The Light and the Heat: Productivity Co-Benefits of Energy-Saving Technology

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 change (Granade et al., 2009). Despite this promise, energy-efficient technologies are usually adopted at low rates (Allcott & Taubinsky, 2015). Recent studies point to several explanations 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 adoption (Allcott & Greenstone, 2012). The second is behavioral factors such as consumer inattention to energy costs (Allcott, Mullainathan, & Taubinsky, 2014). The third possible explanation is that returns are smaller, or costs higher, in practice than engineering projections predict (Burlig et al., 2017; Fowlie, Greenstone, & Wolfram, 2013; Ryan, 2017). Furthermore, behavioral responses to energy-efficiency (such as increased consumption) may offset returns to energy efficiency investments. Thus, estimating the true returns to energy efficiency requires testing for mechanisms that may drive a wedge between engineering and economic returns, including imperfect maintenance of the investments and rebound effects.

In this study, we estimate the productivity consequences of the adoption of energy-saving technology, using daily production line data from a large garment firm operating factories in and around Bangalore, India. First, we show that days with higher outside temperatures have lower productivity, measured as production line efficiency (realized output 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, factories replaced a substantial fraction of CFL bulbs with LED bulbs. LED lighting reduces ambient temperature on the factory floor because less electricity is converted to waste heat, relative to CFL lighting. This lower ambient temperature reduces 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 factories.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 lighting. 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 temperature and humidity, since the impact of temperature on thermal 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 temperature equivalent at average humidity levels in our sample is 27$∘$ to 28$∘$C), temperature has a very small impact on efficiency. But for mean daily temperatures above this cutoff (about one-quarter of production days), there is a large, negative 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, flattens 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 robust to the inclusion of a variety of fixed effects and controls, including factory by year by quarter fixed effects, as well as 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 nonlinear nature of the gradient itself and is likely due to a leftward movement along the gradient. This movement would generate 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 always 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. First, if factories respond to energy savings by increasing working hours, then the cobenefits to these investments may change: they may be higher if workers respond to the more comfortable environment on hotter days by continuing to be more productive for extra hours, and they may be lower or 0 if workers respond to longer hours by slowing their productivity per hour. Using data on working hours, we can directly test for this response by the factory managers. Second, if the temperature-productivity relationship is driven by lower attendance on hotter days, and not by workers responding to a less comfortable work environment, then LED lighting may not mitigate this relationship (e.g., 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. Third, if workers respond to the lighting 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. Finally, our results indicate that energy-efficient lighting 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 manufacturing workplaces in low-income countries).

Finally, we perform cost-benefit calculations for LED adoption, combining the above estimates with the firm's actual cost data for LED replacement and projected energy savings. The results of this analysis show that the productivity co-benefits of LED adoption are substantially larger than the energy savings. Indeed, accounting for productivity increases significantly shifts the break-even point for the firm, from over three and half years to less than eight months. With 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.

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 generate positive externality co-benefits such as greater electricity grid reliability (Carranza & Meeks, 2020). Other studies 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, for a review). We study a novel, private co-benefit of climate change mitigation. This distinction is important because the success of most mitigation strategies rests on individuals' and firms' willingness to adopt them, and this willingness is largely driven by private 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 underestimate the private returns to adoption.

We also contribute to the understanding of the effects of environmental and infrastructural factors (which are often related to the environment) on productivity in developing countries (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 consistently 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 adaptation to higher temperatures in settings characterized by low air-conditioning adoption and significant indoor heat exposure 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. Section 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, section VI offers additional robustness checks, and section VII reviews the cost-benefit analysis and concludes.

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 occupational safety standards for workers exposed to very high or 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 complexity, duration of exposure, and the worker-level skill and acclimatization level (Pilcher, Nadler, & Busch, 2002). This contributes to the difficulty of setting a specific limit in working environments (Hancock et al., 2007).

