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Adapting to the Weather: Lessons from U.S. History Hoyt Bleakley † Sok Chul Hong ‡ Draft in July 2012 Abstract An important unknown in understanding the impact of climate change is the scope for adaptation, a long-term process that requires observations on historical time scales. We consider the impact of weather across much of US history on various measures of productivity. Using cross-sectional and panel methods, we document economically and statistically significant responses of agricultural and individual productivity to weather. We find particularly strong effects of hotter and wetter weather early in US history, but these effects are almost completely attenuated in recent decades. We evaluate several possible channels through which these adaptations took place. We contrast our results to the existing literature that makes inferences from shorter time horizons. Keywords: climate change, adaptation, hedonics, farm value, farm output value, cohort effects † University of Chicago, Booth School of Business, and NBER, [email protected] ‡ Sogang University, School of Economics, [email protected] 1 1 Introduction A major issue in forecasting the economic effect of global warming is how much people can adapt to warmer weather over the long term. If weather affects production or investment, adaptation can take several forms: adjusting the mix of factors of production, choosing an alternative technology, or even developing a new technology more suited to the new climate. On the other hand, long-term changes in weather could bring ecological changes, which might be amplified by human action, especially if property rights are not well established (Diamond 1997; Libecap et al.). In any case, understanding the scope of long-term adjustment is key for measuring the impact of climate change. While early approaches to this question relied on simulations of agronomic models, more recent econometric work has used hedonic/cross-sectional or fixed-effect/panel methods. Hedonic models account for the price of land across areas, with various factors, including climate variables. The estimated coefficients are interpreted as the discounted marginal value of these factors to land productivity. Examples of this work are by Mendelsohn, Nordhaus, and Shaw (1994) and Schlenker, Hanemann, and Fisher (2005, 2006). (We review the literature further in Section 2.) If areas have had enough time to adapt to permanent features of their climate, the hedonic estimates can measure long-term effects of climate. A limitation of such cross-sectional comparisons, however, is that they measure the effect of climate on land value plus the value of any omitted variables that happen to be correlated with the measures of climate employed. Further, it bears mentioning that the hedonic approach is not direct evidence of adaptation: hedonic methods do not actually observe adaptation, but rather assume it. A complementary approach to this question uses panel and fixed-effect models, thus considering the impact of across-year variation in weather within an area. Examples of this type of work include Deschênes and Greenstone (2007), Dell, Jones, and Olken (2008), Schlenker et al. (2008), Deschênes, Greenstone, and Guryan (2009), and Guitieras (2009). Both of these methods have their advantages, but are nevertheless limited by the range of technology during their sample window, and, especially for panel methods, by the scope for adjustment over the time horizon implicit in their research design. Meaningful adaptation takes place over at long horizons, and therefore direct evidence of adaptation requires observation over longer periods of time. In the present study, we adopt aspects of both methodologies, but from a longer-term 2 perspective. We examine historical data that span a century and a half in the United States. We employ geospatial data on average weather conditions as well as historical weather information from measurement stations on temperature and rainfall. These data are matched to economic variables at the county and state levels for the analysis below. (Section 3 and Appendix A describe the data.) The US is well suited for this analysis for two reasons: first, the country has substantial variance of climate across and sufficient variability of weather across areas to estimate these models; second, there exists conformable data on both weather and income over a long stretch of time. We first consider the effect of weather on farm productivity, in Section 4. In the late 1800s, the cross section of farm value per acre versus average temperature was characterized by an inverse-U shape. Below the median, higher average temperature meant more valuable farm land, but, above the median, warmer climate implied lower farm-land values. On the other hand, this penalty for heat and rain was attenuated if not reversed in more recent decades: in the second half of the 20th century, farm value per acre increase monotonically with average temperature. A similar pattern is seen for rainfall: above the median, more average rain predicts lower farm value in the 19th century, but higher farm value by the middle of the 20th. Turning to the shorter run, we use panel/fixed-effect methods to measure the response of farm productivity to variation across years in temperature and rainfall. We particularly match long-term weather variables---measured at the decade level---with farm value, and short-term weather variables---measured by the March-to-October average in each census year---with farm output value. It is estimated that the response of both farm productivity measures to high temperature or rainfall is much more negative in the early parts of the sample than in recent years. These results suggest a substantial economic effect of weather at various horizons, but also point to a change in both the short-run and long-run relationships between weather and farm productivity, particularly in warm and wet areas. Next, we examine in Section 5 the relationship between weather early in one’s life and subsequent income as an adult, among cohorts born in 1860-1960. For those born prior to circa 1900, being born in a warmer or wetter area is associated with substantially lower adult income, but this correlation disappears among those born more recently. Similarly, using a fixed-effect estimator, we find effects on adult income of interannual weather fluctuations in early childhood. But we find that this effect is strongest among those born earlier in the sample, and is sharply attenuated among those born in the past 75 years. 3 Once again, we find relationships between weather and income that were strong in the past, but now are considerably weaker. Taken together, these results suggest difficulties in forecasting the effects of climate change with either hedonic or fixed-effect models, particularly if they are estimated on a relatively narrow time window. We review these issues with a simple, neoclassical model in Section 6.1. On the one hand, a merit of the cross-sectional (i.e., hedonic) comparisons is that the observed differences across areas reflect in part adaptation, but a demerit of this approach is that such adaptation is only within the scope of currently available technology. This is plainly an incomplete accounting of adaptation. Putting another way, imagine being in 1920 and being charged with forecasting the effect of a warmer and wetter climate. A projection based on the US historical experience up to that point would vastly overstate the (negative) response by virtue of its failing to account for the subsequent evolution of technology, which improved productivity in warm and wet areas. On the other hand, an advertised merit of panel/fixed-effect methods is that they provide an upper bound on the magnitude of the response to climate change. The argument invokes the so-called Le Chatelier principle: more adaptation is possible in the long run, so the sensitivity to a change in weather should lessen at longer horizons. But in both examples that we present, the short-run response to weather is at least an order of magnitude smaller than the change in hedonic values over the long run. We show that the relationship of panel/fixed-effect estimates to long-run elasticities also depends on (a) if cumulative exposure to different weather also changes the local ecology (be it soil fertility or disease transmission, for example), and (b) if weather's effects on output are by shifting the production function or the productivity of investment. (One of the examples is indeed human-capital investment, and in both examples the effects seem to work in some measure through ecological changes.) In addition, the panel estimates in both cases are sensitive to the time period examined, the advance of technology having had a buffering effect on the response of income to weather. Both for descriptive purposes and perhaps to better target policy, it is also important to understand what might be the mechanisms of adjustment that undergird the results. We provide suggestive evidence on this point in Section 6.2. For both examples, we consider a variety of hypotheses about how areas adapted over time to warm and wet climate. For each candidate mechanism, we see if it can account for the changing sensitivity to weather. Out of possible mechanisms reviewed, malaria emerges as a key mechanism in 4 understanding this result. The adaptation across century in wet and hot areas was more substantial in higher-risk malarial counties than otherwise. We also find that other agronomic factors such as drainage, irrigation, selection of crop, crop mix, farm ownership, fertilizer use, wage in agricultural sector, and farm mortgage rate, and their change over the century can partly account for the adaptation to weather from the long-term perspective. 