United States GCI: 2017p: saar: CC: Federal: Gross Invt: IP: Software data was reported at 0.170 % Point in Mar 2025. This records an increase from the previous number of 0.060 % Point for Dec 2024. United States GCI: 2017p: saar: CC: Federal: Gross Invt: IP: Software data is updated quarterly, averaging 0.060 % Point from Jun 1961 (Median) to Mar 2025, with 256 observations. The data reached an all-time high of 0.590 % Point in Jun 1999 and a record low of -0.270 % Point in Dec 1963. United States GCI: 2017p: saar: CC: Federal: Gross Invt: IP: Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A114: NIPA 2023: Government Consumption Expenditure: Contributions to Change: Chain Linked 2017 Price: saar.

United States GCI: 2005p: saar: CC: Federal: Gross Investment: Eqp & Software data was reported at -1.110 % in Mar 2013. This records a decrease from the previous number of -0.140 % for Dec 2012. United States GCI: 2005p: saar: CC: Federal: Gross Investment: Eqp & Software data is updated quarterly, averaging 0.160 % from Jun 1947 (Median) to Mar 2013, with 264 observations. The data reached an all-time high of 16.690 % in Dec 1951 and a record low of -8.710 % in Sep 1954. United States GCI: 2005p: saar: CC: Federal: Gross Investment: Eqp & Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A284: NIPA 2009: Government Consumption Expenditure: Contributions to Change: Chain Linked 2005 Price: saar.

United States GCI: 2005p: saar: CC: Gross Investment: Equipment & Software data was reported at -1.240 % in Mar 2013. This records a decrease from the previous number of 0.080 % for Dec 2012. United States GCI: 2005p: saar: CC: Gross Investment: Equipment & Software data is updated quarterly, averaging 0.235 % from Jun 1947 (Median) to Mar 2013, with 264 observations. The data reached an all-time high of 16.660 % in Dec 1951 and a record low of -8.650 % in Sep 1954. United States GCI: 2005p: saar: CC: Gross Investment: Equipment & Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A165: NIPA 2009: Contributions to the Change in GDP: Government Consumption Expenditure: 2005 Price.

United States GCI: 2012p: saar: CC: Fed: GI: IP: Software data was reported at 0.020 % in Jun 2018. This records a decrease from the previous number of 0.080 % for Mar 2018. United States GCI: 2012p: saar: CC: Fed: GI: IP: Software data is updated quarterly, averaging 0.050 % from Jun 1961 (Median) to Jun 2018, with 229 observations. The data reached an all-time high of 0.590 % in Jun 1999 and a record low of -0.270 % in Dec 1963. United States GCI: 2012p: saar: CC: Fed: GI: IP: Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A051: NIPA 2018: Contributions to the Change in GDP: Government Consumption Expenditure: 2012 Price.

Business process outsourcing (BPO) companies provide services to clients in all sectors of the economy, with the financial services and public sectors being particularly important markets. Expanding IT adoption and government expenditure have incentivised businesses to invest more heavily in IT systems and upgrades, supporting demand for BPO services. Industry revenue is expected to contract at a compound annual rate of 3% over the five years through 2024-25 to £73.4 billion, including estimated growth of 3.7% in 2024-25. Advances in cloud computing, mobile technology and big data have created new opportunities for BPO service providers to add value to their services. Even so, tumbling business confidence and the freezing of hiring and expansion initiatives following the COVID-19 outbreak took a steep toll on demand for BPO services. In 2021-22, business hiring and expansionary activities picked up as the economy gradually recovered from the pandemic, fuelling demand for BPO services. However, challenging economic conditions have clouded revenue growth in 2022-23 and 2023-24. Soaring inflation and high interest rates subdued business confidence and weakened business activity, constraining spending on outsourcing. Despite lingering economic fears, subsiding inflation and falling interest rates provide a boost to business confidence and encourage greater business expenditure, driving revenue growth in 2024-24. Inflationary pressures and intense competition have weighed on the industry’s average profit margin, which is estimated at 13.2% in 2024-25. Industry revenue is forecast to swell at a compound annual rate of 4.5% over the five years through 2029-30 to £91.7 billion. A positive economic outlook, thanks to normalising inflation and interest rate levels, and the growing adoption of new IT and telecommunications technology will drive revenue growth. Strong appetite from businesses to outsource non-core activities and to cut costs will also fuel demand for BPO service providers. Although public-sector spending on outsourcing has been significant for BPO firms, government plans to cut expenditure on private consultants going forward would hinder growth in demand from this market. Intense competition from in-house services and overseas BPO firms will restrict revenue and profit growth.

