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

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

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

Transcription

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

The new gold standard? Empirically situating the TPP in the investment treaty universe

The new gold standard? Empirically situating the TPP in the investment treaty universe Graduate Institute of International and Development Studies Center for Trade and Economic Integration Working Paper Series Working Paper N IHEIDCTEI2015-08 The new gold standard? Empirically situating

More information

Figure 1.1 The Parade of World Income. Copyright 2005 Pearson Addison-Wesley. All rights reserved. 1-1

Figure 1.1 The Parade of World Income. Copyright 2005 Pearson Addison-Wesley. All rights reserved. 1-1 Figure 1.1 The Parade of World Income Copyright 2005 Pearson Addison-Wesley. All rights reserved. 1-1 Copyright 2005 Pearson Addison-Wesley. All rights reserved. 1-2 Growth and Development: The Questions

More information

Addressing institutional issues in the Poverty Reduction Strategy Paper process

Addressing institutional issues in the Poverty Reduction Strategy Paper process SESSION 1 Addressing institutional issues in the Poverty Reduction Strategy Paper process Scoping notes, detailed diagnostics, and participatory processes Public Sector Reform and Capacity Building Unit

More information

Today s tips for the Country Buy Report

Today s tips for the Country Buy Report High level outline Today s tips for the Country Buy Report Stephen Malpezzi Introduction Overview of the country and economy Basic indicators (GDP, employment, etc.) Key institutions, the setting How does

More information

Does Absolute Latitude Explain Underdevelopment?

Does Absolute Latitude Explain Underdevelopment? AREC 345: Global Poverty and Economic Development Lecture 4 Professor: Pamela Jakiela Department of Agricultural and Resource Economics University of Maryland, College Park Does Absolute Latitude Explain

More information

Ken Jackson. January 31st, 2013

Ken Jackson. January 31st, 2013 Wilfrid Laurier University January 31st, 2013 Recap of the technology models Do the models match historical data? growth accounting Estimating technology change through history A revised model of technology

More information

Building Capacity in PFM

Building Capacity in PFM Building Capacity in PFM Measuring economic governance in the context of national development planning LAMIA MOUBAYED BISSAT Beirut, Lebanon, 13 June 2014 The Institut des Finances Basil Fuleihan 1996

More information

A new metrics for the Economic Complexity of countries and products

A new metrics for the Economic Complexity of countries and products A new metrics for the Economic Complexity of countries and products Andrea Tacchella Dept. of Physics, La Sapienza - University of Rome Istituto dei Sistemi Complessi, CNR Roma CRISISLAB ANALYTICS FOR

More information

THE QUALITY OF GOVERNMENT CA FOSCARI INTERNATIONAL LECTURE

THE QUALITY OF GOVERNMENT CA FOSCARI INTERNATIONAL LECTURE THE QUALITY OF GOVERNMENT CA FOSCARI INTERNATIONAL LECTURE Andrei Shleifer December 12, 2012 1 Richer countries almost always have better governments Less corrupt More efficient Quality of government improves

More information

Estimating Global Migration Flow Tables Using Place of Birth Data

Estimating Global Migration Flow Tables Using Place of Birth Data Estimating Global Migration Flow Tables Using Place of Birth Data Guy J. Abel Wittgenstein Centre (IIASA, VID/ÖAW, WU) Vienna Institute of Demography/Austrian Academy of Sciences 1 Introduction International

More information

Fear of flying: Policy stances in a troubled world economy

Fear of flying: Policy stances in a troubled world economy Fear of flying: Policy stances in a troubled world economy UNCTAD G-24 Technical Meeting Luxor, 10-11 March 2014 Session 1 Global Economy A weakening economic performance reflects inability to address

More information

The Fall of the Final Mercantilism

The Fall of the Final Mercantilism The Fall of the Final Mercantilism Labour Mobility in the Caribbean and the World, from Arthur Lewis to the 21 st Century Eastern Caribbean Central Bank Michael Clemens November 3, 2010 1 2 Migration

More information

Human Resources for Health Why we need to act now

Human Resources for Health Why we need to act now Human Resources for Health Why we need to act now Progress towards the MDGs, particularly in Africa is slow, or even stagnating. Poor people cannot access basic services for want of doctors, nurses and

More information

China: How to maintain balanced growth? Ricardo Hausmann Kennedy School of Government Harvard University

