The association between the Health Promotion Levy and employment in South Africa: an interrupted time series analysis

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However, there is overwhelming evidence linking SSBs to the rising prevalence in obesity and its comorbidities. In South Africa, the prevalence of overweight and obesity is high and is among the highest in Sub-Saharan Africa. In response to rising prevalence in obesity and its comorbidities, on 1 April 2018 the South African government introduced an SSB tax, known as the Health Promotion Levy (HPL). However, the levy has been opposed by the sugar industry, claiming that it leads to jobs losses. Against this backdrop, this study seeks to investigate the association between the HPL and employment in the sugar industry. Methods We employed single-group interrupted time series analyses using the Quarterly Labour Force Survey data from Statistics South Africa. Results Our results show that the HPL has not been associated with job losses (or generation) in the sugar-related industries in South Africa. These findings are consistent with the findings on the effects of SSB taxes on employment in other jurisdictions. Conclusions Considering that the HPL does not impede employment, and the overwhelming evidence on the effectiveness of SSB taxes, together with the relatively low tax burden, it is imperative that the government raises the HPL from the current 8% of the retail price to the WHO-recommended 20% threshold. The government should also consider expanding the HPL to fruit juices. Such strategies are important in encouraging people to reduce the intake of SSBs, while enabling the government to raise additional revenue for the fiscus. Health Promotion Levy employment SSB tax sugar industry South Africa obesity non-communicable diseases Figures Figure 1 Figure 2 Background The consumption of sugar-sweetened beverages (SSBs) have been increasing over the past years, globally [ 1 ]. However, there is overwhelming evidence linking SSBs to the rising prevalence in obesity and its comorbidities (such as diabetes, hypertension, stroke, cardiovascular diseases, dental caries, and many forms of cancer) [ 2 – 4 ]. The global prevalence of obesity nearly tripled since 1975 and is expected to increase further in the coming decades [ 5 ]. The highest prevalence rates have been recorded in in low- and middle-income countries (LMICs)[ 1 , 5 ]. Non-communicable diseases (NCDs) account for over 70% of deaths globally, about 40% of which is attributable to dietary factors. In response to the rising incidence of obesity and a variety of diet-related NCDs, especially considering that SSBs are among the leading sources of free sugar intake in many countries, there has been growing interest in implementing SSB taxes to curb consumption [ 6 , 7 ]. SSB taxes are regarded as a cost-effective measure which can be used to prevent or slow the growing burden of NCDs [ 8 ]. This is happening as the growing affordability of SSBs, especially in LMICs, threatens to worsen existing global health inequalities [ 7 ]. In South Africa, the prevalence of overweight and obesity is high and is among the highest in Sub-Saharan Africa. In 2016, 31% of adult males, 67% of adult females, and 13% of children under five years old were either overweight or obese [ 9 , 10 ], posing a significant challenge to the healthcare system. This impacts heavily and negatively on income due to decreased productivity [ 11 , 12 ]. The economic impact of obesity and its comorbidities on the South African economy is estimated at ZAR30 billion, in 2020 [ 13 ]. In response to rising prevalence in obesity and its comorbidities, in 2016 the South African government announced the introduction of an SSB tax based on sugar content, as recommended by the World Health Organisation (WHO). The announcement was followed by a white paper, evidence reviewing and making recommendations for a sugar-based tax to be levied at ZAR0.028 per gram of sugar, resulting in a tax burden of approximately 20% of the per-litre price of the most popular SSB [ 14 ]. After extensive consultation with the sugar industry, beverage manufacturers, civic society groups, and public health advocates, there were substantial concessions made to both the sugar and beverage industries. The tax was formally implemented on 1 April 2018 referred to as the Health Promotion Levy (HPL). The levy is limited to non-alcoholic sugary drinks, excluding fruit juice. It is levied at a rate of ZAR0.0221 per gram of sugar above a threshold of 4g of sugar per 100ml. Thus, the effective tax burden was reduced to about 10% from the 20% initially proposed. Despite the concessions made, policymakers continue to face substantial opposition to the levy. The primary argument, which has also been raised against tobacco and alcoholic beverages taxes, is that the tax has led (and will continue to lead to) job losses, particularly in the industries involved in the production, distribution, and sale of these products [ 15 , 16 ]. This argument by the industry led the government to suspend till 2025 its intention to increase the levy rate, reduce the threshold to below 4g per 100ml, and expand the tax to fruit juice. However, evidence from independent research globally show no significant changes in employment associated with SSB taxes e.g., in Mexico [ 17 ], Peru [ 18 ], San Fransisco [ 19 ], and Illinois and California [ 20 ]. Considering the persistent argument by the sugar and beverage industry (amid high unemployment rate), and limited evidence on the employment impact of the SSB tax, this study seeks to investigate the association between the HPL and employment in sugar-related industries in South Africa. This knowledge is important especially for policymakers as they consider reviewing the HPL. Methods Data We use the Quarterly Labour Force Survey (QLFS) data [ 21 ] to evaluate the relationship between the HPL and employment levels in South Africa. The QLFS is conducted by Statistics South Africa (Stats SA). The survey is household-based and collects information on labour market activities in all sectors of the economy. It is nationally representative. The information is collected from individuals aged 15 years or older from all nine South African provinces. The survey uses a two-stage stratified sampling technique. Demographic and socioeconomic characteristics (such as race, age, gender, and level of education) are also gathered. The QLFS has been conducted every year (quarterly) since the first quarter (q1) of 2008. The most recent available survey data (at the time of writing) cover the first quarter of 2023. As such, this uses data for the period 2008q1-2023q1. Sugar-related industries are classified into four categories: agriculture, manufacturing, transport, and wholesale and retail. These categories are to some extent proxies. The agricultural industry covers growing of crops, horticulture and mixed (crop and animal) farming. The manufacturing category covers only those that produce beverages, while the transport industry constitutes railway and other land transport. The wholesale and retail category is comprised of the following (as captured in the QLFS): wholesale trade in agricultural raw materials, livestock, food, beverages and tobacco; retail trade in food, beverages and tobacco in specialised stores; restaurants, bars and canteens; and shebeen. All other (sub-)industries were classified as non-sugar related. Tables 1 and 2 respectively show the distribution of aggregate employment levels for each industry, by gender and province for the period 2008–2023. Table 1 Number of employees by industry and gender, 2008–2023 Male Female Total N Percentage of total sample N Percentage of total sample N Percentage of total sample Agriculture 5 005 681 2 3 169 426 1.3 8 175 107 3.3 Manufacturing 830 088 0.3 578 546 0.2 1 408 634 0.6 Wholesale & Retail 4 829 602 1.9 5 760 306 2.3 10 589 908 4.2 Transport 8 678 191 3.5 1 131 938 0.5 9 810 129 3.9 Non-sugar industry 122 324 757 48.6 99 233 224 39.5 221 557 981 88.1 Total 141 668 319 56.3 109 873 440 43.7 251 541 759 100 Table 2 Number of employees by industry and province, 2008–2023 Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal North West Gauteng Mpumalanga Limpopo Total Agriculture N 2 012 334 603 700 654 009 804 934 1 106 784 452 775 528 238 955 859 1 056 475 8 175 107 Percentage of total sample 0.8 0.24 0.26 0.32 0.44 0.18 0.21 0.38 0.42 3.25 Manufacturing N 377 313 108 163 30 185 45 278 133 317 62 885 402 467 52 824 181 110 1 408 634 Percentage of total sample 0.15 0.043 0.012 0.018 0.053 0.025 0.16 0.021 0.072 0.56 Wholesale & retail N 1 987 180 1 031 321 211 295 628 854 1 534 405 679 163 3 270 043 679 163 553 392 10 589 908 Percentage of total sample 0.79 0.41 0.084 0.25 0.61 0.27 1.3 0.27 0.22 4.21 Transport N 1 106 784 981 013 155 956 452 775 2 314 184 352 158 3 194 580 729 471 528 238 9 835 283 Percentage of total sample 0.44 0.39 0.062 0.18 0.92 0.14 1.27 0.29 0.21 3.91 Non-sugar industry N 30 914 482 19 896 953 3 924 051 11 596 075 38 133 731 13 055 017 73 726 890 15 394 356 14 941 580 221 557 981 Percentage of total sample 12.29 7.91 1.56 4.61 15.16 5.19 29.31 6.12 5.94 88.08 Total N 36 423 247 22 588 450 4 980 527 13 532 947 43 240 028 14 614 576 81 122 217 17 784 002 17 255 765 251 541 759 Percentage of total sample 14.48 8.98 1.98 5.38 17.19 5.81 32.25 7.07 6.86 100 Empirical estimation To assess the association between the HPL and employment we employ a single-group panel interrupted time series (ITS) analysis, (also known as segmented analysis). The segmented ITS study design is a quasi-experimental research technique with potentially significant degree of internal validity in cases where multiple observations on the variable of interest exist for pre- and post-intervention periods. The approach (or its variants) is increasingly being used for the evaluation of public health interventions and are particularly suited to interventions introduced at a population level [ 17 , 22 – 25 ]. In this study, we account for the effects of the coronavirus disease that occurred in 2019 (COVID-19) with its associated restrictions. South Africa recorded its first COVID-19 case on 5 March 2020. The government declared a National State of Disaster on 15 March 2020. The COVID-19 regulations were repealed on 22 June 2022. The outcome of interest is the logarithm of the aggregate employment by province in the sugar-related industries measured quarterly from 2008q1 to 2023q1. As such, we transformed the data to reflect the employment levels by quarter and province. The regression model used in this study follows an approach used in by Guerrero-López et al [ 17 ] and Boachie et al [ 25 ] for similar purpose. The model is specified as follows: $$ln\left({Y}_{it}\right)={\beta }_{0}+{\beta }_{1}T+{\beta }_{2}{X}_{it}+{\beta }_{3}T{X}_{it}+{\beta }_{c}{C}_{it}+{\beta }_{q}Q+{\beta }_{p}P+{\epsilon }_{t}$$ where \({Y}_{it}\) is the number of employees for industry i , at time (quarter) t . \(T\) is the time elapsed since the start of the study (2008Q1), \({X}_{it}\) is a dummy variable representing the HPL intervention; it takes the value of 0 for the pre-intervention (HPL) period, and 1 for the post-HPL period. \({TX}_{it}\) is an interaction term of the time trend and the HPL, while \({C}_{it}\) is a dummy variable representing the COVID-19. \({\beta }_{0}\) represents the baseline level of the employment at \(T\) =0, \({\beta }_{1}\) represents the underlying pre-HPL trend (i.e., the change in employment level associated with a single unit increase in time before the HPL). \({\beta }_{2}\) indicates the immediate level (or intercept) change following the introduction of the HPL and \({\beta }_{3}\) represents the change in the slope of the trend due to the HPL, compared with the pre-HPL trend. \(Q\) represents quarterly dummies for potential seasonality in employment levels, while \(P\) accounts for provincial fixed effects. We run regressions for the overall sugar-related industry, and for each sugar-related industry. The primary regression model is an ordinary least-squares (OLS) linear regression (with lags for the dependent variable). For robustness checks, we run two more different regressions: a generalised-least squares (GLS) method, and a random-effects (RE) regression model. All analyses are done with STATA V.18. Results Overall, there were 549 observations for the nine provinces (i.e., 61 quarterly observations for each of the nine provinces). Figures 1 shows the trend of the aggregated number of employees in separate sugar-related industries, while Fig. 2 shows the employment trend for the overall sugar-related industry, for the period 2008q1-2023q1. Thus, Fig. 2 depicts the aggregate of the industry-specific employment levels depicted in Fig. 1 . From both Figs. 1 and 2 , the HPL appears to have had no significant impact on employment levels in the sugar-related industry. Unlike the HPL, Covid-19 appears to have had a significant negative impact on employment. The extent to which these covariates impacted on employment is established through regression analyses. The regression results are shown in Tables 1 and 2 . The results from all the regression models are largely similar. Results from the GLS and RE are included in the tables but are not discussed in this section. Table 1 shows that across all the sugar-related industries, both \({\beta }_{2}\) (the coefficient of the HPL) and \({\beta }_{3}\) (the coefficient of the interactive term of HPL and time) are not significant. The coefficient for COVID-19 \(\left({\beta }_{c}\right)\) is negative and significant for wholesale and retail, transport, and the overall sugar-related industry. The COVID-19 was associated with 0.11% (95% CI -0.17% to -0.06%) and 0.09% (95% CI -0.15% to -0.04%) reduction in the number of employees in the wholesale and retail, and transport industries, respectively. Overall, as shown in Table 2 , the COVID-19 was associated with a 0.05% (95% CI -0.09% to -0.02%) reduction in the number of employees in the sugar-related industry. Table 1 Regression results for the separate sugar-related industries Agriculture Manufacturing Wholesale and retail Transport (1) (2) (3) (1) (2) (3) (1) (2) (3) (1) (2) (3) OLS GLS RE OLS GLS RE OLS GLS RE OLS GLS RE Time 0.002* 0.003* 0.004*** -0.004*** -0.010*** -0.009*** 0.001* 0.001** 0.002** 0.004*** 0.007*** 0.008*** (0.001) (0.002) (0.001) (0.002) (0.003) (0.002) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) HPL -0.038 -0.008 -0.057 -0.034 0.035 0.021 -0.010 0.024 0.009 -0.001 0.050 0.021 (0.039) (0.065) (0.047) (0.086) (0.121) (0.094) (0.027) (0.041) (0.030) (0.028) (0.046) (0.033) HPL*Time -0.002 -0.008 -0.004 -0.001 0.004 0.002 0.001 -0.004 -0.001 0.001 -0.004 -0.002 (0.003) (0.006) (0.004) (0.008) (0.010) (0.008) (0.002) (0.004) (0.003) (0.002) (0.004) (0.003) COVID-19 0.008 0.005 -0.037 0.001 -0.089 -0.069 -0.114*** -0.126*** -0.183*** -0.094*** -0.125*** -0.159*** (0.037) (0.051) (0.045) (0.084) (0.104) (0.090) (0.026) (0.033) (0.029) (0.027) (0.036) (0.032) Province dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Quarter dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 4.518*** 11.793*** 11.763*** 6.260*** 10.413*** 10.384*** 6.461*** 11.600*** 11.593*** 4.742*** 11.013*** 11.007*** (0.488) (0.075) (0.045) (0.543) (0.120) (0.088) (0.519) (0.044) (0.029) (0.460) (0.052) (0.032) Observations 531 549 549 488 527 527 531 549 549 531 549 549 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.10 Table 2 Regression results for the overall sugar industry (1) (2) (3) OLS GLS RE Time 0.002*** 0.003*** 0.004*** (0.001) (0.001) (0.001) HPL -0.016 0.023 -0.008 (0.019) (0.032) (0.023) HPL*Time 0.001 -0.004 -0.001 (0.002) (0.003) (0.002) COVID-19 -0.053*** -0.063** -0.105*** (0.018) (0.025) (0.022) Province dummies Yes Yes Yes Quarter dummies Yes Yes Yes Constant 5.074*** 12.704*** 12.686*** (0.497) (0.039) (0.023) Observations 531 549 549 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.10 Discussion We employed single-group interrupted time series analyses to investigate the association between the HPL and employment levels in sugar-related industries (including agriculture, beverage manufacturing, transport, and commercial establishments that sell food and beverages). We used the Quarterly Labour Force Survey data from Statistics South Africa, the national