Analysing regional level data from Ukraine, we find two sources of inefficiency which the TB epidemic imposes on economy - dampening of both individual and firm's productivity as well as reducing the level of competitiveness of the economy via inefficient use of resources to compensate for risk of infection. Overall, 10% decreases in TB prevalence could lead to gains in productivity equivalent to a 1% of GDP.

1. Impact of TB Epidemic on Worker and Firm
Productivity: Regional Perspective
Olena Nizalova, University of Kent
Oleksandr Shepotylo, Aston University
22 March 2019
Brown University Ukraine Collaboration

2. Outline
• Motivation
• Research questions
• Theoretical considerations
• Empirical Methodology
• Data
• Results
• Benefits from fighting epidemic
• Conclusions

3. Health and Economic Growth

4. Health and Economic Growth
• “Disease and Development” (Acemoglu and
Johnson 2007): no effect of health on GDP
• Impact of AIDS on GDP growth (Arndt and
Lewis 2000, Bonnel 2000, Kambou et al.
1992): 0.3-1.5% lower GDP growth
• “The Long-run Economic Costs of AIDS” (Bell,
Devarajan, and Gersbach 2003): significant
(even catastrophic effects)

5. TB is not an extinct disease
 9th leading cause of death
 1st leading cause of death from a single infectious
agent (ranked above HIV/AIDS)
 Deaths from TB: 1.3 mln in 2016 (1.7 mln in 2000)
among HIV-negative and 374K among HIV-positive
 In 2016 10.4 mln ppl fell ill with TB (90% adults,
65% male, 10% HIV-positive (74% in Africa), 56%
in five countries (India, Indonesia, China, the
Philippines, and Pakistan)

6. TB is not an extinct disease
 9th leading cause of death
 1st leading cause of death from a single infectious
agent (ranked above HIV/AIDS)
 Deaths from TB: 1.3 mln in 2016 (1.7 mln in 2000)
among HIV-negative and 374K among HIV-positive
 In 2016 10.4 mln ppl fell ill with TB (90% adults,
65% male, 10% HIV-positive (74% in Africa), 56%
in five countries (India, Indonesia, China, the
Philippines, and Pakistan)

7. TB poses new threats
 Drug-resistant TB:
 in 2016 600K new cases, of which 490K MDR-TB
 47% of new MDR-TB cases in India, China, and Russia
 Extensively drug-resistant TB (XDR-TB)
 MDR and XDR TB develop:
 Inadequate treatment
 Infected by someone with MDR/XDR
 MDR and XDR TB require the use of second-line anti-
TB drugs, which are more expensive and have more
side-effects than the first-line drugs used for drug-
susceptible TB

8. TB Globally
• Global trends:
– TB mortality rate is falling at 3% per year
– TB incidence is falling at 2% per year
– 16% of TB cases die from the disease
• Recognized as a global health challenge:
– UN SDGs (SDG 3 “Good Health and Wellbeing”)
– WHO End TB Strategy targets b/w 2015 and 2035
• to reduce TB deaths by 95%
• To cut new cases by 90%
• to ensure that no family is burdened
with catastrophic expenses due to TB

9. Unequal TB Burden
• TB incidence varies widely:
– under 10 per 100 000 population in most high-income
countries
– 150–300 in most of the 30 high TB burden countries
– above 500 in a few countries (Democratic People’s
Republic of Korea, Lesotho, Mozambique, the Philippines
and South Africa)
Three lists:
– TB HBC
– MDR-TB HBC
– TB/HIV HBC

10. Unequal TB Burden

11. Financing for TB prevention, diagnosis
and treatment
• In 2017 USD 6.9 bln in 118 low and middle-
income countries (accounting for 97% of reported
TB cases globally) – double the amount available
in 2006
• India: in 2017 USD 525 mln – double the level of
2016
• Most funding from domestic sources (with
substantial variation – BRICS countries which
account for half of the world’s TB cases mostly
have domestic funding)

12. Should we invest more in fighting TB?
$12 billion annual direct loss to the world’s
economy:
– 75% of TB cases during people's most
productive years, between the ages of 15 and 54
– 30% decline in average productivity due to illness
(3-4 month lost annually with lost earnings 20-
30%)
– 2 mln deaths per year, with a resulting average
loss of 15 years’ of household income
– In some countries 4-7% of GDP

13. Should we invest more in fighting TB?
• Indirect losses?
– Reduced productivity of family members due to
caring responsibilities and stress related to the
disease and bereavement
– Cost due to TB-related orphanhood
– Depressed human capital investment
– Depressed creativity and talent development

14. Why Ukraine?
• One of the world’s “leaders” on TB: up to 2015
one of 22 TB HBCs (TB incidence of 127 per 100K
in 2004/05, 91 in 2015), still on the MDR-TB HBCs
list
• Russian aggression in the East disturbed
historically most TB burdened regions and led to
high numbers of internally displaced persons
from these territories
• EU neighbour country
• Underfinanced state healthcare system with little
resources allocated to fight TB, and insufficient
global support.

