Secondary Logo

Share this article on:

Does the private sector care about AIDS? Evidence from firm surveys in East Africa

Ramachandran, Vijayaa; Shah, Manju Kb; Turner, Ginger Lb

doi: 10.1097/01.aids.0000279695.55815.de
Original articles

Objective: Our objective was to identify the determinants of HIV/AIDS prevention activity and pre-employment health checks by private firms in Kenya, Uganda and Tanzania.

Design: We used data from the World Bank Enterprise Surveys for Uganda, Kenya and Tanzania, encompassing 860 formally registered firms in the manufacturing sector.

Methods: Econometric analysis of firm survey data was used to identify the determinants of HIV/AIDS prevention including condom distribution and voluntary counselling and testing (VCT). Multivariate regression analysis was the main tool used to determine statistical significance.

Results: Approximately a third of enterprises invest in HIV/AIDS prevention. Prevention activity increases with size, most likely because larger firms and firms with higher skilled workers have greater replacement costs. Even in the category of larger firms, less than 50% provide VCT. We found that the propensity of firms to carry out pre-employment health checks of workers also varies by the size of firm and skill level of the workforce. Finally, data from worker surveys showed a high degree of willingness on the part of workers to be tested for HIV in the three East African countries.

From the aGeorgetown Public Policy Institute, Washington, DC 20009, USA and Visiting Fellow Center for Global Development, Washington DC 20036, USA

bThe World Bank, Washington, DC 20433, USA.

Correspondence to Vijaya Ramachandran, Georgetown Public Policy Institute, 3520 Prospect Street NW, Washington, DC 20009, USA. Tel: +202 687 2146 (office); e-mail: vr9@georgetown.edu

Back to Top | Article Outline

Introduction

HIV/AIDS has had an enormous impact on the economies of sub-Saharan Africa. Using firm survey data from East Africa, we found that despite the high seroprevalence rate of HIV in the survey area, only a small proportion of firms, approximately 35%, engage in prevention activities. Although prevention activity increases with the size of firm, less than 50% of large firms provide voluntary counselling and testing (VCT).

Using data from the World Bank Enterprise Surveys, which includes a sample of 860 firms in Uganda, Kenya and Tanzania, we examined two discrete actions: providing prevention services, and conducting pre-employment health checks. We found that approximately 35% of firms engage in HIV/AIDS prevention activity, whereas the percentage of firms conducting pre-employment health checks in our sample ranged from approximately 20% in Uganda to over 50% in Tanzania. Finally, our data also indicated that a large proportion of workers are willing to pay to be tested for HIV/AIDS.

We found that larger firms and firms with a higher skilled or better trained workforce tend to do more about HIV/AIDS through prevention activities; such firms are also more likely to conduct pre-employment health checks to screen applicants. Firms in which a majority of workers are unionized are also more likely to carry out HIV/AIDS prevention activities and pre-employment health checks. Finally, managers who are concerned about absenteeism are also more likely to carry out HIV/AIDS prevention activities. We also found that workers have a high willingness to be tested for HIV when asked; this is not consistent with available private sector data on VCT uptake, and therefore suggests that significant barriers remain for workplace provision of VCT.

Back to Top | Article Outline

Economic analysis of HIV/AIDS in East Africa: review of the literature

Kenya, Tanzania and Uganda have all been struggling with the problem of HIV/AIDS for more than a quarter of a century. Table 1 presents the HIV prevalence rates for these countries in 2003 (the period of data collection used in this paper) [1]. Tanzania had the highest prevalence rate and absolute number of HIV-positive individuals, followed by Kenya and Uganda. All three countries have mounted public campaigns to fight HIV/AIDS; these campaigns have increasingly been supplemented by private sector efforts.

Table 1

Table 1

Although there is a large amount of literature on the problem of HIV/AIDS in Africa, there is relatively little rigorous analysis of private sector activity. A global survey in 2003 revealed that the private sector is not doing enough about AIDS [2,3]. The World Economic Forum's Global Health Initiative website (http://www.weforum.org/site/homepublic.nsf/Content/Global+Health+Initiative) summarizes the results of the study as follows: Of the nearly 8000 businesses surveyed in 103 countries: ‘47% felt that HIV will have some impact on their business; this number is much lower in countries that to date have not been hard-hit by HIV. There are important regional variances – in Africa, 89% thought HIV would have some impact, but in the Middle East and North Africa that figure dropped to 33%. Worldwide, 21% of surveyed firms feel that HIV will have a severe impact on their business. Business leaders estimate lower HIV infection rates among their workforce than UNAIDS (official national adult prevalence figures), although 36% of business leaders did not or could not estimate how many of their employees had HIV. The small proportion of firms that have conducted quantitative studies estimates lower rates than other firms.’