2 Background and Related Literature The effect of climate change on agriculture had been extensively studied as the worldly concern of global warming was increasing in the 1980s (IPCC 1990). The early approach of the studies was based on agronomic models of production function or crop response function (Adams 1989; Adams et al. 1990). This traditional approach models a function of crop production from empirical or experimental results using parametric, nonparametric or semi-parametric methods; climatic components such as temperature and precipitation are used as key input variables. Researchers simulate the change in crop yield by varying the input variables to estimate the impact of climate change on agriculture. Using this production-function approach, various studies have predicted that U.S. agriculture would be substantially hurt as a result of global warming. This method is useful and accurate if the response of crop yield to climate is constant across space and time. But it can be misleading if the response changes over time as new technologies and economic conditions reduce the negative effects of drought and extreme temperature on crop production, i.e. adaptation. The significance of adaptation was pointed out by Mendelsohn, Nordhaus, and Shaw (1994). They emphasized the variety of the adaptations and adjustments that farmers customarily make in response to changing economic and environmental conditions. For example, adaptive and profit-maximizing farmers would switch the main crop from wheat to corn and subsequently to grazing as local temperature increases. Without considering the adaptation and substitution, the production-function approach can overestimate the agricultural damage from climate change. As an alternative approach, Mendelsohn, Nordhaus, and Shaw (1994) developed hedonic price model. Based on David Ricardo’s value theory, the hedonic approach mainly assumes that the economic value of adaptive activities to climate change would be 5 reflected in land rent. In other words, if farms had enough to adapt to climate change, the land rent will be equal to the net yield of the highest and best use of the land, i.e. land productivity. In particular, they measure land rent with farm value, which is the present value of future rent; by running the regression of farm value on climatic, environmental, economic and agronomic variables, they estimated the discounted marginal value or price sensitivity of these factors to land productivity. They found a significantly lower impact of global warming on U.S. agriculture than did the traditional production-function approach. Although the newly-developed method and findings have influenced later researches on climate change, the study does not actually observe the adaptation to climate or dynamic adjustment because of cross-sectional comparisons in 1982, but rather assumes it. Other limitations of the hedonic approach have been pointed out by some studies. Cline (1996) questioned the validity of applying cross-section analysis of present-day land values to predict future global-warming impacts because relative price of grain would change due to the global reduction in grain yield. Darwin (1999) argues that the model produces estimates that violate basic agricultural principles in that it inappropriately captures the effect of irrigation on farmland value. Regarding this comment, Schlenker, Hanemann, and Fisher (2005) showed that the economic effects of climate change on agriculture need to be assessed differently in dryland and irrigated areas, emphasizing the role of irrigation and water supply in the hedonic approach. The validity of the hedonic regression was carefully examined by Deschênes and Greenstone (2007). They recognized that unobserved characteristics are spatially correlated so that the standard OLS formulas used in the hedonic regression can be incorrect since the error variance is not spherical. In the case, the hedonic approach can confound climate with other regional factors that happen to be correlated with the measures of climate employed. As a possible solution to the omitted variable bias issue, Deschênes and Greenstone (2007) proposed a fixed-effects model that uses the across-year variation in temperature and precipitation within county to estimate the effect of climate change on agricultural profits and yields. More recently, Schlenker and Roberts (2009) employed a large panel of crop yields and daily weather variables that spans most US counties from 1950 to 2005. Using fixed-effects model, they estimated temperature thresholds above which crop yields sharply decline; the findings suggest that global warming may severely damage US crop yields in the century, which is contrast to the prediction by Deschênes and Greenstone (2007). 6 Each of those previous studies has its own advantage to understand the effect of climate change on agriculture. A merit of hedonic approach is that it can account for adaptive behavior like crop choice and developing new agricultural technologies; the fixed-effects model can solve the omitted variable bias problem. Nevertheless, the big unknown in the existing studies is the scope of adaptation. This is not actually observed, but assumed in the hedonic approach; the studies based on the fixed-effects model is limited by the time window employed in their research design. Meaning full adaptation takes place over a long horizon, and therefore direct evidence of adaptation requires observation over longer periods of time. A merit of this study is that we try to take each advantage of hedonic and fixed-effects methods. But the most strength of this study is that we extend the time window to the past 150 years throughout which human has experienced various range of technological adaptation to the weather. 3 Data To estimate the historical changes in the economic effects of temperature and precipitation across US history, this study matches historical weather variables with county farm productivity measures in Section 4 and with individual income in Section 5. Thus, three types of variables mainly have been searched for in various historical sources: weather variables, county-level farm productivity measures, and individual income variable. This section briefly explains how each variable is constructed or obtained. Detailed information on data sources and related issues are discussed in Appendix A. First, the construction of historical county weather variables is based on the Nineteenth-Century US Climate Data Set Project developed by National Climate Data Center and the Long-Term Daily and Monthly Climate Records from the Stations across the Contiguous United States provided by the US Historical Climatology Network. Both datasets report monthly mean temperature and monthly accumulated precipitation for thousands of weather stations which existed over the past two centuries. However, the weather information is not available for all the counties throughout the past two centuries; the weather variables of the counties without historical weather stations at certain periods need to be estimated. For the estimation, this study uses a geostatistical technique called ‘Kriging’. As a linear least squares estimation, Kriging algorithms impute the climatic value at a given (target) location as a weighted sum of data values at surrounding locations. 7 Kriging assigns weights with inverse distance squared among locations; it puts a lower weight as a surrounding weather station is located in a longer distance from the target location (Stein 1999). Using this method, we estimate monthly mean temperature and monthly accumulated precipitation by county and for every month in 1860-2000. Then, we utilize the monthly estimation results to calculate March-to-October, annual, decadal, or centurial mean temperature and precipitation at the county- or state-level according to the research designs in Sections 4 and 5. Second, this study uses two measures of county farm productivity, which are mainly found in Historical, Demographic, Economic, and Social Data: The United States, 17902002 (ICPSR #2896): farm value per farmland acre and farm output value per farmland acre. ‘Farm value’ is defined as the value of all land, housing, and outbuildings in the farm at the time of census enumeration. Although it is available from the 1850 agricultural census, we use the variable only in 1870-2000 considering the periods when the quality of estimated weather variables is acceptable. ‘Farm output value’ is the total value of crop production and livestock products within the year prior to the enumeration day, i.e. a sort of annual farm profit. But the variable is not found in the above source for the census years of 1910, 1920, and 1930. On the other hand, the changes of county boundaries matter when county fixed effects are considered in Section 5. To partially fix the problem, we adjust these county variables in 1880-2000 on the 1870 county boundary, using the areaweighted average method. Third, as a measure of individual income, we use the occupational income score that is available for a large number of censuses. The variable is an average indicator of labor earnings by disaggregated occupational categories that were calibrated using data from the 1950 census. Occupation has been measured by the census more than a century, and so the income proxy is available for a substantial stretch of cohorts. Using the proxy, we construct a panel of average income by cohort. The units of observation of the panel are year of birth by state of birth, and we use microsamples from ten censuses (1880 and 19001990). This results in an unbalanced panel spanning the year-of-birth cohorts from 1860 to 1960 for forty six states of birth. 4 Weather and Farm Productivity 4.1 Long-Term Weather Conditions and County Farm Value 8 We first look into the difference in farm value across areas by local weather variables measured as decadal average. We particularly look at the historical pattern of the relationships between the county average of farm value and its weather conditions, using scatter plots. This cross-sectional analysis is intended to see how county farm value has adapted to permanent features of local weather over time. For the six selected decades, the upper panel of Figure 1 plots the logarithm of each county’s average farm value per acre against its decadal average of annual mean temperature in Fahrenheit, which is calculated for the period 10 years prior to each census year. We also put a linear trend line and a lowess fit curve on each decade’s scatter plot. The bivariate diagrams suggest that the relevance of farm value to temperature gradually changed over decade. In terms of linear relationship, a high level of temperature depressed farm value in the second half of the nineteenth century; this negative association had been weakened throughout the early twentieth century; in recent decades, farm value has been (weakly) increasing in temperature. In addition, a quadratic relationship is clearly found in the late nineteenth century, whose threshold level is observed around 50 oF. However, this inverse U shape relationship had attenuated over time, and it looks insignificant in recent decades. [Figure 1 Here] A similar historical pattern is observed between county farm value and decadal average of annual accumulated precipitation, which is presented in the lower panel of Figure 1. Most of all, an inverse U shape relationship is observed until the early twentieth century; for the counties whose decadal average of annual accumulated precipitation was below about 35 inches, their farm value increased in precipitation, but a high level of precipitation above the threshold level depressed farm value. The linear pattern indicates that there was a negative correlation between county farm value and precipitation in the late nineteenth century, and the negative slope became flat and insignificant throughout the early twentieth century. The attenuation of the negative relationship and inverse U shape relationship accelerated toward the late twentieth century; county farm value has been strongly increasing in the level of precipitation in recent decades. This bivariate cross-sectional analysis shows that the adaptation to hot and wet 9 weather can be clearly observed if we look at the adaptation at longer time horizons and that the pattern of the adaptation has occurred very gradually. However, this hedonic approach does not say about whether this adaptation to permanent weather conditions resulted from the change in the climate effect or from the change in the effect of other local‒agricultural, socioeconomic, or demographic‒characteristics that happen to be correlated with weather variables. 