rafmacalaba/datause-encoder-data huggingface dataset:
ban against the deadweight loss of a distorting policy. We can show that a small country ( _N →∞_ ) will always prefer redistribution, as the terms-of-trade effect is zero in that case. But when the country is not small, the comparison between an export quota and redistribution is ambiguous. A fuller evaluation of this tradeoff should also acknowledge that both policies likely involve substantial additional frictions, such as rent-seeking to capture export quota rents (Krueger 1974), as well as inefficiencies and leakages that plague domestic redistribution schemes (see e.g. Murgai, Ravallion and van de Walle 2013).

# **3 Conclusion**

When preferences are non-homothetic, even lump-sum redistribution will affect equilibrium prices. We explore the trade consequences of this phenomenon, in the context of food export bans introduced by developing countries during the last commodity price boom. Export bans indeed raise the world price of food and improve the export-banning countries’ terms of trade. What has been underappreciated is that in this context, even purely domestic redistribution policies might have a qualitatively similar effect. The terms-of-trade improvement under domestic lump-sum redistribution is in the limit as large as under export quotas.

# **References**

**Bhagwati, Jagdish and Vangal K Ramaswami** , “Domestic distortions, tariffs and the theory of optimum subsidy,” _Journal of Political Economy_ , 1963, _71_ (1), 44–50.

**Corden, W Max** , _Trade policy and economic welfare_ , London: Oxford University Press, 1974.

- **Epifani, Paolo and Gino Gancia** , “Openness, Government Size and the Terms of Trade,” _The Review of Economic Studies_ , 2009, _76_ (2), 629–668.

- **Faber, Benjamin** , “Trade liberalization, the price of quality, and inequality: Evidence from Mexican store prices,” 2014.

- **Fajgelbaum, Pablo D and Amit K Khandelwal** , “Measuring the unequal gains from trade,” _The Quarterly Journal of Economics_ , August 2016, _131_ (3), 1113–1180.

- **Fajgelbaum, Pablo, Gene M. Grossman, and Elhanan Helpman** , “Income Distribution, Product Quality, and International Trade,” _Journal of Political Economy_ , 2011, _119_ (4), 721 – 765.

11
The two panels of Figure 1, which plot the evolution of the growth rates of the GDP of highincome countries, and the average DM index, terms-of-trade index, and real interest rate for lowincome countries, highlight the importance of considering the impact of shocks to aggregate demand at the global level on low-income countries. Clearly, by omitting these factors one could wrongly attribute the impact of aggregate demand fluctuations to commodity price shocks. This conclusion would not be wrong from a reduced form viewpoint, but would be misleading in the sense that commodity price fluctuations would be a proximate but not ultimate determinant of the variation of output in LIC.

# **3 Estimating the impact of shocks in low-income countries**

The impact of different shocks on various aspects of a country’s economic performance will be estimated using a panel vector auto-regression (panel VAR). For a given country, our structural model corresponds to: where _xi,t_ = ( _zi,t_<sup>_′, y_</sup> _i,t_<sup>_′_)</sup><sup>_′_,</sup><sup>_z_</sup> _i,t_<sup>_′_= (</sup><sup>_GDPHt,DMTTi,t,Rt,GEOi,t,CLIMi,t,HUMi,t_)isavectorof</sup> exogenous variables including the (log) GDP of high-income countries ( _GDPH_ ), the (log) real DM commodity based terms-of-trade index ( _DMTT_ ), the international interest rate ( _R_ ), and three indicator variables capturing the occurrence of geological, climatic, or human disasters ( _GEO, CLIM,_ and _HUM_ respectively), as described in the previous section; _yi,t_<sup>_′_is a vector of “endogenous”</sup> variables, which in our benchmark specification will correspond to _yi,t_<sup>_′_= (</sup><sup>_AIDi,t,GDPi,t_),where</sup> _GDP_ is the (log) real GDP per capita (PPP adjusted) and _AID_ is the (log) aid per capita. However, the composition of the this vector will vary across specifications, as we will replace GDP for the (log) Gross Domestic Income per capita ( _GDI_ ), include the different components of _GDP_ (Consumption, Investment, etc.), or remove _AID_ . In the notation that follows, _GDP_ represents any of these possibilities.

