In particular, I observe the weigh-in time, the berry picker’s unique employee identifier, the field where the berries were picked, and the weight of the picker’s harvest. I divide the harvest’s weight by the time elapsed since the picker’s previous weigh-in to obtain a weight-per-hour measure of worker productivity. For the first weigh-in of the day, I use time elapsed since morning check-in to calculate this measure. As reported in table 1.1, average productivity pooled across both farms is just over nineteen pounds picked per hour. This number, however, masks significant heterogeneity across farm, day, and worker. At the San Diego farm, which grows organic berries, average productivity is slightly under fourteen pounds per hour, while at the Bakersfield farm, which grows conventional berries, average productivity is over twenty-two pounds per hour. Figure 1.3 plots the distribution of workers’ average productivities, while figure 1.4 plots the distribution of each day’s average productivity, in both cases separated by farm. These two figures highlight substantial variation in picker skill, as well as in daily productivity. In southern California and the central valley, where the farms I study are located, temperatures peak in the mid-to-late afternoon. To avoid the hottest part of the day, most pickers begin work as early as 6:00 a.m. and end around 3:00 p.m. This pattern is reflected in figure 1.5: most fruit picking ends by mid-afternoon. The average picker works around eight hours each day, as shown in figure 1.6. Under California law in my sample period , agricultural workers do not earn overtime pay until after working ten hours in a single day. In my data,rolling tables only the San Diego farm ever lets pickers work more than ten hours in any given day. Farms employ pickers on a day-to-day basis, either directly or through a labor contractor.Some pickers only work for a day or two, but others work continuously for several weeks or months as shown in figure 1.7.
Indeed, several employees in my data work for a farm in two or all three of the years I study. Unfortunately, I do not observe each worker’s initial date of hire, so I am unable to confidently measure lifetime worker tenure on either farm.While I know each farm’s daily piece rate wage from the its payroll data, I obtain information on market prices for California blueberries from the Blueberry Marketing Research Information Center of the California Blueberry Commission . As an official agricultural commission, the CBC legally requires all blueberry producers in the state to report daily production and sales figures. The CBC then publishes daily summary statistics of these data through BMRIC. Individual blueberry producers are able to access a daily BMRIC report online that summarizes the high, low, and weighted average prices received by blueberry producers throughout the state on the previous day. Separate statistics are provided for conventional and organic blueberries. In order to capture the information a farmer could have accessed on any particular day, I use each day’s most recent previous BMRIC report as the relevant measure of market prices. Because BMRIC publishes a daily report each weekday except for holidays, the relevant market price data for harvest data collected on a Thursday is from the Wednesday prior. Similarly, the relevant market price data for harvest data collected on a Monday is from the Friday prior. Based on personal conversations, the blueberry farmers I study track these BMRIC reports quite closely throughout the season. From April to June each year, both market prices and piece rate wages fall as the California blueberry season progresses. Figure 1.10 documents this relationship across the three years and two farms in my dataset. Recall that the San Diego farm grows organic blueberries while the Bakersfield farm grows conventional berries. This distinction accounts for why the two farms face differing market prices in the same year.
Market prices and piece rate wages are highly correlated over time, due in large part to seasonality in blueberry production. Figure 1.11 plots each farm’s daily total production over time for each season. At times of high production, blueberry bushes are likely to be full of easily-pickable ripe berries. This abundance of fruit leads farmers to cut the piece rate as described in the previous section. In order to disentangle the various factors that affect farms’ piece rate wages in my empirical exercises, I control both for seasonality in production as well as the field where berries are harvested. In my subsequent econometric analyses, I estimate the causal effects of piece rate wages and temperature on picker productivity. Figure 1.12, in contrast, plots the naïve relationship between average picker productivity and piece rate wages, temperature, and two other observable characteristics: time of observation and worker tenure by season. First, note that productivity and piece rate are negatively correlated, since farmers lower the piece rate when fruit is plentiful in the fields.Second, note that there are no sharp decreases to average productivity at particularly high temperatures, as one may hypothesize. Finally, note that there is a clear increasing and concave relationship between worker tenure within a season and productivity. In other words, there is learning-by-doing in berry picking, and this learning has decreasing marginal returns over time. While most employees out-earn the hourly minimum wage under the piece rate system, some fall below this threshold and are paid according to the minimum wage for the day. As Graff Zivin and Neidell note, if there is not a credible threat that these workers could be fired for their low output, they may shirk and provide less effort than they otherwise would. Figure 1.13 plots the distribution of normalized daily productivity that identifies those picker days where shirking could be a problem. Observations to the left of one are picker-days where the picker’s effective hourly wage is below the minimum wage, and observations to the right of one are picker-days where the picker out-earns minimum wage under the piece rate scheme. A picker with a normalized productivity measure of two is earning twice the minimum wage. Productivity in this figure is normalized because both piece rate wages and the hourly minimum wage vary over the sample period. Shirking, if it occurs, could bias my results. In particular, if high temperatures or low wages lead to more pickers earning the minimum wage, and these pickers subsequently shirk, my econometric estimates will be biased upward. I address this concern in section 1.6 by re-estimating my primary results using only those picker-days where employees out-earn the minimum wage. My findings do not change when I eliminate these observations, suggesting that the threat to a picker of being fired if they consistently slack off is a sufficient incentive to keep them from shirking. The model presented in section 1.2.1 motivates my empirical strategy. In particular, my goal is to estimate the relationship between piece rate wages and labor productivity . The primary challenges to this undertaking are twofold. First,cannabis grow supplies many observable and unobservable factors contribute to worker productivity which – if unaccounted for – could lead to omitted variable bias in my estimates of temperature and wage effects. Second, piece rate wages are endogenous to labor productivity.
