Human Capitalby

Awa ThionganeUNECA/ACS

WorkshopReview of RRSF Implementation

Table of Content• Introduction• Human Resource Policy• Recruitment• Training• Mobility• Staff Retention • Conclusion

Human Resources

“The biggest force of an organization – and the key of its success – is the quality of its staff and leaders.”

K. A. in « Building the future »

P1- Client orientationP2- LeadershipP3- Personnel involvementP4- Process approachP5- Management by system approachP6- Continuous improvement P7- Evidence-based approach for decision

making P8- Mutual benefit relations with data

providers

Source: ISO – ISOTC 176 Technical Committee

Quality Management Principles

Exemple: United Nations

Basic Competences:• Communication• Team Work (P3)• Planning and

organization• Accountability• Creativity• Client Orientation• Commitment to

continuous Learning (P6)• Old Technology

Managerial Competences :

• Leadership (P2)• Vision• Empowerment of others

(P3)• Building confidence• Managing performances• Judgment and decision-

making

Human Resource Policy

The HR policy should ensure:

• Capacity to evaluate needs with respect to personnel

• Standards and techniques for recruitment should be equitable

• Deployment and mobility of staff members should be clearly stated

• Observance of equity in the management of careers

• Training (lifetime training) should be clearly stated

• Motivation of personnel is one condition of staff retention

Human Resource Policy

• Limitations : – Recruitment through civil service

channel – Size of the staff

• To which extent Chief statistician has appropriate means for: – Motivation of personnel – Deployment of personnel – Task rotation – Training

Recruitment• In general by public administration

– Long delays out of the control of statistical agencies

– Risks:* High percentage of losses (wages – status)

• In some case by statistical offices – Advantages:

* predictability* flexibility * lower percentage of losses (wages – status)

• Recruitment plan (should be included in NSDS)

Recruitment: Norms

• A recruitment policy• A recruitment plan• A convenient working environment • Team work • Clear description of tasks• Promotion rules (merit versus seniority)• Motivation (training, study tours,

participation in international fora, etc.….)

• Competences to fill vacancy; etc.

Staff Composition

Statistical operations require a set of talents

– Statisticians– Economists – Demographers– Computer scientists Purchase of – Sociologists of – Econometricians +

consultancy – Model Builders services – Geographers– Anthropologists– Criminologists– Engineers

– etc

Types of personnel to be recruited

Competencey

Tech

nic

al

Qu

ali

ficati

on

s

Too much solicited High mobility but good for their leadershipCan quickly acquire

competences to retain (the target)

To retain for tasks that do not require interactions with other staff members

To avoid

Professional / Other Staff Ratio

OECD countries

• One professional by two other staff members

African countries

• One professional for three or more than ten other staff members

ExamplesAnglophone countries:Kenya 69 1028

7%Ethiopia 118 1444

8%Mauritius 78 235

33%Tanzania 56 133

42%Zimbabwe 44 376

12%

Source: Country Profiles 2003

Training• Introductory training – immersion (new

comers for their quick integration within the organization)

• Intermediary training (in-the-job training – Study tours)

• Training for managerial functions (for those who have a potential to occupy decision-making positions)

• General training for a shared culture and personal development– Communication – Negotiation skills– Team building – Team work - etc.

• Specialized training (for task rotation purposes)

Training (for discussion)

A lifetime activityAre there opportunities for :• General training for a shared

culture and personal development–Qualification in communication–Qualification in negotiation–Team building – Team work

• Other specialized training

Mobility

• Mobility– Objective : Maximise specialized human

capital– Guiding principle : versatility?

• Staff retention – Training – Centralized management of professional

Statisticians

Staff Retention (for discussion)

A major challenge for NSOs

“A way to handle the staff retention problem consists of simply accepting the fact that any statistical agency can keep their most talented staff members for ever” – The Handbook

Contractual relationships with new staff members

• Special programmes:– The “cadet” programme of the Australian

Bureau of Statistics (ABS funds studies for its most talented statisticians)

–Internship (in STCs) etc.

Staff Retention (for discussion)

A major challenge for NSOs• Central banks competing with NSOs

– Training– Change of status– Professionalism – Contractual relationships with the new staff

members – Periods for consultancy services for senior

staff• Mentoring• Special programmes

ConclusionHuman Resources

Management:A coherent set of measures

« Policy makers – directors, human resource officers, committees on human resources – are not free to choose and take measures they believe are necessary to solve a common problem without worrying about the coherence of the outcome ».

Thank you Thank you

Human Resources Management (for discussion)

P2 – Leadership • Motivate individuals towards the

NSO objectives • Minimize poor communication

between various levels of the NSO• And more

Human Resources Management (for discussion)

P3 – Staff involvement • Motivated, committed and involved staff

members in the NSO• Innovation and creativity to serve the

objectives of the organization• Persons responsible for their own

performances• Staff members empressés to participate

and contribute to continuous improvement

Human Resources Management (for discussion)

P6 – Continuous improvement • Provide training on methods and

tools for continuous improvement• To make continuous improvement of

goods and services as well as objective for each individual

• Recognize improvements and show one’s recognition

Definition

African Statistician:Any professional staff and researcher in statistics contributing to the collection, production, analysis or publication of statistical data within the statistical system