The main identification assumption of this paper is that the variables in _z_ do not respond to the _y_ variables at any lags. This assumption is equivalent to imposing the following block diagonal 10
# 1 Introduction

Since the pioneering work on marriage of Becker (1973, 1974), marriage formation is often modeled as a matching process similar to labor market matching between workers and rms, where males and females meet each other randomly or are assisted by commercial agents (Weiss, 1997). This approach ignores a unique feature of marriage matching: marriage is not simply two individuals forming a new family; rather, it directly aects the welfare of their parents. An example of parental welfare being aected is old age support. Throughout history and still so in many developing countries today, old age support depends critically on children (Cheung 1972; Davidson and Ekelund 1997; Anderson 2003). How do parents ensure that old age support will be provided by children after they grow up? An interesting answer is provided by Becker, Murphy and Spenkuch (2015) (BMS (2015) hereafter), who argue that when old age support is mainly provided by adult children, parents will put in resources to meddle with children's preferences (i.e., to make them more altruistic) to ensure their commitment to providing old age support.

In this paper we examine how considerations of old age support shape marriage outcomes when marriages are matched by parents. We argue that parental matchmaking involves a tradeo between children's welfare and parents' welfare. Parents that help matchmaking have a long-term relationship with the couple after the matching is done; such future interactions may distort incentives in the matching process and therefore aect matching outcomes. In particular, consider a son that chooses between self and parental match. His satisfaction with his spouse depends on expected marriage outcomes, including the couple's joint income, household production (such as home-provided old age support and the number of children), and the love between him and his future wife. From the parents' perspectives, they obtain a spillover from the couple's income, enjoy direct household production including the care and services provided in house by the couple (and especially from the wife) when they are old (BMS, 2015). Being altruistic, parents may also obtain an altruistic component originated from the son's welfare from the marriage.

Conict of interest arises from the parents' keen interest in the couple's joint income and household production including old age support. Parents who expect to receive nancial, emotional and old age support from their son after his marriage may care less about how attractive his wife is to him but more about how able she is in contributing to family income, wealth and/or old age support via household production (Cheung 1972). Parents may also care more about the compatibility of the daughter-in-law's preference with their needs for old age support. In other words, parents weigh their harmony with the daughter-in-law more heavily than the harmony between the future couple. As a result, the best wife candidate 2
# Appendix 1. Results Omitting Banco de Provincia de Buenos Aires.

Table 6: Proportional Hazard Model for Bank Privatization (Buenos Aires Omitted)

||(1)<br> <br>Pr<br>|**1**<br>(2)<br> <br>oportional H<br>|1<br>(3)<br>1<br>azard Model (<br>|<br>(4)<br> <br>Monthly Dat<br>|1<br>_(5)_<br>a)<br>|
|---|---|---|---|---|---|
|_Prov. de Buenos Aires Included_|No|No|No|No|No|
|_# of Banks_|22|22|22|22|16|
|_# ofPrivatizations_|16|16|16|16|12|
|_# of Months at Risk of Privatization_|_950_|_950_|_950_|_950_|765|
|_Does the PJ control the assembly_|-0.410|-0.383|-0.347|-0.034|-1.860|
|_and the executive? (I=yes,Ono)_|_(-0.51)_|(-0.49)|(-0.44)|(-0.04)|(-1.40)|
|_Can either UCR or independents_||-2.369**|-2.211**|-0.395|-2.109|
|_block? (I=yes,O=:no)_||_(-2.54)_|(-2.55)|(-0.28)|(-1.46)|
|_Can the UCR block in assembly or_|-2.275**|||||
|_executive? (I=yes,0=no)_|(-2.02)|||||
|_Can independents block in assembly_|-2.504*|||||
|_or executive? (I=yes,0=no)_|(-1.88)|||||
|<br>_Bank net worth over liabilities_|-15.395**|-15.388**|-16.390**|-16.468**|-12.112*|
|_(lagged)_|(-2.53)|(-2.55)|(-2.81)|(-2.76)|(-1.88)|
|_% of bank loans to public sector_|8.622**|8.786**|8.328**|7.964**|1.966|
|_(lagged)_|(2.68)|(2.89)|(2.87)|(2.75)|(0.40)|
|_% of bank loans considered_|-7.843**|-7.892**|-8.209**|-8.094**|-7.613**|
|_"normal" (either definition) (lagged)_|(-3.35)|(-3.37)|(-3.48)|(-3.36)|(-2.50)|
|_% of bank loans considered -_|2.986**|2.945**|2.938**|2.891**|3.376*|
|_"normal" (old definition) (lagged)_|(2.17)|(2.19)|(2.16)|(2.04)|(1.90)|
|_Gov't deficit as share of revenues_|10.069**|10.157**|10.070**|11.241**|8.656**|
|_(lagged)_|(2.79)|(2.85)|(2.82)|(3.03)|(2.35)|
|_Gov't deficit (lagged) * blocking_||||_-15.964_||
|_dummy_||||(-1.29)||
|_Change in net worth over liability_|-4.368|-4.323||||
|_(lagged)_|(-0.58)|(-0.57)||||
|_If the governor is a member of the_|||||2.496*|
|_PJ, can he be re-elected?_|||||(1.95)|
|_Log Likelihood_|-23.33|-23.34|-23.52|-22.35|_-14.56_|