4.2 Fixed-Effects Model As a complementary approach, we use a fixed-effect model in this subsection and compare it with the cross-sectional result. Estimating the following equation (1) estimates the historical pattern of the effect of long-term weather on farm value controlling for stateand year-specific improvements in agricultural technologies and time-invariant county factors.1 (1) Yijt Wijt X ijt DW 2000 t 1880 t t ijt Dt X ijt t t jt i ijt , i : county, j : state, t : year where the Yijt is the logarithm of county i’s average farm value per acre measured at year t, the Wijt is the decadal average of each weather variable (temperature or precipitation) in county i, the Xijt are some vectors of county-level agricultural controls and constant terms, the Dt are dummies indicating year t, the δt are year fixed effects, the δjt are state-by-year fixed effects, and the δi are county fixed effects.2 As the county-level controls, we use county population density, the ratio of white population to county population, the ratio of farmland to total available county area, and the estimated number of farmers per farmland acre. (Sources and construction of each control are discussed in Appendix A.2.) Equation (1) considers the impact of across-decade variation in weather within a county, and it only focuses on the linear relation between county farm value and weather variables. In the estimation model, the reference year is 1870. Thus, the coefficients βt estimate how much 1 Agricultural control variables that we can use in the regression model are very limited because those that appear in some census years are frequently unavailable in other census years. The use of fixed effects models partially solves this limitation. 2 All of the estimates of this equation below are calculated using ordinary least squares (OLS) regressions. 10 the response of farm value at year t to local temperature or precipitation differed from its level in 1870. Each panel in Figure 2 graphically presents the coefficient βt for temperature and precipitation, respectively. Dotted lines are the results of estimating equation (1) only including year fixed effects; dashed lines are the results that additionally control for stateby-year fixed effects; solid lines present the estimation results that also use county fixed effects.3 [Figure 2 Here] Figure 2 shows that the effect of variation in long-term temperature and rainfall on farm value rapidly changed until the mid-twentieth century. The over-time increase in βt during the period implies that a high level of temperature and rainfall became more beneficial to farm value as time goes on. Then βt remains stable after the period of 19601970, which suggests that the adaptation to hot and wet weather slowed down in recent decades. The estimated coefficients depend on the use of fixed effects. County fixed effects estimate smaller effects of across-decade variation in weather on farm value relative to the level of 1870 than do year or/and state-by-year fixed effects. Accordingly a slower adaption to hot and wet weather precipitation is estimated when county fixed effects are used. This means that the effect of weather on farm value is largely explained by non-weather unobservable county characteristics correlated with county weather conditions.4 4.3 Measuring the Adaptation to the Weather across Century So far we tried to measure the historical pattern of the adaptation to hot and wet weather conditions using panel and fixed-effect models. The key findings are that the effect of hot and wet weather on farm value substantially changed over the two centuries, 3 All the coefficients shown in Figure 1 are statistically significant at the 99% level of confidence. See Table B.1 for the results using county fixed effects. 4 In the panel/fixed-effect analysis, we use county-boundary-adjusted data as discussed in section 3. In Appendix B.1, we compare the result in this section with the estimation result based on data without the adjustment, and find that the use of county-boundary-adjusted data does not cause critical problems in estimating fixed-effect models. 11 and that the turnover of the historical pattern mainly took place throughout the first half of the twentieth century. Thus, if we compare the effect of weather between the beginning (the late nineteen century) and ending (the late twentieth century) periods of the sample time window, the magnitude of the adaptation can be more clearly estimated. Moreover, this comparison also will be useful in our seeking possible mechanisms of the adaptation in Section 6.5 The model of measuring the adaptation to the weather across centuries is as follows. (2) Yijt Wijt Wijt D19 X ijt X ijt D19 t jt i ijt , i : county, j : state, t : year . For the estimation, we select two representative periods: the decades ending with 1870, 1880, 1890 and 1900, and the decades ending with 1970, 1980, 1990 and 2000. Accordingly D19 denotes the dummies indicating the decades in the late nineteenth century. The definitions of the other controls and fixed effects are the same as those in equation (1). Then, the coefficient β in equation (2) measures the across-century difference in the response of farm value to variation in long-term weather conditions within a county (say, the across-century weather sensitivity). Panels A and B of Table 1 reports the coefficient of the across-century weather sensitivity and its standard error, clustered on county, by specifications. The results show that high temperature or rainfall was significantly less favorable to farm value in the late nineteenth century than it was in the late twentieth century. Compared with model (1) that is cross-sectional, the fixed-effect models---especially model (4) that uses county fixed effects---estimate smaller coefficients of the across-century weather sensitivity, but still statistically significant. Again this means that the adaptation of farm value to long-term weather conditions is partly explained by other local and year-specific factors correlated with weather conditions. [Table 1 Here] On the other hand, the two weather variables are frequently correlated together. For 5 In the section, we will test some possible mechanisms using various kinds of historical variables. But many variables are available only for the late nineteenth and late twentieth centuries. 12 example, southern counties’ climate is characterized not only by high temperature, but also by wet or humid weather. In the case, the impact of one weather component on farm value can be compounded by another. We consider this issue by running a regression of the logarithm of average county farm value on each weather variable (say, T and P denote temperature and precipitation, respectively), the demeaned interaction of the two weather variables ([T−μT]×[P−μP], where μ denotes the sample mean of each variable), and the interaction terms of these three variables with the dummy of the late nineteenth century together as follows. (3) Yijt T Tijt P Pijt TP (Tijt T )( Pijt P ) [ T Tijt P Pijt TP (Tijt T )( Pijt P )]D19 X ijt X ijt D19 t jt i ijt , i : county, j : state, t : year. The coefficients of the interaction terms with D19 are reported in panel C of Table 1. In particular, the coefficient βTP shows that the across-century sensitivity to hot weather was more substantial when the counties were under wet weather conditions, and vice-versa. When the temperature-precipitation interaction effects are considered, the coefficient of each weather variable interacted with D19 becomes slightly smaller, but its significance and the implication remain unchanged. To have a better sense of what the estimated coefficients imply, we suppose two counties with different levels of annual mean temperature: 54.9 oF for the average county and 62.6 oF for a hot county, which is one-standard-deviation higher than the mean temperature (see Table A.1 in Appendix A for summary statistics of weather variables). We also assume that the levels of precipitation and other agricultural conditions are identical between both counties and across centuries. Then, using the coefficient of T×D19 in model (3) of panel C, the across-century difference in farm value as a ratio (i.e. the level of farm value in the late twentieth century relative to that in the late nineteenth century) is estimated 11.01 for the average county and 15.42 for the hot county.6 Because there is little difference in county farm value by local temperature today, this simulation suggests 6 Say that FV19C and FV20C denote average farm value in a county in the late twentieth and the late twentieth century, respectively. Accordingly, the cross-century difference in farm value as a ratio (FV20C÷FV19C) in a county with specific long-term weather condition is measured by exp(−βT·T) regarding temperature(T) or exp(−βP·P) regarding precipitation(P). Considering the interaction between temperature and precipitation as discussed in text increase the estimated magnitude of the historical changes by a small amount. 13 that the adaptation more rapidly occurred in hotter areas over time. Similarly, using the coefficient of P×D19 in model (3) of panel C, the cross-century difference in farm value as a ratio is estimated 1.60 for a county with average annual precipitation (37.9 inches) and 1.87 for a wet county with 50.6-inch annual precipitation, which is one-standard-deviation higher than the mean precipitation. The rate of the adaptation seems to be smaller than the case of temperature. But the adaptation to precipitation more substantially took place in wetter areas. 4.4 Short-Term Weather Fluctuations and Farm Output Value So far we have looked at the adaptation to weather using county farm value and longterm weather variables. A merit of using farm value is that the variable is consistently available throughout the sample period. But weather conditions affect farm value through various indirect mechanisms rather than directly. Although we have controlled for those possibilities using some measured local variables and fixed effects, those controls still might be not enough to overcome the weakness implicit in farm value and to identify the effect of weather on farm productivity and its historical pattern. In this subsection, we employ an alternative measure of farm productivity: county farm output value. This is a better measure in that the growth of crop is directly affected by local weather conditions.7 But farm output value is not available in the census years of 1910, 1920 and 1930. This allows us to use only the panel regression of equations (2) or (3) used in the previous subsection, which measures the across-century weather sensitivity. Additionally the level of agricultural output more depends on short-term weather conditions rather than decadal climate. We particularly measure the short-term weather variables as average mean temperature and accumulated precipitation between March and October in each census year, assuming that the March-to-October weather conditions are more essential for the growth of crop and other agricultural activities than are those of other months. Per equation (3), panel A of Table 2 reports the coefficients of the interaction between nineteenth-century dummies and each weather variable in the census year, and the 7 Farm output value has been used in various recent studies such as Deschênes and Greenstone (2007) and Schlenker and Roberts (2009). But those studies utilize daily temperature variables in recent decades, which are not available for historical periods. 