Borders or Barriers?
    The Impact of Borders on
Agricultural Markets in West Africa

           Jenny C. Aker, Tufts Uni...
Motivation
• International borders can affect trade flows and
  price dispersion
• Border effects found in high-income cou...
Niger and Nigeria
This Paper
• Goal: Assess the extent to which borders impose
  costs that segment markets between Niger and
  Nigeria
   ...
Preview of Findings
• International borders matter, but they don’t
  deter trade in the Niger-Nigeria sub-region.
   o Com...
Borders and Agricultural Trade
• Borders were drawn to reflect interests of
  colonial powers.
   Berlin Conference (1884...
Borders and Agricultural Trade




                                 7
Commodity Markets in Niger and
Nigeria
Background on Borders and
Trade in West Africa
• West African Economic and Monetary Union
  (UEMOA)
  o Customs and moneta...
Related Research
• Engel & Rogers AER 1996,
• Prices in US and Canadian Cities, Parsley and
  Wei JIE 2001, Prices in Japa...
Engel & Rogers Methodology
Border effect as km-equivalent
    US – US Pair    Canada – Canada Pair   US – Canada Pair




...
Gorodnichenko & Tesar (2009)
• Engel & Rogers method overstates role of border if
  differences in price volatility in the...
Related Research
• Engel & Rogers AER 1996,
• Prices in US and Canadian Cities, Parsley and
  Wei JIE 2001, Prices in Japa...
Empirical Strategy

• Part I: Regression-based estimates of the border
  effect across market pairs within and across
  co...
Data Sets
• Market-Level Panel Data on prices, costs, other
  characteristics

• Market Locations and Distances between
  ...
Market-Level Panel Dataset
• Monthly prices for agricultural products in
  65 markets in Niger and northern Nigeria
  betw...
Data on Market Locations and
Distances


 GIS (latitude and longitude location) of
  each market
 Road and Euclidean dis...
Trader-Level Data
 Panel survey of markets, traders and farmers
  collected between 2005-2007 across 6 regions of
  Niger...
Empirical Strategy
|ln(pi,t /pj,t) |= β1dij+ β2Bij + ΣβxXij,t + ai + aj +εijt
  • dij is the distance between markets i an...
The International Border Effect
                             Table 2. Average International Border Effect
                ...
Threats to Identification

• Different price volatilities in each country
• Market segmentation
• Endogeneity of border ef...
Different price volatilities
   Plot of εijt from regressions

Frequency

            Within Niger
                       ...
Millet
                    Plot of Residuals: Millet
                                 Prs < 150km
  6
  4
  2
  0




    ...
Threats to Identification


• Different price volatilities in each country
• Market segmentation
• Endogeneity of border e...
Is There Trade Across the
       Border?

                   Table 6: Difference in Trader-Level Characteristics between N...
Threats to Identification


• Different price volatilities in each country
• Market segmentation
• Endogeneity of border e...
Regression Discontinuity
1.   Is there a discontinuity in prices across the border?
2.   Does Distance to the border matte...
Regression Discontinuity
• Is there a discontinuous change in price with
  respect to distance to the border?
• Price at t...
30
Is the discontinuity related to
   other factors?

                               Bandwidth      Bandwidth

          Xj
 ...
Table 5: Mean Difference (Niger - Nigeria)
                                  Within 5 km to the Niger-Nigeria border
     ...
Table 3. RDD Regressions of Niger-Nigeria Border Effect on Millet Price
                                   5 km to Border ...
Table 3. RDD Regressions of Niger-Nigeria Border Effect on Millet Price
                                   5 km to Border ...
Ethnicity: Border or Barrier?
• “Common ethnicity” (Hausa) minimizes the
  impact of the international border effect
  o T...
Defining an Ethnic Border




  Source: Trader and farmer panel surveys, 2005-2007
Defining an “Ethnic” Border
• Use demographic data on ethnic composition
  for villages from 1999 (SIL/Niger) and 2007
• C...
Regression Discontinuity
• Price at time t, of good i in market j:
        ln(pij,t) = αi +γi Hj + θi Dj + β‘Xj +εij

   •...
40
Table 7: Mean Difference (Zarma - Hausa)
                          Within 50 km to the Hausa-Zarma border
                ...
Table 8. RD of Zarma-Hausa Border Effect on Log of Millet Price
                                 50 km to Border
         ...
Potential Mechanisms
• Higher transactions costs in Zarma regions
• Differential investment by colonial powers
• Unwilling...
Potential Mechanisms
• Higher transactions costs in Zarma regions
• Differential investment by colonial powers
• Unwilling...
Table 10: Difference in Trader-Level Characteristics between Hausa and Zarma Regions
                                     ...
Table 10: Difference in Trader-Level Characteristics between Hausa and Zarma Regions
                                     ...
Gender and Agricultural Trade
(Correlations)
• Female traders are less likely to take a loan (in
  general) and are 27 les...
Conclusion
• International borders are statistically significant
  between Niger and Nigeria, but of smaller
  economic im...
Next Steps
• Disentangling the time element of the border
  effect (cowpeas)
• Understanding the credit constraints in the...
50
Extra Slides




               51
Kernel Distributions
|ln(pijt/pikt)| = β0 + β1 ln(TCjkt)+ β2urbanjkt +
            β3droughtjkt+ θt + ajk + εjkt

• pijt i...
Cowpeas
                Plot of Residuals: Cowpea
                         Prs < 150km
 4
 3
 2
 1
 0




     -.5    0   ...
54
55
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Project 9 Final Slides Aker Klein O Connell

