Online Appendix to The Missing Food Problem: Trade, Agriculture, and International Income Differences

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1 Online Appendix to The Missing Food Problem: Trade, Agriculture, and International Income Differences Trevor Tombe, Uversity of Calgary Contents 1 Proof of Propositions 2 2 Data and Sample of Countries Data Sources and Construction Labor Productivity Estimates The Main Sample for Quantitative Analysis Key Empirical Patterns for All Possible Countries Labour Productivity International Trade Patterns Trade Costs Calibration Details Production Function Parameters Estimating Productivity Dispersion θ Trade Cost Asymmetries Alternative Model Specifications Eliminating Itial Zeros from the Trade Matrix Unbalanced Trade Tariff Revenue

2 1 Proof of Propositions Proposition 1 If sectoral expenditures X j n and revenues R j n solve equations 4 and 5, households spend all of their income, and total income equals total value added, then S a n = S m n and X a n + X m n = N πinx a a N i + i=1 i=1 π m inx m i, must hold for all n. That is, aggregate trade balances for all countries. Proof: Summing equation 4 across all sectors in each country yields Xn j = I n + j {a,m,s} j {a,m,s} k {a,m,s} (1 φ k )γ k j R k n, = I n + (1 φ k )R k n k {a,m,s} = R k n. k {a,m,s} j {a,m,s} The third line follows from j {a,m,s} γ k j = 1 and j {a,m,s} φ j R j n = I n. Services are not tradable, so R s n = Xn s and therefore Xn a + Xn m = R a n + R m n. From equation 3, Rn j = Xn j + Sn j for j {m,a}, and therefore Sn a = Sn m must hold. The final trade balance condition immediately follows from equation 5; specifically, Rn j = N i=1 π j in X j i. γ k j, Proposition 2 The change in real GDP is Ŷ n = j {a,m,s} ŵn j ˆl j ˆP n j nωn, j (1) where the weights ω j n = φ j R j n/ k {a,m,s} φ k R k n are the itial GDP shares and changes in sectoral real wages (and therefore sectoral labor productivity) are ŵ j n ˆP j n = ( [ ˆπ nn j ) 1 θ j φ j β 1 ˆλ n k {a,m,s} ] ( ) γ ˆP n k jk 1 φ j / ˆP n j φ j. (2) 2

3 Proof: Real GDP in the counterfactual equilibrium values output using the itial prices, Y n = j {a,m,s} = j {a,m,s} = j {a,m,s} φ j Rn j P j n φ j R j n ˆR n j ˆP n j ˆP j n Pn j,, φ j R j n. Dividing both sides by the itial GDP Y n = k {a,m,s} φ k R k n yields Ŷ n = j {a,m,s} = j {a,m,s} ˆR n j ˆP n j ω j n, ŵn j ˆl j ˆP n j nωn j where the second line follows from ˆR n/ j ˆP n j = ŵn j ˆl n/ j ˆP n j and the weights are the itial GDP shares, ωn j = φ j Rn/ j k {a,m,s} φ k R k n. This gives the first result. Next, the change in real wages in each sector is simple to derive. Relative changes in trade shares are and therefore ˆπ j nn = ( ) ˆπ j = ˆτ ĉ j j θ i / ˆP n j j, [ (ŵ j n ) β ˆr 1 β n ( [ ˆπ nn j ) 1 θ j φ j = ŵn j 1 β ˆλ n k {a,m,s} ] [ φ j ( ˆP n k k {a,m,s} ( ˆP k n ) ] 1 φ j γ jk φ j ) γ jk ] 1 φ j / ˆP j / ( ˆP n j ) 1 φ j θ j n, 3

4 ŵ j n ˆP j n = ( [ ˆπ nn j ) 1 θ j φ j β 1 ˆλ n = ( ˆπ nn j ) 1 θ j φ j β 1 ˆλ n [ k {a,m,s} k {a,m,s} ( ˆP k n ) ] 1 φ j γ jk φ j ] ( ) γ ˆP n k jk 1 φ j / ˆP n j φ j, ( ˆP n j ) 1 φ j φ j, which is the same expression as in Caliendo and Parro (2012) except for ˆλ n. Proposition 3 The change in welfare Û n can be decomposed into Û n = ŵ n ˆP n 1 } {{ } Real Wages ˆλ n }{{} Labor Market ˆΓ n }{{} Subsistence Food where ŵ n ˆP n 1 captures standard real-wage effects, ˆλ n captures changes in labor allocations and distortions, and ˆΓ n captures non-homothetic preference effects. Proof: To get welfare changes, determine changes in consumption in excess of subsistence requirements. For agriculture, define C a n = C a n ā for agriculture and C j n = C j n for manufacturing and services. The household s optimal consumption choices imply C j n = (I n āp a n )/P j n. Taking ratios, ˆ C j n = I n ā P a n (I n āp a n ) 1, ( ) ) which can be simplified using I n ā Pn a s a = I n (În n ε a 1 ε ˆP a a n ( 1 s a n 1 ε )I a n to ˆ C j n = ( 1 ε a 1 s a Î n n = În 1 ε a ˆP n j 1 s a n În ˆP a n ˆΓ n, ( 1 ˆP j n ( s a n ε a ) ) 1 ˆP a 1 s a n n ˆP n j, ( s a n ε a ) ˆP n a ), 1 ε a În and I n āp a n = 4

5 ( ( ) ) where ˆΓ n = 1 εa s a 1 s a 1 n ε a ˆP a n 1 ε a n În. As the change in overall welfare is Û n = ( ˆ C a n) εa (Ĉm n ) ε m (Ĉs n ) ε s, inserting the above change in above-subsistence consumption change yields Û n = Î n ˆΓ n / ˆP n, where the change in prices are ˆP n = ( ˆP n a ) ε a ( ˆP n m ) ε m ( ˆP n s ) ε s. 2 Data and Sample of Countries In this section, I list the main sources of data and details behind how certain variables were constructed. All data sources are publicly available, though the recent UN-IDO data is not free. 2.1 Data Sources and Construction The key variables are as follows: Trade Flows Trade flow data is from the BACI product-level trade database (Gaulier and Zignago, 2010), which classifies trade by harmozed system (HS) codes (2002 version). I aggregate products with two-digit HS codes into agriculture and products with codes or into manufacturing. Notice this excludes mineral products and services and treats food preparations as manufactured goods. Tariffs The UN-TRAINS database provides a wealth of tariff data. I use tradeweighted MFN tariffs for 2005, or the closest year (older breaking ties) within 2004 or Products included in agriculture and manufacturing correspond to the HS codes listed above for trade flows. Gross Output The UN-IDO provides gross output and value-added in manufacturing for a large number of countries. For agriculture, gross output is available from the FAO and OECD. Data are available from the FAO using a number of measures. As trade data is in current US dollars, I use production data for 2005 valued in current US dollars. When agricultural output data is available from the OECD, I 5

