Human Resources and Diversification Strategies in Financial Services
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1 Human Resources and Diversification Strategies in Financial Services Hyowook Chiang Cheryl Grim John C. Haltiwanger Larry W. Hunter Ron Jarmin Nicole Nestoriak Kristin Sandusky Jeongil Seo
2 OVERVIEW Deregulation in Fin Services provides new opportunities for diversification Human resources predict changes in diversification Why? Resource-based view of firm
3 THE RESOURCE-BASED VIEW OF THE FIRM Firms are heterogeneous with respect to resources and capabilities. Resources are stocks of available factors; physical, intangible, and financial resources. Capabilities refer to the capacity to deploy resources to affect a desired end. Competitive advantage occurs when resources and capabilities are valuable, relatively rare, and relatively immobile Underused resources create firm-specific opportunities for exploitation.
4 DIVERSIFICATION AND THE RESOURCE BASED VIEW Diversification is one strategy for exploiting existing firm-specific resources Resources are deployed to product markets where the highest rents can be earned.
5 DIVERSIFICATION AND HUMAN RESOURCES Intangible resources, e.g. knowledge, more likely to produce competitive advantage. Difficult to imitate firm-specific processes based on intangible resources; External market failures Intangible resources are linked to human resources Human resources create knowledge and can be exploited profitably by firms
6 Human Resources in Internal Labor Markets (ILMs) and Diversification Firm-level ILMs (Doeringer and Piore 1971) exist in contrast to buying labor on the spot market Diversifying firms are likely to have robust ILMs: ILMs encourage the development of firm-specific skills that diversification seeks to exploit. ILMs enable the development of capabilities beyond the skills of the workers themselves: e.g. teams
7 DATA SOURCES The Longitudinal Business Database (LBD) Establishment employment, payroll, location, industrial classification and firm affiliation The Longitudinal Employer-Household Dynamics (LEHD) Program High quality data on firm workforce composition over time This is novel / unique
8 SAMPLE Financial establishments in 4 digit SIC SICs are listed in table 1 Time period is , though the analysis will focus on diversification over the period
9 Table 1. SIC codes in financial services 1987 SIC Code Description 1987 SIC Code Description 6021 National Commercial Banks 6211 Security Brokers and Dealers 6022 State Commercial Banks 6221 Commodity Contracts Brokers and Dealers 6029 Commercial Banks NEC 6231 Security and Commodity Exchanges 6035 Savings Institutions (Fed) 6282 Investment Advice 6036 Savings Inst (Not Fed) 6289 Securities Exchange Services 6061 Credit Unions (Fed) 6311 Life Insurance 6062 Credit Unions (Not Fed) 6321 Accident and Health Insurance 6081 Branches of Foreign Banks 6324 Hospital & Medical Service Plans 6099 Functions Related to Deposit Banking 6331 Fire Marine and Casualty Insurance 6111 Federal Credit Agencies 6351 Surety Insurance 6141 Personal Credit Inst 6361 Title Insurance 6153 Short Term Business Credit Inst 6371 Pension, Health and Welfare Funds 6159 Miscellaneous Business Credit 6399 Insurance Carriers 6162 Mortgage Bankers & Loan Correspondents 6411 Insurance Agents, Brokers, and Service 6163 Loan Brokers 6712 Offices of Bank Holding Companies
10 DIVERSIFICATION MEASURES 3 measures of overall diversification Defined as 1 minus a Herfindahl index: industry diversification (ind_div); county diversification (county_div); state diversification (state_div) 2 measures of distance relatedness; geographic diversification; geog_dist_div industry diversification; ind_dist_div
11 DIVERSIFICATION MEASURES Example; industry diversification (ind_div) measure; pay jit s jit = H industry!( ) = s it jit pay j" i it ind _ div = 1! H it it industry 2
12 DIVERSIFICATION MEASURES geog_dist_div, geographic diversification d is 1 + the distance from the center ce of the county where est. e is located and the core county c. H county it =! e" i (1/ d ce ) ( s ) eit 2 geog _ dist _ div = 1! H it it county
13 Indicators of Human Resources in ILMs Establishment-level measures, aggregated to firm-level using employment weights 3 Indicators Worker turnover rates in excess of net changes churning Extent to which wage-tenure profiles slope upward Dispersion of wages
14 CHURNING (turnover) Lower churn rates imply skill development that can be leveraged through diversification. ( Accessions + Separations! " Employment ) Average _ Employment( t, t! 1) Captures worker turnover in excess of required for net changes in the number of workers in the business.