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  • Good afternoon, and thank you for the opportunity to present here at the NBER Africa Project. This is a joint project between Michael Klein, Steve O’Connell and myself, and one that has really I think brought together people from three different disciplines to work on a common project, and so I would like to thank Gates and the organizers for that. We are very much looking forward to your questions and feedback.The title of our presentation today is “Borders or Barriers? The Impact of Borders on Agricultural Markets in West Africa.”
  • As economists, we have often recognized the potential welfare-enhancing effects of international trade. There is more debate, however, about the ease with which such trade takes place across international borders. Since Engels and Rogers’ seminal work assessing the role of the international borders on price dispersion between the US and Canada, there has been a large body of literature testing for the presence of a border effect between countries. While a variety of these papers find that a border effect exists for specific products (of the same retailer, like Safeway) between high-income countries, there are wide estimates of the magnitude and economic significance of this effect. Consequently, border effects have been cited as one of the “great puzzles” in international economics.
  • Now understanding the border effect is more than a theoretical concern. Borders incur excessively (or maybe unnecessary) high transaction costs, which is a deadweight loss. This is of particular concern in sub-Saharan Africa, and especially the landlocked countries of West Africa, which are some of the poorest countries in the world. In addition such countries rely primarily upon trade with their neighbors to meet their food and income needs.In this paper, we look at the specific case of West Africa. The countries in this area – particularly Niger – is one of the poorest countries in the world, ranked last on the UN’s Human Development Index (HDI). Their economies are primarily based upon rainfed agriculture, and trade with its coastal neighbors (Nigeria and Benin) and Burkina Faso.
  • This is really the focus of this research. Our goal is to assess the extent to which borders impose “additional” costs – in effect, become barriers -- that segment markets within West Africa. To do this, we rely upon the arbitrary nature of geographic borders in Africa to measure their impact upon price dispersion for grains and cash crops, using both traditional approaches in the intenrational economics literature and a RD design. Next, we try to go beyond this estimate of the international border effect to identify alternative explanations and mechanisms that can mitigate or exacerbate the border effect, namely, ethnicity. As this paper is primarily empirical in nature, we rely upon three distinct and unique datasets. Three datasetsMarket-level time series monthly panel from 1999-2007 for Niger, northern Nigeria, Benin, Burkina Faso and MaliGIS data on market location, road distances between markets and distances to international and internal bordersTrader and farmer-level market data
  • Whatwefindisthatthereis an international border effect, but itseconomicsignificancedoes not detertradebetween the two countries. This effectisrelatively the same for grains and cowpeas, twodifferentcommodities.A commonethnicityacross the international border appears to reducethis border effect.Whilethiscanmitigate the border, thereappears to be an internal border effectalongethniclines. This does not appear to be due to infrastructure differences, or unwillingness to trade, but somefundamentaldifferences in the role of women in agricultural trade and the importance of your social (ethnic) network in providingtradecredit. 3
  • Before we begin, I would like to give you a bit of background on borders and trade in West Africa, primarily focusing on Niger-Nigeria sub-region.
  • The Niger/Nigeria border reached its current configuration (1,500 km) within 20 yrs of the Berlin Conference of 1884/85. Its placement reflected two simultaneous processes, which, while not random, were arbitrary with respect to African interests. For example, the French wanted access to Chad across southern Niger. This moved the border from something roughly corresponding to the desert/arable fault line (a geographical barrier perhaps) to a location within the arable area. The British insisted that the Hausa/Fulani kingdoms of northern Nigeria be kept intact. Now in the centuries before the colonial powers arrived, trade of course existed between these two countries. In particular, the Hausa in the states of Northern Nigeria were linked to the sub-region through a series of trading routes, which were based mainly upon ecological specialization. There were two major routes: The trans-Saharan trade, which primarily focused in trade in slaves, textiles, salt, grains and livestock. The second major trade route was westward, and involved trade in kola nuts between Niger, Nigeria and Ghana.
  • These traditional trade routes were extremely important for trade of different commodities, and many of those for grains and cowpeas still exist until today. Today, both grains, cash crops and livestock are bought and sold through an extensive system of traditional grain markets, each of which occurs on a weekly basis. The average distance between these markets is about 350 km, but distance between markets for which trade occurs ranges from 5 km to over 1000 km. These photos provide some examples of the grains trade in Niger at the weekly grain markets.
  • Now, West Africa has attempted to promote cross-border trade through a system of monetary and trade unions. There are two relevant ones in West Africa: 1) UEMOA, created in 1994, which covers eight Francophone countries in West Africa, and which shares a common currency – the CFA franc – and a common external tariff; and 2) ECOWAS.The actual CFA franc was created in 1945, with two devaluations in 1948 and 1994 (changing the relative value of the CFA to the French franc). UEMOA is also part of ECOWAS, created in 2001 among all West African states. In 2007, ECOWAS CET is an instrument for tariff setting and liberalisation in order to ensure common market access in the West African region.
  • Now, as I mentioned previously, there is a large body of research that has attempted to measure the impact of a border effect, especially in high-income countries. These can be divided into three approaches.
  • The first approach, used by Engel and Rogers, compares within country price dispersion to cross-country dispersion. The approach within these papers is to regress a measure of price dispersion on distance between two cities, a dummy variable for the border and city fixed effects. The idea behind this analysis is that a border is like a “treatment effect” and, but for this, price relationships would be the same in the two countries