One key finding from this literature is that there is a nonmonotonic relationship between ambient temperature and human performance. The overall shape of the relationship is an U: performance suffers at excessively cold and excessively warm temperatures (Parsons, 2010). Moreover, one meta-analysis highlights the dry-bulb threshold of 29.4$∘$C (85$∘$F) as particularly important (Hancock et al., 2007). This threshold value represents the temperature above which the body starts to store heat. As Hancock et al. (2007) put it, “[In] this 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.

India is the world's second largest producer of textile and garments, 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 seventy sewing machine operators (depending on the complexity of the style) arranged in sequence and grouped in terms of parts of the garment (e.g., sleeve, collar).⁵ Completed sections 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. According 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 involved in the production of orders from particular buyers than others. So, for example, if buyer A's environmental regulations become more stringent, then the supplier might choose to upgrade to LED lighting in factories processing many orders 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 factories after the change. That is, only about half the bulbs were replaced, with each fixture now containing one CFL bulb instead 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 workers' 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 operate 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 emissions 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 discuss 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 factories) in 2011, about 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 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

We use mean daily temperature, precipitation, and relative humidity data from the National Centers for Environmental 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 to 2014. It has a spatial resolution of about 38 km; each factory in our sample is matched to the nearest data grid point.¹¹

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 outdoor 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. Accordingly, we use outdoor ambient temperature as discussed above as a proxy for indoor conditions. For outdoor temperature to represent a valid proxy, we would like to verify that fluctuations in outdoor temperature pass through to indoor temperature. Although we do not have indoor temperature 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

We use daily data at the production line level from thirty garment factories in and around Bangalore. Identifiers include 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 industrial 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

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 a.m. to 5 p.m., with all factories in our sample operating a single daytime production shift. Accordingly, a line producing a style with a SAM of 0.5 will have a target of 120 garments per hour, or 960 garments per day. Most important, the target quantity is almost always fixed across days (and, 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 Variations 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 efficiency. 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 systematically across lines and within a line over time due to, for example, absenteeism, machine failures, or working conditions. 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.

We present means and standard deviations of variables used in the analysis in table 1. Our sample consists of 523 production lines across thirty factories. The range of dates over which we have production data spans 1,001 days. However, 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.

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 accounting 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.

We begin by motivating the empirical specifications and techniques with descriptive plots of production and temperature data.¹⁷

Notably, a wet bulb globe outdoor temperature of 19$∘$C corresponds in our data to an outdoor ambient dry bulb temperature 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 estimates from previous studies on the physiological threshold for the absorption of heat into the body, above which temperature affects human functioning (Hancock et al., 2007).

Motivated by the graphical evidence we estimate the regression equations below to causally identify both the effect of temperature on production efficiency at various points along the temperature distribution and the attenuation of this impact driven by the LED replacement. In particular, we address concerns regarding factory-level trends in efficiency, line-level unobservables, seasonality in efficiency, and the exogeneity of the LED introduction along with the nonlinearities depicted in figures 2 and 3.

Here, $E$ 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 $y$⁠; $TL$ 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; $TH$ 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 increase in wet bulb globe temperature on line-level efficiency for temperatures below and above 19$∘$C, respectively.²¹

Here $LEDumy$ is a dummy for the presence of LED lighting in unit $u$ in month $m$ and year $y$⁠. It changes from 0 to 1 in the month of LED introduction in a particular factory unit. The coefficients of interest in the above specification are $β1L$⁠, $β1H$⁠, $β3L$⁠, and $β3H$⁠. $β3L$ and $β3H$ indicate the effect of temperature on productivity below and above the 19$∘$C spline node, respectively, before LED introduction. $β1L$ and $β1H$ are the extent of attenuation of the temperature-productivity gradient below and above the 19$∘$C spline node, respectively, once LED lighting is introduced. The sums $β1L+β3L$ and $β1H+β3H$ give the net effect of temperature on productivity below and above the spline node, respectively, following LED introduction. 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 and 3 clearly show that the relationship between temperature and efficiency (and the difference in this relationship across LED) changes at this point in the temperature distribution and does not vary much on either side of this cutoff, and (b) this point corresponds remarkably well to previous studies of the physiology of heat stress (Hancock et al., 2007).²²

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 introduction occurs at the unit level. However, 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

We also estimate the same specifications presented in equations (2) and (3), but replacing the efficiency outcome on the left-hand side with mean attendance (or probability 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 specifications from equations (2) and (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).

