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The economic impact of AIDS Treatment: Labor supply in Western Kenya 1

Harsha Thirumurthy2, Joshua Graff Zivin3, Markus Goldstein4

January 2006

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Abstract

Using longitudinal survey data collected in collaboration with a treatment program, this paper is the first to estimate the economic impacts of antiretroviral (ARV) treatment in Africa. The responses in two important outcomes are studied: (1) labor supply of adult AIDS patients receiving treatment; and (2) labor supply of children and adults living in the patients’ households. We find that within six months after the initiation of treatment, there is a 20 percent increase in the likelihood of the patient participating in the labor force and a 35 percent increase in weekly hours worked. Since patient health would continue to decline without treatment, these labor supply responses are underestimates of the impact of treatment on the treated. The upper bound of the treatment impact, which is based on plausible assumptions about the counterfactual, is considerably larger and also implies that the wage benefit from treatment is roughly equal to the costs of treatment provision. The responses in the labor supply of patients’ household members are heterogeneous. Young boys work considerably less after initiation of treatment, while girls and other adults in the household do not change their labor supply. In multiplepatient households, only the labor supply of girls remains unaffected. These results suggest that ARV treatment influences intrahousehold time allocation decisions and that it has non-health benefits for patients and their household members.

Introduction

Sub-Saharan Africa is home to 25 million of the nearly 40 million people living with HIV/AIDS (UNAIDS, 2004). In the next decade, AIDS-related mortality will continue to increase the number of orphans in the region (currently 12 million) and reduce life expectancy (already at 35 years in some countries). Following increases in donor support and substantial reductions in the prices of medicines, antiretroviral (ARV) therapy has recently become an important part of the policy response to combat AIDS.5 As of June 2005, roughly one-half million HIV-positive individuals were receiving ARV therapy in sub-Saharan Africa (WHO, 2005). Since this represents only 11 percent of the number of people needing treatment, scaling-up of treatment programs poses a major challenge in many countries.6 At the same time, however, some have questioned the investment in ARV therapy since most low-income countries have limited resources and many competing needs (Marseille, Hofmann, and Kahn, 2002; Kremer, 2002).7

Numerous studies have shown that ARV therapy dramatically reduces morbidity and mortality among HIV-infected individuals, in both industrialized countries (Hammer et al., 1997, Hogg et al., 1998; Palella et al., 1998) and developing countries (Laurent et al., 2002; Marins et al., 2003; Koenig, Leandre, and Farmer, 2004; Wools-Kaloustian et al., 2005). These health benefits have the potential to significantly improve economic well-being, as suggested by a growing literature that shows linkages between health and income in developing countries.8 While this literature examines the economic impacts of several dimensions of health such as nutritional status and morbidity, it provides little guidance when it comes to a highly debilitating and chronic disease like HIV/AIDS. One exception is the recent study by Fox et al. (2004), who analyze retrospective data from a Kenyan tea estate and find significant declines in the labor productivity of HIV-positive workers prior to their death or medical retirement. However, the extent to which treatment can reverse such declines in labor productivity remains to be determined. Little is known about the impact of this important intervention on a broad range of other socio-economic outcomes as well, both at the individual and household level.

In this paper, we use survey data from Kenya to present the first estimates of how quickly and to what degree ARV therapy affects the labor supply of treated patients and their household members. These estimates are a preliminary step in understanding the socio-economic impacts of ARV therapy, which in turn is critical for properly evaluating treatment programs and efficiently allocating resources. For example, if ARV therapy for adult AIDS patients increases the likelihood that their children attend school, then such impacts belong in any cost-benefit analysis. Estimates of these impacts can also contribute to the growing literature on the longterm micro- and macro-economic consequences of AIDS (Bell, Devarajan, and Gersbach (2003) and Young (2005).

Labor is the central productive asset of the poor in most developing countries. Indeed, labor supply and related outcomes like income have been the focus of many studies that examine the impacts of nutrition, morbidity, and AIDS-related mortality.9 Because it is an important outcome, changes in the labor supply of adult AIDS patients can also generate intrahousehold spillover effects on time allocation patterns and influence other measures of household welfare.

Our analysis is based on data from a household survey we conducted in collaboration with a rural treatment program in western Kenya. Over the course of one year, longitudinal socio-economic data were collected from AIDS patients who receive treatment. These data have been linked to longitudinal medical data containing clinical and laboratory measures of the patients’ health status. The presence of individuals whose HIV status is known, the ARV treatment program, and the linked medical data combine to offer us a unique opportunity to measure the effects of treatment.

To identify the response to treatment, we examine changes over time in the labor supply of treated patients and their household members. Since ARV treatment eligibility is defined by biological markers that are not easily influenced by the behavior of patients with late-stage HIV disease, treatment and the resulting changes in health are exogenous. Using data collected simultaneously from a large random sample of non-patient households, we control for timevarying factors (such as seasonality) that could bias the estimates. The analysis is strengthened by variation in the length of time that patients had been exposed to treatment prior to the survey. As we show with the linked medical data, health has a non-linear temporal response to treatment—it improves dramatically in the first months of treatment but more gradually thereafter. We exploit this non-linearity to test for heterogeneous treatment responses in the labor supply of patients.