14 temperature-precipitation interaction. We find that hot and wet weather was less favorable for producing agricultural goods in the late nineteenth century than in the late twentieth century, and that the adaptation of farm output value to weather rapidly occurred across centuries. To check out whether this result is correlated with the effect of weather in other years around the census year, we add short-term weather variables measured in the periods one year after and before the census year in equation (3), and report the key estimated coefficients in panel B. The across-century weather sensitivity is strongly estimated for the current-year weather variables; the estimated effect of weather in adjacent years is insignificant and negligible. Moreover, the size of the estimated coefficients of the currentyear weather variables in model (3) are very similar with what we estimated using farm value and long-term weather variables in the previous subsection. Finally, the panel regression model (4) with county fixed effects estimates smaller across-century weather sensitivity than does the cross-sectional regression model (1), which implies that the weather sensitivity is partly explained by unobservable county characteristics correlated with county weather conditions. [Table 2 Here] This section is summarized as follows. First, the adaptation to weather has taken place over longer periods of time and can be observed from a longer-term perspective. Second, the adaptation took place more substantially in hot and wet areas. Third, the effect of weather on farm productivity and its historical pattern is significantly estimated in terms of farm value and farm output value, using fixed-effect models. In Section 6, we will seek and evaluate possible mechanisms that can account for the historical pattern of the adaptation to weather. 5 Early-Life Weather and Adult Income A large number of studies have revealed that climate and its change directly or indirectly affect individuals’ activities and life-time health through causing malnutrition from crop failure (Ó Oráda 2007), spreading infectious diseases (Lafferty 2009), distorting health conditions over the course of life (WHO, WMO and UNEP 2003; Patz et al. 2005; Campbell-Lendrum and Woodruff 2006), changing ecological system (Thomas et al. 2003), 15 taking lives due to heat and natural disasters (Davis et al. 2003; Kahn 2005), decreasing water supply (Vörösmarty et al. 2000), resulting in migration (Brown 2008), affecting economic sectors, technological innovation and energy use under greenhouse gas emission regulation (Nordhaus and Boyer 2003; Stern 2007; Tol 2009), and so on. Although some studies predict that global warming can increase crop production in some areas (Deschênes and Greenstone 2007) and reduce cold weather-related mortality (Kalkstein and Greene 1997), most studies predict that the overall impact of climate change would be adverse to human life. From economic aspect, the above channels are closely related to reduction in individuals’ economic productivity.8 On the other hand, existing literatures have shown more concerns over developing countries because they have a large proportion of marginal populations who are much vulnerable to the impact of hot and wet weather. While, the adverse link has been mitigated or prevented in many developed countries with the help of effective technologies and policies. From historical perspective, we thus expect that the US relation between weather and individuals’ economics productivity would have changed over longer-time horizons along with technological adaptation to weather. Recently, Deschênes, Greenstone, and Guryan (2009) show that extreme temperature in utero can increase the risk of having low-birth-weight babies (also see Murray et al. 2000; Lawlor, Leon, and Smith 2005). Although further evidence is required, they point out that this can be caused by the association between weather in utero and fetal nutrient intake or stress. The relation between weather in utero and birth outcome is significant in that adverse birth outcome can affect life-time human capital accumulation such as schooling, disability and adult income on the basis of fetal origin hypothesis, which has been supported by many existing studies. Those studies include Barker (1994), Almond (2006), and Black, Devereux, and Salvanes (2007). In addition, it has been much studied that maternal exposure to infectious diseases promoted by weather---notably malaria--increases the probability of having low-birth-weight babies (Desai et al. 2007), and that exposure to the disease environment can reduce cognitive ability and so the acquisition of human capital (Holding and Snow 2001; Sachs and Malaney 2002). 5.1 Cross-Sectional Evidence 8 For example, the adverse health impacts of hot and wet climate can deteriorate individuals’ productivity at work and so income. Changes in economic sectors due to climate change can affect industrial structure and labor market and so their wage. 16 In consideration of this mechanism, our study focuses on the significance of early-life weather in later economic outcome and how it has been changed throughout the US history. We first look for cross-sectional evidence that shows the effect of weather on adult income from a longer-term perspective, using simple bivariate analysis. The variable of adult income used here is average income by state-of-birth cohorts. Using the county weather variables imputed by Kriging interpolation technique, we construct the 1860-2000 average of annual mean temperature and annual accumulated precipitation by state. Therefore, the weather variables represent the overall weather conditions which each cohort might have experienced in early life rather than at a point of time. In Figure 3, we plot average adult income against long-term average weather variables in the state of birth, by two cohort groups: 1860-1900 and 1930-1960. The figure shows that adult income of the cohorts born before 1900 decreases by temperature and precipitation in the state of birth. In other words, if people were born or had spent their early periods in hotter and wetter states, they earned less income in late life on average than otherwise. But this relation almost completely disappears for the cohorts born after 1930; there is little difference in adult income by early-life average weather conditions. [Figure 3 Here] The bivariate analysis has various limitations for interpretation. Socioeconomic conditions of the state of birth are not controlled for, and so the significance of this pattern cannot be asserted. Even if it is statistically significant, the scatter plots do not provide explanations for the historical pattern. Further investigation is thus required. Nevertheless, the figure may suggest a possibility that technological adaptation took place throughout the early twentieth century, which mitigated the adverse impacts of high temperature and rainfall on individuals’ economic productivity 5.2 The Diminishing Effect of Short-Term Weather Fluctuations Emphasizing the historical change of the relation between long-term weather in early life and adult income, we turn our attention to the effect of short-term weather fluctuations around the year of birth on adult income, using fixed-effects models. The use of long-term 17 average weather not only captures the effect of weather around birth, but also likely measures the accumulated or life-time effect of weather if a large proportion of each stateof-birth cohort had lived in the same state during considerable time in their life. The use of short-term weather variables can better identify the effect of early-life weather on adult income. In the following estimation model, we search for the specific point of time around birth whose weather conditions have significant effects on adult income by running a regression of cohort average occupational income score on annual mean temperature and accumulated precipitation at the year of birth, at the three years after birth, and at two years before birth, per equation (4) below. The regression is conducted for cohort samples defined by state of birth and year of birth in 1860-1960. The model also contains dummies for each cell of state of birth times year of birth ( jk ), dummies for each cell of year of birth times census year ( kt ), and dummies for each cell of sate of birth times census year ( jt ). These fixed effects will partially capture omitted and unmeasured characteristics in state of birth, year of birth and census year that might commonly affect those in a given cohort. 3 (4) Y jkt ( l T j ,k l l Pj ,k l ) jk kt jt jkt , l 2 j : state of birth, k : year of birth, t : census year. We summarize the key results of the regression per equation (4) in Figure 4. The y axis in each plot displays the estimated regression coefficient on the interaction of the indicated weather variable and a dummy for the calendar year minus year of birth, i.e. l or l ; the x axis in each plot denotes the calendar year minus the year of birth. The error bars reflect 95% confidence intervals for each coefficient. [Figure 4 Here] The main finding is that adult income has been highly associated with across-year weather fluctuations for specific years. Weather conditions before birth do not have meaningful effects. The year in which temperature has the most substantial, significant 18 effect on adult income is one year after birth, and the effect declines over next two years. The effect of precipitation is most substantial at the year of birth, and then it becomes insignificant. These results for short-term weather have implications different with what we find from the cross-sectional analysis in the previous subsection that suggests the negative correlation between long-term weather conditions and adult income. The state-of-birth fixed effects average out state-specific climatic characteristics. Thus, the above estimation measures the effect of within-state across-year variation in weather conditions around birth. Although many literatures suggest the negative effect of hot and wet weather in utero on birth outcome as discussed above, some studies have found a positive association between hot/wet weather in early life and condition at birth. Especially, seasonal difference in birth outcome has been estimated in historical periods (Beardsley 1987; Costa 2004) and for developing countries (Ceesay et al. 1997). For example, Costa (2004) shows that babies born in the spring weighted less than babies born in the summer using birth records on white babies born at Johns Hopkins in the early twentieth century, and suggests that this might result from the nutritional stress their mothers experienced during the winter months. From the perspective of fetal origin hypothesis, the positive effect of temperature on birth outcome can lead to a higher level of human capital accumulation and so adult income. Similarly, some studies find that rainfall in early life can be beneficial for later outcomes such as health, schooling, and socioeconomic status (Maccini and Yang 2009). Following this line of research, it is interesting that the meaningful association among weather, nutrition in utero, birth outcome, and later outcome is frequently found in studies on historical experiences and developing countries; the