China: How to maintain balanced growth? Ricardo Hausmann Kennedy School of Government Harvard University China: How to maintain balanced growth? Ricardo Hausmann Kennedy School of Government Harvard University China s growth process An unprecedented miracle China has been the fastest growing country in the

More information

Economic Complexity and the Wealth of Nations

Economic Complexity and the Wealth of Nations Economic Complexity and the Wealth of Nations Cesar A. Hidalgo ABC Career Development Professor MIT Media Lab Faculty Associate, Center for International Development Harvard University EARTH WIND WATER

More information

Natural Resources and Development in the Middle East and North Africa: An Alternative Perspective

Natural Resources and Development in the Middle East and North Africa: An Alternative Perspective Natural Resources and Development in the Middle East and North Africa: An Alternative Perspective Daniel Lederman and Mustapha K. Nabli The World Bank Presentation at the Workshop on Natural Resources

More information

Economic Growth: the role of institutions

Economic Growth: the role of institutions ECON 184 Economic Growth: the role of institutions ECON 184: Institutions and Growth January 26, 2010 1 Contents 1 Institutions and growth: initial analysis 3 2 How can institutions affect economic growth?

More information

FACTSHEET. Figure [1]: Economic Complexity Index vs. Opportunity Value (Positive Product Complexity Index, 2011) 2 ESP ITA POL BEL DNK PRT

FACTSHEET. Figure [1]: Economic Complexity Index vs. Opportunity Value (Positive Product Complexity Index, 2011) 2 ESP ITA POL BEL DNK PRT Opportunity Value Median = -0.06 FACTSHEET Knowledge Economy Assessment of Tunisia - Identifying and addressing capability and innovation gaps in the Southern and Eastern Mediterranean region (SEMED) Overriding

More information

Infrastructure and Economic. Norman V. Loayza, World ldbank Rei Odawara, World Bank

Infrastructure and Economic. Norman V. Loayza, World ldbank Rei Odawara, World Bank Infrastructure and Economic Growth thin Egypt Norman V. Loayza, World ldbank Rei Odawara, World Bank Motivation Questions How does Egypt compare internationally regarding public infrastructure? Is Egypt

More information

Lecture 21: Institutions II

Lecture 21: Institutions II Lecture 21: Institutions II Dave Donaldson and Esther Duflo 14.73 Challenges of World Poverty Institutions II: Plan for the lecture Discussion of assigned reading (Acemoglu, Johnson and Robinson) Causes

More information

Movement and development. Australian National University Jan. 17, 2013 Michael Clemens

Movement and development. Australian National University Jan. 17, 2013 Michael Clemens Movement and development Australian National University Jan. 17, 2013 Michael Clemens ? 60% Benefits Little Haiti Cap-Haïtien Gibson and McKenzie 2010 Tongan seasonal workers in NZ NZ$1,400/family

More information

Trade Policy Restrictiveness in Transportation Services

Trade Policy Restrictiveness in Transportation Services Trade Policy Restrictiveness in Transportation Services Ingo Borchert, Batshur Gootiiz and Aaditya Mattoo Development Research Group Trade and International Integration, The World Bank OECD Expert Meeting

More information

Economic Growth: The Neo-classical & Endogenous Story

Economic Growth: The Neo-classical & Endogenous Story Density of countries Economic Growth: The Neo-classical & Endogenous Story EC307 ECONOMIC DEVELOPMENT 1960 Dr. Kumar Aniet University of Cambridge & LSE Summer School Lecture 4 1980 2000 created on July

More information

Bringing Up Incentives: A Look at the Determinants of Poverty. Alice Sheehan

Bringing Up Incentives: A Look at the Determinants of Poverty. Alice Sheehan Bringing Up Incentives: A Look at the Determinants of Poverty Alice Sheehan Outline presentation What s going on out there? Growth, Human Development indicators, Poverty rates, etc. A look at determinants

More information

Deep Roots of Comparative Development

Deep Roots of Comparative Development Deep Roots of Comparative Development Oded Galor AEA Continuing Education Program Lecture III - AEA 2014 Oded Galor Roots of Comparative Development Lecture III - AEA 2014 1 / 41 Deep Roots of Comparative

