rafmacalaba/datause-encoder-data huggingface dataset:
methodology (see Annex 4 for more details). Interest rates ( _Rear)_ have been calculated from IFS and _TenSyst_ comes from Keefer et al. (2001). All other data are from WDI.

**33** . Oil export also becomes significant in “Administrative Quality” equation at a 10 percent level.

**34** . _PA_ contributes significantly and with the right sign to _GOV_ (see the results of the PCA in _Annex_ 4).

**35** . For more details on the methodology, see Nagaraj, Varoudakis and Véganzonès (2000).

**36** . Theses sub-components are corruption, bureaucracy quality, investment friendly profile of administration and law and order (see section 2).

**37** . Political Stability has been proxied by aggregating the following indicators: government stability, internal and external conflicts, and ethnic tensions.

**38** . Public Accountability has been calculated by using civil liberties and political rights.

**39** . The Structural Reform indicator contains trade policy and financial development.

**40** . Human Capital is defined from life expectancy, and years of primary, secondary and tertiary education. **41** . For comparison purposes, we have benchmarked the effort represented by the improvement of one standard deviation of each initial indicator entering the governance ( _GOV_ ), structural reforms ( _SR_ ) and human capital ( _H_ ) indicators, to the reforms achieved by the East Asian economies. These calculations are shown in Table A.6., _Annex_ 6. Figures of the table are the level of reforms potentially reached by our MENA countries divided by the ones achieved by the East Asian economies. For example, the value 1 means that an improvement of one standard deviation has increased the reform indicator in MENA to the level of the East Asian economies. As well, the value 1.2 means that the level reached by MENA is 20 percent higher than the one observed in East Asia. Most of the time, the effort considered in the simulations gives to our MENA countries an advance in “Administrative Quality”, “Political Stability” and “Public Accountability” (during the 1990s in this case). MENA, however, would not catch up with East Asia in terms of structural reforms (except in the case of Tunisia).

41
# **Annex B: Dropout predictors**

Using the full universe of students who had entered the COMIPEMS in the academic year 20082009, and who were supposed to complete high school in Spring 2012, we regress the dummy for taking part in the 2012 ENLACE 12<sup>th</sup> grade - a proxy for on-time EMS completion – on the lower secondary GPA score and the score in the COMIPEMS admission exam, both measured in the school year 2008-2009.<sup>12</sup> While the intervention aims at tackling both academic and socioemotional issues that can lead to the decision of dropping out of high school or EMS, at the time of the pilot’s design, we had no information on proxies for the socioemotional skills of 9<sup>th</sup> grade students in Mexico. Once we condition on school fixed effects, GPA and the COMIPEMS admission exams could explain 19% of the total variation in the probability of completing, on-time, upper secondary.

With the OLS estimates of the coefficients on GPA and the COMIPEMS exam in hand, for all the first-year EMS students in each of the 20 schools in the evaluation sample, we construct a predicted probability of not finishing EMS.

|**Dropoutpre**|**dictors**<br>|
|---|---|
|COMIPEMS scores|-0.003***<br>(0.018)|
|Secondary average|-0.174***<br>(0.018)|
|Male|0.004<br>(0.002)|
|Observations|186,293|
|Mean Dep. Control|0.518|
|Group||
|SD Dep. Control Group|0.500|
|R-square|0.185|

> 12 For a discussion on the reliability of ENLACE 12th grade as a proxy for EMS completion see de Janvry, Dustan and Sadoulet (2017) and Avitabile and de Hoyos (2018).

26
**Figure 1: Evolution of World Mineral Prices: 2000–2006**

<!-- Start of picture text --> 650 14<br>600<br>12<br>550<br>500<br>10<br>450<br>400<br>8<br>350<br>300<br>6<br>250<br>200<br>4<br>150<br>100<br>2<br>50<br>0 0<br>2000 2001 2002 2003 2004 2005 2006<br>SILVER NATURAL GAS GOLD TIN<br>COPPER ZINC LEAD OIL<br>US$<br><!-- End of picture text --> Source: London Metal Exchange and London Bullion Market Association and NGA, annual reports, Oil Patch Research Group Note: Copper, zinc, lead, and tin are measured in US$/lb., gold and silver are measured in US$/oz.tr. Oil is measured in US$/bbl. and natural gas in US$ per 1000 feet<sup>3</sup> . Silver and natural gas are measured on the right vertical axis.

