THE LIGHT AND THE HEAT: PRODUCTIVITY CO-BENEFITS
OF ENERGY-SAVING TECHNOLOGY

Achyuta Adhvaryu, Namrata Kala, and Anant Nyshadham*

Abstract—We study the adoption of energy-efficient LED lighting in gar-
ment factories around Bangalore, Indien. Combining daily production line–
level data with weather data, we estimate a negative, nonlinear productivity-
temperature gradient. We find that LED lighting raises productivity on hot
Tage. Using the firm’s costs data, we estimate that the payback period for
LED adoption is less than one-third the length after accounting for produc-
tivity co-benefits. The average factory in our data gains about $2,880 in power consumption savings and about $7,500 in productivity gains.

ICH.

Einführung

INNOVATIONS in energy efficiency and regulation-driven

adoption of energy-efficient technologies have been cited
as a primary means of curbing the acceleration of climate
ändern (Granade et al., 2009). Despite this promise, Energie-
efficient technologies are usually adopted at low rates (Allcott
& Taubinsky, 2015). Recent studies point to several expla-
nations for this “energy-efficiency gap.” The first is market
failures such as information frictions or credit constraints that
drive a wedge between socially and privately optimal adop-
tion (Allcott & Greenstone, 2012). The second is behavioral
factors such as consumer inattention to energy costs (Alle-
cott, Mullainathan, & Taubinsky, 2014). The third possible
explanation is that returns are smaller, or costs higher, in prac-
tice than engineering projections predict (Burlig et al., 2017;
Fowlie, Greenstone, & Wolfram, 2013; Ryan, 2017). Weiter-
mehr, behavioral responses to energy-efficiency (such as in-
creased consumption) may offset returns to energy efficiency
investments. Daher, estimating the true returns to energy ef-
ficiency requires testing for mechanisms that may drive a
wedge between engineering and economic returns, inkl-
ing imperfect maintenance of the investments and rebound
Effekte.

In this study, we estimate the productivity consequences
of the adoption of energy-saving technology, using daily pro-

Received for publication January 31, 2018. Revision accepted for publi-

cation April 5, 2019. Editor: Rohini Pande.

∗Adhvaryu: Universität von Michigan, NBER, BREAD, and Good Business
Labor; Kala: MIT Sloan School of Management, BREAD, NBER, and JPAL;
Nyshadham: Boston College, NBER, Universität von Michigan, and Good
Business Lab.

We are incredibly thankful to Anant Ahuja, Chitra Ramdas, Shridatta
Veera, Manju Rajesh, Raghuram Nayaka, Sudhakar Bheemarao, Paul
Ouseph, and Subhash Tiwari for their coordination, enthusiasm, support,
and guidance. Thanks to Prashant Bharadwaj, Michael Boozer, Rahul Deb,
Josh Graff Zivin, Catherine Hausman, Tom Lyon, Robyn Meeks, Nicholas
Ryan, Antoinette Schoar, Tavneet Suri, and Joseph Shapiro, sowie
seminar participants at Yale, MIT, Michigan, Carnegie Mellon, CEGA,
the NBER, the IGC, the World Bank, PacDev, and NEUDC for helpful
comments and suggestions. Robert Fletcher and Karry Lu provided excel-
lent research assistance. A.A. gratefully acknowledges funding from the
NIH/NICHD (5K01HD071949), and all of us acknowledge funding from
PEDL, a CEPR-sponsored grant initiative. All errors are our own.

A supplemental appendix is available online at http://www.mitpress

journals.org/doi/suppl/10.1162/rest_a_00886.

duction line data from a large garment firm operating fac-
tories in and around Bangalore, Indien. Erste, we show that
days with higher outside temperatures have lower produc-
tivity, measured as production line efficiency (realized out-
put over target output). We then show that the replacement
of compact fluorescent lamps (CFLs) with light-emitting-
diode (LED) lighting on factory floors attenuates the negative
relationship between mean daily outdoor temperature and
efficiency. Driven by buyers’ environmental standards, fac-
tories replaced a substantial fraction of CFL bulbs with LED
bulbs. LED lighting reduces ambient temperature on the fac-
tory floor because less electricity is converted to waste heat,
relative to CFL lighting. This lower ambient temperature re-
duces the effect of higher outside temperature on efficiency.
We study the impacts of the staggered rollout of LEDs over
more than three years on the sewing floors of 26 garment fac-
tories.1 We use rich administrative data on worker attendance,
working hours, and productivity to test for mechanisms that
would mitigate or offset the returns to energy-efficient light-
ing. We also demonstrate in a variety of checks that the timing
of the rollout across factories was not systematically related
to business processes or working conditions, such as time of
the start or end of the workday, total working hours, wages,
or the composition of hiring patterns by worker skill levels.
Our measure of mean daily temperature exposure, wet bulb
globe temperature (WBGT), takes into account both temper-
ature and humidity, since the impact of temperature on ther-
mal regulation varies by humidity levels. Impacts of outdoor
temperature on productive efficiency, estimated using a spline
regression (controlling for factory by year, factory by month,
production line, and day of the week fixed effects), are quite
nonlinear: for mean daily WBGT of below 19◦C (the tem-
perature equivalent at average humidity levels in our sample
is 27◦ to 28◦C), temperature has a very small impact on ef-
ficiency. But for mean daily temperatures above this cutoff
(about one-quarter of production days), there is a large, nega-
tive impact on efficiency of approximately 2 efficiency points
per degree Celsius increase in temperature.2 We then estimate
the extent to which the introduction of LED lighting, likely
through the reduced dissipation of heat on factory floors, flat-
tens the temperature-productivity gradient. LED installation
has no impact on the gradient below the 19◦C WBGT cutoff
but attenuates the negative slope of the gradient by more than
80% for temperatures above this threshold. Our results are ro-
bust to the inclusion of a variety of fixed effects and controls,