t-stats in parentheses.

* indicates significance at 10% level ** indicates significance at 5% level.

27
Table A1l4 Balance of Payments (USS Million)

|||1987|1988|Source|
|---|---|---|---|---|
|NDXt<br>HD INt<br>HD_INl_<br>HD INC<br>ND_INV_|Exports<br>imports<br>Investi<br>tgoods<br>Consumptiongoods<br>Intermediate<br>goods|14 196.0<br>15,284.0<br>3 817.0<br>2,287.0<br>9,180.0|17,398.0<br>15,600.0<br>3 389.0<br>2,370.0<br>9,241.0|(SoP data)<br>(SoP data)<br>(SoPdata)<br>(BoP data)<br>(BoP data)|
|ND RB|Resource<br>Balance|-1,088 0|1,798.0|(CalcuLated)|
|NDiFt<br>ND iFGb<br>ND iFGo<br>HDiFGp<br>ND iFGd<br>ND iFGc<br>ND iRESc<br>NDiRESd|Interest<br>Paoments<br>Budget<br>SEEs<br>Private<br>BankingSyste'<br>Central<br>Bank<br>C8's ForeignReserves(-)<br>BS'sForeign Reservest-)|2,209 0<br>1,215.0<br>322.0<br>61.8<br>141.9<br>676.4<br>208.2<br>145.7|2,527.0<br>1,358.0<br>360.0<br>23.2<br>239.0<br>792.7<br>245.8<br>169.8|(SoPdata)<br>(F20)<br>(F20)<br>(iF-FGptt-1])<br>(iF-lFGd-RESdOIt-1])<br>(iF*FGc[t-1])<br>(iFRESc(t-11)|
|NDPR|Profit<br>Remittances|-213.0|-388.0|(BoP data)|
|HDiFt&PR|Factor<br>Payents|1,996.0|2,139.0|(Calculated)|
|NDTfb<br>ND Tfp<br>HDUR|Foreign Transfers<br>to Budget<br>Foreign Transfers<br>to PS<br>Vorkers Remittances|81.0<br>0.0<br>_2,021.0_|89.0<br>0.0<br>1,755.0|(SoP data)<br>(SoP data)<br>(SoP data)|
|ND Tft|Total Foreign Transfers|2,102.0|1,844.0|(Calculated)|
|ND Sf|Foreign Savings|982.0|-1,503.0|(BoP data)|
|NDDFI|Foreign Investment|110.0|352.0|(BoP data)|
|ND dFGb<br>ND dFGo<br>NDdFGp<br>NDdFGd<br>HD dFGc|Capital<br>Inflows:<br>**Bucxoet**<br>SEEs<br>Private<br>BankinSystem<br>Central Bank|-764.6<br>1,498.4<br>-899.2<br>1,237.8<br>895.7|468.3<br>1,199.7<br>-1,052.2<br>906.2<br>-1,668.9|(Fiscat<br>data)<br>(Fiscal<br>data)<br>(Residual)<br>(Calc.<br>from CS data)<br>(Catc.<br>fromCBdata)|
|HDdFt|TotalCapitatInftows|872.0|-1,855.0|(Calculated)|
|NDdRESc<br>NDcdESd|Central<br>Bank<br>Banking System|993.0<br>103.0|888.0<br>821.0|(From BoP)<br>(FromCBdata)|

Source: See explanation in text **and** right m*st coluu.

order to rnake the data on the subcategories<sup>of imports consistent</sup> with the total we choose to leave<sup>imports of consumption goods as</sup> the residual.