6 use this instead of the FAO data. There are 30 countries for which this data is available. The manufacturing, 64 countries have gross output data from the UN-IDO. For the remaing 26, I infer output from value-added data according to the average value-added to gross-output ratio for the countries in the UN-IDO data. Agricultural Employment Agricultural employment data are mainly from the FAO, though I augment it with data from the WDI or the CIA World Factbook as needed. The specific adjustments are occasionally necessary. In cases where FAO employment data results in implausible productivity values, I use the WDI employment data. Specifically, WDI employment is used for Armea, Bhutan, Bulgaria, Burkina Faso, China, Kyrgyzstan, Macedoa, Moldova, Rwanda, and Slovea. Data from the CIA World Factbook are used when WDI values are unavailable. I use this data for Bosa and Herzegovina, Nepal, and Nigeria. Agriculture s labor share for all 90 countries used in the main quantitative exercise is reported later in this appendix. Agricultural Consumption Share The World Bank International Comparison Program (ICP, version 2005) provides a list in their Final Report of the share of consumption expenditures allocated to food. I use this as s a n to solve the itial equilibrium of the model. Labor market distortion The labor market distortion for each country is inferred from agriculture s share of employment and GDP. I described the employment data in the section for Labor Productivity Estimates. Agriculture s share of nominal GDP is readily available from the World Bank s WDI data. For three countries (Greece, Israel, and Qatar) the WDI share is unavailable and I use agriculture s GDP share reported in the CIA World Factbook. 2.2 Labor Productivity Estimates To compare productivity across countries one requires value-added per worker adjusted for price differences. I construct real labor productivity for agriculture and non-agriculture for a large set of countries following Caselli (2005) and Restuccia et al. (2008). The UN Food and Agricultural Orgazation s FAOSTAT reports agricultural 6

7 net output at international prices for Fortunately, these data use producer prices that exclude distribution costs, which vary systematically with a country s level of development (Adamopoulos, 2011). Unfortunately, value-added in international prices is not available; so, assume it is 50% of output (consistent with evidence documented in appendix section 3.1). 1 Non-agricultural value-added is aggregate value-added (from the Penn World Table 8.0) less agricultural value-added. A complication results from differences in how the PWT and the FAO normalize international prices (relative prices equal but overall levels differ). Following Caselli (2005), I rescale agricultural value-added uformly across countries such that agriculture s share of GDP in the US matches the share reported in the World Development Indicators (WDI). Finally, employment in each sector is needed to construct value-added per worker. I describe the sources for this data in the next section. With these employment data, I simply take the ratio of value-added in agriculture to the number of agricultural workers. Similarly for non-agriculture. The results of this exercises are reported in the main paper for as many countries for which sufficient data exists, not just the 90 countries used in the main quantitative exercises. 2.3 The Main Sample for Quantitative Analysis The main quantitative analysis uses a set of 90 countries for which data exists for aggregate GDP and employment, agricultural employment and expenditure shares, and trade flows, tariff rates, and production by sector. Overall, the sample of 90 countries I work with include all major countries around the world and span a wide range of levels of development. Combined, these 90 countries account for roughly 90% of global GDP, population, and employment. I list each country in the sample, along with key data, in the Table 1. 1 Restuccia et al. (2008) exploit internationally priced value-added for 1985 from Rao (1993). The correlation between our measures are 0.87 for agriculture and 0.82 for non-agriculture. 7

8 Table 1: Selected Data and Estimates for 90 Country Sample GDP per Ag. Food Worker Home Trade Shares Labor Budget Labor Country (US=1) Ag. Nonag. Share Share Distortion Albaa Argentina Armea Australia Austria Azerbaijan Bangladesh Bhutan Bolivia Bosa Brazil Bulgaria Burkina Faso Cameroon Canada China Colombia Congo Côte d Ivoire Croatia Cyprus Czech Republic Ecuador Egypt Ethiopia Fiji Finland France Germany Ghana Greece Guinea Hungary Iceland India Indonesia Iran Ireland Israel Italy Japan Jordan Kazakstan Kenya Kyrgyzstan Laos Latvia Lebanon Lithuaa

9 Macedoa Malawi Malaysia Mauritius Mexico Moldova Morocco Mozambique Nepal New Zealand Nigeria Norway Pakistan Paraguay Peru Poland Portugal Qatar Russia Rwanda Saudi Arabia Senegal Slovea South Africa South Korea Spain Sri Lanka Sudan Sweden Tajikistan Thailand Togo Tusia Turkey Ukraine Uted Kingdom Uted States Uruguay Venezuela Vietnam Yemen

