Within U.S. trade and the long shadow of the American secession.

Felbermayr, Gabriel ; Groschl, Jasmin

I. INTRODUCTION

One hundred fifty years after Confederate troops attacked Fort Sumter in South Carolina, a recent U.S.-wide survey by the Pew Research Center summarizes the findings as: "The Civil War at 150: Still Relevant, Still Divisive." (1) The poll reports that 56% of Americans believe that the Civil War is still relevant to politics and public life today. And that 4 in 10 Southerners sympathize with the Confederacy. But does the long defunct border between the Confederation and the Union still affect economic relations between U.S. states that belonged to different alliances today? Is the former border still relevant, still divisive, for economic transactions? This paper sheds light on this question using bilateral trade flows between states.

The Civil War has cost 620,000 American lives, more than any other military conflict. Goldin and Lewis (1975) document that it has retarded the economic development of the whole nation and of the South in particular. And, as the Pew poll shows, the nation is still divided along the lines of the former alliances over whether the war was fought over moral issues--slavery--or over economic policy. Yet, long before the war, the Southern and the Northern economies differed: The South was dominated by large-scale plantations of cotton, tobacco, rice, and sugar, whose profitability relied on forced labor. It exported crops to Europe and imported manufacturing goods from there. The North, dominated by smaller land-holdings, was rapidly urbanizing; slavery was practically abolished north of the Mason-Dixon Line by 1820. (2) Its infant manufacturing industries were protected by import tariffs against European competition.

The North-South divide is very visible in contemporaneous state-level data. On average, the South is still poorer, more rural, more agricultural, less educated, more religious, and has different political views. The economic gap may have narrowed (Mitchener and McLean 1999), in particular after the end of segregation in the 1960s. But, political disagreement, in particular on the role of federal government, continues to beset the country. A special sense of Southern identity continues to mark a cultural divide within the United States.

This paper contributes to a growing literature on the long shadow of history for economic transactions (Falck et al. 2010; Head, Mayer, and Ries 2010; Nitsch and Wolf forthcoming). It shows that the former border still constitutes a discontinuity in the economic geography of the United States. The modern literature has identified cultural differences across countries as impediments of international trade, but typically not within the same country. Estimates of various border effects abound in the literature and there are well-tested empirical methods to measure their trade-inhibiting force. The more challenging question in this paper is: Can the estimated border effect be interpreted as a genuine Union-versus-Confederation effect?

We proceed in three steps. First, employing an OLS approach with state fixed effects for bilateral trade between states, we find a robust, statistically significant, and economically meaningful trade-inhibiting effect of the former border. In the preferred 1993 data, on average, the historical border reduces trade between states of the former Confederation or Union by about 13% to 14%. In comparison, the Canada--U.S. border restricts trade by 155% to 165% (Anderson and Van Wincoop 2003). Nitsch and Wolf (forthcoming) find that the former border between East and West Germany restricts trade by about 26% to 30% in 2004. Running a million placebos, we show that no other border between random groups of (old) U.S. states yields a stronger trade-reducing effect.

The result is robust to employing alternative methodologies (in particular a Poisson model), using different waves of the Commodity Flow Survey (CFS) (1997, 2002, 2007), drawing on sectoral rather than aggregate bilateral trade data, measuring transportation costs differently (travel time instead of sheer geographical distance), or allowing for more flexibility by using distance intervals as in Eaton and Kortum (2002) instead of a log-linear distance measure. Including the rest of the world, or different treatment of states, whose allegiance to either the Union or the Confederation is historically not obvious, does not change the results. The estimated border effect represents an ad valorem tariff equivalent of about 2% to 7%. Interestingly, the effect is stronger (and more robust) in the food, manufacturing, and chemicals sectors than in mining, which is characterized by a completely standardized good, or machinery, where the pattern of specialization across North and South is very strong.

In a second step, we add a large array of contemporaneous variables to the original model to account for observable differences between the South and the North. The controls are meant to capture migrant, ethnic, or religious networks. While these variables matter empirically, they do not reduce the estimated border effect. We account for cultural differences expressed by different colonial relations across states, and for different patterns of urbanization. We include variables that relate to the institutional setup of states, or that measure differences in the judicial system. We control for differences in endowment proportions, or for differences in the structure of the states' economies. Finally, we add demographic factors and test the Linder hypothesis. Most of these controls have some explanatory power, but they do not undo the border effect. The estimate falls from 13% to 11%. This finding survives the same battery of robustness checks applied to the parsimonious model.

Third, we acknowledge that the North-South border, marked by the Secession, is likely not to be exogenous. Engerman and Sokoloff (2000, 2005) suggest that it is related to endowment differences between Northern and Southern states in cropland, or in the size and structure of agricultural production. The emergence of the border may have to do with historical ethnic patterns, historical educational achievements of the population, or institutional differences as captured by the historical incidence of malaria as in Acemoglu, Johnson, and Robinson (2002). Finally, and most importantly, it may result from the incidence of slavery. Not all of these variables matter empirically for contemporaneous trade patterns, but they cannot easily be excluded from the explanation of contemporaneous bilateral trade on conceptual grounds. Including them into the gravity equation does not undo the "Secession effect." Quite to the opposite, the estimated effect actually increases. Finally, we extend the analysis to Western states, but keep the same coding of the border. Thus, we add pairs of states which have been completely unaffected by the Secession. Then, the border dummy essentially captures whether two states have been on opposing sides of the Civil War rather than belonging to the North or the South. We continue to find a border effect (7% to 19%), which can now be attributed more plausibly to the Secession.

The literature offers explanations of border effects in terms of "political barriers," "arte-fact," and "fundamentals." The first should be largely absent in an integrated economy such as the United States. The second relates to difficulties in separating the impact of border-related trade barriers from the impact of geographical distance (Head and Mayer 2002) or to problems of statistical aggregation (Hillberry and Hummels 2008). We deal with these issues by using alternative measures of trade costs and by a large number of placebo exercises. We view our results as consistent with the "fundamentals" approach: historical events have shaped cultural determinants of trade which still matter today.

Our results show that the United States is not a single market, even 150 years after the Civil War. The historical conflict still is divisive today. This is an important lesson for the European integration process, which is more complex due to the lack of a common language, a common legal/judicial system, common regulatory framework, and--most important in our context--the fact that the last huge conflict is not 150 but only 67 years away. Hence, one should not be too optimistic in assessing the economic effects of political union. From a welfare perspective, our results allow two interpretations. First, it could be that the Secession has had lasting effects on trade costs. By shaping the distribution of (railway) infrastructure or business networks (production clusters), and more generally, by affecting bilateral trust, South-North trade frictions are still higher than intra-group frictions. To the extent that our estimates measure this, it signals a long-lasting welfare loss due to the Secession. Second, it could be that the Secession had lasting effects on preferences. The trade embargo during the war could have led to persistent preferences for local goods due to habit formation. In that case, a welfare interpretation of our findings is more problematic, in particular quantitatively. However, if the divergence of preferences was indeed caused by the war, depending on the precise characterization of preferences, the estimate can still be interpreted as an indication of welfare losses.

The literature on border effects was pioneered by McCallum (1995), who finds that trade volumes between Canadian provinces were about 22 times larger than those between Canada and the United States in 1988. Subsequent research shows that states usually trade 5 to 20 times more domestically than internationally. (3) Few studies have moved from simply exploring border barriers to investigating and explaining potential causes. Wei (1996) and Hillberry (1999) do not find that tariffs, quotas, exchange rate variability, transaction costs, and regulatory differences can explain the border effect. Recent studies illustrate that the impact of borders also extends to the sub-national level, implying that additional reasons for high local trade levels must exist. Examples are Wolf (1997, 2000), Hillberry and Hummels (2003), Combes, Lafourcade, and Mayer (2005), Buch and Toubal (2009), and Nitsch and Wolf (forthcoming).

The remainder of the paper is structured as follows. Section II provides details of the empirical strategy. Section III describes the benchmark results, placebo estimations, and a sensitivity analysis. Section IV uses a large array of contemporaneous controls to address a potential omitted variables problem. Section V attempts to explain the "Secession effect" by historical variables and by adding Western states to the analysis. The last section concludes.

II. EMPIRICAL STATEGY AND DATA

A. Empirical Strategy

Our empirical strategy follows Anderson and Van Wincoop (2003), henceforth AvW, and the subsequent research. Based on a multi-country framework of the Krugman (1980) constant elasticity of substitution (CES) model with iceberg trade costs, the literature stresses that the consistent estimation of bilateral barriers requires to take multilateral trade resistance into account.

Anderson and Van Wincoop (2003) show that the CES demand system with symmetric trade costs can be written as

(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

and [z.sub.ij] [equivalent to] [x.sub.ij] /([Y.sub.i][Y.sub.j]) is the value of bilateral exports [x.sub.ij] between state i and state j relative to the product of the states' gross domestic products (GDPs), [Y.sub.i] and [Y.sub.j]. [[beta].sub.0] is a constant across state pairs, [[beta].sub.1] = -[alpha]([sigma] - 1) and [[beta].sub.2] = -[rho]([[sigma] - 1]), where [sigma] > 1 is the elasticity of substitution. [Border.sub.ij] = (1 - [[delta].sub.ij]) represents the historical border line between Union and Confederate states, which takes a value of unity if states in the pair historically belonged to opposing alliances and zero otherwise, ln [Dist.sub.ij] is the log of geographical distance between states. [X.sub.iy] denotes a vector of additional controls, and the multilateral resistance terms are defined as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] where [[theta].sub.k] is the share of income of state k in world income. In our exercise, we substitute multilateral resistance terms with state fixed effects and switch y on and off and work with various vectors [X.sub.ij]. [[epsilon].sub.ij] is the standard error term.

The complication with estimating that model is that the multilateral resistance terms In [P.sub.i.sup.1-[sigma]] and In [P.sup.1-[sigma].sub.j] depend on estimates of [[??].sub.1] and [[??].sub.2] in a nonlinear fashion. We follow a large strand of literature (Anderson and Van Wincoop 2003; Feenstra 2004; Hummels 1999; Redding and Venables 2004) and apply origin and destination fixed effects in an ordinary least squares (OLS) gravity regression. The fixed effects capture all time-invariant origin and destination specific determinants, such as multilateral resistance terms, but also geographical characteristics and historical or cultural facts. The model deploying state fixed-effects accounts for any state-level unobserved heterogeneity. We proxy trade costs by geographical distance, adjacency, and the historical border between the former alliances of states in the Union and the Confederacy.