  • Now, a major criticism of this approach, as noted by Gorodnichenko and Tesar (2005), is that these estimates of the border effect are unidentified unless the degree of within-country price dispersion is the same across countries. They attempt to “control” for this by including country heterogeneity in the regressions.
  • Now, as I mentioned previously, there is a large body of research that has attempted to measure the impact of a border effect, especially in high-income countries. These can be divided into three approaches.
  • The concerns raised byGorodnichenko and Tesar inform our empirical strategy. During the first part of the strategy, we use a regression-based approach using market pairs to assess the impact of international borders on price dispersion. This approach examines whether the border can increase price dispersion (extra transaction costs) when there is trade between markets.However, recognizing the endogeneity concerns inherent in this approach, we use the RD design – as proposed by Gopinath et al – to measure the border effect, effectively limiting our sample to markets located close to the border. This of course has some of its own issues, but we can discuss those later.
  • To measure the border effect, we rely upon three primary datasets.
  • To giveyou an idea of whatthese data look like for the two countries, here are somebriefsummarystatistics. I willdiscussthese in more detaillater, but I wantyou to mainly focus on the pricedifferences for the twocommodities – millet isrelatively more expensive in Niger as comparedwithNorthern Nigeria, and cowpeaisrelativelycheaper, although the differenceis not statisticallysignificant.
  • Now, as a first brush, we estimate the border effect by using the approach in much of the literature, which is based upon market pairs. This involves the log difference in prices as a function of the distance (or transport costs) between markets and border.ln(pij,t / pik,t) = βDdj,k+ βDBj,k+Σ βzDz+εij,k,tDistance between cities j and k: dj,kBorder between cities j and k?: Bj,kdummy variableMarket dummy variables: DzOur primary coefficient of interest is b2, the coefficient on the border effect. In this case, we do not use all of the market pairs, but limit the market pairs that are within a certain distance of each other (ie, 200 km).
  • Using this approach reveals a positive and statistically significant border effect between the two countries. Using the basic specification, the international border increases price dispersion by 2.1 percent, which increases slightly once other covariates are included. Mindful of the criticism by Gorodnichnenko and Tesar, we include a dummy variable for country-specific market pairs, first for Niger, then for Nigeria. In doing so, the coefficient estimates change slightly, but not of the magnitude seen in other papers. And finally, when including market-specific fixed effects, the border effect increases price dispersion by 1.8 percent. The last column includes an additional term, namely, whether the markets in the pair share a common ethnicity or not (1 = different, 0 – same). What we see here is that the international border effect is relatively larger when trade is between markets of a different ethnicity. This will be important later. But in general, the magnitude of the border effect is small -- the km-equivalent border effect is 5 km, which is in contrast to the km-equivalent in the other border effects literature. The last column includes The interaction term reveals the interaction of two barriers: 1) the presence of a border; and 2) different ethnicities between two markets.The first joint effect shows that, in the presence of different ethnicity, the international border effect is higher.The second joint effect shows that, in the presence of an international border, price dispersion is higher between two markets of different ethnicity. (These two mean the same?)Km-equivalent border effect: exp(b1/b2), where b1 is coefficient on ln(distance) and b2 is coefficient on border.The coefficients are b1=.033 and b2=.024
  • Nowobviously, there are numerousthreats to our identification of this border effect.
  • The first of these is the criticism included by Gorodnichenko and Tesar, which point out that the border effect will be underidentified if the underlying price volatilities. We checked for this by including the country-specific market pair dummies. Another way to do this is to plot the residuals from a regression of the absolute value of the differences in the log of prices on a variety of covariates, assuming those residuals show deviations from the LoP.
  • In doing so, we see a slight difference in the distributions, but some difference, providing some support of the previous regression results. Similarity of distributions for within country market pairs suggests that different underlying price distributions is not a concern
  • The second major threat to our identification – althoughperhaps an alternative explanationratherthan a threat – isthat the markets are segmented (thereis no tradebetweenthem), sowewould not expectthere to be LOP. In this case, the border effect
  • Lookingat the trader-level data for border markets in Niger and nothern Nigeria, wefindthat traders in bothmarketstradewith cross-border marketswithin a 50-km radius – although a slightlyhigherpercentage of Nigerien traders – and that the magnitude of thistradeis about the same for traders in bothmarkets.
  • Now, a large criticism of this type of estimate of the border effect is the potential for bias in the results, since borders (albeit arbitrary) are not necessarily randomly assigned. One approach that has been used in the literature recently – byGopinath et al and by Ryan Bubb at Harvard – is the regression discontinuity approach. The idea here is that The intuition in applying the RD in this case is the following: The border represents a discontinuity in “treatment”, which is determined by your assignment to either side of the border. Since the level of treatment jumps discontinuously at the border, any discontinuous change in the outcome variable at the border could be attributed to the change in treatment status. Usually, people criticize RD because it only helps identify some treatment effect at the border, which limits its external validity. In our setting, the effect at the border is exactly what we need to know: why should there be a discontinuous change in price at the border