Daily temperature could reflect short-term serial correlation, which would make it difficult to identify the impacts of contemporaneous exposure to temperature. Following previous studies, we augment equations 2 and 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 contemporaneous spline and interaction terms as the incremental impacts of contemporaneous temperature exposure after controlling for lagged exposure. This isolates the impact of contemporaneous 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 exposures. We can recover the composite impact of both the contemporaneous temperature exposure and of lagged exposures by summing up the coefficients from contemporaneous temperature and the full set of lagged exposures, but this composite impact will be nearly identical to that estimated from the original specification presented in equations (2) and (3).

We report results from the estimation of the parametric spline specifications presented in equations (2) and (3) in table 2. Columns 1 and 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. Columns 3 and 4 report estimates of $β1L$⁠, $β1H$⁠, $β2$⁠, $β3L$⁠, and $β3H$ from equation (3), once again with column 4 reporting results after controlling for precipitation.

The spline regression estimates from columns 1 and 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 indistinguishable from 0) and the slope above 19$∘$C being strongly negative and statistically significant at the 1% level. Point estimates 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 comparison of estimates across columns 1 and 2 shows that including an additional control for precipitation has a minimal impact on results.

The results in columns 3 and 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 attenuating impact on the negative slope of the gradient above 19$∘$C. That is, 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 literature, suggesting that temperature has the highest impact on human functioning at temperatures above this level. The estimate of the main effect of LED is positive and large, but it is imprecisely estimated and statistically indistinguishable from 0.

The results reported in table 3 correspond to the regression of mean line-daily worker attendance on the identical specifications to those in table 2, as described in section IVB. The estimates from table 3 suggest a negative impact of temperature on attendance at temperatures below 19$∘$C, however, the magnitudes of the point estimates are extremely small (less than 1% of the mean). All other estimates of coefficients, including those reflecting the impacts of LED, are statistically indistinguishable from 0. In general, we interpret the results in table 3 as indicating no real impacts of temperature on worker attendance. These results imply that it is unlikely that the impact of temperature on worker attendance contributes to the estimated impacts of temperature and LED installation on efficiency.

Next, we investigate whether the estimated impacts of contemporaneous temperature exposure on efficiency reflect contemporaneous exposure alone rather than a composite of contemporaneous exposure and lagged exposure. Similarly, we check that the estimated attenuation from LED installation is working through contemporaneous temperature exposure. Although persistent impacts of lagged exposures and serial correlation in temperature would not invalidate our analysis, the interpretation of the point estimates will change based on the underlying sources of variation. As discussed in section IV, we repeat the analysis reported in table 2 but include seven-day distributed lag temperature spline terms and, where appropriate, their interactions with LED installation. The results are reported in table 4. All results in table 4 correspond to specifications including seven-day distributed lag temperature spline terms and results in columns 3 and 4 correspond to specifications, also including interactions of distributed lag spline terms with the LED installation dummy.

Overall, the results in table 4 are qualitatively identical to the main results reported in table 2, but with slightly larger magnitudes for coefficients on the above 19$∘$C temperature spline and the corresponding LED interaction terms. These results indicate that the estimates of temperature impacts and attenuation from LED installation are being driven by contemporaneous exposures and not the correlation of contemporaneous and lagged temperature. Daily temperature is generally believed to reflect some degree of serial correlation, so the similarity in results with and without distributed lags is not altogether surprising in our study. The baseline specifications already include a large set of heterogeneous nonlinear trends (e.g., unit by month FE) to control for this less transitory variation in temperature. Indeed, the correlations between contemporaneous temperature and lagged temperature values after partialing out the full set of controls are not very high (never more than .25 and mostly below .1).²⁴

In this section, we check for the exogeneity of the timing of LED installation.

