6. Measuring management practices
6. 衡量管理实践 Working in Excel  在 Excel 中工作

Learning objectives for this part
本部分的学习目标

• Explain how survey data is collected, and describe measures that can increase the reliability and validity of survey data.
  说明如何收集调查数据，并描述可以提高调查数据的可靠性和有效性的措施。
• Use column charts and box and whisker plots to compare distributions.
  使用柱状图和箱形图比较分布。
• Calculate conditional means for one or more conditions, and compare them on a bar chart.
  计算一个或多个条件的条件均值，并在条形图上比较它们。
• Use line charts to describe the behaviour of real-world variables over time.
  使用折线图来描述实际变量随时间的变化。

First download the data used in the paper to understand how this information was collected. The dataset is publicly available and free of charge.
首先下载论文中使用的数据，以了解这些信息是如何收集的。该数据集是公开可用的，并且是免费的。

• Go to the World Management Survey public data download page.
  转到 World Management Survey 公共数据下载页面。
• Under the heading ‘Manufacturing: 2004–2010 combined survey data (AMP)’, click the ‘Download’ button.
  在标题为“制造业：2004-2010 年综合调查数据 （AMP）”下，单击“下载”按钮。
• Unzip the files in the downloaded zip folder into your working directory (the folder you will be working from).
  将下载的 zip 文件夹中的文件解压缩到您的工作目录 （您将使用的文件夹） 中。
• You may also find it helpful to download the Bloom et al. paper ‘Management practices across firms and countries’.
  下载 Bloom 等人的论文“跨公司和国家的管理实践”可能还会有所帮助。

1. To learn about how Bloom et al. (2012) conducted their survey, read the sections ‘How Can Management Practices Be Measured?’ and ‘Validating the Management Data’ (pages 5–9) of their paper.
   要了解 Bloom 等人（2012 年）如何进行调查，请阅读他们论文的“如何衡量管理实践”和“验证管理数据”（第 5-9 页）部分。

• Briefly describe how the interviews with managers were conducted, and explain some of the methods that the researchers used to improve the reliability and validity of their data. (There are a few technical terms that you may not understand, but these are not necessary for answering this question.)
  简要描述如何对经理进行访谈，并解释研究人员用来提高其数据可靠性和有效性的一些方法。（有一些技术术语您可能不理解，但这些不是回答这个问题所必需的。

• Three aspects of management practices were evaluated: monitoring, targets, and incentives. Do you think that these are the best criteria for assessing management practices? What (if any) important aspects of management are not included in this assessment? (You may also find it helpful to refer to the ‘Contingent Management’ section on pages 23–25 of the paper.)
  评估了管理实践的三个方面：监测、目标和激励措施。您认为这些是评估管理实践的最佳标准吗？本次评估不包括哪些（如果有的话）管理的重要方面？（您可能还会发现，参考论文第 23-25 页的“应急管理”部分也会有所帮助。

Now we will create some charts to summarize the data and make comparisons across countries, industries (manufacturing, healthcare, retail, and education), and firm characteristics.
现在，我们将创建一些图表来汇总数据，并在国家/地区、行业（制造业、医疗保健、零售和教育）和公司特征之间进行比较。

1. In ‘Manufacturing: 2004–2010 combined survey data (AMP)’, open the Excel document ‘AMP_graph_manufacturing.csv’. Use this data on manufacturing firms to do the following:
   在“制造业：2004-2010 年合并调查数据 （AMP）”中，打开 Excel 文档“AMP_graph_manufacturing.csv”。使用制造公司的此数据执行以下操作：