More information

Addressing The Marketing Problem of the Social Market Economy

Addressing The Marketing Problem of the Social Market Economy Addressing The Marketing Problem of the Social Prepared for: KAS-Conference on 60 Years of Social Market Economy Sankt Augustin, November 30, 2009 Marcus Marktanner, American University of Beirut Outline

More information

Financial services and economic development

Financial services and economic development GDP per capita growth 03/11/2014 Financial services and economic development Thorsten Beck Finance why do we care? 0.04 BWA 0.02 0.00-0.02 COG SLE ALB GAB IND KOR TUR SGP MUS SDN MOZ IRLLUX IDN MAR EGY

More information

Macroeconomics II. Growth

Macroeconomics II. Growth Macroeconomics II Growth Growth Possibilities We previously referred to the aggregate production function Y = A K α L 1- α. The growth rate of real GDP, Y, is generated by the contributions of A, K and

More information

Relative Prices and Sectoral Productivity

Relative Prices and Sectoral Productivity Relative Prices and Sectoral Productivity Margarida Duarte University of Toronto Diego Restuccia University of Toronto August 2012 Abstract The relative price of services rises with development. A standard

More information

ECON 260 Theories of Economic Development. Instructor: Jorge Agüero. Fall 2008. Lecture 1 September 29, 2008 1

ECON 260 Theories of Economic Development. Instructor: Jorge Agüero. Fall 2008. Lecture 1 September 29, 2008 1 ECON 260 Theories of Economic Development. Instructor: Jorge Agüero. Fall 2008. Lecture 1 September 29, 2008 1 General information Time and location: TR 2:10-3:30 p.m. SPR 3123 Office hours: T 10am-11am,

More information

Lecture 9: Institutions, Geography and Culture. Based on Acemoglu s L. Robbins lectures

Lecture 9: Institutions, Geography and Culture. Based on Acemoglu s L. Robbins lectures Lecture 9: Institutions, Geography and Culture Based on Acemoglu s L. Robbins lectures 1 The Wealth of Nations Vast differences in prosperity across countries today. Income per capita in sub-saharan Africa

More information

Geography and Economic Transition

Geography and Economic Transition Global Spatial Analysis at the Grid Cell Level Mesbah Motamed Raymond Florax Will Masters Department of Agricultural Economics Purdue University March 2009 Urbanization at the grid cell level Growth regimes

More information

Political Economy of Growth

Political Economy of Growth 1 Political Economy of Growth Daron Acemoglu Department of Economics, MIT Milan, DEFAP June 11, 2007 The Wealth of Nations Vast differences in prosperity across countries today. Income per capita in sub-saharan

More information

Accounting For Cross-Country Income Di erences

Accounting For Cross-Country Income Di erences Accounting For Cross-Country Income Di erences January 2011 () Aggregation January 2011 1 / 10 Standard Primal Growth Accounting Aggregate production possibilities frontier: where Change in output is )

More information

Does Export Concentration Cause Volatility?

Does Export Concentration Cause Volatility? Does Export Concentration Cause Volatility? Christian Busch 14. Januar 2010 Overview Countries with undiversified export structure are plausibly more vulnerable to external shocks. But difficult to evaluate

More information

Evaluation with stylized facts

Evaluation with stylized facts Evaluation with stylized facts AMPERE Subgroup on IAM Validation Valeria Jana Schwanitz Potsdam Institute for Climate Impact Research 27. Mai 2013 Content Fundamental laws and stylized facts Systematic

More information

Lecture 12 The Solow Model and Convergence. Noah Williams

Lecture 12 The Solow Model and Convergence. Noah Williams Lecture 12 The Solow Model and Convergence Noah Williams University of Wisconsin - Madison Economics 312 Spring 2010 Recall: Balanced Growth Path All per-capita variables grow at rate g. All level variables

More information

Institute for Development Policy and Management (IDPM)

Institute for Development Policy and Management (IDPM) Institute for Development Policy and Management (IDPM) Development Economics and Public Policy Working Paper Series WP No. 33/212 Published by: Development Economics and Public Policy Cluster, Institute

More information

Subjective Well-Being, Income, Economic Development and Growth

Subjective Well-Being, Income, Economic Development and Growth Subjective Well-Being, Income, Economic Development and Growth Dan Sacks, Betsey Stevenson and Justin Wolfers Wharton School, University of Pennsylvania and NBER Annual Bank Conference on Development Economics--Stockholm,