In this paper, we also use the Poisson pseudo maximum likelihood (PPML) method with state fixed effects suggested by Santos Silva and Tenreyro (2006). The PPML approach has important advantages when trade flows are measured with error. Then, heteroskedastic residuals do not only lead to inefficiency of the log-linear estimator, but also cause inconsistency. This is because of Jensen's inequality which says that the expected value of the logarithm of a random variable is different from the logarithm of its expected value. This suggests that E(ln [z.sub.ij]) not only depends on the mean of [z.sub.ij], but also on higher moments of the distribution. Heteroskedasticity in the residuals, which at first glance only affects efficiency of the estimator, feeds back into the conditional mean of the dependent variable, which, in general, violates the zero conditional mean assumption on the error term needed to guarantee consistency.

For robustness reasons, we also estimate the nonlinear least squares (NLS) model suggested by Anderson and Van Wincoop (2003) to identify the border effect. (4) Finally, we implement the idea of Baier and Bergstrand (2009) to linearize the model with the help of a first-order expansion of the multilateral resistance terms and estimate by OLS.

B. Data Sources

For within--and cross-state trade flows, we focus on bilateral export data from the 1993, 1997, 2002, and 2007 CFS collected by the Bureau of Transportation Statistics. The CFS tracks shipments in net selling values in millions of dollars. The CFS covers 200,000 (100,000; 50,000; 100,000) representative U.S. firms for 1993 (1997; 2002; 2007). The literature is concerned about the low number of firms surveyed in the waves after 1993, see Erlbaum, Holguin-Veras, and Hancock (2006). For this reason, existing studies have usually focused on the 1993 wave which represents about 25% of registered U.S. firms; we follow in this tradition. GDP by state stems from the Regional Economic Accounts, provided by the Bureau of Economic Analysis. Bilateral distance is calculated as the great circle distance between state capitals.

Our primary sample consists of 28 U.S. states divided into two groups that originate from the split caused by the Secession (Figure 1). The South comprises 11 states, while the North consists of 17 states, as listed in Table 1. Five states (Delaware, Kentucky, Maryland, Missouri, and West Virginia) are excluded from the benchmark sample since soldiers from these states fought on both sides of the Civil War and the allegiance to either group of states is unclear. Still today, these five states do not belong to the (fuzzily defined) "deep South." (5) Somewhat abusing terminology, we call these five states border states. We conduct sensitivity analysis with respect to the choice of excluding those states. (6)

[FIGURE 1 OMITTED]

Table A1 in the Appendix shows averages and standard deviations (for the year 1993) of the variables used in this study. Southern states have on average substantially larger shares of Afro-Americans (22.9% vs. 7.4%); the share of Christians is higher while the share of Jewish citizens is smaller (0.8% vs. 2.1%). The percentage share of urban population is lower in the South than in the North (65.7% vs. 72.9%). Historically (as of 1860), average farm sizes were substantially larger in the South than in the North; this gap has closed since then. The same is true for educational outcomes (illiteracy and average schooling). The GDP per capita average across the South is about 12% lower than the average across the North. The most dramatic differences in 1993 data pertain to institutional variables: The North is much more unionized than the South. All Northern states had a minimum wage while only 45.5% of the Southern states had one. In the 1992 presidential election, 64% of Southern states voted Republican while only 12% of Northern states did. (7)

Figure 2 plots cumulative distribution functions (CDFs) of bilateral trade flows scaled by both states' GDPs. (8) For all years, the cumulative distribution function for North-South flows lies to the left of flows within the North or the South. Interestingly, South-South flows stochastically dominate North-North flows. In 1993, where data quality is best, the median flow is about 30% larger within the South than across South and North. This is of course a rough exercise as it does not control for other variables, such as distance, but it gives a first visual sense of how big the border effect is.

III. THE EFFECT OF THE FORMER UNION-CONFEDERATION BORDER

A. Benchmark Results

Estimating Equation (1) allows to assess the average impact of the border on cross-border North-South trade flows relative to within-region flows. Table 2 provides our benchmark results for the year of 1993. In line with the gravity literature, the estimated elasticity of distance is very close to -1 and highly significant at the 1% level. In our sample, and in accordance with the literature, adjacency increases bilateral trade. Due to the omission of border states from our baseline estimations, adjacency correlates negatively with the border. If adjacency increases trade, its omission would bias the border effect away from zero. In column (1), we estimate the model using origin and destination fixed effects, which account for all unobserved importer and exporter characteristics. Our model explains 84% of the variation in trade patterns. Under fixed effects, cross-border trade is on average 12.8% ([e.sup.-0137] - 1) smaller than within region trade. Hence, the border equals a tariff of 2% to 7%, depending on the choice of elasticity of substitution. (9) Compared to international border effects, this is a substantial amount for a sub-national barrier caused by an event more than a century ago. Anderson and Van Wincoop (2003) find that cross-border trade for the Canada-U.S. case is about 80.8% lower than within trade. (10) This amounts to a tariff equivalent of 20% to 128%. Results by Nitsch and Wolf (forthcoming) suggest that the former East-West border within Germany reduces cross-border trade by about 20.5% relative to within-region trade. (11)

In column (2), we use two indicator variables to measure within-group trade relative to cross-border trade separately for the North and the South. We find that trade within the South is 1.66 times larger than cross-border trade with the North in 1993. Counter-intuitively, the North trades 1.26 times less within the region than across the border. This is puzzling, but fits the evidence displayed in Figure 2. Next, we estimate a PPML approach with state fixed effects suggested by Santos Silva and Tenreyro (2006). Column (3) shows that the border estimate remains very close compared to the OLS fixed-effects estimation. The border impeding trade effect between the North and the South persists with a magnitude of 14%.

[FIGURE 2 OMITTED]

Importantly, the puzzle on North-North trade is not robust. The negative effect turns positive but insignificant when estimating the model using PPML, while results on other variables remain very much the same; see column (4). PPML can account for zeros in the trade data (16 observations in our data set). However, the main difference to OLS lies in the fact that it obtains consistent estimates even in the presence of measurement error causing heteroskedasticity. Therefore, we interpret the puzzling finding in column (2) as an artifact. (12)

In column (5), we estimate a "multicountry" model. We consider trade between U.S. states, between 20 OECD countries (13) and exports from individual U.S. states to OECD countries (14) into the PPML fixed-effects model of column (3). We use OECD trade, distance, and GDP' data provided by AvW and U.S. state exports to OECD countries from Robert Feenstra's webpage. (15) Column (5) reports that the distance parameter remains relatively close to -1, while the border reduces North-South trade within the United States by 13.4%. Sample size increases to 1,764 observations, while the explanation power of our model increases only slightly.

In the final step we explore the CFS data in more detail, as disaggregated trade flows at the two-digit commodity level are available. This is in the spirit of Hillberry (1999), who estimated commodity specific border effects for products traded between Canada and the United States in 1993. We pool over all commodities available in the specific year. As commodities are subject to varying transportation costs, we include Origin x Commodity and Destination x Commodity fixed effects following Chen (2004). For 1993, results for the pooled commodity fixed-effects estimation are depicted in Table 2, column (6). We find that the border reduces North-South trade by 7.7%.

Estimates of the Anderson and Van Wincoop (2003) NLS model indicate that the border reduces trade flows between the North and the South by about 19.6% in 1993. When we estimate the model by including MR terms into the gravity estimation as suggested by Baier and Bergstrand (2009), we find that the adjusted explanation power of the estimation slightly falls to 67%, while the border estimate remains very close compared to the fixed-effects estimation. The impeding trade effect of the border between the North and the South remains at 12%. (16)

B. Placebo Estimations

Is there something special about trade across the former Union-Confederation border as opposed to trade across other hypothetical borders? To deal with this question, we randomly assign 11 out of the 28 "old" U.S. states to a hypothetical "South" and the remainder to a hypothetical "North." (17) Based on regression (1) of Table 2, we run a million placebo regressions. We find a negative and significant (at the 10% level) border effect in 7% of the cases. In 12 cases, the border effect is slightly larger than the 12.8% found in our benchmark case. The largest effect we find is 1.2 percentage points larger than our original effect, but the standard error is so large that one cannot reject the hypothesis that the effect is identical to the 12.8 benchmark result. In all 12 cases, the "South" consists predominantly of New England and the Great Lakes States.

[FIGURE 3 OMITTED]

Figure 3 compares the hypothetical South to the "true" sample by counting the number of misallocated states (put into the "wrong" group). Diagram (a) depicts that all samples, where one state was misallocated, yield a negative and statistically significant border effect. If two states are misallocated that share drops to about 58%; if more than five states are put into the "wrong" group the share falls below 10%. Diagram (b) displays the absolute value of the average border effect found in different subsamples. If one state is allocated to the "wrong" group, the average border effect is about 0.11 (as compared to 0.14 in the "correct" grouping). The average effect falls quickly as more states are misallocated and is below 0.01 if five or more states are exchanged.

In further placebo exercises, we investigate border effects between coastal and interior states as well as between Eastern and Western states in the whole United States. We do not find a border effect between coastal and interior states. There is no border effect either at a hypothetical East-West border (approximately drawn at the 90[grados] longitude line). Differences between these states can be explained by our contemporaneous controls. (18) To provide further falsification tests, we consider regions where states are clustered together and split the 28-state sample into Eastern and Western states. We find no significant border effect. (19) Further, we arbitrarily break North and South into two regions (Northeast-Midwest; Southeast-Southwest) each. We find no evidence of a border effect within the subregions. (20)

C. Sensitivity Analysis

Table 3 summarizes border effect estimates obtained from using the 1997, 2002, or 2007 waves of the CFS rather than the more reliable 1993 data. Across the OLS fixed-effects model, the PPML fixed-effects approach, and the commodity-level regression, we find negative border effects that are all highly statistically significant. Interestingly, there is no evidence that the border effect shrinks over time. Comparison across time is hindered by different sampling across waves. The former border reduces trade by between 7% and 16%, with the average effect clustering around at about 12%.

The use of geographical distance as a measure of transportation costs has been criticized by Head and Mayer (2002). Since 71% to 75% of shipments in the United States are transported by truck (Department of Transportation), we use actual travel time from Google maps as an alternative measure of transportation costs. Ozimek and Miles (2011) provide a tool to retrieve these data. We find that the use of travel time reduces the estimated border effect in the preferred 1993 sample from 10% to 7%, thereby confirming the hypothesis that geographical distance slightly inflates the estimated border effects. However, across waves, the effect remains negative and statistically significant. (21)

As it is important to measure distance correctly, we allow for further flexibility and use distance intervals as in Eaton and Kortum (2002) instead of a log-linear distance measure. We therefore create five distance intervals (in kilometers) including distances as: [0,250), [250,500), [500,1000), [1000,2000), and [2000,max] and include dummies thereof into the regression. We find border effects to be slightly more trade impeding compared to when using the log-linear distance measure and still highly significant for all years. (22) Interestingly, we find a similar distance ranking as in Eaton and Kortum (2002) for U.S. states. Distance intervals that capture relatively close state pairs have a smaller negative effect on trade than pairs that are further apart relative to the closest distance interval [0,250). From this we conclude that our border results are not qualitatively affected by the distance measure.