• In a new tab, create a table like Figure 6.2a, that shows the average management scores for all the firms in each of the twenty countries, and fill it in with the required values. The variables for the overall score and three individual criteria are ‘management’, ‘monitor’, ‘target’, and ‘people’. You may find it helpful to use Excel’s PivotTable option—see Excel walk-through 3.1 if you need guidance. For each criterion, rank countries from highest to lowest, then create and fill in a table like Figure 6.2b (see Excel walk-through 4.4 for help on how to assign ranks). Do countries with a high overall rank also tend to rank highly on individual criteria?
  在新选项卡中，创建一个类似于图 6.2a 的表格，该表格显示了 20 个国家/地区中每个国家/地区所有公司的平均管理分数，并填写所需的值。总分和三个单独标准的变量是 “管理”、“监控”、“目标” 和 “人员”。您可能会发现使用 Excel 的数据透视表选项很有帮助 - 如果您需要指导，请参阅 Excel 演练 3.1。对于每个标准，从最高到最低对国家/地区进行排名，然后创建并填写一个表格，如图 6.2b 所示（有关如何分配排名的帮助，请参阅 Excel 演练 4.4）。总体排名较高的国家是否也倾向于在单个标准上排名靠前？

• Create a bar chart that shows the average overall management score (the variable ‘management’) for each country, ordered from highest to lowest. (Hint: You will need to sort your data from highest to lowest, so that it appears correctly in the chart.) Your chart should look similar to Figure 6.1.
  创建一个条形图，显示每个国家/地区的平均总体管理评分（变量 'management'），从高到低排序。（提示：您需要从最高到最低对数据进行排序，以便数据正确显示在图表中。您的图表应类似于图 6.1。

• Compare your chart with Figure 1 in Bloom et al. (2012). Can you explain why your chart is slightly different? (Hint: See the note at the bottom of Figure 1.)
  将您的图表与 Bloom et al. （2012） 中的图 1 进行比较。您能解释一下为什么您的图表略有不同吗？（提示：请参阅图 1 底部的注释。

To look at how management quality varies within countries, instead of just looking at the mean, we can use column charts to visualize the entire distribution of scores (as in Empirical Project 1). To compare distributions, we have to use the same horizontal axis, so we will first need to make a frequency table for each distribution to be used. Also, since each country has a different number of observations, we will use percentages instead of frequencies as the vertical axis variable.
为了了解国家内部管理质量的变化，我们可以使用柱状图来可视化分数的整个分布，而不仅仅是查看平均值（如实证项目 1）。为了比较分布，我们必须使用相同的横轴，因此我们首先需要为要使用的每个分布制作一个频率表。此外，由于每个国家/地区的观测值数量不同，因此我们将使用百分比而不是频率作为纵轴变量。

1. For three countries of your choice and for the US, carry out the following:
   对于您选择的三个国家/地区和美国，请执行以下操作：

• Using the overall management score (variable ‘management’), create and fill in a frequency table similar to Figure 6.3 below for the US, and separately for each chosen country. The values in the first column should range from 1 to 5, in intervals of 0.2. (Hint: To count observations for a specific country only, you will need to use the IF function and FREQUENCY function together, as shown in Excel walk-through 6.1).
  使用总体管理分数（变量 'management'），为美国创建并填写类似于下图 6.3 的频率表，并为每个选定的国家单独填写。第一列中的值应介于 1 到 5 之间，间隔为 0.2。（提示：要仅计算特定国家/地区的观测值，您需要同时使用 IF 函数和 FREQUENCY 函数，如 Excel 演练 6.1 中所示。

• Plot a column chart for each country to show the distribution of management scores, with the percentage of firms on the vertical axis and the range of management scores on the horizontal axis. On each country’s chart, plot the distribution of the US on top of that country’s distribution, as shown in Excel walk-through 6.2.
  为每个国家绘制柱状图以显示管理分数的分布，纵轴上是公司的百分比，横轴上是管理分数的范围。在每个国家/地区的图表上，将美国的分布绘制在该国家/地区的分布之上，如 Excel 演练 6.2 中所示。

• Describe any visual similarities and differences between the distributions of your chosen countries and that of the US. (Hint: For example look at where the distribution is centred, the percentages of observations on the left tail or the right tail of the distribution, and how spread out the scores are.)

Excel walk-through 6.1 Using Excel’s IF function

Follow the walk-through in the CORE video, or in Figure 6.4, to find out how to use Excel’s IF function.

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How to use Excel’s IF function

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The data

In this example, we will make a frequency table for the US data in Columns A to C. It’s a good idea to put all the tables in a separate place from the data.

