rafmacalaba/datause-encoder-data huggingface dataset:
|`Foreign double counting`|`5458.62`|_`0.23`_|
|---|---|---|
|`GVC-related trade (GVC)`|`781287.59`|_`32.21`_|
|`GVC-backward (GVCB)`|`408693.30`|_`16.85`_|
|`GVC-forward (GVCF)`|`372594.29`|_`15.36`_|

The detailed decomposition can be also computed for a particular sector of export, e.g., “Manufacture of computer, electronic and optical products” (sector code 17 for the loaded ICIO table) by using<sup>15</sup> ``` .icio,exporter(chn,17) ``` ``` Decompositionofgrossexports: Perspective:exporter Exporter:CHN Importer:totalCHNexports Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`560552.89`|_`100.00`_|
|`Domestic content (DC)`|`417041.87`|_`74.40`_|
|`Domestic Value-Added (DVA)`|`404306.15`|_`72.13`_|
|`VAX -> DVA absorbed abroad`|`386215.71`|_`68.90`_|
|`DAVAX`|`315342.79`|_`56.26`_|
|`Reflection`|`18090.44`|_`3.23`_|
|`Domestic double counting`|`12735.72`|_`2.27`_|
|`Foreign content (FC)`|`143511.01`|_`25.60`_|
|`Foreign Value-Added (FVA)`|`140161.87`|_`25.00`_|
|`Foreign double counting`|`3349.14`|_`0.60`_|
|`GVC-related trade (GVC)`|`245210.09`|_`43.74`_|
|`GVC-backward (GVCB)`|`156246.74`|_`27.87`_|
|`GVC-forward (GVCF)`|`88963.36`|_`15.87`_|

``` DAVAX:Value-Addeddirectlyabsorbedbytheimporter ``` We now show how the results can change by using a different perspective on the same trade flow. We move from the _default_ - exporting country perspective - to a sectoral-exporter perspective by adding the option `perspective(sectexp)` ``` .icio,exporter(chn,17)perspective(sectexp) Decompositionofgrossexports: Perspective:sectexp Exporter:CHN Importer:totalCHNexports Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`560552.89`|_`100.00`_|

> 15Run `icio, info` after `icio_load` to get the complete country and sector lists.

19
cut emissions without hurting the economic growth needed to lift hundreds of millions of people out of poverty.

But what matters is global emissions. And while it is true that global emissions grew slower than GDP between 1960 and 2000, since then decoupling has stalled and global emissions have grown as fast—or even faster—than global GDP. Figure 1 shows that the emissions/GDP ratio declined in high income and developing countries until the late 1990s but since then the global emissions/GDP ratio has been climbing. How can this be?

The answer is that fast-growing countries such as China and India account for an increasing share of global GDP, and these fast-growing countries have higher emissions/GDP ratios than the slower growing high income counties. So even though these countries’ emissions/GDP ratio is falling, as they account for a larger share of the global economy, the global emissions/GDP ratio is rising.

# **Figure 1. Global Emissions as share of Global GDP**

_(kilograms per dollar of GDP, measured in US dollars at 2000 prices)_ <!-- Start of picture text --> 2.5<br>2<br>Low and middle income countries<br>1.5<br> Averting runanway climate change<br>World Bending the curve Target (15 gtons,<br>1<br>High income countries<br>0.5<br>Emissions-GDP ratio of 0.5 in 2050 = 90 gtons<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> 4
Table E.2: Impact of treatment on COVID-19 outcomes: full-controls regressions

|_Panel A_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Protective<br>measures|Effective<br>measures|Display<br>symptoms|Contract<br>change|Lost<br>income|Income<br>decrease|HH income<br>decrease|
|Treated|-0.390***|-0.404***|0.0278|0.0217|-0.0916*|-0.0476|-0.0100|