To make sure that our treatment of border states (i.e., states whose allegiance was unclear and that are therefore excluded from our benchmark sample), does not bias our results, we assign them alternatively to the South or to the North. The border states were slave states, but officially never seceded, so it is counterfactual to include them into the South. We find that the assignment of those border states does not matter qualitatively for our findings. Estimated effects are slightly lower than when border states are excluded altogether. (23) California, Oregon, and Nevada fought on the side of the Union and may thus be included in the sample on the side of the North, even though they were separated from the other states by a large distance and the territories that did not yet belong to the United States. Results do not change qualitatively if we include the three states in the North. The inclusion of the three states rather increases the border effect, which turns out to reduce North-South trade by 17% under OLS fixed-effects and 18% under PPML fixed effects. (24) In addition, the Northern states trade more with another under the OLS fixed-effects approach if we include the three states in the North. (25)

D. Estimates by Sector

Finally, we also run regressions sector-by-sector. Table 4 provides summary results, suppressing other coefficients except the one on the border dummy. (26) The estimated border effect is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], confounding the elasticity of substitution and the trade-cost increasing effect of the border. It is therefore not surprising that the low-[sigma] agricultural sector features a high but only moderately robust estimate, while the low-[sigma] mining sector does not display a border effect (except in 1997). No border effect exists in the machinery sector, neither. This is presumably due to North-South differences in comparative advantage that the simple model does not capture. The border effect is most pronounced in the chemical and manufacturing sectors, where the degree of product differentiation is high (hence, [sigma] low).

One may conjecture that the Secession has continuing negative effects on the level of trust between market participants. It may also have affected the strength of preferences for local products. Both mechanisms should have no bearing on standardized (homogeneous) goods whose quality can easily be verified and where idiosyncratic features of demand should not matter (e.g., steel). It is therefore comforting that the border effect is largest in sectors with typically strongly differentiated output. The finding therefore supports the view that the former border reflects a cultural divide.

IV. ACCOUNTING FOR OBSERVED CONTEMPORANEOUS HETEROGENEITY

A. Benchmark Results

In this section, we investigate whether observable characteristics of state pairs bias the estimated coefficient. We include a large number of contemporaneous determinants of trade that are discussed in the empirical literature stepwise into the regression. If the variables are not bilateral in nature, we bilateralize them by either taking the absolute difference of variables in state i and state j, denoted by the operator [DELTA], or by using the product of variables in state i and state j, denoted by the operator x. The product of variables relates to network effects between pairs, while the [DELTA] operator focuses on the difference between state pairs. (27) Table 5 reports results for our benchmark year 1993. All estimations include origin and destination fixed effects.

Column (1) of Table 5 depicts the benchmark result including geographical variables with a border effect of 13%. In column (2), we account for the impact of ethnic, religious, or cultural networks (Combes, Lafourcade, and Mayer 2005; Rauch 1999; Rauch and Trindade 2002) and migration within the United States (Helliwell 1997; Head and Ries 1998; Millimet and Osang 2007). The literature reasons that common culture and tastes increase trade flows as they facilitate contracts and instill trust; they also make it more likely that states produce and consume similar goods. Migration and networks might bias the border effect estimate upwards as they increase trade but are negatively associated with the border. To test the impact of networks we include (1) cross-state migration stocks of people residing in one state but were born in another taken from the American Community Survey Decennial Census; (2) the product of the share of Afro-Americans in total state population from the Population Estimates Program; (3) the product of the Jewish population in total state population from the American Jewish Yearbook; and (4) self-reported affinity to Christianity, other religious groups, or no religion from the American Religious Identification Survey 2008 Report, into the estimation. We find that migration networks, high shares of Afro-Americans, of population shares affiliated to Buddhism, Hinduism, or Islam, and of people not self-identifying with any religious group spur trade flows. A 1% increase in the bilateral migration stock indicates an increase in trade by 13% in column (2). (28) If we include network controls, the border still turns out to reduce bilateral trade by 11.3%. In addition, common colonial heritage, also included in column (2), may have lasting effects on bilateral trade. (29) We construct an indicator variable that takes value one if a pair of states had a common colonizer (Britain, France, or Spain) and zero otherwise. We find that a common colonial past increases bilateral trade by about 22%. Yet, while most of those network variables matter statistically, they reduce the estimated border effect only slightly.

Column (3) examines the impact of labor market and political institutions. We control for labor market institutions by including dissimilarities in union membership and density from Hirsch, Macpherson, and Vroman (2001), as well as a dummy for the existence of minimum wage legislation provided by the U.S. Department of Labor. In theory, differences in labor market institutions could increase bilateral trade, because differential legislation acts as a source of comparative advantage as in Cunat and Melitz (2012). In our analysis, we find that institutional differences tend to reduce trade (albeit statistical precision of estimates is nonexistent). This may signal that institutional differences are caused by some deeper differences in cultural norms and that the latter discourage trade by more. Column (3) also controls for differences in the political alignment in the 1992 presidential election (Clinton against Bush sen.) and whether states elect or appoint the judiciary. Voting behavior has no statistically measurable effect on trade, while the difference in judiciary appointment procedure turns out to depress bilateral trade flows. The estimated border effect, however, remains virtually unchanged.

In column (4), we include controls for the difference in relative factor endowments of states, thereby accounting for the Heckscher-Ohlin trade theory. Omitting differences in factor proportions might lead to an upward bias of the border coefficient, as differences in factor proportions should increase trade flows and appear to be more pronounced when the border is present. To measure contemporaneous differences in relative factor proportions and human capital accumulation, we include the absolute difference in (1) capital-labor shares from Turner, Tamura, and Mulholland (2008); (2) shares of high and low skilled in the population (30); (3) average years of schooling for the population over 25 from Turner et al. (2007); (4) cropland from the National Resource Inventory Summary Report; (5) average farm size from the Census of Agriculture; (6) agricultural relative to total output; and (7) manufacturing relative to total output from the Bureau of Economic Analysis. As in other gravity exercises, classical Heckscher-Ohlin variables do not show up statistically significant, though both the variables on the difference in the capital-labor ratio and the difference in relative skill endowment bear the right sign. Differences in the availability of cropland reduce bilateral trade. Contemporaneous differences in factor endowments do not capture the border, which still reduces North-South trade by 10.3%.

Column (5) includes demographic variables such as the difference in contemporaneous population and population density from the Population Estimates Program, as well as fertility rates from the Vital Statistics of the United States. Common demographic features across states may suggest common preferences, so that bilateral trade is larger for such states. The estimated parameters, however, are insignificant throughout. The border effect remains negative and significant.

Finally, following the literature on the Linder effect, we include the difference in the log of per capita income as in Thursby and Thursby (1987), Bergstrand (1989), and Hallak (2010). The hypothesis is that states with dissimilar GDP per capita should have differing preference structures and, hence, trade less. Since the border correlates negatively with GDP per capita in the data, omitting the Linder term may bias the border effect away from zero. This is, however, not what we find. In column (6), we find no support for the Linder hypothesis; the estimated border effect does not move. We have also experimented with direct measures of inequality (Gini coefficients), but without success.

Column (6) represents our most comprehensive and preferred model. The border effect is about 11.2%. It explains 87% of the variation in bilateral trade flows, 67% of which are attributable to included variables and controls. (31)

B. Sensitivity Analysis

Table 6 summarizes sensitivity results pertaining to the comprehensive model in column (6) of Table 5. (32) Panel A deploys the OLS fixed-effects approach. Our baseline border effect of -0.119 is reported in column (A1). We find a negative and significant border effect for 1993 and 2002, while the effect for 1997 and 2007 are insignificant. Results based on the CFS from 1997 onwards suffer from the fact that the number of firms surveyed is only around 25% of those surveyed in 1993. In Panel B, we turn to the PPML model that includes fixed effects. The border barrier turns out to be strong only in 1993. If we use the pooled commodity fixed effects setup with ImporterxCommodity and Destination x Commodity fixed effects following Chen (2004) in Panel C, we find a strong trade impeding effect for all years (except for 2002). Overall, we can conclude that the findings on the border effect compare well to our earlier results. The border reduces cross-border trade by 7% to 21%, depending on the year and the specification. (33)

V. ACCOUNTING FOR HISTORICAL DETERMINANTS

A. Benchmark Results

The economic literature on the emergence of armed conflicts depicts that strong bilateral trade links decrease the probability that two countries go to war, while multilateral openness increases the odds of conflict (Martin and Thoenig 2008). If determinants of bilateral trade are persistent over time, the border could not be considered exogenous in the statistical sense. Historical bilateral trade data are, however, not available. But, one can include historical variables that may, through their impact on historical trade patterns, affect the probability of conflict (and thus the incidence of the border). Moreover, Eichengreen and Irwin (1998) suggest that history might affect contemporaneous trade flows through persistent effects on institutions.

According to Engerman and Sokoloff (2000, 2005), dissimilarities in agricultural land use, driven by soil endowments and climate, led to the South adopting slavery and, more broadly, to the emergence of conflicting economic interests between the North and the South, and ultimately, to the Secession. The different economic models may have long-lasting effects on inequality within states, which may, in turn, be relevant for today's level of economic transactions (Linder effect). It may also have persistent effects on institutions, which affect contemporaneous bilateral trade. The historical settlement structure may have induced networks along cultural lines that survived over time. (34) Absolute differences in historical variables are positively correlated to the border, so that their omission may bias the estimated border effect away from zero.

To account for these possibilities, Table 7 includes historical differences in (1) cropland; (2) average farms size; (3) population density; and (4) illiteracy rates of the non-slave population. (35) In columns (1) to (3), we find that none of these variables matter statistically, except for historical farm size differences which are significant at the 5% level. Including farm size increases rather than decreases the border coefficient to -0.234. This is surprising as historical farm size differences correlate positively with the border.

One would expect the legacy of slavery to partly capture the border barrier in column (4). However, we find that differences in slave shares in 1860 exert no impact on bilateral trade patterns and do not explain away the border barrier. (36) Interestingly, the inclusion of the absolute difference in shares of free blacks in 1860 exerts a positive and significant effect on contemporaneous trade in column (7).

