rafmacalaba/datause-encoder-data huggingface dataset:
where we categorize people into one of 183 categories defined by their gender, age, education, formal or informal and full- or part-time employment status.<sup>23</sup> We estimate equation (4) using two-stage least squares. Then we obtain the predicted employment rate for each group after the inflow of refugees ���,� ���,� � 0.020 �̂�, where 2.0 percent is the mean ratio of refugees to Turkish people in 2014. The wage estimates for the whole sample, see equation (1), provide the causal impact of the arrival of refugees on the log wages of employed Turkish workers (with a coefficient γ��). Then the absolute refugee-induced wage change is given by Δ������ ���� ���<sup>**�**�∗�</sup> �1�∗���.

# **3.3 Instrument**

To allow for a causal interpretation of the impact of refugee flows, see equations (1) and (4), we instrument for the ratio of refugees to working-age population (����.<sup>24</sup> Our instrumenting strategy is based on the idea that travel distance, from the Syrian governorate from which the refugee is fleeing to each potential destination Turkish subregion, is a key determinant of refugee location decisions. We use Google Maps to calculate the travel distance _Tsr_ from each Syrian governorate capital ( _s_ ), to the most populous city in each Turkish NUTS 2 subregion ( _r_ ). The instrument for the number of refugees at a given point of time in each Turkish subregion is given by: where _Rt_ is the total number of registered Syrians in Turkey in a year and �� the fraction of the Syrian population that lived in each governorate in 2010 (pre-war).<sup>25</sup> Since all our

> 23 The education categories are at most primary school, secondary school, and higher education. The age categories are 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, and 60–64 years. There are 183 groups since we exclude groups containing less than 40 observations.

> 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that - conditional on the fixed effects and control variables - the flows of Syrian refugees are uncorrelated with the instrument.

> 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria,

16
# **Table 5: Multi-tasking**

## **Dependent Variable: Completion Binary [yes=1] Standard Errors: Clustered by Organization Interactions in Deviation from Mean OLS Estimates**

|**OLS Estimates**|**Internal**<br>**(1) Ethnic**<br>**Fractionalization**|**External**<br>**(2) Stakeholder**<br>**Engagement**|
|---|---|---|
|**CS-Autonomy**|0.31***|0.28***|
||(0.04)|(0.04)|
|**CS-Incentives/Monitoring**|-0.18***|-0.18***|
||(0.04)|(0.05)|
|**CS-Other**|-0.08***|-0.07***|
||(0.02)|(0.02)|
|**CS-Incentives/Monitoring x Ethnic Fractionalization**|-0.05*<br>(0.03)||
|**Ethnic Fractionalization**|0.06*<br>(0.03)||
|**CS-Incentives/Monitoring x Stakeholder Engagement**||-0.06**<br>(0.02)|
|**Stakeholder Engagement**||0.05**<br>(0.02)|
|**Noise, General and Capital Controls**|Yes|Yes|
|**Project Controls**|Yes|Yes|
|**Fixed Effects**|Project Type, Sector|Project Type, Sector|
|**Observations (clusters)**|3620 (30)|3620 (30)|