4
# **2 Background on Temporary Migration from South Asia**

Bangladesh, Nepal and Pakistan are major migrant-sending countries globally, both in terms of the absolute magnitude of flows and stocks as well as their incidence in the working age population at origin (Ahmed and Bossavie 2021). In Nepal, the incidence of international migration is especially high: 27 percent of households have at least one member working abroad, and over a third of the male working age population has ever worked abroad (Bossavie and Denisova 2018; Bulmer, Shrestha, Marshalia, et al. 2020). In Bangladesh, about 15 percent of the male working age population is either currently working overseas or did in the past, according to data from the Household Income and Expenditure Survey (HIES). In absolute terms, Pakistan and Bangladesh respectively rank 5th and 6th in the global ranking of countries with the largest diasporas overseas. Migrants from South Asia account for 15 percent of all global migrants, and for two thirds of migrants in the Gulf Cooperation Council (GCC) countries.

Labor migration from South Asia is regionally concentrated in a handful of destinations in the GCC economies, and to a smaller extent in Southeast Asia. The GCC is a major destination of migrants globally hosting over 10 percent of the total stock of international migrants. It attracts many foreign workers, primarily from lower income countries, to fill labor shortages in low-skilled occupations in the private sector which are unattractive to natives. They provide large wage gain opportunities for workers from South Asia who can expect to earn three to four times what they earn back home (Ahmed and Bossavie 2021). Migration from Bangladesh, Nepal and Pakistan is especially concentrated in Saudi Arabia and the United Arab Emirates: over 80 percent of Pakistanis and over half of Bangladeshis working overseas are employed in one of these two countries, while half of temporary migrants from Nepal are employed in Saudi Arabia alone.

Given the low skill level of the workforce in Bangladesh, Nepal and Pakistan, combined with high demand for low-skilled labor in the main destinations, temporary migration from South Asia is low-skilled for international standards. The average migrant from Bangladesh, Nepal or Pakistan has incomplete secondary education and a large share of migrants have completed primary education at most: 40 percent of labor migrants from Pakistan to the United Arab Emirates (UAE) have low education, compared to 70 percent of Nepalese migrant workers to Qatar. In Bangladesh, the share of international migrants who have at most completed primary education is 34 percent. In comparison, low-skilled migrants represent only 6 percent and 5 percent of workers from the Philippines going to Qatar and Saudi Arabia, respectively.

5
The sources of productivity enhancements, _technological progress_ and _investment_ , may be directly affected in corrupt environments. For instance, entrepreneurs may have incentives to adopt inefficient “fly-by-night” technologies of production with an inefficiently high degree of reversibility, which allows them to react more flexibly to future demands from corrupt officials—and more credibly threaten to shut down operations (Svensson, 2003). Additionally, vested interests may directly oppose the adoption of new technologies, which would threaten their position of influence by rendering obsolete the older technological vintages they control (Krusell and Rios-Rull, 1996). Finally, corruption may erect de facto entry barriers into otherwise competitive markets with discouraging effects for investment decisions, in a mechanism similar to the one proposed by Alesina et al. (2005). Alternatively, the monetary cost involved in the payment of bribes may simply limit the amount of resources available to expand productive capacity via investment.

In addition to distortionary allocation effects, the discretionary power of state officials will increase the _risk of expropriation_ thus reducing the appropriability of returns to investment and innovation (Demsetz, 1967 and Alchian and Demsetz, 1973). This will further diminish rewards for entrepreneurial behaviour, while propping up inefficient firms engaged in corrupt practices. In this spirit, Johnson, McMillan, and Woodruff (2002), using firm-level data from former communist countries in Europe, find a negative effect of paying bribes on investment and interpret this finding as the effect of insecure property rights.

Whereas corruption can impact economic performance through all these channels, its adverse effects may be non-linear and depend on the overall level of institutional quality (or governance) in the country. Two studies – both based on country level data - find such non-linearities in the relationship between corruption and growth, namely a _more negative effect when institutional quality is poor_ . Méon and Sekkat (2005), based on sample of 71 countries between 1970 and 1998 and using various proxies for both corruption and governance,<sup>7</sup> find that corruption is most harmful to growth where governance is weak. Méndez and Sepúlveda (2006) examine country-

> 7 The authors use the Transparency International CPI, as well as a number of indicators from the World Bank Governance Indicators, as proxies for corruption and governance.