More information

Governance, Rule of Law and Transparency Matters: BRICs in Global Perspective

Governance, Rule of Law and Transparency Matters: BRICs in Global Perspective Governance, Rule of Law and Transparency Matters: BRICs in Global Perspective Daniel Kaufmann * Senior Fellow, Brookings Institution http://www.brookings.edu/experts/kaufmannd.aspx Panel on Transparency

More information

Subjective Well Being, Income, Economic Development and Growth

Subjective Well Being, Income, Economic Development and Growth Subjective Well Being, Income, Economic Development and Growth Dan Sacks, Betsey Stevenson and Justin Wolfers Wharton School, University of Pennsylvania and NBER CSLS ICP Conference on Happiness December

More information

Trends in global income inequality and their political implications

Trends in global income inequality and their political implications Trends in global income inequality and their political implications LIS Center; Graduate School City University of New York Talk at the Stockholm School of Economics, September 1, 2014 A. National inequalities

More information

Specialization Patterns in International Trade

Specialization Patterns in International Trade Specialization Patterns in International Trade Walter Steingress November 16, 2015 Abstract The pattern of specialization is key to understanding how trade affects the production structure of an economy.

More information

Growing Together with Growth Polarization and Income Inequality

Growing Together with Growth Polarization and Income Inequality Growing Together with Growth Polarization and Income Inequality Sudip Ranjan Basu, Ph.D. Economist, United Nations ESCAP UN DESA Expert Group Meeting on the World Economy (LINK Project) United Nations

More information

Trade and International Integration: A Developing Program of Research

Trade and International Integration: A Developing Program of Research Trade and International Integration: A Developing Program of Research World Bank Development Economics Research Group Geneva, June 2013 Three areas of focus I. Implications of the changing patterns of

More information

The Role of Trade in Structural Transformation

The Role of Trade in Structural Transformation 1 The Role of Trade in Structural Transformation Marc Teignier UNIVERSIDAD DE ALICANTE European Summer Symposium in International Macroeconomics 23 May 2012, Tarragona Question Contributions Road Map Motivation

More information

Diversification versus Polarization: Role of industrial policy in Asia and the Pacific

Diversification versus Polarization: Role of industrial policy in Asia and the Pacific TOWARDS A RETURN OF INDUSTRIAL POLICY? ARTNeT SYMPOSIUM 25-26 JULY 211 ESCAP, BANGKOK Diversification versus Polarization: Role of industrial policy in Asia and the Pacific Sudip Ranjan Basu* International

More information

Infrastructure and Economic Growth in Egypt

Infrastructure and Economic Growth in Egypt Public Disclosure Authorized Policy Research Working Paper 5177 WPS5177 Public Disclosure Authorized Public Disclosure Authorized Infrastructure and Economic Growth in Egypt Norman V. Loayza Rei Odawara

More information

Export Survival and Comparative Advantage

Export Survival and Comparative Advantage Export Survival and Comparative Advantage (Work in progress) Regional Seminar on Export Diversification, October 27-28, 2010 Bolormaa Tumurchudur, UNCTAD Miho Shirotori, UNCTAD Alessandro Nicita, UNCTAD

More information

Rethinking the Wealth of Nations. Daron Acemoglu, MIT FEEM Lecture, December 14, 2009.

Rethinking the Wealth of Nations. Daron Acemoglu, MIT FEEM Lecture, December 14, 2009. Rethinking the Wealth of Nations Daron Acemoglu, MIT FEEM Lecture, December 14, 2009. 1 The Failure of Nations Vast differences in prosperity across countries today. Income per capita in sub-saharan Africa

More information

Fertility Convergence

Fertility Convergence Fertility Convergence Tiloka De-Silva a Silvana Tenreyro a,b a London School of Economics, CfM; b CEP, CEPR July 2015 Abstract A vast literature has sought to explain large cross-country differences in

More information

NGO PERSPECTIVE: FROM WORDS TO DEEDS

NGO PERSPECTIVE: FROM WORDS TO DEEDS MMSD & IIED Managing Mineral Wealth NGO PERSPECTIVE: FROM WORDS TO DEEDS Miguel Schloss Executive Director Transparency International Issues Policy distortions Institutional incentives Governance Implications

More information

The distribution of household financial contributions to the health system: A look outside Latin America and the Caribbean

