rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Big HS city<br>Medium HS city<br>Small HS city<br>Big work city<br>Medium work city<br>Small work city<br>−2 −1 0 1 2 3 4 5<br>Standardized Saber 11<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br><!-- End of picture text --> Figure 2: Ability distributions by high school and work location

# _Determinants of college move_

We estimate a probit model of the likelihood to move for college: where _Move collegei_ = 1 if individual _i_ moved for college to a city larger in size than her high school city, 0 otherwise; _Φ_ (.) is the cdf of the standard normal distribution; _Xi_ denotes a vector of student characteristics as of the end of high school; and _Zi_ is a vector of variables associated with the availability of higher education institutions in or near _i_ ’s high school city. Later on, in the estimation of the mnp-cf, _Z_ will serve as our exclusion restriction, as it is reasonable to assume that the availability of higher education institutions may affect the college migration choice but not the work migration choice. In particular, _Z_ contains the number of public and private colleges available within 50 km of the high school city, and their interactions with student ability and household income. We include these interactions because the mere existence of heis within that radius does not necessarily give the student the choice of attending them. In effect, she must be admitted by the hei, which depends on her ability. She must also be able to pay for it, which depends on her household income.

In table 6, columns (1) and (2) report parameter estimates, while columns (1a) and (2a) report the marginal effects corresponding to columns (1) and (2), respectively. In this discussion, and others, we focus on coefficients that are significantly different from zero. Estimates from column (1) are consistent with our descriptive comparison of college movers and non-movers above. According to our estimates, more able individuals, with more educated mothers, and who attended high school in small or medium cities are more likely to move for college. Individuals from households with very high income are the most likely to move. Further, those with fewer public or private 15
# **5.4 Investment Policy Objectives and Strategic Asset Allocation**

The Board is responsible for setting the strategic asset allocation of the fund. It hired an external adviser, Mercer Investment Consulting, in February 2003 to help determine its asset strategy. The report of the adviser was reviewed by the Board, senior management, and a second adviser - Russell Investment Group. The Board also researched the processes and structure of similar funds overseas.

All this research and advice pointed in the same directions. First, international best practice demanded wide diversification of investments across different investment sectors and localities. Second, the best way to maximize long-term returns is to invest a large proportion of the fund in growth assets, such as equities and property. While subject to greater short-term volatility, these assets are expected to substantially outperform over a 20-year period defensive assets, such as cash and fixed-income securities.

The first strategic asset allocation was finalized in July 2003. It emphasized the importance of investing in ‘growth’ assets and risk diversification. The basic allocation was 80 percent ‘growth’ assets and 20 percent ‘defensive’ assets. Allocations to local equities were limited to 7.5% of total assets. However, total assets to be invested in New Zealand, including bonds and property, were targeted at 22 percent. The fund's performance target is set to exceed the risk-free rate of return by 2.5 percentage points (250 basis points) on average over rolling five-year periods.

The asset allocation was reviewed in fiscal 2005 and a decision was taken to expand allocations in alternative assets to 35 percent of total assets (Table 5.2). This implied a move away from listed equities and also required a major effort to create an appropriate infrastructure. Progress was made in establishing specific strategies and appointing specialist advisers.

|Table 5.2: StrategicAssetAllocation|
|---|

|percent|2003 Report|2005 Review|
|---|---|---|
|NZ Equities|7.5|7.5|
|Large/Mid Cap|44.5|34.5|
|Small Cap|12|6.0|
|Emerging Markets|3|2.0|
|**Total Listed Equities**|**67**|50|
|Property|6|10|
|Other|7|25|
|**Total Alternative Assets**|**13**|**35**|
|**Total Growth Assets**|**80**|**85**|
|NZ Fixed Interest|10||
|Global Fixed Interest|10||
|**Total Defensive Assets**|**20**|**15**|

