rafmacalaba/datause-encoder-data huggingface dataset:
Bilateral import data for 18 MENA countries (all except Iraq and West Bank of Gaza) and 239 partners is from United Nation’s Comtrade<sup>10</sup> . We use data for the period 1990-2009 (20 years) as in the 1980s MENA regionalism was non-existent. Data for Libya is mirrored because Libya does not report to the United Nations system. These differences in data sources for Libya are partly controlled for in our empirical specification by the importer-year and exporter-year fixed effects.

We use total import data, but also data on non-oil imports<sup>11</sup> . In a robustness check we also used data that subtract re-exports from bilateral import data<sup>12</sup> , but the results are almost identical to those reported in the next section.

We use WTO notifications to capture the year of entry into force of the agreement<sup>13</sup> , and these available in the appendix. As already mentioned, we looks at the trade effects of PAFTA, The Gulf Cooperation Council (GCC), AGADIR, COMESA, all Euromed agreements signed by MENA countries, all FTAs with EFTA countries and all FTA with Turkey.<sup>14</sup> We decided not to include dummies for the FTA signed by some MENA countries and the US because these are too recent to meaningfully estimate their impact. We also do not control for the Economic Cooperation Organization (ECO) for three reasons. First, the only MENA country in ECO is Iran. Second, it starts in 1992 and therefore captures almost our entire time variation. Including ECO would request expanding the time-span. Third, and more importantly, it is well known that ECO has been suffering from serious implementation problems, and therefore not much should

> 10 were obtained through the World Bank’s web platform: WITS (World Integrated Trade Solution) 11 We exclude HS27: Mineral fuels, oils & product of their distillation from total imports.

> 12 Hence results are not driven by re-export from UAE which is classified a resource-rich country, and whose re-exports are indeed large. To check the sensitivity of our results to the presence of UAE, we took UAE from the sample, and re-estimated the regressions. None of the coefficients change sign or loses significance and none is statistically different from the ones currently reported in the paper.

> 13 Since we take account of agreement solely after their entry in force we do not capture possible anticipation effect.

> 14 Some of these agreements overlap and this is sometimes referred to as MENA’s spaghetti’s bowl. However, there are always differences between these agreements in terms of membership and timing which allow for the identification of their impact on trade flows. Note, however, that we focus in the paper on the RR and RP trade relationship, which is PAFTA’s specific, as it is the only MENA trade agreement where both RR and RP countries are present.

11
# **REFERENCES CITED**

- 1) Abhijit Banerjee, Esther Duflo and Nancy Qian. 2012. “On the Road: Access to Transportation Infrastructure and Economic Growth in China.” Mimeo MIT.

- 2) Aker, Jenny. 2010. “Information from Markets Near and Far: The Impact of Mobile Phones on Agricultural Markets in Niger.” _American Economic Journal: Applied Economics_ , 2 (3) : 46–59.

- 3) Aker, Jenny, Michael Clemens and Christopher Ksoll. 2011. “Mobiles and Mobility: The Effect of Mobile Phones on Migration in Niger.” Unpublished Manuscript.

- 4) Aker, Jenny, Paul Collier and Pedro Vicente. 2016. “Is Information Power? Using Mobile Phones and Free Newspapers during an Election in Mozambique.” _Review of Economics and Statistics_ (Forthcoming).

- 5) AFP, 2013. “Niger Cuts off third of Mobile Phones to Stop Crime.” (27 November), Accessed 29 January 2014.

- 6) Atif Ansar, Bent Flyvbjerg, Alexander Budzier, and Daniel Lunn. 2016. “Does infrastructure investment lead to economic growth or economic fragility? Evidence from China.” _Oxford Review of Economic Policy_ , 32(3): 360–390.

- 7) Annan, Francis and Aly Sanoh. 2016. “Social protection in Niger: What has Shocks and Time Got To Say?” Mimeo World Bank.

- 8) Batista, Catia and Gaia Narciso. 2016. “Migrant Remittances and Information Flows: Evidence from a Field Experiment.” _World Bank Economic Review_ (Forthcoming).