The distribution of household financial contributions to the health system: A look outside Latin America and the Caribbean The distribution of household financial contributions to the health system: A look outside Latin America and the Caribbean Priyanka Saksena and Ke Xu 3 November, 2008 Santiago 1 The distribution of household

More information

ECONOMIC DIVERSIFICATION: BUILDING RESILIENCE TO CLIMATE CHANGE

ECONOMIC DIVERSIFICATION: BUILDING RESILIENCE TO CLIMATE CHANGE ECONOMIC DIVERSIFICATION: BUILDING RESILIENCE TO CLIMATE CHANGE Sarwat Jahan Bonn September 7 th, 2015 The team also comprises Chris Papageorgiou (lead), Giang Ho, Ke Wang, Lisa Kolovich, Camelia Minoiu,

More information

2006/SOM1/ACT/WKSP/007a Recasting Governance for the XXI Century - Presentation

2006/SOM1/ACT/WKSP/007a Recasting Governance for the XXI Century - Presentation 2006/SOM1/ACT/WKSP/007a Recasting Governance for the XXI Century - Presentation Submitted by: Miguel Schloss, Managing Partner DamConsult Ltd. APEC Workshop on Anti-Corruption Measures for the Development

More information

Infrastructure investment and growth

Infrastructure investment and growth Infrastructure investment and growth Luis Servén The World Bank IMF, November 2010 Background How important is infrastructure for economic growth? Old question even in Adam Smith s Wealth of Nations Empirically

More information

Bands (considered to be) Shared on an Equal Basis Between Space and Terrestrial Services (for Region 1)

Bands (considered to be) Shared on an Equal Basis Between Space and Terrestrial Services (for Region 1) Bands (considered to be) Shared on an Equal Basis Between Space and Terrestrial Services (for Region 1) Source: RR2012 Art 5, Art 9, Art 21, App 5, App 7; Rules of Procedure 2012 Rev. 5 Lower 137 137.025

More information

International Investment Patterns. Philip R. Lane WBI Seminar, Paris, April 2006

International Investment Patterns. Philip R. Lane WBI Seminar, Paris, April 2006 International Investment Patterns Philip R. Lane WBI Seminar, Paris, April 2006 Introduction What determines aggregate capital inflows and outflows? What determines bilateral patterns in international

More information

DEPENDENT ELITES IN POST- SOCIALISM: ARE LAND-BASED POST- COLONIAL SYSTEMS SO DIFFERENT FROM THE TRANSCONTINENTAL ONES? by Pal TAMAS [Institute of

DEPENDENT ELITES IN POST- SOCIALISM: ARE LAND-BASED POST- COLONIAL SYSTEMS SO DIFFERENT FROM THE TRANSCONTINENTAL ONES? by Pal TAMAS [Institute of DEPENDENT ELITES IN POST- SOCIALISM: ARE LAND-BASED POST- COLONIAL SYSTEMS SO DIFFERENT FROM THE TRANSCONTINENTAL ONES? by Pal TAMAS [Institute of Sociology, HAS Budapest] STRUCTURE OF THE PAPER 1. STATE

More information

Causes of Cross-country Income Gaps

Causes of Cross-country Income Gaps Causes of Cross-country Income Gaps Prof. Lutz Hendricks Econ520 January 14, 2016 1 / 24 Objectives We start looking into the question: Why are some countries rich and others poor? We think about methods

More information

The contribution of trade in financial services to economic growth and development. Thorsten Beck

The contribution of trade in financial services to economic growth and development. Thorsten Beck The contribution of trade in financial services to economic growth and development Thorsten Beck Finance why do we care? 0.04 BWA GDP per capita growth 0.02 0.00-0.02 COG SLE ALB GAB IND KOR TUR SGP MUS

More information

In Defense of Wall Street. The Social Productivity of the Financial System

In Defense of Wall Street. The Social Productivity of the Financial System In Defense of Wall Street The Social Productivity of the Financial System Finance is powerful Mobilizes Researches and allocates Monitors and exerts corporate control Provides risk diversification and

More information

Non-market strategy under weak institutions

Non-market strategy under weak institutions Lectures 5-6 Non-market strategy under weak institutions 1 Outline 1. Does weakness of institutions matter for business and economic performance? 2. Which institutions matter most? 3. Why institutions