In addition, similarities in culture due to similar settlement structures in U.S. states before the war could have induced social and business networks that have survived over time and still affect trade. We therefore include the product in the shares of French, Spanish, Irish, British, and German settlers in 1860. While Spanish, German, or British heritage has no particular impact on trade, Irish heritage decreases bilateral trade significantly in column (5). States with a large share of French settlers trade more amongst each other.

According to Acemoglu, Johnson, and Robinson (2002), historical climatic differences measured by the incidence of malaria, may have affected the characteristics and quality of institutions. In the present case, it is conceivable that the high risk of malaria in the South has led to the acceptance of slavery by the local elite and may therefore constitute a deep reason for the conflict. It may also affect contemporaneous trade flows through its lasting effect on institutions. So, we include the malaria risk index in 1860 from Hong (2007). We find neither a significant effect on trade nor does historical climate explain away the border. In the last column, we include all historical controls simultaneously in our model. All in all, we find that the border reduces trade by 22%, even when we include variables capturing the historical determinants of the Secession. (37)

B. Including the West

From the previous analysis, one cannot conclude that the Secession has caused the observed border effect in contemporaneous trade data. Including historical variables that relate to the deep reasons for the Civil War goes some way in dealing with reverse causation. However, it fails to account for unobserved shocks that both make the odds for Secession and today's bilateral trade flows larger. Unfortunately, no instrument is ready-to-use in an IV approach.

One way to nudge the analysis closer to identifying a causal effect consists in separating the whole of the United States--including the West--into states that underwent a treatment by the Secession and states that were not affected by these historical events. We separate the states into three groups--the North, the South, and the West--still excluding border states, the District of Columbia, Alaska, and Hawaii. (38) The border dummy is unity for states that found themselves on opposite sides of the Civil War and zero for all other pairs of states. Adding the West adds a control set of state pairs that are characterized by their absence of a past shaped by the Civil War.

Table 8 reports the results. All models include additional contemporaneous controls. (39) In columns (1), (3), and (5), we find for the OLS fixed effects, the PPML fixed effects, and the pooled commodity fixed-effects regression a significant trade impeding effect of the Secession treatment. The effect ranges between 7% and 19%. In addition, we again find in column (2) that the South trades more amongst each other while the effect on the North is negative, but turns insignificant when we control for heteroskedasticity in the PPML fixed-effects approach in column (4). There seems not to be any particular trade effect within Western states. (40)

When we estimate border effects for a sample of South and West states (41) and a separate sample of North and West states, (42) we see no border effect. In some cases, we even find a positive and significant coefficient such that Southern and Western states trade more rather than less with another.

VI. CIVIL WAR AT 150: STILL RELEVANT, STILL DIVISIVE

The former border between the Union and the Confederation is still relevant today: The defunct border represents a trade barrier that lowers trade between U.S. states by on average 7% to 22%. In a million placebo estimations, we find supportive evidence that the magnitude of this border effect is unique. The result is robust to using alternative waves of the CFS, to different econometric methods, or to the inclusion of Western states or the rest of the world. It cannot be substantially attenuated, let alone eliminated, by adding a vast array of contemporaneous and historical variables that correlate both with the border dummy and, potentially, also with bilateral trade.

The great Mississippi novelist and poet William Faulkner famously writes "The past is never dead. It's not even past" ("Requiem for a Nun," 1951). This holds true for the Secession that tore the United States apart 150 years ago, even when the judgment is based on bilateral trade data and econometric analysis: Trade between the former Confederation and the former Union is about 13% smaller on average than within the alliance. Several additional results stand out: First, the effect of the long defunct border on today's trade is not attributable to the legacy of slavery alone. It becomes weaker if not the Secession but the status of slave states is the criterion for belonging to one of the two groups. Second, the border effect is not merely a North-South effect. When the border is redefined to reflect whether two states have been on opposing sides in the Civil War, it remains significantly negative. Third, the trade inhibiting force of the former border has to do with the degree of differentiation of products: the higher, the stronger. This suggests that the channel through which the border still matters may be through cultural affinity or trust.

Our results imply that one cannot view the United States as a single market. The effect of the former Union-Confederation border persists after 150 years. The finding suggests that one should not be overly optimistic as to other regional integration projects. This applies most notably to Europe, where the last major war ended only 67 years ago and the history of conflict is much longer and bloodier. Moreover, in contrast to the United States there is no pre-war history of integration, and other frictions related to languages, legal systems etc. are plentiful.

In terms of welfare, our results imply that trade disruptions in the past can still constitute barriers today. By distorting the flow of trade away from the structure that would have obtained without the Secession, they present continuing welfare losses. So, by its long-run effects on economic integration armed conflicts may cast a very long shadow on the welfare of future generations.

ABBREVIATIONS

CDFs: Cumulative Distribution Functions

CES: Constant Elasticity of Substitution

CFS: Commodity Flow Surveys

GDP: Gross Domestic Product

MR: Multilateral Resistance

NLS: Nonlinear Least Squares

OECD: Organization of Economic Cooperation and Development

OLS: Ordinary Least Squares

PPML: Poisson Pseudo Maximum Likelihood

doi: 10.1111/j.1465-7295.2012.00510.x

Online Early publication January 10, 2013

APPENDIX

TABLE A1
Summary Statistics by State, 1993

Unit of Observation: State Level

Sample                         North (N = 17)      South (N = 11)

Variable                        M        SD         M        SD

Black Share                    7.412     5.519    22.855     7.871
Jewish Share                   2.105     2.339     0.809     1.285
Christian Share               86.882     3.059    91.636     3.139
Other Religion Share           3.235     2.278     1.727     1.272
No Religion Share              7.647     1.998     5.000     1.673
Urban Share                   72.853    16.095    65.655    12.098
In 1860 Cropland              15.038     1.045    15.228     0.806
In 1860 Farm Size              4.785     0.184     5.940     0.291
In 1860 Population Density     3.338     1.384     2.454     0.929
In 1860 Illiteracy Rates       1.604     0.415     2.683     0.303
1860 Slave Share               0.000     0.001    39.700    11.369
1860 Free Black Share          1.018     0.999     1.170     1.326
1860 French Share              0.302     0.202     0.254     0.619
1860 Spanish Share             0.004     0.005     0.032     0.076
1860 Irish Share               6.890     4.303     0.918     1.057
1860 German Share              4.772     4.244     0.886     1.271
1860 British Share             4.250     2.216     0.306     0.204
1860 Malaria Risk              0.126     0.073     0.351     0.057
In Capital-Labor Ratio        11.610     0.261    11.520     0.227
In High-Low Skilled Ratio      0.264     0.316    -0.256     0.256
In Average Schooling           2.579     0.023     2.538     0.023
In Cropland                    7.821     2.223     8.574     0.656
In Farm Size                   5.309     0.570     5.574     0.424
In Agri. / Tot. Output        -4.515     0.687    -4.159     0.427
In Manuf. / Tot. Output       -1.615     0.250    -1.661     0.364
In Population                 15.237     1.009    15.534     0.624
In Population Density          5.175     1.145     4.602     0.485
In Fertility                   4.127     0.071     4.184     0.065
In Income Per Capita          10.129     0.129    10.011     0.115
Union Membership              18.106     5.470     8.436     2.826
Union Density                 19.812     5.218    10.382     3.009
Minimum Wage                   1.000     0.000     0.455     0.522
Republican                     0.118     0.332     0.636     0.505
Judiciary Election             1.824     0.883     1.182     0.405

Unit of Observation: State Level

Sample

Variable                     Description

Black Share                  Share (%) of blacks in population.
Jewish Share                 Share (%) of Jewish in population.
Christian Share              Share (%) of Christian in population.
Other Religion Share         Share (%) of people with other religion.
No Religion Share            Share (%) of people with no religion.
Urban Share                  Share (%) of urban population.
In 1860 Cropland             1860 cropland in 1,000 acres.
In 1860 Farm Size            1860 average farm size in acres.
In 1860 Population Density   1860 population by square km.
In 1860 Illiteracy Rates     1860 share of non-slave illiterate.
1860 Slave Share             1860 slaves in population.
1860 Free Black Share        1860 free blacks in population.
1860 French Share            1860 French in population.
1860 Spanish Share           1860 Spanish in population.
1860 Irish Share             1860 Irish in population.
1860 German Share            1860 German in population.
1860 British Share           1860 (American) British in population.
1860 Malaria Risk            1860 Malaria risk index.
In Capital-Labor Ratio       Capital relative to labor.
In High-Low Skilled Ratio    Bachelor to high school, age [greater
                               than or equal to] 25.
In Average Schooling         Years of schooling.
In Cropland                  Cropland in 1,000 acres.
In Farm Size                 Average farm size in acres.
In Agri. / Tot. Output       Agri. over total output, mio US $.
In Manuf. / Tot. Output      Manuf. over total output, mio US $.
In Population                Total population in thousands.
In Population Density        Population by square km.
In Fertility                 Live births per 1,000 women, age 15-44.
In Income Per Capita         Total GDP per capita.
Union Membership             Percentage of union membership.
Union Density                Percentage of union density.
Minimum Wage                 1 if state has minimum wage, 0 else.
Republican                   1 if republ., 1992 pres. election. 0
                               else.
Judiciary Election           1 if judiciary is elected. 0 else.

Notes: Data sources as in Table A2 (Appendix).