||(0.003)|(0.001)|(0.592)|(0.693)|(0.056)|(0.338)|(0.831)|
||[0.033]|[0.017]|[0.995]|[0.991]|[0.473]|[0.985]|[0.973]|
|Constant|3.767***|3.717***|0.550***|0.715***|0.441***|0.880***|0.849***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Std. coeff.|-0.308|-0.327|0.0562|0.0434|-0.194|-0.0997|-0.0221|
|Basel. controls|✓|✓|✓|✓|✓|✓|✓|
|N|368|368|368|346|365|368|368|
|R-sq|0.0610|0.0632|0.0304|0.0111|0.1110|0.0512|0.0403|
|_Panel B_|(8)<br>Transfers received<br>decrease|(9)<br>Food expend.<br>increase|(10)<br>Health expend.<br>increase|(11)<br>Educ expend.<br>increase|(12)<br>Transfers sent<br>increase|(13)<br>Savings<br>decrease|(14)<br>Borrowing<br>increase|
|Treated|0.0057|-0.0445|-0.0194|-0.0359|0.0232|0.0282|0.0127|
||(0.910)|(0.350)|(0.587)|(0.115)|(0.407)|(0.586)|(0.774)|
||[0.911]|[0.978]|[0.998]|[0.741]|[0.980]|[0.988]|[0.987]|
|Constant|0.280**|0.519***|0.493***|0.119*|0.110|0.689***|0.251**|
||(0.047)|(0.000)|(0.000)|(0.054)|(0.165)|(0.000)|(0.037)|
|Std. coeff.|0.0118|-0.0995|-0.0553|-0.162|0.0859|0.0570|0.0302|
|Basel. controls|✓|✓|✓|✓|✓|✓|✓|
|N|368|368|368|368|368|368|368|
|R-sq|0.0409|0.0299|0.0689|0.0383|0.0126|0.0429|0.0525|

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. List et al. (2019) p-values adjusted for multiple hypothesis testing in square brackets. _Baseline controls_ : sex, age, completed middle school, number of beds in household, number of children in household, number of adults in household, total (formal and informal) household debt, total household savings, household income.
_Table 1: Sets used in model definition_

|_Set_<br><br>|_Description_|
|---|---|
|i<br><br>|Full set of SAM Accounts|
|a(i)<br><br>|Set of Armington agents—includes all production activities and final demand|
|a(a)<br><br>|Set of production activities|
|oa(a)<br><br>|Other Armington agents—mostly final demand accounts|
|f(oa)<br><br>|Final demand accounts (excludes the trade and transport margin accounts)|
|h(oa)<br><br>|Household accounts|
|f(oa)<br><br>|Other final demand accounts|
|i(i)<br><br>|Commodities|
|e(i)<br><br>|Energy commodities|
|i(i)<br><br>|Non-energy commodities|
|k<br><br>|Consumed commodities|
|in(k)<br><br>|The energy bundle in consumed commodities|
|ii(i)<br><br>|Institutions (for transfers)|
|f(i)<br><br>|Factors of production|
|l(f)<br><br>|Labor categories|
|ul(l)<br><br>|Unskilled labor types|
|il(k)<br><br>|Skilled labor types|
|lif(f)<br><br>|Land types|
|caf(f)<br>|Capital types|
|v|Vintages (Old and New)|

_Table 2: sectoral coverage_

|**Agriculture**|**Sewerage-Waste**|
|---|---|
|**Forestry**|Construction|
|**Fishing**|Trade-of-motor-vehicles|
|**Mining**|Wholesale-trade|
|**Coal**|Retail-trade|
|**Oil**|Land-transport|
|**Gas**|Water-transport|
|**Food-products**|Air-transport|
|**Textiles**|Warehousing-and-stransportation|
|**Wood**|Postal|
|**Paper**|Accommodation-food-serv|
|**Printing**|Publishing|
|**Coke-and-pp**|Motion-picture-TV|
|**Chemicals**|Telecommunications|
|**Pharmaceutical**|Computer-prog-Information-serv|
|**Rubber-and-plastic**|Financial-serv|
|**Non-metallic-mineral-products**|Insurance|
|**Basic-metals**|Aux-to-financial-svcs-insurance|
|**Metal-products**|Real-estate|

4
quotas on the part of poreer households. The share of products bought via the quota system varies from 30.6% for semolina to 100% for milk for children and flour for bread. Flour for bread and pasta are the subsidized products with the largest consumption. These products are basic staples for Libyans and quotas for these products are larger than those for other products.