association is probably mitigated in modern developed countries by enough food supply throughout year, public programs and technological adaptation. This suggests that the coefficients presented in Figure 4 can vary across year. To check out this possibility, we now look at how the effect of short-term weather fluctuations on adult income has changed over cohort. We measure this long-term pattern by estimating equation (4) on a 40-year-wide moving window. So the sample for each regression includes cohorts born 20 years before and after the target year. Consequently the target years are between 1880 and 1940. Figure 5 presents the estimated regression coefficient on the interaction of the indicated weather variable and a dummy for the year chosen according to the results in Figure 4 (i.e. one year after the year of birth for temperature and the year of birth for precipitation). We also add 19 the 95% confidence intervals for each coefficient. [Figure 5 Here] We mainly find that the effect of short-term weather fluctuations had diminished over cohorts. As it is estimated above, the effect is generally positive among the cohorts born in the second half of the nineteenth century and its across-cohort variation is large. But as the target year approaches to 1910-1920, the magnitude, significance and across-cohort variation of the effect had attenuated. After then, adult income became little relevant to weather in early life. The pattern of attenuation over time in Figure 5 is similar with what we observe in the cross-sectional analysis using long-term weather variables, although the role of weather in early life is differently estimated between two analyses. Moreover, it is worthy to remind that the diminishing effect of weather was similarly found in the analysis of farm productivity in Section 4. The findings so far suggest that the adaptation to weather largely occurred throughout the early twentieth century not only in agricultural sector but also at the individual level. In the following section, we discuss theoretical explanation and possible mechanisms through which the economic implications of weather have changed across centuries. 6 Discussion * Note that the following section is very incomplete. 6.1 Theory [To be added] 6.2 Mechanisms This section revisits the adaptation of farm productivity to weather in Section 4 to discuss its possible mechanisms. The results in Section 4 suggest that the level of agricultural technology of adapting to hot and wet weather in the nineteenth century was inferior to that of modern periods, which led to a lower level of farm value and farm output value in hot and wet areas. Explanations of this historical pattern can be found from 20 various kinds of technological adaptations, which have been advanced throughout the twentieth century. The variables of mechanisms and their statistics are summarized in Table 3. First, historically drainage was the key technology to improve the productivity of farmland by preventing the damage from heavy rainfalls and frequent flooding, increasing areas for cultivation, changing the hydrology of the soil system, and reducing erosion. As population pressure and a demand for better farmland increased, drainage practice and wetland conversion had been expanded to the East in the colonial periods and early-19th century, to the Midwest and Mississippi river valleys in the mid-19th century, and to the South and West in the late-19th century. But the scale and effectiveness of drainage efforts in local areas depended not only on size of local population and demand for arable land, but also on the level of federal and state governments’ policy and supports, costs of land reclamation, and drainage technology. In particular, a substantial drainage practice in southern states had been much delayed until the early 20th century when more advanced and low-cost technology was introduced. This implies that less drained areas in the nineteenth century would more sensitive to heavy rains and Therefore, because wetlands and swamps were generally fertile for agriculture, the extension of drainage was key to they were little developed primarily due to lack of drainage technology and great costs of reclamation work in the colonial periods. Until the early twentieth century, a substantial drainage practice Over the course of the nineteenth century, population pressure and a need for better farmland demanded efforts for converting wetlands to farmlands (Pavelis 1987). In particular, as immigrants began to settle in the Midwestern states and Mississippi river valleys whose large proportions were swampland unsuited to cultivation, a substantial drainage practice became prevalent in those areas in the 1850s. But a large scale of wetland conversion in the Midwest occurred since the 1870s when new drainage technology of using steam power and drainage tiles were introduced. After the Civil War in 1861-1865, western ward expansion took the nation’s attention, and the railroads opened new lands in the West and South including wetlands. As agriculture moved A broad scale of drainage in southern states occurred in the early twentieth century in response to federal and local governments’ financial supports and improvements in drainage technology. Drainage was also a key factor that improved local sanitary and ecological conditions. Because wetlands and swamps were the main source of water-borne diseases such as 21 malaria, yellow fever and cholera, draining those areas could make local communities healthy. However, drainage with sanitary intention [more discussion will be added here] … For instance, the technological improvement for irrigation is one of the notable technological adaptations to overcome high temperature (Mendelsohn and Dinar 2003). Drainage has been utilized to prevent the damage from flooding and to increase areas for cultivation (Pavelis 1987). The selection of crops, crop mix or inventing hybrid seeds more resistant to hot and wet weather is another key technique to adapt to the change of local climate (Olmstead and Rhode 2008). The change of farm ownership and the use of fertilizer are also important for increasing farm productivity, although they are less directly related to weather conditions than drainage or irrigation (Gallup and Sachs 2000; Garrett and Xu 2003). The supply and cost of farm labor and farm land mortgage can be correlated with local climate conditions, and this can affect farm productivity (Bodenhorn and Rockoff 1992; Engerman and Goldin 1993). Besides these mechanical and structural advances of farming, farm productivity largely depends on farm laborers’ health status and their labor productivity. This is more likely in the periods when disease environments were rampant and medical knowledge for disease controls was less accumulated. Thus the eradication of infectious diseases that deteriorated farm workers’ labor productivity can be a good explanation of the historical pattern of the adaptation across the centuries. We pay a special attention to the significance of malaria and its eradication that occurred throughout the first half of the twentieth century. Besides malaria eradication, other technological improvements in the first half of the twentieth century could mitigate the negative effects of hot and wet weather on farm productivity, and account for the historical pattern. The significance of malaria environments estimated above can be robust by examining the role of other technological improvements. From the respect, we consider various aspects of agricultural technologies related with weather such as drainage, irrigation, farm improvement for crop land, crop mix, selection of crop, farm organization, fertilizer use, and the cost of farm labor and farm land mortgage. We have searched for proxy variables from various historical resources, which measure the levels of these agricultural technologies. Each type of technology above is measured as follows: (1) the ratio of drained and (2) irrigated areas out of total county areas, (3) the ratio of cropland out of total available farm land, (4) the ratio of cotton areas 22 in farm land, (5) the level of crop concentration measured by Herfindahl-Hirschman index for the 10 major US crops, (6) the ratio of share croppers and (7) tenants, (8) the fertilizer cost per farmland acres, (9) annual agricultural wage per farmland acres, and (10) interest paid on farms per mortgage value of farms. (Sources and constructions of each variable are discussed in Appendix A.5.) We examine the role of these technologies by revising the set of K in equation (5) to K={T, P, M, F, T×P, T×M, P×M, T×F, P×F }. The new variable F denotes each county’s agricultural factor introduced above, and T×F and P×F are the interaction term between agricultural factor and (long-term or short-term) temperature or precipitation, respectively, depending on the type of dependent variable. We control for each agricultural factor as the value at the ‘pre-existing’ level, which is found in the earlier periods (mostly in the late nineteenth century) when significant improvements were not yet made. Accordingly, the coefficients βTFD19 and βPFD19 measure whether the across-century weather sensitivity of farm productivity depended on the pre-existing level of each agricultural factor. Tables 4.1 and 4.2 present the estimated coefficients and standard errors clustered on county of key variables. According to panel A in the tables, the across-century difference in the response of farm value to long-term hot weather was significantly greater as counties are characterized by less irrigated areas, less cropland ratio, less crop concentration, less share-cropper ratio, more tenant ratio, and lower mortgage rate in the earlier periods. The across-century sensitivity to wet weather was significantly substantial in the counties with less drained areas, less cropland ratio, less crop concentration, less fertilizer cost, and lower agricultural wage. Although all the estimated coefficients and their implications are not consistent with historical fact and economic intuition, it is generally estimated that the counties where advanced agricultural technologies were not yet introduced experienced the negative effect of hot and wet weather on farm value at a greater level than otherwise. Technological improvements and their introduction to hot and wet areas well account for the historical pattern of the relationship between farm value and weather conditions. [Table 4.1 Here] [Table 4.2 Here] Another key finding is that adding the pre-existing agricultural factors in the regression model does not hurt the significance of malaria in general. Compared to the 23 results in model (2) of Table 3, the coefficients of malaria (T×Malaria×D19 and P×Malaria×D19) in Tables 4.1 and 4.2 are still significantly negative and substantial across the regression models. Moreover, the coefficients of weather variables (T×D19 and P×D19) do not change much by adding the pre-existing agricultural factors. Consequently, malaria eradication, a particular technological adaptation, is thought as a considerable explanation for the historical pattern of the relation between farm value and long-term weather conditions. On the other hands, panel B of Tables 4.1 and 4.2 reports the main results that use county farm output value as the dependent variable and the March-to-October average weather variables in the census year, using the same format with Table 4. The general implication is that the counties that had insufficient levels of agricultural technologies in terms of drainage, crop land, crop mix, farm ownership, fertilizer use, farm labor wage, and mortgage rate experienced the negative effect of hot and wet weather on farm output value at a greater level than otherwise. The role of malaria is still significantly estimated especially for precipitation even adding agricultural factors in the model. So far we have looked into two aspects of technological adaptations that primarily occurred in the first half of the twentieth century in order to seek possible mechanisms through which the adaptation to hot and wet weather substantially took place. 