TABLE A2
Summary Statistics and Data Sources, 1993

Unit of Observation: Pairs of States

                                  Full                 North-South

Sample                          (N = 756)               (N = 374)

Variable                      M          SD           M          SD

In [z.sub.ij]               -16.257       0.863     -16.590       0.637

[z.sub.ij]                 1.31e-07    1.64e-07    7.36e-08    5.15e-08
[Border.sub.ij]               0.495       0.500       1.000       0.000
In [Disti.sub.ij]             6.854       0.663       7.139       0.411
[Adjacency.sub.ij]            0.112       0.316       0.000       0.000
In Migration
  [Stock.sub.ij]              9.722       1.528       9.482       1.525
x Black [Share.sub.ij]      178.111     203.585     169.404     140.489
x Jewish [Share.sub.ij]       2.393       5.376       1.702       4.218
x Christian
  [Share.sub.ij]           7876.034     465.705    7961.583     376.909
x Other Religion
  [Sharer.sub.ij]             1.081       0.993       1.039       0.886
x No Religion
  [Share.sub.ij]             43.471      20.768      38.235      15.907
x Urban [Share.sub.ij]        0.490       0.144       0.478       0.134
[Colonizer.sub.ij]            0.540       0.499       0.524       0.500
[DELTA] In 1860
  [Cropland.sub.ij]           1.062       0.816       1.025       0.778
[DELTA] In 1860 Farm
  [Size.sub.ij]               0.698       0.525       1.155       0.331
[DELTA] In 1860
  Population
  [Density.sub.ij]            1.429       1.120       1.492       1.069
[DELTA] In 1860
  Illiteracy
  [Rates.sub.ij]              6.462       5.127       9.901       4.663
x 1860 Slave
  [Share.sub.ij]             21.596      20.239      39.700      10.854
x 1860 Free Black
  [Share.sub.ij]              1.200       1.029       1.215       1.043
x 1860 French
  [Share.sub.ij]            0.07-1        0.167       0.077       0.218
x 1860 Spanish
  [Share.sub.ij]             0.0001       0.001       0.000       0.000
x 1860 Irish
  [Share.sub.ij]             19.925      32.596       6.326       8.991
x 1860 German
  [Share.sub.ij]              9.995      20.730       4.226       8.473
x 1860 British
  [Share.sub.ij]              7.052      11.245       1.302       1.140
[DELTA] 1860 Malaria
  [Risk.sub.ij]               0.151       0.105       0.225       0.089
[DELTA] In
  Capital-Labor
  [Ratio.sub.ij]              0.281       0.211       0.276       0.208
[DELTA] In High-Low           0.454       0.308       0.565       0.324
  Skilled
  [Ratio.sub.ij]
[DELTA] In Average
  [Schooling.sub.ij]          0.036       0.025       0.045       0.027
[DELTA] In                    1.959       1.619       1.924       1.383
  [Cropland.sub.ij]
[DELTA] In Farm               0.580       0.465       0.567       0.467
  [Size.sub.ij]
[DELTA] In Agricultural
  To Total
  [Output.sub.ij]             0.711       0.503       0.709       0.486
[DELTA] In
  Manufacturing To
  Total [Output.sub.ij]       0.329       0.254       0.338       0.259
[DELTA] In
  [Population.sub.ij]         1.000       0.735       0.953       0.703
[DELTA] In Population
  [Density.sub.ij]            1.109       0.814       1.090       0.769
[DELTA] In
  [Fertility.sub.ij]          0.081       0.065       0.084       0.070
[DELTA] In Income Per         0.150       0.118       0.159       0.128
  [Capita.su8b.ij]
[DELTA] Union
  [Membership.sub.ij]         7.627       5.431       9.939       5.497
[DELTA] Union
  [Density.sub.ij]            7.434       5.267       9.668       5.420
[DELTA] Minimum
  [Wage.sub.ij]               0.087       0.124       0.081       0.105
[DELTA]
  [Republican.sub.ij]         0.452       0.498       0.604       0.490
Judiciary
  [Election.sub.ij]           0.426       0.495       0.428       0.495

Sample

Variable                  Data Source

In [z.sub.ij]             Commodity Flow Survey; Bureau of
                            Economic Analysis.
[z.sub.ij]                Commodity Flow Survey.
[Border.sub.ij]           Own calculations.
In [Disti.sub.ij]         Anderson and van Wincoop (2003).
[Adjacency.sub.ij]        Own calculations.
In Migration
  [Stock.sub.ij]          American Community Survey.
x Black [Share.sub.ij]    Population Estimates Program.
x Jewish [Share.sub.ij]   The American Jewish Yearbook.
x Christian
  [Share.sub.ij]          ARIS 2008 Report.
x Other Religion
  [Sharer.sub.ij]         ARIS 2008 Report.
x No Religion
  [Share.sub.ij]          ARIS 2008 Report.
x Urban [Share.sub.ij]    Census of Population and Housing.
[Colonizer.sub.ij]        Own calculations.
[DELTA] In 1860
  [Cropland.sub.ij]       Census of Agriculture 1860.
[DELTA] In 1860 Farm
  [Size.sub.ij]           Census of Agriculture 1860.
[DELTA] In 1860
  Population
  [Density.sub.ij]        Census of Population and Housing 1860.
[DELTA] In 1860
  Illiteracy
  [Rates.sub.ij]          Census of Population and Housing 1860.
x 1860 Slave
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 Free Black
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 French
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 Spanish
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 Irish
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 German
  [Share.sub.ij]          Census of Population and Housing 1860.
x 1860 British
  [Share.sub.ij]          Census of Population and Housing 1860.
[DELTA] 1860 Malaria
  [Risk.sub.ij]           Hong (2007).
[DELTA] In
  Capital-Labor
  [Ratio.sub.ij]          Turner et al. (2008).
[DELTA] In High-Low       Census of Population; American
  Skilled                   Community Survey.
  [Ratio.sub.ij]
[DELTA] In Average
  [Schooling.sub.ij]      Turner et al. (2007).
[DELTA] In                National Resource Inventory Summary
  [Cropland.sub.ij]         Report.
[DELTA] In Farm           Census of Agriculture.
  [Size.sub.ij]
[DELTA] In Agricultural
  To Total
  [Output.sub.ij]         Bureau of Economic Analysis.
[DELTA] In
  Manufacturing To
  Total [Output.sub.ij]   Bureau of Economic Analysis.
[DELTA] In
  [Population.sub.ij]     Population Estimates Program.
[DELTA] In Population
  [Density.sub.ij]        Population Estimates Program.
[DELTA] In
  [Fertility.sub.ij]      Vital Statistics of the United States.
[DELTA] In Income Per     Bureau of Economic Analysis; Population
  [Capita.su8b.ij]          Estimates Program.
[DELTA] Union
  [Membership.sub.ij]     Hirsch et al. (2001).
[DELTA] Union
  [Density.sub.ij]        Hirsch et al. (2001).
[DELTA] Minimum
  [Wage.sub.ij]           US Department of Labor.
[DELTA]
  [Republican.sub.ij]     The American Presidency Project.
Judiciary
  [Election.sub.ij]       Own calculations.

Notes: Data from the Bureau of Economic Analysis stem from the
Regional Economic Accounts. Contemporaneous variables if not stated
otherwise. The operator A denotes the absolute difference of
variables between state i and state j. The operator x denotes the
product of variables in state i and state j. In [z.sub.ij] has 740
observations for the full sample and 364 for the North-South sample.

TABLE A3
1993 Standard Transportation Commodity Codes (STCC)

Commodity   Meaning                                        Agriculture

1           Farm products                                       x
8           Forest products                                     x
9           Fresh fish or other marine products                 x
10          Metallic ores
11          Coal
13          Crude petroleum, natural gas. gasoline
14          Non-metallic minerals
19          Ordinance or accessories
20          Food or kindred products                            x
21          Tobacco products, excluding insecticides            x
22          Textile mill products
23          Apparel or other finished textile products
24          Lumber or wood products. excluding furniture
25          Furniture or fixtures
26          Pulp, Paper, allied products
27          Printed matter
28          Chemicals or allied products
29          Petroleum or coal products
30          Rubber or miscellaneous plastics products
31          Leather or leather products
32          Clay, concrete, glass, stone products
33          Primary metal products
34          Fabricated metal products
35          Machinery, excluding electrical
36          Electrical machinery, equipment, supplies
37          Transportation equipment
38          Instruments, photographic and optical goods
39          Miscellaneous products of manufacturing
40          Waste or scrap materials
41          Miscellaneous freight shipments
99          LTL-general cargo

Commodity   Mining   Chemical   Machinery   Manufacturing

1
8
9
10
11            x
13            x
14            x
19            x
20
21
22                                                x
23                                                x
24                                                x
25                                                x
26                                                x
27                                                x
28                      x
29                      x
30                      x
31                                                x
32                                                x
33                                                x
34                                                x
35
36                                  x
37                                  x
38                                  x
39                                  x
40                                                x
41
99

TABLE A4
1997, 2002, 2007 Standard Classification of Transported Goods (SCTG)

Commodity   Meaning

1           Live animals and live fish
2           Cereal grains
3           Other agricultural products
4           Animal feed and products of animal
              origin, n.e.c.
5           Meat. fish, seafood. and preparations
6           Milled grain products, bakery products
7           Other prepared foodstuffs, fats, oils
8           Alcoholic beverages
9           Tobacco products
10          Monumental or building stone
11          Natural sands
12          Gravel and crushed stone
13          Nonmetallic minerals n.e.c.
14          Metallic ores and concentrates
15          Coal
17          Gasoline and aviation turbine fuel
18          Fuel oils
19          Coal and petroleum products, n.e.c.
20          Basic chemicals
21          pharmaceutical products
22          Fertilizers
23          Chemical products and preparations, n.e.c.
24          Plastics and rubber
25          Logs and other wood in the rough
26          Wood products
27          Pulp, newsprint. paper, and paperboard
28          Paper or paperboard articles
29          Printed products
30          Textiles, leather, articles of textiles or leather
31          Nonmetallic mineral products
32          Base metal in primary or semifinished forms
33          Articles of base metal
34          Machinery
35          Electronic and office equipment and components
36          Motorized and other vehicles (including parts)
37          Transportation equipment, n.e.c.
38          Precision instruments and apparatus
39          Furniture, mattresses and supports, lamps
40          Miscellaneous manufactured products
41          Waste and scrap
43          Mixed freight

Commodity   Agriculture   Mining   Chemical   Machinery   Manufacturing

1                x
2                x
3                x
4                x

5                x
6                x
7                x
8                x
9                x
10                          x
11                          x
12                          x
13                          x
14                          x
15                          x
17                          x
18                          x
19                          x
20                                    x
21                                    x
22                                    x
23                                    x
24                                    x
25               x
26                                                              x
27                                                              x
28                                                              x
29                                                              x
30                                                              x
31                                                              x
32                                                              x
33                                                              x
34                                                x
35                                                x
36                                                x
37                                                x
38                                                x
39                                                              x
40                                                              x
41
43

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

TABLE S1. Cross Correlations of [Border.sub.ij] with all other Variables, 1993

TABLE S2. Alternative Methods: AvW and OLS with MR Terms

TABLE S3. Placebo Coast-Interior and East-West, 1993

TABLE S4. Robustness: In-Sample Eastern-Western States

TABLE S5. Robustness: Subsamples

TABLE S6. Sensitivity Analysis Various Years

TABLE S7. Alternative Distance Measure (fixed-effects estimation)

TABLE S8. Sensitivity Analysis: Allocation of Border States, 1993

TABLE S9. Additionally Including California, Oregon and Nevada, 1993

TABLE S10. Sectoral Regressions (fixed-effects estimation)

TABLE S11. Additional Controls, Alternative Samples and Models: Summary Results

TABLE S12. Sectoral Regressions Including Controls (fixed-effects estimation)

TABLE S13. Additionally Including the West: Sensitivity

TABLE S14. Robustness: Alternative Samples Including the South-West

TABLE S15. Robustness: Alternative Samples Including the North-West

GABRIEL FELBERMAYR and JASMIN GROSCHL *

* We thank the editor, Cedric Tille, and two anonymous referees for excellent comments and suggestions. We are also grateful to Mario Larch, Doug Nelson, Katheryn Russ and to seminar participants at the ETSG meeting in Copenhagen, 2011, the Munich-Tuebingen International Economics Workshop in Munich, 2011 and the Royal Economic Society meeting in Cambridge, 2012. We thank the Leibniz Gemeinschaft (WGL) for financial support under project Pact 2009 Globalisierungsnetzwerk.