Interest payments are obtained in two<sup>different ways. First,</sup> data on interest payments of the budget<sup>and the SEEs are taken from</sup> the external debt service table. However, this table does not detail payments made/receivad by the<sup>other three sectors. Hence,</sup> 15
Section 4.2. Before that, we describe our data.

# 3 Data and Sources

To perform the empirical analysis we build a balanced panel data set for 89 countries over the period 1990-2011, covering more than 96 percent of world GDP and for more than 94 percent of FDI throughout the sample period.<sup>16</sup> Our data set includes the following variables: foreign direct investment, trade agreements, trade ows, gross domestic product (GDP), employment, physical capital, bilateral investment treaties, sanctions, and WTO membership. We describe the sources to obtain these variables, as well as their construction, in turn next.

 FDI Data. We use two sources to construct the FDI variable, ( _FDIij,t_ ), which takes a central stage in our analysis. The main source for FDI data is the Bilateral FDI Statistics database of the United Nations Conference on Trade and Development (UNCTAD). These data can be accessed at http://unctad.org/en/Pages/DIAE/FDI%20Statistics/ FDI-Statistics-Bilateral.aspx. UNCTAD's FDI data covers inows, outows, inward stock, and outward stock for 206 countries over the period 1990-2011. Data are collected from national sources and international organizations and to ensure maximum coverage the data are mirrored. The second source of FDI data is the International Direct Investment Statistics database, which is constructed and maintained by the Organization for Economic Co-operation and Development (OECD). OECD's data oers detailed statistics for inward and outward foreign direct investment ows and positions (stocks) of the OECD countries, including transactions between the OECD members and non-member countries. We use the OECD data to ensure consistency and maximum coverage. Finally, we note that, given our theory, we focus our analysis on FDI stocks (positions), which is also the FDI category for which most data are available.<sup>17</sup>

> 16The list of countries and their respective alpha ISO3 codes appear in the rst two columns of Table 5.

> 17Anderson et al. (2019) utilize the same sources to construct a cross-section FDI data set. For the

17
Tseng, et al. (2006). Association between chronic exposure to arsenic and slow nerve conduction velocity among adolescents in Taiwan. _J Health Popul Nutr,_ 182-189.

UNICEF. (2015). Bangladesh MICS 2012-2013 Final Report.

Wasserman, G. A., Liu, X., Parvez, F., Ahsan, H., Factor-Litvak, P., Kline, J., . . . Graziano, J. H. (2007). Water Arsenic Exposure and Intellectual Function in 6-Year-Old Children in Araihazar, Bangladesh. _Environmental Health Perspectives_ , 285-289.

Wasserman, G. A., Liu, X., Parvez, F., Ahsan, H., Factor-Litvak, P., van Geen, A., . . . Graziano, J. H. (2004). Water Arsenic Exposure and Children’s Intellectual Function in Araihazar, Bangladesh. _Environmental Health Perspectives_ , 1329-1333.

WHO. (2011). Guidelines for Drinking Water Quality, 4th edition. Geneva.

Yu, W. H., Harvey, C. M., & Harvey, C. F. (2003). Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies. _Water Resources Research_ .

von Ehrenstein, O. S., Poddar, S., & Yuan, Y. (2007). Children’s Intellectual Function in Relation to Arsenic Exposure. _Epidemiology_ , 44-51.

29
**_Figure 2:   Education Outputs_**

<!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>Panel A. Secondary School<br>Enrollment<br>Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2005<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>Panel C. Tertiary School<br>Enrollment<br>150<br>100<br>in 2006 50<br>School enrollment, secondary (% gross)<br>100<br>80<br>60<br>in 2005<br>40<br>20<br>school enrollm ent, tertiary (% gross)<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2005<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>1 20<br>1 00<br>8 0<br>in 20 05<br>6 0<br>P ro gre ssio n to se con da ry sch oo l(% )<br>4 0<br><!-- End of picture text --> Panel B. Progression to Secondary School Source: World Development Indicators.

