Our knowledge about the past is fragmentary for a number of reasons, and there are many challenges facing our approach.Many of these are by no means unique to the study of agricultural systems, and relate to the nature of the historical and archaeological records.Firstly, we must deal only with the limited material remains of past societies , or the marks they left on the world around them.Features of behavior and practice are not preserved directly but instead have to be inferred from what does remain.Secondly, some regions and time periods are better-studied than others, and this reflects a number of factors, including ecological conditions, that make some regions easier to excavate than others, the personal interests or theoretical persuasions of researchers, and broader social and historical factors that shape which countries and institutions have the money to conduct such research.In other words, there are certain biases in our records of the past of which we need to be aware when attempting to draw broader inferences.One potential source of error comes from the fact that although our units of analysis are the NGAs and the societies that have inhabited them in the past, we often only have information from a small number of archaeological sites or a limited set of historical records.For example, our inferences about the early stages of agriculture in the Kachi Plain, discussed above, are extrapolated from the single site of Mehrgarh.A potential risk here is that this site may not be representative of what was going on in the wider NGA.On a practical point, there is not much that can be done here except to recognize that we must work with whatever information we have, be aware of the potential limitations and sources of error, and be ready and willing to update our understanding as and when new information is discovered.Our general strategy is to make as clear as possible the assumptions involved in defining and coding these variables,ebb and flow trays and in how they will get incorporated into further inferences about the productivity of past agricultural systems.

One way to make sure we are using the best available data and are aware of its limitations is by engaging fully with academic historians and archaeologists who are experts on our focal regions and time periods.Such experts enter into the process in a number of ways.Firstly, they help us navigate what is known already, identifying the most relevant literature, aiding in the design of the codebooks, and advising on what information is or is not feasible to obtain.Secondly, they verify that the information being collected is based on the most up-to-date knowledge and scholarship.Finally, as we move forward and begin analyzing these data statistically, experts will also be able to provide important background information and context that can help in the interpretation of the results.It should be noted that researchers can sometimes come to different conclusions about how historical and archaeological records should be interpreted.In other words, there is often conflicting evidence, or a lack of consensus about what the data are telling us.We try to avoid imposing any kind of typological scheme or monolithic theoretical perspective on the data, and the coding framework allows us to indicate where there are disagreements.Another practical step we have taken in dealing with how to interpret the information that is present in the archaeological and historical records is to try and make the coding process as objective as possible.This is done by focusing on presence/absence-type features, which are often much easier to code with certainty, and almost by definition are more consistent across different situations than quantitative estimates or judgments.We have found it often helps to break things down into component parts that can be more readily coded in a presence/absence manner, particularly for societies known only archaeologically.For example, rather than simply asking whether irrigation was practiced or not, we can ask if certain features related to irrigation systems were present or not.In our experience, certain features, such as the presence or absence of metal tools or food storage facilities have proven relatively straightforward to code.

Other variables have proven more challenging because, due to their annual or cyclical nature, many agricultural practices can be quite hard to discern archaeologically.A solution to this is to have very specific criteria about justifying the presence of such techniques.For example, for crop rotation, a sensible justification could be that within a particular site, a spectrum of crops were grown that is compatible with the rotation of crops.The point here is not that these justifications are without error, or cannot be challenged, but rather, that the reason given should be made clear.Attempting to code data systematically across different regions highlights that, for many variables of interest, there will be many cases in which not much, if anything, is known.Because this project is primarily interested in the broad patterns and processes of human history, the issue of missing data or scholarly disagreement perhaps matters less than if we are trying to find the particular details of a given time or place.In other words, as long as we have information on enough variables and enough regions then major trends should still be discernible.On a practical level, we are able to incorporate such sources of uncertainty into any statistical analyses that we perform, and we can explore whether making different assumptions about the data affects our overall results and conclusions.Although sometimes frustrating from an analytical perspective, highlighting where there are gaps in our understanding also serves a useful purpose in the wider sense in that it highlights those areas where future research efforts can be productively targeted.Having outlined out the general factors that will be important in assessing potential productivity in past societies, in this section, we sketch out how we are combining earth system science approaches with historical and geographical information to create a model of carrying capacity in past societies.Our general approach is to take estimates of productivity based on the output of simulations of modern crop growth and then modify these estimates based on the kind of historical information discussed above.