11
**Table 4. Major cities at risk from intensification of storm surges Indicator: Percent of coastal area exposed***

|**Rank**|**EAP**|**SAR**|**AFR**|**LAC**|**MENA**|
|---|---|---|---|---|---|
|1|Hai Phong<br>(Vietnam)|Barisal<br>(Bangladesh)|Bugama<br>(Nigeria)|Ciudad del<br>Carmen<br>(Mexico)|Port Said<br>(Egypt)|
|2|San Jose<br>(Philippines)|Mumbai<br>(India)|Okrika<br>(Nigeria)|Manzanillo<br>(Cuba)|Dubai<br>(UAE)|
|3|Vung Tau<br>(Vietnam)|Cox’s Bazar<br>(Bangladesh)|George<br>(South Africa)|Georgetown<br>(Guyana)|Rabat<br>(Morocco)|
|4|Manila<br>(Philippines)|Khulna<br>(Bangladesh)|Quelimane<br>(Mozambique)|Bahia Blanca<br>(Argentina)|Kenitra<br>(Morocco)|
|5|Roxas<br>(Philippines)|Bhaunagar<br>(India)|Mahajanga<br>(Mozambique)|Cienfuegos<br>(Cuba)|Aden<br>(Yemen)|
|6|Cotabato<br>(Philippines)|Karachi<br>(Pakistan)|Nacala<br>(Mozambique)|Vina del Mar<br>incl. Concon<br>(Chile)|Abu Dhabi<br>(UAE)|
|7|Ansan<br>(Korea)|Jamnagar<br>(India)|Bathurst<br>(Gambia)|Aracaju ♦<br>(Brazil)|Al Ain<br>(UAE)|
|8|Poryong<br>(Korea)|Surat<br>(India)|Beira<br>(Mozambique)|Puerto la Cruz<br>incl. Pozuelos<br>(Venezuela)|Ajman<br>(UAE)|
|9|Rach Gia<br>(Vietnam)|Thane<br>(India)|Tanga<br>(Tanzania)|La Plata<br>(Argentina)|Mohammedia<br>(Morocco)|
|10|Hue<br>(Vietnam)|Vadodara<br>(India)|Free Town<br>(SierraLeone)|Acapulco de<br>Juarez(Mexico)|Nador<br>(Morocco)|

***** In the Urban Risk Index database allocation of urban extent to adjacent city limits may sometimes have a margin of error due to potential inaccuracies associated with the thiessen polygon technique. ♦Aracaju in Brazil has been identified as one such example of allocation error.

# **IV. Conclusions**

Coastal areas of the world face a range of risks related to climate change (IPCC 2007). Anticipated risks include an accelerated rise in sea level, an intensification of cyclones, and larger storm surges among others. This paper assesses the vulnerability of the world’s coastal zones to intensification of storm surges. A detailed GIS analysis is used to estimate the impact of future storm surge increases associated with more intense storms and a 1 m sea-level rise. After delineating future inundation zones, this information is overlaid with indicators for coastal populations, settlements, economic activity, and 33
**Table 10: Land Rights, Decision-Making and Permissions Variables by Cluster (Ethiopia)**

||Male La|ndowners by|Cluster|Female L|andowners b|y Cluster|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.49|0.85***|0.76***|0.55|0.86***|0.76***|
||(0.50)|(0.35)|(0.43)|(0.50)|(0.35)|(0.43)|
|Plot Decision Making|0.79|1.00***|0.97***|0.62|0.89***|0.89***|
||(0.41)|(0.05)|(0.16)|(0.49)|(0.31)|(0.32)|
|Economic Decision Making if Plot|0.89|1***|0.98**|0.82|0.98***|0.96***|
|Sold|(0.31)|(0.00)|(0.14)|(0.38)|(0.13)|(0.19)|
|Right to Improve|0.27|0.92***|0.91***|0.28|0.89***|0.91***|
||(0.44)|(0.27)|(0.29)|(0.45)|(0.31)|(0.28)|
|Tenure Security|0.57|0.59|0.63|0.55|0.65|0.68*|
||(0.50)|(0.49)|(0.48)|(0.50)|(0.48)|(0.47)|
|Need permission for at least 1 right#||1.00***|0.84***||1.00***|0.75***|
|||(0.01)|(0.37)||(0.00)|(0.43)|
|Need permission for some rights (at||1.00***|0.79***||1.00***|0.72***|
|least 3 out of 5 inc. right to improve)||(0.01)|(0.40)||(0.00)|(0.45)|
|Need permission for all rights||0.88***|0.63***||0.85***|0.59***|
|||(0.33)|(0.48)||(0.36)|(0.49)|
|Perc. of rights needing permission||0.99***|0.78||1.00**|0.72***|
|||(0.06)|(0.38)||(0.03)|(0.43)|
|Observations<br>Notes:Permission statistics for non-ho|153<br>lders were no|286<br>t provided|609<br>as very few|253<br>individuals i|283<br>n this categ|430<br>ory held righ|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u>

# **6. Sensitivity Analyses**

# **6.1 Comparing Reported Owners versus Individuals with Use Rights**

The analysis above is based on respondents identifying themselves as landowners. In Ethiopia and Tanzania, rights and decision-making variables were asked to both owners and users of land (even if they were not self-reported owners). This allows us to investigate whether including the useronly group will change the categorization of bundles. Table 11 shows that our findings are quite robust — incorporating individuals who are only users in the analysis still leads to three cluster categories based on the rights to transfer. Users were assigned to clusters which included the owners instead of populating their own cluster category. This suggests that transfer rights and distinguishing exclusivity/jointness determine users’ right bundles similarly to owners.

