Tag: greenhouse vertical farming

The data for workers in these sectors come from the March Current Population Survey

Apprehensions by the U.S. border patrols dropped from 876,803 in 2007 to 556,032 in 2009. Because immigrants often send money home, we can use remittances from the United States to Mexico to infer whether the number of immigrants changed substantially during a recession. Figure 2 shows quarterly remittances to Mexico in millions of U.S. dollars as reported by Banco de México . The figure shows that remittances increased during the relatively mild 2001 recession but decreased substantially during the 2008–2009 Great Recession. These data again support the view that the number of Mexican immigrants to the United States fell during the Great Recession but not during the previous, milder recession. Moreover, Warren and Warren estimated that the net change of undocumented immigrants was negative during the Great Recession, which was related to a sharp decrease of new undocumented immigrants. The United States Department of Agriculture, Economic Research Service estimated number of full- and part-time agricultural workers fell from 1.032 million in 2007 to 1.003 million in 2008 and 1.020 million in 2009, before rising to 1.053 million in 2010.5 That is, the number of workers in 2008 was 3% to 5% lower than in the years before and after the Great Recession. Presumably the share of workers dropped by even more in seasonal agriculture, which employs most of the undocumented workers.Our agricultural workers data comes from the National Agricultural Workers Survey.The NAWS is a national, random sample of hired seasonal agricultural employees, who work primarily in seasonal crops.The NAWS is an employer-based survey. That is, it samples worksites rather than residences to overcome the difficulty of reaching migrant farm workers in unconventional living quarters.

These employers are chosen randomly within the U.S. Department of Agriculture’s 12 agricultural regions .Surveyors randomly select 2,500 employees of these growers to obtain a nationally representative sample of crop workers. Surveyors interview the more than 2,500 crop workers outside of work hours at their homes or at other locations selected by the respondent. The NAWS has a long,vertical growers visible history within farming communities, and the survey design incorporates questions aimed at data validation about legal status. Respondents receive a pledge of confidentiality and a nominal financial incentive for participation. As a result, only one to two percent of workers in the overall sample refuse to answer the legal status questions. The NAWS contains extensive information about a worker’s compensation, hours worked, and demographic characteristics such as legal status, education, family size and composition, and workers’ migration decisions. We dropped workers from the sample who were missing any relevant variable, 23% of the original survey sample. The NAWS is conducted in three cycles each year year to match the seasonal fluctuations in the agricultural workforce. Unfortunately, the public-use data, which we use, suppresses information about the cycle and aggregates the 12 regions into 6 regions. As a result, our data set consists of repeated annual cross sections of workers from 1989 through 2012. Column 1 of Table 1 presents national summary statistics for the variables used in our empirical analysis. Columns 2 and 3 provide data for California and for the rest of the country, because 37% of the sample works in California. Compared to workers in the rest of the country, Californian workers tend to have less education; have more farm experience; are more likely to be non-native, Hispanics; and are more likely to work in fruit and nut crops and less likely to work in horticulture.

After analyzing the effects of recessions on agricultural workers, we replicate the analysis for workers in construction, hotels, and restaurants, which also employ many immigrants.In March of each year, workers in the basic CPS sample are administered a supplemental questionnaire in which they are asked to report their income such as hourly wage rate and additional labor force activity such as hours worked in the previous week.8 Because information on immigration is available only since 1994, our sample period is 1994–2013. We include all workers who are 18 years and older.Three recessions occurred during our 1989–2012 sample period . The economy recovered quickly from the first of these recessions in 1990–1991. The second, 2001 recession was also relatively mild. However, the third recession, the 2008–2009 Great Recession, was much more severe and had longer-lasting economic and labor market effects than the first two. We analyze the effects of recessions on hourly earnings, the probability of receiving a bonus, and weekly hours of work of employed workers. For workers paid by time, hourly earnings are a worker’s hourly wage. For piece-rate workers, we use the workers’ reported average hourly earnings. The bonus dummy equals one for workers who receive a money bonus from an employer in addition to the wage, and zero otherwise. Weekly hours of work are the number of hours interviewees reported work at their current farm job in the previous week. The explanatory variables in all these equations are the same. The explanatory variables include all the usual demographic variables: age, years of education, years of farm experience, job tenure , gender, whether the workers is Hispanic, whether the worker was born in the United States, and whether the worker speaks English.The specification uses a legal status variable to capture the bifurcated labor markets for documented and undocumented workers. It also includes crop and regional dummies.