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Lammers (2000), “Global Water Resources: Vulnerability from Climate Change and Population Growth,” Science 289(5477): 284-8. 29 World Health Organization, WMO, and UNEP (2003), Climate Change and Human Health-Risks and Responses, Geneva: World Health Organization. 30 Appendix A. Data Sources and Construction A.1 Weather Variables Raw Data from Weather Stations Monthly mean temperature and accumulated precipitation are obtained from two historical sources: the records of the Nineteenth-Century US Climate Data Set Project developed by National Climate Data Center and the Long-Term Daily and Monthly Climate Records from the Stations across the Contiguous United States provided by the US Historical Climatology Network. The former dataset covers the periods of 1822-1900 and includes 4,056 individual weather stations with different names. But all the stations did not exist throughout the nineteenth century. The number of weather station was less than 100 prior to 1854, and it increased to about 400 in the 1860s, and to about 1,000 after the 1880s. The later dataset has been utilized to obtain monthly weather information in 1901-2000. The number of weather stations in the dataset is 1,221; each weather station existed over most years of the twentieth century so that each year’s number of stations is over 1,000. On the other hand, two datasets include the information on weather stations’ latitude and longitude, which is used to locate them on map. Spatial Averaging (“Kriging”) A Kriging technique is used to interpolate monthly weather variables for the counties whose weather records are not found in the above two sources. The method is based on a linear least squares estimation algorithm, which minimizes the variance of the prediction error. Thus a Kriging estimator is a liner combination of the value at nearby locations; the distance between neighboring points is generally employed as weights. In this study, the maximum distance between points is set as 200 miles. An enough number of weather stations within 200-mile radius of the target location are required to get a reliable estimation result. It was thought that the size of weather stations in the years prior to 1860 is insufficient, and so those years were dropped from the analysis. Measurement by County 31 To estimate the county-level monthly mean temperature and accumulated precipitation in 1860-2000, the geographical center of each county was used as the target location in the Kriging estimation, and the actual weather information of weather stations within the 200-mile buffer around the target location was utilized. The county-level decadal (long-term) or within-year March-to-October (short-term) mean temperature is calculated by averaging monthly estimated results over the specified periods. The decadal precipitation is the average value of annual accumulated precipitation for the 10 years prior to each census year; the March-to-October precipitation is calculated by summing up monthly precipitation over the months within the year. Table A.1 presents the mean and standard error of the estimated long-term and short-term weather variables throughout the sample periods. The table also provides the actual census year that we referred to for measuring long-term and short-term climate variables. [Table A.1 Here] A.2 County-Level Agricultural, Economic, Demographic and Environmental Data Farm Productivity Two variables are used to measure US farm productivity: farm value per farmland acre and farm output value per farmland acre. Farm value is the value of all land, housing, and outbuildings in the farm at the time of census enumeration; it can be interpreted as land price because the value of land in the farm takes a large portion of farm value, following the hedonic-approach studies. Farm output value is the total value of all farm products such as crop and livestock products within the year prior to the enumeration day. Both variables are generally obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790-2002 (ICPSR #2896), which digitized the key variables in US federal population and agricultural censuses in 1850-1930 and US county data book in 1940-2000. We tried to construct the variables for the 14 decades in 18702000, but the variables of county-level farm output value are missing for three decades in 1910-1930 from the ICPSR dataset. All the value is converted in constant 2000 US dollar using the BLS GDP deflator. 32 Controls The long-period analysis substantially limits the use of various controls that account for historical changes in the relationship between climate and farm productivity. We constructed a panel of four variables at the county level in 1870-2000: population density, the ratio of white population to county population, the ratio of farmland to total available county area, and the estimated number of farmers per farmland acre. The first three variables were obtained from the dataset of ICPSR #2896 above. To estimate the number of farmers per farmland acre, we first calculated the county ratio of farmers from the IPUMS dataset, which is a subsample of census, and applied the ratio to the actual population per farmland acre. Sample means of each farm-productivity measure and controls by census year are reported in Table A.2. A.3 Boundaries of Consistent Counties Changes in county boundaries over time can make county-fixed-effects analyses problematic. As a solution, we adjusted all the county variables in 1880-2000 on the 1870 county boundary in Figure A.1 by tracking the overlap of county boundaries over decade and using the (overlapped) area-weighted average method. On the other hand, Appendix B.1 below shows that the use of county-boundary-adjusted data does not cause critical problems in estimating fixed-effect models. [Figure A.1 Here] A.4 Measuring Various Agricultural Technologies and Data Sources To discuss possible explanations of across-century difference in agricultural response to climate, we employ various county variables that show the level of agricultural technologies and economic conditions and its change over the century. The technologies for drainage, irrigation, selection of crops and crop mix have been developed to assimilate the permanent pattern of local climate and its change. Although some variables such as 33 average wage in agriculture, farm structure, use of fertilizer, and farm mortgage rate are less related with climate change, the control of those variables would show whether the adaptation to weather resulted from the change in agricultural conditions that are correlated to local climate. The year and source of each variable is summarized in Table A.3. Because the variables are not available for all the census years in 1870-2000, we searched for historical sources in which each variable was first reported. Although one year’s variable cannot measure technological changes over time, it may represent the overall level of agricultural technologies in the periods when substantial improvements were not yet observed, i.e. the pre-existing condition. We also searched for the variables that measure the agricultural and economic conditions in the second half of the twentieth century when it is thought that advanced technologies were largely introduced into the most US counties. The difference between the pre-existing and recent conditions is used in Appendix B.3 to estimate the role of technological improvements in explaining the historical pattern of the adaptation to weather. [Table A.3 Here] Appendix B. Supplementary Analyses B.1 Fixed-Effects Analysis Using Data with the County Boundary Adjustment Throughout this study, we use county-boundary-adjusted data in estimating countyfixed-effect regressions, as discussed in Appendix A.3 above. Using boundary-adjusted data, models (1) and (3) present the estimated coefficients of the interaction terms between each weather variable and each year dummy, and their standard errors, clustered on county, after regressing equation (1) with county fixed effects. These coefficients are also graphically depicted by solid lines in Figure 2. In models (2) and (4), we conduct the same regressions, but use data without the county boundary adjustment. No substantial disparity is found between two estimation results with and without the adjustment. [Table B.1 Here] 34 B.2 Fixed-Effects Analysis for South and North In Section 4, we showed that the relationship between weather condition and agricultural productivity has substantially changed over centuries. We particularly measured the across-century difference in the response of farm value or farm output value to weather conditions by running the regressions per equation (3). Hot and wet weather significantly depressed agricultural productivity in the late nineteenth century, but the effect has been attenuated over time. Thus, the historical pattern of the adaptation to weather would be different across region. To see the regional difference, we estimate equation (3) by two regions: the southern and northern counties. Table B.2 reports the coefficients that measure the across-century weather sensitivity. As we did in Section 4, we control for long-term weather variables when farm value is used as the dependent variable, and short-term weather variables for farm output value. The estimated coefficients of the interaction terms between each weather variable and the dummy of the nineteenth century are more negative for the southern counties than for the northern counties; in the case that farm output value is used as the dependent variable, the coefficients for the northern counties is near zero and statistically insignificant. Those results suggest that the adaptation to weather occurred more substantially in the South than in the North. [Table B.2 Here] 35 Table 1. Across-Century Difference in the Response of County Farm Value to Long-Term Weather Conditions Dependent variable: ln(county average farm value per acre) (1) Key Control Variables Temperature(T)*D19 (2) Panel A: Only Temperature -0.0592*** -0.0664*** (0.0030) (0.0029) (3) (4) -0.0603*** (0.0056) -0.0447*** (0.0052) -0.0291*** (0.0028) -0.0170*** (0.0028) Panel B: Only Precipitation Precipitation(P)*D19 -0.0459*** (0.0022) -0.0411*** (0.0022) Panel C: Temperature and Precipitation Temperature(T)*D19 -0.0313*** (0.0031) -0.0484*** (0.0033) -0.0565*** (0.0057) -0.0437*** (0.0053) Precipitation(P)*D19 -0.0411*** (0.0021) -0.0305*** (0.0022) -0.0232*** (0.0027) -0.0124*** (0.0028) (T-µT)*(P-µP)*D19 0.0008*** (0.0002) -0.0003 (0.0002) -0.0005 (0.0003) -0.0006* (0.0003) Year FE NO YES YES YES State by Year FE NO NO YES YES County FE NO NO NO YES Notes: We estimate the across-century difference in the response of county farm value to long-term weather conditions, per equation (2) for panels A and B, and equation (3) for panel C. The long-term weather conditions are measured by the 10-year average of annual mean temperature (T) and annual accumulated precipitation (P) prior to each census year, respectively. µT and µP denote the mean value of temperature and precipitation across counties, respectively. D19 is the dummy variable that indicates the census years in the 19th century (i.e. 1870, 1880, 1890 or 1900). Standard errors, reported in parentheses, are clustered on county. Single asterisk denotes statistical significance at the 90% level of confidence, double 95%, triple 99%. 