In Ethiopia, 19 percent of the sample are users who have mostly exclusive transfer rights, which amounts to 58 percent of users. Thus, research that focuses only on reported ownership would miss out a large portion of individuals who would identify as non-owners but would still be exclusive 27
- 5 -

in 2021, five times higher than the level prior to the COVID crisis. Road transport costs also increased substantially. The harmonized consumer price index for transport in Europe jumped from 106.7 in 2020 to 127.6 in 2022 (Eurostat). No doubt transport connectivity is now more important than ever.

10. Regional integration and transport connectivity are of particular importance for the Caucasus and Central Asia region, which is landlocked and located at a strategic place between Europe and East Asia, and between the Russian Federation and South Asia (e.g., Incaltarau et al., 2022). After the collapse of the Soviet Union in 1991, the region experienced long economic stagnation and transition as well as regional disintegration. In recent years, the region has started recovering with relatively robust growth (Pomfret, 2010, 2021). As pointed out by Cheong and Turakulov (2022), trade facilitation and regional reintegration are of vital importance to sustain growth in the region. Since the early 2010s, a number of large transport investments have been made to reestablish the regional connectivity. For instance, Kazakhstan embarked upon a massive road and rail investment program, Nurly Zhol, to connect major cities, logistics centers and free-trade zones to the regional market, i.e., border crossings, including ports on the Caspian Sea dry ports. The country spent about US$37 billion over five years: 2013-2017 (UNECE, 2019).

11. Due to such efforts, the regional connectivity seems to have been improved. According to the ADB CAREC Corridor Performance Measurement and Monitoring (CPMM) program, average transportation costs on the regional corridors were halved from US$1,400 per 20-ton cargo per 500 km in 2014 to US$700 in 2019 ( **Figure 1** ). Average speed also increased gradually. Note that the recorded travel times include not only driving time but also various delays and roadside stops, such as loading and offloading, and security checks. With such delays excluded (i.e., data “without delays”), the average speed had a slight increasing trend since 2015 ( **Figure 2** ).
<!-- Start of picture text --> Figure 1: Primary sector contribution to GDP growth (%)<br>7.0<br>6.14<br>5.75<br>6.0<br>5.0<br>4.0 3.53<br>3.0 2.36<br>2.0<br>0.99 0.97<br>1.0 0.40 0.48 0.35<br>0.0<br>food crop industrial breeding and forestry, forest Fishing<br>‐1.0<br>agriculture agriculture or hunting harvesting,<br>‐2.0 export<br>(1.96)<br>‐3.0<br>1995‐2005 2006‐2013<br><!-- End of picture text --> Source: Senegal country sources However, livestock and forestry are bright spots in the sector. Efforts to strengthen irrigation infrastructure, address the challenges of agro-processing and deal with constraints in the fishing sector, while promoting livestock and forestry, are needed to reverse the decline in the sector. In addition, horticulture products, such mango, sweet potato, tomato, melon, banana, ginger, and tamarind, hold immense potentials for the future of agriculture in the country, both as important export earners as well as opportunities to strengthen industrial agriculture through agribusiness development. Export (tonnage) of these products grew at 16 percent per annum during 1991-2011 and domestic conditions such as favorable ecology, low cost of labor and proximity to European markets create conditions for growth of exports (Brethenoux et al 2011). Within industrial agriculture, horticulture has been performing well, increasing its exports from 10,728 tons in 2003 to 60,000 tons in 2013, or an annual average increase of 19 percent. A refocus on value chains in these products through agribusiness will help reduce wastages, strengthen local production, and encourage farmers to diversify their activities.