In summarizing the findings of their paper, Bloom and colleagues [2] argued that firms have taken little action regarding HIV in Africa. They write that the largest discrepancy between firm perceptions and actual data is to be found in Africa, where 45% of firms report less than 1% prevalence, despite data from UNAIDS that show that only 10% of respondent firms in Africa are located in low-prevalence countries. They argue that as of 2003–2004, the response to AIDS by the private sector has been piecemeal, with only a few firms having HIV/AIDS policies; the response is limited even when firms are quite concerned about HIV. In these cases, businesses seem to rely more on the public sector to deal with the problem.

In the analysis of Nigeria by Rosen [4], the author also argues that managers are doing little about AIDS. Survey data used in that paper in 2001 showed that AIDS was not yet a big problem in the Nigerian workplace, and most managers have had little experience dealing with it. Rosen [4] also makes the interesting argument that given the high cost of the business environment in Nigeria (power, water), it is unlikely that AIDS would enter the ‘top 10’ list of concerns for a while. Using a similar firm-level dataset, Biggs and Shah [5] looked at the impact of AIDS in the mid-1990s through worker attrition as a result of sickness and death on firm performance and concluded that there was no significant measurable impact.

A recent study of agricultural workers in Kenya provided empirical estimates of the impact of HIV/AIDS on labor productivity, by comparing healthy workers with workers who later left the company as a result of HIV, through retrospective measures of output for several years before their exit [6]. Workers terminated because of AIDS-related causes earned 16–18% less in the 2 years before termination, as well as choosing less strenuous tasks and using more sick leave days. Rosen et al. 2004 [7] examined the cost of AIDS to six large employers in South Africa, estimating the cost at 0.4–5.9% of the total wage and salary bill, with each infected employee costing the employer an average of 0.5 to 3.6 times his or her annual salary. Rosen and Simon [8] observed elsewhere that many large employers are actively taking steps to shift the economic burden of AIDS onto employees and governments, through such practices as outsourcing unskilled jobs and capping benefits premiums.

In a survey of 80 small and medium enterprises in South Africa, Connelly and Rosen [9] found that managers on average ranked HIV/AIDS as nine out of 10 on the list of priorities. Managers attributed a low percentage of productivity losses to HIV and found worker replacement inexpensive. In addition, the study found lack of information to be a major constraint; managers were unaware of free services available nearby. Aurum Health [10] recently demonstrated the profitability of AIDS workplace programmes in nine large firms, including Anglo-American Mining. It observed a 60% decrease in absenteeism, which compensated for 70% of the costs of the AIDS workplace programmes, the rest of which were covered by other cost savings.

The South African Business Coalition Against HIV/AIDS (SABCOHA) has recently targeted small and medium-sized enterprises with its small and medium-sized enterprise toolkit, for sale at approximately US$215, which has attracted very low uptake [11].

A number of studies have quantified the projected macroeconomic impact of HIV/AIDS on the labor force [12–14]. It has been more challenging to demonstrate the microeconomic cost to firms of HIV-related absenteeism and lower productivity, mostly because of the difficulty in gathering firm data and confidential worker health information (often not known by managers or even workers themselves) within the same survey instrument. Although Biggs and Shah [5] found no significant impact of HIV on productivity across a large survey of manufacturing firms, possibly because of the ease of replacing workers in the earlier years of the epidemic, more recent case studies [6,15,16] have been able to identify a link between HIV infection, higher absenteeism, and lower productivity.

Back to Top | Article Outline

Firm behavior in East Africa

The analysis contained in this paper is based on a sample of 860 firms across Uganda, Kenya and Tanzania and 4955 workers. The data for this study come from the World Bank's Enterprise Surveys, collected in 2002–2003, in collaboration with local organizations in Africa. More information about this dataset is available at www.enterprisesurveys.org. The collaborating institutions for the design and enumeration of the East African surveys were the Kenya Institute for Public Policy Research (KIPPRA), the Economic and Social Research Foundation–Tanzania (ESRF) and the Uganda Manufacturers' Association Consulting Services (UMACIS). These firms are located in ‘traditional’ sectors such as agroprocessing, wood/furniture, textiles/garments/leather, paper and publishing, construction, chemicals and plastics, and metal-working. Each firm was interviewed in person by a team of enumerators, in most cases, the manager, accountant, and up to 10 workers were interviewed separately to collect the information used in this analysis.

The sampling strategy that was followed was standardized across the East African surveys. In each of the three countries, a stratified random sample was drawn, based on available data regarding the population of formal, registered firms. (It is important to note that these surveys do not cover firms in the informal sector.) Firms were sampled primarily by the number of employees and secondarily by sector and region. In Kenya, a sample of 284 firms was surveyed, constituting a sampling rate of 15.2% for formal manufacturing firms. Approximately 62% of the firms were located in the Nairobi and 10.6% in the Eldoret/Kisumu regions. Other key regions included in the survey were Mombasa and Nakuru.