- 9) Blumenstock, Joshua, Nathan Eagle and Marcel Fafchamps. 2016. “Airtime Transfers and Mobile Communications: Evidence in the Aftermath of a Natural Disaster” _Journal of Development Economics_ (Forthcoming).

- 10) Dinkelman, Taryn. 2011. “The Effects of Rural Electrification on Employment: New Evidence from South Africa.” _American Economic Review_ , 101(7): 3078–3108.

- 11) Duflo, Esther, and Pande, R. 2007. “Dams.” _Quarterly Journal of Economics_ , 122(2), 601–46.

- 12) Fafchamps, Marcel and Bart Minten. 2012. “Impact of SMS-Based Agricultural Information on Indian Farmers.” _World Bank Economic Review_ , 26(3): 383–414.

- 13) Gonzalez, Robert. 2015. “Social Monitoring and Electoral Fraud: Evidence from a Spatial Regression Design in Afghanistan.” Mimeo University of North Calorina, Chapel-Hill.

- 14) Goyal, Aparajita. 2010. “Information, Direct Access to Farmers and Rural Market Performance in Central India.” _American Economic Journal: Applied Economics_ , 2(3): 22–45.

- 15) Hansen, H., Andersen, O. W., and White, H. 2013. Impact Evaluation of Infrastructure Interventions, London, Routledge.

- 16) Imbens, G. W., and T. Lemieux. 2008. “Regression Discontinuity Designs: A Guide to Practice.” _Journal of Econometrics_ , 142 (2): 615–635.

- 17) Jack, William and Tavneet Suri. 2014. “Risk Sharing and Transactions Costs: Evidence from Kenya’s Mobile Money Revolution.” _American Economic Review_ , 104(1): 183– 223.

- 18) Jensen, Robert. 2007. “The Digital Provide: Information (Technology), Market Performance and Welfare in the South Indian Fisheries Sector.” _Quarterly Journal of Economics_ , 122(3): 879–924.

‐ 21 ‐
# **Figure A14– Respondents’ most trusted source of information about the COVID-19 vaccine, disaggregated by willingness to get the COVID-19 vaccine**

<!-- Start of picture text --> Most trusted source of information about COVID-19<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Radio 17.9 Radio 19.2 Radio 16.9<br>Internet 7.8 Internet 10.3 Internet 6.2<br>Newspaper 24.5 Newspaper 17.7 Newspaper 18.0<br>TV 2.8 TV 3.6 TV 3.2<br>Health clinic 31.5 Health clinic 28.7 Health clinic 39.0<br>Family and friends 9.5 Family and friends 11.5 Family and friends 10.6<br>Other 6.0 Other 8.9 Other 6.1<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 29 in Appendix B.

**Figure A15 – People’s main source of information about the COVID-19 vaccine disaggregated by their willingness to get vaccinated**

<!-- Start of picture text --> Main source of information about COVID-19 vaccine<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Radio 16.9 Radio 20.7 Radio 18.2<br>Internet 8.2 Internet 10.4 Internet 8.2<br>Newspaper 25.4 Newspaper 20.1 Newspaper 19.3<br>TV 2.9 TV 4.8 TV 5.2<br>Health clinic 29.7 Health clinic 24.7 Health clinic 37.2<br>Family and friends 10.7 Family and friends 12.3 Family and friends 10.0<br>Other 6.2 Other 7.1 Other 2.0<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 28 in Appendix B.

37
**Plc,Planning, and Research . Macroeconc-mic Adjustment and Growth**

If developing countries follow the same paths industrialized countries have followed, saving for retirement will initially become more important as the population growth rate declines.

years of high reproductive rates reaches adulthood, the proportion of working-age population rises sharply. Then, as baby boomers retire and die off, it declines toward the steady-state level.

To calculate the potential importance of lifecycle saving (saving for retirement), Webb and Zia set up a simulation model that translates demographic projections into savings-rate projections. Modeling explicitly the behavior of each cohort of households separates the effects of changing population shares of children and retirees. These shares behave differently and have different effects on saving as the population growth rate changes.