Abstract

The overriding aim of the SDA Priority surveys is to provide relevant statistical information on the socio-economic effects of structural adjustment policies being implemented by the government and in particular how such policies affect living standards at the household level. The Priority survey is a household based survey but data was also collected at the individual level. The survey has two primary objectives. The first is to provide a quick identification of policy target groups. The second is to provide a mechanism, whereby key socio-economic variables can be easily and regularly produced to describe and monitor the well-being of different groups of households.

The Priority Survey places emphasis on five basic needs indicators. These are education, health, nutrition, food expenditure and housing. Structural adjustment programs involve the implementation of a series of policy measures designed to correct imbalances in the national economy and to promote a desirable or targeted economic growth. The type of structural adjustment programs that have been carried out in Zambia include:

• Introducing market foreign exchange rates • Liberalizing interest rates • Privatizing state owned companies • Liberalizing foreign trade so that domestic and international producers compete • Liberalizing domestic trade by removal of price controls on commodities • Removal of subsidies on consumption and production • Reforming and restructuring the civil service

These measures and other adjustments to the national economy have impacts on the Zambian society and the Priority Survey is intended to highlight and monitor these impacts. Structural adjustments involve both fiscal and monetary reforms which seek to redress imbalances in the economy. Fiscal policy includes such issues as reduction in Government expenditure and tax reform while monetary reforms involve such issues as reducing money supply and liberalizing the interest and foreign exchange rates. In highlighting the social dimensions of adjustment attention is generally focused on the identification of the poor and most vulnerable groups in the population.

Geographic coverage

Coverage was national. The Priority survey II covered both urban and rural parts of Zambia in all the nine provinces. In all 651 Standard Enumeration Areas were selected across the country. In urban areas the same 250 Standard Enumeration Areas (SEAs) that were selected for Priority survey I were canvassed in Priority survey II. In Rural areas 401 Standard Enumeration Areas were covered based on the CSO Agriculture post harvest (1993) survey.

In urban SEAs 25 households were selected in each sample SEA. In the rural areas 10 households were selected from the 20 sample households in the 401 sample SEAs earmarked for the 1993 Agriculture survey. In all about 10,000 households were interviewed in Priority survey II.

In the Priority survey I on which the PSII sample is based, a three stage stratified random sample method was used for the survey. The first stage constituted primary sampling units (PSUs) which were Census Supervisory Areas, (CSA), delineated for the 1990 Census of Population, Housing and Agriculture. Standard Enumeration Areas (SEAs) were second stage sampling units, while households formed third-stage sampling units. The household as well as individuals formed the units of analysis. The sampling frame consisted of 4,144 CSAs and 12,999 SEAs.

Analysis unit

• Households
• Individuals

Universe

The sample frame of this survey was the list of SEAs developed from the 1990 Census of Population, Housing and Agriculture. The eligible household population constisted of all civilian households. Excluded from the survey were the institutional population in (hospitals, boarding schools, prisons, hotels, refugee camps, orphanages, military camps and bases, etc) and diplomats accredited to Zambia in embassies and high commissions. However, private households living around these institutions were enumerated such as teachers whose houses are on school premises and doctors and other workers living on hospital premises.