In Tanzania, the survey covered 276 firms from the manufacturing sector. These manufacturing firms operated in eight industrial sectors and in 10 regions of mainland Tanzania, as well as Zanzibar. The sectors and regions covered in the survey were selected because they had relatively high concentrations of manufacturing firms. The regions covered in the survey were four regions on the eastern coast (Arusha, Kilimanjaro, Tanga, and Dar es Salaam), three in the center (Morogoro, Iringa, and Mbeya), one island (Zanzibar), and three in the north (Kagera, Mwanza, and Mara). Most of the manufacturing firms in the survey were small, with the median of 31 employees. Larger firms were most common in Iringa/Mbeya and Tanga, where the median establishments had 234 and 68 workers, respectively.

Finally, in Uganda, the sample was drawn following the same rules as Tanzania and Kenya, stratified by size, and then sector and location. With assistance from the Uganda Bureau of Statistics, a sample of 300 manufacturing firms was surveyed, covering the central, northeast and southwest regions and the major areas of manufacturing activity. It is important to note that in each survey, firms with a larger number of employees were correspondingly more likely to be drawn; this method of sampling enables an adequate number of firms to be sampled in each size class. Consequently, the observations are weighted, where relevant, according to the probability of being sampled. Detailed tables of the sample for each country are available from the authors and can also be found in the World Bank's Investment Climate Assessment for each country, available at www.worldbank.org/rped. Appendix 1 shows the distribution of firms in each of the three surveys, by size, sector, and location.

In the survey data, we identify two actions that firms may take in response to the HIV/AIDS impact on the workplace: (i) Conducting prevention activities; and (ii) Conducting pre-employment health checks of workers.

Back to Top | Article Outline

HIV/AIDS prevention

HIV/AIDS prevention activities are defined as the following activities in the survey: Prevention messages, which mostly consists of putting up posters around the factory; Distributing condoms on the premises of the firm; Providing HIV/AIDS counselling; and Offering VCT.

Before discussing our results, it is worth asking why firms invest in prevention activity at all. One might argue that the beneficial effects of prevention activities are low because of high turnover rates or because of the length of time before individuals become ill with HIV. Similar questions are often asked about why firms invest in training workers. We recognize the validity of the question and offer several plausible explanations. First, the average length of tenure for a full-time worker is fairly long, 9 years in Kenya, 7 years in Tanzania, and approximately 5 years in Uganda. Second, the length of time before individuals become ill with HIV is not very long in East Africa, it could be as short as 2 or 3 years. At least two studies have shown that the length of time before the onset of illness is quite short in Africa [17,18]. One study, however, reported a longer time from serocoversion to AIDS [19]. Third, firms may be trying to retain at least some types of workers but cannot target them and therefore carry out prevention for the entire workforce. Firms may consider these activities to be an extra benefit to the workers, may use them as signalling devices to attract and retain better or more skilled workers, and may see gains to worker productivity from these investments. Fourth, the activities we describe in this paper, posters, condom distribution, and VCT, are generally not very costly to implement. Perhaps most importantly, prevention activities may be driven by what the manager believes to be its returns, based on the manager's beliefs about the nature of HIV. We do not have information about antiretroviral programmes in this dataset. In the period 2002–2003, however, it is highly unlikely that workers in any firm in East Africa had access to employer-provided antiretroviral treatment. These treatments were prohibitively expensive and restricted to a few individuals, at best. Only South Africa was beginning to see antiretroviral provision in this time period. We found that overall, approximately 35% of all firms in our sample conducted HIV prevention activities. Of this set of firms, 15.6% of firms provided HIV education (prevention messages via posters) and distributed condoms, whereas another 19.5% conducted these activities as well as VCT. Table 2 shows HIV/AIDS prevention activity by country and by region.

Table 2

Table 2

Prevention activity in Tanzania was lowest, which is interesting, given that Tanzania has the highest HIV prevalence rate. Uganda has the highest proportion of firms putting up prevention messages and distributing condoms. These activities tend to be less expensive, and may be driven by increased awareness created by publicly funded programmes. Kenya has the highest percentage of firms engaging in counselling and testing. Both Kenya and Uganda have visible public awareness campaigns to fight HIV/AIDS; this may have some effect in terms of influencing firms to undertake prevention activities.

Table 2 also shows that prevention activity has a positive association with regional HIV prevalence. (Low, medium and high prevalence are defined as the following: low is less than 5% of the population infected with HIV, medium is 5–10%, high is more than 10%.) Firms in high-prevalence regions are almost twice as likely to engage in prevention activity than firms in low-prevalence zones, for each category of prevention. High-prevalence regions such as Nyanza in Kenya; Iringa, Mbeya, and Dar es Salaam in Tanzania; and Kampala and Entebbe in Uganda, are more likely to have firms that conduct prevention activities. It is, however, worth pointing out that the vast majority of our firms (60%) are in medium or high prevalence zones.