We have seven main explanatory variables: dummies for each of the three recessions, the recession dummies interacted with the legal status dummy , and regional unemployment rates for workers in all sectors of the economy. We use separate dummies for each recession to allow for differential effects across the recession . The interaction terms capture whether employers treat undocumented workers differently than legal workers during a recession. We include the unemployment rate because it peaks after the end of each recession . We do not report the unemployment rate interacted with the undocumented dummy because we cannot reject that its coefficient is zero in any equation. We treat all these variables as exogenous to the compensation and weekly hours of individual agricultural workers. We start by examining the effects of recessions on NAWS workers’ hourly earnings. Column 1 of Table 2 presents regression estimates for the ln hourly earnings equation. The coefficients on the demographic variables have the expected signs and are generally statistically significantly different from zero at the 5% level. Undocumented workers’ hourly earnings are 2.1% less than those of documented workers. Females earn 6.4% less than males. Hispanics earn 4.9% less than non Hispanics. Unlike most previous studies, we find a statistically significant effect of education. English speakers earn 3.9% more than non-English speakers. The coefficients on the recession dummies reflect the effect of the recession on documented workers. Documented workers’ hourly earnings rose 1.8% during the 1990–1991 recession, 4.2% during the 2001 recession, and 6.9% during the Great Recession. We draw two conclusions about the effects of recessions on documented workers. First, the hourly earning effect of the Great Recession was larger than that of the relatively minor recessions, which is consistent with literature on business cycles and the farm labor market in the 1970s . Second, in all recessions, documented workers’ wages rose, which suggests that recessions cause the hired-agricultural-worker supply curve to shift leftward relatively more than did the demand curve. The sum of the coefficients on the recession dummy and its interaction with the undocumented dummy captures the effect of a recession on undocumented workers. The 1990–1991 recession did not have a statistically significant effect on undocumented workers.

Hourly earnings for undocumented workers rose by 3.4% during the 2001 recession and 1.9% during the Great Recession. In contrast to the pattern for documented workers, the undocumented workers’ earnings rose by less during the Great Recession than during the 2001 recession. Thus, not only do undocumented workers earn less than documented workers do in general, but their hourly earnings rise less during recession than do the earnings of documented workers. That is, the wage gap between documented and undocumented workers widens during recessions. In addition to hourly earnings, 28% of the workers in our sample receive bonus payments , which supplement relatively low wage payments . These deferred payments play a similar function to that of efficiency wages in other sectors . We use a binary indicator equal to one if a worker receives a money bonus. Column 2 of Table 2 shows the results of a regression using a linear probability model . For documented workers, the probability of receiving a bonus did not rise during the two relatively minor recessions but increased by 5.8 percentage points during the Great Recession. Thus, the Great Recession not only raised documented workers’ hourly earnings, but it raised the probability that they received a bonus substantially. For undocumented workers, the probability of receiving a bonus fell by 2.9 percentage points during the 1990–1991 recession and rose by 9 percentage points during the Great Recession. Again, this result is consistent with the theory that the Great Recession caused a large supply side shock. Thus,vertical grow for both documented and undocumented workers, the Great Recession had a larger, positive effect on the probability of receiving a bonus than did earlier recessions. The unemployment rate has a statistically significant effect on the probability of receiving a bonus payment. A one percentage point increase in the unemployment rate raised the probability of receiving a bonus by 0.9 percentage points.Because our data set includes information about only employed workers, we cannot directly observe the effect of a recession on total employment. However, we can examine the effect on workers’ weekly hours. When employers have difficulty recruiting workers, they have employees work more hours per week to compensate for an unusually small workforce. For documented workers, weekly hours fell by 2.2 hours during the 1990–1991 recession, but rose by 1.1 hours during the 2001 recession, and 2.3 hours during the Great Recession. For undocumented workers, weekly hours were not statistically significantly affected during the two relatively minor recessions, but rose by 2.6 hours during the Great Recession—more than for documented workers. An increase in the overall unemployment rate by 1 percentage point lowered the weekly hours by 0.3 hours. Thus, an increase in the overall unemployment rate lowered weekly hours, but weekly hours rose during relatively large recessions.We conducted five robustness checks. First, we estimated all three equations separately for documented and undocumented workers. That is, we allowed all the coefficients to vary between these two groups instead of only the recession dummies.