Source: NZSF In addition to expanding the allocation to alternative assets, the fund adopted multistrategy mandates and started paying performance fees to active managers. During 2006 57
<!-- Start of picture text --> Vijva = Orr FemaleCommyg . Fj + OpyFemaleCommyg . Mj +<br>OurMaleCommyg . Fj + OymMaleCommyg .Mj + AF) +0 Xivg + Dt + Da + €iva.j (2)<br>FemaleComm,q and MaleCommy,q<br>MOF;<br>Xiva AF;<br><!-- End of picture text -->
**Table F4: Relationship between food security measures and height-for-age z-scores (Nepal): Limited Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Heig|ht-for-age z-s|cores|||
|Per capita kcal|0.101***<br>(0.029)|||||||
|1-SSR||0.199***<br>(0.033)||||||
|1 - SSEXR|||0.202***<br>(0.033)|||||
|FCS||||0.256***<br>(0.032)||||
|DDS|||||0.230***<br>(0.032)|||
|PC consumption||||||0.279***<br>(0.043)||
|Food share|||||||-0.267***<br>(0.035)|
|Rural|-0.596***|-0.427***|-0.444***|-0.407***|-0.432***|-0.324***|-0.298***|
||(0.069)|(0.073)|(0.072)|(0.072)|(0.072)|(0.078)|(0.078)|
|Eastern|0.066|0.130|0.130|0.153*|0.118|0.140|0.176**|
||(0.090)|(0.090)|(0.090)|(0.089)|(0.090)|(0.090)|(0.089)|
|Central|0.103|0.129|0.125|0.162*|0.139|0.077|0.089|
||(0.085)|(0.085)|(0.084)|(0.084)|(0.085)|(0.085)|(0.085)|
|Midwestern|-0.150|-0.055|-0.077|-0.020|-0.050|-0.094|-0.094|
||(0.097)|(0.099)|(0.097)|(0.097)|(0.097)|(0.096)|(0.095)|
|Far Western|-0.052|0.037|0.045|0.000|0.017|0.049|-0.010|
||(0.114)|(0.115)|(0.116)|(0.113)|(0.113)|(0.113)|(0.114)|
|Constant|-1.258***|-1.434***|-1.416***|-1.468***|-1.431***|-1.491***|-1.517***|

57
Health: Lessons from New Jersey” _Journal of Health Economics_ 28 (3): 688–703.

Currie, J., and R. Walker. 2011. “Traffic Congestion and Infant Health: Evidence from E-Z Pass.” _American Economic Journal: Applied Economics_ 3 (1): 65–90.

Davis, L. W. 2008. “The Effect of Driving Restrictions on Air Quality in Mexico City.” _Journal of Political Economy_ 116 (1): 38–81.

DMRC. 2008. “A Journey to Remember.” Public Relations Department, Delhi Metro Rail Corporation Limited, New Delhi.

———. 2012. “Annual Report 2011–12.” Delhi Metro Rail Corporation Limited, New Delhi.

Doll, N.H.C., and O. Balaban. 2013. “A Methodology for Evaluating Environmental Co-Benefits in the Transport Sector: Application to the Delhi Metro” _Journal of Cleaner Production_ 58 (1): 61–73.

Duranton, G., and M. A. Turner. 2011. “The Fundamental Law of Road Congestion: Evidence from US Cities.” _American Economic Review_ 101 (6): 2616–52.

Ghosh, A., and A. Mukherji. 2014. “Air Pollution and Respiratory Ailments among Children in Urban India: Exploring Causality.” _Economic Development and Cultural Change_ 63 (1): 191–222.

Goel, R., S. K. Guttikunda, D. Mohan, and G. Tiwari. 2015. “Benchmarking Vehicle and Passenger Travel Characteristics in Delhi for On-road Emissions Analysis.” _Travel Behaviour and Society_ 2: 88–101.

Govt. of NCT of Delhi. 2008a. “Economic Survey of Delhi 2008–2009.” Planning Department, Government of NCT of Delhi.

