Post-Acquisition Restructuring, HRM Policies and Performance:

Insider Econometrics in a Multi-unit Firm

Hein Bogaard & Jan SvejnarThe 16th Dubrovnik Economic Conference

June 23-25, 2010

Goals

• Advance “insider econometrics” literature – Usually analyzing effects of intra-firm (HRM) reforms on productivity– Usual problem – potential endogeneity of reforms (more likely to be

implemented in units where they have high marginal impact )

• Provide new/different analysis of reforms in Central-East European (CEE) firms after foreign acquisition– Most analyses use limited data on many firms– We use detailed data on one firm/bank (with about 180 branches)

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Literature: Insider Econometrics -- Banks

• Insider Econometrics -- measuring impact of HRM policies on performance within a well-understood production process (Ichniowski and Shaw, 2003) – usually in advanced economies

• Research on banks -- branch-level determination and implementation of work practices (Bartel, 2004), employee attitudes/satisfaction (Bartel et al., 2003) and training (Jones et al., 2008) -- all affect productivity

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Literature:Ownership-governance-performance

• Foreign ownership is associated with better performance of firms and banks in CEE region (e.g. Bonin et al, 2005a,b; Fries and Taci, 2005; Havrylchyk, 2008; Estrin et al., 2009)

• Differences are attributed to:– Corporate governance (e.g. Majnoni et al, 2003)– Financial relationships with foreign parents (e.g. De Haas and

Naaborg, 2005b)

• We consider role of work practices and HRM policies transferred by a foreign parent

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Background and Approach

• Studies of individual (Lazear, 2000) and team (Hansen, 1997; Hamilton et al., 2003) incentives have considered homogenous tasks

• We analyze reforms involving task differentiation -- reforms in our bank => greater individual responsibility and higher-powered incentives for a subset of employees

• Approach– Examine sales productivity in 180 branches of a foreign-owned bank– Have five years (20 quarters) of data on sales performance and

organizational structure of each branch– Exploit structure of data and fact that the reforms were determined

centrally and gradually implemented bank-wide => can deal better with endogeneity of HRM reforms at branch level (IV estimation and propensity score matching)

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Our results

• Reforms improved sales productivity, but• Differentiation of incentives among co-workers has limited

success of reforms -- in particular with regard to quality / profitability improvements

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Data

• Bank in the CEE region, acquired by a Western-European owner around 2000

• Bank’s branches serve retail and SME clients and are incentivized to maximize “sales” of loans and deposits

• Quarterly balance sheets and profit/loss accounts for up to 200 branches between 2003 and 2007 (≈ 3,250 obs.)

• Quarterly data on the number of employees and the functional structure of the branches

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Institutional setting

• Conservative incumbent executives• Team-based incentives in branches• Foreign acquirers sought to improve sales and client

retention/acquisition– Stratification of functions within branches giving subset of employees

(“Bankers” and “Advisors”) individual incentives– Bankers and advisors expected to improve sales quantity and quality

(profitability, product mix)

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Branch Manager

Middle Management

Client Services

SMEs

Client Services

Retail

Client Services Private Banking

Branch Manager

Middle Management

Retail

Middle Management

SMEs

Middle Management

Payment Comfort

SME Bankers + Assistants

SME Client

Advisors

Retail Bankers + Assistants

Retail Client

Advisors

OLD Branch Operational Model

Cashiers / Admin Staff

Arrow Indicates hierarchical relationship

Functions in box with dashed outline only in select (mostly large) branches

NEWBranch Operational Model

Branch Incentives

Branch Incentives

Branch Incentives

Branch Incentives

Individual IncentivesIndividual Incentives

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Incentive Structure

Bankers

Advisors & Other Staff

70% 100% 150% 200%

10%

16%

25%

40%

75%

Realized Sales / Planned Sales (%)Note: Bankers’ and advisors' final bonuses are based on a 70/30 weighted average of personal and branch performance. All other non-managerial staff receives a bonus based on branch performance

Summary Stats: Branch Staffing, by YearYear Branches Employees

Retail Bankers

SME Bankers

AdvisorsLeadership Academy

    (FTE) (% FTE) (% FTE) (% FTE) (% Br. Mng.)

2003 182 15.8 3.7% 2.4%

2004 179 15.9 7.0% 4.3%

2005 180 15.3 6.9% 4.5% 0.5%

2006 180 14.6 8.5% 4.6% 10.8% 23.2%

2007 178 14.1 8.3% 4.7% 10.2% 79.8%

All figures are yearly averages based on quarterly data

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Empirical Approach

• Baseline model used to assess the impact of HRM reforms on productivity

• Footing is the sum of loans outstanding plus savings and deposits held by the branch (Bartel et al, 2003)

• Index i is for branches, j for region and t for period (quarter/year)

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( )

( )( )

21 12

2

2 3

4

Footing Bankers+Advisorsijti jtFTE FTEijti jtFTE FTEijti jt

Bankers+Advisors ijt Bankers+Advisors ijtFTEijtLeadershipAcademy city region periodtijti j j ijt

Empirical Approach (2)

• Estimation of equation (3) is subject to two sources of endogeneity bias:– Bankers and advisors are assigned to well-performing branches – bias

can be eliminated by using fixed effects– Bankers and advisors are assigned to branches where they have the

largest incremental effect on productivity – fixed effects will not eliminate bias

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Empirical Approach

• Exploit structure of data to develop an IV strategy– Reforms mandated by headquarters for all branches– For each branch i in period t, the state of reforms in

branches k, (k, i) Є K, i ≠ k, is informative about the reforms in branch i, but not correlated with εijt

– As instruments for (bankers + advisors)ijt /FTEijt and interactions, we use averages of(bankers + advisors)kjt/FTEkjt for branches in the same region or size group as branch i