There were only small differences in prevention activity across sectors, the agroprocessing/food sector has the highest share, followed by the furniture/wood sector and the construction/machinery sector, and prevention activity is least likely in the textile/garments/leather sector. Prevention activity varied in approximately a 10% range, and there was no obvious difference between these sectors with respect to labor intensity. Table 3 reveals interesting differences in prevention activity by firm size. Not only are large firms more likely to do prevention activity, but they are more likely to do more high-cost prevention activity, such as VCT, and provide financial aid for employees. The figure was highest for large and very large firms in our sample; close to 50% of large firms and over 70% of very large firms are engaged in some type of prevention. Overall, approximately 35% of firms carry out some sort of prevention activity.

Table 3

Table 3

Firms that train workers are twice as likely to engage in prevention activity, and 60% of firms that do AIDS prevention also provide training to their workers. Similarly, 61% of firms that provide VCT also provide worker training; only half that percentage provide training in the category of firms that do not provide VCT and other high-cost services.

Finally, do firms do more prevention activity when the perception of worker absenteeism is higher? Our data showed that firms reporting a higher rate of absenteeism are more likely to conduct HIV/AIDS prevention activities. Approximately 43% of firms that say that absenteeism is a problem carry out HIV/AIDS prevention activities; this number falls to 29% for firms that do not report absenteeism as a problem. One explanation is that managers who are likely to observe absenteeism may also be more likely to do HIV/AIDS prevention. The Tanzania survey asked about ‘high’ HIV-related and general absenteeism; the Uganda and Kenya surveys asked about ‘high’ HIV-related absenteeism and ‘increased’ general absenteeism. The question did not specify the time period of increase but the past 12 months was clearly implied from the flow of questions.

Back to Top | Article Outline

Pre-employment health checks

Approximately a third of firms in our survey engaged in pre-employment health checks. Pre-employment health checks that do not specifically test for HIV/AIDS may not detect workers' HIV infection status. Our survey did not ask whether pre-employment health checks included HIV testing, or whether managers understood that HIV/AIDS status would be visible from general health examinations. Some managers may not make the connection between HIV prevention and general health testing; others may make guesses as to the reasons for symptoms observed during the pre-employment check.

Pre-employment health checks of workers are controversial, to say the least. Opinions vary about whether pre-employment health checks are illegal in East Africa. National policies are in existence that ban HIV testing of workers in Tanzania and Kenya, but these do not seem to be implemented in a uniform manner. Although it appears that HIV testing as a condition of employment is illegal, the law appears to be less clear on the issue of pre-employment health checks. These checks are largely conducted outside the employer–employee relationship, i.e. before the potential employee is hired. It is also unclear to what extent pre-employment health checks can identify the HIV status of the employee; it may well be the case that potential employers are making no more than a guess about HIV status, particularly in cases in which the CD4 cell count can be ascertained.

Table 4 shows the incidence of pre-employment health checks, by country and by region. Approximately 33% of firms in our sample engaged in pre-employment health checks of potential employees.

Table 4

Table 4

The proportion of firms conducting a pre-employment health check of workers was highest in Tanzania (51.9%), followed by Kenya (34.5%) and Uganda (19.7%). Uganda and Kenya did more HIV/AIDS prevention compared with Tanzania in our sample of firms. Tanzania also has the highest HIV/AIDS prevalence across the three countries, as reported in the first section of this paper. Country-wide prevalence rates could influence firms' concerns about HIV/AIDS, and therefore cause them to conduct health checks in the hiring process. Table 5 shows that pre-employment health checks are positively correlated with the prevalence of HIV.

Table 5

Table 5

Like prevention activity, pre-employment health checks vary by firm characteristics. They are more likely to be carried out by foreign-owned firms; our data show that almost 50% of foreign-owned firms carry out health checks compared with 30% of domestically owned firms. Table 6 shows the incidence of pre-employment health checks by firm size. The proportion of firms performing a pre-employment health check of workers increases with size, with over half of large and very large firms engaging in pre-employment checks.

Table 6

Table 6

The proportion of firms performing a pre-employment health check is highest in the agro/food processing sector (approximately 50%), followed by the chemicals/plastics and textiles/garments, and is lowest in the paper/printing sector. There is no obvious reason for this difference among sectors, but it may be caused by a third factor, such as the differences in worker demographics and education levels across sectors. Health concerns may also be higher in the food industry, for safety reasons.

Pre-employment health checks are likely to be greater in firms that invest in worker training and in which worker replacement is costly, and for firms with a higher skill composition of their workforce. Our data show that 56% of the firms that provide pre-employment health checks also provide training to their employees; this number drops to 34% for firms in which no pre-employment health check is carried out. Firms that do pre-employment checks also have a slightly higher ratio of skilled workers than those that do not, 38% versus 34%.