15 WAGE-TENURE PROFILE Wage-tenure profiles that slope more sharply upward imply skill development within ILMs Measured through growth of workers wages with at least 5 years of tenure
16 DISPERSION OF WAGES Wage compression is positively related to worker cooperation Builds routines and skills for leveraging diversification Log of ratio of earnings of the worker at the 90 th percentile to the worker at the 10 th percentile Less dispersion leads to more diversification
17 COMPLEMENTARITY Each ILM indicator may have effects on diversification that depend on the other indicators Example: within-job-wage growth has a stronger effect in companies with low churning Measured through multiplicative interactions
18 OTHER CONTROLS Firm age Firm size (# of workers) Home state Home sub-industry (4 digit) Net employment growth Share of high-skill workers Share of female workers
19 Table 6. Summary of variable definitions Independent Variable firmage1997 growth size lnsize shr_fem shr_low shr_high shr_hw wjwg chr diff Definition Firm age in 1997 Average net employment growth Average number of full quarter workers Average log number of full quarter workers Average share of female workers Average share of low human capital workers Average share of high human capital workers Average share of high wage workers Average within job wage growth (five years) for new hires Average churning Average within firm log wage differential Dependent Variable county_div state_div ind_div geog_dist_div ind_dist_div Definition Change in geographic diversification at county level Change in geographic diversification at state level Change in diversification at industry level Change in geographic diversification at county level, weighted by distance Change in diversification at industry level, weighted by relatedness The independent variables in this table are five-year ( ) averages. The dependent variables indicate the change in the indices (construction described in the text) from 1997 to 2000.
20 EMPIRICAL APPROACH Use the average level of ILM variables over period at the firm level to predict change over in firm-level diversification measures Control model Study variables: main effects Study variables: interactions
21 Table 9. Ordinary least squares regression results for control model (1) county_div (2) state_div (3) ind_div (4) geog_dist_div (5) ind_dist_div (Constant) ** ** ** ** (0.023) (0.014) firmage1997 b 1-6 years (0.026) (0.023) (0.014) 7-10 years (0.020) (0.020) (0.020) (0.018) (0.011) years * ** (0.009) (0.009) (0.009) (0.008) (0.005) lnsize ** ** ** ** ** (0.001) (0.001) (0.001) (0.001) (0.001) Growth * * ** ** (0.026) (0.023) (0.014) shr_fem ** ** ** ** (0.022) (0.014) shr_high ** ** (0.022) (0.023) (0.023) (0.021) (0.013) N R Industry dummies and state dummies are not reported. Standard errors are in parentheses; b over 20 years old is omitted; * p < 0.05, ** p <
22 Table 10. (1) county_div (2) state_div (3) ind_div (4) geog_dist_div (5) ind_dist_div (Constant) ** ** ** ** ** (0.023) (0.014) firmage1997 b 1-6 years * * (0.023) (0.014) 7-10 years ** ** ** (0.019) (0.020) (0.020) (0.018) (0.011) years ** ** ** (0.008) (0.009) (0.009) (0.008) (0.005) lnsize ** ** ** ** ** (0.001) (0.001) (0.001) (0.001) (0.001) Growth ** ** (0.023) (0.014) shr_fem ** ** ** (0.023) (0.022) (0.014) shr_high ** ** ** (0.023) (0.022) (0.013) chr ** ** ** ** (0.057) (0.059) (0.060) (0.054) (0.033) diff ** ** ** ** ** (0.006) (0.007) (0.007) (0.006) (0.004) wjwg ** ** ** ** ** (0.049) (0.050) (0.051) (0.046) (0.028) N R R ** 0.03 ** 0.03 ** 0.04 ** 0.05 **