**Notes:** *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses, and are clustered by organization throughout. All columns report OLS estimates. The dependent variable in all columns is a dummy variable that takes the value 1 if the project is completed and 0 otherwise. The variable with which management scores are interacted is z- score defined across the 30 organizations we study. In Column 1, 'Ethnic Fractionalization' is the organization-level Herfindahl index that is equal to 1 minus the sum of squares of ethnicity proportions in an organization. In Column 2, 'Stakeholder Engagement' is the proportion of projects officials at an organization report that they interact with stakeholders outside of their organization. Sector fixed effects relate to whether the project is in the administration, environment, finance, infrastructure, security/diplomacy/justice or social sector. Project controls comprise project-level controls for whether the project is regularly implemented by the organization or a one-off, whether the project is a bundle of interconnected outputs, and whether the division has to coordinate with actors external to the government to implement the project. Capital controls comprise a count of the number of interviews undertaken, which is a close approximation of the total number of employees. General controls comprise organization-level controls for the share of the workforce with degrees, the share of the workforce with postgraduate qualifications, and the span of control. Noise controls are averages of indicators of the seniority, gender, and tenure of all respondents, the average time of day the interview was conducted and of the reliability of the information as coded by the interviewer. Figures are rounded to two decimal places.
|**Country name**|**Date enacted /**<br>**revised**|**Table A**<br>**Foreign**<br>**currencies**<br>yes=1 no=0|**.1.2**(continue<br>**Inter-bank**<br>**deposits**<br>yes=1 no=0|d)<br> <br>**Coverage limits as of 2003**|**Coverage**<br>**limits as of**<br>**2003 in**<br>|
|---|---|---|---|---|---|
||||||**US$**|
|Isle of Man|1991|1|0|lesser of 15,000 pounds or 75% of<br>amount deposited|<br> <br>35694|
|Italy|1987/1996|1|0|<br>ITL 200 Mil.|130457|
|Jamaica|1998|1|0|J$ 300,000|4957|
|Japan|1971|0|0|<br>10000000 yen|93371|
|<br>Jordan|2000|0|0|<br>JD 10,000|14104|
|Kazakhstan|1999/2003|1|0|<br>400,000 Tenges|2774|
|Kenya|1988|1|1|<br>K Sh 100,000|1313|
|<br>Korea|1996|0|0|50 Mil Won|41925|
|Latvia|1998|1|0|3000 Lat|5545|
|Lebanon|1967|0|1|LL 5,000,000|3317|
|Liechtenstein|1992/2003|1|0|<br>EUR 20,000|25260|
|Lithuania|1996|1|0|LTL 45,000|16293|
|Luxembourg|1989|1|0|EUR 20,000|25260|
|<br>Macedonia|1996/2000/2002|1|0|<br>EUR 20,000|25260|
|Malaysia|1998|||Blanket guarantee||
|<br>Malta|2003|0|0|<br>EUR 20,000, about 8600 Maltese<br>lira|<br>25260|
|Marshall Islands|1975|1|1|US $ 100,000|100000|
|Mexico|1986/1990/1999|1|0|32,262,340 Pesos|2871337|
|Micronesia|1963|1|1|US$ 100,000|100000|
|Netherlands|1978/1996/1998|1|0|EUR 20,000|25260|
|Nicaragua|2001|1|0|US$ 20,000|20000|
|Nigeria|1988/1989|0|1|N 50,000|366|
|Norway|1961/1997|1|0|NOK 2,000,000|299401|
|Oman|1995|1|0|RO 20,000 or 75% of net deposits,<br>whichever is less|<br>52016|
|Paraguay|2003|n.a.|n.a.|75 * monthly minimum salary|10500|
|<br>Peru|1991|1|0|<br>S 68,474|19773|
|Philippines|1963|1|1|<br>P 100,000|1800|
|<br>Poland|1995|1|0|<br>100% of up to EUR 1,000; 90% of<br>|<br>28418|
|||||EUR 1,000 to EUR 22,500||
|Portugal|1992/1995|1|0|<br>EUR 25,000|31575|
|Romania|1996|1|0|ROL 125,222,000|3842|
|Russia|2003|n.a.|n.a.|<br>100,000 rubles|3395|
|Serbia & Montenegro|2001|1|0|5,000 Dinars|87|
|Slovak Republic|1996/2001|1|0|90%, not to exceed EUR 20,000|25260|
|Slovenia|2001|1|0|5100000 tolars|26931|
|<br>Spain|<br>1977/1996|<br>1|<br>0|,,<br>EUR 20,000|<br>25260|
|<br>Sri Lanka|1987|0|0|<br>Rs. 100,000|1034|
|Sweden|1996|1|0|<br>SEK 250,000|34364|
|Switzerland|1984/1993|0|0|<br>CHF 30,000|24254|
|Taiwan|1985|0|0|NT$ 1,000,000 since Aug 15 1987||
|Tanzania|1994|1|1|<br>TZS 250,000|<br>235|
|Thailand|1997|1|1|Full coverage (blanket<br>|<br>Full|
|||||government guarantee since 1997)||