**Table 7: Quantities of subsidized food products consumed (Kilograms or Liters)**

||**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Total**|**% at subsidized**<br>**Prices (quotas)**|
|---|---|---|---|---|---|---|---|
|Flour (kg)|35.4|31.7|29.2|26.0|19.6|141.9|66.9|
|Flour-bread (kg)|96.9|89.7|77.9|64.9|51.5|380.9|100|
|Semolina ( kg)|12.8|12.6|10.9|9.0|6.7|52.0|30.6|
|Rice (kg)|39.1|37.2|34.2|30.0|24.0|164.3|67.5|
|Sugar (kg)|38.3|37.0|33.9|30.8|25.2|165.2|59.3|
|Tea (kg)|5.1|5.4|5.1|4.7|4.3|24.6|32.8|
|Macaroni (kg)|47.1|44.2|40.1|35.9|30.0|197.4|47.4|
|Vegetable Oil (liter)|37.8|35.3|33.0|29.2|24.2|159.6|55.5|
|Paste tomatoes (kg)|23.5|22.3|21.0|18.5|15.9|101.2|58.6|
|Milk for children (kg)|0.6|0.9|1.1|1.3|1.2|5.1|100|
|Milk concentrated(kg)|16.8|17.1|15.6|13.0|11.2|73.8|51.5|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Poorer households spend a much greater share of total expenditure on subsidized food items than richer households. Indeed, while expenditure on food products at subsidized prices represents 9.3% of total household expenditure (Table 8) on average, this share is higher for the first (12.32%) and second (10.68%) quintiles, and falls to 7.12 for the fifth quintile. The fact that poorer households are larger in size explains part of this observation. If we focus on quotas only (the share bought at subsidized prices), the first quintile’share is 3.61% against the fifth quintile share of 1.07%.

**Table 8: Share of spending on subsidized food in total expenditure (percent)**

||**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Total**|**Total**<br>**(quotas)**|
|---|---|---|---|---|---|---|---|
|Flour|0.61|0.50|0.46|0.43|0.34|0.45|0.07|
|Flour-bread|0.19|0.15|0.11|0.09|0.06|0.11|0.11|
|Semolina|0.41|0.35|0.28|0.23|0.16|0.27|0.01|
|Rice|1.01|0.90|0.78|0.74|0.63|0.79|0.12|
|Sugar|1.16|1.03|0.90|0.85|0.71|0.90|0.2|
|Tea|0.97|0.88|0.77|0.71|0.62|0.77|0.1|
|Macaroni|1.81|1.52|1.29|1.17|0.97|1.30|0.15|
|Vegetable Oil|3.15|2.66|2.38|2.15|1.80|2.35|0.42|
|Paste tomatoes|1.31|1.13|1.01|0.91|0.78|1.00|0.28|
|Milk for children|0.25|0.30|0.32|0.35|0.29|0.31|0.31|
|Milk (concentrated)|1.44|1.27|1.08|0.89|0.75|1.04|0.3|
|Total|12.32|10.68|9.39|8.50|7.12|9.30|2.07|

10
A large proportion of the discrepancies observed can be attributed to differences in algal oil yield per unit of area. This indicates that improvements in algal oil yields per unit of area should be targeted as a cost reducing strategy. The importance of yields in driving costs can be observed in Figure 2, along with a break down on cost components. The major two components each accounting for roughly 30% of the operating costs are water and the supply of CO2 (Pienkos 2008). The three cases in the figure refer to differences in biomass and oil yield per unit of land, and correspond to the NREL (current, aggressive, and maximum) scenarios presented in figure 1. The “aggressive” and “maximum” indicate assumptions regarding yields that need to be attained, and assume algae oil yields of roughly 73 and 131.4 tons per hectare. Clearly, significant breakthroughs are needed to achieve these yield levels on a consistent basis.

Figure 2: **Impacts of productivity on costs of production** <!-- Start of picture text --> 3.50<br>25% oil<br>3.00 20 g/m2/d Coproduct credit<br>Operating Total<br>2.50 Capital Indirect<br>Capital-Facility<br>2.00<br>Capital-Land<br>1.50<br>50% oil 60% oil<br>1.00 40 g/m2/d 60 g/m2/d<br>0.50<br>0.00<br>Current Case Aggressive Case Maximum Case<br>-0.50<br>$/lt<br><!-- End of picture text --> Source: Pienkos (2008).

