Inverted Pareto Coefficient For all top groups, we also provide the inverted Pareto coefficient. It is the ratio of the group average over the group threshold. They are a measure of the fatness of the tail of the distribution of income or wealth. Beware that people are not always ranked with respect to the quantity that the variable measures. That is true in particular for subcomponents of income or wealth. The description of the variable specifies which ranking is used. These different aggregates have their specific five-letter codes, which are described below.
Their cover the sequence of accounts from primary incomes i. This section presents these five-letter codes in the different decompositions of national incomes into components and subcomponents. GDP is a measure of all that is produced in a country over a given year, while national measures the income that accrues to residents. These quantities differ for two reasons. First, some of the domestic production is done by companies that are owned by foreigners, and by foreign workers.
Conversely, residents can own foreign companies or work abroad. Farm-level solutions must be found to help farmers undertake this challenge and achieve sustainable intensification in comprehensive terms. This article explores the potential of an agricultural technology to concurrently increase farm efficiency and provide wider environmental and social benefits in the context of dairy farming. Technology adoption has traditionally been considered as a key mechanism for increasing farm productivity Ali et al.
While it is reasonable to expect concurrent sustainability benefits from the adoption of certain agricultural technologies Guerci et al. In the present article, we address this issue and focus on the case study of milk recording on Irish dairy farms. Following the European Union EU milk quota abolition in , a process of dairy expansion and intensification was set in motion in Ireland Eurostat, n.
Two main challenges arise from the estimation of technology impact on farm sustainability. On the one hand, new technologies can only be evaluated if their productive, environmental and social performances can be reliably estimated Fumagalli et al. Additionally, the quantification of environmental and social sustainability is limited by data availability, and subjectivity and complexity in delineating these terms Lebacq et al.
This inevitably results in a greater representation of easily-defined and -recorded economic performance and thus an imbalance between sustainability dimensions in the literature Lebacq et al. We overcome this problem by using the rich and original Teagasc National Farm Survey NFS dataset, which comprises a nationally representative sample of Irish dairy farms.
Based on this data, we apply an indicator approach to measure farm sustainability Hennessy et al. On the other hand, as farmers ultimately decide whether or not to adopt a particular technology, self-selection must be accounted for when estimating technology impact. Drawing on previous theoretical and empirical literature Dehejia and Wahba, ; Fentie and Beyene, ; Imbens and Wooldridge, ; Rosenbaum and Rubin, ; Schilling et al.
As a robustness check, additional estimation methods i. Moreover, as PSM is based on the strong assumption that selection occurs only on observed characteristics, Rosenbaum bounds are estimated to test the sensitivity of treatment-effects estimates to hidden bias Becker and Caliendo, ; DiPrete and Gangl, ; Rosenbaum, The present study has direct policy relevance by addressing the topical issue of sustainable intensification and adds value to the existing literature in at least two ways.
Secondly, the study also contributes to the literature on the development and application of sustainability indicators by extending their use for measuring the impact of new technologies. A set of indicators has already been created specifically for the Teagasc NFS dataset Hennessy et al. Thus far, farm sustainability indicators have mostly been used to assess time trends in sustainability Buckley et al.
Subject to data availability, this approach can be replicated in other agricultural settings. The remainder of the article is structured as follows: section 2 reviews relevant literature. Section 3 introduces background information on Irish dairy expansion and milk recording. Section 4 outlines the methodology, followed by a description of the sustainability indicators used in the study and the data in section 5. Section 6 presents and discusses the results, while the final section provides the conclusions and policy implications.
Relevant literature Agricultural technologies encompass a wide array of innovative practices implemented on farms. Among others, they can refer to new seed varieties, fertilisers or irrigation procedures Doss, ; Kassie et al. In the literature, technology adoption has been identified as a main driver of farm productivity and profitability, and thus of farm economic sustainability Ali et al.
However, technologies allowing for productivity gains might only resolve part of the sustainable intensification challenge. Not all technologies are equally suited to achieve this goal as their adoption might not always lead to synergies but also to trade-offs across sustainability dimensions Dawkins, ; Lanigan et al. For instance, from the perspective of environmental synergies, Lanigan et al.
Therefore, the adoption of these technologies can result in enhanced farm economic and environmental sustainability. Additionally, technology-driven productivity gains can only show GHG mitigation benefits if emissions associated with intensification, particularly from off-farm sources, are offset by higher levels of efficiency Crosson et al.
In other words, increased productivity can mitigate GHG emissions of agricultural production if excessively high levels of external input application e. This can be a concern in intensive pasture-based production systems such as the Irish one.
In fact, since higher-yielding cows might have greater nutritional requirements, not always achievable from grazing alone Charlton et al. In the New Zealand context, Basset-Mens et al. Therefore, not all technology-driven productivity gains might enhance farm environmental sustainability. Similarly, Lanigan et al. From an animal welfare perspective, mastitis provides an interesting example of sustainability synergies and trade-offs.