32
**Table H4: Relationship between food security measures and height-for-age z-scores (Tanzania): Limited Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||Height-for-|age z-scores|||
|Per capita kcal|-0.004<br>(0.053)||||||
|1-SSR||0.005|||||
|||(0.047)|||||
|1-SSEXR|||0.049||||
||||(0.046)||||
|DDS||||0.147***<br>(0.045)|||
|PC expenditure|||||-0.002<br>(0.054)||
|Food share||||||-0.149***<br>(0.046)|
|Rural|-0.522***|-0.519***|-0.513***|-0.431***|-0.524***|-0.452***|
||(0.135)|(0.129)|(0.131)|(0.133)|(0.135)|(0.132)|
|Dodoma|-0.549|-0.550|-0.553|-0.443|-0.546|-0.511|
||(0.591)|(0.589)|(0.585)|(0.567)|(0.593)|(0.574)|
|Arusha|-0.222|-0.221|-0.188|-0.218|-0.199|-0.198|
||(0.618)|(0.616)|(0.614)|(0.599)|(0.623)|(0.605)|
|Kilimanjaro|0.201|0.206|0.236|0.170|0.202|0.145|
||(0.614)|(0.614)|(0.605)|(0.594)|(0.615)|(0.599)|
|Tanga|-0.108|-0.109|-0.098|-0.151|-0.108|-0.107|
||(0.611)|(0.601)|(0.596)|(0.588)|(0.609)|(0.583)|
|Morogoro|0.035|0.037|0.041|0.013|0.036|0.037|
||(0.608)|(0.606)|(0.598)|(0.585)|(0.610)|(0.588)|
|Pwani|-0.092|-0.092|-0.093|-0.077|-0.094|-0.132|
||(0.670)|(0.660)|(0.653)|(0.639)|(0.661)|(0.646)|
|Dar es salaam|-0.193|-0.194|-0.201|-0.255|-0.193|-0.282|
||(0.574)|(0.567)|(0.563)|(0.551)|(0.579)|(0.555)|
|Lindi|-0.301|-0.301|-0.274|-0.207|-0.300|-0.284|
||(0.605)|(0.600)|(0.594)|(0.579)|(0.604)|(0.586)|
|Mtwara|-0.405|-0.406|-0.387|-0.381|-0.405|-0.381|
||(0.592)|(0.584)|(0.576)|(0.563)|(0.591)|(0.567)|
|Ruvuma|-0.752|-0.750|-0.733|-0.714|-0.751|-0.719|
||(0.580)|(0.578)|(0.570)|(0.557)|(0.579)|(0.561)|
|Iringa|-0.796|-0.795|-0.773|-0.799|-0.794|-0.795|
||(0.584)|(0.582)|(0.575)|(0.562)|(0.587)|(0.567)|
|Mbeya|-0.430|-0.430|-0.411|-0.461|-0.428|-0.464|
||(0.589)|(0.585)|(0.579)|(0.568)|(0.592)|(0.571)|

90
Appendix 1B: Total direct & indirect value added export (VAX) of business service, 1995-2007

||X||Ratio1 =|||Ratio2 =|
|---|---|---|---|---|---|---|
|Country|(gross export)|VAX|VAX/X|dVAX|indVAX|indVAX/dVAX|
|AUS|512|1128|2.20|250|879|3.60|
|AUT|1012|1184|1.17|579|604|1.11|
|BEL|1964|2657|1.35|964|1693|1.76|
|BGR|11|26|2.41|8|18|2.23|
|BRA|303|665|2.19|193|472|2.38|
|CAN|1322|2380|1.80|812|1568|1.93|
|CHN|1765|1921|1.09|757|1164|1.33|
|CYP|12|21|1.78|8|13|1.66|
|CZE|274|352|1.28|121|231|1.97|
|DEU|3821|12761|3.34|2596|10166|3.74|
|DNK|429|680|1.59|236|444|1.80|
|ESP|1576|1957|1.24|924|1033|1.08|
|EST|27|38|1.41|15|24|1.62|
|FIN|474|654|1.38|278|376|1.25|
|FRA|3138|8361|2.66|1801|6560|3.69|
|GBR|6748|9602|1.42|4635|4966|1.06|
|GRC|99|130|1.31|53|77|1.39|
|HUN|287|388|1.35|163|225|1.38|
|IDN|29|48|1.63|17|32|1.31|
|IND|1588|1279|0.81|1084|195|0.19|
|IRL|1563|1321|0.85|862|458|0.51|
|ITA|1713|4178|2.44|965|3212|3.29|
|JPN|1011|4660|4.61|608|4052|7.06|
|KOR|591|1624|2.75|374|1249|3.27|
|LTU|16|25|1.55|10|15|1.87|
|LUX|231|240|1.04|121|118|0.90|
|LVA|14|25|1.85|7|18|2.67|
|MEX|88|793|9.06|63|730|12.17|
|MLT|24|24|1.02|15|9|0.67|
|NLD|3266|3954|1.21|1896|2058|1.06|
|POL|258|511|1.98|148|363|2.28|
|PRT|175|299|1.70|92|206|2.16|
|ROM|106|119|1.12|52|66|1.23|
|RUS|72|524|7.27|45|480|12.01|
|SVK|85|121|1.42|43|78|1.86|
|SVN|50|99|2.00|27|72|2.68|
|SWE|1444|1903|1.32|811|1092|1.27|
|TUR|1.34|146|109.57|1|146|148.14|
|TWN|423|516|1.22|216|300|1.61|
|USA|9517|20777|2.18|6230|14547|2.20|
|ROW|9776|8234|0.84|5458|2776|0.51|
|**TOTAL**|**55815**|**96323**|**1.73**|**33535**|**62788**|**1.87**|