  • In a regression framework, this essentially means regressing the log of prices in market j at time t on a variable for Niger, the distance of the market to the border, an interaction term and other potential market-specific variables. The variable of interest here is γi, which measures the size of the border effect. This reveals a counterfactual: the price remains higher to one side of the border even if the distance shrinks to zero. As is the case in the RD literature (Imbens and Lemieux), the regression is estimated suing a local linear regression framework, whereby the regression is only estimated for prices in those markets that are within a certain distance from the border. The border effect therefore answers the question: How do prices change when crossing from a positive to a negative distance from the border, where this distance is some small number (epsilon)?*NOTES*:RD can be either sharp (level of treatment, N, goes from 0 to 1 at Z0) or fuzzy (where the level of treatment at Z0 increases discontinuously, but not from zero to one.The treatment effect is either the change in y at D0 in the sharp design, or the change in y over the change in treatment at Z0, which is the local Wald estimate of the causal impact. The log function log (x) is only defined for x>0, and it is <0 when x is inbetween 0 and 1. In this case, then, the change in the log of the price is equal to gamma times the change in N (going from treatment or not). This means as we go from Niger to Nigeria, the change in prices increases by x% (also known as a semi-elasticity).
  • Is thisapproachvalid? This is an oldmapthatprovides a bit of the intiution for the validity of thisapproach. Herewesee a close-up of the international border from the time of the international border, with villages on eitherside. In many cases, thesemarkets are «arbitrarily close » to the border.
  • Now, one of the primary assumptions in using the RD approach is whether the discontinuity in the outcome is related to discontinuities in other observable and unobservable characteristics. While we can’t directly test for the discontinuity in unobservables, we can test for the discontinuity in observable characteristics. #4 can be tested by comparing the means in small bins of Z to the left and right of the threshold, or as a regression with various powers of Z. But, since the goal is to compute the effect at Z0, most often use the local linear regression. The biggest issue is how to choose the bandwidth or kernel. The distance is uniquely identifying the villages and/or the markets in both slides.
  • Whenusing the regressionresults, theseconfirm the visual inspection. Crossing the border leads to a 25 percent jump in prices, and thisjumpisstatisticallysignifcantacross all specifications. Whenwe change the bandwidth to 50 km of the border, the effectstillremains, although the magnitude isrelativelysmaller.What is the reason behind this result? Have Niger-Nigeria done a good job of promoting free trade, or have they done a « bad » job at patrolling the border? Or are there other factors that could be explaining the magnitude of the border effect? Well, similar to the market pair results, we add in a variable for ethnicity. Once we include this we find belonging to the Hausa group - -which is common across the borders – significantly reduces the magnitude of this price jump, and the border effect falls from 25 percent to about 10 percent. The similar finding exists for cowpeas.
  • Whenusing the regressionresults, theseconfirm the visual inspection. Crossing the border leads to a 25 percent jump in prices, and thisjumpisstatisticallysignifcantacross all specifications. Whenwe change the bandwidth to 50 km of the border, the effectstillremains, although the magnitude isrelativelysmaller.Whatis the reasonbehindthisresult? Have Niger-Nigeria done a good job of promoting free trade, or have theydone a « bad » job atpatrolling the border? Or are thereotherfactorsthatcouldbeexplaining the magnitude of the border effect? Well, similar to the market pair results, weadd in a variable for ethnicity. Once weincludethiswefindbelonging to the Hausa group - -whichiscommonacross the borders – significantlyreduces the magnitude of thispricejump, and the border effectfallsfrom 25 percent to about 10 percent. The similarfindingexists for cowpeas.
  • The question is, of course, defining an ethnic border. Within Niger alone, there are 8 major ethnic groups, the major ones are the Zarma, Hausa, Kanuri, Tuareg, Puehl. The Hausa are the dominant ethnic group, representing 55% of the population, followed by the Zaram/Songhai (21%), Toureg (9.3%), Peuhl (8.5%), Kanuri (5%) and other (1.2%). They are strongly spatially differentiated throughout the country, with the Toureg in the north, with the Kanuri to the far southeast, and the Hausa to the southeast, and the Zarma/Songhai to the southwest. The Peuhl are somewhat interspersed with the Zarma in the Western region of the country, along the Burkina-Niger border. This map shows the general locations of different ethnic groups. .
  • Now, defining an ethnic border istrickier, but not impossible. This isactuallylessarbitrarythanyoumightthink – when I was in Niger in March, I was right along the area of the Hausa-Zarmaregion. Weleftfrom one village – SabonYayi – whereby the predominant composition was Hausa. Wethentraveledapproximately 5-10 km on a dirt road to another village – Zozo TomboKoida – whichwaspredominantlyZarma. So being able to identify a border, whilesubject to error, is not impossible.
  • The idea here is to use the similar type of RD design to determine the internal border effect, again by restricting our observations close to the actual Zarma/Hausa border.
  • The issue, of course, istwofold.
  • Once we focus on thosemarketswithin 50 km of the border, the effectissimilar in magnitude of the international border effect but slightlylarger, and robust to the inclusion of othercovariates. Once weexpandthis to 100 km, the effectisrelativelysmaller and lesssignificant, whichwewouldexpect. But westill have the « distance to border » issue.
  • Whatcouldbeexplainingtheseresults? Coulditbedifferent transport costsbetween the markets? This seemsunlikely, sincetheywere the same. Coulditbedifferentinvestments due by the colonial powers? Again, given the similarity in the market-levelcharacteristics, thisseemsunlikely. Coul
  • Whatcouldbeexplainingtheseresults? Coulditbedifferent transport costsbetween the markets? This seemsunlikely, sincetheywere the same. Coulditbedifferentinvestments due by the colonial powers? Again, given the similarity in the market-levelcharacteristics, thisseemsunlikely. Coul
  • Now, before we discuss the regression-based results, let’s look at some kernel distributions to see the intuition for where we’re going.In these distributions, we regress the absolute value of the log of price ratios in two different markets for product i at time t on a variety of factors we think might explain percentage changes in price dispersion.We then capture and plot the eit to show the measure of price dispersion. te |ln(pit/pjt)| = b0 + b1 ln(total transport cost)(i,j,t) + b2 (1 if i urban, j not urban, or conversely) + b3 (1 if i drought, j not drought, or conversely)(t) + e(i,j,t)We plot the εijt as a measure of deviation from the Law of One Price.
  • We see the same thing for cowpeas – similar underlying price distributions, so this would appear not to be driving the results.
  • RDD ResultsCowpea
  • Project 9 Final Slides Aker Klein O Connell