In this line, many attempts have been made in recent years to refine the design and materials of algae cultivation systems in order to increase their productivity. The enhanced interest and research in this area is yielding fruit. As an example, GreenFuel Technologies Corporation developed a new production system called 3D Matrix System (3DMS), that achieved productivities of 98 g/m<sup>2</sup> /d over a 19 period trial under field conditions. These yields, which were externally evaluated (Pulz 2007), are remarkably close to the theoretical maximum indicated above. However, despite the rapid advances 26
# 7 Conclusion

This paper uses microeconomic data to analyze the e¤ects of a business registration reform in Mexico on registration, employment, prices, and income. It also traces out the e¤ects of the reform on di¤erent pre-reform occupation groups, thereby o¤ering an insight into the channels through which the e¤ects operate.

First, the paper provides evidence that simplifying entry regulation increases the number of registered businesses. After the business registration reform in Mexico, the total number of registered businesses increased by 5 percent in eligible industries. This ...nding is in line with previous cross-country studies which show that countries with simpler regulation have more business entry. The paper then illustrates that the increase in registered businesses was due to former wage workers opening businesses, and not due to unregistered business owners registering their businesses. This e¤ect is consistent with a model where unregistered business owners are low-ability individuals in a residual sector. The evidence does no support models that are in line with Hernando De Soto’s view that informal business owners are medium-range ability individuals who choose a small scale production technology over wage work and who cannot register since registration is too costly and complicated.

The fact that I do not ...nd informal business owners registering their businesses after the reform could also imply that complexity of business registration is not the relevant constraint keeping ...rms informal. After a ...rm is registered, it is presumably under more pressure to pay taxes and to comply with labor regulation (even though compliance is far from universal in Mexico, even for registered ...rms). The associated costs could be so big that changing only registration procedures may not be enough to push informal businesses over the threshold to formality.

This paper also shows that employment in eligible industries increased by 2.8 percent after the reform. In particular, those previously unemployed and out of the labor force were more likely to work as wage earners after the reform as the number of businesses increased. This e¤ect mirrors the cross-country results on output growth, where less complicated regulation is associated with higher growth in output. It also provides evidence that lowering barriers to entry bene...ts consumers. Another piece of evidence suggesting that consumers bene...t from the reform is that prices decreased by 0.6 percent after the reform in Mexico. I attribute this to the fact that new entrants increase total output in a market with a downward sloping demand curve. The price decline was concentrated among low-risk industries in the non-tradable goods sector, which indicates that it was due to competition. The increased competition also decreases income of incumbent ...rms by 3.2 percent.

Interestingly, this paper does not ...nd an increase in the income of wage earners who opened 27
**<u>Table 7: Effects of reservation on NREGS governance</u>**

||<br>**Get dated**<br>**receipt**|<br>**Payment to**<br>**account**|**Payment less**<br>**than assessed**|**If less, did**<br>**complain**|**Complaint**<br>**addressed**|
|---|---|---|---|---|---|
|**Panel A**||||||
|Res. now (α1)|0.268***|0.024|-0.014|0.271***|-0.014|
||(0.039)|(0.030)|(0.043)|(0.015)|(0.008)|
|Res. before (α2)|0.496***|0.174***|-0.444***|-0.048|0.241***|
||(0.037)|(0.032)|(0.034)|(0.093)|(0.057)|
|R-squared|0.604|0.698|0.390|0.558|0.687|
|Test:||||||
|F test(α1+α2=0; pval)|5.80e-11|0.00355|3.79e-07|0.0123|0.00124|