Kind of data

Sample survey data [ssd]

Sampling procedure

The PSII covered all the nine (9) provinces of Zambia, both rural and urban areas on a sample basis. The domains of study and data disaggregation for this survey were:- - Rural - Urban - Province

Stratification The whole country is divided into nine provinces that are subdivided into 57 districts by the Local government Administration. Central Statistical Office has delineated the Districts into Census Supervisory Areas and then CSAs into Standard Enumeration Areas. A CSA has about three SEAs in it.The sample standard enumeration areas were selected with a probability proportional to the number of inhabitants in each area.For urban areas stratification was done based on the main type of housing in the area. Urban households were classified into low, medium and high cost areas. In the case of rural areas stratification was done based on the scale of Agricultural activity. Rural households were classified into small scale, medium scale, large scale and non-agricultural. In PSII small scale and non-agricultural households were lumped together as one since the rural sample was a sub-sample of the sample areas selected for the agriculture survey and that is how the agriculture survey lumped the two. The large scale agricultural households were left out of the PSII analysis because of the small number that were interviewed.

Sampling Frame The sampling frame consisted of 4,144 CSAs and 12,999 SEAs. It was obtained from the 1990 Census of Population and Housing. The SEAs in the frame were sorted by rural/urban and by low cost, medium cost and high cost areas. All in all, the frame gives information on the population size of each SEA throughout the country, the number of households, information about rural/urban, and low cost, medium cost and high cost areas.

Sample Size In all , 651 Standard Enumeration Areas were selected across the country. In urban areas the same 250 Standard Enumeration Areas (SEAs) that were selected for Priority survey I were canvassed in Priority survey II. In Rural areas 401 Standard Enumeration Areas were covered based on the CSO Agriculture post harvest (1993) survey. In urban SEAs 25 households were selected in each sample SEA. In the rural areas 10 households were selected from the 20 sample households in the 401 sample SEAs earmarked for the 1993 Agriculture survey. In all about 10,000 households were interviewed in Priority survey II. In the Priority survey I on which the PSII sample is based, a three stage stratified random sample method was used for the survey. The first stage constituted primary sampling units (PSUs) which were Census Supervisory Areas, (CSA), delineated for the 1990 Census of Population, Housing andformed third-stage sampling units. The household as well as individuals formed the units of analysis.

Sample Selection Sampling with probability proportional to size (PPS) was used in selecting the sample of CSAs and SEAs. In selecting CSAs and SEAs the measure of size was the cartographic mapping population estimates.

Allocation Allocation of SEAs to provinces was done using the Probability Proportional to Size (PPS) method.This means that the total sample size was proportionally allocated to each province according to the population in the province. First, allocation was done on provinces considering the population share of each province from the total population. Then allocation was done at district level in the same way. Within the districts, allocation was done by rural/urban by the same method. Within the urban strata, allocation was done by low cost, medium cost, and high cost areas using the same method. (See Appendix I).

Listing In each selected SEA, households were listed and each household given a unique sampling serial number. A circular systematic sample of households was then selected. Vacant residential housing units and non-contact households were not assigned sampling serial numbers.

Sampling deviation

Due to logistical problems the actual number of SEAs enumerated in rural strata was 392 and 250 in urban areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two basic instruments were used in collecting data during the survey: the listing form and the main questionnaire.

Cleaning operations

Training: The provincial data entry operators were trained for a week to facilitate capturing of the Priority survey data. A total of 18 data entry operators were trained.

For data entry the IMPS (Integrated Microcomputer Processing System) software designed by the U.S. Bureau of Census was used. This software contains three components; CENTRY -for data entry and verification, CONCOR - for range, skip and consistency checks in the data and CENTS - for tabulation. Only the first two (CENTRY and CONCOR) components of IMPS were used.For tabulation and analysis the SAS (Statistical Analysis System) software was used. This software was developed in the U.S.A. as well. The software has the advantage of being able to handle large amounts of data and also to compute statistical and complex tables. For typing the report, the Word Perfect software was used. For Anthropometry EPI-INFO was used.