Webb and Zia simulated aggregate rates for life-cycle savings for Brazil, China, Korea, Mexico, Nigeria, Pakistan, and Turkey.

The savings rates increase 5 or 6 percentage points when the last baby boomers enter the work force and begin to save after their children leave home. The effect on life-cycle saving is dramatic; the effect on total savings rates, which are often three or four times as high, is not.

Baseline World Bank population projections assume that by the middle of the twenty-first century, if not sooner, the net reproductive rate of women in every country will decline to 1.0, a level that will eventually lead to a stable population. As the last cohort of those born in the Simulated life-cycle savings rates peak at an absolute 10 percent or less in all cases. The pattems in these projections seem robust with regard to assumptions about productivity growth, interest rates, and age-specific participation in the labor force.

This paper is a product of the Macroeconomic Adjustment and Growth Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Emily Khine, room NI _I_ - 067, extension 61765 (27 pages with charts and tables) The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations **are** less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **Appendix.  Construction of Variables Used in Commute Mode Choice Models**

<u>Out-of-vehicle travel time:</u>

- Walking: Distance from home to job/0.067  (Equivalent to speed of 4km/hour)

- Rail: Distance to nearest rail station (from home and from job)/0.067

- Bus: Answer to “How far is the nearest bus stop?” (from work and from home) from household survey.  (Midpoint of the selected range is used.)

- Two-wheeler: 0

- Car: 0

<u>In-vehicle travel time:</u>

- Walking: 0

- Rail, Bus, Two-wheeler, Car: Distance traveled/Average speed of the mode by distance category, short (1-5km) / medium (5-10km) / long(>10km).  [Average speed of mode calculated for each distance category using (actual in vehicle time)/(distance to work) for persons who chose that mode. Those who traveled less than 1km is excluded to from the estimation of travel speed because of the relatively large error involved in distance traveled.]

# <u>Money cost:</u>

- Walking : 0

- Rail, Bus: Calculated based on the fare tables and distance traveled. The fare tables are taken from <u>http://www.indianrail.gov.in/</u> (rail) and the Mumbai Metropolitan Region Development Authority (bus).

- Two-wheeler, Car: Gas price (Rs. 37.74 /litre)/Gas mileage (24km/litre for twowheeler and 10km/litre for car)*Distance

<u>The distance from home to job</u> is estimated as the distance between the worker’s home (whose location is geo-reference in the survey) and his approximate work location. The work location is approximated by the centroid of the intersection of the section and pin code in which the job is located.<sup>19</sup> The distances to rail stations from the home and workplace have been calculated using the geo-referenced locations of train stations. The travel distance for rail is the network distance, calculated from actual rail network data.

<u>The wage per minute</u> is calculated as follows:

- Personal income per month/206/60 for full time workers (assuming 8 hours per day, 6 days per week)

> 19 If the pin code (section) of the work place is unavailable, the centroid of the section (pin code) is used.
**Table A2.CR.B: Intergenerational Persistence in Schooling in Rural China -** **<u>(18 30 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.27|0.32|0.054|0.27|0.38|0.11|
||(0.017)***|(0.019)***|(0.025)**|(0.048)***|(0.053)***|(0.071)|
||[0.039]***|[0.049]***|[0.028]*|[0.10]**|[0.12]***|[0.051]**|
|Quadratic Coefft.||||-0.00047|-0.0056|-0.0052|
|||||(0.0039)|(0.0043)|(0.0058)|
|||||[0.0067]|[0.0075]|[0.0037]|
|Intercept|7.91|7.37|-0.54|7.90|7.31|-0.60***|
||(0.13)***|(0.15)***|(0.20)***|(0.14)***|(0.16)***|(0.22)|
||[0.34]***|[0.41]***|[0.27]*|[0.38]***|[0.47]***|[0.26]*|
|No. Observations|2833|2814|5647|2833|2814|5647|
|||**Estima**<br>|**tes of Mobility**|**from Quadrat**|**ic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.91|7.37||**_IGRC_**|0.27|0.32|
|**_ES6 _**|9.53|9.29|||||
|**_ES9_**|10.34|10.25|||||
|**_ES16_**|12.23|12.49|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
Abouleinein, El-Laithy and Kheir-El-Din, which makes use of the same input-output information made available for this present study. The importance of subsidies can be appreciated by comparing for various energy sectors the value added at factor cost and value added at market prices (exchange prices paid by buyers). As seen in Table 2, subsidies account for a large percent of value added at factor prices, especially in the case of LPG. These subsidies are in exchange for lower energy prices to other industries, which to some degree translate into lower prices generally for consumers. Abouleinein, El-Laithy and Kheir-El-Din examine in some detail simulated impacts on the consumer price index of various scenarios for reducing energy subsidies; the effects on consumer prices of removing subsidies appear to be considerable. In this present study, we examine the impact of removing the subsidy at the industry level through the subsidies contribution to the ERPs.