Felbermayr: Ifo-Leibniz Institute for Economic Research at the University of Munich, Poschingerstr. 5, 81679 Munich, Germany; CESifo & GEP. Phone +49 (0)89 9224 1428, Fax +49 (0)89 985369, E-mail felbermayr@ifo.de

Groschl: Ifo-Leibniz Institute for Economic Research at the University of Munich, Poschingerstr. 5, 81679 Munich, Germany. Phone +49 (0)89 9224 1317, Fax +49 (0)89 985369, E-mail groeschl@ifo.de

(1.) Pew Research Centre for the People and the Press, "Civil War at 150: Still Relevant, Still Divisive," April 8, 2011; available at http://pewresearch.org/pubs/1958/.

(2.) The Mason-Dixon Line settled a conflict between British Colonies and set the common borders of Pennsylvania, Maryland, Delaware, and West Virginia.

(3.) Helliwell (1997, 1998, 2002); Wei (1996); Hillberry (1999, 2002); Woff (1997, 2000); Nitsch (2000); Parsley and Wei (2001); Hillberry and Hummels (2003); Anderson and Van Wincoop (2003); Chen (2004); Feenstra (2004); Combes, Lafourcade, and Mayer (2005); Millimet and Osang (2007); Baier and Bergstrand (2009); Buch and Toubal (2009); Nitsch and Wolf (forthcoming) to name only a few.

(4.) Anderson and Van Wincoop (2003) propose to estimate their gravity model by means of an iterative procedure that minimizes the sum of squared residuals, while simultaneously obtaining values for the multilateral resistance terms.

(5.) Reed and Reed (1997) define the "deep South" as an area roughly coextensive with the old cotton belt from eastern North Carolina through South Carolina west into East Texas, with extensions north and south along the Mississippi.

(6.) Note that California, Oregon, and Nevada were officially part of the Union but played no particular role in the Civil War. So, we exclude them from our benchmark sample, but include them in our robustness check in Table S9 in the Supporting Information.

(7.) North-South differences are also clearly visible when looking at pairs of states. Table A2 in the Appendix differentiates between the sample of all pairs (N = 756) and the sample of cross-border pairs (states from different sides of the historical border; N = 374).

(8.) We have estimated Epanechnikov Kernel density functions, with the width of the density window around each point set to the "optimal" level; see Silverman (1992). Optimal bandwidths are approximately 0.17, 0.25, and 0.32 for North-South, North-North and South-South flows, respectively.

(9.) Broda, Greenfield, and Weinstein (2006) estimate elasticities of substitution with a median of 3.8 and a mean of 12.1. The elasticity of substitution they estimate for the United States is 2.4. We follow the recent literature and calculate tariff equivalents according to a range of the elasticity of substitution between 3 and 10.

(10.) Table 2 in AvW, two-country model: [e.sup-1.65-]- 1.

(11.) Table 2a in Nitsch and Wolf (forthcoming), pooled OLS in 2004: [e.sup.-0.229] - 1.

(12.) The puzzle also vanishes when counting the border states into the South (Table S8, Supporting Information) or when including California, Oregon, and Nevada into the Union (Table S9, Supporting Information).

(13.) These include Canada, Australia, Japan, New Zealand, Austria, Belgium-Luxembourg, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom.

(14.) We focus on exports from U.S. states to the OECD as import data of individual U.S. states from OECD states (and vice versa) are not available.

(15.) http://cid.econ.ucdavis.edu/

(16.) Detailed results are found in Table S2 (Supporting Information).

(17.) The number of potential "South" subsamples and hence of state groups is huge: 21,474,180. Estimating all possible border effects between these groups of states is computationally extremely costly. A single regression takes about 1 second. Computation time then amounts to 249 days.

(18.) Detailed results are found in Table S3 (Supporting Information).

(19.) Detailed results are found in Table S4 (Supporting Information).

(20.) Detailed results are found in Table S5 (Supporting Information).

(21.) The 1997 wave is an exception. Detailed results are found in Table S7 (Panel A) (Supporting Information).

(22.) Detailed results are found in Table S7 (Panel B) (Supporting Information).

(23.) Detailed results are found in Table S8 (Supporting Information).

(24.) The increase in the border effect when the three "disconnected" states are included supports the view that the border effect is really about a "genuine" Union-versus-Confederation effect.

(25.) Detailed results are found in Table S9 (Supporting Information).

(26.) Detailed results are found in Table S10 (Supporting Information).

(27.) We tried a range of other variables and combinations, as well as network and difference variables separately and combinations thereof. The results are robust to these modifications.

(28.) A similar effect has been identified by Combes, Lafourcade, and Mayer (2005) for trade within France.

(29.) See, for instance, Head, Mayer, and Ries (2010).

(30.) We measure high skilled by a Bachelor's degree or above and low skilled by a High School degree or below. Data stem from the Census of Population and the American Community Survey.

(31.) A model that explains bilateral trade solely using importer and exporter fixed effects can only explain 20% of the variation in the dependent variable.

(32.) Details are relegated to Table S11 (Supporting Information).

(33.) When we work with sectoral data and include the additional controls, results suggest that the trade impeding effect is mainly caused by barriers to manufacturing products in all years. Compared to our earlier results, the border effect is negative but less robust for agriculture and chemicals--except for 2002 and 2007. Mining and machinery products again depict in most cases an indistinguishable coefficient from zero. Table S12 (Supporting Information) reports detailed results.

(34.) The analysis relates to the literature on the long-term impact of factor endowments and institutions (Acemoglu, Johnson, and Robinson 2002; Galor, Moav, and Vollrath 2009; Nunn 2009).

(35.) Additionally, all models include our additional contemporaneous controls from Table 5 column (6) and importer as well as exporter fixed effects.

(36.) If we use the difference in the share of slaves in 1840, when there were still slaves also living in the North, we still find robust results on the border effect but an insignificant coefficient close to zero for the slave share. In column (7), the effect of differences in 1840 slaves is still zero, while the effects of all other historical controls prevail. The border effect remains negative and significant on the 1% level.

(37.) We have also experimented with direct measures for the historical transportation system (differences or networks of railroad miles per 100 square miles of land area after the Civil War in 1870). The result is robust to the inclusion of the historical transportation system.

(38.) West includes all U.S. states that were not assigned to the North, the South, or the border states in Table 1, excluding the District of Columbia, Alaska, and Hawaii.

(39.) Historical controls are not available for most of the Western states before the war, as these were only Territories in 1860.

(40.) Results are similar for the other years and can be found in Table S13 (Supporting Information).

(41.) Detailed results are found in Table S14 (Supporting Information).

(42.) Detailed results are found in Table S15 (Supporting Information).

TABLE 1
Sample

                                        Excluded/
North = Union    South = Confederacy    Border States

Connecticut      Alabama                Delaware
Illinois         Arkansas               Kentucky
Indiana          Florida                Maryland
Iowa             Georgia                Missouri
Kansas           Louisiana              West Virginia
Maine            Mississippi
Massachusetts    North Carolina         California
Michigan         South Carolina         Nevada
Minnesota        Tennessee              Oregon
New Hampshire    Texas
New Jersey       Virginia
New York
Ohio
Pennsylvania
Rhode Island
Vermont
Wisconsin

TABLE 2
Basic Border Effect Results

Dependent Variable: In bilateral exports between i and j relative to
states' GDPs

Year of Data:                           1993

Data:                                Aggregated

                                      OLS FE

Specification:                   (1)           (2)

Border [dummy.sub.ij]        -0.137 ***
                               (0.03)
North-North [dummy.sub.ij]                  -0.230 **
                                             (0.09)
South-South [dummy.sub.ij]                  0.504 ***
                                             (0.10)
In [Distance.sub.ij]         -0.919 ***    -0.919 ***
                               (0.03)        (0.03)
[Adjacency.sub.ij]            0.434 ***     0.434 ***
                               (0.06)        (0.06)
Fixed effects
  Importer                       YES           YES
  Exporter                       YES           YES
  Importer x Commodity           --            --
  Exporter x Commodity           --            --

Observations                     740           740
Adjusted/pseudo-[R.sup.2]       0.841         0.841

Year of Data:                           1993

Data:                                Aggregated

                                     PPML FE

Specification:                   (3)           (4)

Border [dummy.sub.ij]        -0.152 ***
                               (0.03)
North-North [dummy.sub.ij]                    0.063
                                             (0.08)
South-South [dummy.sub.ij]                    0.241
                                             (0.09)
In [Distance.sub.ij]         -0.953 ***    -0.953 ***
                               (0.03)        (0.03)
[Adjacency.sub.ij]            0.426 ***     0.426 ***
                               (0.05)        (0.05)
Fixed effects
  Importer                       YES           YES
  Exporter                       YES           YES
  Importer x Commodity           --            --
  Exporter x Commodity           --            --

Observations                     756           756
Adjusted/pseudo-[R.sup.2]       0.030         0.030

Year of Data:                           1993

Data:                        Aggregated      Commodity

                             PPML Multi    Chen (2004) FE

Specification:                   (5)            (6)

Border [dummy.sub.ij]        -0.144 ***      -0.080 ***
                               (0.04)          (0.02)
North-North [dummy.sub.ij]

South-South [dummy.sub.ij]

In [Distance.sub.ij]         -0.828 ***      -0.670 ***
                               (0.03)          (0.02)
[Adjacency.sub.ij]            0.629 ***        0.492
                               (0.05)          (0.04)
Fixed effects
  Importer                       YES             --
  Exporter                       YES             --
  Importer x Commodity           --             YES
  Exporter x Commodity           --             YES

Observations                    1,764          12.271
Adjusted/pseudo-[R.sup.2]       0.060          0.601

Notes: Constant and fixed effects not reported. Robust standard errors
reported in parentheses. States in sample as in Table 1. District of
Columbia is excluded. In column (5), we adapt a multi-country PPML
fixed-effects approach, respectively, and add exports of individual
U.S. states to 20 OECD countries and between OECD trade.

