rafmacalaba/datause-encoder-data huggingface dataset:
���+ ��,� > 0 where the vector _zj_ includes individual-level characteristics such as gender, income, educational attainment and age.

# **_<u>4.1 The First Stage: Measuring Gaps in Need by Gender and Income Level</u>_**

Table 3 presents the Stage 1 results of the Heckman Two Stage Regression. Stage 1 measures if “need” for government provided goods vary across gender or income level.

**Table 3: Stage 1 of Heckman Regression (Selection Equation)**

|**Stage 1: Estimati**|**ng Differences i**<br>**Driving License**|**n Need for Go**<br>**Register Land**|**vernment Pr**<br>**Utilities**|**ovided Goods**<br>**Public School**|**and Services**<br>**Healthcare**|
|---|---|---|---|---|---|
|**Women**|-0.081***|-0.112***|-0.170***|0.036|0.057**|
||(0.030)|(-0.026)|(-0.026)|(-0.026)|(-0.027)|
|**Illiterate**|0.007|-0.137***|-0.108**|-0.125***|-0.163***|
||(0.053)|(-0.046)|(-0.047)|(-0.047)|(-0.046)|
|**Primary**|0.028*|-0.107***|-0.058|-0.082**|-0.223***|
||(0.045)|(-0.040)|(-0.041)|(-0.040)|(-0.040)|
|**Poor**|0.051*|0.116***|0.075***|0.216***|0.125***|
||(0.031)|(-0.028)|(-0.028)|(-0.029)|(-0.028)|
|**Age**|0.013|6.12E-05|0.012***|0.03***|0.003|
||(0.005)|(-0.004)|(-0.004)|(-0.004)|(-0.004)|
|**Age2**|-0.0001*|0.0001**|-7.79e-05*|-0.0003***|-1.97E-05|
||(6.27e-05)|(-4.92E-05)|(-4.71E-05)|(-4.58E-05)|(-4.48E-05)|
|**Rural**|0.067**|0.088***|-0.084***|-0.058**|0.059**|
||(0.033)|(-0.029)|(-0.030)|(-0.030)|(-0.028)|
|**Development**|0.073**|0.035***|0.005|0.073***|0.022***|
|**Spending / State GDP**|(0.005)|(-0.004)|(-0.004)|(-0.004)|(-0.004)|
|**State Per-capita GSDP**|-2.83e-05***|3.07e-05***|7.64e-05***|7.48e-05***|1.20e-05*|
||(7.78e-06)|(-6.52E-06)|(-6.81E-06)|(-6.80E-06)|(-6.31E-06)|
|**Share of Agriculture**<br>**i Stt GSDP**|-0.022***|-0.025***|0.012***|-0.018***|-0.042***|
|**n ae**|(-0.003)|(-0.003)|(-0.003)|(-0.003)|(-0.003)|
|**Lambda**|-0.445***|0.144**|-2.343***|0.483***|-0.236***|
||(-0.054)|(-0.071)|(-0.863)|(-0.055)|(-0.064)|
|**Total**|13000|13000|13000|13000|13000|
|**Censored**|2467|2697|2543|2511|3020|

_Note_ : This table shows the results for the first stage of the Heckman Two Stage regression. Sage 1consists of the selection equation and shows the probability that an individual will express a “need” for a good or service given his/her individual characteristics and state-level characteristics. Base categories are male, above primary school, richest 60 percent, urban. The selection equation uses three state-level variables: development spending by state GDP, state GDP per capita, and share of agriculture in state GDP.

22
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**3**

massive restructuring of industry was needed as part of restructuring the economy. This included down-sizing the industrial sector to free resources for the service sector, down-sizing or liquidating inefficient enterprises, finding new markets, and introducing new products and more efficient production methods, etc.

No single paper can hope to capture the breadth of experience in the past four years in CEE and the FSU with bank and enterprise restructuring. At the country level the approaches, implementation capacity, and the degree of progress have differed. Rather this paper outlines some of the common policy issues and problems arising in these countries, focussing specifically on the role of banks in the financial restructuring of enterprises. In the discussion on transformation the tern "restructuring" has been used to refer to physical changes in production processes, organizational changes including corporatization and privatization, and to financial changes in corporate balance sheets. In order to reduce the confusion somewhat, we shall distinguish among the different aspects of restructuring. Of course, the various aspects are related and financial restructuring can force physical changes.

# **The Macroeconomic hnIetus for Resbuctine of Enterses**

The first stage of the adjustment process in CEE/FSU has been driven by traumatic economic collapse. The disintegration of regional and CMEA trade relations, sharply higher energy prices, high inflation, and severe economic dislocation reduced demand forcing the downsizing of the industrial sectors. Between 1989 and 1993 GDP in real terns fell by 30% in CEE and 40% in FSU. Industrial production in most countries declined by 40% and industrial employment by at least one third (see Annex Tables I and 2 and Figure 1).

Most of the decline in industrial employment is permanent. The socialist countries were overindustrialized with very small service sectors. Services accounted for only 30-40% of GDP as against over 60% in typical market economies (see Annex Table 3)._/ A decline in industrial employment was certain as the economies shifted resources to the service sectors. That it came so rapidly has led to high unemployment. As the transforming economies recover from the present depression, most of those laid off will be reemployed, not in industry, but in the service sectors.31 2/ For example, in 1988 Polish industry accounted for 61% of GDP, whereas in OECD the average at the time was 33 % (see _ThIe Economist. Book of Vital World Statistics._ New York, 1990). Moreover, the industrial sector was excessively concentrated and reliant on very large firms. In 1990 nearly half of the Polish workforce was employed in enterprises with between 500 and 2500 workers and most of fte remainder worked in enteprises employing over 2500.