The coefficients on our seven key recession variables were virtually unchanged . Second, we estimated all three regressions eliminating all newcomers , about 3,300 people or 7.5% of the sample, to see if compositional changes in the workforce during recessions are driving our results. However, the coefficients were virtually unchanged . Third, we estimated all three regressions leaving out the unemployment rate. Doing so had negligible effects on the other recession variable coefficients . Fourth, we excluded the crop dummies, in case they are endogenous. The recession variable coefficients were unaffected .Do recessions have different effects in agriculture than in other sectors of the economy that employ many undocumented immigrants, such as construction, hotels, and restaurants? To answer this question, we constructed a comparable data set based on the March Current Population Survey for 1994–2013. We can look at the effects from only two recessions, 2001 and the Great Recession, because the CPS does include certain key variables prior to 1994. It also lacks a variable on bonus payments. In contrast to the NAWS, the CPS data does not record whether an immigrant is undocumented. Therefore, we focus on immigrants in general and form interaction terms between immigrant status and the recession dummies. Otherwise, we use as similar a set of demographic variables as possible. Table 4 presents the regression results for the ln wage and weekly hours in the three sectors. In none of these three sectors did either recession affect the wages of non-immigrants or of immigrants. Presumably, wages are sticky in these sectors, partially due to union and other contracts and minimum wage laws. The unemployment rate had a statistically significant effect only in the construction sector, and that positive effect is small, as in the agricultural sector. The 2001 recession did not affect the weekly hours in these sectors.

The tools to facilitate such an accounting can only be developed within a whole-systems perspective

Our educational and research institutions tend to mirror this shortcoming,8 with the result that the larger system contexts of research questions are infrequently investigated and poorly understood. Difficulties in apprehending and resolving problems whose constituents are grounded in several interrelated systems are compounded by the international community’s disparate, competitive political and economic systems. Nations act to promote their own priorities but affect, often negatively, globally shared resources and globally interdependent societies. Although nations and other sociopolitical groups generate impacts beyond their borders, they are generally incapable or unwilling to assess and react equitably to the results of their actions. Pierre Crosson and Norman Rosenberg 18 note the inadequacy of information feedback about significant environmental problems in modern societies, an inadequacy which characterizes feedback about social problems as well. Accounting for the system-wide implications of local actions should be a primary objective for sustainable agricultural systems. The definition of sustainability offered here places a priority on broad-based equity considerations. We believe it is inadequate to exclude social justice as a priority and that there is an ethical requirement for greater equity in the agricultural system. Some have combined concern for how we treat the environment with how we treat our fellow human beings.19, 20, 21,22 For those focusing on the latter, it is essential to look beyond sustaining our environmental and economic ability to produce agricultural goods. It is equally important to ensure that those goods are produced and distributed in an equitable manner. A concern with this human values aspect of agriculture involves a sweeping rather than localized concept of who constitutes “us.” Typically, resource conservation is dis- cussed in terms of its implications for farmers’ profit- ability or our descendants’ food-producing capabilities. The sustainability definition offered in this paper does not limit equity considerations to these groups. A concern with equitable social relations in agriculture requires defining “us” in terms of all fellow humans – not only farmers and future generations, but also farm workers, consumers, non-farm rural residents, Third World urban poor, and others.

