Ageing Workforce, Productivity and Labour costs of Belgian Firms Vandenberghe, Vincent (IRES- UCL)

Waltenberg, Fabio (CEDE, Universidade Federal Fluminense)

ZEW seminar, MannheimJune 15, 2010Universit Catholique de Louvain

*Presentation outlineMotivationExisting literatureMethodologyDataResults and conclusions

*1. Context, motivationPolicy and scientific context- Ageing population, political initiatives to increase older empl. rates but (very) low employment in some EU countries (e.g. Belgium, France, Luxembourg)- Existing literature looks mainly at the consequences of an ageing population, in terms of welfare cost or growth (Gruber and Wise, 2004)the retirement behaviour of older individuals (replacement rates, pension, early-retirement schemes, role of health, joint-decision within households) (Mitchell & Fields, 1983) supply sideNot so much the determinants of the labour demand by firms (e.g. labour costs, productivity...) demand side

Despite country-level evidence suggesting that it could matter

*1. Context, motivation (cont)Belgium

*1. Context, motivation (cont)Belgium

*1. Context, motivation (cont) StandardParameter Estimate Error t Value Pr > |t|

Intercept 0.20 0.16800741 1.23 0.2312rwage -.58 0.17990096 -3.26 0.0038rp 0.17 0.22314542 0.76 0.4560proc glm data=silc.corr;model emplg= rwage rp /solution;run;

*1. Context, motivation (cont.)Our main motivation here is to answer two questions

Do ageing workforces negatively affect productivity performance of firms? [growth/ GDP]

Are employers willing to (re)employ older workers? [Employment rate]=> Key assumption: a sizeable negative productivity- vs. labour costs gap is likely to adversely affect the labour demand for older workers

*2. Existing literature on age, productivity (and labour costs)Individual-level data

Individual job performance is found to decrease from around 50 years of age, which contrasts with life-long increases in wages. Productivity reductions at older ages are particularly strong for work tasks where problem solving, learning and speed are needed, while in jobs where experience and verbal abilities are important, older individuals maintain a relatively high productivity level. (Skirbekk, 2004: SURVEY)

*2. Existing literature (cont.)Country-level data

() large macro-data panel () explores the impact of the age composition of the labour force on levels and growth rates of output per worker as well as on total factor productivity (TFP). The results point to an inversely U-shaped relationship between the share of workers in different age groups (...) the impact of projected ageing of the labour force on productivity and per-capita growth could be really substantial in some cases (Werding, 2007)

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Firm-level data***Hellerstein et al. (1999) [USA]: wages and productivity tend to grow with age, but no significant gap.Malmberg, Lindh, & Halvarsson (2006) [Sweden]: an accumulation of high shares of older adults in Swedish manufacturing plants does not seem to have a negative effect on plant-level productivityGrnd & Westergrd-Nielsen (2008) [DK]: find that mean age (and age dispersion) in Danish firms are inversely u-shaped related to firm productivitySkirbekk, (2008) [International survey]: The most common finding from these studies is a hump-shaped relation between job performance and age. Of the 14 studies considered, 11 find a productivity decline in the 50s relative to the 30s and 40s, two have inconsistent results, while one finds that productivity peaks among the oldest workers.

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Aubert & Crpont (2003), Economie & Statistiques [France], productivity rises with age until around the age of 40, before stabilizing, a path which is very similar to those of wages. A wage-productivity gap is observed only for workers aged more than 55Dostie (2006), IZA [Canada] obtains concave (inversely U-shaped) age-productivity profiles. Significant wage-productivity gap occurs with educated males aged 55 +

Ilmakunnas & Mliranta(2007) [Finland]. Older workers separations are correlated with higher productivity, lower cost=> higher profitsGbel, Ch. and Zwick, Th. (2009) [Germany] find that productivity increases with the share of employees until the age of 50-55 and only decreases slightly afterwardsvan Ours, J.C & Stoeldraijer, L. (2010), [Netherlands] find little evidence of an age related pay-productivity gap

*3. MethodologyEqu.1: productivitylog Yit = log LitA + logKit + Fit + it

where: Yit is the firm value added

and LitA a labour quality index -la-Hellerstein

LitA = k k Likt = ref Lit + k (k - ref) Likt

k being the productivity of type (e.g. age) k workers

*Assuming k=0 18-29k=1 30-49 [ref]k=2 50-65

log Yit yit = A + li,t + 0 Pi0t + 2 Pi2t+ kit +Fit + it

with Pi2t= Li2t/Li1t 2 = (2 1) and 2= 2/1;

*Equ.2: labour costs

ln LCit = ln 1 + ln Lit + 0Pi0t + 2Pi2t + it with 2 2-1= 2/ 1 -1 being the relative labour cost of the considered type of workersKey question2= ??? 22= relative productivity of 50-652= relative labour cost of 50-65

*Identification challenge

yit = A + li,t + 0 Pi0t + 2 Pi2t+ kit +Fit + itit = i + it + it

i unobservable (time-invariant) heterogeneity between firmsit short-term (asymmetrically) observed productivity shocks, it it random error E(it) = 0

*Production/productivity (cont.)

We report the results of several estimations methods: OLS, first-difference, within (fixed-effect), System GMM -la Blundell-Bond Our preferred approach = proxying the short-term productivity shocks it using with demand for intermediate inputs (Levinshon & Petrin, 2003)

intit =I(it , kit)[5]

Assuming this function can be inverted the residual it becomes ii + it(intit) + it[6]

with it(intit) that can be approximated by a polynomial expansion in int.

