Figure A1
Source: own calculations. For the data sources see text.
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Figure A2
Source: own calculations. For the data sources see text.
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Figure A3
Source: own calculations. For the data sources see text.
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Figure A4
Source: own calculations. For the data sources see text.
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Figure A5
Source: own calculations. For the data sources see text.
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Table A1: Price series.
South Pacific Rest
1850 110 236 84
1860 112 113 93
Notes: Prices come from Carter et. al. (2006).
Table A2: Mean efficiency values per region
Technical Efficiency Scale Efficiency Mix Efficiency
New England 0.5193 0.5458 0.7072
Middle Atlantic 0.6070 0.6430 0.8437
West 0.5764 0.6525 0.8410
Pacific 0.6344 0.6979 0.8228
South 0.5856 0.6126 0.8377
Source: own calculations. For the data sources see text. The numbers show what share of the output of a firm on the frontier the actual firm achieved.
Appendix 2: The Constant Returns to Scale specification
As was already pointed out in the introduction, efficiency is a relative concept, and the DEA is the mathematical representation of this concept as it estimates efficiency of an observation relative to the other observations in the dataset. Using variable returns to scale leads to an effi-ciency frontier as depicted in figure A 6:
Figure A 6: The Variable Returns to Scale Model.
In the picture above the small establishments will be evaluated against the frontier that is constituted by the firms A, B, C, D and E and linear connections between them. Therefore, although the technology inhibits significant decreasing returns to scale and the ratio of output to input is much higher for the small establishments, all three groups will have high efficiency values, as they are all close to the efficiency frontier. This means that efficiency values show in this case more about how homogeneous the three groups are than about their respective performances. If we used a constant returns to scale model, the resulting frontier would look like in figure A 7.
Figure A 7: The Constant Returns to Scale Model.
Now, Firms D and E would receive efficiency values of less than 100 percent, they are identified as inefficient. Additionally, as E is now no longer on the frontier, the large firms would all receive lower efficiency values. This indicates that they cannot reach the ratio of output to input that the smaller ones achieve. This example shows that the constant returns to scale model is more appropriate to evaluate the efficiency of different size classes compared to each other.
3 The Impact of the American Civil War on Manufacturing Effi-ciency.
Abstract:
This paper uses the Data Envelopment Analysis to measure the development of efficiency around the American Civil War and to compare the counties that were affected by battles with those that were not. The results show that the battleground counties developed better in tech-nical and scale efficiency, and had a disadvantage in mix efficiency relative to the counties that were not affected. Comparing the impact of the abolition of slavery and the war destruc-tion we find the former to have had a stronger impact on manufacturing.
This chapter is based on a working paper with the same title currently under review at the journal of business history.
3.1 Introduction
The American Civil War is an event that has been intensively studied. With regard to manu-facturing, scholars have asked what impact the war had on the firms and whether this can ex-plain Southern backwardness. In looking at the consequences of the war for industry one can take two different views. One is to look at the structure of the manufacturing sector and see how many establishments were destroyed and how output expanded or contracted. A second view is to look inside the industry and look at the production processes. Did the war introduce new production methods? How strong was the loss of capital, e.g. how many machines were missing after the war? These questions relate to the efficiency of firms, which will be altered through damages and will have an impact on the development of industry. This topic has not been studied quantitatively before, and in order to accomplish this task we use the Data En-velopment Analysis, which has not been used widely in the war literature, to gain direct evid-ence on efficiency changes through the war.
War destruction has a negative influence on industry, as capital is destroyed, workers are killed and communication lines are disrupted. However, destruction may also have posit-ive consequences. If more advanced technology is available at the time of reconstruction, it will give the newly developed industry a technological advantage. Olson (1982) argued that this cannot be beneficial to the economy, as better technology would have been implemented even without the war (see Arbetman and Kugler 1989), but if capital markets are imperfect, war can lead to the use of superior technology that otherwise would not have been used. This may increase efficiency in industry.
In this study, we provide direct evidence of efficiency changes by a war while we do not have to rely on conclusions drawn from growth studies. Specifically, we consider the ef-fect of the American Civil War on manufacturing efficiency. The Civil War has some features that are favorable for our analysis. First, we have data shortly before and after the war, which minimizes the effect of third factors. Second, we can compare regions that suffered from the war and regions that were not directly affected by the fighting which have a similar institu-tional context and are therefore comparable. Third, we have a large data set from the Americ-an MAmeric-anufacturing Census at hAmeric-and which decreases the likelihood of finding spurious results that are due to the small sample sizes and are not related to the war.
The discussion about the effects of the Civil War on industry has a long tradition in the economic history literature, and we will discuss this literature below. A systematic discussion of the effect of the Civil War on efficiency is though still missing in the literature. The impact of the War on industry is often treated in an aggregated way, such as in the literature dis-cussed below, or in small samples like Gallman’s (1990) study on Philadelphia. Contrary to that we use a large firm-level data set.