# **2.3. LOSS OF MANUFACTURING COMPETITIVENESS**

The decrease in manufacturing growth was widespread across subsectors ranging from food manufactures to machine and chemical manufactures (figure 2). Comparing the two periods, only two subsectors, food packaging (preservation) and tobacco products, recorded higher growth rates 8
64 Table D.3: Timing of the Correlations btw Urbanization & the Measures, 10-Year Panel

|**Dependent Variable:**||**Urbaniz**|**ation Rate (%)**|**in Year**_t_||
|---|---|---|---|---|---|
|Correlations with:|Baseline|1 Lead|l Lag|2 Lags|2 Lags|
|NRXGDP (%)_t_||0.05||||
|||[0.062]||||
|NRXGDP (%)_t_-10|0.19**|0.18***|0.12*|0.03||
||[0.089]|[0.065]|[0.065]|[0.080]||
|NRXGDP (%)_t_-20|||0.23***|0.23***||
||||[0.072]|[0.068]||
|NRXGDP (%)_t_-30||||0.14***||
|||||[0.046]||
|MFGSERV (%)_t_+10||0.15||||
|||[0.096]||||
|MFGSERV (%)_t_|0.43***|0.26***|0.33**|0.37**|0.37**|
||[0.141]|[0.090]|[0.142]|[0.159]|[0.168]|
|MFGSERV (%)_t_-10|||0.26**|0.16**|0.17**|
||||[0.103]|[0.077]|[0.078]|
|MFGSERV (%)_t_-20||||0.31**|0.32**|
|||||[0.127]|[0.125]|
|DEINDU (%) 1980-_t_+10||0.27||||
|||[0.238]||||
|DEINDU (%) 1980-_t_|0.42|0.15|0.37|0.11|0.10|
||[0.266]|[0.152]|[0.252]|[0.271]|[0.272]|
|DEINDU (%) 1980-_t_-10|||0.01|-0.09|-0.08|
||||[0.295]|[0.170]|[0.162]|
|DEINDU (%) 1980-_t_-20||||0.35|0.35|
|||||[0.443]|[0.440]|
|FMXGDP (%)_t_-10|||||0.05|
||||||[0.090]|
|FMXGDP (%)_t_-20|||||0.23***|
||||||[0.069]|
|FMXGDP (%)_t_-30|||||0.14***|
||||||[0.047]|
|AGXGDP (%)_t_-10|||||-0.04<br>|
||||||[0.160]|
|AGXGDP (%)_t_-20|||||0.23|
||||||[0.195]|
|AGXGDP (%)_t_-30|||||0.15|
||||||[0.161]|
|Sum for NXGDP|||0.35***|0.40***||
||||[0.12]|[0.15]||
|Sum for MFGSERV|||0.60***|0.85***|0.85***|
||||[0.20]|[0.29]|[0.29]|
|Sum for DEINDU|||0.37|0.37|0.37|
||||[0.32]|[0.46]|[0.46]|
|Sum for FMXGDP|||||0.42***|
||||||[0.15]|
|Sum for AGXGDP|||||0.33|
||||||[0.34]|
|CntryFE,Yr FE,Ctrls;Obs|Y;694|Y;578|Y;578|Y;462|Y;462|

_Notes_ : Robust SEs are clustered at the country level. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
<!-- Start of picture text --> oO<br>N<br>1995 2010<br>A “<br>: ; 1<br>ar x 2 4 7<br>iS “ + /<br>va rd sd Y 7<br>° A LFe 7 7s<br>x ca hd an<br>Go “iY? Le 7<br>3” KA 7<br>> im -,<br>c 2** “<br>; MEE 2019<br>O10 part hag rang -—---- 2015<br>aaeeTM rode ---------—*— 20102005<br>—-@#-—- 2000<br>—) ”* ----Ae--- 1995<br>6 7 8 9 10 11<br>Log GDP per capita<br><!-- End of picture text -->
22 machinery and equipment.20 Second, our protection estimates for 1967 are crude. Third, with respect to specification, it must also be recognized that our crude measure of labor input is probably not strictly exogenous. So if labor usage depends on output demand and some omitted variables -- e.g. the wage-price ratio -- changes in the omitted variable can change the estimated relationship between labor and output. (Changes in the structure of protection alter the cross-sectoral pattern of wage-price ratios, and hence in principle could change the apparent pattern of scale economies.) About these shortcomings little could be done. However, we did test for robustness by trying alternative variable definitions and model specifications. With respect to data, we experimented with two alternative industry-specific deflators for gross output and found that our results were robust to the selection of industry-specific gross output deflator (results available upon request). We also developed an extension of the model treating labor as endogenous (see footnote 15). Unfortunately, this yielded implausibly low estimates for returns to scale, probably reflecting problems with our wage variable and the imposition of too much structure on the data.2<sup>1</sup>

> 20/ However, tests for the association between protection and efficiency were not performed until we had settled on our instrument, nor did we even compare results from the two census years industry-byindustry. Hence our search in itself did not bias the results in favor of our priors.

- 21/ Os'r data do not specify location of establishments, so we are unable to control for the possibility that firms in different localities may face different wage-price ratios.