A variety of process-based models have been developed to simulate crop growth and productivity using detailed physical and biological processes.The details of the inputs required by these models can vary greatly depending on the question being addressed and the scale at which a simulation is being applied.For this project, we are making use of the LPJmL global crop model of , which simulates the productivity of a limited number of broad crop functional types through an explicit model of crop growth and development based on the features of such plants, their ecophysiology, and the management techniques applied to them.The model uses inputs relating to climate and weather in order to assess productivity under a variety of scenarios.One of the advantages of LPJmL for our purposes is that it is constructed at a suitable level of abstraction, with a limited number of inputs, which is suitable for projecting back into the past.A downside of simulations is that they are often time consuming to run and require specialist expertise to develop.To overcome this constraint, emulators have been developed that are computationally fast, statistical representations of process-based models.6 In previous work, developed an emulator of the LPJmL model.It generates a spatial map of maize, rice, cereal, or oil crop yields at approximately 50 km resolution.The crop emulator allows for variable management levels, allowing us to capture the effects of developing agricultural technologies.The emulation framework takes a matter of minutes to derive a global map of crop yield for a specified climate input and crop management level.This speed compares to several days of computing that would be required if we were using its underlying simulators.We are, therefore, using this emulator approach for reasons of tractability, flexibility, and to facilitate the analysis of modelling uncertainties.To date, LPJmL has been used primarily to simulate current and future conditions.However, the emulation approach allows us to take the modelled associations between climate and crop productivity and project these back into the past using information about past climate.For this project, we have developed a crop-modelling framework that uses emulation of a crop simulator and a paleo climate simulator.This paleo climate emulator generates spatially and seasonally resolved fields of temperature, precipitation,4×8 flood tray and cloud cover as functions of the Earth’s orbital configuration.

The climate data is then passed to an emulator of the LPJmL crop model.As with the original emulator, the outputs of this crop emulator are spatial maps of crop yields, but this time, those maps are derived from the estimated climate at defined periods in the past.To make the outputs more relevant to past societies, the suite of crops being considered will be extended beyond the maize, rice, cereal, or oil crops of the original emulator to take into account additional important classes of crops, such as tropical cereals and tropical roots.The simulator and the emulator also estimate productivity under rain-fed and irrigated conditions, which can help inform our historical estimates of productivity based on knowledge about the presence of irrigation techniques in the past.With these estimates of productivity in hand, we can set about adjusting them based on the historical data on past agricultural systems described above.The basic idea is that within a Geographical Information System framework, we can take the initial coverage maps supplied as output from the emulator and apply a formula that shifts the estimated yields up or down depending on the particular crops, agricultural techniques and practices, and other relevant factors at different points in time and in different regions.As a first step in adjusting the emulator output, our data can tell us when agriculture began being practiced in different regions and which crops are most important to assess for different regions.Adjustments will have to be made based on the human-induced biological improvements that crop varieties have undergone.For example, was able to estimate the improvement in maize yields that occurred over time in Oaxaca, Mexico, based on the increase in the length of ears of corn evident in the archaeological record.Furthermore, information about variables, such as the size of fields and whether land is farmed permanently or more sporadically , will affect the amount of land that could be devoted to food production and, therefore, the carrying capacity under that system.The effects of other variables can also be assessed with reference to the literature about the degree to which techniques such as fertilizing, mulching, or crop rotation affect crop productivities.This process can be conducted at several scales.Firstly, because the historical data relate directly to particular NGAs, we can make adjustments at this level to estimate productivity and carrying capacity at the NGA level.

However, because our NGAs are well-sampled geographically, we can use this information in conjunction with other sources to make reasonable extrapolations out from these points to assess potential productivity on regional and global scales.The relative efficiency of private and state-owned enterprises has been a subject of interest throughout recent history, and has gained attention in recent decades following a wave of privatizations that began with the Thatcher government in the UK in the 1980s, and escalated after the collapse of the Soviet Union.While theoretical literature in the last 3 decades has been nearly unequivocal in favoring the productive efficiency of private firms, results in the empirical literature have been less conclusive.Boardman and Vining survey 54 studies that examine the effects of ownership on performance, and find that 32 of them conclude that private firms outperform state-owned firms, and 22 of them are either inconclusive, or find that public firms outperform private firms.More recently, Megginson and Netter find greater evidence that the empirical literature favors the efficiency of private firms, but cite important exceptions.In addition, measures of efficiency vary greatly between studies, and may have important implications to findings.In this paper, I explore whether the variation in findings on the effect of firm ownership on productive efficiency can be partially explained by differences in the level of industry competition faced in each setting.In addition, I identify the elements of “competitiveness”that theoretically matter in determining efficiency differences between state-owned and private firms, to shed light on what elements seem to matter most, and how strongly these elements are tied to standard measures of competitiveness.