10 2.4 Key Empirical Patterns for All Possible Countries Section 2 of the main paper is restricted to my sample of 90 countries outlined in appendix section 2.3. None of the patterns are particular to this sample. In this section, I replicate all of the figures from section 2 for the broadest possible set of countries for each. Keep in mind the countries included may vary from one figure to the next Labour Productivity Figure 1 displays the results for 158 countries with sufficient data. Agricultural labor productivity differences are an order of magtude greater than non-agricultural. Agricultural productivity among the richest 10% of countries is nearly 90 times higher than among the poorest 10%; the comparable figure is only 14 for nonagriculture. Other measures of variation give similar results. The 90/10 ratio for agriculture is 70 while the ratio for non-agriculture is 9. Despite such low productivity, the vast majority of poor country employment is agricultural, as illustrated in panel (b) of Figure 1. Figure 1: Labor Productivity and Employment (a) Real Labor Productivity (b) Agriculture s Share of Employment Log Agricultural GDP/Worker, LBR BEL DNK CAN USA FRA NZL AUS ISR NLD MLT DEULUX GBR ARG ITA ESP AUT URY SWE CYPFIN MKD HUN CHE BLR LTU HRV CZE BRB GRC KWT SRB LBN SGP BRA JOR BGR ROU VEN SVK SVN CRI CHL EST ISL JPN UKR BHR QAT MDA BLZ RUS MYS LVA IRQ KAZ POL PRT TWN SYR ECU BIH DOM BHS KOR PRY SAU GEOCOL MUS TUN MEX IRN UZB TKM SUR PAN BRN MNG HND EGYPER TTO JAMVCT SWZ KGZ AZE ALB CIV TJK MAR PAK BOL SDN FJI GTM PHLNAM ARM BTN GAB BMU BEN LCA THA OMN GHA NGA CMR ATG BGD STP MLI MRT VNM CAF IND LKA NERCOG KEN GIN LAO GNB BWA MDGKHM NPL UGA TGOSLE TCDYEM MDV LSO MWI RWA TZA COM BFA SEN AGO ZMB ETH BDI ZWE GMB DJI MOZ GNQ Log Non Agricultural GDP/Worker, 2005 Agriculture s Employment Share BDI NERBFA MOZ ETH MWI GNB GIN RWA TZA UGA MLIGMB NPL LAO DJI MDG COM KEN SEN TCDNGAAGO CAF KHM LBR ZMB VNM SLE STP ZWE TGO GHA IND SDN CMR BENBGD MRT THA ARM CIV ALB BOL YEM LKA BTN BWA LSO PAK GTM KGZ FJI COGPHLNAM TJK SWZTKM GAB OMN HND MAR PRY EGY AZE PER UZB BLZ IRN MNG SYR VCT ECU TUN LCA ATG JAM BIH MDAGEO COL SURCRI MDVMEX PAN POL KAZSRB MYS BRA DOM CHL GRC UKR MUS BLR ROU URY BGR LVAEST IRQJOR ARG MKD LTUPRT RUS LBNHUN HRV CZECYP NZL VEN TTO SVKSVN KOR ESP ISL TWN SAU BHS BRB BMUDNK CHE FIN AUT AUS BRN ISR JPN MLT SWE GBR DEU FRA CAN ITA NLD SGP BHR BEL USA LUXKWT QAT Log Aggregate GDP/Worker, 2005 Labor productivity measured in international prices for agriculture and non-agriculture. Calculations follow Caselli (2005) and Restuccia et a. (2008). Agriculture s share of employment primarily from the UN-FAO. Details in appendix. 10

11 Figure 2: Key Trade Patterns for Agriculture and Manufacturing (a) Share of Total Expenditures on Domestically Produced Goods Share of Spending on Domestic Output BDI MWI NERWA ETH BFA MOZ MDG SLV TGO Correlation: 0.47 KHM LAONGA TKM NPL IND SDN MLI KGZ BRA ARG GHA CMR BOL GIN KEN TJKPAK PRY UKR KAZ ROU BTN COL IRN BGD CIV VNM VEN PHL ALB BLR POL FIN AUSUSA GEO THA AZE ARM EGY HUNKOR JAM CPVMAR MDA ECU PER DOM MEX NZL MNG RUS GRCJPN HND BGR FRA CAN COG TUN MKDSVK CZE ISR YEM CHE SVN BIH ESP FJI ITA ESTHRV AUT GNQ PRT SWE GMB LBN CYP GBR MUS MYS PAN LVA LKA BRBDEU JOR URY LTU SAU SEN Agriculture BLZ SURCRI Log(GDP/Worker), 2005 ATG TTO MLT ISL QAT Share of Spending on Domestic Output Manufacturing Correlation: 0.10 BRA JPN CMR ARG IND VEN PER DOM KOR PAK USA BRN BDI TCD RUS BGD CHLTTO ITA KWT NPL AUS SLV BFA COL DEU GTM BLR NZLESP FRA CIV BOLKA UKR URY RWA IRN GNQ POL SAU EGY ECU FIN UGA SDN HRV GBR KEN PRY THA LBN PRT MOZ VNM GRC MAR SWE MRT CAN SEN YEM TUN CRI NGAZWE TJK HND JOR UZB LCA BGR CZE ISL OMN LAO JAM LTU HUN BEN AGO AZE DNK FJI SUR MEX AUT KAZ MYS MKD MUS ISR ETH SVN GMB STP KGZ BLZ MDV BIH BRB MWI TZA CYP QAT LVA GAB TGO NLD GIN COM ALB COGMDA GHA BTN BEL ARM Log(GDP/Worker), 2005 (b) Number of Trading Partners Number of Trade Partners ETH MDG SLV NER MOZ TGO MWI BFA RWA BDI Agriculture Correlation: 0.64 FRA DEUUSA CAN JPN GBR ESPITA POL CHE CZE AUT RUS AUS IND THA KOR LBN GRCSAU PAK HRV ROU MEX FIN SVKSVN PRT SWE MAR BGRMYS BLR EST HUNNZL VNM BRA TUN BIH BGD CYP ISR KEN MDA MKD CIV GEO COL MUS KAZ LTU TTO BRB ISL SEN UKR GHA EGY IRN AZE LKARM ALB CRI CMR ECU ARG GIN HND JAM JOR LVA MLT MLI URY NGA PHL PER VEN PAN KGZ COGBOL DOM BLZ SDN YEM FJI KHMGMB MNG PRY ATG NPL TJK SUR TKM GNQ LAO CPV BTN Log(GDP/Worker), 2005 QAT Number of Trade Partners BDI ETH TZA MOZ TGO SLV RWA BFA MWI Manufacturing Correlation: 0.48 DEU FRA MEX KORGBR CAN CZE ESP JPN ITA NLD USA PAK POL IND THA DNK AUT AUS BEL SVN SWE LBN BGD BRA COL MYS HRV NZL GRC FIN KEN PRT SAU RUS VNM BGR BIHHUN MAR TUN CIV GHA HND GTM PER CRI CYP ISR LKA MUS BRB ALB BLR ARG CHL UGA SEN CMR JAM KAZ LTU TTO IRN MKD GIN FJI GAB ISL ZWE URY BOL MDA ECU LVA AZE PRY ARM UKR EGY COM NGAAGO KGZCOG JOR VEN OMN KWT MRTSDN DOM BLZ BEN GMB YEM NPL SUR MDV UZB TCD TJK GNQ BRN LCA LAO STP BTN Log(GDP/Worker), 2005 QAT Displays the share of total expenditures allocated to domestically produced goods (π j nn). Trading partners is the number number of exporters from which each country imports. Trade data are from CEPII s BACI database and production data are from the UNIDO, OECD, and FAO. All data is for International Trade Patterns What fraction of a country s total expenditures are spent on imports? The pattern of trade differs substantially across countries and sectors. Figure 2 displays home shares for agriculture (140 countries) and manufacturing (128 countries). Among the poorest countries, the share of agricultural expenditures allocated to domestically produced goods is well over 90%. While among rich countries the share is highly variable, the average is closer to 50%. For manufacturing goods, the pattern is very different. There is little relationship between πnn m and a country s level of 11