*** Significant at the 1% level; ** significant at the 5% level; *
significant at the 10% level.

TABLE 3
Sensitivity Across Different Survey Waves

Dependent Variable: In bilateral exports between i and
j relative to states' GDPs

Data:                             Aggregated             Commodity

                                OLS          PPML         FE Chen
Specification:                  FE            FE          (2004)

                            Panel A: 1997

                               (A1)          (A2)          (A3)

Border [dummy.sub.ij]        -0.070 **    -0.096 ***    -0.132 ***
                              (0.03)        (0.03)        (0.02)
Observations                    738           756         10,342
Adjusted/pseudo-[R.sup.2]      0.821         0.030         0.795

                            Panel B: 2002

                               (B1)          (132)         (133)

Border [dummy.sub.ij]       -0.120 ***    -0.141 ***    -0.177 ***
                              (0.03)        (0.04)        (0.02)
Observations                    711           756          6,979
Adjusted/pseudo-[R.sup.2]      0.816         0.030         0.767

                            Panel C: 2007

                               (C1)          (C2)          (C3)

Border [dummy.sub.ij]       -0.110 ***    -0.143 ***    -0.172 ***
                              (0.03)        (0.04)        (0.02)
Observations                    740           756         11,834
Adjusted/pseudo-[R.sup.2]      0.847         0.030         0.763

Notes: Constant, fixed effects, effects on log distance and
adjacency are not reported. Robust standard errors reported
in parentheses. Table S8 (Supporting Information) contains
full results. Column (3) includes Importer x Commodity and
Exporter x Commodity fixed effects following Chen (2004).
States in sample as in Table 1. District of Columbia is
excluded.

*** Significant at the 1% level; ** significant at the 5%
level; * significant at the 10% level.

TABLE 4
Sectoral Results (Fixed-Effects Estimation)

Dependent Variable: In bilateral exports between i and j relative to
states' GDPs

Sector                  Agriculture    Mining      Chemical

                                    Panel A: 1993

                           (A1)         (A2)         (A3)

Border [dummy.sub.ij]   -0.254 ***     -0.052     -0.236 ***
                          (0.08)       (0.26)       (0.07)
Observations               4,585        1,156       2,940
Adjusted [R.sup.2]         0.659        0.611       0.545

                                    Panel B: 1997

                           (B1)         (B2)         (B3)

Border [dummy.sub.ij]     -0.133      -0.453 **     -0.065
                          (0.08)       (0.18)       (0.06)
Observations               5.210        2,403       3,075
Adjusted [R.sup.2]         0.720        0.658       0.688

                                    Panel C: 2002

                           (C1)         (C2)         (C3)

Border [dummy.sub.ij]     -0.158       -0.123      -0.150 *
                          (0.10)       (0.36)       (0.08)
Observations               4,190        1,377       2.680
Adjusted [R.sup.2]         0.679        0.623       0.659

                                    Panel D: 2007

                           (D1)         (D2)         (D3)

Border [dummy.sub.ij]   -0.242 ***      0.007     -0.287 ***
                          (0.07)       (0.17)       (0.06)
Observations               3,910        1,679       2,976
Adjusted [R.sup.2]         0.752        0.674       0.715

Sector                  Machinery   Manufacturing

                             Panel A: 1993

                          (A4)          (A5)

Border [dummy.sub.ij]    -0.036        -0.051
                         (0.07)        (0.05)
Observations              4,140        11,484
Adjusted [R.sup.2]        0.565         0.684

                             Panel B: 1997

                          (B4)          (B5)

Border [dummy.sub.ij]    -0.078      -0.181 ***
                         (0.05)        (0.04)
Observations              3,315         7,340
Adjusted [R.sup.2]        0.681         0.752

                             Panel C: 2002

                          (C4)          (C5)

Border [dummy.sub.ij]    -0.037      -0.246 ***
                         (0.07)        (0.06)
Observations              3,065         6,800
Adjusted [R.sup.2]        0.618         0.722

                             Panel D: 2007

                          (D4)          (D5)

Border [dummy.sub.ij]    -0.016      -0.238 ***
                         (0.07)        (0.04)
Observations              3,332         7,156
Adjusted [R.sup.2]        0.614         0.766

Notes: Importer and exporter fixed effects included in all
regressions. Constant, fixed effects and effects on log distance and
adjacency not reported. Robust standard errors reported in
parentheses. Table S 10 (Supporting Information) contains full
results. Commodities pooled into sectors as listed in Tables A3 and A4
in the Appendix. States in sample as in Table 1. District of Columbia
excluded.

*** Significant at the 1% level; ** significant at the 5% level; *
significant at the 10% level.

TABLE 5
Contemporaneous Controls, 1993 (Fixed-Effects Estimation)

Dependent Variable: In bilateral exports between i and j relative to
states' GDPs

                                                 (1)           (2)

Border [dummy.sub.ij]                        -0.137 ***    -0.120 ***
                                               (0.03)        (0.03)
Geographical controls
In [Distance.sub.ij]                         -0.919 ***    -0.631 ***
                                               (0.03)        (0.04)
[Adjacency.sub.ij]                            0.434 ***     0.356 ***
                                               (0.06)        (0.05)
Network controls
In Migration [Stock.sub.ij]                                 0.129 ***
                                                             (0.03)
x Black [Share.sub.ij]                                      0.001 ***
                                                             (0.00)
x Jewish Share.sub.ij]                                       -0.005
                                                             (0.00)
x Christian [Share.sub.ij]                                    0.002
                                                             (0.00)
x Other Religion [Share.sub.ij]                             0.062 **
                                                             (0.03)
x No Religion [Share.sub.ij]                                  0.007
                                                             (0.00)
x Urban [Share.sub.ij]                                      3.494 ***
                                                             (0.77)
Common [colonizer.sub.ij]                                   0.198 ***
                                                             (0.04)
Labor market/political institutions
[DELTA] Union [membership.sub.ij]

[DELTA] Union [density.sub.ij]

[DELTA] Minimum [wage.sub.ij]

[DELTA] [Republican.sub.ij]

Judiciary [election.sub.ij]

Heckscher-Ohlin controls
[DELTA] In Capital-labor [ratio.sub.ij]

[DELTA] In High-low skilled [ratio.sub.ij]

[DELTA] In Average [schooling.sub.ij]

[DELTA] In [Cropland.sub.ij]

[DELTA] In Farm [size.sub.ij]

[DELTA] In Agricultural to total
  [output.sub.ij]
[DELTA] In Manufacturing to total
  [output.sub.ij]
Demography
[DELTA] In [Population.sub.ij]

[DELTA] In Population [density.sub.ij]

[DELTA] In [Fertility.sub.ij]

Linder hypothesis
[DELTA] In Income per [Capita.sub.ij]

Observations                                     740           740
Adjusted [R.sup.2]                              0.841         0.865

                                                 (3)           (4)

Border [dummy.sub.ij]                        -0.1 17 ***   -0.109 ***
                                               (0.04)        (0.04)
Geographical controls
In [Distance.sub.ij]                         -0.633 ***    -0.627 ***
                                               (0.04)        (0.05)
[Adjacency.sub.ij]                            0.352 ***     0.380 ***
                                               (0.05)        (0.05)
Network controls
In Migration [Stock.sub.ij]                   0.125 ***     0.089 **
                                               (0.03)        (0.03)
x Black [Share.sub.ij]                        0.001 ***     0.001 ***
                                               (0.00)        (0.00)
x Jewish Share.sub.ij]                         -0.004        -0.003
                                               (0.00)        (0.00)
x Christian [Share.sub.ij]                      0.002         0.002
                                               (0.00)        (0.00)
x Other Religion [Share.sub.ij]               0.064 **      0.067 **
                                               (0.03)        (0.03)
x No Religion [Share.sub.ij]                    0.006         0.007
                                               (0.00)        (0.00)
x Urban [Share.sub.ij]                        3.425 ***     3.675 ***
                                               (0.81)        (0.92)
Common [colonizer.sub.ij]                     0.202 ***     0.173 ***
                                               (0.04)        (0.04)
Labor market/political institutions
[DELTA] Union [membership.sub.ij]              -0.003        -0.013
                                               (0.02)        (0.02)
[DELTA] Union [density.sub.ij]                  0.003         0.013
                                               (0.02)        (0.02)
[DELTA] Minimum [wage.sub.ij]                  -0.177        -0.207
                                               (0.15)        (0.15)
[DELTA] [Republican.sub.ij]                    -0.001        -0.003
                                               (0.03)        (0.03)
Judiciary [election.sub.ij]                   -0.074 **     -0.073 **
                                               (0.03)        (0.03)
Heckscher-Ohlin controls
[DELTA] In Capital-labor [ratio.sub.ij]                       0.024
                                                             (0.16)
[DELTA] In High-low skilled [ratio.sub.ij]                    0.076
                                                             (0.09)
[DELTA] In Average [schooling.sub.ij]                        -1.404
                                                             (1.13)
[DELTA] In [Cropland.sub.ij]                               -0.053 ***
                                                             (0.02)
[DELTA] In Farm [size.sub.ij]                                 0.021
                                                             (0.05)
[DELTA] In Agricultural to total                              0.066
  [output.sub.ij]                                            (0.04)
[DELTA] In Manufacturing to total                            -0.102
  [output.sub.ij]                                            (0.11)
Demography
[DELTA] In [Population.sub.ij]

[DELTA] In Population [density.sub.ij]

[DELTA] In [Fertility.sub.ij]

Linder hypothesis
[DELTA] In Income per [Capita.sub.ij]

Observations                                     740           740
Adjusted [R.sup.2]                              0.866         0.868

                                                 (5)           (6)