    1. 1. Borders or Barriers? The Impact of Borders on Agricultural Markets in West Africa Jenny C. Aker, Tufts University Michael W. Klein, Tufts University Stephen A. O’Connell, Swarthmore College December 12, 2009 Gates Africa Meeting
    2. 2. Motivation • International borders can affect trade flows and price dispersion • Border effects found in high-income countries • In West Africa, intra-national market segmentation may be more pronounced o Limited infrastructure (roads, distances) o Corruption and transaction costs o Countries comprised of ethno-linguistic groups corresponding to different regions within country
    3. 3. Niger and Nigeria
    4. 4. This Paper • Goal: Assess the extent to which borders impose costs that segment markets between Niger and Nigeria  Determine the impact of the Niger–Nigeria border on price dispersion for agricultural products  Identify the mechanisms that can mitigate or exacerbate border effects • Three datasets
    5. 5. Preview of Findings • International borders matter, but they don’t deter trade in the Niger-Nigeria sub-region. o Common ethnicity across borders appears to reduce the international border effect • There is a statistically significant border effect along ethnic lines within Niger. o Differences regarding the role of women and the importance of social networks for providing credit
    6. 6. Borders and Agricultural Trade • Borders were drawn to reflect interests of colonial powers.  Berlin Conference (1884/5) starts “scramble for Africa”  1890 – French (expanding from Senegal and Algeria) and British (expanding north from southern Nigeria) established Niger’s southern border.  Borders passed through political, ethnic, and economic groupings
    7. 7. Borders and Agricultural Trade 7
    8. 8. Commodity Markets in Niger and Nigeria
    9. 9. Background on Borders and Trade in West Africa • West African Economic and Monetary Union (UEMOA) o Customs and monetary union in 1994 o Common external tariff (CET) in 1998 • Economic Community of West African States (ECOWAS) o Created in 2001 o Harmonize their import tariffs with UEMOA CET in 2007 • Niger-Nigeria Joint Commission (1971) 9
    10. 10. Related Research • Engel & Rogers AER 1996, • Prices in US and Canadian Cities, Parsley and Wei JIE 2001, Prices in Japan and US. • Ceglowski, CJE 2003, Intra-national price comparisons in Canada. • Gorodnichenko and Tesar, AEJ-Macro 2009 • Gopinath, Gourinchas, Hsieh, and Li, 2009, RDD approach for supermarket prices in western US & Canada.
    11. 11. Engel & Rogers Methodology Border effect as km-equivalent US – US Pair Canada – Canada Pair US – Canada Pair ln(pi) - ln(pj) Border Effect Distance Between Km Equivalent of Cities i and j Border Effect
    12. 12. Gorodnichenko & Tesar (2009) • Engel & Rogers method overstates role of border if differences in price volatility in the two countries. (e.g. Parsley and Wei US-Japan border effect is 43,000 trillion miles) • Price dispersion between the two countries exists because low-volatility Canadians trading with high- volatility Americans. • Border effect under-identified: Cannot have dummy variables for US-US pairs, Canada-Canada pairs and border (i.e. US-Canada) pairs due to multicollinearity.
    13. 13. Related Research • Engel & Rogers AER 1996, • Prices in US and Canadian Cities, Parsley and Wei JIE 2001, Prices in Japan and US. • Ceglowski, CJE 2003, Intra-national price comparisons in Canada. • Gorodnichenko and Tesar, AEJ-Macro 2009 • Gopinath, Gourinchas, Hsieh, and Li, 2009, RDD approach for supermarket prices in western US & Canada.
    14. 14. Empirical Strategy • Part I: Regression-based estimates of the border effect across market pairs within and across countries. • Part II: Regression Discontinuity Design: As distance to the border shrinks to zero, is there a price change at the border?
    15. 15. Data Sets • Market-Level Panel Data on prices, costs, other characteristics • Market Locations and Distances between markets (road and Euclidean) • Trader and farmer-level datasets
    16. 16. Market-Level Panel Dataset • Monthly prices for agricultural products in 65 markets in Niger and northern Nigeria between 1999-2007 • Market-level rainfall statistics • Monthly gasoline prices • Urban status • Date of mobile phone coverage in each market • Monthly CFA-Naira exchange rates
    17. 17. Data on Market Locations and Distances  GIS (latitude and longitude location) of each market  Road and Euclidean distances between each set of market pairs
    18. 18. Trader-Level Data  Panel survey of markets, traders and farmers collected between 2005-2007 across 6 regions of Niger and in cross-border markets o Traders’ demographic characteristics and marketing behavior o Number of traders operating per market o Market-level institutional characteristics  Ethnolinguistic mapping of villages by SIL/Niger (1998)
