Papers in Regional Science (RSAI) Volume 78, Issue 1, p.47-67.

Burgess, S dan S. Profit. 2001.”Externalities in the Matching of Workers and Firms in Britain”, Labour Economics 8: 313-333.

Elhorst, Paul, J., 2011. Spatial Panel Model. The University of Groningen, Department of Economics, Econometrics and Finance, September 2011.

Fajnzylber, Pablo. 2000. “Minimum Wage Effects Throughout the Distribution: Evidence from Brazil’s  Formal and Informal Sectors”. Working Paper, Brazil, Cedeplar.

Gramlich, Edward M., 1976. “Impact of Minimum wage on Other wages, Employment, and Family Incomes”. Broking Papers on Economic Activity 2: 409- 61.

Getis,  Arthur dan Jared Aldstadt, 2004.”Constructing the Spatial Weights Matrix Using a Local Statistic”. Journal Of Geographical Analysis, Vol. 36, Issue 2, p.90-104. Blackwell Publishing Ltd.

Lemos, Sara. 2004.”The Effect of the Minimum Wage in the Formal and Informal Sectors in Brazil”. Brazilia, Discussion Paper Series IZA DP No.1089.

Longhi, Simonetta, Peter Nijkamp dan Jaques Poot. 2006. “Spatial Heterogeneity And The Wage Curve Revisited ” — Journal of Regional Science, Volume 46, Issue 4, p.707 – 31.

Motellбn, Elisabet, Enrique Lбpez-Bazo dan Mayssun El_Attar.2011. “Regional Heterogeneity in Wage Distribution: Evidence From Spain”. Journal of Regional Science, Volume 51, Issue 3. p. 558- 84.

Neumark, David, Schiwetzer, Mark dan Wascher, William. 2004. “The Effect of Minimum Wage Throughout the Wage Distribution”. The Journal of Human Resources 39(2): 425 -50

Pratomo, Devanto Shasta. 2010. “The Effects of Changes in Minimum Wage on Employment in the Covered and Uncovered Sectors in Indonesia”. Journal of Indonesian Economy and Business 25(3): 278 – 92.

Rama, Martin. 2001. “The Consequences Doubling of the Minimum Wage: The Case of Indonesia”. Industrial and Labor Relations Review 54(4); 864-81.