The low level of skills raises the question: Do individuals have an incentive to acquire skills? Some hard evidence is provided in World Bank (2008b) through estimates of earnings equations for Mozambican workers based on household data for 2003. In Figure 3 we reproduce the estimated percent increase in wages for each degree of education completed, relative to the average wages paid to workers with no education. The numbers suggest a high private return to higher education, substantially higher than the private return to secondary education (ES2) and to any other lower levels of education. This ordering of returns to education is similar to that in other low-income countries, as shown by Psacharopoulos and Patrinos (2002). These returns suggest that even if the educated were destined to work only in Mozambique, there would still be clear incentives to acquire tertiary education in Mozambique.

13
# Annex Tables

- Cl Classification of countries by policy environment 52

- Dl Average nominal protection coefficients for major export commodities 53

- D2 Nominal protection coefficient for principal export commodities _54_

- D3 Real protection coefficients for principal export commodity 55

- D4 Average real producer price for major export commodities 56

- D5 Average nominal protection coefficients for ti.ded food crops 57

- D6 Real producer prices for major food crops 58

- D7 Comparison of estimated transport costs 59

- D8 Ratio of implicit financial outflow from agriculaire 60

- D9 Weather variable (deviation from trend of cereal yields, in logarithms) 61

- D1O "Disasters" data: number of people affected by major disasters, in thousands 62 D1I Food crop price and policy effects 63

- 012 Comparison of domestic staple food prices to imported rice and wheat prices 64 D13 Various specifications of regression equation for agricultural supply 65

# Figures

- 1 Policy, incentives, and agricultural exports in Sub-Saharan Africa 4

- 2 Nominal and real protection coefficients for major agricultural exports 6

- 3 NPCs and RPCs in CFA countries for major export crops 8

- 4 Adjusted RPCs for Africa's major export crops 10

- _5_ Decomposition of changes in RPCs for African export crops 11

- 6 The impact of weather on African agriculture 14

- 7 Real producer prices for major crops in Sub-Saharan Africa 24

- 8 Ratio of export/food producer prices in Sub-Saharan Africa 24

- 9 Africa's food imports 28

- 10 Africa's import capacity 28

- 11 Africa's agricultural exports 36

- 12 Total agricultural production 36

- 13 Total food production 37

- 14 Africa's GDP 37

- 15 Real effective exchange rates 38

- 16 Weather patterns in Africa 38

- 17 Ghana's price incentives and agricultural exports 40

- 18 Zaire's price incentives and agricutural exports 40

- 19 Tanzania's price incentives 42

- 20 Togo's price incentives ad agricultural exports 42

# ACKNOWLEDGEMENTS

This paper was largely prepared while the author was an economist at the World Bank. Data for this research was compiled under UNDP project RAP 861058. The author is gratefial to Charles Humphreys, Michael Sarris, and Gilbert Uwujaren for comments on earier draft. The research assistance of Katharina Katterbach, James Teffi, and Peter Rosendorff, and the secrearial support of Azeb Yideru, are gratefuily acknowledged.
the distance to the frontier is higher at the intensive margin. Overall, the gap between Senegal and the state of Cear ́a, one of the poorest states in Brazil, is between 36% and 16% across the different indices. The results suggest that firms in Senegal are not only far from the technological frontier, but they are also far from adopting technologies that have been widely available and adopted by firms in other developing or emerging regions.

Table 3: Cross-Country Difference in Technology

||General Bus|iness Function|Specifc Bus|iness Function|
|---|---|---|---|---|
||Extensive|Intensive|Extensive|Intensive|
|Average|2.67|1.90|2.30|1.66|
|Cear ́a (Brazil)|3.35|2.49|2.75|1.92|
|Vietnam|2.75|1.92|2.55|1.80|
|Senegal|1.92|1.29|1.59|1.27|
|Gap: BR - SN|1.43|1.20|1.16|0.65|
|Relative Gap**|36%|30%|29%|16%|

Source: Cirera et al. (2020) Note: Average is the average of Brazil, Vietnam, and Senegal. Relative gap is the difference between Brazil and Senegal relative to the maximum technology gap of 4 ((BrazilSenegal)/Maximum Gap(4)).Technology measures are weighted by the sampling weights.