    19. 19. Empirical Strategy |ln(pi,t /pj,t) |= β1dij+ β2Bij + ΣβxXij,t + ai + aj +εijt • dij is the distance between markets i and j • Bij is a dummy variable for the presence of an international border between two markets • Xij,t is a vector of other exogenous covariates • θt is time fixed effects • ai and aj are market-specific fixed effects • Use dyadic standard errors
    20. 20. The International Border Effect Table 2. Average International Border Effect Millet Dependent variable: |ln (P it /P jt )| (1) (2) (3) (4) (5) (6) .021*** .025*** .025*** .032*** .018*** .024*** Niger-Nigeria border (.003) (.003) (.003) (.007) (.002) (.003) .007 Niger Market Pair (.008) -.007 Nigeria Market Pair (.008) Km- equivalent .014*** Inter-ethnic (.003) border .019*** effect is Inter-ethnic*border (.007) only 5 km .141*** .095*** .052*** .095*** .038*** .097*** Constant (.005) (.003) (.006) (.003) (.006) (.003) Other covariates No Yes Yes Yes Yes Yes Market-Specific Fixed Effects No No Yes No Yes No Monthly time dummy Yes Yes Yes Yes Yes Yes # of observations 23760 23760 23760 23760 23760 23760 Dyadic s.e. 0.005 0.005 0.005 0.005 0.006 0.006 R2 0.0109 0.0505 0.1609 0.0831 0.2956 0.086 .034*** Joint effect (different) ethnicity (.007) .044*** Joint effect border (.007)
    21. 21. Threats to Identification • Different price volatilities in each country • Market segmentation • Endogeneity of border effect 22
    22. 22. Different price volatilities Plot of εijt from regressions Frequency Within Niger Cross-Border: Niger- Nigeria 0 ln(pij,t / pik,t)
    23. 23. Millet Plot of Residuals: Millet Prs < 150km 6 4 2 0 -.2 0 .2 .4 .6 .8 x Niger-Nigeria Pairs Niger-Niger Pairs Nigeria-Nigeria Pairs
    24. 24. Threats to Identification • Different price volatilities in each country • Market segmentation • Endogeneity of border effect 25
    25. 25. Is There Trade Across the Border? Table 6: Difference in Trader-Level Characteristics between Niger and northern Nigeria Niger Nigeria Coefficient S.e. Mean s.d. Mean s.d. Trading Behavior Number of markets followed 4.35 3.90 5.29 2.21 -0.93 0.84 Number of market contacts 4.24 3.89 5.00 5.59 -0.76 2.12 Number of purchase and sales markets 4.36 2.85 5.38 1.92 -1.01 0.68 Trade in cross-border markets within a 50-km radius 0.27 0.22 0.55 0.07 -0.28** 0.05 Quantity traded in 2005/2006 12936 59696 10025 14106 -2911 36096 Notes: Data from the Niger trader survey and secondary sources collected by Aker. N=415 traders, 37 markets. Huber-White robust standard errors are in parentheses. * is significant at the 10% level, ** significant at the 5% level, *** is significant at the 1% level. 26
    26. 26. Threats to Identification • Different price volatilities in each country • Market segmentation • Endogeneity of border effect 27
    27. 27. Regression Discontinuity 1. Is there a discontinuity in prices across the border? 2. Does Distance to the border matter? 3. Is the discontinuity related to other factors? 4. What is the relevant range for comparison? Bandwidth Bandwidth ln(pj) γ = Border Discontinuity d→0 0←d Nigeria 0 Niger (distance to border < 0) (distance to border > 0)
    28. 28. Regression Discontinuity • Is there a discontinuous change in price with respect to distance to the border? • Price at time t, of good i in market j: ln(pij,t) = αi +γi Nj + θi Dj + δ Dj * Nj + β‘Xj +εij Nj=1 if market in Niger, 0 if in Nigeria Dj is distance of market j from the border, >0 for markets within Niger and <0 for cross-border markets Xj other market-specific variables Local linear regression
    29. 29. 30
    30. 30. Is the discontinuity related to other factors? Bandwidth Bandwidth Xj Border Discontinuity in Covariate Nigeria Niger (distance to border < 0) (distance to border > 0)
    31. 31. Table 5: Mean Difference (Niger - Nigeria) Within 5 km to the Niger-Nigeria border Coefficient Robust standard error t -ratio p -value Market size -138.92 75.74 -1.830 0.126 Urban status -0.417 0.411 -1.010 0.358 Road quality -0.083 0.411 -0.200 0.847 Gas/kg 0.000 4.411 0.000 1.000 Cellphone coverage 0.320 0.035 8.400 0.000 Drought status -0.010 0.012 -0.850 0.390 Number of police controls -0.167 0.693 -0.240 0.816 Sample size 960 Within 50 km to the Niger-Nigeria border Coefficient Robust standard error t -ratio p -value Market size -84.977 73.277 -1.160 0.267 Urban status -0.295 0.283 -1.040 0.316 Road quality -0.205 0.283 -0.720 0.483 Gas/kg 0.000 3.370 0.000 1.000 Cellphone coverage 0.219 0.024 9.160 0.000 Drought status -0.006 0.010 -0.560 0.575 Number of police controls -0.286 0.664 -0.430 0.677 Sample size 1,920