Sustainability in this sense is framed in terms of both intergenerational and intragenerational equity. Thus,greenhouse vertical farming issues such as farm worker rights and inner-city hunger are as central as issues of soil erosion and groundwater contamination to the goals of agricultural sustainability. One of the most profound challenges facing agriculture is creating a decision-making process which will fairly resolve equity issues. Such a process must assess competing interests; evaluate agriculture’s costs and benefits, and the recipients of each; decide fairly what the compromises must be; recognize and encourage shared goals and common ground. In most discussions of sustainability either environmental quality or social justice issues are emphasized, but neither can be sup- ported wholly at the expense of the other. Nourishing humans, ensuring social justice, and providing a reasonable quality of life cannot be accomplished if agriculture’s resource base and environmental constraints are neglected. Likewise, few would argue that environmental considerations should be pursued at the expense of satisfying basic human needs. An equitable agricultural system must foster a decision-making process which is truly democratic, one which identifies not only what the costs and benefits are but how to distribute them fairly among all sectors of society.Many sustainability definitions, particularly those which guide applied sustainable agriculture programs, are based on the primacy of farm production and short-term profitability. As sustainable agriculture programs have increasingly been incorporated into long-established agricultural institutions they have manifested the largely unquestioned intellectual assumptions and infrastructural constraints which characterize their parent institutions. This is problematic because conventional agricultural institutions have fostered many technologies and policies counter to sustainable agriculture goals.Such institutions have, for example, contributed to concentration within agriculture; have not generally benefited agricultural labor; and have systematically failed to examine their impact on the environment, the structure of rural households and communities, and the consequences of rural resident displacement.

To situate new pro- grams designed to address these problems within the framework which produced them is of questionable value unless steps are taken to change the nature of that framework, for it determines the way its re- searchers see the world, pose questions, and define problems. When agriculture is viewed in a whole-systems context and sustainability is defined comprehensively, it is clear why the current popular focus on farm production practices is insufficient for achieving agricultural sustainability. Developing non-chemical pest management methods, for example, will effectively reduce pesticide use only if economic structures and policies encourage their adoption by farmers. More importantly, one cannot conclude that improved production practices will transform the agricultural system into one that meets all environmental, economic, and social sustainability goals. Social goals must be addressed explicitly. This is why production techniques such as organic farming, while a likely component of a sustainable food and agricultural system, cannot be thought of as synonymous with sustainable agriculture. Given the conventional institutional context of most state and federal sustainable agriculture programs it is not surprising that they tend to focus research on conventional priorities such as production practices and efficiency and have not, for the most part, aggressively addressed social and economic issues. Sustainability priorities – and the definitions which embody them – must be expanded to encompass the many factors affecting production and distribution as well as the larger environmental, economic, and social systems within which agriculture functions. This has been the focus of the Agroecology Program since its inception in 1982. Through conferences and publications* we have worked to expand the discussion and practice of integrating these aspects of sustainability. Recently, the University of California Sustainable Agriculture Research and Education Program has broadened its agronomic focus to include social, economic, and policy issues. SAREP defines sustain- able agriculture as integrating “…three main goals – environmental health, economic profitability, and social and economic equity.”Their grant program, which encourages research and education on social, economic, and public policy issues affecting food and agriculture, could become a model for other sustain- able agriculture programs such as LISA. We believe that it is important to continue exploring the meaning of agricultural sustainability. Before an improved agricultural system can be developed the biases and structures that have led to agricultural problems must be closely examined and concrete goals articulated, based upon a broadened concept of agricultural sustainability. The concept of sustainability offered in this paper emphasizes that social goals are as important as environmental and economic goals, and widens the opportunity to move beyond the narrow agricultural priorities expressed in the past.