|**Panel B**||||||
|Res. now (α1)|0.264***|0.029|-0.004|0.294***|-0.007|
||(0.038)|(0.030)|(0.039)|(0.023)|(0.014)|
|Res. before (α2)|0.488***|0.175***|-0.456***|-0.036|0.254***|
||(0.041)|(0.034)|(0.035)|(0.100)|(0.058)|
|Res now × fem (β1)|0.013|-0.017|-0.046|-0.052|-0.009|
||(0.018)|(0.021)|(0.035)|(0.040)|(0.038)|
|Res. before × fem (β2)|0.040*|-0.008|0.034|-0.067**|-0.058***|
||(0.022)|(0.010)|(0.040)|(0.026)|(0.019)|
|Obs.|6,736|6,736|6,736|2,747|2,747|
|R-squared|0.605|0.698|0.390|0.560|0.688|
|Dep. var mean|0.712|0.883|0.408|0.568|0.420|
|**Test:**||||||
|F test (α1+α2=0; p val)|1.57e-10|0.00323|1.27e-07|0.00559|0.000463|
|F test (β1+β2=0; p val)|0.0931|0.346|0.848|0.000621|0.0684|
|F test (α1+ β1=0; p val)|9.74e-07|0.745|0.440|3.04e-08|0.554|
|F test (α2+ β2=0; p val)|0|6.27e-06|3.14e-09|0.253|0.00126|
|F test (α1+β1+α2+β2=0; p val)|0|0.0108|6.78e-05|0.127|0.0126|

_Note:_ ‘Reserved now’ and ‘reserved before’ are indicator variables of whether village panchayats are reserved in the current or the previous panchayat periods and the sample is limited to those who worked under NREGS. Control variables included throughout but coefficients on which are not reported include household size, composition, land ownership, and the head’s marital status, gender, age, and education; village-level access to road, distance to town and district HQ, population, share of SCs, STs, and key religions; years since the last village election; _pradhan_ characteristics (education, caste, religion, previous tenure and candidacy for office) and for individual-level regressions individuals’ gender, marital status, age, education and their squared terms. Standard errors are clustered at panchayat level. Robust standard errors reported in parentheses and multiple hypotheses tests are adjusted using the Bonferroni method *** p<0.01, ** p<0.05, * p<0.10.

22
# **[Table 8 about here]**

# _6.1.1. Compositional effect_

To verify if the trade collapse during the Great Recession was concentrated in industries with large vertical specialization, we first need to identify in which type of industry China’s processing exports are most prevalent. For this purpose, we use the Organization of Economic Cooperation and Development’s (OECD) technology classification (Hatzichronoglou, 1997) to disaggregate China’s exports into four categories: _high technology_ exports, _medium-high technology_ exports, _medium-low technology_ exports, _low technology_ exports. In Figure 10, we depict the share of processing exports in China’s total exports for each technology category. The figure shows that vertical specialization is more prevalent in the higher technology categories than in the lower technology categories. In 2007, processing exports accounted for 84.9% of _hightechnology_ exports; 45.6% of _medium-high-technology_ exports; 26.6% of _medium-lowtechnology_ exports; and 29.8% of _low technology_ exports.

# **[Figure 11 about here]**

The higher technology categories are also the industries that saw a larger exports collapse during the Great Recession. As it is shown in Table 8, _high technology_ exports dropped 24.1% in the first quarter of 2009 compared to a year earlier; _medium-high technology_ exports dropped 22.0%; _medium-low technology_ exports dropped 21.6%; _low technology_ exports dropped 8.9%.

Furthermore, the higher technology categories are also the industries that contributed most to the drop in China’s exports during the Great Recession. As it is shown in Table 8, _high technology_ exports, which accounted for almost one third of China’s exports in the first quarter of 2008, contributed to 37.6% of the exports collapse; _medium-high technology_ exports contributed to 25.4%; _medium-low technology_ exports contributed to 16.0%; _low technology_ exports contributed to 10.9%; and _other_ non-manufacturing exports contributed to 10.1% 30
7 *(P) gt(p) = Elytje-1@)] —Ve-1(P) = Me — Mea + (Bt-1 — Doe_-1 Br-1 Br-1 gt (yp) — of'4(p) =»: (p) - Hrjt-1 (P) = (6, —Br1%-1 ~*(P) O¢ — Be-1%t-1 “1(p) <0 p<0.5 Bt-1 p Br-1 of 2 Bi-10¢-1
**20**

**Table 2:** Household and Parcel characteristics across treatment groups

||Tr|eated|Co|ntrol|D|iff.|