statistical agency. Our results show that the HPL has not been associated with job losses or generation in the sugar-related industries in South Africa. These results compare favourably with findings from other peer-reviewed non-industry-funded studies on the employment impact of SSB taxes. For instance, our results are consistent with the findings on the effects of SSB taxes on employment in Peru [ 18 ], San Francisco [ 19 ], Philadelphia [ 26 ], and California and Illinois [ 20 ]. In all these studies, SSBs taxes were found to have no significant impact on employment levels. However, the key difference is that our study is the first to look at the subject in Africa. The lack of effects of the SSB tax (the HPL) on employment can be attributed to at least four reasons [ 18 – 20 ]. First, multiproduct firms in affected industries may internally reallocate their labour force to products unaffected by these policies. Second, beverages have non-nutritive sweetener options that allow producers to quickly reformulate, as research has shown in South Africa [ 2 , 27 ] and Portugal [ 28 ]. Reformulation allows producers to avoid the tax and retain most consumer preferences [ 18 , 27 ]. As a result, they have no need to reduce employment (or change wages) [ 18 ]. Third, if the demand for the affected products does not decline (or declines slightly) after the implementation of the HPL, the industry finds no incentive to adjust employment levels. Fourth, consumers may substitute untaxed for the taxed products from the same producers. The increase in the demand for unaffected products may offset the decline in demand for affected products [ 2 , 18 ]. To reduce the intake of SSBs, the government should consider raising the HPL from the current 8% of the retail price to the minimum 20% recommended by the World Health Organisation [ 29 ]. The government should also consider expanding the HPL to fruit juices. Increasing the HPL and expanding it to fruit juices are important in incentivising people to reduce the consumption of SSBs as evidence has shown, while enabling the government to raise additional revenue for the fiscus. Although this study provides useful information for devising suitable SSB tax policy measures, there is one limitation to consider. The sugar-related industries are broadly defined, which may include other activities that are unrelated to the sugar industry. Conclusions Contrary to the sugar-related industry claims of employment losses due to the HPL, we found no association between the levy and employment levels based on the QLFS. Considering that the HPL does not impede employment, and the overwhelming evidence on the effectiveness of SSB taxes, together with the relatively low tax burden, it is imperative that the government raises the HPL from the current 8% of the retail price to the WHO-recommended 20% threshold. The government should also consider expanding the HPL to fruit juices. Such strategies are important in encouraging people to reduce the intake of SSBs, while enabling the government to raise additional revenue for the fiscus. Thus, primary prevention of NCDs such as type 2 diabetes and cardiovascular diseases can be implemented without harm to employment. Abbreviations SSB Sugar-sweetened beverage HPL Health Promotion Levy LMICs Low- and middle-income countries NCDs Non-communicable diseases WHO World Health Organisation QLFS Quarterly Labour Force Survey OLS Ordinary least-squares regression GLS Generalised-least squares regression RE random-effects regression Declarations Ethics approval and consent to participate: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research, and a waiver of informed consent were approved by the Human Research Ethics Committee at the University of Witwatersrand (HRECNMW24/03/04). Consent for publication: All authors have approved the final article for publication. Availability of data and materials: Data are available in a public, open access repository. Data are publicly available on https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/?page=1&sort_by=title&sort_order=asc&ps=15. Competing interests : None declared. Funding : This study received funding from Bloomberg Philanthropies through the University of North Carolina, USA (grant number 5106249), with additional support from the South African Medical Research Council (grant number 23108). Authors' contributions : CD, MKB and ET conceptualised the study. CD and MKB conducted the data analysis. CD, MKB, SG and ET contributed to the drafting and revision of the manuscript. Acknowledgements : Special thanks to Shu Wen Ng who reviewed earlier drafts. References Lara-Castor L, Micha R, Cudhea F, Miller V, Shi P, Zhang J, et al. Sugar-sweetened beverage intakes among adults between 1990 and 2018 in 185 countries. Nature communications. 2023;14(1):5957. Stacey N, Mudara C, Ng SW, van Walbeek C, Hofman K, Edoka I. Sugar-based beverage taxes and beverage prices: Evidence from South Africa's Health Promotion Levy. Social Science & Medicine. 2019;238:112465. Juul F, Hemmingsson E. Trends in consumption of ultra-processed foods and obesity in Sweden between 1960 and 2010. Public health nutrition. 2015;18(17):3096-107. Rauber F, Steele EM, Louzada MLdC, Millett C, Monteiro CA, Levy RB. Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008-2016). PloS one. 2020;15(5):e0232676. Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Elsevier; 2022. p. 155217. Andreyeva T, Marple K, Marinello S, Moore TE, Powell LM. Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis. JAMA Network Open. 2022;5(6):e2215276-e. Organization WH. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets2022. Abdool Karim S, Kruger P, Hofman K. Industry strategies in the parliamentary process of adopting a sugar-sweetened beverage tax in South Africa: a systematic mapping. Globalization and Health. 2020;16(1):1-14. National Department of Health (NDoH). Strategy for the Prevention and Management of Obesity in South Africa, 2023 - 2028. Pretoria, South Africa.2023. Danquah FI, Ansu-Mensah M, Bawontuo V, Yeboah M, Kuupiel D. Prevalence, incidence, and trends of childhood overweight/obesity in Sub-Saharan Africa: a systematic scoping review. Archives of Public Health. 2020;78:1-20. Tremmel M, Gerdtham U-G, Nilsson PM, Saha S. Economic burden of obesity: a systematic literature review. International journal of environmental research and public health. 2017;14(4):435. Specchia ML, Veneziano MA, Cadeddu C, Ferriero AM, Mancuso A, Ianuale C, et al. Economic impact of adult obesity on health systems: a systematic review. The European Journal of Public Health. 2015;25(2):255-62. Boachie MK, Thsehla E, Immurana M, Kohli-Lynch C, Hofman KJ. Estimating the healthcare cost of overweight and obesity in South Africa. Global Health Action. 2022;15(1):2045092. Stacey N, Edoka I, Hofman K, Swart EC, Popkin B, Ng SW. Changes in beverage purchases following the announcement and implementation of South Africa's Health Promotion Levy: an observational study. The Lancet Planetary Health. 2021;5(4):e200-e8. Wesbound (PTY) Ltd. Economic Impact of the Health Promotion Levy on the Sugar Market Industry: Impact Assessment Report. 2020. Bureau for Food and Agricultural Policy. Potential impact of an increase in the current level of the Health Promotion Levy. 2023. Guerrero-López CM, Molina M, Colchero MA. Employment changes associated with the introduction of taxes on sugar-sweetened beverages and nonessential energy-dense food in Mexico. Preventive medicine. 2017;105:S43-S9. Díaz J-J, Sánchez A, Diez-Canseco F, Miranda JJ, Popkin BM. Employment and wage effects of sugar-sweetened beverage taxes and front-of-package warning label regulations on the food and beverage industry: Evidence from Peru. Food Policy. 2023;115:102412. Marinello S, Leider J, Powell LM. Employment impacts of the San Francisco sugar-sweetened beverage tax 2 years after implementation. Plos one. 2021;16(6):e0252094. Powell LM, Wada R, Persky JJ, Chaloupka FJ. Employment impact of sugar-sweetened beverage taxes. American journal of public health. 