More information

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits 19992013 8 9 10 11 12 13 01Jan99 22Jan99 19Mar99 1Jun99 14Oct99 24Jan00 29Jan99 26Mar99 24Jun99

More information

Finance, Growth & Opportunity. Implications for policy

Finance, Growth & Opportunity. Implications for policy Finance, Growth & Opportunity Implications for policy Today, I will make three points 1) Finance matters for human welfare beyond crises. 2) Financial innovation is associated with arguably necessary for

More information

Incen%ves The Good, the Bad and the Ugly

Incen%ves The Good, the Bad and the Ugly Incen%ves The Good, the Bad and the Ugly Vale Columbia Center Interna%onal Investment Conference New York, Nov 13-14, 2013 Sebas%an James The World Bank Group 1 Prevalence of Tax Incen%ves around the Number

More information

Institutional Change and Growth-Enabling Governance Capabilities

Institutional Change and Growth-Enabling Governance Capabilities Institutional Change and Growth-Enabling Governance Capabilities Nicolas Meisel Strategy and Research Dept - French Development Agency (AFD) Jacques Ould Aoudia Treasury and Economic Policy Directorate

More information

Income Differences Across Countries

Income Differences Across Countries Income Differences Across Countries Pete Klenow Stanford University Society for Economic Dynamics July 6th, 2006 Vancouver, Canada 1 2000 PPP Income per capita 90 th /10 th 25.6 75 th /25 th 8.8 S.D. of

More information

Life-cycle Human Capital Accumulation Across Countries: Lessons From U.S. Immigrants

Life-cycle Human Capital Accumulation Across Countries: Lessons From U.S. Immigrants Life-cycle Human Capital Accumulation Across Countries: Lessons From U.S. Immigrants David Lagakos, UCSD and NBER Benjamin Moll, Princeton and NBER Tommaso Porzio, Yale Nancy Qian, Yale and NBER Todd Schoellman,

More information

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits 19992013 8 9 10 11 12 13 01Jan99 22Jan99 19Mar99 1Jun99 14Oct99 24Jan00 29Jan99 2Mar99 24Jun99

More information

The Role of Women in Society: from Preindustrial to Modern Times

The Role of Women in Society: from Preindustrial to Modern Times CESifo Economic Studies Advance Access published May 22, 2014 CESifo Economic Studies, 2014, doi:10.1093/cesifo/ifu019 The Role of Women in Society: from Preindustrial to Modern Times Paola Giuliano UCLA

More information

Informality in Latin America and the Caribbean

Informality in Latin America and the Caribbean WPS4888 Policy Research Working Paper 4888 Informality in Latin America and the Caribbean Norman V. Loayza Luis Servén Naotaka Sugawara The World Bank Development Research Group Macroeconomics and Growth

More information

The Global Crisis in Low- and Middle-Income Countries: How the IMF Responded

The Global Crisis in Low- and Middle-Income Countries: How the IMF Responded The Global Crisis in Low- and Middle-Income Countries: How the IMF Responded Andrea F. Presbitero Alberto Zazzaro 1 Università Politecnica delle Marche 2 Money and Finance Research group (MoFiR) Real and

More information

Session 5x: Bonus material

Session 5x: Bonus material The Social Statistics Discipline Area, School of Social Sciences Session 5x: Bonus material Mitchell Centre for Network Analysis Johan Koskinen http://www.ccsr.ac.uk/staff/jk.htm! johan.koskinen@manchester.ac.uk

More information

Department of Economics

Department of Economics Department of Economics Dr. Seo-Young Cho Platz der Göttinger Sieben 3, D-37073 Göttingen Tel. +49 (0) 551 / 39-7368 Fax +49 (0) 551 / 39-7302 scho@uni-goettingen.de Göttingen, 17.02.2012 Several Developed

More information

Industrial Policy, Capabilities, and Growth: Where does the Future of Singapore lie? Jesus Felipe Asian Development Bank

Industrial Policy, Capabilities, and Growth: Where does the Future of Singapore lie? Jesus Felipe Asian Development Bank Industrial Policy, Capabilities, and Growth: Where does the Future of Singapore lie? Jesus Felipe Asian Development Bank Purpose of the talk Understand the economic challenges that Singapore faces Discuss

More information

BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY

BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY Région et Développement n - BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY Marco FUGAZZA *, Jan HOFFMANN *, Rado RAZAFINOMBANA * Abstract - This paper presents a unique database reporting the shortest

More information

Adaptation to land constraints: Is Africa different?