We use the Data Envelopment Analysis technique to estimate manufacturing effi-ciency and to separate it into scale, mix, and technical effieffi-ciency.11 Afterwards, regressions are used to separate the effects of the war from third factors, especially the abolition of slavery. As this event freed black slaves in manufacturing and as it is possible that slaves and free workers are not equally productive, it could influence efficiency and therefore our regres-sions separate the effects of the two events.
11 Technical efficiency refers to the amount of waste produced in the production process, scale efficiency means how successful scale economies were exploited while mix efficiency looks at the overutilization of production factors. Below we provide a more detailed treatment of the concepts.
There are some studies that come close to the method and period used in the present paper (Grabowski and Pasurka 1988 and 1989, Field-Hendrey 1995, Craig and Field-Hendrey 1996). These articles are concerned with agricultural efficiency and the effect of slavery.
None of these studies is concerned with the effect of the Civil War on manufacturing as is the case here. Hutchinson and Margo (2006) study capital intensity and labor productivity with a production function approach. As a by-product they look at the development of economies of scale as we do, but our approach differs as we also study both technical and mix efficiency.
This work develops as follows: The next section describes which economic influences the war had on industry. Then the Data Envelopment Analysis is briefly explained and how it can be used to decompose measured efficiency into technical-, mix-, and scale-efficiency.
From some theoretical considerations, testable hypotheses are derived about the effect of the war on efficiency. We then describe the data, present our estimates and discuss the results.
The last section concludes.
3.2 The Civil War and its Economic Impact on American Industry
The American Civil War was the most devastating event in the history of the United States as the direct costs alone can be estimated at about seven billion dollars (in 1860 US-$) while the GDP of the United States in 1869 was just about four billion dollars (O’Brien 1988, Ransom 1998). From a total population of around 30 million people, 620,000 were killed during the war. Destruction in the South was severe and the old plantation system based on slavery was dismantled by the emancipation of the slaves. In the major cotton producing states in the
South output per person of the rural population failed to reach its pre-war level for the rest of the nineteenth century (O’Brien 1988). William Russel attempted to pin down the impact of the war and the end of slavery on the South and concludes that the negative effect from the abolishment of slavery affected the incomes of southerners more strongly than the war itself (Russel 2001).
Destruction in the South was unequal. Georgia and the Shenandoah Valley for ex-ample were severely devastated as Union troops tried to destroy the crop supplies of the con-federate armies. Other states experienced less direct fighting. In Georgia, General Sherman actually used the census of 1860 to help identify the rich counties that had many valuable goods to seize (Hughes 1994).
There was a lively debate in the literature concerning the impact of the Civil War on manufacturing (Engelbourg 1979). Charles and Mary Beard (1927) and Louis Hacker (1940) claimed that the Civil War was a decisive event that strengthened economic growth. This view was later tested intensively and largely rejected (Engerman 1966, Cochran 1961, Sals-bury 1962). It seems as if the Civil War had no boosting effect on manufacturing at all, and although the period of the Civil War was a period that led to the increased distribution of agri-cultural machinery (Schmidt 1930), few new machines were invented (Wright 1943). In some industries however, such as small arms and textiles, the production processes were modified by the Civil War. But other important industries were not at all changed by the war (Clark 1962).
3.3 The Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a technique that allows the estimation of efficiency val-ues of decision making units (DMU). In this paper every establishment is a decision making unit. It is a non-parametric technique that uses a given set of DMUs to estimate a production frontier so that every DMU lies on or below the frontier. Every DMU is then compared with the frontier. An establishment that produces with the same amount of input only 70% of the output of a second establishment on the frontier will receive an efficiency score of 70%. No assumption regarding the underlying production function or market structure is necessary, only an assumption concerning the returns to scale. It can deal with multiple inputs and out-puts, and can be seen as a multivariate generalization of the simple output/input measure.12 No weights have to be assigned to the inputs or outputs a priori, as those are estimated from the data. A basic DEA-model for n DMUs can be represented by the following linear program (Cooper, Seiford and Tone 2006).
12 It is the fraction of a weighted sum of outputs over a weighted sum of inputs.
Here θ denotes the efficiency score, µ denotes the weights for the s outputs, y the corresponding output, ν denotes the weights for the m inputs, while xdenotes the input. All weights have to be non-negative, and the weighted output must be smaller or equal to the weighted input, so that the efficiency score is between 0 and 1.
The DEA has its origin in a paper by Farrell (1957) and is common in the literature since the 1980s. Today it is widely used to measure the efficiency of different institutions like US Army bases or hospitals (See Taveres 2003 for a literature review).
There are a number of different models available which use different assumptions, in particular concerning the nature of returns to scale in the production function. These different models can be used to decompose the efficiency of DMUs into different efficiency-categories, namely pure technical efficiency, scale efficiency and mix efficiency (Cooper, Seiford and Tone 2006, chapter 5, Diacon, Starkey and O’Brien 2002). Pure technical efficiency (pte) means how efficient the DMU used the available inputs, so that two DMUs using the same in-puts but with different levels of output will have different levels of pure technical efficiency.
