The China Productivity Project: Results and conclusions

Abstract

Experiments were conducted to determine what factors cause variation in individual work output (economic productivity). Forty-five young male Chinese cycle haulers from Beijing were assessed for physiological work capacity, size and body composition, health, nutritional status, cold resistance, household social environment, and motivation. Experiments were conducted in the laboratory as well as under actual working conditions; ethnographic observations were made in the household and on the job during the Beijing winter of 1992. Overall work motivation correlated to actual monthly distance/load measures of productivity the most strongly (r = 0.518), followed by physiological capacity estimated by heart rate:speed ratio during field experiments (r = -0.473). Alcohol consumption (a negative factor), household health, and carbohydrate intake were all moderate predictors. Maximum oxygen uptake showed lower correlation (r = 0.261), and among anthropometric values only relatively long lower legs were predictive (r = 0.298). Since many of these variable categories were relatively independent of each other, multiple regression analysis showed that together they explained 61.6% of the work output variance. Simultaneous prediction by FASEM (LISREL) is also very strong.

Factors affecting the work productivity of Oraon agricultural laborers of Jalpaiguri district, West Bengal

“…Blood pressures do not differ among productivity groups in either sex (Table 3), in contrast with the findings of Weitz (1982) in Sherpas and Tibetan migrant groups. Pulse rates also did not differ significantly in the high-output groups compared to the low-output groups in both sexes, in contrast with the findings of Steegman et al (1997), who found strong correlation between heart rate and work output among Chinese cycle haulers.…”

Commercial porters of eastern Nepal: Health status, physical work capacity, and energy expenditure

“…O 2 peak of the porter sample (2.38 ± 0.27 L O 2 /min, normalized for body mass to 47.1 ± 5.3 ml/kg/min) is similar to values reported by Strickland and Tuffrey (1997) for two groups of Nepali males from the western hills: Mongoloid, 2.61 ± 0.60 L O 2 / min, or 48.9 ± 11.7 ml/kg/min; non-Mongoloid, 2.37 ± 0.71 L O 2 /min or 46.9 ± 12.9 ml/kg/min. Comparison of Nepali porters with other populations shows that the aerobic capacity of the present porter sample is comparable to that of Chinese cycle haulers (49.6 ± 6.3 ml/kg/min; Steegman et al, 1997); high-altitude Aymara male agriculturalists temporarily employed as porters (46.5 ± 4.7 ml/kg/min; Greksa et al, 1984); two high-altitude Tibetan samples (51.2 ± 1.4 and 48.5 ± 0.2 ml/kg/ min; Curran et al, 1998); high-altitude natives in Bolivia (52.0 ± 1.7ml/kg/min; Brutsaert et al, 1999); and a Guatemalan (Flores et al, 1984): sugar-cane cutters (445 ± 0.9 ml/kg/min), coffee pickers (49.1 ± 0.8 ml/kg/min), and well-nourished males (46.3 ± 0.8 ml/kg/min). The Nepali porters have a peak oxygen uptake that places them within the normal range for other male populations living in rugged mountainous areas, for whom mean V .…”

Field test of self-paced work capacity: Ifugao rice farmers

“…An immediate impetus for this self-paced physiological work capacity test was the unexpected outcome of a similar study of Chinese haulers. Traditional markers of work capacity, such as maximum oxygen uptake and muscle mass, were poor predictors of economic productivity as measured by mean monthly income (Steegmann et al, 1997). Of the several work capacity measures tested, the single strongest income predictor was the ratio of heart rate to cycle speed during standardized but self-paced field experiments.…”

“…Traditional markers of work capacity, such as maximum oxygen uptake and muscle mass, were poor predictors of economic productivity as measured by mean monthly income (Steegmann et al, 1997). The people practice irrigation rice agriculture as their primary subsistence.…”

“…This pilot report is part of a larger, ongoing project in which we are testing proximate, multivariate models for their capacity to predict outcomes of importance to human adaptation (Steegmann et al, 1997). It should ultimately be possible to predict a substantial percentage of the variance in economic productivity, and that of more mainline human biology concerns such as physical health, growth, and broader wellbeing as defined by the World Health Organization (Evans and Stoddart, 1994).…”