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Table 2: Sales (ΔFooting / FTE) and Branch Characteristics (1) (2) (4) (5) OLS GMM OLS GMMBankers + Advisors / FTE 0.035 0.021 0.095 0.452 [0.019]* [0.024] [0.045]** [0.137]***(Bankers + Advisors / FTE) squared -0.194 -1.724 [0.122] [0.638]***Bankers + Advisors 0.004 0.018 [0.003] [0.011]Leadership Academy 0.004 0.004 0.006 0.009 [0.006] [0.006] [0.006] [0.008]FTE -0.000 -0.000 -0.003 -0.005 [0.000] [0.000] [0.001]** [0.002]***FTE Squared 0.000 0.000 [0.000]*** [0.000]Observations 3245 3245 3245 3245Number of Branches 188 188 188 188Hansen J test 1.172 1.50p-value 0.279 0.220Note In the GMM estimates, (Bankers + Advisors )/ FTE, its square and its interaction with FTE are instrumented using the strategy discussed on the previous slide. All regressions include Region x Year x Quarter Fixed Effects. Robust standard errors, clustered by branch, in brackets

Robustness

• Results are robust to:– inclusion of Operational Expenses (includes

wages, a proxy for “worker quality”)– one-by-one exclusion of regions– Exclusion of branch “i” in first stage (JIVE

estimator; Angrist, Imbens and Krueger, 1999)• Consistent results in loglinear specification• Also in Retail / SME segments (except SME

loans)

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Robustness (2)

• We also implement an estimator based on the generalized propensity score (Hirano and Imbens, 2004; Imai and Van Dyk, 2004)– Difference-in-difference estimates of the impact

of bankers and advisors on sales productivity respectively about a year after these functions were introduced to the branches

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Table 4: Impact of Bankers on Sales Per Employee - Generalized Propensity Score EstimatesBankers /

FTE 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Average, Qrtr 7 to 10

0.005 0.011 0.021 0.034 0.048 0.061 0.072 0.077 0.076 0.067[0.002]***[0.003]***[0.004]***[0.004]***[0.004]***[0.004]***[0.003]***[0.002]***[0.001]***[0.002]***

Notes The estimates are based on difference-in-difference analysis conditional on the propensity score for the share of bankers per employee (the treatment). See Appendix 1 for details. Standard errors are bootstrapped with 1,000 repetitions. * significant at 10%; ** significant at 5%; *** significant at 1%

Table 5: Impact of Advisors on Sales Per Employee - Generalized Propensity Score EstimatesAdvisors / FTE 5% 10% 15% 20% 25%

Average Qrtr 17 to 20 -0.006 0.042 0.098 0.168 0.245 [0.001]*** [0.000]*** [0.001]*** [0.003]*** [0.001]***Notes The estimates are based on difference-in-difference analysis conditional on the propensity score for the share of bankers per employee (the treatment). See Appendix 1 for details. Standard errors are bootstrapped with 1,000 repetitions. * significant at 10%; ** significant at 5%; *** significant at 1%

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Advisors / Employee

The Quality of Sales

• We do not find that the introduction of bankers and advisors had a positive impact on the quality of sales in terms of product mix (mortgages, mutual funds), or productivity

• Exception: the arrival of advisors had a positive effect on profits and mortgage sales (but negative on mutual fund sales)

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Summary of Results• Productivity is lower in branches with more

employees• The contribution to productivity of bankers and

advisors to sales is higher in branches with more employees

• There is a concave relationship between (Bankers + Advisors)/FTE and sales productivity

• The arrival of advisors had a bigger impact on productivity than that of bankers

• The impact of bankers and advisors on sales quality is mixed at best

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Discussion

• The finding of low productivity + high marginal contribution of bankers and advisors in large branches is consistent with free riding (Alchian and Demsetz, 1972)

• In our setting (transition economy, heterogeneous tasks), there are limitations to the efficacy of group incentives (contrast with Wageman, 1995; Hansen, 1997; Hamilton et al, 2003)

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Discussion (2)

• We develop a model of employee behavior within the incentive structure to further analyze the results

• Focus on– Concavity in marginal contribution of bankers and

advisors to sales → local demand factors– Lack of quality improvements / large contribution of

advisors to productivity → collusion / imperfectly observable quality (Baker, 2000; Agarwal and Wang, 2009)

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Discussion

• Under standard assumptions about cost and productivity– Employee productivity depends on (local) demand for

bank services (+, more demand raises productivity), incentives (+), the marginal cost of effort (-) and individual and branch level lending targets (-)

– Banker/advisor effort also depends on the weight given to individual performance (+) relative to branch level performance

– Lending by other employees may reduce productivity

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Discussion

• Based on the model:– The concavity in the relationship between the

number of bankers and advisors per employee and productivity could be due to:

• An over-allocation of bankers and advisors to branches relative to local demand

• Lack of cashiers to provide the “services” that make bankers and advisors productive

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Discussion (3)• What if HQ (the principal) cannot observe

quality?– Due to differentiation in incentives, branch employees

have an incentive to misrepresent cashier’s loans as banker or advisor loans

– Bankers could bribe cashiers to allow them to book a sale about to be made by the cashier

– Bribery becomes more difficult when differences in incentives are smaller

– Hence, the large positive impact of advisors on performance despite the fact that they have less powerful incentives than bankers

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Conclusions• In collecting data for insider econometrics studies,

collecting a sample of units / firms that were subject to a common impulse for organizational reforms provides ready instruments to control for endogeneity

• The reforms introduced were ambitious and relied on internal controls for quality assurance. Considering the outcome, it is important to calibrate organizational structures to local manager’s ability

• This work contributes to the ownership-governance-performance literature in banking and can provide an impetus for further research into organizational choices and performance

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