Adaptation to land constraints: Is Africa different? Adaptation to land constraints: Is Africa different? Derek Headey International Food Policy Research Institute (IFPRI) Thom Jayne Michigan State University (MSU) 1 1. Introduction Some 215 years ago, Malthus

More information

TRADE WATCH DATA JANUARY T RVSFRRTVL

TRADE WATCH DATA JANUARY T RVSFRRTVL Public Disclosure Authorized TRADE WATCH DATA JANUARY T RVSFRRTVL Public Disclosure Authorized A C F D H T W B DECRG Public Disclosure Authorized Public Disclosure Authorized *TRADE WATCH is a monthly

More information

BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY. Marco Fugazza Jan Hoffmann Rado Razafinombana

BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY. Marco Fugazza Jan Hoffmann Rado Razafinombana U N I T E D N AT I O N S C O N F E R E N C E O N T R A D E A N D D E V E L O P M E N T POLICY ISSUES IN INTERNATIONAL TRADE AND COMMODITIES STUDY SERIES No. BUILDING A DATASET FOR BILATERAL MARITIME CONNECTIVITY

More information

Reported measles cases and incidence rates by WHO Member States 2013, 2014 as of 11 February 2015 2014 data 2013 data

Reported measles cases and incidence rates by WHO Member States 2013, 2014 as of 11 February 2015 2014 data 2013 data Reported and rates by WHO s 2013, 2014 as of 11 February 2015 Number of by confirmation rate AFR Algeria DZA 49 0 0.00 0.12 0 0.00 0.22 AFR Angola AGO 12301 12036 547 11173 316 54.37 1.20 6558 30.54 1.20

More information

Design of efficient redistributive fiscal policy

Design of efficient redistributive fiscal policy Fiscal Policy and Income Inequality Sanjeev Gupta Deputy Director Fiscal Affairs Department, IMF IMF-Hitotsubashi University Workshop March 12, Tokyo Structure of the presentation Trends in inequality

More information

Informality in Latin America and the Caribbean

Informality in Latin America and the Caribbean Public Disclosure Authorized Policy Research Working Paper 4888 WPS4888 Public Disclosure Authorized Public Disclosure Authorized Informality in Latin America and the Caribbean Norman V. Loayza Luis Servén

More information

Tripartite Agreements for MEPC.2/Circ. Lists 1, 3, 4 received by IMO following issuance of MEPC.2/Circ.20

Tripartite Agreements for MEPC.2/Circ. Lists 1, 3, 4 received by IMO following issuance of MEPC.2/Circ.20 The following is a list of tripartite agreements reported to IMO during the period between the issuance of the annual MEPC.2/Circular, disseminated in December of each year. Any countries wishing to join

More information

The Effects of Infrastructure Development on Growth and Income Distribution

The Effects of Infrastructure Development on Growth and Income Distribution The Effects of Infrastructure Development on Growth and Income Distribution César Calderón Luis Servén (Central Bank of Chile) (The World Bank) ALIDE - The World Bank - Banco BICE Reunión Latinoamericana

More information

Human Rights and Governance: The Empirical Challenge. Daniel Kaufmann World Bank Institute. www.worldbank.org/wbi/governance/

Human Rights and Governance: The Empirical Challenge. Daniel Kaufmann World Bank Institute. www.worldbank.org/wbi/governance/ Human Rights and Governance: The Empirical Challenge Daniel Kaufmann World Bank Institute www.worldbank.org/wbi/governance/ Presentation at Human Rights and Development: Towards Mutual Reinforcement Conference,

More information

Global Value Chains in the Current Trade Slowdown

Global Value Chains in the Current Trade Slowdown MARCH 14 Number 137 Global Value Chains in the Current Trade Slowdown Michael J. Ferrantino and Daria Taglioni Real growth in global trade has decelerated significantly since its sharp recovery in 1. Year-on-year

More information

The Impact of Primary and Secondary Education on Higher Education Quality 1

The Impact of Primary and Secondary Education on Higher Education Quality 1 The Impact of Primary and Secondary Education on Higher Education Quality 1 Katharina Michaelowa University of Zurich katja.michaelowa@pw.unizh.ch 1. Introduction Undoubtedly, the overall education system