12 development, with home share ranging between 40-50%. The lack of agricultural trade by poor countries is also evident in the number of trading partners each country has. Counting the number of partners from which each country imports reveals a strong positive relationship between the number of trade partners and a country s level of development. In agriculture, poor countries typically import (what little they do import) from 50 sources while rich countries import from closer to 200. For manufacturing, the positive relationship still holds, though it is far less pronounced. Poor countries have between partners for manufactured goods imports Trade Costs Why do poor countries import so little food despite having such low productivity in that sector? Trade costs are an obvious candidate, though they come in many forms and are difficult to measure. First, consider average tariff levels. Trade-weighted average MFN tariff rates are available from the UN-TRAINS database, classified by sector using the HS codes listed earlier. I plot these tariffs in Figure 3 for 151 countries. While poor countries do have larger average tariffs than rich countries, the magtudes are fairly small at 15-20% among the poorest countries compared to less than 5% among the richest. Trade costs go beyond tariffs; non-tariff barriers and other costs are far more important. Consider border delays. For perishable agricultural goods, these long delays may be particularly costly. Hummels and Schaur (2013) recently estimate the ad-valorem cost of time to import. They find for food and beverages each day is equivalent to a 3.1% tariff, compared to 2% for consumer and capital goods generally. Using their estimates, I construct a measure of the overall trade costs in agriculture and manufacturing associated with time delays. Panel (b) of Figure 3 plots the results of this calculation for 119 countries for agriculture 128 countries for manufacturing. The difference in magtude between rich and poor countries is stark. On average, the ad-valorem cost of time delays to import into poor countries is approximately 400% in agriculture and 200% in manufacturing. The time cost for rich countries are an order of magtude lower, varying around 30% for agriculture and 20% for manufacturing. 12

13 Figure 3: Trade Costs in Agriculture and Manufacturing (a) Average Tariff Rates Average Tariff Rate Agriculture IND MAR BDICAF TCD SDN GNQ LAO VNM COG IRN GAB BGR ETH DJI UGA CMR NGA UZB PAN ZMB JAM COL ATG SLV MDG RWA KEN BEN LKA VEN TTO BFA GNBGIN GEO NER CIV GHA ECU MDV BHS KHM MLI SEN THA TGO TZA CPVBOL JOR DOM BGD AGOHND LCA MDA CRI GTM PER MOZ NPL FJI BRABLZ LBN HRV ISR MWI KGZ PAK PHL VCT PRY RUS URY ARM ALB ARG MKD MRT TJKMNG YEM LVA CHL EST LTU POL HUN SVKSVN PRT CZE MLT GRC CYP ESP TWN DNK GBR DEU FRA FIN AUT ITA BEL AZE BIH BMU JPN SWE NLDLUX NAM LSO SWZ UKR EGY BWA KAZ MYS ISLBHR KWT MUS SAU NZL OMN USA CAN QAT CHE AUS SGP HKG BRN Log(GDP/Worker), 2005 BTN TUN MEX BRB Correlation: 0.48 Average Tariff Rate Manufacturing DJI BHS BMU Correlation: 0.49 MAR TUNMDV BDI CAF RWA LSO CMR CPV COG BTN EGY GAB BRB ETH GNB NPL BEN BGD ATG GNQ LAO VNM SDN LCA NER BFA GIN INDPAK IRN JOR BWA BLZ ARG MWI TGO UGA TCD VEN NAM CIV FJI ECU BRA COL MEX TZAKHM MLI MOZ ZMBSEN NGA GHA PRY UZB BOL JAM SWZ URY KEN DOM BGR GEO ALB BIH AGO LKA PER RUS SLV MDG MRT TJK MKD HND AZE CHL BHR GTM THA MUS PANTTO YEMUKR VCT LBN BRN KGZ MNG MDA CRIMYS AUS ARM EST HUN HRVKOR CZEGRC CYP ISR ESP ISL DNK GBR DEU FRA FIN CAN AUT ITA KWT PHL LVALTU POL PRT NZLSAU MLT OMN QAT SVKSVN SWE NLD BEL LUX KAZ TWN JPN USA CHE SGP HKG Log(GDP/Worker), 2005 (b) Time Costs to Import Tariff Equivalent Cost of Days to Import RWA TCD Agriculture UZB SDN KGZ KAZ BDI CAFNER UGA MLI ZWE GHA LAO TJK KENBGD COG ZMB AGO MWIBFA KHM TZA NGA GEO MDG LSO MNG COL CIV ECU SRB ETH TGO BWA IRN BEN MRT IND VENGNQ PAK MOZ BOL BTN AZE ARM BLR SLE NPL CMR MDA GTMUKR CRI GIN PRY EGY LBN SAU SLV STP MAR COM DJI YEM SWZ TUN PER JOR ROU SEN LKA GNB FJI BRA BIHTTO GMB SYR VNMCPV HND JAMTHA BLZ ALB URY BGR RUS GAB SVK NAM LCA SUR ARG MDV SVN FRA DOM HUN HRV CZEGRC KWT PHL ITA ATG CHL MEX POL HKG VCT MKD PRT OMN MUS LVA LTU KOR ISR TWN JPN MYS ESP CAN PAN NZLISL AUT BEL GBR CHE FIN AUS DEU SWE NLD EST DNK USA SGP IRQ Correlation: Log(GDP/Worker), 2006 Tariff Equivalent Cost of Days to Import RWA TCD Manufacturing UZB SDN BDI KGZ KAZ CAFNER UGA MLI ZWE KENBGD GHA LAO COG TJK ZMB AGO MWIBFA KHM GEO MDG TZA NGA LSO MNG COL ETH TGO CIV ECUBWA SRB IND IRN BEN MRT VENGNQ PAK MOZ BOL BTN AZE ARM BLR SLE NPL CMR GTM CRI GIN EGY LBN MAR MDA UKR PRY SAU SLV STP GNB COM DJI PER SEN YEM SWZ TUN LKA JOR ROU GMB FJI SYRBRA BIHTTO VNMCPV HND JAMTHA BLZ ALB URY BGR RUS GAB SVK NAM LCA SUR ARG MDV SVN FRA DOM HUN HRV CZEGRC KWT PHL ITA ATG CHL MEX POL HKG VCT MKD PRT OMN MUS LVA LTU KOR ISR TWN MYS ESP JPN CAN PAN NZLISL AUT BEL GBR CHE FIN AUS DEU SWE NLD EST DNK USA SGP Log(GDP/Worker), 2006 IRQ Correlation: 0.41 Displays observable measures of trade costs in agriculture and manufacturing. First, observable trade-weighted MFN tariffs from UN-TRAINS. Second, the ad-valorem equivalent cost of border delays. Days to import are from the World Bank Doing Business Index for 2006 (2005 is unavailable). The results of Hummels and Schaur (2013) suggest a tariff-equivalent cost of 3.1% per day for food and beverages, and roughly 2% per day for consumer and capital goods. These rates are used to convert the single Days to Import variable to ad-valorem rates that differ by sector. Beyond these observable measures, Novy (2013) generalizes Head and Ries (2001) to provide an aggregate summary measure of bilateral trade costs. Following the main text, I construct this measure (include asymmetries) for many countries. Specifically, I measure agricultural export costs tn a for 123 countries and manufacturing export costs tn m for 141. I summarize these in Figure 4 as the trade-weighted average across country pairs. The top panel reports the average cost by importer; the bottom panel, by exporter. Poor countries systematically face higher trade costs, 13