Lederman (2005) presents rough estimates of the impacts of energy subsidies on ERPs for selected industries taken from the 2005 input-output information and the 2007 Egyptian tariff schedule. Although recognizing that energy subsidies enter the valueadded equation via costs, the basic formula used by Lederman for the ERP of an industry g takes the form where the aI is the share of intermediate factors (ideally in terms of prices without intervention) and Sg are net subsidies: indirect taxes and other subsidies plus energy subsidies estimated in terms of an ad valorem equivalent addition to output price. In his appendix, Lederman does discuss the ERP accounting for an adjustment in the price of tradable inputs by both tariffs and a subsidy rate on inputs, SI: which is the approach we take here.<sup>7</sup> For those five inputs with subsidies of interest – namely, natural gas, fuel oil (mazot), diesel oil (solar), LPG and gasoline – the cost shares of gross revenues that would H otherwise prevail without both protection and subsidies for an individual input, a gi , can again be written in terms of the currently observed cost share, a gi , and the ad valorem protection rates: And so the practical ERP formula above in Section 3.a. above for applied tariffs and the ad valorem tariff equivalents due to NTBs should be modified somewhat to account for

> 7 Lederman (2005) does not appear to apply the energy subsidies per unit of energy use from a particular source, SI, likely due to lack of appropriate detail in the input-cost-share data. As discussed further on, such source-specific subsidy data is now available from Abouleinein, , El-Laithy and Kheir-El-Din (2009).

7
5 so-called more objective measures less than fully comparable across nations. Thus In the former West Germany, older persons became eligible for a special unemployment benefit after being jobless for a specffied time; on accepting this special benefit, they were removed from the official unemployment count, and were thereafter excluded from the labor force count (KohIl and others 1991). Elsewhere, as In the United States, older workers are not privy to a special unemployment subsidy and are thus more likely to be included In :abor force statistics as long as they seek work.

Which specffic retirement concept Is used Is also important for policy reasons, and the concepts of most interest may depend on which policy concern is preeminent. Analysts concerned with a pension plan's funding status will tend to focus on the age of pension acceptance for the purpose of predicting benefit outflows. In contrast, labor force measures (e.g. labor force participation or hours of work) may be more useful in determining whether tax collections will be sufficien **t** o meet pension financing needs. Analysts focusing on alleviation of poverty in old age will wish to know not only older persons' labor force status in the wage labor market, but might also wish to examine whether income is generated by self-employment, and the employment and eamings status of other family members.

While no single retirement concept is appropriate for all policy matters, the best data for the countries of central interes **t** o this study are labor force participation rates for older persons. Hence a brief examination of these Is useful, with the cavea **t** hat cross-country comparisons may be somewhat Inaccurate, as compared to within-country time series labor force participation pattems.

Retirement Trends in the OECD: Labor force participation rates of older individuals are by no means uniform across the developed countries (see Table 2). For example, only 4 percent of the men age 65 and older were in the labor force in 1985 in the Netherlands, while in Japan as many as 37 percent participated. Similar cross-national dffferences are observed for men age 55-64, with rates of 19 percent In Austria, and 76 percent in Sweden.