It is based upon the whole-systems, interactive nature of all aspects of the agricultural system – that problems and their resolutions must be conceived not only in terms of their immediate time frames and local impacts, but just as importantly, in terms of their future time frames and their global impacts. It encourages emphasis on optimum production over maximum production, the long term along with the short term, the public’s best interest over special interests, and the contextualization of disciplinary work within interdisciplinary frameworks. Our hope is that this definition helps advance the discussion on developing a food and agriculture system that is sustainable for everyone. While aggregate growth in an economy may improve the welfare of both wealthy and poor households,vertical agriculture the latter are most usually rural, and rural households have employment and incomes that depend disproportionately on agriculture. It is natural to wonder if growth in aggregate agricultural income has a different effect on the welfare of poorer households than does growth elsewhere in the economy. The question is an important one for many policy issues. Faced with continuing extensive poverty, many development agencies and scholars have suggested the need to refocus growth on agriculture , arguing that the alternatives of redistributing income generated outside of agriculture or migration out of agriculture to urban areas are difficult to achieve and create other problems. Of course, we are not the first to wonder whether growth in agriculture may be more effective than growth in the rest of the economy in reducing poverty; an extensive theoretical and empirical literature already exists on the subject which we discuss in Section 2. The theoretical literature focuses on the different transmission mechanisms of an exogenous gain in agricultural productivity on poverty, while the empirical literature analyzes the reduced form relationship, and generally documents a stronger association between poverty reduction and growth originating in agriculture compared to growth originating in non-agriculture, with the exception of Latin American countries. In this paper we tackle this question by comparing changes in the level and distribution of household expenditures due to growth in both aggregate agricultural and aggregate non-agricultural income. We use growth in household expenditures as the outcome of interest because we believe expenditures to be the best available indicator of material well-being; also, these are the data generally used for poverty calculations for most low-income countries. However, our analysis differs from most other studies in several aspects. First, we consider growth in expenditures across the entire distribution rather than the simple poverty headcount ratio, giving a richer picture of the effect of sectoral growth on welfare. Second, we use the deciles as defined within each country, rather than a common international benchmark of expenditures.

To correct the underlying assumption that deciles of very different countries have similar relationship with agriculture, we then pursue some heterogeneity analysis. Finally, we tackle the issue of simultaneity between sectoral income and expenditures using an instrumental variable approach, allowing us to take a stand on the causality of sectoral growth on welfare. The simple regression we would like to estimate relates expenditure growth for differently positioned households to growth in sectoral income, the latter weighted by its share in total aggregate income; this is described in Section 4. The question of whether the poor benefit more from agricultural income growth than growth in other sectors could then be answered simply by examining the relative size of the coefficients on aggregate income growth from agriculture and from other sectors. In practice, there is a series of challenges we must face before estimating such a regression. First, we do not have household level data that would allow us to make comparisons across countries. Instead, we use data from the World Bank’s Povcal Net project and consider estimates of household expenditures from different expenditure deciles; in effect we construct a panel of ten representative ‘households’ for each country, each representing an expenditure decile.1 We discuss these data in Section 3.1. Second, the resulting ‘panel’ is extremely unbalanced, since the underlying expenditure surveys are conducted at irregular intervals. This creates some important accounting issues when we turn to estimation, treated in Section 4.1. Third, some countries, some years, and perhaps some deciles can naturally be expected to have different expenditure growth rates for reasons unrelated to sectoral income growth. A global financial shock may cause expenditure growth to slow for everyone; households’ risk attitudes or time preferences may imply different rates of expenditure growth across deciles ; the endowments of a particular country or some aspect of the structure of its economy may imply systematically different rates of expenditure or income growth even over long periods; and variation in the global price of agricultural commodities will change the composition of income across sectors for many countries. We attempt to deal with these kinds of alternative sources of variation in expenditure and income growth in a fairly agnostic manner, by using fixed effects and related methods for dealing with what Wooldridge calls “unobserved effects.” So: we account for aggregate cross-country shocks using a collection of time effects; and for systematically different rates of expenditure growth across the distribution we use a set of decile fixed effects. We would be inclined to also use a complete set of country fixed effects to deal with differences in endowments, but with these we reach the limits of our dataset; instead we employ a set of continent fixed effects, which in practice seems to be effective. Fourth, the stochastic process governing country-level agricultural income exhibits more time-series variance than does income from other sectors.