14 Figure 4: Trade Costs in Agriculture and Manufacturing (a) Average Trade Costs, by Importer Tariff Equivalent Trade Costs BDI SDN Agriculture TKM Correlation: 0.40 UKR NER MLI COG GIN GMB GHA ARG CMR IND IRN MDG NGA YEM RUS MOZ JOR MWI KEN VNM KGZ CPV TJK KAZ MDA BFA EGY ALB USA RWA BGD CIV PAK THA TGO ARMBRA BGR FIN LAO GEOECU NZL MAR TUN MKD LBN POL SAU GNQGRC AUS AZE ATG CYP ISRFRA PHL MUS VEN HUN HRV ESP SEN MNG JPN DEU CHE LKA BLR BIH ESTCZE KORGBR ITA KHM SUR PER PAN SWE BOL ETH JAM COL MYSLTU ISL NPL SLV BTN DOM LVA SVKSVN CAN PRT AUT HND PRY URY BLZ MLT BRB FJI CRI TTO MEX Log(GDP/Worker), 2005 QAT Tariff Equivalent Trade Costs BDI RWA BFA SLV MOZ ETH MWI TZA TGO TCD MRT Manufacturing Correlation: 0.41 GMB CMR STP TJK SDN YEM BGD BRA MDV KWT UGA SEN NGAAGO EGY ECU ARG GNQ IND PAK KEN LKA BOL BLZ CIV AZE GTM DOM CHL IRN BEN COL GIN JAM JOR LCA NPL SUR PER LBN PRY UKR MUSURY VEN TTO UZB COM BRB AUS COG FJI CRI CYP ISL SAU VNM KGZ RUS NZL OMN ZWEMAR BGR BLR GRCJPN HND KAZ MKD GAB HRVKOR ESP FIN ITA GHA LAO THATUN LVA MDA ALB MYS LTU BIH POL PRT BTN CZE DNK GBR DEU FRA HUNSVNISR SWE USA MEX CAN AUT NLD ARM BEL BRN Log(GDP/Worker), 2005 QAT (b) Average Trade Costs, by Exporter Tariff Equivalent Trade Costs BDI MOZ ETH SLV MWI RWA NER TGO BFA MDG KHMGMB NGA COG Agriculture JAM MNG Correlation: 0.45 BGD CMR TJK CPV FJIARM MUS NPL MLI GHA LAO KGZ VEN BOL GEO GIN SDN BTN DOM LBN BRB PAK MDA MKD MLT FIN SUR HRV KEN TUN CIV IRN BIH CYP HND PER BLZ BGR SVN SEN IND AZE COL KAZ EST ISR JPN LKA JOR TTO YEM KOR CHE MAR PHL EGY ECU BLR LVA SAU PAN HUN BRA CZE GRC ISL PRY SVK SWE UKR URY THA RUS ARG MEX VNM LTU POL PRT AUT CRI MYS AUS NZL GBR CAN ITA ESP DEU FRA USA Log(GDP/Worker), 2005 QAT Tariff Equivalent Trade Costs BDI ETH RWA BFA MWI MOZ TGO SLV TZA GMB MRT Manufacturing Correlation: 0.53 STP BRN MDV COM GNQ TCD LCA BLZ NPL CMR TJK SDN UGABEN BOL BRB FJI GIN JAM SUR GAB KWT SEN KENBGD NGA PRY LAO ZWE LBN AZE LKA JOR URY CIV COG TTO PAK YEM ECUMUS MKD ISL KGZ HND UZB PER ALB BIH AGO GTM EGY DOM VEN COLCRI IRN HRVCYP OMN VNM MAR MDA TUN KAZ BGR BLRARG CHL NZL LVALTU GRCSAU GHA IND BRA AUS UKR RUS FIN POL PRT SVN BTNTHA HUN ISR CZEKORDNK ARM ESPAUT MYS JPN SWE GBR DEU FRA ITA MEX CAN NLDUSA BEL Log(GDP/Worker), 2005 QAT Displays trade costs in agriculture and manufacturing. The top panel averages trade costs τ j across all exporters i for each importer n, weighted by trade volumes. The bottom panel averages across importers for each exporter. particularly in agriculture. The typical poor countries faces import costs of approximately 300% in agriculture and % in manufacturing. The average cost of exporting for these countries is even higher. 3 Calibration Details This section outlines details behind calibrating the production function parameters and the elasticity of trade. I also provide a brief set of results that confirms Waugh 14