Back to Top | Article Outline

Econometric estimations of firm behavior

In this section, we examined the determinants of HIV/AIDS prevention activities and pre-employment health checks, in a multivariate framework, using a Probit model. We based our econometric analysis on a simple cost–benefit model, as discsused earlier.

This model leads to several hypotheses: (i) Firms that use a higher ratio of skilled labor are more likely to invest more in AIDS prevention or pre-employment health checks because of higher replacement costs; (ii) Firms that carry out training programmes are more likely invest more in HIV/AIDS prevention or pre-employment health checks because of a higher level of investment in employees. It is important to note that skill ratios are independent of whether the firm invests in the training of workers; the skill ratio is defined by job status, i.e. the ratio of managers and professionals to total workers. In each skill category, the firm may or may not provide formal training. Therefore, the first hypothesis captures formal schooling (pre-employment human capital formation), whereas the second captures post-employment learning; (iii) Prevention activity varies across sectors according to the degree of mobility in each; firms will invest more in HIV/AIDS prevention in sectors in which workers are less mobile.

We controlled for firm-specific characteristics such as size, ownership and degree of unionization as a measure of the bargaining power of labor. The hypothesis is that a more unionized labor force will lead to greater HIV/AIDS prevention activity. Related to this, it may also lead to more pre-employment health checks as firms anticipate that they need to offer a higher level of services to their unionized employees. We did not have data on the per-worker costs of HIV/AIDS prevention or pre-employment health checks, but we assumed that these do not vary significantly across firms.

The Probit model used is as follows:

where Y * represents the unobservable variable measuring the net benefit to a firm from investing in any of these activities. The actual variable observed is y (whether or not a firm carries out HIV/AIDS prevention or pre-employment health checks), measured as a dummy variable, equal to 1 if Y * is greater than 0, and 0 otherwise. The function f is a distribution function, X is a vector of explanatory variables, and u is the unobserved error term.

The following equation is estimated for firm i, based on the simple model described above:

where Y is whether any HIV/AIDS prevention is carried out, whether high-cost HIV/AIDS prevention activities are carried out, whether the firm does pre-employment health checks; X 1 is the size of the firm, as measured by the total number of workers; X 2 is whether the firm is foreign owned (0/1); X 3 is the ratio of skilled to total labor; X 4 is whether or not a firm does training; X 5 is whether or not a firm is majority unionized; and X 6X 12 are sector and country dummies.

The definitions of the dependent variables are as follows. The size of the firm is measured by the total number of workers employed, part-time workers are assigned the value 0.5. A firm is foreign owned if it has more than 10% foreign ownership. Skill ratio is defined as the number of managers, professionals, and skilled production workers as a proportion of total workers. Formal training is a dummy variable, equal to 1 if an enterprise has a training programme for its workers, 0 otherwise. The majority unionized dummy is set to 1 if more than 50% of workers in the enterprise belong to a union. Sector dummies are assigned to firms in food processing, textile and garments, wood and furniture, and metal working; the sector effects for these key sectors are measured relative to all others.

Unfortunately, we do not have data on the per-worker cost of HIV/AIDS prevention or pre-employment health checks. It is unlikely, however, that these vary substantially by firm for the simple activities we are considering (posters, condom distribution, VCT). In alternative specifications of the above-described model, we also included a variable to measure firm attrition as a measure of worker mobility. The rationale is that firms are more likely to invest in HIV/AIDS prevention if workers are less able to leave the firm. This variable was not significant, largely because there was little variance in this measure of worker mobility in our cross-sectional dataset. Presumably, panel data will be more useful in this regard. Table 7 presents the results of the Probit estimations for firm behavior. We estimated three econometric models that focus on: (i) whether the firm carries out a pre-employment health check; (ii) whether the firm engages in HIV/AIDS prevention; (iii) whether the firm engages in counselling and testing (VCT).

Table 7

Table 7

Equation [1] presents the results examining the determinants of pre-employment health checks. Specifications that controlled for the age of the firm and included a dummy for whether or not the firm is credit constrained did not yield any additional significance. The dependent variable is defined as a dummy variable, equal to 1 if the firm conducts a pre-employment check, zero otherwise. Firm size is extremely significant, larger firms are much more likely to conduct pre-employment tests compared with smaller firms. After controlling for firm size, firms that provide a formal worker training programme (beyond on-the-job) are much more likely to test new workers. In addition, firms with a higher proportion of skilled workers are more likely to engage in pre-employment checks.

It is interesting to note that after controlling for size, foreign ownership is not significant in the multivariate estimation; foreign firms are not more likely to screen out potentially sick applicants or carry out HIV/AIDS prevention. The coefficient on foreign ownership is small and the variance is large, indicating that there is not enough variance in foreign firms in our sample. As most foreign firms are large, the size coefficient captures the significance and foreignness alone does not give additional information. There is also a fair bit of sectoral variation, which may reflect differing degrees of labor mobility among other things. Pre-employment health checks are significantly greater in the food sector, perhaps for reasons of health and consumer safety. Finally, Kenya and Uganda do significantly fewer pre-employment health checks than Tanzania.