1Our data examine thirty factories (all owned by the same garment firm),

four of which did not receive LED lighting.

2This nonlinear gradient is remarkably consistent with the physiology
of temperature effects: at high ambient temperatures, the body loses the
ability to dissipate heat, which negatively affects performance (Hancock
et al., 2007).

The Review of Economics and Statistics, Oktober 2020, 102(4): 779–792
© 2019 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Veröffentlicht unter einer Creative Commons Namensnennung 4.0
International (CC BY 4.0) Lizenz.
https://doi.org/10.1162/rest_a_00886

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780

THE REVIEW OF ECONOMICS AND STATISTICS

including factory by year by quarter fixed effects, sowie
alternative specifications such as semiparametric estimation.
The reason that LED installation flattens only the top of the
temperature-productivity gradient has to do with the nonlin-
ear nature of the gradient itself and is likely due to a leftward
movement along the gradient. This movement would gener-
ate large increases in efficiency in high temperature ranges
and small efficiency increases elsewhere.3

While engineering estimates of the heat dissipation of LED
(versus those of CFL) bulbs exist, those estimates are not al-
ways reflective of economic returns, as Fowlie et al. (2013)
and Burlig et al. (2017) showed recently. In our setting too,
a field study has several advantages in estimating the true
productivity returns to energy efficiency. Erste, if factories
respond to energy savings by increasing working hours, Dann
the cobenefits to these investments may change: they may
be higher if workers respond to the more comfortable envi-
ronment on hotter days by continuing to be more productive
for extra hours, and they may be lower or 0 if workers re-
spond to longer hours by slowing their productivity per hour.
Using data on working hours, we can directly test for this re-
sponse by the factory managers. Zweite, if the temperature-
productivity relationship is driven by lower attendance on
hotter days, and not by workers responding to a less comfort-
able work environment, then LED lighting may not mitigate
this relationship (z.B., temperatures outside of working hours
may affect workers’ health, and therefore their propensity to
attend work). Using data on worker attendance, we can rule
out that this is the case. Dritte, if workers respond to the light-
ing by changing their attendance (either because they are now
more comfortable or because they are uncomfortable with the
new lighting), the productivity cobenefits may be higher or
lower. Endlich, our results indicate that energy-efficient light-
ing can generate these co-benefits in settings where workers
are exposed to heat generated by conventional bulbs, and air-
conditioning is not cost-effective (which is typical of manu-
facturing workplaces in low-income countries).

Endlich, we perform cost-benefit calculations for LED
adoption, combining the above estimates with the firm’s ac-
tual cost data for LED replacement and projected energy
savings. The results of this analysis show that the produc-
tivity co-benefits of LED adoption are substantially larger
than the energy savings. In der Tat, accounting for productivity
increases significantly shifts the break-even point for the firm,
from over three and half years to less than eight months. Mit
some assumptions on how worker productivity translates into
profits (detailed in section VII), we estimated that the average
factory gained about $2,880 in power consumption savings and about $7,500 in productivity gains.

3One major drawback of our study is that we do not have indoor tem-
perature data in the factories before and after LED installation. Daher, andere
aspects of LED lighting that affect the productivity-temperature gradient
such as unmeasured light quality changes may contribute to the aggregate
effect of LED lighting mitigating the productivity-temperature relationship,
as long as these unmeasured changes affect productivity only on hotter days.

Our study contributes to the literature on the returns to
climate change mitigation and energy efficiency. Recent
studies have indicated that energy-efficient lighting can both
reduce electricity consumption (Burlig et al., 2017) and gen-
erate positive externality co-benefits such as greater electric-
ity grid reliability (Carranza & Meeks, 2020). Other stud-
ies that examine co-benefits, or additional gains, of climate
change mitigation broadly speaking, such as carbon taxes,
also focus largely on the indirect public returns (Knittel &
Sandler, 2011; see IPCC, 2013, für eine Rezension). We study a
novel, private co-benefit of climate change mitigation. Das
distinction is important because the success of most mitiga-
tion strategies rests on individuals’ and firms’ willingness
to adopt them, and this willingness is largely driven by pri-
vate returns. If energy-saving technologies like LEDs do have
substantial private co-benefits, this should meaningfully alter
firms’ benefit-cost calculations. By our estimation, ignoring
the productivity benefits of LEDs would significantly under-
estimate the private returns to adoption.