———. 2008b. “Statistical Abstract 2008.” Directorate of Economics and Statistics, Government of NCT of Delhi.

———. 2012. “Economic Survey of Delhi 2012–2013.” Planning 33
|**Policy R**<br>**Title**|**esearch Working Pape**<br>**Author**|**r Series**<br>**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2468 Pricing, Subsidies, and the Poor:<br>Demand for Improved Water Services<br>in Central America|Ian Walker<br>Fidel Ordohez<br>Pedro Serrano<br>Jonathan Halpern|November 2000|S. Delgado<br>37840|
|WPS2469 Risk Shifting and Long-Term<br>Contracts: Evidence from the<br>Ras Gas Project|Mansoor Dailami<br>Robert Hauswald|November 2000|W. Nedrow<br>31585|
|WPS2470 Are Larger Countries Really More<br>Corrupt?|Stephen Knack<br>Omar Azfar|November 2000|P. Sintim-Aboagye<br>38526|
|WPS2471 Validating Operational Food<br>Insecurity Indicators against a<br>Dynamic Benchmark: Evidence<br>from Mali|Luc J. Christiaensen<br>Richard N. Boisvert<br>John Hoddinott|November 2000|L. Christiaensen<br>81463|
|WPS2472 Uzbekistan and Kazakkhstan:<br>ATale of Two Transition Paths|Asad Alam<br>Arup Banerji|November 2000|L. Henson<br>84026|
|WPS2473 Banking Risks around the World:<br>The Implicit Safety Net Subsidy<br>Approach|Luc Laeven|November 2000|R. Vo<br>33722|
|WPS2474 Exports and Information Spillovers|Alessandro Nicita<br>Marcelo Olarreaga|November 2000|L. Tabada<br>36896|
|WPS2475 Industrial Growth and the Quality of<br>Institutions: What Do (Transition)<br>Economies Have to Gain from the<br>Rule of Law?|David A. Grigorian<br>Albert Martinez|November 2000|A. Nelson<br>37117|
|WPS2476 Measuring Banking Efficiency in the<br>Pre- and Post-Liberalization<br>Environment: Evidence from the<br>Turkish Banking System|Cevdet Denizer<br>Mustafa Ding<br>Murat Tarimcilar|November 2000|M. Ding<br>36233|
|WPS2477 Picking the Poor: Indicators for<br>Geographic Targeting in Peru|Norbert R. Schady|November 2000|T. Gomez<br>32127|
|WPS2478 Institutions, Politics, and Contracts:<br>The Attempt to Privatize the Water<br>and Sanitation Utility of Lima, Peru|Lorena Alcazar<br>Lixin Colin Xu<br>Ana Maria Zuluaga|November 2000|P. Sintim-Aboagye<br>37644|
|WPS2479 Estimating the Elfects of Corruption<br>Implications for Bangladesh|: Aminur Rahman<br>Gregory Kisunko<br>Kapil Kapoor|November 2000|S. Powell<br>33526|
and planned centrally, but for which local officials are active in the front line operation, credit allocation could be difficult for voters if partisan control was divided between central and local government. This is rarely the case in Tanzania, which is strongly dominated by the ruling party at all levels of government.

Finally we document a significant fading of the effect over time. This is an intrinsically important pattern, since such dynamic effects, if clearly understood, must shape the calculations of politicians about the timing or frequency of such programs. But it may also help explain variation in the effects identified in this literature. This suggests that handouts to voters, whether clientelistic or as part of organized programmatic policies, are not an all purpose strategy for politicians. Delivered far in advance of elections, their effect could wear off completely and result in no particular benefit at election time. For example, an explanation for the null response to _Progresa_ found by Imai, King and Velasco Rivera (2016) could be that effects are large in the short run but weaker in the long run. If this were true, comparison of a group that recently got the program to one that got it longer ago could produce a null result, not because the program did not affect voters, but rather because the group that got it longer ago (notwithstanding that they have received a higher “dose”) has already had some fadeout, while the newly treated group is in the middle of their bounce in approval rating for politicians.<sup>20</sup> Similarly, the apparent unresponsiveness of South African voters to infrastructure and sanitation improvements (De Kadt and Lieberman, 2015) could be because of the time dimension, since that analysis examines changes in infrastructure and service delivery over a 10 year period.<sup>21</sup> To conclude, we note that it is commonly understood that governments do not deliver services effectively in developing countries because there are powerful political forces pushing politicians