Notes: The export values in this table are for business service sector (WIOD sector 30) in 100 million U.S. dollars at 2005 constant price, using the U.S. GDP deflator from the Federal Reserve Economic Data (website address: http://research.stlouisfed.org/fred2). _X_ is total gross exports. _VAX_ is total value added exports. _dVAX_ is direct value added exports. _indVAX_ is indirect value added exports through other sectors.

42
Table 4 Gains from Trade - Winners

||Gains|E|xpenditur|e|||Income|||
|---|---|---|---|---|---|---|---|---|---|
|||agric.|manuf.|total|agric.|wage|enter.|rev.|total|
|Cameroon|6.9|8.9|3.6|12.5|-1.7|-1.3|-0.7|-1.9|-5.6|
|Zambia|5.9|7.8|1.2|9.0|-1.0|-0.5|-0.7|-0.8|-3.0|
|Sierra Leone|4.3|5.8|1.7|7.4|-1.5|-0.1|-0.1|-1.5|-3.1|
|Tanzania|4.2|4.7|4.1|8.8|-1.7|-1.1|-0.2|-1.7|-4.6|
|Central African Rep,|4.2|7.1|3.7|10.8|-4.5|-0.0|0.0|-2.1|-6.6|
|Jordan|4.0|6.2|2.1|8.3|-0.3|-0.4|-0.1|-3.4|-4.2|
|Mozambique|3.7|6.0|1.2|7.2|-1.1|-0.3|-0.1|-2.0|-3.6|
|Uzbekistan|3.5|5.0|1.9|7.0|-0.7|-1.1|-0.4|-1.2|-3.4|
|Cˆote d’Ivoire|3.4|4.6|2.6|7.2|-1.7|-0.6|-0.4|-1.1|-3.8|
|Nigeria|3.3|6.2|2.2|8.3|-1.6|-1.8|-0.3|-1.3|-5.0|
|Ukraine|3.2|3.7|0.9|4.6|-0.2|-0.2|-0.0|-0.9|-1.3|
|Ecuador|3.0|5.9|1.5|7.3|-1.6|-1.4|-0.3|-1.0|-4.4|
|Kenya|2.9|6.1|2.5|8.6|-2.8|-1.2|-0.1|-1.6|-5.7|
|Egypt, Arab Rep.|2.9|4.7|1.9|6.6|-1.3|-1.1|-0.4|-1.0|-3.7|
|Guinea|2.8|4.9|2.9|7.8|-1.9|-0.1|-0.2|-2.9|-5.0|
|Bolivia|2.8|4.2|2.3|6.5|-1.2|-0.6|-0.6|-1.3|-3.7|
|Yemen, Rep.|2.7|4.1|1.3|5.4|-0.8|-0.4|-0.1|-1.5|-2.8|
|Azerbaijan|2.5|3.9|2.3|6.2|-2.3|-0.1|0.0|-1.3|-3.7|
|Armenia|2.5|3.8|0.4|4.1|-0.5|-0.3|-0.0|-0.9|-1.7|