We also contribute to the understanding of the effects of
environmental and infrastructural factors (which are often re-
lated to the environment) on productivity in developing coun-
versucht (Adhvaryu, Kala, & Nyshadham, 2016; Allcott, Collard-
Wexler, & O’Connell, 2014; Hsiang, 2010; Sudarshan et al.,
2015) and adaptation to higher temperatures.4 The impacts
of temperature on productivity appear to hold quite consis-
tently across countries and time (Burke, Hsiang, & Miguel,
2015; Dell, Jones, & Olken, 2012). A related literature has
established patterns of adaptation to climate change and the
returns to this adaptation (Barreca et al., 2016). Our results
indicate that energy-efficient lighting can be a form of adap-
tation to higher temperatures in settings characterized by low
air-conditioning adoption and significant indoor heat expo-
sure from conventional lighting. Our results thus highlight an
interaction between high temperatures and the co-benefits of
energy-efficient technologies.

The remainder of the paper is organized as follows. Abschnitt
II describes contextual details regarding garment production
in India and the LED installation. Section III provides details
on the temperature and production data. Section IV describes
our empirical strategy. Section V describes the results, Sek-
tion VI offers additional robustness checks, and section VII
reviews the cost-benefit analysis and concludes.

II. Context

A. Physiology of the Temperature-Productivity Gradient

The physical impact of temperature on human beings is
well studied (Enander, 1989; Parsons, 2010; Seppanen, Fisk,
& Lei, 2006) and has been important for establishing occu-
pational safety standards for workers exposed to very high or

4Several recent studies document this relationship in more developed set-
tings (Chang et al., 2014; Costinot, Donaldson, & Schmied, 2016; Graff Zivin
& Neidell, 2012; Hanna & Oliva, 2015).

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THE LIGHT AND THE HEAT

781

low temperatures for extended periods of time (Vanhoorne,
Vanachter, & De Ridder, 2006). Thermal stress can affect
human beings physically and through lower psychomotor
ability and degraded perceptual task performance (Hancock,
Ross, & Szalma, 2007). The impact on individual subjects
varies based on factors such as the type of task and its com-
plexity, duration of exposure, and the worker-level skill and
acclimatization level (Pilcher, Nadler, & Busch, 2002). Das
contributes to the difficulty of setting a specific limit in work-
ing environments (Hancock et al., 2007).

One key finding from this literature is that there is a non-
monotonic relationship between ambient temperature and hu-
man performance. The overall shape of the relationship is an
U: performance suffers at excessively cold and excessively
warm temperatures (Parsons, 2010). Darüber hinaus, one meta-
analysis highlights the dry-bulb threshold of 29.4◦C (85◦F)
as particularly important (Hancock et al., 2007). This thresh-
old value represents the temperature above which the body
starts to store heat. As Hancock et al. (2007) Leg es, „[In] Das
circumstance, although the individual is dissipating heat at
the maximal rate, he or she experiences a dynamic increase in
core body temperature” (P. 860). In line with this physiology,
measured effects on performance are larger for temperatures
above the 29.4◦C threshold.