    32. 32. Table 3. RDD Regressions of Niger-Nigeria Border Effect on Millet Price 5 km to Border 50 km to Border 1 2 3 4 5 6 7 8 .255*** .306*** .257*** .232*** .065 .140*** .141*** .215*** International border (.009) (.031) (.022) (.020) (.079) (.070) (.076) (.031) .012*** .004 .013*** .013*** .012*** .001 .004 .014** Distance to Border (.002) (.005) (.004) (.007) (.001) (.004) (.003) (.004) -.080*** -.068*** -.079*** -.044* '-.012 '-.001 '-.003 -.015*** Border*Distance (.002) (.008) (.004) (.019) (.002) (.005) (.004) (.004) -.013 .002 Hausa (.018) (.011) -.138 -.203*** Hausa*International Border (.077) (.032) 4.73*** 4.50*** 4.73*** 4.74*** 4.73*** 4.440522 4.71*** 4.74*** Constant (.007) (.114) (.009) (.011) (.007) .0652112 (.009) (.040) Other covariates No Yes Yes Yes No Yes Yes Yes Monthly fixed effects No No Yes Yes No No Yes Yes Observations 588 588 588 588 1267 1267 1267 1267 R-squared 0.1765 0.9239 0.9239 0.8841 0.2519 0.0704 0.7322 0.8436 .093* .011 Joint effect border (.07) (.033) '-.151* -.200*** Joint effect Hausa (.072) (.028) Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Standard errors are robust to market level clustering in the conditional variance-covariance matrix of the disturbance.
    33. 33. Table 3. RDD Regressions of Niger-Nigeria Border Effect on Millet Price 5 km to Border 50 km to Border 1 2 3 4 5 6 7 8 .255*** .306*** .257*** .232*** .065 .140*** .141*** .215*** International border (.009) (.031) (.022) (.020) (.079) (.070) (.076) (.031) .012*** .004 .013*** .013*** .012*** .001 .004 .014** Distance to Border (.002) (.005) (.004) (.007) (.001) (.004) (.003) (.004) -.080*** -.068*** -.079*** -.044* '-.012 '-.001 '-.003 -.015*** Border*Distance (.002) (.008) (.004) (.019) (.002) (.005) (.004) (.004) -.013 .002 Hausa (.018) (.011) -.138 -.203*** Hausa*International Border (.077) (.032) 4.73*** 4.50*** 4.73*** 4.74*** 4.73*** 4.440522 4.71*** 4.74*** Constant (.007) (.114) (.009) (.011) (.007) .0652112 (.009) (.040) Other covariates No Yes Yes Yes No Yes Yes Yes Monthly fixed effects No No Yes Yes No No Yes Yes Observations 588 588 588 588 1267 1267 1267 1267 R-squared 0.1765 0.9239 0.9239 0.8841 0.2519 0.0704 0.7322 0.8436 .093* .011 Joint effect border (.07) (.033) '-.151* -.200*** Joint effect Hausa (.072) (.028) Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Standard errors are robust to market level clustering in the conditional variance-covariance matrix of the disturbance.
    34. 34. Ethnicity: Border or Barrier? • “Common ethnicity” (Hausa) minimizes the impact of the international border effect o The international border effect is reduced by 15 percent for millet and 20 percent for cowpeas • Can different ethnic compositions across markets serve as a intra-national border? 36
    35. 35. Defining an Ethnic Border Source: Trader and farmer panel surveys, 2005-2007
    36. 36. Defining an “Ethnic” Border • Use demographic data on ethnic composition for villages from 1999 (SIL/Niger) and 2007 • Calculate the ethnolinguistic fractionalization (ELF) of each village in the sample • Identify the villages with “high” ELF (ethnic diversity) • Use this to mark the latitude and longitude points of the ethnic border • Compare with trader and farmer survey data 38 from 2005-2007
    37. 37. Regression Discontinuity • Price at time t, of good i in market j: ln(pij,t) = αi +γi Hj + θi Dj + β‘Xj +εij • Hj=1 if market in Hausa area, 0 if in Zarma area • Dj is distance of market j from the border, >0 for markets in Hausa region and <0 for markets in Zarma region • Xj other market-specific variables • But, distance to border issue
    38. 38. 40
    39. 39. Table 7: Mean Difference (Zarma - Hausa) Within 50 km to the Hausa-Zarma border Coefficient t -ratio p -value Market size 28.50 2.11 0.17 Urban status 0.00 0.00 1.00 Road quality 0.00 0.00 1.00 Gas/kg 0.00 0.00 1.00 Cellphone coverage 0.09 1.84 0.07 Drought status 0.00 0.00 1.00 Police controls -1.50 -2.45 0.25 Market tax (CFA/kg) 0.17 1.00 0.37 Within 100 km to the Hausa-Zarma border Coefficient t -ratio p -value Market size 29.83 1.09 0.33 Urban status -0.08 -0.20 0.85 Road quality 0.08 0.20 0.85 Gas/kg 0.00 0.00 1.00 Cellphone coverage 0.08 2.50 0.01 Drought status 0.00 0.00 1.00 Police controls -0.83 -0.62 0.58 41 Market tax (CFA/kg) 0.10 0.71 0.51
    40. 40. Table 8. RD of Zarma-Hausa Border Effect on Log of Millet Price 50 km to Border (1) (2) (3) (4) .282*** .289*** .252*** .480*** Ethnic border (.082) (.080) (.064) (.109) -.005*** -.005*** -.002*** -.005 Distance to Border (.000) (.000) (.000) (.004) .004*** .005*** .002* -.017 Border*Distance (.001) (.001) (.001) (.011) .000 Distance squared (.000) 4.79*** 4.79*** 4.79*** 4.65*** Constant (.036) (.044) (.007) (.022) Other covariates No Yes Yes No Monthly fixed effects No No Yes No Observations 253 253 253 607 R-squared 0.055 0.0671 0.783 0.8019 42
    41. 41. Potential Mechanisms • Higher transactions costs in Zarma regions • Differential investment by colonial powers • Unwillingness to trade across the “ethnic border” • Linguistic costs to trade • Gender differences among Hausa and Zarma markets (women’s market participation) • Credit among social networks 43
    42. 42. Potential Mechanisms • Higher transactions costs in Zarma regions • Differential investment by colonial powers • Unwillingness to trade across the “ethnic border” • Linguistic costs to trade • Gender differences among Hausa and Zarma markets (women’s market participation) • Credit among social networks 44
