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[经济学前沿] 聚集经济文献阅读2(5)- What Causes Industry Agglomeration [推广有奖]

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文献阅读第二个专题 : 聚集经济。
第5篇文献

What Causes Industry Agglomeration, 2010
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关键词:Industry ration Causes ATION ratio

What Causes Industry Agglomeration.pdf

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感兴趣领域——宏观经济、区域经济与技术经济
相关papers

geographic concentration US manifacturing industries.pdf

426.56 KB

EG 指数

附录what causes agglomeration.pdf

464.06 KB

Testing for Localization Using Micro-Geographic data.pdf

1.51 MB

DO 指数

The Geographic Concentration of Industry Does Natural Advantage explain agglomeration.pdf

455.43 KB

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geographic concentration US manifacturing industries,1997

内容简报

文章建立了EG指数和协同聚集指数,并使用美国数据进行了测度。结果表明:几乎所有的产业都具有一定程度的地方化。但很多产业的聚集程度较低。
EG指数的优点是:他考虑产业内企业的分布和区域面积的差异。从而在将产业聚集程度在产业间、区域间进行比较时变得可靠。

精彩文段
Localized industry-specific spillovers, natural advantages, and pure random chance all contribute to geographic concentration.
EG index controls for differences in the size distribution of plants and for differences in the size of the geographic areas.

EG指数
γ=(G-(1-∑_i▒x_i^2 )H)/((1-∑_i▒x_i^2 )(1-H))=(∑_(i=1)^M▒〖(s_i-x_i)〗^2 -(1-∑_i▒x_i^2 ) ∑_(j=1)^N▒z_j^2 )/((1-∑_i▒x_i^2 )(1-∑_(j=1)^N▒z_j^2 ))

s_i is the share of an industry’s employment in each of M geographic areas, x_i is the share of total employment in each of those areas. Herfindahl index H=∑_(j=1)^N▒z_j^2  is the industry plant size distribution.

If the plant’s location decisions are made in accordance with the model of the previous section, then proposition 1 implies that the index γ is an unbiased estimate of the quantity γ^na+γ^s-γ^na γ^s that captures the strength of the agglomeration forces in the model.

EG指数的四个性质
第一,使用企业数据,较容易计算。第二,the scale of the index allows one to make comparisons with a no-agglomeration benchmark in that E(γ)=0 if the data are generated by the simple dartboard model of random location choices with no natural advantages or industry-specific spillovers. 第三,the index is comparable across industries in which the size distribution of firms differs. 第四,the index is also comparable across industries regardless of differences in the level of geographic aggregation at which employment data are available in the different industries。

在使用不同空间维度,计算EG指数并进行比较时,要注意了。模型并没有考虑空间溢出问题,但是现实中却存在这个问题。Our model imposes an extreme limitation on the geographic scope of forces that produce localization in two ways. First, when potential spillovers exist, they are realized only if firms choose to locate in the same geographic area. Second, natural advantages are drawn independently for each geographic area.
In practice, we would expect that spillovers might provide some benefit also to plants locating in nearby areas. In this case, an estimate of EG that is computed from county-level data would be expected to be smaller than an estimate that is computed from state-level data.

We shall generally adopt the convention of referring to those industries with γ’s above 0.05 as being highly concentrated and to those with γ’s below 0.02 as being not very concentrated.

协同聚集指数
γ^c=(G/((1-∑_i▒x_i^2 ) )-H-∑_(j=1)^r▒〖γ ̂_j w_j^2 (1-H_j)〗)/((1-∑_(j=1)^r▒w_j^2 ) )
G^j, H_j, and w_j are the raw concentration, the plant Herfindahl index, and the employment share of the jth industry. γ ̂_j is the value of our index of concentration as computed from the data on the jth industry. G is for the raw concentration of employment in the group as a whole. H=∑_j▒〖w_j^2 H_j 〗


评论
工作量之大,对模型、计量的掌握之深,叹服。

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Testing for Localization Using Micro-Geographic Data, 2005

DO指数的5个优势
In summary, any test of localization should rely on a measure which (i) is comparable across industries; (ii) controls for the overall agglomeration of manufacturing; (iii) controls for industrial concentration; (iv) is unbiased with respect to scale and aggregation. The test should also (v) give an indication of the significance of the results. The approach we propose here satisfies these five requirements.