2014;104(4):672-7. Statistics South Africa. South Africa - Quarterly Labour Force Survey. In: Africa SS, editor. Pretoria: Cape Town: DataFirst; 2023. Xiao H, Augusto O, Wagenaar BH. Reflection on modern methods: a common error in the segmented regression parameterization of interrupted time-series analyses. International journal of epidemiology. 2021;50(3):1011-5. Linden A. Conducting interrupted time-series analysis for single-and multiple-group comparisons. The Stata Journal. 2015;15(2):480-500. Lagarde M. How to do (or not to do)… Assessing the impact of a policy change with routine longitudinal data. Health policy and planning. 2012;27(1):76-83. Boachie MK, Khoza M, Goldstein S, Munsamy M, Hofman K, Thsehla E. The Impact of COVID-19 Lockdown on Service Utilization Among Chronic Disease Patients in South Africa. Health Services Insights. 2023;16:11786329231215040. Marinello S, Leider J, Pugach O, Powell LM. The impact of the Philadelphia beverage tax on employment: A synthetic control analysis. Economics & Human Biology. 2021;40:100939. Essman M, Taillie LS, Frank T, Ng SW, Popkin BM, Swart EC. Taxed and untaxed beverage intake by South African young adults after a national sugar-sweetened beverage tax: A before-and-after study. PLoS medicine. 2021;18(5):e1003574. Gonçalves J, Dos Santos JP. Brown sugar, how come you taste so good? The impact of a soda tax on prices and consumption. Social Science & Medicine. 2020;264:113332. World Health Organization. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. 2022. 2022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2025 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviews received at journal 01 Nov, 2024 Reviews received at journal 23 Oct, 2024 Reviewers agreed at journal 17 Oct, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviewers invited by journal 24 Sep, 2024 Editor invited by journal 24 Sep, 2024 Editor assigned by journal 24 Apr, 2024 Submission checks completed at journal 24 Apr, 2024 First submitted to journal 19 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4291451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294914860,"identity":"910d4bc9-be45-42ed-a0b8-e917ef88f3e7","order_by":0,"name":"Chengetai Dare","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFCCBCDmYWDgZ2BgI1GLZANpWoDA4ACxWvjbk599+CBjF218I/nZgw8VDPL8Ygfwa5E488x45gye5NxtN9LMDWecYTCcOTsBvxYDiQRjZh4eZqCWBDNp3jaGBIPbBLWkf2b+w1Ofu3lG+jditeQYMzPwHM7dIJFDpC0SZ94UM/bwHM+dceZNmeSMMxKE/cLfnr6Z4WdPdW5/e/o2iQ8VNvL80gS0gAFjD5AQAKuUIEI5GPwA2XeAWNWjYBSMglEw0gAAfOFBDdt4QOoAAAAASUVORK5CYII=","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":true,"prefix":"","firstName":"Chengetai","middleName":"","lastName":"Dare","suffix":""},{"id":294914861,"identity":"50fcccef-d7c4-4d5a-aaa5-c9a34cd960d8","order_by":1,"name":"Micheal Kofi Boachie","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Micheal","middleName":"Kofi","lastName":"Boachie","suffix":""},{"id":294914862,"identity":"763d0d07-5796-43e1-9dd7-065dc7f16e0f","order_by":2,"name":"Susan Goldstein","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Goldstein","suffix":""},{"id":294914863,"identity":"81939b05-5477-4f69-9028-435e24ae3cca","order_by":3,"name":"Evelyn Thsehla","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Evelyn","middleName":"","lastName":"Thsehla","suffix":""}],"badges":[],"createdAt":"2024-04-19 07:12:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4291451/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4291451/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-025-01012-6","type":"published","date":"2025-01-31T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55516791,"identity":"6f08dad5-0c1d-4358-b315-74abbb389558","added_by":"auto","created_at":"2024-04-29 13:19:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndustry-specific employment levels\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4291451/v1/2bad0624abc4d19a137e1875.png"},{"id":55515562,"identity":"6fbf767a-55b5-48e7-bf7c-2442dc6da23c","added_by":"auto","created_at":"2024-04-29 13:11:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall employment level in the sugar-related industry\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4291451/v1/a84b7ff9c8d00afe4c137d9a.png"},{"id":75351329,"identity":"def609ae-1eed-479a-b83e-d54e459143c2","added_by":"auto","created_at":"2025-02-03 16:09:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1142107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291451/v1/2a6c351b-2bda-44be-86e0-da8ebe658def.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between the Health Promotion Levy and employment in South Africa: an interrupted time series analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eThe consumption of sugar-sweetened beverages (SSBs) have been increasing over the past years, globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, there is overwhelming evidence linking SSBs to the rising prevalence in obesity and its comorbidities (such as diabetes, hypertension, stroke, cardiovascular diseases, dental caries, and many forms of cancer) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global prevalence of obesity nearly tripled since 1975 and is expected to increase further in the coming decades [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The highest prevalence rates have been recorded in in low- and middle-income countries (LMICs)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-communicable diseases (NCDs) account for over 70% of deaths globally, about 40% of which is attributable to dietary factors. In response to the rising incidence of obesity and a variety of diet-related NCDs, especially considering that SSBs are among the leading sources of free sugar intake in many countries, there has been growing interest in implementing SSB taxes to curb consumption [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. SSB taxes are regarded as a cost-effective measure which can be used to prevent or slow the growing burden of NCDs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This is happening as the growing affordability of SSBs, especially in LMICs, threatens to worsen existing global health inequalities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn South Africa, the prevalence of overweight and obesity is high and is among the highest in Sub-Saharan Africa. In 2016, 31% of adult males, 67% of adult females, and 13% of children under five years old were either overweight or obese [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], posing a significant challenge to the healthcare system. This impacts heavily and negatively on income due to decreased productivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The economic impact of obesity and its comorbidities on the South African economy is estimated at ZAR30\u0026nbsp;billion, in 2020 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to rising prevalence in obesity and its comorbidities, in 2016 the South African government announced the introduction of an SSB tax based on sugar content, as recommended by the World Health Organisation (WHO). The announcement was followed by a white paper, evidence reviewing and making recommendations for a sugar-based tax to be levied at ZAR0.028 per gram of sugar, resulting in a tax burden of approximately 20% of the per-litre price of the most popular SSB [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. After extensive consultation with the sugar industry, beverage manufacturers, civic society groups, and public health advocates, there were substantial concessions made to both the sugar and beverage industries. The tax was formally implemented on 1 April 2018 referred to as the Health Promotion Levy (HPL). The levy is limited to non-alcoholic sugary drinks, excluding fruit juice. It is levied at a rate of ZAR0.0221 per gram of sugar above a threshold of 4g of sugar per 100ml. Thus, the effective tax burden was reduced to about 10% from the 20% initially proposed.