23 Table 11. (Constant) (1) county_div ** (2) state_div ** (3) ind_div * (4) geog_dist_div ** (5) ind_dist_div ** (0.023) (0.014) firmage1997 b 1-6 years * (0.023) (0.014) 7-10 years ** ** ** (0.019) (0.020) (0.020) (0.018) (0.011) years ** * ** (0.009) (0.009) (0.009) (0.008) (0.005) lnsize ** ** ** ** ** (0.001) (0.001) (0.001) (0.001) (0.001) Growth ** ** (0.023) (0.014) shr_fem ** ** ** * (0.023) (0.022) (0.014) shr_high ** ** ** (0.023) (0.022) (0.014) chr ** ** * ** ** (0.059) (0.061) (0.062) (0.056) (0.034) diff ** ** ** ** ** (0.006) (0.007) (0.007) (0.006) (0.004) wjwg ** ** ** ** ** (0.063) (0.065) (0.066) (0.060) (0.037) chr wjwg ** * * ** (0.788) (0.816) (0.821) (0.745) (0.455) chr diff ** ** ** (0.107) (0.110) (0.111) (0.101) (0.062) wjwg diff * ** ** (0.061) (0.063) (0.063) (0.057) (0.035) N R
24 Coefficients for main effects and interactions of ILM variables from Table 11 (1) county_div (2) state_div (3) ind_div (4) geog_dist_div (5) ind_dist_div chr ** ** * ** ** (0.059) (0.061) (0.062) (0.056) (0.034) diff ** ** ** ** ** (0.006) (0.007) (0.007) (0.006) (0.004) wjwg ** ** ** ** ** (0.063) (0.065) (0.066) (0.060) (0.037) chr wjwg ** * * ** (0.788) (0.816) (0.821) (0.745) (0.455) chr diff ** ** ** (0.107) (0.110) (0.111) (0.101) (0.062) wjwg diff * ** ** (0.061) (0.063) (0.063) (0.057) (0.035)
25 RESULTS Each of the three indicators of ILM strength is significantly associated with changes in the diversification measures. The relationships between ILM strength and diversification activity are in the hypothesized direction.
26 MORE SPECIFICALLY Churning is negatively associated with changes in diversification. Steepness of wage profiles is positively associated with changes in diversification. More extensive wage differentials are negatively associated with diversification.
27 EFFECT SIZES Change of.05 in churn rate associated with 4-15% of s.d. change in diversification indices One s.d change in wage dispersion associated with 10-25% of s.d. change in diversification indices One s.d. change in wage growth associated with 10-20% of s.d. change in diversification indices In general, effects are modest
28 COMPLEMENTARITIES? Support for complementarity mixed Overall, some interactions are as hypothesized Non-parametric results even less clear than linear interactions grouped by high and low more investigation required
29 Coefficients for main effects and interactions of ILM variables (1) county_div (2) state_div (3) ind_div (4) geog_dist_div (5) ind_dist_div chr ** ** * ** ** (0.059) (0.061) (0.062) (0.056) (0.034) diff ** ** ** ** ** (0.006) (0.007) (0.007) (0.006) (0.004) wjwg ** ** ** ** ** (0.063) (0.065) (0.066) (0.060) (0.037) chr wjwg ** * * ** (0.788) (0.816) (0.821) (0.745) (0.455) chr diff ** ** ** (0.107) (0.110) (0.111) (0.101) (0.062) wjwg diff * ** ** (0.061) (0.063) (0.063) (0.057) (0.035)
30 Discussion points Overall conclusion is that human resources are associated with diversification A surprise to industry analysts? ILM indicators all matter but do not make up bundles Further analyses could look at Complementarities, more carefully Firm types Mode of diversification Splitting out positive and negative changers Possible moderators
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The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
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The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
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Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
Number of Insurance Companies. U.S. Premiums by NAIC Financial Statement Type. U.S. Premiums by IDRR Premium Type. Insurance Department Data
The mission of the National Association of Commissioners (NAIC) is to assist state insurance regulators, individually and collectively, in serving the public interest and achieving the following fundamental
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