15 (2010) s results hold for agricultural trade, which justifies using an export-cost specification for trade-cost asymmetries. 3.1 Production Function Parameters To calibrate each sector s production function parameters (β,φ j,γ jk j,k {a,m,s}), I use data from the Input-Output tables in the OECD Structural Analysis Database. From this, I construct measures of total output, value-added, and spending on various inputs by sector for the mid-2000s. Industries are classified by ISIC Revision 3, with Agriculture as 01-09, Manufacturing as 15-39, and Services as Ming, quarrying, and raw materials sectors (10-14) are not included in this exercise, as I do not included these sectors in the trade flow measures of the paper. Countries included in the database are typically rich but there is also data for certain poor countries, including India and China, and middle-income countries, such as Turkey, South Africa, and Mexico. To estimate labor s share of value-added, I treat a share of gross operating surplus as labor compensation. This is common in the literature and accounts for the labor of owner operators that are not paid in wages (see, for example, Gollin, 2002). I use a higher share of surplus as labor compensation in agriculture than in manufacturing or services. To reach an aggregate share of nearly two-thirds, I assume 40% of agricultural surplus is labor compensation while I assume 25% for manufacturing and services. This adjustment is not without consequence, although it is necessary. Labor compensation, as reported, implies labor s share of value added is 0.29 in agriculture and 0.53 in aggregate. These values are inconsistent with other evidence and are therefore not likely correct. Consider measures of input use compiled by Fuglie (2010). Aggregating various studies, he finds a worldwide average agricultural labor inputs relative to gross output of The share of land and structures is 0.21, suggesting labor s share of value-added of His evidence also suggests little variation across countries. More broadly, Gollin (2002) finds little variation in labor s aggregate share of value-added across countries. Since a country s employment share in agriculture does vary with income, labor s share of value added 15

16 Table 2: Production Function Parameters Sector j Agriculture Manufacturing Services Labor s Share of Value Added β Value Added Share of Output φ j Agricultural Input s Share γ ja Manufactured Input s Share γ jm Services Input s Share γ js Displays the production-weighted average share of labor in value-added, value-added in output, and the intermediate inputs sources for three broad sectors from the OECD STAN Input-Output (Total) tables for mid-2000s. Industries are classified by ISIC Revision 3, with Agriculture as 01-09, Manufacturing as 15-39, and Services as across sectors must be close to equal. Gollin, Lagakos and Waugh (2014) review more evidence on this point. I report the production-weighted average values in Table 2. The importance of intermediate inputs varies across sectors. The value-added to gross output ratio in services is nearly double that in manufacturing and roughly 50% in agriculture. The source of intermediates also varies substantially across sectors. Agriculture demands inputs from the three sectors in roughly even proportion. Manufacturing and services demand almost no agricultural inputs (and what little agricultural inputs manufacturing uses is largely due to food and beverage processing). Own-sector inputs are, by far, the most important intermediates for non-agricultural sectors. The shares are fairly uform across different levels of development. To show this, I plot the country-specific shares against per-capita GDP in various figures. Figure 5 (a) plots labor s share of value added across countries and sectors. With the exception of China, all countries are very close to the aggregate share of two-thirds. Figure 5 (b) gives the same plot for the value-added share of gross output. Finally, Figures 5 (c)-(e) display the intermediate input shares. This evidence suggests using the same production function parameters across countries is empirically reasonable. 16

17 Figure 5: Input Shares, by Sector and Country (OECD STAN Data) (a) Labor s Share of Value-Added (b) Value-Added Share of Gross Output Agriculture Manufacturing Services Agriculture Manufacturing Services TWN SWE SVN DEU GBR DNK EST CZE GBR CAN DEU DNK MLT FRA CHL LVA ESP SWE AUT SVK EST HUN PRT ITA FIN USA HUN SVN ITA NLD USA LUX CZE JPN CAN AUS ROU LVA MLT ESP FRA ROU NLD FIN AUS MEX LTU POL PRT POL AUT GRC JPN GRC KOR TWN BGR KOR LTU SVK BGR CHL MEX LUX DNK FRA FIN GBR SWE CAN HUN PRT SVN JPN AUT AUS NLD USA CHL MLT ESP TWN KOR DEU LTU EST CZE LVA ITA ROU BGR MEX POL SVK GRC LUX MEX GRC ITA KOR ESP AUS ROU JPN MLT PRT LTU SVN BGR FIN CHL POL SWE SVK CZE FRA AUT HUN TWN DEU EST NLD USA GBR LVA CAN DNK LUX LTU ROU GRC CHL CAN AUS BGR MEX GBR USA DEU DNK AUT FIN NLD SVK JPN SWE POL PRT SVN KOR ESP ITA FRA LVA MLT CZE EST HUN TWN LUX MEX GRC LTU TWN JPN DEU FRA CAN USA ROU SWE POL DNK AUT CHL HUN MLT PRT KOR ITA ESP FIN GBR NLD SVN LVA SVK AUS BGR EST CZE LUX Log(GDP/capita) in 2005, PWT8.0 Log(GDP/capita) in 2005, PWT8.0 Share of Output Share of Output (c) Agricultural Inputs Share of Intermediates (d) Manufactured Inputs Share of Intermediates Agriculture Manufacturing Services Agriculture Manufacturing Services ROU BGR AUT FIN LTU POL USA SVN LVA GRC SVK FRA CAN AUS MEX EST HUN TWN PRT ITA NLD CZE JPN CHL MLT SWE DNK KOR ESP GBR DEU LUX CHL GRC LVA ROU BGR EST DNK AUS MEX POL LTU PRT ESP FIN CAN NLD HUN FRA CZE DEU GBR AUT SVK MLT KOR ITA SVN JPN SWE USA TWN LUX BGR ROU CHL LVA POL LTU MEX SVK EST HUN MLT PRT CZE SVN GRC KORITAJPN ESP TWN FRAGBR FIN DEU SWE DNK AUT CAN AUS NLD USA LUX KOR ESP CHL MLT TWN HUN CZE JPN GBR DEU SWE DNK MEX PRT ITA POL EST FRA CAN SVK SVN AUS NLD GRC FIN AUT USA LTU BGR LVA ROU LUX MLT CZE KOR TWN SVK SVN HUN EST JPN MEX PRT DEU CAN ESP FIN AUT ITA DNK BGR FRA GBR NLD POL SWE USA LVA LTU ROU AUS CHL GRC LUX KOR ROU MEX BGR TWN GRC POL LTU HUN JPN CHL SVK EST SVN ESP FIN CAN LVA MLT PRT ITA SWE CZE AUT AUS USA FRAGBR DNK DEU NLD LUX Log(GDP/capita) in 2005, PWT8.0 Log(GDP/capita) in 2005, PWT8.0 Share of Output Share of Output (e) Services Inputs Share of Intermediates Agriculture Manufacturing Services DEU GBR LVA NLD GRC SWE ROU AUS DNK AUS CHL EST GRC MLT CAN CZE PRT ITA BGR CHL FRA GBR SWE LTU USA LVA ITA MEX LTU SVK JPN SVN ESP FRA USA POL DEU FIN AUT NLD ESP JPN DNK HUN LUX MEX CAN FIN BGR POL KOR TWN PRT SVK AUT HUN SVN EST CZE ROU KOR TWN MLT LUX DEU NLD CZE FRAGBR DNK AUT AUS USA CHL LVA MLT PRT ITA SWE SVK EST SVN ESP FIN CAN POL LTU HUN JPN GRC ROU BGR MEX TWN KOR LUX Share of Output Log(GDP/capita) in 2005, PWT8.0 17