B. Measuring Garment Productivity and Overview

of the LED Installation

India is the world’s second largest producer of textile and
Kleider, with the export value totaling $10.7 billion in 2009–2010. Women comprise the majority of the workforce (Staritz, 2010). Garments are usually sewn in production lines in manufacturing plants. Each line produces a single style of garment at a time (possibly with varying colors or sizes) until the order for that garment is met. Lines consist of sixty to sev- enty sewing machine operators (depending on the complexity of the style) arranged in sequence and grouped in terms of parts of the garment (z.B., sleeve, collar).5 Completed sec- tions of garments pass between these groups, are attached to each other in additional operations along the way, and emerge at the end of the line as a completed garment. The factories began installing LED lighting in October 2009 and completed the installations by February 2013. Ac- cording to senior management at the firm, over the past decade, buyers have become more stringent in their regulation of their suppliers’ production and environmental standards. This prompted a staggered rollout of LEDs across factories within the firm because some factories were more heavily in- volved in the production of orders from particular buyers than others. Also, Zum Beispiel, if buyer A’s environmental regula- tions become more stringent, then the supplier might choose 5In general, we describe here the process for woven garments; Jedoch, the steps are quite similar for knits and even pants, with a varying number and complexity of operations. Even within wovens, the production process varies slightly by style or factory. to upgrade to LED lighting in factories processing many or- ders from buyer A. When buyer B’s regulations change, the firm will prioritize factories servicing buyer B, and so on.6 One thing to note is that there are still CFL bulbs in all facto- ries after the change. Das ist, only about half the bulbs were replaced, with each fixture now containing one CFL bulb in- stead of two. The replacement took the form of substituting a portion of CFLs targeted at individual operations with an equivalent number of small LED lights mounted on individual work- ers’ machines. The replacements were designed to maintain the original level of illumination. On average, each factory replaced about 1,200 CFLs consuming 7 W each with LED lights of 1 W each.7 The LED light bulbs that replaced the CFLs in the factories in our data require about 3 as opposed to 21 kWh/year in electricity in our setting, and thus oper- ate at about one-seventh the cost of CFL lighting.8 Based on the factories’ operating time cost calculation, this meant an energy saving of 18 kWh per bulb per year. Heat emis- sions for a single LED bulb are 3.4 Btus, compared to 23.8 Btus for a single CFL lighting bulb.9 In section VII, we dis- cuss the magnitude of the environmental benefits from the installation. Each factory received the installation within a single month. Eight percent of the LED rollout (2 factories) was completed in 2009, 48% (12 factories) In 2010, 16% (4 fac- tories) In 2011, um 24% (6 factories) In 2012, and the rest (1 factory in 2013. Of the 30 factories from which we have productivity data, LED replacements occurred in 26 factories during the observation period. Since our productivity data range from April 2010 to June 2013, some factories already had LEDs at the beginning of our productivity data, and all 6We check for the endogeneity of LED adoption in tables 5 Und 6 and conduct other robustness checks. We find little evidence that LED adoption at the factory level was correlated with a variety of business operations and outcomes. 7The number of lights installed is a function of the number of machines in the factory and varies from about 100 Zu 2,550, with a mean of about 1,200. 8It should be noted that there are many varieties of LED and CFL bulbs. The energy and lighting specifications and calculations presented and dis- cussed in this paper are specific to the bulbs used in the factory replacements in our data and do not represent universal comparisons. Entsprechend, gen- eralizing our findings would require an understanding of how bulb specifics might differ from those used in this empirical context. 9Changing factory lighting may have consequences for productivity through mechanisms other than temperature changes, as highlighted by the results of the original Hawthorne lighting experiment (Snow, 1927; Mayo et al., 1939), as well as new analysis by Levitt & List (2011). Our analysis allows for this possibility by including the main effect of LED installation, but we find limited evidence for productivity changes through mechanisms other than temperature changes. This is not altogether surprising given the degree of care and attention placed on lighting conditions in the garment production setting. Senior management emphasized that the lighting re- placement was designed such that light quantity and quality at the point of production operation would remain within the strict industry and buyer guidelines before and after the replacement. Jedoch, any unmeasured light quality changes that affect the temperature gradient in addition to indoor temperature would form part of our estimates of LED lighting to mitigate the efficiency-temperature gradient. We cannot distinguish the two, since we do not have measurements of indoor temperature before and after the study. l D o w n o a d e d von h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 782 THE REVIEW OF ECONOMICS AND STATISTICS but four factories had LEDs by the end of our sample period. Figure A1 in the appendix presents the cumulative proportion of factories adopting LED against mean temperature.10 III. Data A. Weather Data We use mean daily temperature, Niederschlag, and relative humidity data from the National Centers for Environmen- tal Prediction Climate Forecast System Reanalysis (CFSR; Saha et al., 2010). The CFSR data is a reanalysis data set that uses historical station-level and satellite data combined with climate models to produce a consistent record of gridded weather variables from 1979 Zu 2014. It has a spatial resolu- tion of about 38 km; each factory in our sample is matched to the nearest data grid point.11 We use a temperature index