End-of-Year Spending and the Long-Run Effects of Training Programs - PowerPoint PPT Presentation

End-of-Year Spending and the Long-Run Effects of Training Programs for the Unemployed B. Fitzenberger a , c , M. Furdas a , C. Sajons b a: Humboldt Uni Berlin / b: ifm Uni Mannheim / c: ZEW Mannheim IRP - Summer Research Workshop - Madison - 20 June 2018 Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 1 / 37

Introduction Motivation Motivation • Training programs for the unemployed: Very important part of active labor market policies (ALMPs) • Focus on providing occupation-specific skills, human capital in general, and/or work experience • Opportunity to obtain vocational training degrees • Lock-in effect: reduced job-search intensity during participation • Intensive Use of ALMP in West Germany • Between 500-700 Tsd. entries into training p.a. in the 80s and 90s • Significant amount of resources: about 3.4 bn DM in the mid-80s Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 2 / 37

Introduction Motivation Related Literature I • Large number of evaluation studies estimating effects on employment / earnings accounting for selection on observables (see e.g. Card et al. 2010, 2015; Lechner et al. 2009; Biewen et al. 2014, for an overview for Germany) • Difficult to account for selection on unobservables: ⇒ Causal evidence accounting for selection on unobservables is scarce Richardson/van den Berg 2013 and Osikominu 2013 [Continuous-time duration modelling, no IVs]; Frölich/Lechner, 2010 [IV: Regional treatment intensity, complier effect]; Aakvik et al. 2005 [IV: Degree of rationing, MTE + ATT]; Caliendo et al. 2014 [No effect of additional covariates, which are typically unobservable] • Evidence for effect heterogeneity with respect to unobservables: Aakvik et al. 2005 - Treatment effect falls with treatment probability, cream skimming • No evidence for cream skimming in Frölich/Lechner 2010 Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 3 / 37

Introduction Motivation This paper • Use IV strategy to account for unobserved selection and estimate the long-run employment effects of training (over 10 years) • Idea: Exploit “end-of-year spending” effects caused by strict budget rules in West Germany in the 1980s and early 1990s as source of conditional exogenous variation in training participation • Non-transferability of funds • Budget largely determined by previous years’ spending ⇒ Incentive to use all remaining funds before the end of the year and tieing up funds for the next ⇒ Use the budget surplus after the 1st half of the year as instrument for treatment in the 2nd half • Implement IV strategy in a dynamic setting using a two-step control function approach for models with binary outcomes and binary endogenous treatment (Wooldridge, 2014) Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 4 / 37

Introduction Motivation Related Literature II • Lots of anecdotal evidence for end-of-year spending in government agencies and company divisions (references in paper). • Little empirical evidence due to lack of data Gao (1998). • Recently, a small literature on end-of-year spending hike associated with a lower quality of the projects • Liebman and Mahoney (2017) [IT procurement decisions by the US government] → precautionary motive plus decreasing returns • Later, Eichenhauer (2017) [contributions to foreign aid for a panel of countries] → lack of planning capacity and bureaucratic effectiveness → dismisses procrastination by bureaucrats as alternative explanation Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 5 / 37

Outline Outline • Institutional background • Data • Model of end-of-year spending • Empirical strategy - Identification • Effects of training on subsequent employment • LATE – 2SLS estimation • ATT – control function approach • Heterogeneous effects by training program • Conclusions Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 6 / 37

Institutional background Organization Federal Employment Office West Germany • Three levels: • Central office in Nuremberg • 9 regional employment offices (REO, Landesarbeitsämter ) • 142 local employment offices (LEO, Arbeitsämter ) • Annual budget determined and managed largely at the federal level • Local offices possess limited discretion in the use of their allocated funds for training programs subject to budget plan of center/region Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 7 / 37