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Calculating frequencies for a particular country

We want to calculate the frequency but only for firms in the US. To do this, add an IF condition within the FREQUENCY function to tell Excel which data to use.

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Calculating frequencies for a particular country

By putting the IF function inside the FREQUENCY function, Excel will only use the data that satisfies the condition we specified (firms in the US).

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Calculating frequencies for a particular country

The formula that is completed in step 4 is: ‘(=FREQUENCY(IF(AMP_graph_manufacturing!N:N=”United States”,AMP_graph_manufacturing!A:A),Sheet2!A2:A22))’. After step 4, all the frequency values will be filled in.

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Using frequencies to calculate percentages

The $ symbol before the row numbers in the formula tells Excel to keep these row numbers the same when copying the formula to other cells. We used it here because we are dividing the frequency value by the total number of observations (cells B2 to B22).

Figure 6.4 How to use Excel’s IF function within another function.

Excel walk-through 6.2 Overlaying one column chart over another

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The data

In this example, we use data for the US (Columns A to C) and Chile (Columns F to H), and plot a column chart of the percentages in Columns C and H (see the steps in Figure 6.4 for how to calculate these).

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Plot a column chart

After step 3, the column chart will look like the one shown above.

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Change the appearance of the columns

First, we will remove the gaps between the columns, and make the columns overlap (rather than being plotted side-by-side). After step 7, there will be a vertical axis on the left and right side of the chart.

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Change the vertical axis values

In order to compare the distributions, make sure the left and right vertical axis have the same labels.

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Change the horizontal axis values and series names

Now, change the horizontal axis values to match the data in our frequency tables.

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Change the horizontal axis values and series names

After step 12, the horizontal axis value for the selected columns will change.

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Change the horizontal axis values and series names

Now, we will change the series names to country names.

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Change the horizontal axis values and series names

After step 14, the legend entry for that country will now be the country name.

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Change the horizontal axis values and series names.

Since we have two vertical axes on our chart (one for each data series), we need to change the horizontal axis labels for both axes before Excel will update the chart.

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Change the horizontal axis values and series names.

Finally, change the shading of the columns so we can see the distributions of both countries. After step 19, the distributions of both countries should be clearly visible.

Figure 6.5 How to overlay one column chart over another.

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Figure 6.6 Example of two overlapping distributions on the same column chart.

box and whisker plot

A graphic display of the range and quartiles of a distribution, where the first and third quartile form the ‘box’ and the maximum and minimum values form the ‘whiskers’.

Another way to visualize distributions is a box and whisker plotbox and whisker plot A graphic display of the range and quartiles of a distribution, where the first and third quartile form the ‘box’ and the maximum and minimum values form the ‘whiskers’.close⁠, which shows some parts of a distribution rather than the whole distribution. We can use box and whisker plots to compare particular aspects of distributions more easily than when looking at the entire distribution.

As shown in Figure 6.7, the ‘box’ consists of the first quartile (value corresponding to the bottom 25 per cent, or 25th percentile, of all values), the median, and the third quartile (75th percentile). The ‘whiskers’ are the minimum and maximum values. (In Excel, the ‘whiskers’ may not be the actual maximum or minimum, since any values larger than 1.5 times the width of the box are considered outliers and are shown as separate points.)

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Figure 6.7 Example of a box and whisker plot.
(Note: In Excel, the mean value is shown by X. In general, the median may not be in the centre of the box, and can differ greatly from the mean. Using the data shown in Figure 6.7 for a variable from the dataset, the mean and median are very similar.)

1. Using the same countries you chose in Question 3:

• Make a box and whisker plot for each country and the US, showing the distribution of management scores. You can either make a separate chart for each country or show all countries in the same plot. To check that your plots make sense, compare your box and whisker plots to the distributions from Question 3.

• Use your box and whisker plots to add to your comparisons from Question 3(c).

Excel walk-through 6.3 Drawing box and whisker plots

Follow the walk-through in the CORE video, or in Figure 6.8, to find out how to draw a box and whisker plot in Excel.

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How to draw a box and whisker plot

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The data

In this example, we will use data for the US (Column A) and Chile (Column B). To create a box and whisker plot of more than one variable, each variable needs to be in a separate column. You will need to filter, then copy and paste the required data into a new tab in Excel.