18 3.2 Estimating Productivity Dispersion θ Firm productivities in sector j are distributed Frechet following the CDF F(x) = e (x/a j i ) θ j, where larger θ j implies smaller variance. In Eaton-Kortum trade models, differences in firm productivity provide the incentive to trade. Recall the expression, π j = ( ( Pn j ) θ j τ j c j A j i ) θ j i γ Greater differences leads to less sensitivity of trade flows to trade costs, as goods are less substitutable. This relationship between trade flows and trade costs can help identify θ j. The difficulty lies in finding measures for overall productivity A j i, prices Pn j, input costs c j j i, and trade costs τ. The country-specific factors in this expression can be canceled by taking differences between pairs of countries. Specifically, ln ( π j π j in π j ih π j hi π j ) ( hn π j = θ j ln nh τ j τ j in. τ j ih τ j hi τ j ) hn τ j. nh The challenge to estimate θ j is now to find a measure of the trade cost ratios on the right hand side. Following Caliendo and Parro (2012), consider overall trade costs τ j as a composite of importer-specific costs µ n, j such as border delays or other non-tariff barriers; exporter-specific costs δ j i, which Waugh (2010) finds particularly important for developing countries; symmetric bilateral trade costs ν j that inhibit trade between two countries in a similar way, such as distance, language, regional trade agreements, and so on; and, finally, asymmetric bilateral trade costs t j that may be different for trade from country i to n than from n to i. Tariffs are an important component of asymmetric bilateral trade costs. Trade costs can then be modeled fairly generally as ln τ j = ln t j + ν j + µ n j + δ j i + ε j, and therefore ln τ j τ j in = ln t j t j + µ n j µ j i + δ j i δn j + ε j ε j in in 18

19 does not depend on symmetric bilateral trade costs. This hold for all other countries pairs, and ln τ j ih τ j hi = ln t j ih t j + µ j i µ j h + δ j h δ j i + ε j ih ε j hi hi ln τ j hn τ j = ln t j hn nh t j + µ j h µ n j + δn j δ j h + ε j hn ε j nh. nh Adding the above three expressions eliminates all country-specific costs, ln ( τ j τ j in τ j ih τ j hi τ j ) ( hn τ j = ln nh t j t j in t j ih t j hi t j hn t j nh ) + ε j, where ε j = ε j ε j in + ε j hi ε j hi + ε j nh ε j hn. The sum of first differences in (log-) trade costs between any three countries will depend only on the asymmetric trade costs between them. These asymmetric trade costs can be measured with data on bilateral tariff rates, which display large asymmetries. Combing this result with the relationship between trade flows and trade costs derived above, ln ( π j π j in π j ih π j hi π j ) hn π j nh = θ j ln ( t j t j ih t j in t j hi t j hn t j nh ) + ε j, where ε j = θ j ε j j. If other factors affecting trade flows ε are unrelated to tariffs between countries, then this expression can be used to estimate θ j. To estimate θ j from the above expression. Complete trade and tariff data on all country triples (i, n, h) are required. I investigate a number of country combinations. The Parro Set countries are those in Parro (2013), who finds θ = 4.6 for capital goods and θ = 5.2 for other manufactured goods in As I am using data on tariffs and trade for 2005, Finland and Sweden are aggregated into the EU. This set is presented for comparison to his results. My preferred estimates use a different set of countries: the biggest ten countries trading entities for which I have complete tariff and trade data. For agriculture, I include the European Uon, the Uted States, Japan, China, Canada, Brazil, Mexico, Australia, Russia, and Argentina. 19

20 Figure 6: Tariff Asymmetries in Agriculture and Nonagriculture, 2005 Tariffs by Country i on Country n Agriculture Tariffs by Country n on Country i Tariffs by Country i on Country n Manufacturing Tariffs by Country n on Country i For non-agriculture, I include the European Uon, the Uted States, China, Japan, Canada, Korea, Taiwan, Mexico, Russia, and India. All remaing countries are aggregated into a rest of the world category. I use data on (trade-weighted) average tariffs in agriculture and manufacturing from the UN-TRAINS database. Similar to how I define trade flows, agricultural tariffs are for products with two-digit HS codes 15 and below and manufactured products are products with codes and Raw materials (codes 25-27) are excluded. The asymmetries are evident in a plot of t j against t j in for all countries pairs in my data. Figure 6 displays this for a large set of countries. If Canada, for example, applies the same tariff against imports from the EU as the EU levies on imports from Canada, then the Canada-EU pair will fall on the figure s 45 line. Most trading relationships feature asymmetric tariff rates. The resulting estimates in Table 3 are largely consistent with other estimates in the literature. For the same set of countries as Parro (2013), I find θ m = Using the big-10 countries, I find θ m = For agricultural goods, I estimate a slightly smaller elasticity, at θ a = Based on these results, I set θ m = 4.63 and θ a = Caliendo and Parro (2012) apply this method to trade flows between Canada, 2 Other combinations of countries yield similar results. For the largest 15 countries, I find θ = 4.42, and for the largest 20 countries, I find θ = The number of observations for the main regression is 990, since there are ten countries plus the rest of the world, which implies there are (11)(10)(9) = 990 triples. 20

21 Table 3: Productivity Dispersion Estimates, Agriculture and Manufacturing, 2005 Manufacturing Agriculture Parro Set Top 10 Top 10 ˆθ j 5.27*** 4.63*** 4.06*** [0.315] [1.267] [0.512] Countries Observations R Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. the Uted States, and Mexico in 1993 (pre-nafta). For agriculture, they find a larger estimate of θ a = It is not surprising that the degree of productivity dispersion between producers in economies is less than for a large set of countries at various levels of development. Waugh (2010), for example, finds a larger value for θ among OECD countries than among non-oecd countries. Also, Caliendo and Parro (2012) estimate this parameter for a agricultural trade at a more disaggregated level and report the average whereas I estimate it for agricultural trade flows as a whole. 3.3 Trade Cost Asymmetries Novy (2013) generalized the Head and Ries (2001) summary measure of trade costs. In a broad class of trade models, the average cost of trade between two countries is τ j τ j τ j in = ( π j nnπ j ii π j π j in ) 1 2θ j, (3) where τ j is the geometric average cost for sector j trade (in both directions) between countries n and i, π j are the expenditure shares defined earlier, and θ j is the (negative) cost-elasticity of trade. This measure is symmetric by construction ( τ j = τ j in ). But, trade cost asymmetries are known to be important. Waugh (2010), for example, demonstrates that poor countries systematically face higher export costs (regardless of the destination) than rich countries in manufacturing. 21