15. Why Ukraine?
• Methodologically:
– Availability of routinely collected regional and
firm-level data (as in developed countries)
– High levels of TB burden (as in developing
countries), with significant regional variation

16. Our contribution
• Indirect costs put into focus
• Addressing issues with causality of estimates
(fixed effects)
• Differentiation among different aspects of TB
epidemic (TB incidence vs. TB prevalence)

17. Research Questions
• Does TB epidemic have an adverse effect on
workers’ productivity?
• Does TB epidemic depress productivity of
firms?
• Is there a difference in productivity impact of
various aspects of TB epidemic?
• Does this impact of TB epidemic have a
spatial dimension?

18. Theoretical considerations
• Wages – productivity of workers (Becker’s Time
Allocation theory)
– Sicker (but yet undiagnosed) ppl have less energy, more
sick days => lower productivity (i.e. lower wage)
– Healthy ppl taking care of sick (already diagnosed) have
less energy/time for work => lower productivity (i.e. lower
wage)
• Wages – compensation for (dis)amenities (Rosen’s
Compensating Wage Differentials theory)
– Firms in regions with worse characteristics (e.g. higher risk
of getting TB) have to pay higher wages to attract
workforce

19. Theoretical considerations
• TFP – productivity of firms
– In regions with worse epidemiological situation, ppl have
lower life expectancy, invest less in Human Capital, engage
less in creative activities and innovation, poorer
managerial talent => firms are less productive
• TFP – resource needed to compensate for
(dis)amenities
– In regions with worse characteristics (e.g. higher risk of
getting TB), only highly productive firms (those with higher
TFP) can survive because they have to offer additional
(non-wage) benefits to workers to attract the workforce

20. Methodology
Mincer-like equation
average wage rate w as a function of TB (incidence and prevalence), labor
quality H, and relevant socio-economic characteristics X of the region that
influence labor market
w = αw0 + αw1TB + αw2H + αw3X + αTT + u
Total Factor Productivity
Y accounting for economic characteristics of the region -
it‘s exports and imports:
T F P = βtfp0 + βtfp1TB + βtfp2H + βtfp3X + βtfp4Y+e

21. Spatial Model Specification
• Spatial lag model
𝑌 = 𝑇𝐵 × 𝛾 + 𝑊𝑇𝐵 × 𝛾 𝑊 + 𝑋 × 𝛽 + 𝜀
• Spatial Durbin model (spatial autocorrelation)
𝑌 = 𝜌 × 𝑊𝑌 + 𝑇𝐵 × 𝛾 + 𝑊𝑇𝐵 × 𝛾 𝑊 + 𝑋𝐵 + 𝜖
• where
Y={wage,TFP}
W spatial weighting matrix

22. Total Factor Productivity
• TFP is estimated at firm-level by Olley and
Pakes (1996), controlling for demand shocks
(Shepotylo and Vakhitov, 2015)
• All Ukrainian firms in 2003-2010
• Mean Residual TFP (adjusted for industry and
time effects) at rayon level weighted by the
firms’ workforce
• Also computed for agriculture, industry, and
services

23. Time Dynamics of Productivity and TB
Epidemic Measures
200400600800
2000 2005 2010
year
-.4-.2
0
.2.4
RegionalTFP
2000 2005 2010
year
150200250300
2000 2005 2010
year
556065707580
TBIncidence,per100,000
2000 2005 2010
year

24. Time and Space Dynamics: Wages
(650,2000]
(500,650]
[0,500]
No data
2003
(650,2000]
(500,650]
[0,500]
No data
2010

25. Time and Space Dynamics: TFP
(.5,1.34]
(.1,.5]
(-.1,.1]
[-1,-.1]
2003
(.5,1.34]
(.1,.5]
(-.1,.1]
[-1,-.1]
No data
2010

26. Time and Space Dynamics:
TB prevalence
(300,1200]
(200,300]
[0,200]
No data
2010
(300,1200]
(200,300]
[0,200]
No data
2003

27. Time and Space Dynamics:
TB incidence
(100,700]
(70,100]
[0,70]
No data
2003
(100,700]
(70,100]
[0,70]
No data
2010

28. Non-parametric relationship: wage400600800
1000
0 200 400 600 800 1000
TB Prevalence TB Incidence
Average Regional Wage and TB Prevalence or TB Incidence

29. Non-parametric relationship: TFP-.5-.4-.3-.2-.1
0
.1.2.3.4.5
0 200 400 600 800 1000
TB Prevalence TB Incidence
Weighted Average Regional TFP and TB Prevalence or TB Incidence

30. Descriptive Statistics
2003 2004 2005 2006 2007 2008 2009
Average regional 312.87 357.94 453.85 527.66 594.60 638.62 617.28
wage, 2001 UAH [137.27] [138.29] [154.71] [158.33] [165.33] [156.82] [145.61]
Average regional -0.32 -0.08 -0.03 0.06 0.10 0.17 0.25
TFP (weighted) [0.31] [0.29] [0.27] [0.26] [0.26] [0.23] [0.25]
TB prevalence 299.63 245.49 237.91 230.05 217.47 211.91 204.35
(per 100,000) [98.86] [85.83] [83.03] [75.10] [73.15] [72.54] [69.27]
TB incidence 71.76 75.99 80.70 81.10 78.17 75.86 71.39
(per 100,000) [29.61] [33.58] [31.67] [32.61] [30.85] [31.16] [27.07]
N obs 493 530 529 519 541 472 471