Institutional background Budget Rules Until 1993 • Stable budget rules within the Federal Employment Office (FEO) during the 1980s and early 1990s • Budget rules (1980 – 1993) • Non-transferability of funds: budget for training programs was planned and allocated separately from other programs like job creation schemes • Allocation of funds top-down to regional and local offices was based primarily on past levels of program participation (“head-count” calculation) and adjusted according to the expected economic development • Unused funds from one fiscal year could not be transferred to the following year • Budget for the next year depended on the degree of utilization in the current year and the comparison of each LEO with the other local offices • LEO has an incentive to spend the whole remaining budget before the end of the year Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 8 / 37

Institutional background The employment office’s budget year Figure : Monthly shares of total year entries into training programs 12 10 Share in percent 8 6 ⇒ Use remaining financial leeway after the first half of the year as a measure for the magnitude 4 1 2 3 4 5 6 7 8 9 10 11 12 Calendar month of possible end-of-year spending behaviour of local officials Shares averaged over regions and time • Best time for readjustment was the period (1983–1993). immediately after the summer holidays ⇒ Consider only programs starting in the months after the summer holiday (August – November) Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 9 / 37

Data and training programs Data • Data sources: • Integrated Employment Biographies (IEB) based on employees’ history and the benefit recipients’ history • Training participation ( Fortbildung und Umschulung, FuU) data • Actual and planned spending (Yearbook of the FEO, 1980-1993) • Sample: 50% sample of all program participants (1980–1993) and 3% sample of all individuals (followed until December, 2004) • Training programs included (by length of duration): • Short-term training (STT) • Practice firms (PF) • Specific professional skills and techniques (SPST) • Retraining (RT) Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 10 / 37

Data and training programs Sample and treatment definition • Sample restrictions: • Unemployment inflows after employment spell lasting at least three months • Individuals living in West Germany and aged 25 to 50 at the beginning of unemployment • Treatment: First training within the first 12 months after entering unemployment by elapsed unemployment duration (stratum) • Dynamic control group (Sianesi, 2004): Individuals with the same elapsed unemployment duration as in the treatment group, but without or with later treatment, i.e., all individuals are replicated for each month they remain unemployed and they are eligible for treatment Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 11 / 37

Model of end-of-year spending behavior Model of end-of-year spending behavior • Stylized model to rationalize end-of-year spending by LEO official in two period model with two types of training program • Trade-off between budget absorption and maximizing returns from training • Random shocks to entries into training • End-of-year spending: Entries in period 1 lower (higher) than planned prompts higher (lower) planned entries in period 2 • Higher entries associated with composition changes towards less effective programs → higher marginal costs to find suitable matches for more effective program Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 12 / 37

Model of end-of-year spending behavior LEO official minimizes expected loss over two periods of the budget year � ( B − e 1 − e 2 ) 2 + w ( e 1 − e ∗ ) 2 + w ( e 2 − e ∗ ) 2 � L = E , (1) where B : training budget (measured in entries), e 1 , e 2 entries into training in periods 1 , 2 e ∗ entries maximizing net returns relative weight of e ∗ , w ≥ 0 w LEO official decides upon planned entries ˆ e 1 and ˆ e 2 at beginning of each period Actual entries: e t (ˆ e t ) = ˆ e t + ǫ t where ǫ t random shock with expectation zero given history and decision on ˆ e t Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 13 / 37

Model of end-of-year spending behavior End-of-year spending : Second period response to actual entries in first period w 1 1 + w · e ∗ + e ∗ ˆ 2 ( e 1 ) = 1 + w · ( B − ˆ e 1 − ǫ 1 ) . (2) � �� � budget left → response of planned entries ˆ e 2 is less than one-for-one wrt budget left ( B − e 1 ) Surprise component ǫ 1 : Unanticipated → leading to a change of � � 1 − · ǫ 1 1 + w e ∗ in planned entries in second period ˆ 2 ( e 1 ) and subsequently in actual entries e 2 . Fitzenberger, Furdas, Sajons End-of-year Spending 20 June 2018 14 / 37