that incorporates tempera- ture and humidity. We incorporate relative humidity into the temperature measure because the effect of relative humidity on thermal comfort may vary with temperature by affecting evaporative heat loss from the human body (Jing et al., 2013), but we also show that our results hold with dry bulb temper- ature. With mean daily temperature and relative humidity data, we construct the wet bulb globe temperature (WBGT) measure that is suitable for indoor exposure (that does not take into account wind or sunlight exposure, since that is not applicable in this context). The formula is from Lemke and Kjellstrom (2012) and is given by WBGT = 0.567Td + 0.216 (cid:2) rh 100 ∗ 6.105 exp (cid:2) (cid:3)(cid:3) 17.27Td 237.7 + Td + 3.38, (1) where Td = dry bulb temperature in Fahrenheit and rh = relative humidity (%). Both measures of temperature—dry bulb temperature and WBGT—are converted into Celsius to ensure interpretative ease across regression specifications. Note that the weather data we use are mean daily out- door temperature measures. While indoor temperature in the factory is what would affect worker productivity, we do not have data on indoor temperature from the period of the LED rollout. Entsprechend, we use outdoor ambient temperature as 10Regression results that omit factories that had LED lighting at the start of the sample period or did not receive LED lighting by the end of the sample period yield very similar estimates. 11There are eight temperature grid points in our sample. The factories are located in and around Bangalore city, so while they are not clustered in a particular part of the city, the identification is largely coming from the time series variation in temperature. The reanalysis data allow us to exploit this cross-sectional relationship slightly better. There are eight reanalysis data points and only one station in Bangalore that regularly report weather data across our sample period that we found in the Global Historical Climate Network (GHCN) Daten. If we compare the time series of the mean daily temperatures from our eight reanalysis points (averaged over each day) with the mean daily temperature from the Bangalore weather station, the correlation in daily temperatures is about 0.8, which seems to suggest that the reanalysis data correlate reasonably well with the station-level data. discussed above as a proxy for indoor conditions. For outdoor temperature to represent a valid proxy, we would like to ver- ify that fluctuations in outdoor temperature pass through to indoor temperature. Although we do not have indoor temper- ature data from the study period, we did collect about a year’s worth of indoor and outdoor temperature from two factories and six months of data from a third factory after the study period.12 In figure 1, we plot mean indoor temperature values for each 0.1 degree bin of outdoor temperature along with a lo- cal polynomial regression fit curve and 95% confidence in- tervals.13 Indoor temperature appears to be a linear function of outdoor temperature with a slope of roughly 0.79. Das ist, there appears to be large but not perfect pass-through of outdoor temperature fluctuations to indoor temperature, and this relationship appears to be constant for all levels of out- door temperature. A positive intercept indicates that at lower outdoor temperatures (z.B., 22◦C wet bulb globe) the indoor temperature is slightly higher than the outdoor temperature, reflecting a flow source of heat inside the factory independent of outdoor temperature (z.B., lighting and machinery, in ad- dition to heat generated by workers’ presence on the factory floor). Außerdem, a regression of indoor temperature on outdoor temperature has an R2 of about 0.84, implying that a very large amount of the variation in indoor temperature is explained by the variation in outdoor temperature. Wie- immer, it is important to note that these data were collected after the introduction of LED in the factories and therefore depict the ex post relationship between indoor and outdoor temperature. B. Factory Data We use daily data at the production line level from thirty garment factories in and around Bangalore. Identifiers in- clude factory number and production line number within the factory. For each line and day within each factory, production measures include actual quantity of garments produced and target quantities of the line on that day. Actual efficiency is actual quantity produced divided by target quantity. The target quantity is derived from an indus- trial engineering measure for the complexity of the garment— the “Standard allowable minute (SAM), which is the estimated number of minutes required to produce a single garment of a particular style. This estimate largely derives from a central database of styles, with potential adjustments by the factory’s industrial engineering (IE) department during “sampling.”14 12We collected data from September 22, 2014, to August 11, 2015, in one factory, from September 27, 2014, to August 10, 2015, in a second factory, and from January 28, 2015, to August 10, 2015, in a third factory. 13Fit reflects kernel-weighted local mean smoothing, using the Epanech- nikov kernel. 14Sampling is the process by which a cost estimate is generated for a buyer when ordering a garment style. Sampling tailors make a garment of l D o w n o a d e d f r o m h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 THE LIGHT AND THE HEAT 783 FIGURE 1.—INDOOR TEMPERATURE VERSUS OUTDOOR TEMPERATURE The SAM measure is used to calculate the target quantity for the line for each hour of production. Each line runs for eight hours during a standard workday from 9 Bin. Zu 5 p.m., with all factories in our sample operating a single daytime production shift. Entsprechend, a line producing a style with a SAM of 0.5 will have a target of 120 garments per hour, oder 960 garments per day. Most important, the target quantity is almost always fixed across days (Und, in fact, across hours within the day) within a particular order of a style. Each line produces only a single style at a time.15 Vari- ations in expected achievable efficiency over the life of a particular garment order due to order size are reflected in a measure that incorporates learning by doing, budgeted effi- ciency. Budgeted efficiency remains fixed for a given line over the life of a particular order and reflects the efficiency that management believes a line might be able to achieve given the expected length of time the line will be producing the order. Actual efficiency of a given order will vary system- atically across lines and within a line over time due to, Zum Beispiel, absenteeism, machine failures, or working condi- tionen. We are interested in variation in actual efficiency due to transitory temperature. We therefore control for budgeted efficiency to account for systematic variation in efficiency deriving from order size and include line fixed effects in the regression analysis that follows. In the robustness checks, we show that our results are not affected by excluding this control variable. TABLE 1.