In column 1 of table 5, we report estimates of the coefficients on the temperature spline terms from the regression of the LED introduction dummy on the main specification in equation (2). We find no evidence that LED installation was timed around particular temperature realizations. In columns 2 and 3 of table 5, we report results from the regression of SAM (a proxy for the complexity of the garments being produced) and budgeted efficiency (a proxy for scope for learning by doing due to order size), respectively, on the LED installation dummy, the date relative to LED installation, and their interaction with the remaining specification identical to that depicted in equation (2). These regressions are meant to check whether garment style and complexity (SAM) and order size (budgeted efficiency) varied systematically in the lead-up to LED installation or immediately after. Significant coefficient estimates in columns 2 and 3 would suggest that the timing of LED introduction is endogenous with respect to these production factors; however, we find no such evidence. In columns 4 through 6, we check that LED installation was not accompanied by other forms of upgrading. Specifically, we regress the proportion of each of the three skill levels of tailors—A, B, and C grade—hired on each day in each factory unit on the same specification reported in columns 2 and 3.²⁵ We do not find any evidence that hiring patterns changed in the lead-up to LED installation or immediately after.

Finally, we test whether there were changes to working hours or wage contracts in the lead-up to LED installation. We use two other data sources for this purpose. The first data source is daily data at the worker level showing when individuals clocked in and out of work, which we use to construct the two measures of daily line-level working hours. The first is the average time at the line level that we observe a worker clock in for a given line on a given day (measured in terms of elapsed minutes since midnight). The second is the average time that we observe a worker on a line clock out on a given day (also measured in terms of elapsed minutes since midnight). These measures are at the production-line daily level.²⁶ The second data source is at the monthly level for each individual worker and measures different aspects of the wage contract as well as the number of paydays each month for the worker. The four variables we consider are wages earned (total wages accruing to the employee, including deductions for taxes, social security, and employees' provident fund), total deductions (contributions to taxes, social security, and employees' provident fund), value of unpaid leave, and the number of days each month that the employee was present and accrued wages (the number of pay days).

Table 6 presents the results. Overall, we do not find that changes in working hours or compensation to workers change with LED adoption; there is no statistically significant change in any of the six variables leading up to LED adoption.

We conduct a variety of additional robustness checks.

To further verify that worker attendance is not a primary mediating mechanism of the impacts of temperature and LED installation on efficiency, we repeat the analysis reported in table 2 with mean line-daily worker attendance as an additional control. The results from these regressions are reported in table A6 and are very similar to those presented in table 2. Overall, we interpret the results in tables 3 and A6 as evidence against the importance of attendance as a primary mediator of the has an impact on of temperature and LED installation on efficiency. That is, we find that exposure to higher temperatures impacts the intensive margin of productivity per unit labor supplied, but does not strongly affect the extensive margin of the quantity of labor units supplied. Similarly, the introduction of LED attenuates greatly the impacts of temperature on the intensive margin of efficiency but has no perceptible impact on the extensive margin of labor supply.

We also present all of our main results with additional fixed effects. We replace factory by year fixed effects with factory by year by quarter fixed effects (and still include factory by calendar month, production line, and day of the week fixed effects, along with daily precipitation as a control variable). Table A3 presents the results using this specification. Columns 1 and 2 present the impact of temperature on productivity and the mitigating impact of LED lighting. Columns 5 and 6 present the impact of temperature on productivity and the mitigating impact of LED lighting controlling for line-level attendance, and columns 3 and 4 do so while including the same splines for seven-day distributed lag specification of temperature as for same-day temperature (as well as with interactions of the LED adoption dummy variable). The results are robust to the inclusion of these fixed effects, though the magnitude reduces a little in some specifications. These results suggest that any unmeasured changes to the working environment that we are not able to pick up in the previous robustness checks but that happened within the quarter are not driving our results.