22 To measure trade cost asymmetries and adjust τ j is straightforward. In the same broad class of trade models for which equation 3 holds, a gravity relationship ln ( ) π j ( ) πnn j = S j i S n j θ j ln τ j exists, where S j denotes a country s sector j competitiveness (productivity, factor prices, and the like). Suppose trade costs τ j depend in part on common bilateral components such as distance, shared border, and shared language. Further suppose there is a country-specific additional cost of trading. In the main text, I follow Waugh (2010) and presume this country-specific cost is an export cost. The alternative is to assume it is an import cost. For either assumption, one can measure the country-specific cost with the following regression ln ( ) π j πnn j = β j X + ιn j + η j i + ε j, where X is a matrix of observable bilateral components, ιn j and η j i are a set of importer and exporter fixed-effects, respectively. The country-specific trade cost is inferred from fixed effects. They are both of the same magtude, despite applying to trade flows in different directions. Specifically, ln tn j = (ιn j + ηn)/θ j j. Combing the export cost estimates with equation 3 yields a measure of trade costs τ j = τ j (t j i /t n) j 1/2. If country-specific costs are import costs, then τ j = τ j (t n/t j j i )1/2 instead. How do we know which to use? One can use additional data to learn about the nature of the trade cost asymmetries. Waugh (2010) demonstrates that in the same broad class of models for which the above regression holds, we have ( τ j P = P j n P j i )( ) π j 1/θ j π j, ii where P j n is the price index for sector j goods. For countries surveyed, one can use the World Bank 2005 ICP cross-country price data to proxy for P j n. We can 22

23 Figure 7: Comparing Regression-Based to Price-Based Estimates (a) Export Cost Specification Manufacturing Log Bilateral Trade Costs, Regression Based Estimates Log Bilateral Trade Costs, Regression Based Estimates Agriculture Log Bilateral Trade Costs, Price Based Estimates Log Bilateral Trade Costs, Price Based Estimates (b) Import Cost Specification Manufacturing Log Bilateral Trade Costs, Regression Based Estimates Log Bilateral Trade Costs, Regression Based Estimates Agriculture Log Bilateral Trade Costs, Price Based Estimates Log Bilateral Trade Costs, Price Based Estimates use this price-based estimate of trade costs to see which regression-based estimate correlates most strongly. Figure 7 compares these estimates. On the horizontal axis, I plot the pricej P based estimates τ and on the vertical axis I plot the regression-based estimates. The export cost specification is the top panel and the import cost specification is the bottom panel. It is clear that there is a strong positive correlation between the export cost specification and the price-based estimates. This is true in manufacturing (as Waugh, 2010, demonstrated) and it is equally true in agriculture. The import cost 23

24 specifications display essentially no correlation and even slightly negative for manufacturing. So, I opt for an export cost specification to augment the symmetric trade cost estimates τ j from the Head-Reis-Novy approach. 4 Alternative Model Specifications This section reports various robustness exercises to ensure the main results of the paper are not overly sensitive to certain modeling choices. Each alternative specification was discussed and introduced in the main text. I display the results, for the poorest 10% of countries, in Table 4. The first column corresponds to a higher value for agriculture s θ parameter. This means the variation in agricultural productivity is lower (and therefore the gains from trade liberalization are lower) than in the baseline model of the paper. The second column shuts down inter-sectoral linkages (and all intermediate inputs) and abstracts from non-labour inputs. The third column uses alternative preference parameters estimated by Herrendorf et al. (2013). In all cases, the broad results of the main paper hold. Namely, a large share of cross-country productivity differences can be accounted for by trade barriers as agricultural productivity grows substantially upon liberalization and labor reallocates to non-agricultural employment. The final two columns require additional discussion, to which I turn in the next two sections. 4.1 Eliminating Itial Zeros from the Trade Matrix There are often potential trade relationships that are not realized, or that do not have trade volumes available in the data. For example, Canada did not record any agricultural exports to Bhutan in 2005 in the data. The presence of zeros in the trade data is well known phenomenon. To ensure these are not driving any of the main results of the paper, I replace zeros with imputed trade values following a simple gravity-regression approach. 24

25 Table 4: Results for Poor Countries, Various Alternative Specifications Agriculture Both Sectors θ a = 8 θ m = 5 β = 1 φ j = 1 ε a = 0.02, ε m = 0.15, ε s = 0.83 No Zeros Unbalanced Trade θ a = 8 θ m = 5 β = 1 φ j = 1 ε a = 0.02, ε m = 0.15, ε s = 0.83 No Zeros Unbalanced Trade Change in Welfare Total Welfare 67.0% 106.3% 116.5% 127.4% 72.4% 638.6% 364.7% 470.1% 607.3% 381.6% Real Wage Effect 29.7% 17.2% 38.4% 55.8% 14.5% 88.3% 53.9% 91.4% 144.4% 88.9% Labor Market Effect -7.1% 20.3% 5.3% -3.4% 14.0% 156.1% 99.7% 94.7% 87.0% 95.7% Subsistence Effect 38.6% 46.3% 48.5% 51.1% 43.6% 53.1% 51.2% 53.0% 54.8% 52.0% Change in Productivity Aggregate 74.6% 152.3% 220.6% 303.3% 98.9% 800.8% 378.1% 767.0% 940.4% 480.5% Agricultural 243.5% 381.1% 774.8% % 343.2% 596.3% 462.7% % % 665.9% Manufacturing 96.3% 87.3% 207.1% 232.3% 56.4% % 470.6% % % 799.9% Services 13.1% 0.0% 16.2% 21.0% 3.1% 7.7% 0.0% 24.7% 27.6% 13.5% Change in Employment and Trade Shares (p.p.) Ag Employment Ag Home Trade Mfg Home Trade Aggregate Productivity Ratio, Rich/Poor (Data: 40.9) Counterfactual Ratio Share Explained Reports main counterfactual of the main paper (eliminating trade costs in agriculture and in both sectors) under various alternative specifications. The effects for the bottom 10% of countries are reported. 25