—SUMMARY STATISTICS: WEATHER, PRODUCTION, AND LED INTRODUCTION Number of line-day observations Number of lines Number of days Number of factories Weather Temperature (Celsius) Relative humidity (%) Wet bulb globe temperature (Celsius) Production Actual efficiency Budgeted efficiency Standard allowable minutes (SAM) Attendance 1(Present for Full Work day) 239,680 523 1,001 30 Mean 24.353 0.647 17.230 55.234 61.981 0.724 0.843 Standard Deviation 2.966 0.174 1.683 26.233 11.545 2.445 0.363 C. Summary Statistics We present means and standard deviations of variables used in the analysis in table 1. Our sample consists of 523 Profi- duction lines across thirty factories. The range of dates over which we have production data spans 1,001 Tage. Jedoch, we do not observe all factories for all dates.16 Altogether, our data include nearly 240,000 line by day observations. About one-third of the observations correspond to days in factories prior to the introduction of LED lighting, and the remainder are post-LED observations. a particular style and recommend any alterations to the SAM for that style to the IE department. 15In der Tat, in our data, lines produce styles for between 1 Und 268 Tage. 16Once a factory starts reporting data, it continues to do so until the end of the sample period. In the appendix, we restrict the analysis of the main productivity specifications to only production lines that have a proportion of missing data less than or equal to 30% of observations. l D o w n o a d e d von h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 784 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 2.—EFFICIENCY AGAINST TEMPERATURE (PRE-LED) IV. Empirical Strategy In this section, we provide preliminary graphs on the shape of the temperature-productivity gradient, the effects of LED introduction, and the persistence of this evidence after ac- counting for various unobservables. We then leverage these motivating facts to develop an empirical strategy to flexibly estimate the impact of LED introduction on productivity as moderated through ambient temperature. A. Descriptive Evidence We begin by motivating the empirical specifications and techniques with descriptive plots of production and temper- ature data.17 Productivity-temperature gradient. To estimate how LED lights have an impact on the relationship between efficiency and temperature, we first investigate the raw relationship be- tween efficiency and wet bulb temperature in the data prior to LED introduction. Figur 2 presents a scatter plot of the average efficiency for each 0.1 degree bin of wet bulb tem- perature observed in the data. We also include in the figure a local polynomial smoothed fit and 95% confidence intervals like those depicted in figure 1.18 Figur 2 shows that in the ab- sence of LED lighting, efficiency appears to be a decreasing function of temperature, and this relationship is quite non- linear, with the largest declines in efficiency occurring at the 17Residualized graphs with fixed effects and controls mentioned in section IVB look very similar and are available on request. 18Fit reflects kernel-weighted local mean smoothing, using the default Epanechnikov kernel and bandwidth of 1. highest wet bulb temperatures. Konkret, the gradient goes from modestly decreasing to strongly decreasing to the right of the vertical line in figure 2. This vertical line, denoting 19◦C in wet bulb temperature, represents a strong break in the slope. Entsprechend, in the parametric regression analysis proposed below, we specify a linear spline with a node at 19 to capture this dichotomous slope in the gradient. Vor allem, a wet bulb globe outdoor temperature of 19◦C cor- responds in our data to an outdoor ambient dry bulb temper- ature of about 27◦C and is likely equivalent to an indoor dry bulb temperature of about 29.5◦C before LED introduction.19 This 29.5◦C dry bulb temperature is quite consistent with es- timates from previous studies on the physiological threshold for the absorption of heat into the body, above which temper- ature affects human functioning (Hancock et al., 2007). Impacts of LED introduction. Having established the shape of the temperature-productivity gradient for the garment fac- tories in our data before the introduction of LED, we next 19This approximate relationship is derived from the indoor-outdoor tem- perature we collected and back-of-the-envelope calculations about how LED affected internal temperature. While a full engineering projection of heat dissipation is beyond the scope of this study, we present a simple heat gain calculation. The difference in energy consumption is 18 kWh per bulb per year, which translates into 0.058 kWh per bulb per day (assuming a six-day workweek). For the average factory, which received 1,000 LED bulbs, that implies a lowered electricity consumption of 58 kWh/day. Tak- ing the heat capacity of air as 1 joule/(g δ◦ C) and the density of air as 1.18 kg/m3, 58 kWh would heat 73,700 m3 of air (oder, zum Beispiel, a factory of 192 von 192 m square with a height of 2 M) by 2.4◦C. This temperature difference is a significant ambient temperature difference that would ex- plain our results, a calculation based on the fact that 1 kWh is 3.6 million joules, and heating 1 m3 of air requires 1 × 1.18 × 1,000 × 2.4 = 2832 joules = 0.00079 kWh. (We thank one of our referees for suggesting this back-of-the-envelope calculation.) l D o w n o a d e d von h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 THE LIGHT AND THE HEAT 785 FIGURE 3.