\u003c/p\u003e \u003cp\u003eDespite the concessions made, policymakers continue to face substantial opposition to the levy. The primary argument, which has also been raised against tobacco and alcoholic beverages taxes, is that the tax has led (and will continue to lead to) job losses, particularly in the industries involved in the production, distribution, and sale of these products [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This argument by the industry led the government to suspend till 2025 its intention to increase the levy rate, reduce the threshold to below 4g per 100ml, and expand the tax to fruit juice. However, evidence from independent research globally show no significant changes in employment associated with SSB taxes e.g., in Mexico [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Peru [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], San Fransisco [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and Illinois and California [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Considering the persistent argument by the sugar and beverage industry (amid high unemployment rate), and limited evidence on the employment impact of the SSB tax, this study seeks to investigate the association between the HPL and employment in sugar-related industries in South Africa. This knowledge is important especially for policymakers as they consider reviewing the HPL.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData\u003c/p\u003e \u003cp\u003eWe use the Quarterly Labour Force Survey (QLFS) data [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to evaluate the relationship between the HPL and employment levels in South Africa. The QLFS is conducted by Statistics South Africa (Stats SA). The survey is household-based and collects information on labour market activities in all sectors of the economy. It is nationally representative. The information is collected from individuals aged 15 years or older from all nine South African provinces. The survey uses a two-stage stratified sampling technique. Demographic and socioeconomic characteristics (such as race, age, gender, and level of education) are also gathered. The QLFS has been conducted every year (quarterly) since the first quarter (q1) of 2008. The most recent available survey data (at the time of writing) cover the first quarter of 2023. As such, this uses data for the period 2008q1-2023q1.\u003c/p\u003e \u003cp\u003eSugar-related industries are classified into four categories: agriculture, manufacturing, transport, and wholesale and retail. These categories are to some extent proxies. The agricultural industry covers growing of crops, horticulture and mixed (crop and animal) farming. The manufacturing category covers only those that produce beverages, while the transport industry constitutes railway and other land transport. The wholesale and retail category is comprised of the following (as captured in the QLFS): wholesale trade in agricultural raw materials, livestock, food, beverages and tobacco; retail trade in food, beverages and tobacco in specialised stores; restaurants, bars and canteens; and shebeen. All other (sub-)industries were classified as non-sugar related. Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively show the distribution of aggregate employment levels for each industry, by gender and province for the period 2008\u0026ndash;2023.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of employees by industry and gender, 2008\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 005 681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 169 426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 175 107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e830 088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e578 546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 408 634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWholesale \u0026amp; Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 829 602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 760 306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 589 908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 678 191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 131 938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 810 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-sugar industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 324 757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 233 224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221 557 981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e141 668 319\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e56.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e109 873 440\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e43.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e251 541 759\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of employees by industry and province, 2008\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern Cape\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEastern Cape\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthern Cape\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFree State\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKwaZulu-Natal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGauteng\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMpumalanga\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLimpopo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 012 334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e603 700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e654 009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e804 934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 106 784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e452 775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e528 238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e955 859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1 056 475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8 175 107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377 313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133 317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62 885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e402 467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52 824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e181 110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1 408 634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWholesale \u0026amp; retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 987 180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 031 321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e628 854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 534 405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e679 163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 270 043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e679 163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e553 392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10 589 908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 106 784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e981 013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155 956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e452 775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 314 184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e352 158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 194 580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e729 471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e528 238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9 835 283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-sugar industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 914 482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 896 953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 924 051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 596 075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38 133 731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 055 017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73 726 890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15 394 356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14 941 580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e221 557 981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of total sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e88.