> 20This does not apply for the null result that they find for the _Seguro Popular_ health program, which compared a treatment group to a completely untreated control group.

21However we note that Pop-Eleches and Pop-Eleches (2012) find significant results two years after the program, and Manacorda, Miguel and Vigorito (2011) find relatively small decrease in effect size between surveys conducted during the PANES cash transfer program and those conducted several months after the program was largely completed.

24
# **Table 2: Pooled OLS and Panel Random-Effects (RE) Regressions for the Gini and the P90/P50 Ratio**

|**_Idd ibl_**|||**Gini**<br>|||**P90/P50**<br>||
|---|---|---|---|---|---|---|---|
|**_nepenent varae_**|_Full model,_<br>_OLS_|_Full model,_<br>_RE_|_No high income,_<br>_RE_|_No high income and_<br>_post-2000, RE_|_Full model,_<br>_RE_|_No high income,_<br>_RE_|_No high income and_<br>_post-2000, RE_|
|_Low income with consumption(SSA)_|-0.187***|-0.156***|-0.201***|-0.314***|-0.631***|-0.818***|-1.206***|
|_Low-middle income with_<br>_consumption (SSA)_|-0.155***|-0.145***|-0.190***|-0.295***|-0.618***|-0.801***|-1.182***|
|_Upper-middle income with_<br>_consumption (SSA)_|-0.219***|-0.151***|-0.225***|-0.317***|-0.562***|-0.877***|-1.206***|
|_Low income with consumption(OTH)_|-0.045***|-0.049***|-0.047***|-0.119***|-0.137***|-0.134***|-0.345***|
|_Low-middle income with_<br>_consumption (OTH)_|-0.059***|-0.063***|-0.058***|-0.124***|-0.151***|-0.143***|-0.342***|
|_Upper-middle income with_<br>_consumption (OTH)_|-0.064***|-0.077***|-0.072***|-0.135***|-0.167***|-0.157***|-0.354***|
|_High income with consumption_<br>_(OTH)_|-0.049***|-0.065***|—|—|-0.171***|—|—|
|_Low income with income_|Baseline|Baseline|Baseline|Baseline|Baseline|Baseline|Baseline|
|_Low-middle income with income_|-0.009|-0.020***|-0.020**|-0.064***|-0.060***|-0.066***|-0.221***|
|_Upper-middle income with income_|-0.043***|-0.041***|-0.040***|-0.099***|-0.135***|-0.140***|-0.341***|
|_High income with income_|-0.099***|-0.073***|—|—|-0.197***|—|—|
|_Population inyear_|0.122***|-0.084|-0.103*|0.052||-0.244|0.242|
|_Age dependency ratio, workforce_<br>_over total population_|0.075***|0.117***|0.141***|0.155***|0.339***|0.363***|0.468***|
|_GDPper capitagrowth_|0.019|0.028|0.041|0.077***|0.060|0.099|0.176**|
|_Fuel exports, percentage over total_<br>_merchandise_|0.871|-0.117|0.493|2.111|-2.609|-1.221|4.358|
|_Remittances,percentage over GDP_|-0.113***|-0.098***|-0.095***|-0.034|-0.204**|-0.216**|-0.106|
**Table 1.A. Literature review of forecasting methods for crude oil prices (continued)**