—EFFICIENCY AGAINST TEMPERATURE BY LED l D o w n o a d e d f r o m h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 Erste, we estimate the following empirical specification of the relationship between production line efficiency and tem- perature using only observations prior to LED installation: Eludmy = α0 + βLT L + βH T H dgmy + ηum + δd + εludmy. dgmy + φBludmy + αl + γuy (2) Hier, E is the actual efficiency of line l of unit u on day d in month m and year y; B is budgeted efficiency for line l of unit u on day d in month m and year y; T L is daily wet bulb globe temperature from grid point g in degrees Celsius up to the spline node of 19, above which it records a constant 19; T H is daily wet bulb temperature minus 19◦C from grid point g above the spline node, below which it records a constant 0; αl are production line fixed effects; γuy are unit by year fixed effects; ηum are unit by month fixed effects; δd are day-of- week fixed effects; and α0 is an intercept. βL and βH are the coefficients of interest, giving the impact of a 1◦C Celsius in- crease in wet bulb globe temperature on line-level efficiency for temperatures below and above 19◦C, respectively.21 We then estimate the extent to which the introduction of LED lighting attenuates the temperature-productivity rela- tionship via the following specification: Eludmy = α0 + βL dgmy × LE Dumy) + βH 1 (T L + β2LE Dumy + βL 3 T H + αl + γuy + ηum + δd + εludmy. 3 T L + βH dgmy dgmy 1 (T H × LE Dumy) dgmy + φBludmy (3) 21While the effect of temperature on productivity may vary within the day, this is not testable given our data, since we only observe mean productivity and outdoor temperature for a production line each day. check for evidence that this gradient is affected by the partial replacement of the CFLs in factories with focused, machine- mounted LED lighting. We repeat the exercise from figure 2 for subsets of the data from before and after the LED rollout in each factory. These plots are presented in figure 3.20 The evidence suggests that factories are somewhat more efficient at all temperatures after the LED introduction, but this gain (or attenuation) increases at high temperatures. Das ist, the pre-LED gradient (red line) in figure 3 replicates the non- linear shape depicted in figure 2, but the post-LED gradient exhibits a flatter slope to the right of the 19 degree verti- cal line, allowing the gap between the before and after LED gradients to widen at higher temperatures and indicating a persistently significant treatment effect above 19 degrees. B. Parametric Spline Regression Analysis Motivated by the graphical evidence we estimate the re- gression equations below to causally identify both the ef- fect of temperature on production efficiency at various points along the temperature distribution and the attenuation of this impact driven by the LED replacement. Insbesondere, we ad- dress concerns regarding factory-level trends in efficiency, line-level unobservables, seasonality in efficiency, and the exogeneity of the LED introduction along with the nonlin- earities depicted in figures 2 Und 3. 20Fit reflects kernel-weighted local mean smoothing, using the Epanech- nikov kernel and bandwidth of 1. Note in each figure from here onward in the paper with both pre- and post-LED plots, we show 83% confidence intervals, which allow the reader to visually assess the hypothesis of a dif- ference between the two curves; if the confidence intervals do not overlap at a given point, then the two curves are significantly different at the 5% level at that point. 786 THE REVIEW OF ECONOMICS AND STATISTICS 3 . βL 1 and βH 3 and βH 3 , and βH Here LE Dumy is a dummy for the presence of LED lighting in unit u in month m and year y. It changes from 0 Zu 1 in the month of LED introduction in a particular factory unit. The coefficients of interest in the above specification are βL 1 , βH 1 , βL 3 indicate the effect of tempera- ture on productivity below and above the 19◦C spline node, jeweils, before LED introduction. βL 1 are the ex- tent of attenuation of the temperature-productivity gradient below and above the 19◦C spline node, jeweils, once + βH LED lighting is introduced. The sums βL 3 1 give the net effect of temperature on productivity below and above the spline node, jeweils, following LED intro- duktion. Note that we choose this spline specification with a single node at 19◦C WBGT for two reasons: (A) the raw data plots in figures 2 Und 3 clearly show that the relationship be- tween temperature and efficiency (and the difference in this relationship across LED) changes at this point in the temper- ature distribution and does not vary much on either side of this cutoff, Und (B) this point corresponds remarkably well to previous studies of the physiology of heat stress (Hancock et al., 2007).22 3 and βH + βL 1 To account for common error distributions within a factory over time, standard errors are clustered at the factory level. This cluster structure is appropriate given that LED intro- duction occurs at the unit level. Jedoch, given the relatively small number of clusters (thirty), we employ wild cluster bootstrap inference and report 95% confidence intervals in parentheses in all tables unless otherwise noted.23 Attendance. We also estimate the same specifications pre- sented in equations (2) Und (3), but replacing the efficiency outcome on the left-hand side with mean attendance (or prob- ability of each worker being present in the factory) at the line-daily level. These regressions are intended to investigate the degree to which temperature affects efficiency, and the corresponding attenuation from LED introduction might be working through effects on worker attendance. In robustness checks, we also estimate the original efficiency specifica- tions from equations (2) Und (3), including mean line-daily worker attendance as an additional control. The combination of these two sets of results allows us to investigate whether temperature and LED introduction affect worker attendance and whether controlling for attendance changes the estimated impacts of temperature and LED on the primary outcome of interest (efficiency). Distributed lags. Daily temperature could reflect short- term serial correlation, which would make it difficult to iden- tify the impacts of contemporaneous exposure to tempera- tur. Following previous studies, we augment equations 2 22Trotzdem, we explored more flexible spline specifications with more nodes and found the results to be qualitatively identical with less precision. 