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e36 423 247\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e22 588 450\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4 980 527\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13 532 947\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e43 240 028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e14 614 576\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e81 122 217\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e17 784 002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e17 255 765\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e251 541 759\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePercentage of total sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e32.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e7.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e6.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEmpirical estimation\u003c/p\u003e \u003cp\u003eTo assess the association between the HPL and employment we employ a single-group panel interrupted time series (ITS) analysis, (also known as segmented analysis). The segmented ITS study design is a quasi-experimental research technique with potentially significant degree of internal validity in cases where multiple observations on the variable of interest exist for pre- and post-intervention periods. The approach (or its variants) is increasingly being used for the evaluation of public health interventions and are particularly suited to interventions introduced at a population level [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we account for the effects of the coronavirus disease that occurred in 2019 (COVID-19) with its associated restrictions. South Africa recorded its first COVID-19 case on 5 March 2020. The government declared a National State of Disaster on 15 March 2020. The COVID-19 regulations were repealed on 22 June 2022.\u003c/p\u003e \u003cp\u003eThe outcome of interest is the logarithm of the aggregate employment by province in the sugar-related industries measured quarterly from 2008q1 to 2023q1. As such, we transformed the data to reflect the employment levels by quarter and province.\u003c/p\u003e \u003cp\u003eThe regression model used in this study follows an approach used in by Guerrero-L\u0026oacute;pez et al [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Boachie et al [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for similar purpose. The model is specified as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$ln\\left({Y}_{it}\\right)={\\beta }_{0}+{\\beta }_{1}T+{\\beta }_{2}{X}_{it}+{\\beta }_{3}T{X}_{it}+{\\beta }_{c}{C}_{it}+{\\beta }_{q}Q+{\\beta }_{p}P+{\\epsilon }_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the number of employees for industry \u003cem\u003ei\u003c/em\u003e, at time (quarter) \u003cem\u003et\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T\\)\u003c/span\u003e\u003c/span\u003e is the time elapsed since the start of the study (2008Q1), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a dummy variable representing the HPL intervention; it takes the value of 0 for the pre-intervention (HPL) period, and 1 for the post-HPL period. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({TX}_{it}\\)\u003c/span\u003e\u003c/span\u003e is an interaction term of the time trend and the HPL, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a dummy variable representing the COVID-19. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the baseline level of the employment at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T\\)\u003c/span\u003e\u003c/span\u003e=0, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e represents the underlying pre-HPL trend (i.e., the change in employment level associated with a single unit increase in time before the HPL). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}\\)\u003c/span\u003e\u003c/span\u003e indicates the immediate level (or intercept) change following the introduction of the HPL and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e represents the change in the slope of the trend due to the HPL, compared with the pre-HPL trend. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Q\\)\u003c/span\u003e\u003c/span\u003e represents quarterly dummies for potential seasonality in employment levels, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e\u003c/span\u003e accounts for provincial fixed effects.\u003c/p\u003e \u003cp\u003eWe run regressions for the overall sugar-related industry, and for each sugar-related industry. The primary regression model is an ordinary least-squares (OLS) linear regression (with lags for the dependent variable). For robustness checks, we run two more different regressions: a generalised-least squares (GLS) method, and a random-effects (RE) regression model. All analyses are done with STATA V.18.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOverall, there were 549 observations for the nine provinces (i.e., 61 quarterly observations for each of the nine provinces). Figures\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the trend of the aggregated number of employees in separate sugar-related industries, while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the employment trend for the overall sugar-related industry, for the period 2008q1-2023q1. Thus, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the aggregate of the industry-specific employment levels depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFrom both Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the HPL appears to have had no significant impact on employment levels in the sugar-related industry. Unlike the HPL, Covid-19 appears to have had a significant negative impact on employment. The extent to which these covariates impacted on employment is established through regression analyses. The regression results are shown in Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results from all the regression models are largely similar. Results from the GLS and RE are included in the tables but are not discussed in this section.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that across all the sugar-related industries, both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}\\)\u003c/span\u003e\u003c/span\u003e (the coefficient of the HPL) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e (the coefficient of the interactive term of HPL and time) are not significant. The coefficient for COVID-19 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({\\beta }_{c}\\right)\\)\u003c/span\u003e\u003c/span\u003e is negative and significant for wholesale and retail, transport, and the overall sugar-related industry. The COVID-19 was associated with 0.11% (95% CI -0.17% to -0.06%) and 0.09% (95% CI -0.15% to -0.04%) reduction in the number of employees in the wholesale and retail, and transport industries, respectively. Overall, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the COVID-19 was associated with a 0.05% (95% CI -0.09% to -0.02%) reduction in the number of employees in the sugar-related industry.