|**Authors**<br>**and year**|**Crude oil**<br>**prices**|**Methods**|**Main findings**|**Real or**<br>**nominal**<br>**prices**|**In-sample or**<br>**out-of-sample**<br>**forecasts**|
|---|---|---|---|---|---|
|Cheng et al.<br>(2019)|Brent;<br>WTI|VEC-NAR,<br>VAR, VECM,<br>and GARCH<br>models|The hybrid VEC-NAR<br>model outperforms other<br>models for longer forecast<br>horizons.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Chernenko,<br>Schwarz and<br>Wright<br>(2004)|WTI|Futures, no-<br>change|Forward or futures rates are<br>not rational expectations of<br>actual future prices.|Nominal<br>prices|Both types of<br>forecasts|
|Chinn and<br>Coibion<br>(2014)|WTI|Futures,<br>GARCH, linear<br>regressions, no-<br>change|Futures prices outperform<br>forecasts from reduced-form<br>empirical models and no-<br>change.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Chu et al.<br>(2022)|Brent|Futures, no-<br>change|No-change forecast<br>performs better than futures<br>prices in the short term.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Coimbra and<br>Esteves<br>(2004)|Crude oil<br>price|Futures,<br>random walk<br>(carry over<br>assumption)|No difference between<br>opting for futures prices or<br>using the carry-over<br>assumption for short-term<br>forecast horizons.|Nominal<br>prices|In-sample<br>forecasts|
|Coppola<br>(2008)|WTI|VEC model,<br>no-change<br>Multifactor<br>|Forecasts from VEC models<br>outperform no-change.|Nominal<br>prices|Both types of<br>forecasts|
|Cortazar,<br>Ortega and<br>Valencia<br>(2021)|WTI|stochastic<br>pricing model;<br>no-change;<br>Bloomberg’s<br>consensus<br>expected price<br>model|Multifactor stochastic<br>pricing models perform<br>better than no-change and<br>Bloomberg’s consensus<br>expected price models.|Both prices|Both types of<br>forecasts|
|||ARIMA,||||
|Drachal<br>(2016)|WTI|futures prices,<br>model<br>averaging<br>methods, no-<br>change|Forecasts based on futures<br>contracts produce larger<br>errors than no-change.|Nominal<br>prices|Out-of-sample<br>forecasts|