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Create box and whisker plots

After step 3, your box and whisker plot will look like the one above. The plots are ordered according to the columns in Excel, so the first plot corresponds to Column A, the second plot corresponds to Column B, and so on.

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Add a chart legend

First, we will add a legend to indicate to which country each plot corresponds.

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Change the legend entries

Now, we will change the labels in the legend. If your legend is already correctly labelled, skip Steps 6–9.

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Change the legend entries

After changing the name of the data series, the label in the legend will change.

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Change the legend entries

After step 9, your data should now be correctly labelled in the chart legend.

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Remove the outliers (optional)

Excel shows any observations that are greater than 1.5 times the box width (in absolute value) separately as outliers. If your chart is too cluttered with outliers, you can remove them to make your chart more readable.

Figure 6.8 How to create box and whisker plots.

From the manufacturing data, firms in the US seem to be managed better (on average) than firms in other countries. To investigate whether this is the case in other sectors, we will use data gathered on hospitals and schools.

1. Using the data for hospitals and schools (AMP_graph_public.csv):

• Create a table for hospitals and schools, showing the mean management score and criteria score (monitoring, targets, incentives) for each country, as in Figure 6.2a. (Hint: You may find it helpful to use Excel’s PivotTable option—see Excel walk-through 3.1.)

• Make separate bar charts for hospitals and schools showing the mean overall management score for each country, sorted from highest to lowest, as in Figure 6.1. Are the country rankings similar to those in manufacturing?

• Using your average criteria scores from Question 5(a), suggest some explanations for the observed rankings in either hospitals or schools. (You may find it helpful to research healthcare or educational policies and reforms in those countries to support your explanations.)

Learning objectives for this part

• Calculate conditional means for one or more conditions, and compare them on a bar chart.
• Construct confidence intervals and use them to assess differences between groups.

Using the management survey data collected by Bloom et al. (2012), we can compare average management scores across countries and industries. When we find differences between groups in the survey, we are interested in what that tells us about the true differences in management practices between the countries.

confidence interval

A range of values centred around the sample mean value, with a corresponding percentage (usually 90%, 95%, or 99%). When we use a sample to calculate a 95% confidence interval, there is a probability of 0.95 that we will get an interval containing the true value of interest.

In Empirical Project 2, we used p‑values to assess differences between groups. A p‑value tells us how unusual it would be to observe the differences between groups that we did, assuming our hypothesis is correct (and additional model assumptions about the data hold). If the p-value is small, we might then conclude that the data is not compatible with our assumptions (for example, that the two groups were drawn from populations with the same mean), and that there is a real difference between the underlying populations. Now we will use another method that helps us to allow for random variation when we interpret data, called a confidence intervalconfidence interval A range of values centred around the sample mean value, with a corresponding percentage (usually 90%, 95%, or 99%). When we use a sample to calculate a 95% confidence interval, there is a probability of 0.95 that we will get an interval containing the true value of interest.close⁠.

When we work with data, we usually have only a small sample from the entire population of interest. For example, the World Management Survey collects information from a selection of all the firms in a particular country. If we calculate the average management score for the sample, we have an estimate of the average management score across all firms in the country (the ‘true value’), but it may not be a very accurate estimate—especially if the sample is small and management scores vary a lot between firms.

A 95% confidence interval is a range of possible values within which the true value might lie. It is estimated from the mean and standard deviation of the data. As in the process of calculating p-values, we use a standard method that gives a good estimate provided that certain statistical conditions are satisfied. We cannot be certain that the true value lies in the range; we might have the bad luck to pick an atypical sample, in which case our estimate of the confidence interval would be atypical too. But we can say that when we use this method, there is a 95% probability that we will find an interval that contains the true value. One way to interpret this is to say that if we were able to repeat the process of sampling and calculating confidence intervals many times, roughly 95% of these confidence intervals would contain the true value.

As the name suggests, confidence intervals tell us how much confidence we can place in our estimates, or in other words, how precisely the sample mean is estimated. The confidence interval gives us a margin of error for our estimate of the true value. If the data varies a lot, the 95% confidence interval may be quite wide. If we have plenty of data, and the standard deviation is low, the estimate will be more precise and the 95% interval will be narrow.