*4. Our DataEmployers-employees matched data~10.000 firms with 20+ workers (BELFIRST- BNB) using firm identifiers, we are able to inject information from banque Carrefour de la scurit sociale on the age of (all) workers employed by these firms: ~1.200.000 workers ..we do not need to assign workers to firms using matching methods like in Hellerstein et al. (1999)Data aggregated at firm level Long Panel 1998-2006 (9 years)

*Information on firms from the (now dominant) service sector, where administrative and intellectual work is predominant

Like Aubert & Crpon (2003) and Dostie (2006), we have a measure of firms productivity (the net valued added), which is measured independently from firms wage cost

Contrary to Dostie (2006), we do have a measure of firms capital stock, such that no imputation method is required.

Mean age and value added per employee

Denmark BelgiumJune 09*

*5. Results

*Estimating age differencials. Calculating the produtivity/labour cost gap2 22 = (2 1); 2= 2/1; 2 2-1= 2/ 1 -1 lnY; Y being value added (productivity) or labour cost

Testing the significance of the gap (pooled data)*

Testing the significance of the gap (by sector)*

Testing the significance of the gap (firm-size)*

Other robustness checks- Sub-sample of (big) firms properly reporting on part-time work- Sub-samble of (big) firms reporting on human capital attainment of recruits and separating workers + share of blue-collar workers

=> no major qualitative impact on estimates*

ConclusionAn increase of 10 percentage points in the share of older workers (>50) in a firm depresses its added value by 3.2% (preferred model & cross-model average)Large productivity differential for olders workers, only partially compensated by lower relative labour costs which could (negatively) affect the labour demand for older workers.

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Conclusion (cont.)The dominant service sector does not seem to offer working conditions that mitigate the negative relationship between age and productivityOlder workers in smaller firms display a larger productivity gap, and their productivity is less aligned on labour costs. Small firms might be less inclined to employ them*

*Other stylised facts

*Other stylised facts (cont.)

Profitablity of firms located in Belgium and workforce age (intervalles de confiance au seuil de 2,5% confidence interval). Year1998-2006

Source: Belfirst & CarrefourNote: Profits : value added/labour cost, centered using year and NACE4 fixed effects. Age data correspond to the 75th percentile of the firms age distribution. Resutls on display are obtained using non prarametric estimation methods

*Productivity/labour cost gaps and employment contract -la-LazearABMandatory departure from firm 1

ReferencesAubert. P. and B. Crpon (2003). La productivit des salaris gs: une tentative destimation. Economie et Statistique. 368. 95-119. Dostie. B. (2006). Wages. Productivity and Aging. IZA. Discussion Paper No. 2496. Bonn. Germany. Gbel, Ch. and Zwick, Th. (2009), "Age and productivity: evidence from linked employer-employee data,"ZEW Discussion Papers09-020, ZEW - Zentrum fr Europische Wirtschaftsforschung / Center for European Economic Research. Grund and Westergrd-Nielsen (2008). International Journal of Manpower. Vol. 29(5). pp. 410-422Hellerstein. J.K. and Neumark. D. (1995). Are Earning Profiles Steeper than Productivity Profiles: Evidence from Israeli Firm-Level Data. The Journal of Human Resources. vol. 30. 1. pp. 89-112.Ilmakunnas, P. and M. Maliranta, (2007), Ageing, Labour Turnovers and Firm Performance, ETLA DP, No 102, The Research Institute of the Finnish Economy, Helsinki *

References (cont.)Levinsohn. J. and A. Petrin (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies. 70 (2). 317-341 Malmberg. B. Lindh. T & Halvarsson. M., (2005). Productivity consequences of workforce ageing -Stagnation or a Horndal effect?. Arbetsrapport No 2005:17. Institute for Futures Studies. Stockholm. Skirbekk, V. (2004), Age and individual productivity: a literature survey, In: Feichtinger, G. (Editor): Vienna yearbook of population research 2004. Vienna: Austrian Academy of Sciences Press, pp. 133-153. Skirbekk, V. (2008), Age and productivity capacity: Descriptions, causes and policy options,Ageing Horizons, 8, pp. 4-12.van Ours, J.C & Stoeldraijer, L. (2010), Age, Wage and Productivity, IZA Discussion Papers 4765, Institute for the Study of Labor (IZA), Bonn.Werding, M. (2007). "Ageing, Productivity and Economic Growth: A Macro-level Analysis,"PIE/CIS Discussion Paper338, Center for Intergenerational Studies, Institute of Economic Research, Hitotsubashi University

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Employment gaps are estimated by OLS (LPM), by regressing the employment (0,1) responses of all respondents on AGEGROUP (ref=35-45) , centering on highest degree (ISCED4). Relative wages are obtained by regressing (log) gross monthly wages on AGEGROUP (ref=35-45), centering on highest degree, part-time/full-time status, and sector(NACE1) [reported values= est. Coeff. +1]*Employment gaps are estimated by OLS (Linear probability model), by regressing the employment (0,1) responses of all respondents on AGEGROUP (ref=35-45) , centering on highest degree (ISCED4). Replacement rates are the country-average of cell-average TR/W, where a cell is defined by the highest degree and the occupation (ISCO1). Transfers= all welfare transfers+ pension received by 55-66 no longer in employment. Wage= gross monthly earnings of 55-66 still in employment.*******Introduire autre graphique Y/L vs share of old workers**Rf= travailleurs de 35 49 ans*Ref= workers aged 30 -49System GMM: we do not pass Sargan/Hansen test of overidentifying restrictions****Why do productivity labour-cost gaps matter? Even if negative gaps are compensated by early-career positive gap (i.e. as predicted by Lazear), these could imply: [1] forced early departure from historical employers (firm 1) and [2] i) significant wage reduction (if older worker resumes work), ii) long unemployment spells (if he/she first refuses lower pay, or lower wage are forbidden) iii) or early retirement (particularly if welfare money is available).*