Data entry was done in the respective nine provinces by the provincial data entry operators. Central Statistical Office has decentralised its computer data capturing process since 1991. After all the data was captured in the provinces, it was brought to the headquarters office in Lusaka as well as the questionnaires

United States GCI: 2009p: saar: CC: SL: GI: IP: Software data was reported at 0.070 % in Mar 2018. This records an increase from the previous number of -0.020 % for Dec 2017. United States GCI: 2009p: saar: CC: SL: GI: IP: Software data is updated quarterly, averaging 0.020 % from Mar 1960 (Median) to Mar 2018, with 233 observations. The data reached an all-time high of 0.130 % in Mar 1997 and a record low of -0.080 % in Dec 2001. United States GCI: 2009p: saar: CC: SL: GI: IP: Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A096: NIPA 2013: Contributions to the Change in GDP: Government Consumption Expenditure: 2009 Price.

United States GCI: 2009p: saar: CC: Fed: GI: IP: Software data was reported at 0.010 % in Mar 2018. This records a decrease from the previous number of 0.020 % for Dec 2017. United States GCI: 2009p: saar: CC: Fed: GI: IP: Software data is updated quarterly, averaging 0.040 % from Jun 1961 (Median) to Mar 2018, with 228 observations. The data reached an all-time high of 0.600 % in Jun 1999 and a record low of -0.270 % in Dec 1963. United States GCI: 2009p: saar: CC: Fed: GI: IP: Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A096: NIPA 2013: Contributions to the Change in GDP: Government Consumption Expenditure: 2009 Price.

United States GCI: 2009p: saar: CC: Fed: D: GI: IP: Software data was reported at 0.010 % in Mar 2018. This stayed constant from the previous number of 0.010 % for Dec 2017. United States GCI: 2009p: saar: CC: Fed: D: GI: IP: Software data is updated quarterly, averaging 0.010 % from Jun 1961 (Median) to Mar 2018, with 228 observations. The data reached an all-time high of 0.210 % in Jun 1999 and a record low of -0.160 % in Mar 1999. United States GCI: 2009p: saar: CC: Fed: D: GI: IP: Software data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A207: NIPA 2013: Government Consumption Expenditure: Contributions to Change: Chain Linked 2009 Price: saar.

美国 GCI: 2012p: saar: CC: Fed: GI: IP: Software在2018-06达0.020 %，相较于2018-03的0.080 %有所下降。美国 GCI: 2012p: saar: CC: Fed: GI: IP: Software数据按季度更新，1961-06至2018-06期间平均值为0.050 %，共229份观测结果。该数据的历史最高值出现于1999-06，达0.590 %，而历史最低值则出现于1963-12，为-0.270 %。CEIC提供的美国 GCI: 2012p: saar: CC: Fed: GI: IP: Software数据处于定期更新的状态，数据来源于Bureau of Economic Analysis，数据归类于Global Database的USA – Table US.A051: NIPA 2018: Contributions to the Change in GDP: Government Consumption Expenditure: 2012 Price。

GCI：2017年价格：按年率季节性调整后：变化贡献比：联邦：总投资：IP：软件在03-01-2025达0.170百分点，相较于12-01-2024的0.060百分点有所增长。GCI：2017年价格：按年率季节性调整后：变化贡献比：联邦：总投资：IP：软件数据按季更新，06-01-1961至03-01-2025期间平均值为0.060百分点，共256份观测结果。该数据的历史最高值出现于06-01-1999，达0.590百分点，而历史最低值则出现于12-01-1963，为-0.270百分点。CEIC提供的GCI：2017年价格：按年率季节性调整后：变化贡献比：联邦：总投资：IP：软件数据处于定期更新的状态，数据来源于Bureau of Economic Analysis，数据归类于全球数据库的美国 – Table US.A114: NIPA 2023: Government Consumption Expenditure: Contributions to Change: Chain Linked 2017 Price: saar。