Despite the differences on the average across countries, an important finding of our analysis in Senegal is the heterogeneity across firms. Figure 5 presents the distribution of the technology index for GBFs and SSBFs in Senegal across firms. The right-skewed distribution shows that a large share of firms in Senegal do rely on basic technologies to perform either GBFs or SSBFs. Yet, those firms with higher level of technology measured by the index we propose also have better performance in terms of productivity, measured by value added per worker.<sup>12</sup> While these results do not suggest any causal relationship between technology and performance, they are consistent with previous literature. Although further investigation is needed to determine the causal relationship between technology adoption and performance, available evidence suggest that adopting technology pays off. Easterly and Levine (2001) and Comin and Hobijn (2010) and Comin and Mestieri (2018a) show that technology is a key driver of productivity differences across countries. Kwon and Stoneman (1995) show this relationship for firms in manufacturing and an extensive literature on agriculture has shown the impact of technology adoption on farm productivity.

Sections 4 and 5 provide more details of the heterogeneity of adoption for general and sector specific functions.

> 12The elasticity of the technology index with respect to value added per worker is 1.2 for the intensive margin GBFs. Table C2 in the appendix provides the full results for these estimates.

11
# **Annex III. TFP Growth Results by Country**

# **Table A.1. TFP with and excluding natural resources, by time periods**

||**1996–**|**2006**|**2007**|**– 2014**|**1996**|**– 2014**|
|---|---|---|---|---|---|---|
|**Country**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|
|Armenia|7.5%|7.5%|0.1%|0.2%|4.3%|4.3%|
|Azerbaijan|5.6%|6.3%|1.2%|-0.5%|3.8%|3.4%|
|Belarus|6.1%|6.0%|0.9%|0.7%|3.9%|3.7%|
|Benin|1.7%|1.7%|0.9%|0.9%|1.4%|1.4%|
|Bolivia|0.2%|0.3%|0.7%|0.6%|0.4%|0.4%|
|Bosnia and<br>Herzegovina|9.0%|8.9%|-0.2%|-0.3%|5.0%|4.9%|
|Botswana|-1.2%|-1.4%|-1.3%|-2.1%|-1.2%|-1.7%|
|Brazil|0.2%|0.3%|0.8%|0.8%|0.5%|0.5%|
|Bulgaria|-0.7%|-1.0%|-1.7%|-1.9%|-1.1%|-1.3%|
|Burkina Faso|3.1%|2.8%|0.1%|-0.5%|1.8%|1.4%|
|Burundi|-1.3%|-1.1%|2.5%|0.0%|0.3%|-0.6%|
|Cameroon|1.5%|1.3%|0.7%|0.4%|1.1%|0.9%|
|Central African<br>Republic|1.2%|1.4%|-3.5%|-3.6%|-0.8%|-0.8%|
|Chad|3.0%|3.6%|2.0%|-0.5%|2.6%|1.9%|
|China|5.1%|4.7%|4.5%|4.0%|4.9%|4.4%|
|Colombia|-0.1%|-0.2%|0.2%|0.2%|0.1%|0.0%|
|Costa Rica|0.6%|0.5%|0.6%|0.4%|0.6%|0.4%|
|Côte d'Ivoire|0.5%|0.8%|2.6%|2.3%|1.4%|1.4%|
|Djibouti|-1.1%|-1.1%|1.2%|1.0%|-0.1%|-0.2%|
|Dominican<br>Republic|1.0%|0.9%|1.9%|1.2%|1.4%|1.0%|
|Ecuador|-0.1%|-0.1%|1.0%|0.3%|0.4%|0.1%|
|Egypt, Arab<br>Rep.|0.3%|-0.2%|-0.3%|-1.0%|0.0%|-0.6%|
|Fiji|0.7%|0.6%|0.4%|0.3%|0.6%|0.5%|
|Gabon|0.3%|-0.8%|1.8%|0.1%|0.9%|-0.4%|
|Georgia|6.6%|6.4%|3.6%|3.5%|5.3%|5.2%|
|Guatemala|-0.2%|-0.2%|1.1%|1.1%|0.4%|0.4%|
|Guinea|0.0%|-0.5%|-2.0%|-4.2%|-0.8%|-2.1%|
|Honduras|0.8%|0.7%|-0.2%|-0.4%|0.4%|0.3%|
|India|2.3%|2.1%|1.9%|1.5%|2.1%|1.8%|
|Indonesia<br>|-0.7%|-1.0%|2.0%|1.6%|0.5%|0.1%|
|Iran, Islamic<br>Rep.|1.3%|0.7%|1.4%|-0.4%|1.3%|0.3%|

30