For example, if a new machine or organizational invention enables a firm to produce more goods with the same amount of labor and capital then its pte scores will increase. Also a re-duction in the amount of waste that is produced increases technical efficiency. Contrary to that a DMU will be called mix-inefficient if it can reduce input of one factor without reducing output. This DMU could increase the produced output by using the inputs in a different mix, thereby gaining efficiency. Scale efficiency refers to the case when a DMU does not produce in the most productive scale size. This means for example that a DMU which is operating un-der increasing returns to scale will be called scale-inefficient. When this DMU increases the
size of its operations, it would, due to the increasing returns to scale, achieve a higher ratio of outputs to inputs. Of course, all three sources of inefficiency may appear simultaneous.
In the following we will first report the development of the median efficiency of an in-dustrial sector, which means the median of all efficiency scores from that sector obtained from input-oriented models. Looking at the median efficiency gives us information about the distribution of the firms with respect to efficiency. If the median efficiency is, for example, around 0.9 then most firms produce efficiently. If the estimated median efficiency is around 0.5, then the distribution of efficiency scores is much wider and hence in this sector some firms do not make proper use of their inputs.
Looking only at the median efficiency would not allow direct comparisons of indus-tries over time. If the median efficiency decreases from one point in time to a later date it is still possible that the technology in all firms improved, but some firms improved more than others. What is observed here is only how similar the firms are.
We avoid these problems by estimating the frontier using all firms in the data set, but the median efficiency is estimated separately for those firms that were located in the states which were directly impacted by the war and firms that were in the other states. If there was no war impact, the development of the industry in different parts of the country would have been equal with respect to the efficiency measures. So the level of the median efficiency of the Southern firms should develop similarly to the median efficiency of the Northern firms. If the development of efficiency was different in the South compared to the North (evaluated against all enterprises), this indicates a real effect of the war. Because of this we will mainly
discuss the differences in efficiency development between the North and the South, and not whether efficiency actually improved or not.
The North and the South had a different economic structure. The North was more in-dustrialized than the South, which relied heavily on cotton production. This does not influence our results for several reasons. First, we only compare the industries available and differences in agriculture will not influence this comparison. Second, if there are differences between the regions, such as different institutions, these differences will mostly influence the level of effi-ciency and also the development in the decades around the Civil War. We focus solely on dif-ferences in the Civil War decade that are not present in the other decades, hence the influence of institutions is minimized. It was argued in the literature (Pessen 1980) that the institutional situation in the North and the South was roughly equal. What about institutional differences between the United States and the Confederacy? The data point in 1870 could be affected, but the Confederacy lasted only four years and had ended 5 years before the next census.
Moreover, there was no peace-time Confederacy, and the war-time effects are exactly what we want to measure. We conclude therefore that institutional differences clearly influence ef-ficiency, but they pose no problem in the setting we use.
For identifying outliers a super-efficiency model is employed. In this model the ana-lyzed firm is excluded from the estimation of the frontier, which means that the efficiency score may exceed 100% if the firm constitutes a part of the frontier in a normal model. An es-timated score of 200% for example would mean that the firm could double the quantities of all their inputs, and, producing the same output, would still be defined efficient. DMUs that receive a very high score in a super-efficiency model seem to be much more efficient than the
rest of this industry, which is unlikely in a competitive market. Therefore, those highly effi-cient establishments are regarded as data errors.
3.4 The Civil War and Manufacturing Efficiency
Regarding the technical side of production, a differentiation between two points in time is necessary. Directly after the war, when most factory-equipment was destroyed, tech-nical efficiency should have lower in the battlefield-regions because fewer machines were available and older equipment was used, which was not as efficient as its more modern coun-terpart. So in the years directly following the war technical efficiency will be lower in the damaged regions. But as the economy recovers from the war many new machines will be bought and the old equipment will be replaced. The new equipment will be technologically more advanced than the older one because of the technology developed during the time of the war. Wars also lead to many inventions for the military that can be used afterwards for civil-ian purposes. It may also be the case that governments intervene in the industry to strengthen important military suppliers, which will lead to efficiency gains in those factories. Some years after the war technical efficiency of the firms impacted by the war will be higher than in the other firms, as they use new equipment, while in the other firms not all of the old equipment will be replaced, for example because of financial constraints. But then over time also firms from regions which saw no fighting replace their old machines with the new equipment, so the technical efficiency scores should converge in the long-run (Modernization hypothesis).
During battles and campaigns factories will be destroyed which have to be rebuilt after the war. If the economy before the war produced under constant returns to scale, scale ineffi-ciency after the war will be observed, as the firms are smaller than before (Scale
During battles and campaigns factories will be destroyed which have to be rebuilt after the war. If the economy before the war produced under constant returns to scale, scale ineffi-ciency after the war will be observed, as the firms are smaller than before (Scale