Despite persistent long-run differences In levels of labor force participation across countries, there has been a common downward trend in men's participation in developed nations since World War II. This persistent long-run labor marke **t** rend was widespread across developed nations
and domestic electricity produced: Given that we have the solution for each locally produced fossil fuel and renewable price index we can then determine the demand for renewables and fossil fuels. The quantities for fossil fuels are: i.e., aggregate fossil fuel demand (only that which is used in electricity production) is a CES of the price of fossil fuels (derived above) and the price of electricity. Renewable demand in the model is the residual after subtracting fossil fuel demand from total electricity demand:<sup>8</sup> Now it is simply a matter of allocating produced quantities to sources. The demand for domestic fuel electricity is: where _j ∈{oil, coal, gas}_ and _h ∈{_ 3 _,_ 4 _,_ 5 _}_ .

The same logic holds for different renewables. The equations show that it is relative prices that cause the transition. In the first nest, we have the relative price of fossil fuels vs. renewables and in the second nest the type of fossil fuels or renewables. For energy transition one would need a large elasticity of substitution to move away from fossil fuels, and a large increase in relative prices. A large carbon tax, a renewable energy subsidy or low operational costs for renewables can achieve such a transition.

> 8Note that we have the following that holds in the data: _QED,t_<sup>=</sup><sup>_Q_</sup> _t_<sup>_F OSS_</sup> + _Q_<sup>_REN_</sup> _t_ as well as _PD,t_<sup>_EQ_</sup> _D,t_<sup>_E_=</sup> _Pt_<sup>_F OSS_</sup> _Q_<sup>_F OSS_</sup> _t_ + _Pt_<sup>_REN_</sup> _Q_<sup>_REN_</sup> _t_ . There is thus additivity both in nominal and in quantity terms. In CGE models, one way to incorporate additivity and maintain the substitutability assumption is to change the utility function slightly where we still pick quantities but with prices included in the utility function and the constraint being the additive quantity identity. The first-order conditions look very similar, but the share parameters are slightly different (see van der Mensbrugghe, 2020).

12
Table 7: The World Bank’s IEG Project Ratings and Missions

||(1)|IEG <br>(2)|rating: sat<br>(3)|isfactory <br>(4)|(0/1)<br>(5)|(6)|
|---|---|---|---|---|---|---|
|Fraction of locations with missions|0.02|0.02|_−_0.02|0.01|0.02|_−_0.01|
||(0.07)|(0.07)|(0.09)|(0.07)|(0.07)|(0.10)|
|Sector dummies|No|No|No|Yes|Yes|Yes|
|Mean dependent variable|0.67|0.67|0.66|0.67|0.67|0.66|
|_R_-squared|0.00|0.05|0.16|0.06|0.11|0.20|
|Observations|324|324|188|324|324|188|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A _Note_ : Robust standard errors are given in parentheses. The dependent variable equals 1 if the IEG rating is at least moderately satisfactory and equals 0 otherwise. Regressions are done without controls in columns 1 and 4.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 In terms of project-level variables, the controls include project length (in years), the log of total committed funds, a dummy for new projects (versus follow-ups), a dummy for investment projects, sector dummies, share in the largest sector, and the log of completion and preparation costs relative to total committed funds.

Table 7 presents estimates from six different variants of equation (2). In column 1 the regression does not include any controls, in column 2 it includes all controls except cost variables (which are not available for many projects), and in column 3 it includes the entire control set. Columns 4–6 replicate the same specifications but also include sector fixed effects. The coefficient on the fraction of locations in the vicinity of a mission is small, and its sign is not consistent across specifications. The standard errors are at least three times as large as the coefficient. In short, table 7 shows no evidence that mission presence is correlated with better (or worse) project performance, as measured by IEG ratings.

The results in table 7 should be interpreted with some caution: the sample size is relatively small, and the explanatory variable is subject to measurement error due to the need to keep the dataset at the project level. These factors imply low statistical power to reject the null hypothesis. Furthermore, the IEG rating is an imperfect measure of project performance. It measures performance with respect to a goal, which is not the same across sectors, and it is at least partly based on documentation produced by the team leader.

Bank, but these data for the period before 2005 could not be located for the present study.

27