To address factors other than the piece rate wage that could drive labor productivity, I exploit the richness of my data and include flexible controls for temperature, and a host of fixed effects. Most importantly, I include time fixed effects to capture seasonality , work patterns , and season-specific shocks . I also include field-level fixed effects to capture variation in the productivity of different varieties and plantings of blueberry bushes. The combination of time- and field-level fixed effects gives me a credible control for the average density of blueberries available for harvest at a given time in a given field. In other words, these fixed effects allow me to control for resource abundance . Further, I include worker-specific fixed effects to capture heterogeneity in picker ability. Lastly, I include a quadratic of worker tenure to allow for learning-by-doing. When estimating the effect of temperature on productivity, my identifying assumption is that individual realizations of temperature are as good as random after including the controls described here and the piece rate wage. To address the endogeneity of piece rate wages to labor productivity, I instrument for these wages using California market prices for blueberries. In order for these prices to be a valid instrument for wages, they must be correlated with farms’ piece rates, but not affect labor productivity through any other channel. Figure 1.10 plots piece rate wages and market prices over time and suggests a strong correlation between the two variables. I provide formal evidence of this relationship in table 1.4, which I describe in detail in the following section. As evidence that the exclusion restriction holds – that market prices do not affect labor productivity except through wages – I rely on the size and heterogeneity of the California blueberry industry. Statewide market prices capture supply shocks from growing regions around the globe, each with different weather, growing conditions, and labor markets. To the extent that environmental conditions agronomically drive blueberry production, they do so differentially across different growing regions of California. Therefore, any one farm’s temperature shocks in a given growing season do not determine aggregate blueberry supply.Additionally, both of the farms I study are quite small in comparison to the statewide market: they are price-takers and cannot independently affect average prices. As a result, market prices capture exogenous variation in aggregate supply shocks and serve as an effective instrument for piece rate wages. Table 1.2 presents the results of estimating my primary specification, equation , with different sets of controls. In column , I include only the instrumented piece rate wage and five-degree temperature bins. As expected, without controlling for seasonality or harvest field, I find a statistically significant negative effect of wages on productivity. I also find large and negative effects of cool temperatures on productivity. In each subsequent column, I add more controls: farm fixed effects, field fixed effects, worker tenure controls, time fixed effects , and worker fixed effects. Including time fixed effects to column makes the largest difference to the sign and significance of my results. This makes sense, since seasonality and time-of-day dynamics are particularly relevant in the California blueberry context. Column of table 1.2 contains the results of my preferred specification using the temperature bins described in equation . By controlling for field and time fixed effects, , the point-estimate for piece rate wages’ effect on worker productivity switches from negative and statistically significant to positive but statistically indistinguishable from zero. The standard error on this effect is qualitatively small, meaning that I can reject even modest effects of wage on productivity. I also find statistically significant negative effects of both cool temperatures and very hot temperatures on picker productivity. The solid line in figure 1.14 plots this temperature-response function with a 95%- confidence interval. The relevant temperature point estimates represent the change in conditional average picker productivity expected by replacing a picking period with a time-weighted average temperature between 80–85◦F with a picking period having a time-weighted average temperature within the corresponding temperature bin. I find that temperatures between 50 and 55 degrees lower productivity by 3.22 pounds per hour – a nearly 17% decrease, while temperatures over 100 degrees lower productivity by 2.33 pounds per hour – just over a 12% decrease. Table 1.3 re-estimates my preferred specification using the piece wise-linear spline described in equation . I find that at temperatures below 88.5 degrees Fahrenheit, an additional degree of heat increases productivity by 0.088 pounds per hour, on average. At temperatures above 88.5 degrees, however, an additional degree of heat lowers productivity by 0.20 pounds per hour. The dashed line in figure 1.14 plots these effects, which are significant at the 0.001 and 0.05 levels, respectively. In table 1.4, I provide evidence that blueberry market prices are an effective instrument for piece rate wages. Column reports the results of estimating equation by ordinary least squares without instrumenting for wages. While the estimated effect of wages on productivity in this specification is statistically insignificant, the point estimate is negative. Column presents the results of regressing market prices, temperature, and other controls on the piece rate wage: my first stage. There is a large, positive, and statistically significant effect of prices on wages, while temperature has no meaningful effects on piece rates below95 degrees Fahrenheit.