Rule of thumb for comparing means

When comparing two distributions, if neither mean is in the 95% confidence interval for the other mean, the p-value for the difference in means is less than 5%.

This rule of thumb is handy when looking at charts. If two 95% confidence intervals don’t overlap, we can say immediately that the observed difference between the means for the two groups is unlikely to have arisen by chance alone (given that our hypothesis that there were no differences in the populations from which these groups were drawn and that our other assumptions about the data, such as random sampling, are correct). For a more definite conclusion, we can calculate the actual p-value (see Empirical Project 2) or construct a confidence interval for the difference in means. (This method involves more mathematics, so we will discuss that in Empirical Project 8.)

It is possible to calculate a confidence interval for any probability: however wide the 95% confidence interval, a 99% confidence interval would be wider, and an 80% one would be narrower. 95% is a common choice: it gives us quite a high degree of confidence, and to go higher tends to lead to very wide intervals. We will use 95% confidence intervals throughout this project.

To sum up: A confidence interval is a range of values centred around the sample mean value, with a corresponding percentage (usually 90%, 95%, or 99%). When we use a sample to calculate a 95% confidence interval, there is a probability of 0.95 that we will get an interval containing the true value of interest.

We will now build on the results from the Bloom et al. (2012) paper by using 95% confidence intervals to make comparisons between the mean overall management score for different countries and types of firms. The confidence interval for the population mean (mean management score for that country) is centred around the sample mean. To determine the width of the interval, we use the standard deviation and number of firms.

1. First look at manufacturing firms in different countries. Using the manufacturing data (AMP_graph_manufacturing.csv) for three countries of your choice and for the US:

• Create a summary table for the overall management score as shown in Figure 6.9, with one row for each country. (Hint: Use Excel’s PivotTable option.)

• Use Excel’s CONFIDENCE.T function to determine the width of the 95% confidence interval (this is the distance from the mean to one end of the interval). See Excel walk-through 6.4 for help on how to do this. You should get a different number for each country.

• Plot a column chart that shows the mean management score and add the confidence intervals to your chart.

• Use the width of the confidence intervals to describe how precisely each mean was estimated.

• Using your chart from Question 1(c) and this rule of thumb, what can you say about the differences between the US management score, and the scores of other countries? How would your results change if you use a different specified probability (for example, 99%)?

Excel walk-through 6.4 Creating confidence intervals and adding them to a chart

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Calculate the width of the confidence interval

In this example, we will use data for the US and Chile (shown in Columns A and B). To calculate the width of the 95% confidence interval (distance from the mean to one end of the interval), we first need to calculate the standard deviation and number of observations for each country. The 0.05 in the CONFIDENCE.T function is 1 − 0.95 (our specified probability).

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Plot a bar chart

After completing step 4, your bar chart will look similar to the one above.

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Add confidence intervals to the chart

The ‘error bars’ option in Excel plots confidence intervals. We will use the calculated width values from step 1 to determine the size of the error bars.

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Add confidence intervals to the chart

The positive error value determines the right end of the interval (the value above the mean). The negative value determines the left end of the interval (the value below the mean). Confidence intervals are symmetric, so we use the same values for both.

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The finished chart

After completing step 9, your chart will look similar to the one above. You can also add horizontal and vertical axis titles and a chart title.

Figure 6.10 How to create confidence intervals and add them to a chart.

1. Using the data for hospitals or schools (AMP_graph_public.csv), using all available countries:

• Create a summary table like Figure 6.9 for the overall management score, with one row for each country. (Hint: Use Excel’s PivotTable option.) Add a column containing the widths of the confidence intervals for the country means.

• Plot a column chart that shows the confidence intervals. Compare the management practices in the US with those in other countries. Are there any countries for which you can be confident that management practices are either better, or worse, on average than in the US? Explain your answer.

1. Look at the width of your confidence intervals and the corresponding standard deviation and number of observations for each one. Explain whether or not the relationship between them is what you would expect.