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for the separate sugar-related industries\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eWholesale and retail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.009***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.008***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPL*Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.114***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.183***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.094***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.125***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.159***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarter dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.793***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.763***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.260***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.384***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.461***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.600***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.593***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.742***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.007***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eStandard errors in parentheses\u003c/p\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for the overall sugar industry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPL*Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.053***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.063**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.105***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarter dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.704***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.686***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eStandard errors in parentheses\u003c/p\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe employed single-group interrupted time series analyses to investigate the association between the HPL and employment levels in sugar-related industries (including agriculture, beverage manufacturing, transport, and commercial establishments that sell food and beverages). We used the Quarterly Labour Force Survey data from Statistics South Africa, the national statistical agency.\u003c/p\u003e \u003cp\u003eOur results show that the HPL has not been associated with job losses or generation in the sugar-related industries in South Africa. These results compare favourably with findings from other peer-reviewed non-industry-funded studies on the employment impact of SSB taxes. For instance, our results are consistent with the findings on the effects of SSB taxes on employment in Peru [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], San Francisco [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Philadelphia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and California and Illinois [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In all these studies, SSBs taxes were found to have no significant impact on employment levels. However, the key difference is that our study is the first to look at the subject in Africa.\u003c/p\u003e \u003cp\u003eThe lack of effects of the SSB tax (the HPL) on employment can be attributed to at least four reasons [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. First, multiproduct firms in affected industries may internally reallocate their labour force to products unaffected by these policies. Second, beverages have non-nutritive sweetener options that allow producers to quickly reformulate, as research has shown in South Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Portugal [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Reformulation allows producers to avoid the tax and retain most consumer preferences [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As a result, they have no need to reduce employment (or change wages) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Third, if the demand for the affected products does not decline (or declines slightly) after the implementation of the HPL, the industry finds no incentive to adjust employment levels. Fourth, consumers may substitute untaxed for the taxed products from the same producers. The increase in the demand for unaffected products may offset the decline in demand for affected products [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo reduce the intake of SSBs, the government should consider raising the HPL from the current 8% of the retail price to the minimum 20% recommended by the World Health Organisation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The government should also consider expanding the HPL to fruit juices. Increasing the HPL and expanding it to fruit juices are important in incentivising people to reduce the consumption of SSBs as evidence has shown, while enabling the government to raise additional revenue for the fiscus.\u003c/p\u003e \u003cp\u003eAlthough this study provides useful information for devising suitable SSB tax policy measures, there is one limitation to consider. The sugar-related industries are broadly defined, which may include other activities that are unrelated to the sugar industry.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eContrary to the sugar-related industry claims of employment losses due to the HPL, we found no association between the levy and employment levels based on the QLFS. Considering that the HPL does not impede employment, and the overwhelming evidence on the effectiveness of SSB taxes, together with the relatively low tax burden, it is imperative that the government raises the HPL from the current 8% of the retail price to the WHO-recommended 20% threshold. The government should also consider expanding the HPL to fruit juices. Such strategies are important in encouraging people to reduce the intake of SSBs, while enabling the government to raise additional revenue for the fiscus. Thus, primary prevention of NCDs such as type 2 diabetes and cardiovascular diseases can be implemented without harm to employment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSugar-sweetened beverage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Promotion Levy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-communicable diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQLFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuarterly Labour Force Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrdinary least-squares regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralised-least squares regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erandom-effects regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research, and a waiver of informed consent were approved by the Human Research Ethics Committee\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eat the University of Witwatersrand (HRECNMW24/03/04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All authors have approved the final article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData are available in a public, open access repository. Data are publicly available on https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/?page=1\u0026amp;sort_by=title\u0026amp;sort_order=asc\u0026amp;ps=15.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study received funding from Bloomberg Philanthropies through the University of North Carolina, USA (grant number 5106249), with additional support from the South African Medical Research Council (grant number 23108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: CD, MKB and ET conceptualised the study. CD and MKB conducted the data analysis. CD, MKB, SG and ET contributed to the drafting and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Special thanks to Shu Wen Ng who reviewed earlier drafts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLara-Castor L, Micha R, Cudhea F, Miller V, Shi P, Zhang J, et al. Sugar-sweetened beverage intakes among adults between 1990 and 2018 in 185 countries. Nature communications. 2023;14(1):5957.\u003c/li\u003e\n\u003cli\u003eStacey N, Mudara C, Ng SW, van Walbeek C, Hofman K, Edoka I. Sugar-based beverage taxes and beverage prices: Evidence from South Africa\u0026apos;s Health Promotion Levy. Social Science \u0026amp; Medicine. 2019;238:112465.\u003c/li\u003e\n\u003cli\u003eJuul F, Hemmingsson E. Trends in consumption of ultra-processed foods and obesity in Sweden between 1960 and 2010. Public health nutrition. 2015;18(17):3096-107.\u003c/li\u003e\n\u003cli\u003eRauber F, Steele EM, Louzada MLdC, Millett C, Monteiro CA, Levy RB. Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008-2016). PloS one. 2020;15(5):e0232676.\u003c/li\u003e\n\u003cli\u003eBoutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Elsevier; 2022. p. 155217.\u003c/li\u003e\n\u003cli\u003eAndreyeva T, Marple K, Marinello S, Moore TE, Powell LM. Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis. JAMA Network Open. 2022;5(6):e2215276-e.\u003c/li\u003e\n\u003cli\u003eOrganization WH. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets2022.\u003c/li\u003e\n\u003cli\u003eAbdool Karim S, Kruger P, Hofman K. Industry strategies in the parliamentary process of adopting a sugar-sweetened beverage tax in South Africa: a systematic mapping. Globalization and Health. 