Marco conceptual de la migración. Delimitación conceptual. 62

The fact that number of interviews completed per hour declines on hot days with little change in the numerator suggests a lengthening in working hours. As mentioned previously, the opposite phenomenon has been observed in the U.S., where workers appear to work fewer hours on hot days (Graff Zivin and Neidell, 2014). Given these differences in results, in this section, I examine the types of adjustments workers are making to their workdays on hot days in developing countries. I first examine summary statistics on the starting times and ending times of interviewer days in my sample by wet bulb bin. The results, displayed in Panel A of Figure 2.9, show that interviewers’ days begin monotonically earlier as the wet bulb temperature rises, while end times appear to be less responsive. The result is that interviewers work more hours on hot days on average, and the difference between the hottest and the coldest bin is quite stark: work days are about four hours shorter on days with wet bulb temperatures less than 30 degrees than for days over 85 degrees wet bulb. This is

to be workers who were brought in later in the survey round to replace those who were fired. Figure B5 examines the same relationship but using interviewer fixed effects. The results are very similar, suggesting that selection into experience does not drive these results.

consistent with anecdotal evidence from developing countries suggesting that workers may have to work longer hours in order to hit daily production targets (Kjellstrom et al., 2016b).

To more closely investigate this pattern, Panel B of Figure 2.9 gives kernel density plots of individual interview start times throughout the day for each wet bulb bin. The figure shows the same pattern as the previous one, where interviewers start their morning interviews earlier but don’t end their afternoons significantly earlier. From this figure, it also becomes clear that the interviewers take longer mid-day breaks on hotter days. Therefore, it appears that interviews are allocated towards times of day with more pleasant temperatures:

in the morning and later afternoon on hot days and mid-day on cold days.

These figures draw from summary statistics, so there are many possible explanations for the patterns shown in the average interviewer schedules. One such explanation could be the relationship between daylight hours and temperature: if the sun rises earlier on warmer days on average, then this could account for the earlier start times, rather than temperature itself. However, as Table B5 shows, this does not appear to be the case. The table shows correlations from regressions without controls or fixed effects; daylight hours have the expected negative correlation with start time: interviewing days start earlier on days with more daylight. However, when added as a control, daylight hours do not appear to mediate the relationship between temperature and start time.

To separate the causal effect of temperature on working hours from other place-specific factors, in Figure 2.10, I investigate the relationship between wet bulb temperature and start time using multiple specifications. The dark blue line gives the cross sectional relationship between temperature and start time; this is the same relationship displayed in Figure 2.9, except in regression form with clustered standard errors and non climate-related controls

added. The red line adds controls for usual wet bulb temperature in the month of interview for that cluster (this is the 10-year average used in the main specification). The slope is significantly shallower in this specification, implying that much of the relationship between temperature and start time is due to a relationship between climate and start time; that is, places that are hotter on average start work days earlier on average (at least in times of year that are hot). The light blue line gives the results of the full specification, with survey round by region of country fixed effects and other controls. The coefficient on the hottest wet bulb bin remains negative and statistically significant in this specification, but the relationship across the rest of the distribution has disappeared. Again, these results suggest that most of the response along this margin is to usual seasonality, rather than “surprise” weather days.

This suggests that a lengthening in the work day may be a form of long-term adaptation in these settings: workers accommodate the slowing pace of production by starting earlier.

An implication of this is that in the longer run leisure hours and productivity may be more negatively affected by high temperatures than in the shorter run. In addition, workers may start earlier on days they expect to be hot; this ability to forecast may be factored out in specifications that narrow in on surprising weather days.

Finally, in Figure 2.11, I examine the contribution of increases in interviewing du-ration to the increase in overall time in the field. I find that even in the main regression specification, with region of country fixed effects and climate controls, time spent through-out the day conducting interviews increases significantly on hot days relative to mild days.

Therefore, interviewers are both spending more time in the field on hot days and more time actively conducting interviews.

The previous results show that workers increase labor supply along the intensive

margin on hot days, but another way they could respond is through adjustments along the extensive margin. That is, they could not conduct interviews at all on days with extreme temperatures. In Figure 2.12 I investigate this possibility by running a version of Equation 2.1 using a dummy variable for whether an interviewer was observed conducting interviews on a given day. I include in the sample all days between the interviewer’s first and last interviewing date in the survey round.²⁸ The results of this exercise show that interviewers are significantly less likely to work on hot days relative to mild days and more likely to work on colder days. The magnitudes are large, hovering around 20 percentage points at the highest temperatures. There are several potential channels for these effects. Interviewing teams may schedule travel days (between survey clusters) to coincide with unpleasantly warm temperature days in order to avoid the effects on interviewing performance. Interviewing teams also generally take regular breaks so that the interviewing teams can return home and visit their families; it may be that these are also strategically scheduled. Or, it may simply be that individual interviewers are less likely to show up for work on hot days. In any case, it appears that adjustments in labor supply on the extensive margin are one manner in which interviewing teams avoid some productivity and utility consequences of extreme temperature.

28This exercise is complicated by the fact that interviewers are observed in many different places throughout the survey round. I assign each non-working day the weather from the most recent survey cluster visited by the interviewer. To minimize the possibility that they have travelled large distances since their last observed interview, I limit the sample to interviewers observed working within the last 2 days, though the results look similar if that period is extended.