|---|---|---|---|---|---|---|
||Mean|Std. Dev.|Mean|Std. Dev.|coeff.|s.e.|
|**Panel A: Household characteristics**|||||||
|Female-headed HH<br>_†_|0.17|0.37|0.16|0.37|0.01|(0.02)|
|Age of HH head (years)<br>|46.78|15.21|47.12|15.38|-0.32|(0.64)|
|Household head can read/write<br>_†_|0.24|0.43|0.23|0.42|0.03|(0.02)|
|Household size|6.28|3.27|6.50|3.37|-0.22|(0.13)*|
|Number of households|2,002||970||2,972||
|**Panel B: Parcel characteristics**|||||||
|Type of soil :|||||||
|-_sandy_|0.19|0.39|0.14|0.35|0.04|(0.02)*|
|-_lateritic_|0.31|0.46|0.30|0.46|0.01|(0.03)|
|-_hydromorphic_|0.17|0.38|0.15|0.35|0.00|(0.03)|
|-_ferralitic_|0.27|0.45|0.36|0.48|-0.03|(0.03)|
|-_other_|0.05|0.22|0.05|0.23|-0.01|(0.01)|
|Walking distance from homestead:|||||||
|-_00–05 minutes_|0.16|0.36|0.15|0.36|-0.02|(0.01)|
|-_06–15 minutes_|0.15|0.35|0.12|0.33|0.02|(0.01)|
|-_16–30 minutes_|0.17|0.38|0.18|0.39|-0.00|(0.01)|
|-_31–45 minutes_|0.14|0.35|0.15|0.36|-0.01|(0.01)|
|-_46–60 minutes_|0.12|0.33|0.12|0.33|0.01|(0.01)|
|-_> 1 hour_|0.26|0.44|0.27|0.44|0.00|(0.02)|
|Number of parcels|3,559||1,770||5,329||

<u>Note: The table compares household and parcel characteristics across treated and control villages. Stan-</u> dard errors (s.e.) are reported in parentheses and are clustered at the village level. The coefficients reported in column “coeff.” are obtained from regressing each variable on the treatment variable controlling for the lottery pool fixed effects. Significance levels are reported as follows: * p<0.10, ** p<0.05, *** p<0.01.

> _†_ Indicates dummy variables.

**Table 3:** Status of land demarcation activities in treatment villages in March 2011

||Obs|Min|Mean|Median|Std. dev.|Max|
|---|---|---|---|---|---|---|
|Land survey has started<br>_†_|191|1|1|1|0|1|
|-_nb. of months since start_<br>|191|1|13.9|14|4.42|21|
|-_land survey is completed_<br>_†_|191|0|0.96|1|0.19|1|
|-_nb. of months since completion_|184|0|11.4|11|4.05|19|
|-_duration in months_|184|1|2.95|3|1.75|14|
|Land survey ended 12 months|191|0|0.48|0|0.50|1|
|ago|||||||

<u>Note:</u> The table shows statistics on the implementation of land demarcation activities — _enquêtes topo-foncieres_ (ETF) — in treated villages in our sample as of March 2011. _†_ Indicates dummy variables.

no significant difference between treated villages where the survey started earlier and those where the survey started later (see Table A-9) except for the fact that the program started earlier in the northern region of the country
- MENA free trade with trade facilitation (GAFTA+TF)

- MENA-EU free trade with trade facilitation(GAFTA-EU+TF)

# **Results and Discussion:**

# **_Non-Trade Facilitation FTA simulations_**

<!-- Start of picture text --> Figure 4: Welfare increase from liberalization with the EU is significantly higher than liberalization just<br>within GAFTA for most MENA countries (%)<br>1.88<br>2.0<br>1.76<br>1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.53<br>0.6<br>0.4<br>0.18<br>0.13 0.11<br>0.05 0.05<br>0.2<br>0.0<br>ROMIDE RONAF TUN MOR<br>GAFTA GAFTAEU<br><!-- End of picture text --> The welfare gains to MENA from implementing the free trade agreements are positive. Under GAFTA, the total static welfare gains to MENA amounts to $913 million, a 0.1% boost to regional income. Under GAFTAEU, the static welfare gains to MENA increase to some $1.84 billion, a 0.21% boost to regional income. These results show that integration with the EU provides significantly greater welfare gains than only intraMENA trade. This is mainly due to the much larger size of the EU market and the greater importance of EU trade compared with intra-regional trade.

The distribution of the welfare gains however reveal marked differences in gains to trade liberalization between the various MENA sub-regions. Under GAFTA, Tunisia gained the most with a 0.53% increase in welfare, followed by relatively less substantial gains to the Rest of the Middle East (0.13%), Morocco (0.05%) and the Rest of North Africa (0.05%) (figure 4). Under GAFTAEU, both Morocco (1.88%) and Tunisia (1.72%) gained the most followed by the rest of North Africa (0.18%). The differences in the 12