    43. 43. Table 10: Difference in Trader-Level Characteristics between Hausa and Zarma Regions Zarma Hausa Coefficient S.e. Mean s.d. Mean s.d. Demographic Characteristics Years of Education 2.18 2.70 3.05 2.56 -0.88 0.60 Age 43.20 13.40 44.10 11.05 -0.91 2.78 Speak Hausa Language 0.20 0.40 1.00 0.00 -0.80*** 0.06 Speak Zarma Language 0.70 0.48 0.00 0.00 0.70*** 0.08 Gender 0.29 0.46 0.05 0.23 0.24*** 0.08 Firm Characteristics Association Membership 0.33 0.48 0.47 0.51 -0.14 0.11 Years of Experience 11.48 8.22 15.20 9.38 -3.78* 1.99 Number of employees 3.44 5.10 4.00 3.11 -0.56 0.94 Have partners 0.29 0.46 0.24 0.49 0.06 0.11 Change original market 0.08 0.27 0.11 0.31 -0.03 0.07 Retailer 0.59 0.50 0.55 0.50 0.03 0.11 Have financial account 0.11 0.32 0.24 0.44 -0.13 0.09 Trading Behavior Number of markets followed 2.82 1.92 3.50 3.96 -0.68 0.71 Use mobile phone for trading 0.44 0.50 0.38 0.49 0.06 0.08 Number of market contacts 2.53 2.47 2.93 4.09 -0.40 0.88 Number of purchase and sales markets 3.51 1.96 4.53 2.88 -1.01* 0.56 Trade in markets within a 50-km radius 0.85 0.24 0.94 0.14 -0.09* 0.05 Take a loan 0.36 0.48 0.38 0.49 -0.02 0.10 Take a loan from a fellow trader 0.21 0.42 0.23 0.42 -0.01 0.09 Number of credit institutions 0.50 0.70 0.67 0.67 -0.02 0.10 Notes: Data from the Niger trader survey and secondary sources collected by Aker. N=415 traders, 35 45 markets. Huber-White robust standard errors are in parentheses. * is significant at the 10% level, ** significant at the 5% level, *** is significant at the 1% level.
    44. 44. Table 10: Difference in Trader-Level Characteristics between Hausa and Zarma Regions Zarma Hausa Coefficient S.e. Mean s.d. Mean s.d. Demographic Characteristics Years of Education 2.18 2.70 3.05 2.56 -0.88 0.60 Age 43.20 13.40 44.10 11.05 -0.91 2.78 Speak Hausa Language 0.20 0.40 1.00 0.00 -0.80*** 0.06 Speak Zarma Language 0.70 0.48 0.00 0.00 0.70*** 0.08 Gender 0.29 0.46 0.05 0.23 0.24*** 0.08 Firm Characteristics Association Membership 0.33 0.48 0.47 0.51 -0.14 0.11 Years of Experience 11.48 8.22 15.20 9.38 -3.78* 1.99 Number of employees 3.44 5.10 4.00 3.11 -0.56 0.94 Have partners 0.29 0.46 0.24 0.49 0.06 0.11 Change original market 0.08 0.27 0.11 0.31 -0.03 0.07 Retailer 0.59 0.50 0.55 0.50 0.03 0.11 Have financial account 0.11 0.32 0.24 0.44 -0.13 0.09 Trading Behavior Number of markets followed 2.82 1.92 3.50 3.96 -0.68 0.71 Use mobile phone for trading 0.44 0.50 0.38 0.49 0.06 0.08 Number of market contacts 2.53 2.47 2.93 4.09 -0.40 0.88 Number of purchase and sales markets 3.51 1.96 4.53 2.88 -1.01* 0.56 Trade in markets within a 50-km radius 0.85 0.24 0.94 0.14 -0.09* 0.05 Take a loan 0.36 0.48 0.38 0.49 -0.02 0.10 Take a loan from a fellow trader 0.21 0.42 0.23 0.42 -0.01 0.09 Number of credit institutions 0.50 0.70 0.67 0.67 -0.02 0.10 Notes: Data from the Niger trader survey and secondary sources collected by Aker. N=415 traders, 35 46 markets. Huber-White robust standard errors are in parentheses. * is significant at the 10% level, ** significant at the 5% level, *** is significant at the 1% level.
    45. 45. Gender and Agricultural Trade (Correlations) • Female traders are less likely to take a loan (in general) and are 27 less likely to borrow from another trader • Female traders sell is 2 fewer markets and consult 1.2 fewer people for market information • Female traders have 1.5 years less schooling (mean is 3 years) and 4.5 years less experience 47
    46. 46. Conclusion • International borders are statistically significant between Niger and Nigeria, but of smaller economic importance than in industrialized countries • Impact larger for semi-perishable commodity (cowpea) • Common ethnicities mitigate the impact of an international border effect  There is an internal border along ethnic lines
    47. 47. Next Steps • Disentangling the time element of the border effect (cowpeas) • Understanding the credit constraints in the agricultural market – are social credit networks along ethnic lines? • International border effect for agricultural trade between Niger and the CFA zone (Benin, Burkina Faso) as compared with the CFA-Naira (role of the exchange rate)
    48. 48. 50
    49. 49. Extra Slides 51
    50. 50. Kernel Distributions |ln(pijt/pikt)| = β0 + β1 ln(TCjkt)+ β2urbanjkt + β3droughtjkt+ θt + ajk + εjkt • pijt is the price of good i in market j at time t • pikt is the price of good i in market k at time t • TCjkt is transport costs between markets j and k at time t • urbanjkt is an urban variable (1 if one market is greater than 35,000 people, 0 otherwsie) • droughtjkt is a dummy variable for drought at time t • aj,k is market-pair fixed effect, used in some specifications
    51. 51. Cowpeas Plot of Residuals: Cowpea Prs < 150km 4 3 2 1 0 -.5 0 .5 1 1.5 x Niger-Nigeria Pairs Niger-Niger Pairs Nigeria-Nigeria Pairs
    52. 52. 54
    53. 53. 55

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