Border [dummy.sub.ij]                        -0.120 ***    -0.119 ***
                                               (0.04)        (0.04)
Geographical controls
In [Distance.sub.ij]                         -0.611 ***    -0.612 ***
                                               (0.05)        (0.05)
[Adjacency.sub.ij]                            0.397 ***     0.399 ***
                                               (0.05)        (0.05)
Network controls
In Migration [Stock.sub.ij]                   0.088 **      0.086 **
                                               (0.03)        (0.03)
x Black [Share.sub.ij]                        0.001 ***     0.001 ***
                                               (0.00)        (0.00)
x Jewish Share.sub.ij]                         -0.002        -0.002
                                               (0.00)        (0.00)
x Christian [Share.sub.ij]                     0.002 *        0.002
                                               (0.00)        (0.00)
x Other Religion [Share.sub.ij]                0.055 *       0.056 *
                                               (0.03)        (0.03)
x No Religion [Share.sub.ij]                    0.005         0.005
                                               (0.00)        (0.00)
x Urban [Share.sub.ij]                        3.648 ***     3.651 ***
                                               (1.13)        (1.13)
Common [colonizer.sub.ij]                     0.168 ***     0.169 ***
                                               (0.04)        (0.04)
Labor market/political institutions
[DELTA] Union [membership.sub.ij]              -0.016        -0.017
                                               (0.02)        (0.02)
[DELTA] Union [density.sub.ij]                  0.016         0.017
                                               (0.02)        (0.02)
[DELTA] Minimum [wage.sub.ij]                  -0.167        -0.168
                                               (0.15)        (0.15)
[DELTA] [Republican.sub.ij]                    -0.002        -0.002
                                               (0.03)        (0.03)
Judiciary [election.sub.ij]                   -0.072 **     -0.072 **
                                               (0.03)        (0.03)
Heckscher-Ohlin controls
[DELTA] In Capital-labor [ratio.sub.ij]         0.022         0.000
                                               (0.16)        (0.20)
[DELTA] In High-low skilled [ratio.sub.ij]      0.079         0.080
                                               (0.09)        (0.09)
[DELTA] In Average [schooling.sub.ij]          -1.473        -1.584
                                               (1.15)        (1.27)
[DELTA] In [Cropland.sub.ij]                 -0.052 ***    -0.052 ***
                                               (0.02)        (0.02)
[DELTA] In Farm [size.sub.ij]                   0.010         0.007
                                               (0.07)        (0.07)
[DELTA] In Agricultural to total                0.038         0.038
  [output.sub.ij]                              (0.04)        (0.04)
[DELTA] In Manufacturing to total              -0.075        -0.064
  [output.sub.ij]                              (0.11)        (0.12)
Demography
[DELTA] In [Population.sub.ij]                 -0.018        -0.019
                                               (0.03)        (0.03)
[DELTA] In Population [density.sub.ij]          0.029         0.030
                                               (0.04)        (0.04)
[DELTA] In [Fertility.sub.ij]                  -0.675        -0.658
                                               (0.41)        (0.41)
Linder hypothesis
[DELTA] In Income per [Capita.sub.ij]                         0.069
                                                             (0.29)
Observations                                     740           740
Adjusted [R.sup.2]                              0.869         0.868

Notes: Importer and exporter fixed effects included in all
regressions. Constant and fixed effects not reported. Robust standard
errors reported in parentheses. The operator [delta] denotes the
absolute difference of variables in state i and state j. The operator
x denotes the product of variables in state i and state j.

*** Significant at the 1% level; ** significant at the 5% level; *
significant at the 10% level.

TABLE 6
Controls, Alternative Samples, and Models: Summary Results

Dependent Variable: In bilateral exports between i and j relative to
states' GDPs

Year of Data:              1993         1997        2002       2007

                        Panel A: OLS FE

                           (A1)         (A2)        (A3)       (A4)

Border [dummy.sub.ij]   -0.119 ***     -0.039     -0.119 *     0.016
                          (0.04)       (0.05)      (0.06)     (0.06)
Observations               740          738         711         740
Adjusted [R.sup.2]        0.868        0.854       0.844       0.874

                        Panel B: PPML FE

                           (BI)         (B2)        (B3)       (B4)
Border [dummy.sub.ij]   -0.133 ***     -0.027      -0.019      0.115
                          (0.05)       (0.05)      (0.07)     (0.07)
Observations               756          756         756         756
Pseudo-[R.sup.2]          0.028        0.028       0.033       0.031

                        Panel C: Pooled commodity FE (Chen 2004)

                           (C1)         (C2)        (C3)       (C4)

Border [dummy.sub.ij]   -0.234 ***   -0.101 ***    -0.050    -0.076 **
                          (0.04)       (0.03)      (0.04)     (0.04)
Observations              12.271       10,342      6.979      11.834
Adjusted [R.sup.2]        0.611        0.805       0.775       0.773

Notes: Constant, fixed effects, and controls not reported. Robust
standard errors reported in parentheses. All models include variables
of column (6) in Table 5 as additional controls. Full results are
reported in Table S11 (Supporting Information).

*** Significant at the 1% level; ** significant at the 5% level:
* significant at the 10% level.

TABLE 7
Contemporaneous and Historical Controls, 1993
(Fixed-Effects Estimation)

Dependent Variable: In bilateral exports between i and j relative to
states' GDPs

                                    (1)          (2)          (3)

Border [dummy.sub.ij]            -0.234 ***   -0.121 ***   -0.129 **
                                   (0.07)       (0.04)       (0.06)
Controls as of Table 5              YES          YES          YES
column (6) included
Historical controls
[DELTA] In 1860                    -0.027
  [Cropland.sub.ij]                (0.02)
[DELTA] In 1860 Farm              0.160 **
  [size.sub.ij]                    (0.08)
[DELTA] In 1860 Population                      0.032
  [density.sub.ij]                              (0.02)
[DELTA] In 1860 Illiteracy                                   0.001
  [rates.sub.ij]                                             (0.00)
[DELTA] 1860 Slave
  [share.sub.ij]
[DELTA] 1860 Free Black
  [Share.sub.ij]
x 1860 French [Share.sub.ij]
x 1860 Spanish [Share.sub.ij]
x 1860 Irish [Share.sub.ij]
x 1860 German [Share.sub.ij]
x 1860 British [Share.sub.ij]

[DELTA] 1860
  [Malaria Risk.sub.ij]
Observations                        740          740          740
Adjusted [R.sup.2]                 0.869        0.869        0.868

                                    (4)          (5)

Border [dummy.sub.ij]            -0.177 **    -0.118 ***
                                   (0.08)       (0.04)
Controls as of Table 5              YES          YES
column (6) included
Historical controls
[DELTA] In 1860
  [Cropland.sub.ij]
[DELTA] In 1860 Farm
  [size.sub.ij]
[DELTA] In 1860 Population
  [density.sub.ij]
[DELTA] In 1860 Illiteracy
  [rates.sub.ij]
[DELTA] 1860 Slave                 0.002
  [share.sub.ij]                   (0.00)
[DELTA] 1860 Free Black            0.030
  [Share.sub.ij]                   (0.02)
x 1860 French [Share.sub.ij]                  0.492 ***
                                                (0.16)
x 1860 Spanish [Share.sub.ij]                   2.462
                                               (16.94)
x 1860 Irish [Share.sub.ij]                   -0.002 **
                                                (0.00)
x 1860 German [Share.sub.ij]                    0.001
                                                (0.00)
x 1860 British [Share.sub.ij]                   0.002
                                                (0.00)
[DELTA] 1860
  Malaria [Risk.sub.ij]
Observations                        740          740
Adjusted [R.sup.2]                 0.869        0.873

                                    (6)          (7)

Border [dummy.sub.ij[            -0.141 ***   -0.251 **
                                   (0.04)       (0.10)
Controls as of Table 5              YES          YES
column (6) included
Historical controls
[DELTA] In 1860                                -0.035 *
  [Cropland.sub.ij]                             (0.02)
[DELTA] In 1860 Farm                            0.100
  [size.sub.ij]                                 (0.09)
[DELTA] In 1860 Population                      0.028
  [density.sub.ij]                              (0.02)
[DELTA] In 1860 Illiteracy                      0.006
  [rates.sub.ij]                                (0.01)
[DELTA] 1860 Slave                              0.000
  [share.sub.ij]                                (0.00)
[DELTA] 1860 [Free Black                       0.035 *
  Share.sub.ij]                                 (0.02)
x 1860 French [Share.sub.ij]                  0.474 ***
                                                (0.17)
x 1860 Spanish [Share.sub.ij]                   0.085
                                               (17.74)
x 1860 Irish [Share.sub.ij]                   -0.002 **
                                                (0.00)
x 1860 German [Share.sub.ij]                    0.001
                                                (0.00)
x 1860 British [Share.sub.ij]                   0.003
                                                (0.00)
[DELTA] 1860                       0.345        0.255
  Malaria [Risk.sub.ij]            (0.25)       (0.29)
Observations                        740          740
Adjusted [R.sup.2]                 0.869        0.873

Notes: Importer and exporter fixed effects included in all
regressions. All models include variables as of column (6), Table 5
as additional controls. Constant, fixed effects, and contemporaneous
controls not reported. Robust standard errors reported in
parentheses. The operator [DELTA] denotes the absolute difference of
variables in state i and state j. The operator x denotes the
product of variables in state i and state i.

*** Significant at the 1% level; ** significant at the 5% level;
* significant at the 10%n level.

TABLE 8
Additionally Including the West, 1993

Dependent Variable: In bilateral exports between i
and j relative to states' GDPs

Data                               Aggregated

                                     OLS FE

Specification:                  (1)          (2)

Border [dummy.sub.ij]         -0.068 *
                               (0.04)
South-South [dummy.sub.ij]                0.235 ***
                                            (0.07)
North-North [dummy.sub.ij]                -2.665 **
                                            (1.24)
West-West [dummy.sub.ij]                    -0.039

                                            (0.09)
In [Distance.sub.ij]         -0.421 ***   -0.422 ***
                               (0.05)       (0.05)
[Adjacency.sub.ij]           0.463 ***    0.458 ***
                               (0.07)       (0.07)
Additional controls             YES          YES
Observations                   1,696        1,696
Adjusted/Pseudo-[R.sup.2]      0.808        0.808

Data                              Aggregated             Commodity

                                    PPML FE            Chen (2004) FE

Specification:                  (3)          (4)            (5)

Border [dummy.sub.ij]         -0.090 *                     -0.213
                               (0.05)                      (0.03)
South-South [dummy.sub.ij]                0.267 ***
                                            (0.09)
North-North [dummy.sub.ij]                  -0.307
                                            (0.30)
West-West [dummy.sub.ij]                    -0.084
                                            (0.11)
In [Distance.sub.ij]         -0.275 ***   -0.282 ***     -0.238 ***
                               (0.06)       (0.05)         (0.03)
[Adjacency.sub.ij]           0.338 ***    0.326 ***      0.475 ***
                               (0.06)       (0.06)         (0.04)
Additional controls             YES          YES            YES
Observations                   1,806        1,806          23,400
Adjusted/Pseudo-[R.sup.2]      0.039        0.039          0.567

Notes: Constant, fixed effects, and controls not reported. Robust
standard errors reported in parentheses. All models include variables
as of column (6), Table 5 available for all U.S. states as additional
controls.

*** Significant at the 1% level; ** significant at the 5% level;
* significant at the 10% level.