Learning objectives for this part

• Calculate conditional means for one or more conditions, and compare them on a bar chart.
• Construct confidence intervals and use them to assess differences between groups.
• Evaluate the usefulness and limitations of survey data for determining causality.

Besides documenting and comparing management practices across industries and countries, another purpose of the World Management Survey was to investigate factors that affect management quality.

One possible factor affecting differences in management is firm ownership. To look at the data for this factor in the healthcare and education sectors, we will focus on broad groups (public versus privately-owned firms), and for manufacturing firms we will focus on different kinds of private ownership.

1. Using the data for hospitals and schools (AMP_graph_public.csv):

• Create a pivot table for hospitals and schools, showing the average management score, standard deviation (StdDev), and number of observations, with ‘country’ as the row variable, and ‘ownership’ (public or private) and ‘ind’ as the column variables.

• Use your pivot table from Question 1(a) to calculate the confidence interval widths for management in public and private hospitals. Then do the same for schools.

• Plot a bar chart (one for hospitals and another for schools) showing the means from Question 1(a) and the confidence intervals from Question 1(b). Describe the differences between public and private firms within countries and compare management scores for the same firm type across countries. (For example, is one type of firm generally better managed than the other? Are there similar patterns for hospitals and schools? If you have done Question 5 in Part 6.1, you may want to discuss whether the rankings change after conditioning on ownership type.

Besides ownership type, management practices may vary depending on firm size, though it is difficult to predict what the relationship between these variables might be. Larger firms have more employees and could be more difficult to manage well, but may also attract more experienced managers. We will look at the conditional means for manufacturing firms, depending on whether they are above or below the median number of employees (calculated from the data), and see if there is a clear relationship.

1. Using the data for manufacturing firms (AMP_graph_manufacturing.csv):

• In a new column in the original spreadsheet, use Excel’s IF function (see Excel walk-through 6.1) to create a variable that equals ‘Smaller’ if a firm has less than the median number of employees (330) and ‘Larger’ otherwise. In natural log terms, ‘Smaller’ corresponds to log employment of less than 5.80.

• For two countries of your choice and the US, create a pivot table that shows the mean overall management score, standard deviation, and number of observations, with ‘country’ and ‘ownership’ as the row variables, and firm size (from Question 2(a)) as the column variable. (Note: When there is only one observation in a group, there is no standard deviation.)

• Use your pivot table from Question 2(b) to calculate the confidence interval width for each firm size and ownership type.

• Plot a column chart for each country, showing the means from Question 2(b) and the confidence intervals from Question 2(c). Describe any patterns you observe across ownership types and firm size in each country.

Excel walk-through 6.5 Using Excel’s IF function

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The data

This is what the manufacturing data looks like. We will create a new variable in Column T, according to the log employment values in Column E. We have labelled our new column ‘size’ (cell T1).

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Create a new variable

After completing step 2, you will have a variable for firm size. We used the IF function to fill the cells in Column T with the word ‘Smaller’ if log employment is smaller than 5.8, otherwise the cell is filled with the word ‘Larger’.

Figure 6.11 How to use Excel’s IF function.

So far, we have looked at associations between firm characteristics and management practices, but have not made any causal statements. We will now discuss the difficulties with making causal statements using this data and examine how we might determine the direction of causation.

1. For each of the following variables, explain how it could affect management practices, and then explain how management practices could affect it:

• education level of managers (percentage with a college degree)

• number of competitors

• firm size (number of employees).

1. One way to establish the direction of causation is through a randomized field experiment. Read the discussion on pages 22–23 of the Bloom et al. paper (the section ‘Experimental Evidence on Management Quality and Firm Performance’) about one such experiment that was conducted in Indian textile factories.

• Briefly describe the idea behind a randomized field experiment, and explain, with reference to the results of the experiment in India, whether we can use it to determine the direction of causation between management practice and firm performance. The paper ‘Does Management Matter? Evidence from India’ provides more details about the experiment (pages 9–10 are particularly useful).

• Figure 12 in the paper shows productivity in treatment and control firms over time, with 95% confidence intervals. Use the information in the chart to describe the effect of the treatment on firm productivity.