2020;16(1):1-14.\u003c/li\u003e\n\u003cli\u003eNational Department of Health (NDoH). Strategy for the Prevention and Management of Obesity in South Africa, 2023 - 2028. Pretoria, South Africa.2023.\u003c/li\u003e\n\u003cli\u003eDanquah FI, Ansu-Mensah M, Bawontuo V, Yeboah M, Kuupiel D. Prevalence, incidence, and trends of childhood overweight/obesity in Sub-Saharan Africa: a systematic scoping review. Archives of Public Health. 2020;78:1-20.\u003c/li\u003e\n\u003cli\u003eTremmel M, Gerdtham U-G, Nilsson PM, Saha S. Economic burden of obesity: a systematic literature review. International journal of environmental research and public health. 2017;14(4):435.\u003c/li\u003e\n\u003cli\u003eSpecchia ML, Veneziano MA, Cadeddu C, Ferriero AM, Mancuso A, Ianuale C, et al. Economic impact of adult obesity on health systems: a systematic review. The European Journal of Public Health. 2015;25(2):255-62.\u003c/li\u003e\n\u003cli\u003eBoachie MK, Thsehla E, Immurana M, Kohli-Lynch C, Hofman KJ. Estimating the healthcare cost of overweight and obesity in South Africa. Global Health Action. 2022;15(1):2045092.\u003c/li\u003e\n\u003cli\u003eStacey N, Edoka I, Hofman K, Swart EC, Popkin B, Ng SW. Changes in beverage purchases following the announcement and implementation of South Africa\u0026apos;s Health Promotion Levy: an observational study. The Lancet Planetary Health. 2021;5(4):e200-e8.\u003c/li\u003e\n\u003cli\u003eWesbound (PTY) Ltd. Economic Impact of the Health Promotion Levy on the Sugar Market Industry: Impact Assessment Report. 2020.\u003c/li\u003e\n\u003cli\u003eBureau for Food and Agricultural Policy. Potential impact of an increase in the current level of the Health Promotion Levy. 2023.\u003c/li\u003e\n\u003cli\u003eGuerrero-L\u0026oacute;pez CM, Molina M, Colchero MA. Employment changes associated with the introduction of taxes on sugar-sweetened beverages and nonessential energy-dense food in Mexico. Preventive medicine. 2017;105:S43-S9.\u003c/li\u003e\n\u003cli\u003eD\u0026iacute;az J-J, S\u0026aacute;nchez A, Diez-Canseco F, Miranda JJ, Popkin BM. Employment and wage effects of sugar-sweetened beverage taxes and front-of-package warning label regulations on the food and beverage industry: Evidence from Peru. Food Policy. 2023;115:102412.\u003c/li\u003e\n\u003cli\u003eMarinello S, Leider J, Powell LM. Employment impacts of the San Francisco sugar-sweetened beverage tax 2 years after implementation. Plos one. 2021;16(6):e0252094.\u003c/li\u003e\n\u003cli\u003ePowell LM, Wada R, Persky JJ, Chaloupka FJ. Employment impact of sugar-sweetened beverage taxes. American journal of public health. 2014;104(4):672-7.\u003c/li\u003e\n\u003cli\u003eStatistics South Africa. South Africa - Quarterly Labour Force Survey. In: Africa SS, editor. Pretoria: Cape Town: DataFirst; 2023.\u003c/li\u003e\n\u003cli\u003eXiao H, Augusto O, Wagenaar BH. Reflection on modern methods: a common error in the segmented regression parameterization of interrupted time-series analyses. International journal of epidemiology. 2021;50(3):1011-5.\u003c/li\u003e\n\u003cli\u003eLinden A. Conducting interrupted time-series analysis for single-and multiple-group comparisons. The Stata Journal. 2015;15(2):480-500.\u003c/li\u003e\n\u003cli\u003eLagarde M. How to do (or not to do)\u0026hellip; Assessing the impact of a policy change with routine longitudinal data. Health policy and planning. 2012;27(1):76-83.\u003c/li\u003e\n\u003cli\u003eBoachie MK, Khoza M, Goldstein S, Munsamy M, Hofman K, Thsehla E. The Impact of COVID-19 Lockdown on Service Utilization Among Chronic Disease Patients in South Africa. Health Services Insights. 2023;16:11786329231215040.\u003c/li\u003e\n\u003cli\u003eMarinello S, Leider J, Pugach O, Powell LM. The impact of the Philadelphia beverage tax on employment: A synthetic control analysis. Economics \u0026amp; Human Biology. 2021;40:100939.\u003c/li\u003e\n\u003cli\u003eEssman M, Taillie LS, Frank T, Ng SW, Popkin BM, Swart EC. Taxed and untaxed beverage intake by South African young adults after a national sugar-sweetened beverage tax: A before-and-after study. PLoS medicine. 2021;18(5):e1003574.\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves J, Dos Santos JP. Brown sugar, how come you taste so good? The impact of a soda tax on prices and consumption. Social Science \u0026amp; Medicine. 2020;264:113332.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. 2022. 2022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Promotion Levy, employment, SSB tax, sugar industry, South Africa, obesity, non-communicable diseases","lastPublishedDoi":"10.21203/rs.3.rs-4291451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4291451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe production and consumption of sugar-sweetened beverages (SSBs) have been increasing over the past years, globally. However, there is overwhelming evidence linking SSBs to the rising prevalence in obesity and its comorbidities. In South Africa, the prevalence of overweight and obesity is high and is among the highest in Sub-Saharan Africa. In response to rising prevalence in obesity and its comorbidities, on 1 April 2018 the South African government introduced an SSB tax, known as the Health Promotion Levy (HPL). However, the levy has been opposed by the sugar industry, claiming that it leads to jobs losses. Against this backdrop, this study seeks to investigate the association between the HPL and employment in the sugar industry.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed single-group interrupted time series analyses using the Quarterly Labour Force Survey data from Statistics South Africa.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results show that the HPL has not been associated with job losses (or generation) in the sugar-related industries in South Africa. These findings are consistent with the findings on the effects of SSB taxes on employment in other jurisdictions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eConsidering that the HPL does not impede employment, and the overwhelming evidence on the effectiveness of SSB taxes, together with the relatively low tax burden, it is imperative that the government raises the HPL from the current 8% of the retail price to the WHO-recommended 20% threshold. The government should also consider expanding the HPL to fruit juices. Such strategies are important in encouraging people to reduce the intake of SSBs, while enabling the government to raise additional revenue for the fiscus.\u003c/p\u003e","manuscriptTitle":"The association between the Health Promotion Levy and employment in South Africa: an interrupted time series analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 13:11:13","doi":"10.21203/rs.3.rs-4291451/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-11T16:09:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T08:27:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T11:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16664798412572574373788014407298275363","date":"2024-10-17T06:55:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73668722914239839660717427724049917470","date":"2024-10-14T08:05:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-24T17:13:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-24T15:38:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T09:16:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T06:46:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-04-19T07:09:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"169e5f5c-fd13-4ae1-86f3-f18fa9482786","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-4291451","link":"https://doi.org/10.1186/s40795-025-01012-6","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2025-01-31 15:57:56","publishedOnDateReadable":"January 31st, 2025"},"versionCreatedAt":"2024-04-29 13:11:13","video":"","vorDoi":"10.1186/s40795-025-01012-6","vorDoiUrl":"https://doi.org/10.1186/s40795-025-01012-6","workflowStages":[]},"version":"v1","identity":"rs-4291451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4291451","identity":"rs-4291451","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}