In table A2, we present our main results without including budgeted efficiency as a control variable, to show that our results do not depend on using this control variable. In table A4, we restrict the analysis of the main productivity specifications to only production lines that have a proportion of missing data that is lower than or equal to 30% (this includes data from 344 of about 500 production lines).²⁷ The results are nearly identical to our original results, suggesting that the missing data are not substantially affecting our main results.²⁸

In table A5, we replicate results from table 2, except that we use dry bulb temperature instead of WBGT and control for relative humidity separately. Finally, in the online appendix, we present our main results using a more flexible semiparametric estimation rather than parametric spline regressions, and obtain very similar estimates.

The promise of climate change mitigation is tempered by the willingness of individuals and firms to adopt beneficial technologies on a large scale. This willingness in turn, is a function of the private returns to adoption. In this study, we show that the introduction of energy-efficient LED lighting in Indian garment factories had substantial productivity co-be-nefits that accrue privately to the adopting firm.

Specifically, we find that the introduction of LEDs eliminates about 85% percent of the negative impact of temperature on worker efficiency on relatively hot days. Using the probability that mean daily outdoor temperature reaches or exceeds 19$∘$C WBGT (20% of all days), we estimate an average total increase in production efficiency of 0.4 percentage points (0.2 times the mitigation coefficient on LED lighting, which is 1.95 percentage points of efficiency).²⁹

We combine our estimate of average total efficiency gains with actual production and cost data from the firm to calculate annual costs and benefits of LED installation. The calculations are shown in table 7. Senior management at the firm estimated that the profit gain for each percentage point gain in efficiency was 0.1875 percentage points.³⁰ Thus, a 0.4 percentage point gain in efficiency from LED installation translates to a .075 percentage point gain in profits (or a 1.5% increase in profitability from the 5% baseline profit margin of the firm). At an approximate profit per factory per year of $500,000 (amounts in US dollars) the introduction of LED delivers productivity gains worth $7,500 per factory per year.

How does this estimate change the cost-benefit calculations of LED adoption for the firm? We calculated this based on the energy cost calculations the firm used for its LED adoption choices. The total energy cost savings per year per factory unit of LEDs (as compared with CFL bulbs, which were being used before LED introduction) were approximately $2.40 per bulb replaced or $2,880 in total for an average replacement of about 1,200 bulbs per factory in our sample.³¹ The additional annual profits from efficiency gains we computed are more than two and a half times this amount. The cost of replacing the average factory's bulbs with LEDs is $10,240. Thus, if only electricity expenditure were taken into account, it would take about three and a half years to break even. However, when the productivity benefits are included, the firm breaks even within twelve months of LED installation. After the initial payback period, the firm benefits from an ongoing combined increase in profitability from energy savings and efficiency gains.

These results are of course generalizable only to settings where air-conditioning is not available in the workplace. However, since air-conditioning remains quite rare in factories in developing countries, our results indicate that energy-efficient lighting can have substantial co-benefits for worker productivity for a large section of the workforce.

In addition to the private benefits of increased productivity and energy cost savings, the replacement of CFL lighting with LEDs has public benefits of avoided damages due to reduced carbon emissions. On average, the LED introduction saves 21,600 kWh of electricity per factory unit per year, which in this case reduces electricity emissions by about 3.73 tC emissions per unit per year.³² Valuing this reduction of carbon emissions at the Nordhaus (2011) estimate of $44/tC (a 2005 carbon price) gives us avoided damages of $197.04 per factory per year, and valuing this at the mean value of the review by Tol (2005) of $93/tC yields avoided damages of $416.47 per factory per year. At the current estimates of carbon prices, these benefits are relatively small in comparison to the annual private benefits.³³

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