23See Cameron, Gelbach, and Miller (2008) for a thorough treatment of clustering approaches with few clusters and a discussion of their relative performance, which highlights that wild cluster bootstrap inference works best in a setting with few clusters. Und 3 to include 7-day distributed lag spline terms and their interactions with LED, in addition to the contemporaneous spline and LED interaction terms of primary interest. In the distributed lag models, we interpret the coefficients on con- temporaneous spline and interaction terms as the incremental impacts of contemporaneous temperature exposure after con- trolling for lagged exposure. This isolates the impact of con- temporaneous exposure from that of lagged exposure. If the coefficients on the contemporaneous temperature terms are similar with and without the inclusion of the 7-day distributed lag terms, we interpret the results as indicating a minimal role for serial correlation and persistence in impacts of lagged ex- posures. We can recover the composite impact of both the contemporaneous temperature exposure and of lagged expo- sures by summing up the coefficients from contemporaneous temperature and the full set of lagged exposures, but this com- posite impact will be nearly identical to that estimated from the original specification presented in equations (2) Und (3). V. Results A. Main Results We report results from the estimation of the parametric spline specifications presented in equations (2) Und (3) in table 2. Columns 1 Und 2 report estimates of βL and βH from equation (2), with column 2 estimates corresponding to a specification with an additional control for precipitation. The precipitation control ensures that impacts are being driven by temperature exposure alone and are not composite effects reflecting the impacts of other correlated weather conditions. 3 , and βH Columns 3 Und 4 report estimates of βL 3 from equation (3), once again with column 4 reporting results after controlling for precipitation. 1 , B 2, βL 1 , βH The spline regression estimates from columns 1 Und 2 reflect the pattern shown in figure 2 with the slope of the efficiency-temperature gradient below 19◦C of wet bulb globe temperature being slightly negative (statistically indis- tinguishable from 0) and the slope above 19◦C being strongly negative and statistically significant at the 1% Ebene. Point es- timates indicate that at wet bulb globe temperatures above 19◦C, a 1◦C increase in temperature leads to a reduction of more than 2.1 percentage points in actual efficiency. A com- parison of estimates across columns 1 Und 2 shows that in- cluding an additional control for precipitation has a minimal impact on results. The results in columns 3 Und 4 are consistent with the pattern reflected in figure 3, with the introduction of LED having no significant impact on the slope of the efficiency- temperature gradient below 19◦C, but a significant attenuat- ing impact on the negative slope of the gradient above 19◦C. Das ist, the introduction of LED offsets the negative impacts of temperature on efficiency by about 85%, attenuating the magnitude of the negative slope above 19◦C from about −2 to about −0.3. LED shows no significant impact below 19◦C, which is consistent with the ergonomics and physiology l D o w n o a d e d f r o m h t t p : / / Direkte . m i t . e du / r e s t / l a r t i c e – p d f / / / / 1 0 2 4 7 7 9 1 8 8 1 3 3 6 / r e s t _ a _ 0 0 8 8 6 p d . f by gu e s t o n 0 7 S e p e m b e r 2 0 2 3 THE LIGHT AND THE HEAT 787 TABLE 2.—IMPACT OF TEMPERATURE ON PRODUCTION EFFICIENCY AND MITIGATIVE IMPACT OF LED LIGHTING 1 2 3 4 Efficiency (Actual Production / Targeted Production) × 100 Wet bulb globe temperature <19 Wet bulb globe temperature ≥19 1(LED) × (Wet <19) ≥19) −0.299 [−1.803, 0.532] −2.135∗∗∗ [−3.312, −1.395] Fixed effects −0.318 [−1.813, 0.510] −2.169∗∗∗ [−3.369, −1.399] −0.0940 [−1.017, 0.421] −1.953∗∗ [−3.00, −1.206] −0.106 [−0.847, 0.852] 1.671∗∗∗ [0.718, 2.787] 3.447 [−18.34, 16.85] Factory Year, Calendar Month Production Line, Day of the Week −0.105 [−1.008, 0.404] −1.981∗∗∗ [−3.020, −1.230] −0.103 [−0.843, 0.853] 1.681∗∗∗ [0.725, 2.809] 3.393 [−18.39, Precipitation control Observations Mean dependent variable Wild-cluster bootstrap 95% CIs in brackets: significant at ∗∗∗1%, ∗∗5%, and ∗10%. Clustering is done factory level. All measures are degrees Celsius. regressions include daily budgeted Y 239,680 55.234 N 74,939 53.73 efficiency as a variable. TABLE 3.—IMPACT OF TEMPERATURE ON ATTENDANCE AND MITIGATIVE IMPACT LED LIGHTING 1 2 3 4 <19 −0.0061∗∗ [−0.0166, −0.000630] 0.0003 [−0.00585, 0.00973] Worker Presence (Line-Level Daily Probability) −0.0059∗∗ −0.0011 [−0.00534, 0.00162] [−0.0167, −0.000305] 0.0056 0.0007 [−0.00409, 0.0186] [−0.00533, 0.00989] [−0.00283, 0.00442] −0.0051 [−0.0197, 0.00771] −0.0065 [−0.0708, 0.0427] 136,062 0.846 392,601 0.829 −0.0007 [−0.00506, 0.00204] 0.0064 [−0.00341, 0.0193] 0.0002 [−0.00298, 0.00428] −0.0053 [−0.0199, 0.00743] −0.0054 [−0.0701, 0.0441] literature, suggesting that has highest impact on human functioning temperatures above this The estimate main effect positive large, but it imprecisely estimated statistically indistinguishable from 0. results reported table correspond to regression mean line-daily worker attendance identical speci- fications those 2, described section IVB. estimates suggest negative tempera- ture below 19◦C, however, magnitudes point extremely small (less than 1% mean). other coefficients, in- cluding reflecting impacts LED, In general, we interpret indicating no real attendance. These imply unlikely contributes installation efficiency. Next, investigate whether contemporaneous exposure reflect alone rather composite lagged exposure. Similarly, check attenuation working through Although persistent exposures serial correlation would not invalidate our analysis, interpretation will change based underlying sources variation. As discussed sec- tion IV, repeat analysis clude seven-day distributed lag spline terms and, where appropriate, their interactions with installation. 4. cor- respond specifications including l D o w n d e f r m h t p : >PDF Herunterladen