|South Africa|2.5|1.2|2.9|4.2|-0.0|-1.0|0.0|-0.7|-1.7|
|Malawi|2.4|4.1|2.8|6.9|-2.4|-0.6|-0.3|-1.2|-4.5|
|Pakistan|2.4|2.0|3.7|5.7|-1.4|-0.8|-0.3|-0.8|-3.3|
|Benin|2.3|4.8|2.9|7.7|-2.0|-0.2|-0.0|-3.2|-5.4|
|Ethiopia|2.2|3.7|3.6|7.3|-2.9|-0.0|-0.7|-1.4|-5.1|
|Togo|2.1|5.3|1.8|7.1|-0.9|-1.0|-0.9|-2.3|-5.0|
|Guinea-Bissau|2.0|4.7|0.8|5.5|-0.9|-0.3|-0.0|-2.3|-3.5|
|Tajikistan|2.0|3.3|1.4|4.7|-0.2|-0.6|-0.0|-2.0|-2.8|
|Uganda|2.0|5.4|1.1|6.6|-2.0|-1.0|-0.3|-1.4|-4.6|
|Niger|1.9|4.3|2.0|6.3|-2.4|-0.0|-0.0|-1.9|-4.4|
|Nicaragua|1.9|5.0|1.1|6.1|-1.7|-0.9|-0.3|-1.3|-4.1|
|Gambia, The|1.9|6.5|1.5|8.0|-0.7|-1.1|-0.6|-3.7|-6.1|
|Guatemala|1.9|3.6|1.2|4.8|-0.8|-1.0|-0.2|-0.9|-2.9|
|Indonesia|1.9|2.8|0.5|3.2|-0.2|-0.6|-0.0|-0.6|-1.4|
|Papua New Guinea|1.7|4.3|0.4|4.7|-2.3|-0.2|-0.0|-0.5|-3.0|
|Iraq|1.6|1.5|2.0|3.5|-0.3|-0.3|-0.1|-1.2|-1.8|
|Liberia|1.6|3.3|1.3|4.6|-0.8|-0.4|-0.5|-1.3|-3.0|
|Nepal|1.4|2.7|1.6|4.4|-0.5|-0.3|-0.1|-2.0|-3.0|
|Vietnam|1.1|5.1|2.0|7.1|-2.8|-1.0|-0.4|-1.8|-5.9|
|Georgia|1.0|2.1|0.1|2.2|-0.6|-0.0|-0.0|-0.6|-1.2|
|Moldova|0.7|1.4|1.5|2.9|-0.6|-0.1|-0.0|-1.4|-2.1|
|Burkina Faso|0.7|3.8|2.3|6.1|-2.4|-0.6|-0.5|-1.9|-5.4|
|Kyrgyz Republic|0.6|1.8|1.4|3.2|-0.7|-0.2|-0.0|-1.6|-2.6|
|Bangladesh|0.5|4.9|2.3|7.2|-3.8|-1.5|-0.1|-1.3|-6.7|
|Burundi|0.4|6.9|2.2|9.0|-6.2|-0.5|-0.0|-1.8|-8.6|
|Mongolia|0.1|2.7|0.6|3.4|-0.6|-0.2|-0.1|-2.4|-3.3|
|Average|2.4|4.5|1.9|6.4|-1.5|-0.6|-0.2|-1.6|-3.9|
|Pop. weighted av.|2.3|4.1|2.1|6.2|-1.6|-0.9|-0.2|-1.2|-3.9|
|GDPihtd|24|37|18|54|10|09|02|10|31|
|wege av.<br>’|.<br>|.<br>|.<br><br>4|.<br><br>8|-.<br>|-.<br>|-.<br>|-.<br>|-.<br>|