More information

The Macroeconomic Implications of Financial Globalization

The Macroeconomic Implications of Financial Globalization The Macroeconomic Implications of Financial Globalization Eswar Prasad, IMF Research Department November 10, 2006 The views expressed in this paper are those of the author(s) ) only, and the presence of

More information

Food and Agriculture. The State of. Investing in agriculture for a better future. Food and Agriculture The State of

Food and Agriculture. The State of. Investing in agriculture for a better future. Food and Agriculture The State of 2012 The State of Food and Agriculture Investing in agriculture for a better future Kostas G. Stamoulis Director, Agricultural Development Economics Division Food and Agriculture European Parliament, Committee

More information

Subjective Well Being and Income: Is There Any Evidence of Satiation? *

Subjective Well Being and Income: Is There Any Evidence of Satiation? * Subjective Well Being and Income: Is There Any Evidence of Satiation? * Betsey Stevenson The Gerald R. Ford School of Public Policy, University of Michigan & CESifo and NBER betseys@umich.edu www.nber.org/~bstevens

More information

Rodolfo Debenedetti Lecture

Rodolfo Debenedetti Lecture Rodolfo Debenedetti Lecture Andrei Shleifer March 2005 Legal Origin Distribution Legal Origins = English = French = German = Scandinavian = Socialist Institution Procedural Formalism Outcomes Time to evict

More information

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits. Classification Code Country Name (1)

Country Risk Classifications of the Participants to the Arrangement on Officially Supported Export Credits. Classification Code Country Name (1) 1 AFG Afghanistan 7 7 2 ALB Albania 6 6 3 DZA Algeria 4 4 4 AND Andorra - - (5) 5 AGO Angola 5 6 6 ATG Antigua and Barbuda 7 7 (8) 7 ARG Argentina 7 7 8 ARM Armenia 6 6 9 ABW Aruba 4 4 10 AUS Australia

More information

Econ 1340: World Economic History

Econ 1340: World Economic History Econ 1340: World Economic History Lecture 16 Camilo Gracía-Jimeno University of Pennsylvania April 4, 2011 Camilo Gracía-Jimeno (University of Pennsylvania)Econ 1340: World Economic History April 4, 2011

More information

Metrics Matters: Measures of Governance and Security and the Business Perspective An initial empirical exploration

Metrics Matters: Measures of Governance and Security and the Business Perspective An initial empirical exploration Metrics Matters: Measures of Governance and Security and the Business Perspective An initial empirical exploration Daniel Kaufmann, World Bank Institute www.worldbank.org/wbi/governance For presentation

More information

Employment, Structural Change, and Economic Development. Dani Rodrik March 15, 2012

Employment, Structural Change, and Economic Development. Dani Rodrik March 15, 2012 Employment, Structural Change, and Economic Development Dani Rodrik March 15, 2012 A remarkable reversal in fortunes since 1990s -.04 -.02 0.02.04.06 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

More information

Malaysia is rich in natural resources

Malaysia is rich in natural resources WORLD BANK 1 Malaysia is rich in natural resources Composition of Malaysia s national wealth, 2005 19.7 Intangible Capital 13% 8% Subsoil assets 26.0-1.2 55.5 Net Foreign Assets Produced Capital Natural

More information

Tripartite Agreements for MEPC.2/Circ. Lists 1, 3, 4 received by IMO following issuance of MEPC.2/Circ.21

Tripartite Agreements for MEPC.2/Circ. Lists 1, 3, 4 received by IMO following issuance of MEPC.2/Circ.21 The following is a list of tripartite agreements reported to IMO during the period between the issuance of the annual MEPC.2/Circular, disseminated in December of each year. Any countries wishing to join

More information

Technical partner paper 8

Technical partner paper 8 The Rockefeller Foundation Sponsored Initiative on the Role of the Private Sector in Health Systems in Developing Countries Technical partner paper 8 Regulation of Health Service Delivery in Private Sector:

More information

Political Economy of Development and Underdevelopment

Political Economy of Development and Underdevelopment Political Economy of Development and Underdevelopment Daron Acemoglu Department of Economics Massachusetts Institute of Technology October 10, 2005 The State of the World Economy Vast differences in prosperity

More information