Equation [2] describes the results of the Probit estimation for whether or not the firm engages in HIV/AIDS prevention activity consisting of posters, condom distribution or VCT. Size also matters here; larger firms tend to do more prevention. After controlling for size, it is important to note that firms with better trained workers and higher-skilled workers tend to do more prevention. It could be argued that firms that do more AIDS prevention carry out more training, i.e. that the causality goes in the opposite direction. We do not believe that this is the case; anecdotal and other evidence suggests that firms' decisions to do training greatly precedes their decision to do AIDS prevention. Four sectors, food-processing, wood, metal, and construction, tend to do more HIV/AIDS prevention than other sectors. It is probably the case that sectors in which employees are not easily replaceable will do more AIDS prevention; this may be as a result of issues such as lack of ease in hiring or the difficulty of losing workers during peak seasons. The country dummies are not significant; there is no real variance in prevention activity across countries, after controlling for firm size and other firm characteristics. This result is also reassuring in terms of the decision to pool the data across countries. The third column in Table 7 describes the determinants of more significant HIV/AIDS intervention (VCT). Again, larger firms and firms that have higher-skilled workers who are trained in-house tend to do more VCT activity.

Why is size significant in the three regressions? Large firms may have better quality managers, greater resources, and other unobserved characteristics that enable them to do HIV/AIDS prevention. Anecdotal evidence suggests that large firms in the textile and garment sector in Lesotho carry out very little AIDS prevention; this may be because of the highly mobile nature of firms in this sector. In countries that serve as temporary homes to firms, one would expect less correlation with size or workforce quality. Larger firms may also have already-established facilities for conferences or training that can be easily adapted for HIV/AIDS education sessions. Apart from the fact that large firms may find HIV/AIDS interventions more affordable, they may also be more aware of the risks of HIV. Available evidence suggests that small and medium firms may be less aware of the risks of HIV, lack the staff and resources to carry out prevention activities, and are sometimes unaware of the options available to them to address the problem of HIV [7,20]. It is also worth noting that foreign ownership is not significant after controlling for size. Finally, Kenyan firms do more VCT activity than other firms, as do firms in the construction sector; the latter perhaps because of the migratory nature of the workforce and/or the difficulty in replacing workers in this sector.

Unionization is significant in determining HIV/AIDS prevention, only when a majority of workers are unionized. A simple union dummy set to 1 if the firm has a union is not significant, but a dummy recording whether more than 50% of workers are unionized is significant in determining whether or not the firm carries out HIV/AIDS prevention activity. Interestingly, it is also significant in determining whether or not the firm carries out a pre-employment health check; this may be because firms with a unionized workforce are aware that they have to provide a higher level of HIV/AIDS-related services and may consequently do more to screen out sick workers. Other econometric specifications that included measures of the regional and city location of the firm, and age and education level of the manager, did not yield different results to those reported here. The lack of panel data prevented us from testing other hypotheses some attempts were made to do address this issue in alternative econometric specifications. The addition of regional or city dummies did not change the results; these dummies were not statistically significant. One might argue that larger firms do HIV prevention because of legal requirements. Most legal requirements are de jure rather than de facto in the East African context; analyses of the investment climate as well as of governance factors have found that the regulatory and legal requirements are largely not binding [21].

Manager characteristics and experience with HIV may affect the decision to engage in HIV prevention or pre-employment health checks; specifications of the econometric model that included the manager's age and education did not show these variables to be significant, probably because of a lack of variation across the sample of countries. Finally, there were not many stakeholder conflicts in the firms surveyed, the vast majority were entrepreneur or family-owned on a privately held, limited liability basis, and less than 4% were publicly traded. These variables, legal requirements, managerial and stakeholder attitudes, might be significant if a wider range of countries and geographical areas are considered and if panel data are used. We are hopeful that panel data will be available in the future to enable the investigation of HIV prevention activity in East Africa and elsewhere. Our results are quite robust to variations in specification.

Table 5 shows the probability of a firm of 66 employees (mean size for our sample) carrying out pre-employment health checks and doing high-cost HIV/AIDS prevention, based on the econometric results obtained in Table 7. We examined four scenarios for each country, from a base case of a mean-size firm with a training programme that is majority-unionized, and has 35% of its workforce skilled. We calculated probability values from the cumulative density function underlying the numbers estimated in Table 7. This firm has a 65% probability of doing a pre-employment health check in Tanzania. This number drops to 54% if it does not have a training programme or to 52% if it is not majority unionized. The impact of unionization and a training programme are thus very similar in terms of the likelihood of carrying out a pre-employment health check. Increasing the firm size to 212 employees (one standard deviation larger) raises the likelihood of a pre-employment check by 9% to 74%. The numbers for Kenya and Uganda are also shown below. The results from this exercise confirm the data reported in the descriptive tables in previous sections, larger firms, especially those with higher investments in workers, tend to do more HIV/AIDS prevention and to screen employees more carefully; for such firms, it does appear that the benefits of HIV-related activities outweigh the costs.