Notes: Authors’ calculations. The gain from trade, expressed in percentage points, is the population weighted average of the proportional change in household real expenditure.
|**Table 1 : Salient Enterprise**|**Characterist**<br>**Large urb**<br>**manufactu**|**ics**<br>**an**<br>**rers**<br> <br> <br>|**Urban**<br>**manufact**<br>**microente**|**uring**<br>**rprises**<br> <br>|**Manufactur**<br>**rural towns**|**ers in**<br> <br> <br>|**Manufactu**<br>**remote rur**|**res in**<br>**al areas**|
|---|---|---|---|---|---|---|---|---|
||**Mean**|**sd**|**Mean**|**sd**|**Mean**|**sd**|**Mean**|**sd**|
|**Output**|||||||||
|<br>LnY|6.85|2.18|<br>3.42|1.04|<br>0.78|1.57|<br>0.01|1.76|
|Ln Value-added|6.19|2.05|<br>2.65|0.98|<br>0.23|1.86|<br>-0.56|1.95|
|**Factors of Production**|||||||||
|LnL (full-time equivalent)|3.38|1.55|<br>1.12|0.59|<br>-0.17|0.48|<br>-0.41|0.61|
|Log of permanent workers|3.55|1.53|<br>1.15|0.61|<br>0.14|0.29|<br>0.16|0.30|
|Share of unpaid labour|na|na|0.11|0.25|<br>0.96|0.18|<br>0.98|0.13|
|<br>LnK|5.82|2.74|<br>2.26|1.68|<br>-1.47|2.24|<br>-2.52|2.65|
|LnM|5.94|2.46|<br>2.33|1.47|<br>-1.57|1.82|<br>-2.06|2.23|
|**Characteristics of the**|||||||||
|Female management|0.27|0.44|<br>0.21|0.41|<br>0.75|44|<br>60|49|
|<br>Years of schooling|11.2|4.13|<br>9.34|3.84|<br>1.67|3.06|<br>82|1.97|
|**Activity**|||||||||
|Food|30%|46%|<br>0%|0%|<br>62%|49%|<br>36%|48%|
|Garments|33%|47%|<br>36%|48%|<br>33%|47%|<br>47%|50%|
|Leather|6%|23%|<br>0%|0%|<br>0%|0%|<br>3%|17%|
|Wood|28%|45%|<br>0%|0%|<br>5%|22%|<br>9%|29%|
|Other manufacturing|5%|21%|<br>64%|48%|<br>0%|0%|<br>6%|23%|
|**Growth**|||||||||
|Firm-age|18.30|15.26|<br>9.55|8.94|<br>10.32|11.06|<br>11.45|11.37|
|Average annual growth|||||||||
|(log)|0.09|0.15|<br>0.05|0.13|<br>0.00|0.04|<br>0.00|0.03|
|Permanent Workforce at|||||||||
|Start-up (log)|2.49|1.75|<br>0.77|0.67|<br>0.11|0.27|<br>0.13|0.28|
|Invested|0.50|0.50|<br>0.27|0.45|<br>0.20|0.40|<br>0.20|0.40|
|**Per-worker variants**|||||||||
|Ln (Value-added/L)|2.87|1.18|<br>1.54|0.72|<br>0.40|1.77|<br>-0.14|1.83|
|<br>Ln(Y/L)|3.47|1.17|<br>2.30|0.90|<br>0.95|1.52|<br>0.43|1.61|
|<br>Ln(K/L)|2.44|1.69|<br>1.14|1.66|<br>-1.30|2.21|<br>-2.10|2.58|
|Ln(M/L)|2.57|1.54|<br>1.21|1.35|<br>-1.39|1.91|<br>-1.68|2.14|
||**Median**|**USD**|**Median**|**USD**|**Median**|**USD**|**Median**|**USD**|
|Value-added per worker|15.23|1673|<br>4.72|518|<br>1.04|114|<br>0.63|69|
|<br>Capital per worker|10.32|1134|<br>4.40|483|<br>0.32|35|<br>0.22|24|
|Inputs per worker|13.53|1486|<br>4.80|527|<br>0.83|91|<br>0.36|40|
|NB Amounts measured in thou|sands of Bir|r unless o|therwise in|dicated.|||||

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remarkable dynamic of growth. India has followed a similar path, but more slowly in terms of opening up the economy and with less success in creating good investment climates.<sup>4</sup> The rest of developing Asia has some pockets of notable success such as Malaysia, Thailand, and Vietnam, but other large population centers such as Indonesia, Bangladesh, Pakistan, and the Philippines have been held back, primarily by poor investment climates and weaker connections to the global market.