文章思路:产业内每对企业的距离会形成一个分布,比较这个分布与随机分布的差异性。
We build on work by quantitative geographers on spatial point patterns (see Cressie, 1993, for a comprehensive review) that we extend to address issues of spatial scale and significance. The basic idea in our geo-computations is to consider the distribution of distances between pairs of establishments in an industry and to compare it with that of hypothetical industries with the same number of establishments which are randomly distributed conditional on the distribution of aggregate manufacturing.

结论
We apply our approach to an exhaustive U.K. manufacturing data-set. Four main conclusions emerge with respect to four-digit industries: (i) 52% of them are localized at a 5% confidence level, (ii) localization takes place mostly between 0 and 50 km, (iii) the degree of localization is very skewed across industries, and (iv) industries that belong to the same industrial branch tend to have similar localization patterns.


方法-四步
We first select the relevant establishments. The second step is to compute the density of bilateral distances between all pairs of establishments in an industry. The third step is to construct counterfactuals. Finally we construct local confidence intervals and global confidence bands to take care of our fifth requirement. 提示:第三步使用全部产业(整体)的情况,作对比,构建反事实。

评论
关键和难点是:处理反事实。即从哪个样本中抽样

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What Causes Industry Agglomeration,2010

内容简报
构建coagglomeration indices,将这一指数与产业关联、劳动力池、知识溢出和自然禀赋联系起来,考察各个因素对聚集的影响。发现自然禀赋解释了产业聚集的40%,马歇尔的理论也都得到了验证。


精彩部分

考虑的是产业间的协同聚集,而不是产业内的聚集。
产业的聚集,已经超越了地理空间。其布局是基于全球战略和全球资源的。
The EG coagglomeration index of industry i and j is
γ_ij^c=(∑_(m=1)^M▒〖(s_mi-x_m)(s_mj-x_m)〗)/(1-∑_(m=1)^M▒x_m^2 )
where m indexes geographic areas. s_mi is the share of industry i’s employment contained in area m. x_m measures the aggregate size of area m, which we model as the mean employment share in the region across manufacturing industries.

DO indices (就业人口加权)
K ̂_ij^Emp (d)=1/(h∑_(r=1)^(n_i)▒∑_(s=1)^(n_j)▒〖e(r)e(s)〗) ∑_(r=1)^(n_i)▒∑_(s=1)^(n_j)▒〖e(r)e(s)f((d-d_(r,s))/h)〗
where d_(r,s) is the Euclidean distance between plants r and s, f is a Gaussian kernel density function with bandwidth h, n_i and n_j are the number of plants in industries i and j, respectively.

具体见DO-指数一文的1095页,公式(6)。


四大指标
(1)产业间的投入比例和产出比例:表达货物的运输成本
we define undirectional versions of the input and output variables by Inputij =        max {Inputi←j, Inputj←i}and Outputij =max {Outputi→j, Outputj→i}. We also define a combined
InputOutputij=        max {Inputij, Outputij}.

(2)劳动力池
the 1987 National Industrial-Occupation Employment Matrix (NIOEM) matrix provides industry level employment in 277 occupations, and we define Shareio as the fraction of industry i’s employment in occupation o. We measure the similarity of employments in industries i and j through the correlation of Shareio and Sharejo across occupations.

(3)知识溢出
We  base  our  metrics  of  information  flows  on  patents  and  research  and  development (R&D), which reflect only the highest level of information flows, rather than worker level spillovers.

(4)自然禀赋
This  methodology  follows  Ellison  and  Glaeser (1999),  who  model  16  state  level  characteristics that afford natural advantages in terms of natural resources, transportation costs, and labor inputs. Combining these cost differences with each industry’s intensity of factor use, Ellison and Glaeser (1999)estimate a spatial distribution of manufacturing activity by industry that would be expected due to cost differences alone.
We employ these expected spatial distributions of industries across states to calculate expected coagglomeration levels CoaggijnA for industry pairs. Separate expected coagglomerations due to natural advantages are constructed for the EG and DO metrics. These measures simply substitute the predicted spatial employments by industry into the EG and DO formulas outlined in Section I. Essentially, this approach measures how coagglomerated the two industries would be if their locations were determined entirely by the interactions of industry characteristics and local characteristics. The DO metric again requires some slight modifications, which we document in the online Appendix. The pairwise correlation between expected and actual coagglomeration using this technique is 0.2 and 0.4 for the EG and DO techniques, respectively.

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