Mastitis is a contagious production disease widely spread on dairy farms at global scale Sharma et al. In the last thirty years, its incidence has risen due to genetic selection heavily focused on milk production traits Algers et al. These traits are genetically antagonistic towards mastitis resistance, which is now increasingly taken into account in breeding programmes Algers et al.
Decreasing mastitis occurrence and improving herd health and welfare are a promising path towards more sustainable dairy systems Dawkins, ; Llonch et al. Indeed, the disease leads to substantial milk yield losses, decreased raw milk quality and avoidable culling decisions Geary et al.
VELKOMSTBONUS BETTING LINES
The ignore option of destring If there are non-numeric characters in our dataset, destring command will show an error non-numeric characters found For example, our data might have comma separators, therefore, destring will generate the above error.
Suppose that our variable strvar contains non-numeric values tabulate strvar if missing real strvar Suppose that the above code comes up with a list of the following non-numeric characters. Percent Cum. We can specify these in the ignore option. The real function The destring command is useful in a sense that it does not convert data to missing observations.
Instead, it gives you an error message when there are non-numeric characters in the variable. If you are sure that observations with non-numeric characters are not needed , you can use the real function with generate command. This may seem obvious, but I have had many students nonchalantly say "oh, so we can just replace those with zeros Consider this in the context of gas mileage. Different statistical software code missing data differently.
In Stata, if your variable is numeric and you are missing data, you will see. If you are working with string variables, the data will appear as [blank]. Missing data values will affect how Stata handles your data. It is possible that you might want the percentages to be computed out of the total number of observations, and the percentage missing for each variable shown in the table. This can be achieved by including the missing option which can be shortened to m after the tabulation command.
We would expect that it would perform the computations based on the available data and omit the missing values. Here is an example command. The output is show below. Note how the missing values were excluded.
Stata will perform listwise deletion and only display correlation for observations that have non-missing values on all variables listed. Stata also allows for pairwise deletion. Correlations are displayed for the observations that have non-missing values for each pair of variables. This can done using the pwcorr command. We use the obs option to display the number of observation used for each pair.
As you can see, they differ depending on the amount of missing. It is important to understand how missing values are handled in assignment statements. Consider the example shown below. The list command below illustrates how missing values are handled in assignment statements. The variable sum1 is based on the variables trial1 , trial2 and trial3. If the value of any of those variables were missing, the value for sum1 was set to missing.
Therefore sum1 is missing for observations 2, 3, 4 and 7. Whenever you add, subtract, multiply, divide, etc. In our reaction time experiment, the total reaction time sum1 is missing for four out of seven cases. We could try totaling the data for the non-missing trials by using the rowtotal function as shown in the example below. The results below show that sum2 now contains the sum of the non-missing trials. Note that the rowtotal function treats missing as a zero value. When summing several variables it may not be reasonable to treat missing as zero if an observations is missing on all variables to be summed.
The rowtotal function with the missing option will return a missing value if an observation is missing on all variables. Other statements work similarly. For example, observe what happened when we try to create an average variable without using a function as in the example below. If any of the variables trial1 , trial2 or trial3 are missing, the value for avg1 is set to missing.
Alternatively, the rowmean function averages the data for the non-missing trials in the same way as the rowtotal function. Here is a shortcut you could use in this kind of situation:. Finally, you can use the rowmiss and rownomiss functions to determine the number of missing and the number of non-missing values, respectively, in a list of variables. This is illustrated below.
For the variable nomiss , observations 1, 5 and 6 had three valid values, observations 2 and 3 had two valid values, observation 4 had only one valid value and observation 7 had no valid values. The variable miss shows the opposite; it provides a count of the number of missing values. It is important to understand how missing values are handled in logical statements.
Recording missing variables in stata forex airbnb stock valuation analysis Stata recode variables Delirium, forex options vs forex futures trading consider Login or Register Log in with. Recording missing variables in stata forex Ideally, the code that I am looking for would create a new time observation for each panel in case there is none only if f. You're welcome. Posts Latest Activity. Last edited by Steve Samuels ; 08 Feb Search in titles only. Thanks for providing a small data set, but the FAQ also ask that you show results which illustrate your problem.
The complete idiot guide to value investing blogs 48 Recording missing variables in stata forex Recording missing variables in stata forex Filtered by:. Dear List I have asked this question in different ways before, but it turns out I'm sure what i am doing right or wrong.
Recording missing variables in stata forex Jp morgan report impact investing G p forex Going back to my example, the first observation on panel 1 is in m2 and since x is the outcome of a variable in m1, I would like to add an observation for that month too. Last edited by Steve Samuels ; 23 Dec If I understand the problem correctly, some months of the year never appear in the data if for example data is not collected in March.
As my arguments apparently don't convince you, I won't try further.
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