China’s sustained growth fueled historically unprecedented poverty reduction. The World Bank uses a poverty line based on household real consumption (including consumption of own-produced crops and other goods), set at $1 per day measured at Purchasing Power Parity. In most low-income countries this amount is sufficient to guarantee each person about 2000 calories of nutrition per day, plus other basic necessities. In contemporary China this line corresponds to about 888 RMB per year. Based on household surveys, we estimate that the poverty rate in China in 1981 was 64% of the population. This rate declined to 10% in 2004, indicating that about 500 million people have climbed out of poverty during this period (Figure 1).

This poverty reduction has occurred in waves. The shift to the household responsibility system propelled a large increase in agricultural output, and poverty was cut in half over the short period from 1981 to 1987. From 1987 to 1993 poverty reduction stagnated, then resumed again. From 1996 to 2001 there was once more relatively little poverty reduction. Since China joined the WTO in 2001, however, poverty reduction resumed at a very rapid rate, and poverty was cut by a third in just three years.

# **_3. Economic reform and increased inequality_**

China’s growth has been so rapid that virtually every household has benefited significantly, fueling the impressive drop in poverty. However, different people have benefited to very different extents, so that inequality has risen during the reform period. This is true for inequality in household income or consumption, as well as for inequality in important social outcomes such as health status or educational attainment. Concerning household consumption, the Gini measure of inequality increased from 0.31 at the beginning of reform to 0.45 in 2004, a level similar to that of the U.S. To some extent 6
an average monthly wage of 7,500 MAD i.e. 45 MAD/hour, assuming 8 hours of work per day and 20.833(=250/12) work days per month<sup>9</sup> . The annual income is _y_ = 90,000 (7,500 x 12) MAD.

The fleet of vehicles in this study are either diesel-fueled (58%) or petrol-fueled (42%). The after-tax consumer price of fuel in the model is taken as the weighted average of the price of diesel and the price of petrol or 9.42 MAD/liter.<sup>10</sup> The supplier fuel price, in the model is _p_ = 6.12 MAD/liter. The excise tax rate on fuel in the base year of 2014 is 0.54 which means that about 35% of the after-tax consumer price of fuel is tax and 65% is the supplier price.

The total road area<sup>11</sup> is 22.7 square kilometers which is used in the denominator of the congestion function (9). The parameter, _A_ , is calibrated to match the base travel times. The average daily number of bus journeys, i.e.<sup></sup> 4<sup>, is 7.8. If we multiply the number of journeys with the average</sup> bus occupancy per journey, i.e. 106 riders, and by the total bus supply, then we get the average daily bus ridership given in the column “Base” of Table 3. In the case of taxi, given the average taxi occupancy of 5.5 riders per journey along with the total number of taxis of 15,000 and the total daily taxi ridership, we calibrate for the total journeys,<sup></sup> 3<sup>, made by a taxi in a day on average,</sup> which comes to 29 daily journeys. For tram, the average capacity of a double tramcar is 454 which includes 118 for seating and 336 for standing. There are 37 tramcars currently operating in Casablanca, and the average number of daily journeys made by a tram i.e. �� �� 10. Given these parameters along with the daily transit ridership information, the average occupancy per tram per trip is 324 passengers calculated from (8b). The standing area of bus, �� is 10.7 square meters. The average seating capacity of a bus is 45. Therefore, 61 out of 106 passengers are standing which gives a high crowding density of 5.75 passengers per meter squared. The bus, as modeled here, is a 12 meter by 2.5 meter vehicle giving a floor area of 30 square meters. Hence the ratio of seating

> 9 From the Survey of Income and Household spending (2006, 2007), the 2007 average monthly household income in Morocco was 6,124 MAD (for cities) and 3,954 (for rural areas). We found no other data, but we think that the imputation of average monthly income from the transportation expenditure share is reasonable.

> 10 The after-tax prices of diesel and petrol are about 9 MAD and 10 MAD per liter respectively.

> 11 The percentage of urbanized land area occupied by roads in developing countries is lower than it is in developed countries. The length of roads by type were available. Assuming widths for the road types, the road area in Grand Casablanca to be around 10% (see Appendix). In Western Europe it would be 15% - 20% and in the U.S. 30% of the urbanized area. <u>https://people.hofstra .edu/geotrans/eng/ch6en/conc6en/ch6c1en.html</u>

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