We find that the provision of ARV therapy leads to a large and significant increase in the labor supply of AIDS patients. This increase occurs very soon after the initiation of ARV therapy: within six months, there is a 20 percent increase in the likelihood of participating in the labor force and a 35 percent increase in hours worked during the past week. Since AIDS patients left untreated will experience continued declines in health and possibly death within six months, our estimated labor supply responses are underestimates of the impact of treatment on the treated. It is important to note that due to the clinical effectiveness and life-saving nature of ARV therapy, randomized evaluations of treatment interventions are not feasible on ethical grounds. Thus, the results here represent the best available method of studying the impact of treatment. However, we also calculate an upper bound of the impact of treatment on the treated by assuming that patients would be too sick to work (or even dead) without treatment. Clinical evidence on the evolution of untreated HIV disease suggests that this is a reasonable assumption, and that the upper bound estimate is close to the ‘true’ impact of treatment on the treated. This upper bound is very large: labor force participation for those initiating therapy in Round 1 increases by 85 percentage points and hours worked increases by 26 hours per week relative to what would have happened if AIDS had progressed untreated.

Given this effect on patients’ labor supply, treatment can also have spillover benefits within the household. However, an analysis of how ARV therapy influences the labor supply of treated patients’ household members is complicated, as the effects are theoretically ambiguous. On the one hand, the increase in a patient’s labor supply has an income effect that allows other household members to work less. On the other hand, the improvement in the patient’s health reduces the care-taking and housework burden on family members, thereby having a time endowment effect that allows for more work and leisure. We find that the labor supply of younger boys in patients’ households declines after the initiation of ARV therapy. In multiplepatient households, both younger and older boys, as well as other adults in the household, work less after patients receive treatment. This suggests that intrahousehold decisions about time allocation are influenced by the provision of treatment, and that the welfare of some household members beyond the patient may increase considerably as a result.

This paper is organized as follows: in Section 2, we provide a brief overview of the key stages of HIV infection and the role of ARV therapy in treating infected individuals. We then discuss our survey data in Section 3. Section 4 uses medical data from the HIV clinic where this study was conducted to show that measurable dimensions of patient health improve after initiation of treatment. We discuss our strategy for estimating the response in treated patients’ labor supply in Section 5 and present the results in Section 6. In Section 7, we examine the labor supply of children and adults living with ARV recipients. Section 8 concludes and discusses the policy implications of this research.


Footnotes:
  1. This project would not have been possible without the support of the Academic Model for Prevention and Treatment of HIV/AIDS (AMPATH) and members of the IU-Kenya partnership. We are grateful to Michael Boozer, T. Paul Schultz, and Christopher Udry for many comments and suggestions. We have also benefited from discussions with Richard Akresh, Joseph Altonji, Janet Currie, Amit Khandelwal, Germano Mwabu, Matt Neidell, Tavneet Suri, and participants in seminars at Yale and the Center for Interdisciplinary Research on AIDS. We thank Kara Wools-Kaloustian and especially, Beverly Musick, for guidance in using the AMPATH medical records. Many individuals contributed to the implementation of the household survey under the direction of the authors and Mabel Nangami. Giovanna d’Adda assisted in managing the second round of the survey and the data collection was facilitated by the excellent field supervision of Irene Muhunzu. We also acknowledge the tremendous contributions of Andrew Anyembe, Caroline Amuyunzu, Jayne Chaina, Norbert Ketter, James Mungai, June Ochanda, and Jacklyne Tetee for administering questionnaires; and Chelimo Cherono, David Marende, Maurice Mungai, Florence Oduor, and Martha Simiyu for computer entry of questionnaires. Financial support for this project was received from the Economic and Social Research Council (UK), Pfizer, Inc., The World Bank, Yale University's Center for Interdisciplinary Research on AIDS (CIRA) through a grant from the National Institute of Mental Health to Michael Merson, M.D. (No. P30 MH 62294), the Social Science Research Council, and the Calderone Program at Columbia University. All errors and opinions are our own.


  2. Department of Economics, Yale University. E-mail:


  3. Department of Health Policy and Management, Joseph L. Mailman School of Public Health, Columbia University.


  4. The World Bank.


  5. For example, in 2003 the World Health Organization (WHO) launched the prominent “3 by 5” campaign, with the goal of treating three million people by 2005 (WHO, 2003).


  6. As explained below, not all HIV-positive individuals are currently in need of ARV therapy.


  7. Furthermore, advocates of treatment have also noted that questions concerning economic effectiveness have served as obstacles to obtaining greater donor support (Binswanger, 2003; Clinton, 2003).


  8. See Strauss and Thomas (1998), Ruger, Jamison, and Bloom (2001), and Thomas and Frankenberg (2002) for reviews and discussions of the micro-economic literature on linkages between health and income.


  9. Yamano and Jayne (2004) examine the impacts of working-age adult mortality on a range of household outcomes including crop and non-farm income. Beegle (2005) examines the impacts of adult mortality on the labor supply of household members.


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