36 Table 2. Across-Century Difference in the Response of County Farm Output Value to Short-Term Weather Conditions Dependent variable: ln(county average farm output value per acre) (1) Key Control Variables (2) (3) (4) Panel A: Weather Variables in Current Year Current Year Temperature(T)*D19 0.0055 (0.0039) 0.0064 (0.0042) -0.0401*** (0.0082) -0.0389*** (0.0081) Precipitation(P)*D19 -0.0324*** (0.0027) -0.0290*** (0.0029) -0.0218*** (0.0033) -0.0156*** (0.0029) (T-µT)*(P-µP)*D19 0.0023*** (0.0004) 0.0018*** (0.0004) 0.0015*** (0.0006) 0.0010* (0.0006) Panel B: Weather Variables in Current, Next and Previous Years Current Year Next Year Previous Year Temperature(T)*D19 -0.0613*** (0.0060) -0.0434*** (0.0100) -0.1032*** (0.0217) -0.0432*** (0.0156) Precipitation(P)*D19 -0.0174*** (0.0021) -0.0163*** (0.0021) -0.0203*** (0.0029) -0.0152*** (0.0024) (T-µT)*(P-µP)*D19 -0.0001 (0.0003) 0.0002 (0.0003) 0.0005 (0.0005) 0.0004 (0.0004) Temperature(T)*D19 0.0568*** (0.0104) 0.0439*** (0.0122) 0.0036 (0.0190) 0.0117 (0.0143) Precipitation(P)*D19 -0.0122*** (0.0018) -0.0135*** (0.0020) -0.0024 (0.0028) -0.0024 (0.0022) (T-µT)*(P-µP)*D19 0.0019*** (0.0004) 0.0016*** (0.0004) 0.0025*** (0.0006) 0.0009** (0.0004) Temperature(T)*D19 0.0229** (0.0096) 0.0119 (0.0118) 0.0578*** (0.0186) -0.0064 (0.0137) Precipitation(P)*D19 -0.0078*** (0.0022) -0.0066*** (0.0022) -0.0101*** (0.0025) -0.0016 (0.0021) (T-µT)*(P-µP)*D19 0.0013*** (0.0003) 0.0008** (0.0003) -0.0007* (0.0004) 0.0001 (0.0003) NO YES YES YES Year FE State by Year FE NO NO YES YES County FE NO NO NO YES Notes: We estimate the across-century difference in the response of county farm output value to short-term weather conditions, per equation (3). The short-term weather conditions are measured by the March-to-October mean temperature (T) and accumulated precipitation (P) in the census year (denoted by Current Year) and in the periods one year after and before the census year. µT and µP denote the mean value of temperature and precipitation across counties in the given year, respectively. D19 denotes the dummy variable that indicates the census years in the 19th century (i.e. 1870, 1880, 1890 or 1900). Standard errors, reported in parentheses, are clustered on county. Single asterisk denotes statistical significance at the 90% level of confidence, double 95%, triple 99%. 37 Table 4.1. Across-Century Difference in the Response of County Farm Productivity to Weather and Pre-Existing Agricultural Factors % Drained Areas Key Control Variables % Irrigated Areas % Cropland Crop HHI (1) (2) (3) (4) Panel A: Farm Value and Long-Term Weather Variables % Cotton Acres (5) Temperature(T)*D19 -0.0283*** (0.0080) -0.0287*** (0.0085) -0.0310*** (0.0087) -0.0111 (0.0096) -0.0115 (0.0088) Precipitation(P)*D19 0.0012 (0.0052) 0.0039 (0.0056) 0.0021 (0.0060) 0.0177*** (0.0063) 0.0041 (0.0058) (T-µT)*(P-µP)*D19 0.0009** (0.0004) 0.0009** (0.0004) 0.0004 (0.0004) 0.0011** (0.0004) 0.0010** (0.0004) T*Malaria*D19 -0.1008*** (0.0311) -0.1017*** (0.0317) -0.1625*** (0.0359) -0.0776** (0.0315) -0.1236*** (0.0393) P*Malaria*D19 -0.1034*** (0.0224) -0.0926*** (0.0230) -0.0647** (0.0276) -0.1009*** (0.0235) -0.1007*** (0.0268) T*Factor*D19 -0.0042 (0.0118) 0.1625*** (0.0483) 0.0435*** (0.0128) 0.1232* (0.0641) 0.0711 (0.1038) P*Factor*D19 0.0404*** (0.0085) 0.0012 (0.0374) -0.0112 (0.0091) 0.1735*** (0.0410) -0.0359 (0.0450) Panel B: Farm Output Value and Short-Term Weather Variables Temperature(T)*D19 -0.0349*** (0.0115) -0.0407*** (0.0118) -0.0442*** (0.0120) 0.0100 (0.0121) 0.0036 (0.0119) Precipitation(P)*D19 0.0320*** (0.0086) 0.0367*** (0.0082) 0.0315*** (0.0097) 0.0420*** (0.0087) 0.0403*** (0.0092) (T-µT)*(P-µP)*D19 0.0037*** (0.0007) 0.0041*** (0.0007) 0.0031*** (0.0007) 0.0042*** (0.0007) 0.0046*** (0.0007) T*Malaria*D19 -0.1094** (0.0487) -0.0932* (0.0489) -0.1758*** (0.0508) -0.0752 (0.0478) -0.1591*** (0.0558) P*Malaria*D19 -0.2183*** (0.0329) -0.2169*** (0.0312) -0.1877*** (0.0360) -0.2046*** (0.0314) -0.2236*** (0.0374) T*Factor*D19 0.0164 (0.0109) 0.1295 (0.0914) 0.0729*** (0.0181) 0.4672*** (0.0738) 0.2291* (0.1357) P*Factor*D19 0.0267*** (0.0068) -0.2460*** (0.0942) -0.0060 (0.0151) 0.1512*** (0.0522) 0.0421 (0.0409) Notes: We estimate the role of various agricultural factors (F)---specified in the headings---in explaining the historical pattern of the adaptation to weather, per equation (5) where the set of K also includes {F, TF and PF}. Panel A employs farm value and long-term weather variables (decadal average); Panel B uses farm output value and short-term weather variables (March-to-October average). This table reports only the coefficients of key variables---particularly the interactions of each specified agricultural factor with weather variable and 19th-century dummy. Standard errors, clustered on county, are reported in parentheses. Single asterisk denotes statistical significance at the 90% level of confidence, double 95%, triple 99%. 38 Table 4.2. Across-Century Difference in the Response of County Farm Productivity to Weather and Pre-Existing Agricultural Factors Annual ln(Fertilizer Agricultural % Share Cost Wages Cropper % Tenant per Acre) per Acre (6) (7) (8) (9) Panel A: Farm Value and Long-Term Weather Variables -0.0277*** -0.0209** -0.0288*** -0.0316*** (0.0084) (0.0084) (0.0086) (0.0082) -0.0990*** (0.0208) Precipitation(P)*D19 0.0048 (0.0056) 0.0037 (0.0056) 0.0077 (0.0057) -0.0013 (0.0056) 0.0244* (0.0136) (T-µT)*(P-µP)*D19 0.0007 (0.0004) 0.0008* (0.0004) 0.0006 (0.0004) 0.0002 (0.0004) 0.0011** (0.0005) T*Malaria*D19 -0.1025*** (0.0348) -0.0899*** (0.0331) -0.0869*** (0.0318) -0.0907*** (0.0317) -0.1211*** (0.0318) P*Malaria*D19 -0.0972*** (0.0245) -0.1050*** (0.0238) -0.1008*** (0.0235) -0.0810*** (0.0236) -0.1014*** (0.0250) T*Factor*D19 0.0361* (0.0217) -0.0913** (0.0423) 0.0006 (0.0012) -0.0005 (0.0005) 0.9009*** (0.2332) P*Factor*D19 -0.0071 (0.0153) 0.0374 (0.0245) 0.0013* (0.0008) 0.0014*** (0.0004) -0.2102 (0.1307) Key Control Variables Temperature(T)*D19 Mortgage Rate (10) Panel B: Farm Output Value and Short-Term Weather Variables -0.0351*** -0.0309*** -0.0246** -0.0371*** (0.0116) (0.0117) (0.0123) (0.0113) -0.0696** (0.0288) Precipitation(P)*D19 0.0365*** (0.0089) 0.0345*** (0.0085) 0.0309*** (0.0087) 0.0262*** (0.0085) 0.0901*** (0.0204) (T-µT)*(P-µP)*D19 0.0038*** (0.0007) 0.0039*** (0.0007) 0.0035*** (0.0007) 0.0029*** (0.0007) 0.0045*** (0.0008) T*Malaria*D19 -0.0827* (0.0491) -0.1166** (0.0511) -0.1112** (0.0495) -0.0922* (0.0487) -0.1005** (0.0504) P*Malaria*D19 -0.2174*** (0.0310) -0.2392*** (0.0339) -0.2056*** (0.0319) -0.1950*** (0.0324) -0.2049*** (0.0354) T*Factor*D19 0.0795** (0.0315) 0.0237 (0.0425) 0.0043** (0.0018) -0.0014 (0.0011) 0.4893 (0.3501) P*Factor*D19 0.0006 (0.0216) 0.0904*** (0.0256) -0.0015 (0.0013) 0.0027*** (0.0009) -0.7319*** (0.2505) Temperature(T)*D19 Notes: We estimate the role of various agricultural factors (F)---specified in the headings---in explaining the historical pattern of the adaptation to weather, per equation (5) where the set of K also includes {F, TF and PF}. Panel A employs farm value and long-term weather variables (decadal average); Panel B uses farm output value and short-term weather variables (March-to-October average). This table reports only the coefficients of key variables---particularly the interactions of each specified agricultural factor with weather variable and 19th-century dummy. Standard errors, clustered on county, are reported in parentheses. Single asterisk denotes statistical significance at the 90% level of confidence, double 95%, triple 99%. 39 Table A.1. Summary Statistics: County Weather Variables Denote d census year Actual census year Number of counties 1870 1869 2,162 1880 1879 2,454 1890 1889 2,791 1900 1899 2,767 1910 1909 2,916 1920 1919 3,026 1930 1929 3,059 1940 1939 3,074 1950 1949 3,071 1960 1959 3,081 1970 1969 3,079 1980 1978 3,078 1990 1988 3,078 2000 1998 3,080 Average for the entire sample years Mean temperature (oF) Accumulated precipitation (inches) Longterm 54.1 55.0 53.8 54.5 54.6 54.5 55.1 56.0 55.3 55.3 54.5 54.5 54.5 54.6 S.E . 5.7 7.3 7.8 7.9 7.7 8.0 8.3 7.9 7.9 8.1 7.7 7.7 7.7 7.8 Shortterm 61.9 64.9 62.6 63.8 63.0 64.3 63.9 65.1 64.2 64.3 62.9 65.1 64.1 62.7 S.E . 5.9 6.1 6.2 7.2 7.1 6.8 7.1 7.0 6.4 6.5 6.6 6.6 5.8 6.8 Longterm 41.8 40.2 38.8 36.4 37.7 36.2 37.3 34.8 37.8 36.1 35.7 38.3 38.1 38.8 54.9 7.7 63.9 6.6 37.9 6.6 11.2 12.2 12.3 12.3 13.0 13.8 13.4 13.6 13.4 13.4 14.3 13.1 14.4 Shortterm 29.8 25.7 25.6 24.3 26.5 29.5 28.9 23.5 27.8 28.7 26.7 28.4 23.7 27.3 7.5 7.8 9.7 7.3 10.2 11.1 12.0 9.9 11.1 11.6 9.8 10.1 7.8 9.5 12.7 27.2 9.9 S.E. S.E. Notes: County-level monthly weather mean temperature and accumulated precipitation were estimated by Kriging interpolation methods as discussed in the text. This table presents sample mean and standard errors across counties for the given census year. The long-term weather variables are calculated by the 10-year average of annual mean temperature and annual accumulated precipitation prior to each census year; the short-term weather variables are measured by the March-to-October mean temperature and accumulated precipitation in each census year. 40 Table A.2. Summary Statistics: Farm Productivity Measures and Controls Productivity Measures Denoted census year Actual census year Number of counties 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 1869 1879 1889 1899 1909 1919 1929 1939 1949 1959 1969 1978 1988 1998 2,162 2,454 2,791 2,767 2,916 3,026 3,059 3,074 3,071 3,081 3,079 3,078 3,078 3,080 Controls Farm Value per Farmland Acres Farm Output Value per Farmland Acres Population Density Ratio of White Population Farm Acres / Total Area Farmer / Farmland Acres 354.2 335.2 476.1 499.1 753.2 720.3 678.1 765.3 681.4 941.3 1147.7 2477.9 1577.7 2452.9 90.3 76.6 88.9 112.6 147.6 243.2 198.9 242.1 464.0 381.8 365.4 0.0523 0.0825 0.0723 0.0870 0.1093 0.1382 0.1185 0.1264 0.1434 0.1013 0.0996 0.1404 0.1506 0.1165 0.8362 0.8389 0.8582 0.8581 0.8646 0.8761 0.8642 0.8862 0.8920 0.8938 0.9001 0.8862 0.8638 0.8537 0.5207 0.5962 0.5821 0.6740 0.6714 0.6656 0.6441 0.6672 0.6909 0.6419 0.6001 0.5611 0.5290 0.5194 0.0202 0.0191 0.0110 0.0161 0.0191 0.0211 0.0308 0.0155 0.0447 0.0201 0.0407 0.0093 0.0088 0.0093 Notes: Farm value and farm output value are converted in constant 2000 dollars. Population density is measured as the number of county populations per square mile divided by 1,000. Farm output value is not available at the county level in 1910-1930. 