Back to Top | Article Outline

Worker perceptions about HIV/AIDS

The dataset used in this analysis also contains information provided by workers within the firms surveyed. A sample of 4950 workers was interviewed in East Africa, of which 80% of workers were men and 20% were women. Broken down by country, 1922 workers were interviewed in Kenya, 1597 in Tanzania, and 1436 in Uganda. Most workers interviewed (84%) had permanent status, the rest were temporary employees. The majority of workers interviewed were between 20 and 40 years of age. The age, occupational, and educational distribution of the workforce is shown in Table 8. Unskilled production workers make up the largest share of the sample, followed by skilled workers. Approximately a third of workers have completed primary school, another third have completed secondary school or vocational training, and 12% have a university degree.

Table 8

Table 8

The worker survey included questions about worker perceptions of HIV/AIDS. Workers were asked to rank from 1 to 5 if HIV/AIDS was of concern to them. Close to 85% of workers surveyed indicated that they are very concerned about HIV/AIDS, rating the problem either 4 or 5 on the scale provided; there was little variation in the responses of workers across age, occupational or educational status. Our data also show that approximately 75% of workers surveyed are willing to pay to be tested. This result is in sharp contrast to anecdotal and case-study evidence that indicates that the uptake on free testing provided by firms is very low. A recent case-study provided by Debswana [22], a diamond mining company in Botswana, shows a VCT uptake of approximately 20–25%. This high number may reflect, to some extent, the workers' perception of the risk of being exposed to HIV. One explanation of our result is that workers are telling us what we want to hear, i.e. they know that getting tested is ‘good for them’, and are consequently saying that they are willing to be tested. Another explanation is that there is in fact a real interest in being tested but because of social stigmas or the visibility of company clinics and VCT facilities, workers are reluctant to visit these health facilities. If the second explanation is to be believed, there may be significant latent demand for HIV testing. An informal discussion with Debswana staff was consistent with the second hypothesis; there is considerable social stigma associated with being HIV positive, and the VCT service provided by the firm is highly visible to all employees, perhaps explaining the low uptake.

Finally, Table 9 shows the amount that workers are willing to pay to get tested; there is a correlation between work status and the amount that workers are willing to pay. Interestingly, it appears that some workers are willing to pay an amount above the actual cost of the test. If there is indeed latent demand, this might be realized if VCT were part of a continuum of services whereby workers have treatment options available after learning their HIV status.

Table 9

Table 9

Back to Top | Article Outline

Conclusion

The analysis in this paper indicates that firms that are larger, have trained workers or workers with greater skill levels or are unionized do more to prevent HIV/AIDS. These factors are also significant in determining whether firms do pre-employment health checks.

Several questions emerge from this analysis: how we can create stronger incentives for private sector intervention such as tax credits or other financial incentives? If larger firms are doing more prevention, as these results suggest, increasing their incentives to provide HIV/AIDS prevention will increase the proportion of workers covered by some prevention activity. If the result that larger firms do more is partly a result of the lower perceived benefit of AIDS interventions by smaller firms, can we raise awareness in the small and medium enterprise sector about the true cost of HIV/AIDS? If size of the firms drives the degree of intervention, the public sector will need to take the lead on HIV/AIDS in most African countries for at least the near future, given the high proportion of small firms compared with large firms.

We also need to find the means by which firms are motivated to do fewer pre-employment health checks and more prevention activity. The screening process, as it currently stands, is probably burdening the state as well as individuals and households by imposing an additional constraint on income generation and the ability to deal with HIV/AIDS.

If latent demand does indeed exist for HIV testing, both the public and private sectors need to find ways to meet that demand; removing the social stigma attached to HIV/AIDS testing or providing a continuum of services beyond VCT may be necessary to ensure that workers are able to get tested. One implication is that if a fee-based testing option were made available and all employees took the test, it would become routine and might help end the stigma attached to testing because it would be a market, consumer-oriented transaction.

Back to Top | Article Outline

Acknowledgements

The authors are grateful for detailed comments to four anonymous reviewers, and to James Habyarimana, Elizabeth Ashbourne, Nancy Birdsall, George Clarke, Bill Cline, Sabine Durier, Judy Feder, Alan Gelb, Alvaro Gonzalez, John Kline, Maureen Lewis, Taye Mengistae, Agata Pawlowska, Axel Peuker, and seminar participants at the World Bank, the Center for Global Development, and Georgetown University for helpful comments and suggestions. The data for Sub-Saharan Africa used in this paper were collected by the Regional Program on Enterprise Development in the Africa Private Sector Unit of the World Bank. The views expressed in this paper are the authors'own and do not necessarily reflect the views of the institutions with which they are affiliated.