31. Wage TFP TFP
Robust
OLS
Fixed
Effects
Robust
OLS
Fixed
Effects Agriculture
Manufact
uring Services
(1) (2) (3) (4) (5) (6) (7)
Log of TB
prevalence 0.0438* 0.0548** 0.0423* 0.0506** 0.0340 -0.0021 0.1338
per 100,000
people (0.0171) (0.0095) (0.0185) (0.0148) (0.0755) (0.1298) (0.3590)
Log of TB
incidence -0.0098 -0.0333* -0.0467+ -0.0893** 0.0991 -0.0122 -0.0811
per 100,000
people (0.0260) (0.0148) (0.0272) (0.0187) (0.1273) (0.1760) (0.2945)
Number of
Observations 3,555 3,555 3,555 3,555 2,340 1,7451 817
R-squared/F-
stat 0.6248 941.49 0.2825 179.62 62.72 8.14 6.13
Note: Robust standard errors in
parentheses; ** p<0.01, * p<0.05, +
p<0.1
Estimated Effect of TB-incidence and TB-prevalence on Weighted
Average Regional TFP Growth and Average Regional Wage
Growth

32. Spatial lag model
Wage TFP TFP
Agr Manuf Services
(1) (2) (3) (4) (5)
Log of Initial TB incidence 0.0397** 0.0401** 0.0367 -0.0355 0.1395
per 100,000 people (0.0086) (0.0124) (0.0746) (0.1201) (0.2380)
Log of Average TB incidence 0.1221** 0.0838** 0.0327 0.2158 -0.0694
rate in neighbouring regions (0.0127) (0.0182) (0.1288) (0.1889) (0.2894)
Log of Initial TB prevalence -0.0216+ -0.0757** 0.0842 0.0924 -0.0286
per 100,000 people (0.0122) (0.0175) (0.1166) (0.1766) (0.2896)
Log of Average TB prevalence -0.0491** -0.0608** -0.0181 -0.4762** -0.1595
rate in neighbouring regions (0.0108) (0.0154) (0.1183) (0.1659) (0.2756)
Number of Observations 3,555 3,555 2,340 1,745 817

33. Economic impact
2014 2009
Gross Domestic Product (UAH) 1,586,900,000,000 947,042,000,000
Employment (persons) 18,073,300 20,191,500
Average monthly wage 3368 1906
Considered decrease in TB prevalence (%) -10 -10
TFP elasticity with respect to TB prevalence -0.0893 -0.0893
Wage elasticity with respect to TB prevalence -0.0333 -0.0333
Total national wage bill 730,450,492,800 461,819,988,000
Gain from fighting TB in terms of wages (UAH) 2,432,400,141 1,537,860,560
Gain from Fighting TB in terms of wages (% of
GDP) 0.15 0.16
Gain from fighting TB in terms of TFP (% of GDP) 0.89 0.89
Total gain from fighting TB (% of GDP) 1.05 1.06
TB-related expenditure (UAH) 568,836,440 n/a
TB-related expenditure (% of GDP) 0.04 n/a

34. Conclusions
• TB epidemic has considerable indirect economic cost in
terms of lost productivity which is documented both
through lower wages and lower TFP.
• Consistent with the Compensating Wage Differentials
theory and after controlling for the prevalence of the TB,
the risk of contracting the disease - TB incidence rate - is
associated with higher wages and higher productivity - a
kind of premium for individuals and firms to operate in
risky environment.
• There exist spatial effects in TB incidence for both wages
and TFP, but only for wages in TB prevalence

35. Conclusions
Both – negative effect of the prevalence of TB
(level of sickness of population) and positive
effect of the incidence of TB (risk of contracting
the disease) are sources of inefficiency:
- Reduction in TB prevalence and incidence will
boost productivity at worker and firm level and will
remove the need to spend resources compensating
for this risk

36. Economic Gains from Fighting TB are enormous!
• 10% lower TB prevalence level implies:
– ~0.15% higher GDP via improved individual
productivity;
– ~0.89% higher GDP via improved firms’
productivity.

37. Acknowledgements
• The Project “Feasibility Study: Effectiveness of Public Health System
(Programmes/Policies) in Combating Severe Population Health
Crisis in Ukraine” was funded by MRC/Wellcome Trust/UKAid
International Health System Initiative
• Initial stage of the analysis for this research was funded by Brown
University
• The authors are thankful to Vadym Bizyaev for compiling together
the administrative statistics from all small regions of Ukraine and to
Nataliia Shapoval for excellent research assistance within the period
of the above mentioned project.

38. THANK YOU
Brown University Ukraine Collaboration