41 Table A.3. Year and Source of Variables for Agricultural Technologies and Economic Conditions Type of technology Variable Drainage Irrigation Land improvement for crops Crop mix Selection of crops Farm structure Use of fertilizer Farm labor market Farm mortgage rate Year of early condition Year of later condition Ratio of drained areas 1930 - Ratio of irrigated areas Ratio of cropland 1890 1880 1980 1980 ICPSR 2896 ICPSR 2896 Crop Herfindahl-Hirschman index 1880 1980 ICPSR 2896: County-level Herfindahl-Hirschman index was calculated using county acres for 10 major crops: wheat, corn, barley, oats, rye, potato, cotton, tobacco, rice and hay 1880 1880 1960 ICPSR 2896 ICPSR 2896 1880 1960 ICPSR 2896 1880 1960 ICPSR 2896 1870 - ICPSR 2896 1890 - ICPSR 2896 Crop acres out of total farmland Ratio of crop-share farms Ratio of tenant (cash rental) farms ln(fertilizer cose per farmland acre) Annual agricultural wage per farmland acre Interest paid on farms per mortgage value of farms Source and note 1930 Agricultural Census: Drainage of Agricultural Lands Notes: ICPSR 2896 is the dataset developed by Michael Haines, which is available at Interuniversity Consortium for Political and Social Research (ICPSR). 42 Table B.1. The Effects of Long-Term Temperature and Precipitation on Farm Value: Using Data with and without County Boundary Adjustment Dependent variable: ln(county average farm value per acre) Key control variables Weather*D1880 Weather*D1890 Weather*D1900 Weather*D1910 Weather*D1920 Weather*D1930 Weather*D1940 Weather*D1950 Weather*D1960 Weather*D1970 Weather*D1980 Weather*D1990 Weather*D2000 Weather = Decadal average of annual mean temperature (1) (2) Using countyUsing data boundarywithout adjusted data adjustment Coeff. S.E. Coeff. S.E. 0.0243 0.0075 0.0271 0.0071 0.0293 0.0081 0.0323 0.0080 0.0368 0.0086 0.0388 0.0088 0.0457 0.0091 0.0491 0.0092 0.0400 0.0093 0.0429 0.0092 0.0550 0.0096 0.0539 0.0092 0.0555 0.0094 0.0521 0.0089 0.0682 0.0097 0.0648 0.0091 0.0772 0.0099 0.0789 0.0095 0.0738 0.0096 0.0731 0.0094 0.0768 0.0095 0.0741 0.0092 0.0696 0.0094 0.0703 0.0092 0.0685 0.0093 0.0714 0.0090 Weather = Decadal Average of Annual Accumulated Precipitation (3) (4) Using countyUsing data boundarywithout adjusted data adjustment Coeff. S.E. Coeff. S.E. 0.0058 0.0057 0.0086 0.0053 0.0064 0.0054 0.0072 0.0055 0.0114 0.0055 0.0100 0.0061 0.0056 0.0055 0.0047 0.0061 0.0041 0.0056 0.0018 0.0058 0.0065 0.0057 0.0037 0.0056 0.0149 0.0057 0.0114 0.0055 0.0101 0.0057 0.0080 0.0056 0.0156 0.0060 0.0162 0.0061 0.0223 0.0058 0.0246 0.0060 0.0214 0.0057 0.0211 0.0059 0.0215 0.0058 0.0212 0.0060 0.0209 0.0058 0.0198 0.0058 Notes: We run the fixe-effect estimation regression that includes all the three fixed effects, per equation (1), for two datasets with and without county boundary adjustment. The table reports only the coefficients of the interaction terms between long-term temperature or precipitation and each year dummy. Standard errors are clustered on county. All the coefficients are statistically significant at the 99% level of confidence. 43 Table B.2. Across-Century Difference in the Response of Farm Productivity to Weather Conditions by Region Farm Value Farm Output Value (1) Entire Counties (2) Southern Counties (3) Northern Counties (4) Entire Counties (5) Southern Counties Temperature(T)*D19 -0.0437*** (0.0053) -0.0923*** (0.0088) -0.0288*** (0.0066) -0.0389*** (0.0081) -0.0973*** (0.0161) 0.0046 (0.0074) Precipitation(P)*D19 -0.0124*** (0.0028) -0.0389*** (0.0051) -0.0175*** (0.0045) -0.0156*** (0.0029) -0.0460*** (0.0057) 0.0046 (0.0049) (T-µT)*(P-µP)*D19 -0.0006* (0.0003) 0.0027*** (0.0005) -0.0011* (0.0006) 0.0010* (0.0006) 0.0045*** (0.0010) 0.0033*** (0.0010) Key Control Variables (6) Northern Counties Notes: We run the regressions per equation (3) in the text, respectively, for the entire counties and two regional groups: the southern and northern counties. This table reports the key estimated coefficients of the interaction terms among weather variables (long-term weather for farm value analysis and short-term weather variable for farm output value analysis) and the dummy that indicates the 19th century. Standard errors, clustered on county, are reported in parentheses. Single asterisk denotes statistical significance at the 90% level of confidence, double 95%, triple 99%. 44 Figure 1. Scatter Plots: Log County Average Farm Value per Acre by 10-Year Average Temperature and Precipitation Decadal Average of County Annual Mean Temperature 1900 10 8 6 4 2 0 -2 12 10 8 6 4 2 0 -2 35 40 45 50 55 60 65 70 75 Degree Fahrenheit 8 6 4 2 0 8 6 4 2 0 35 40 45 50 55 60 65 70 75 Degree Fahrenheit 1970 -2 12 2000 Log of Farm Value per Acre 10 10 35 40 45 50 55 60 65 70 75 Degree Fahrenheit Log of Farm Value per Acre 12 12 -2 1950 Log of Farm Value per Acre 1920 Log of Farm Value per Acre Log of Farm Value per Acre Log of Farm Value per Acre 1870 12 10 8 6 4 2 0 -2 35 40 45 50 55 60 65 70 75 Degree Fahrenheit 12 10 8 6 4 2 0 -2 35 40 45 50 55 60 65 70 75 Degree Fahrenheit 35 40 45 50 55 60 65 70 75 Degree Fahrenheit Decadal Average of County Annual Accumulated Precipitation 1900 10 8 6 4 2 0 -2 12 10 8 6 4 2 0 -2 0 10 20 30 40 50 60 70 80 Inches 0 8 6 4 2 0 -2 12 10 8 6 4 2 0 -2 0 10 20 30 40 50 60 70 80 Inches 8 6 4 2 0 10 20 30 40 50 60 70 80 Inches 0 10 20 30 40 50 60 70 80 Inches 2000 Log of Farm Value per Acre 10 10 1970 Log of Farm Value per Acre 12 12 -2 1950 Log of Farm Value per Acre 1920 Log of Farm Value per Acre Log of Farm Value per Acre Log of Farm Value per Acre 1870 12 12 10 8 6 4 2 0 -2 0 10 20 30 40 50 60 70 80 Inches 0 10 20 30 40 50 60 70 80 Inches Notes: Each weather variable in the x axis is calculated by the 10-year average of annual weather values prior to each census year. The y axis denotes the logarithm of the county average farm value per farmland acre. Solid curves are lowess fit curves, and dotted lines are linear fit lines. 45 Figure 2. Trend of the Estimated Effect of Long-Term Temperature and Precipitation on County Farm Value per Acre Precipitation .05 .14 Temperature Year FE and State by Year FE and State by Year FE and County FE and County FE Regression Coefficient .02 .03 Year 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 1880 0 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 1880 0 .02 .01 .04 Regression Coefficient .06 .08 .1 .04 .12 Year FE Year Notes: We run the regressions of the logarithm of county average farm value on the 10-year average of annual mean temperature or annual accumulated precipitation prior to each census year, standard controls, and their interactions with the dummies indicating the 1880-2000 census years, per equation (1). Thus, the reference year is 1870. Each panel is the graphical presentation of regression coefficients of each weather variable. The dotted and dashed lines are the estimation results that employ year and state-by-year fixed effects, respectively; the solid lines additionally employ county fixed effects. All the regressions datasets that adjust county boundary changes to the 1870 county boundary. 46 Figure 3. The Relation between Average Weather Conditions in Early-Life and Adult Income by Two Cohorts Average Occscore 3 3.25 3.5 Born after 1930 DC NM 40 CA OK LA AZ TN NC AR FL TX SCMS GA AL ND DC CT NY UT NJ MA DE RIPAOH IL MD WY CA IN MO LA KS WI WAORNV NHMI CO ID IA NE MNMT OK AZ SC MS TX GA VA NC AL VT SD TN ME AR WV NMKY 50 60 Average Temperature 70 40 50 60 Average Temperature DC AZUT LA 20 30 40 50 Average Precipitation UT WY NVCOID AZ MTND SD NM DC NJ CT NY MA IL DE MD RI OHIN PA CA MI KS WI FLMSLA WA NH MO SC IA NEMN TXOKOR VA NCGA AL VT TN ME WVKY AR 2.75 2.75 MA RI CT MD NJ NH WA NYPA ME DE VT OK MI OH WI IA OR IL MO IN VA WVKY SC TX FL GA MS NCTN AL AR NM 10 CA KS NEMN 70 Born after 1930 Average Occscore 3 3.25 3.5 Average Occscore 3 3.25 3.5 Born before 1900 CO NVMT WY ID ND SD FL 2.75 ND MARI CO CT NJ NV MD MT NH NY WY PA WA KS ME DE MN NE VT SD ID OH WI MI IA UT OR IL MO VA IN WV KY 2.75 Average Occscore 3 3.25 3.5 Born before 1900 60 10 20 30 40 50 Average Precipitation 60 Notes: This figure plots a proxy of adult income against the state-of-birth average temperature and precipitation for earlier- and later-born cohorts in the U.S. A cohort is defined by year of birth and state of birth. Data on native adult males are drawn from U.S. censuses of 1880-1990, which span the years of birth 1861-1960. The outcome variable is the average occupational income score, transformed into natural logarithms. Cohorts are grouped into those born before 1900 (in the left-hand plots) and those born after 1930 (in the right-hand plots). The x axis is the state-of-birth average temperature in the upper plots or precipitation in the bottom plots. The y axis in each plot refers to the cohort group’s average income score. State abbreviations are used to denote the position of each point. The solid line is the best-fit regression line between the points. Sources and methods of data construction are described in the appendix. 47 Figure 4. Short-Term Weather Fluctuations around Year of Birth and Adult Income Temperature Precipitation .015 .005 .002 .005 0 0 -.005 -.002 -.015 -.005 -2 -1 0 1 2 3 -2 -1 0 1 2 3 Notes: This figure summarizes regressions of cohort outcome on early-life weather conditions, per equation (4) in the text. A cohort is defined by year of birth and state of birth. Data on native adult males are drawn from U.S. censuses of 1880-1990, which span the years of birth 1861-1960. The outcome variable is, at the cohort level, the occupational income score transformed into natural logarithms. The x axis in each plot refers to the calendar year minus the year of birth. The y axis in each plot displays the estimated regression coefficient on the interaction of the indicated weather variable (temperature or precipitation) and a dummy for the calendar year minus year of birth. The error bars reflect 95% confidence intervals for each coefficient. Sources and methods of data construction are described in the appendix. 48 Figure 5. The Diminishing Effects of Early-Life Weather Fluctuations on Adult Income -.02 0 .02 .04 .06 Temperature 1880 1890 1900 1910 Year of Birth 1920 1930 1940 1920 1930 1940 -.005 0 .005 .01 .015 Precipitation 1880 1890 1900 1910 Year of Birth Notes: This figure summarizes regressions of cohort adult income on early-life weather conditions, per equation (4) in the text. The regressions are conducted for a 40-year-wide moving window so that the sample for each regression includes cohorts born 20 years before and after the target year. Regarding weather variables, we only control for temperature in the period one year after birth and precipitation at the year of birth according to the results in Figure 4. The x axis in each plot refers to the average year of birth for cohort samples used. The y axis in each plot displays the estimated regression coefficient on the interaction of the indicated weather variable (temperature or precipitation) and a dummy for the year chosen as explained above. The error bars reflect 95% confidence intervals for each coefficient. Sources and methods of data construction are described in the appendix. 49 Figure A.1. 1870 County Boundaries Notes: To resolve the over-time changes in county boundaries, we adjusted all the county variables on the 1870 boundary shown in this figure. 50