Conflicts of interest: None.

The authors are grateful to the World Bank for partial funding of this research.

A version of this article was previously published as Working Paper no. 76 by the Center for Global Development in January 2006.

Back to Top | Article Outline

References

1. UNAIDS. Center for HIV information. University of California, San Diego, California, USA; 2004.
2. Bloom D, Bloom L, Steven D, Weston M. Business and HIV/AIDS: who me? A global review of the business response to HIV/AIDS. Prepared for the World Economic Forum's Global Health Initiative. World Economic Forum, Geneva 2004.
3. Taylor K, DeYoung P, Boldrini F. Business and HIV/AIDS: a global snapshot. World Economic Forum, Geneva July 2004.
4. Rosen S. The implications of HIV/AIDS for Nigerian manufacturing firms. RPED Working Paper no. 113; World Bank, Washington DC July 2001.
5. Biggs T, Shah M. The impact of the AIDS epidemic on African firms. RPED Working Paper no. 72; World Bank, Washington DC 1997.
6. Fox M, Rosen S, MacLeod W, Wasunna M, Bii M, Foglia G, Simon J. The impact of HIV/AIDS on labour productivity in Kenya. Trop Med Int Health 2004; 9:318–324.
7. Rosen S, Simon J, Vincent JR, MacLeod W, Fox M, Thea DM. AIDS is your business. Harvard Bus Rev 2003; 81:613–626.
8. Rosen S, Simon J. Shifting the burden: the private sector's response to the AIDS epidemic in Africa. Bull WHO 2003; 81:131–137.
9. Connelly P, Rosen S. Will small and medium firms provide HIV/AIDS services to employees? An analysis of market demand. South Afr J Econ 2005; 73(Suppl 1):613–626.
10. Aurum Health. Cost–benefit analysis of eight South African companies' HIV/AIDS treatment programs. Presented at the South Africa AIDS Conference. Durban, June 2005. Available at: http://www.aurumhealth.org. Accessed: June 2007.
11. Mears B. SABCOHA presentation at South Africa AIDS Conference. Durban, June 2005.
12. Over M. Confronting AIDS: public priorities in a global epidemic. World Bank. Oxford: Oxford University Press; 1997.
13. Dorrington R, Bradshaw D, Budlender D. HIV/AIDS profile in the provinces of South Africa: indicators for 2002. MRC, ASSA, UCT Technical Report; University of Capetown, South Africa 2002.
14. Coulibaly I. The impact of HIV/AIDS on the labor force in sub-Saharan Africa: a preliminary assessment. Geneva: International Labor Office; 2005.
15. Maffessanti A. HIV absenteeism study. AIC Insurance Company. Presented at the International AIDS Conference, Toronto, August 2006.
16. Maffessanti A. The impact of absenteeism on productivity at Welfit Oddy. AIC Insurance Company. Presented at the International AIDS Conference. Toronto, August 2006.
17. N'Galy B, Ryder RW, Bila K, Mwandagalirwa K, Colebunders RL, Francis H, et al. Human immunodeficiency virus infection among employees in an African hospital. N Engl J Med 1988; 319:1123–1127.
18. Whittle H, Egboga A, Todd J, Corrah T, Wilkins A, Demba E, et al. Clinical and laboratory predictors of survival in Gambian patients with symptomatic HIV-1 or HIV-2 infection. AIDS 1992; 6:685–689.
19. Morgan D, Mahe C, Mayanja B, Okongo JM, Lubega R, Whitworth JAG. HIV infection in rural Africa: is there a difference in median time to AIDS and survival compared with that in industrialized countries? AIDS 2002; 16:597–603.
20. Durier S. Meeting to discuss IFC Against AIDS Program. International Finance Corporation, Washington DC, USA 9 November 2005.
21. Kaufmann D, Kraay A, Mastruzzi M. Governance matters IV: governance indicators for 1996–2004. World Bank Policy Research Working Paper 3630. Washington, DC: World Bank; June 2005.
22. Debswana AIDS Office. Presentation on Debswana HIV/AIDS Program. Washington, DC; September 2005.
23. World Bank. Investment climate assessment for Kenya. 2002. Available at: www.worldbank.org/rped. Accessed: June 2007.
    24. World Bank. Investment climate assessment for Uganda. 2003. Available at: www.worldbank.org/rped. Accessed: June 2007.
      Back to Top | Article Outline

      Appendix 1: the sample of firms

      Table A

      Table A

      Table A

      Table A

      Table A

      Table A

      25. World Bank. Investment climate assessment for Tanzania. 2003. Available at: www.worldbank.org/rped. Accessed: June 2007.
        Keywords:

        AIDS; East Africa; economics; private sector

        © 2007 Lippincott Williams & Wilkins, Inc.