23
|P107050|2010|2011|4/13/2011|2 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P107050|2010|2012|11/27/2011|3 Closed|Yemen,Republic of|
|P107050|2010|2012|6/24/2012|4 Closed|Yemen,Republic of|
|P107050|2010|2013|1/15/2013|5 Closed|Yemen,Republic of|
|P107050|2010|2014|9/5/2013|6 Closed|Yemen,Republic of|
|P107050|2010|2014|4/16/2014|7 Closed|Yemen,Republic of|
|P107050|2010|2014|5/20/2014|8 Closed|Yemen,Republic of|
|P107050|2010|2015|12/24/2014|9 Closed|Yemen,Republic of|
|P107050|2010|2015|3/31/2015|10 Closed|Yemen,Republic of|
|P107062|2009|2009||0 Closed|Vietnam|
|P107062|2009|2010||0 Closed|Vietnam|
|P107069|2010|2012|6/26/2012|1 Closed|India|
|P107069|2010|2013|6/22/2013|2 Closed|India|
|P107069|2010|2015|3/10/2015|3 Closed|India|
|P107100|2008|2008||0 Closed|Sri Lanka|
|P107100|2008|2009||0 Closed|Sri Lanka|
|P107100|2008|2010||0 Closed|Sri Lanka|
|P107100|2008|2011||0 Closed|Sri Lanka|
|P107100|2008|2020|6/30/2020|1 Closed|Sri Lanka|
|P107101|2009|2009|5/25/2009|1 Closed|Afghanistan|
|P107101|2009|2010|11/26/2009|2 Closed|Afghanistan|
|P107101|2009|2010|5/31/2010|3 Closed|Afghanistan|
|P107101|2009|2011|7/20/2010|4 Closed|Afghanistan|
|P107101|2009|2011|11/1/2010|5 Closed|Afghanistan|
|P107101|2009|2011|1/4/2011|6 Closed|Afghanistan|
|P107101|2009|2012|8/29/2011|7 Closed|Afghanistan|
|P107101|2009|2012|1/3/2012|8 Closed|Afghanistan|
|P107137|2012|2012|10/5/2011|1 Active|Bolivia|
|P107137|2012|2012|4/22/2012|2 Active|Bolivia|
|P107137|2012|2013|11/2/2012|3 Active|Bolivia|
|P107137|2012|2013|6/10/2013|4 Active|Bolivia|
|P107137|2012|2014|12/13/2013|5 Active|Bolivia|
|P107137|2012|2015|7/3/2014|6 Active|Bolivia|
|P107137|2012|2015|12/3/2014|7 Active|Bolivia|
|P107137|2012|2015|5/26/2015|8 Active|Bolivia|
|P107137|2012|2016|8/26/2015|9 Active|Bolivia|
|P107137|2012|2016|3/9/2016|10 Active|Bolivia|
|P107137|2012|2016|4/21/2016|11 Active|Bolivia|
|P107137|2012|2017|11/4/2016|12 Active|Bolivia|
|P107137|2012|2017|1/23/2017|13 Active|Bolivia|
|P107137|2012|2017|6/5/2017|14 Active|Bolivia|
|P107137|2012|2018|12/27/2017|15 Active|Bolivia|
|P107137|2012|2018|6/24/2018|16 Active|Bolivia|
|P107137|2012|2019|12/18/2018|17 Active|Bolivia|
|P107137|2012|2019|4/10/2019|18 Active|Bolivia|
|P107137|2012|2020|10/20/2019|19 Active|Bolivia|
|P107137|2012|2020|3/10/2020|20 Active|Bolivia|
Asia region (ECA). There are three rounds of surveys which are conducted in 2002, 2005, and 2008. The survey conducted in 2002 includes 6343 and the one in 2005 includes 9265 firms. A panel dataset is constructed from a part of the cross-sectional data. Data from 2008 survey is only used in the analysis with this panel dataset. There are 1552 observations in the panel. 1025 of these firms are surveyed in 2002 and 2005 and 527 of them are surveyed in 2005 and 2008<sup>11</sup> . The size of the panel data for manufacturing sector is 943. The countries that are included in the analysis are Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Croatia, FYR Macedonia, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Russian Federation, Tajikistan, Turkey, Ukraine, and Uzbekistan. In addition to these countries there are 10 European Union (EU) members: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

The randomly selected sample is stratified by 2-digit industry, size, and region. The survey covers the major industries in each sector across countries. The list of the sectors, the industries they cover, and the fraction of exporters in each sector are given in Table 1.

Table 2 shows the same statistics for industries covered in manufacturing sector. The industries included in each sector are selected based on their contribution to GDP and they represent the majority of output in each country. All of the sectors described are covered in each country. The classification of the industries is made according to ISIC classification revision 3.1. Manufacturing and wholesales-retail sectors are the largest sectors in terms of firms included.

Definition of exporting varies across sectors. A guideline of export in services is described in the General Agreement on Trade in Services (GATS). World Trade Organization and World Bank provide detailed description of different possible methods of trade in services. In the Enterprise Surveys, the exact wording of the question for exporting is “ _Does your firm currently sell its products or services directly to customers outside the country?_ ” Hence it covers both exporting of products and services. In the data, two of the most export intensive sectors are transport and communication and manufacturing. Both sectors have more than 30% of firms that export products or services. A more detailed table for the fraction of exporters in each sector covered in each country for 2005 is given in Table 3. Data for 2002 is quite similar. Net job

> 11The survey conducted in 2008 is not included in the cross-sectional analysis because the question on net job creation is only available in 2002 and 2005 surveys. However, I use the information on export status for the panel firms in 2008.

9