3/ In Poland and Hungary over 50% of the workforce is now in the private sector and newly established private firms tend to be concentrated in the trade and service sectors.
WPS3425

# **Can Student Loans Improve Accessibility to Higher Education and Student Performance?**

**An Impact Study of the Case of SOFES, Mexico**

Erik Canton<sup>a,b</sup> and Andreas Blom<sup>b</sup> a CPB Netherlands Bureau for Economic Policy Analysis b World Bank JEL: I2; J24

## **Abstract**

_Financial aid to students in tertiary education can contribute to human capital accumulation through two channels: increased enrollment and improved student performance. We analyze the quantitative importance of both channels in the context of a student loan program (SOFES) implemented at private universities in Mexico. With regard to the first channel, enrollment, results from the Mexican household survey indicate that financial support has a strong positive effect on university enrollment. Given completion of upper secondary education, the probability of entering higher education rises 24 percent. Two data sources are used to investigate the second channel, student performance. Administrative data provided by SOFES are analyzed using a regression-discontinuity design, and survey data enable us to perform a similar analysis using a different control group. Empirical results suggest that SOFES recipients show better academic performance than students without a credit from SOFES. However, the results cannot be interpreted as a purely causal impact of the student loan program, since the impacts also could reflect (self-) selection of students._

## World Bank Policy Research Working Paper 3425, October 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
follows closely the oil price. A second period is characterized by a steep surge in oil prices between 2003 and 2008 when price volatility increases and the price of gasoline follows less closely the oil price. The third period starting in 2009 follows the global financial crisis and is characterized by high and volatile oil prices where the U.S. and European prices and the price of gasoline increasingly diverge. The fourth period shows the most recent decline in oil and gasoline prices and also continued price volatility. The period considered by this paper (20102014) is unique in terms of both the level and volatility of oil prices.

Figure 1: U.S. and European Oil Prices, May 1987–April 2016 <!-- Start of picture text --> 160 3.5<br>140 3<br>120<br>2.5<br>100<br>2<br>80<br>1.5<br>60<br>1<br>40<br>20 0.5<br>0 0<br>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)<br>Europe Brent Spot Price FOB (Dollars per Barrel)<br>New York Harbor Conventional Gasoline Regular Spot Price FOB (Dollars per Gallon)<br>May‐1987 May‐1988 May‐1989 May‐1990 May‐1991 May‐1992 May‐1993 May‐1994 May‐1995 May‐1996 May‐1997 May‐1998 May‐1999 May‐2000 May‐2001 May‐2002 May‐2003 May‐2004 May‐2005 May‐2006 May‐2007 May‐2008 May‐2009 May‐2010 May‐2011 May‐2012 May‐2013 May‐2014 May‐2015<br><!-- End of picture text --> _Source:_ Elaborated from data available at EIA 2015.

The picture is similar if we consider the commodities indexes for fuel and food (figure 2). The fuel energy index, which combines fuel products, shows that its trend overlaps with the oil trend up until the 2008 global financial crisis when we start to see more volatility and a certain divergence between the two trends. The food price index is naturally less associated with changes in the oil price but nevertheless is correlated with it. In particular, during the period that we consider more closely (2010–14) both the food and energy indexes show high levels and high volatility. In their reform efforts, governments faced increasing fiscal pressure and increasing 8
where the parameters _ξ_ 0<sup>_w_,</sup><sup>_ξ_</sup> 1<sup>_w,_and</sup><sup>_ξ_</sup> 2<sup>_w_correspondtolong-runforesthar-</sup> vesting costs and short-run adjustment costs of harvesting and harvested land conversion to agricultural land. We calibrate short-run adjustment costs of harvesting and conversion of harvested forest land to agricultural land to match recent dynamics in commercial land-use.

# C.1.10 Timber Processing Sector

The growth of TFP in the timber processing sector is described by the following equation: where the parameters _θ_ 0<sup>_w,y_</sup> and _κw,y_ reect the initial level and annual growth rate in the technology of the timber processing sector. We calculate the technology index of the timber processing sector in 2004 using GTAP v.7 data, by dividing the output of timber products (GTAP sectors 30-31) by the output of commercial forestry sector (GTAP sector 13). We set the growth rate of the technology in the timber processing sector equal to growth rate of the economy's TFP. We obtain the timber processing costs from GTAP v.7 database.

# C.1.11 Ecosystem Services Sector

The parameters for production of ecosystem services in production function B.38 are based on the estimates of Costanza et al. (1997), who estimated values for 17 ecosystem services from 16 ecosystem types at global scale.<sup>5</sup> We exclude the services from the production of food and timber, as well as from based climate abatement, as those are determined endogenously in the model. We also exclude the production of ecosystem services from ecosystems not represented in the model (e.g., marine, grasslands and deserts). We use agroecological zone (AEZ) representation of GTAP land use database to dierentiate between tropical and temperate/boreal forest land. Based on ecological literature (Ehrlich and Mooney 1983) we assume that there is a limited substitution between different land types in production of ecosystem services. Because eectiveness of protected land areas is very dicult to quantify (Chape et al. 2005), we set the parameter _θ_<sup>_R_</sup> large enough to make sure new protected areas are established.

> 5We are familiar with multiple criticisms of this approach National Research Council (2005, p. 188-189). However, there have been very few attempts to evaluate production of ecosystem services at global scale, and the work of Costanza et al. (1997) still remains most inuential.

21
_The Madab Projcc_ _9_ Table 1. Contraceptive prevalence rates (yearly means) in the Treatment and Comparison Areas in Bangladesh: 197S-1981

||_Matlab Treat-_<br>_ment Areab_|_Matlab Com-_<br>_parison Area'_|_Bangl_|_adeshd_|
|---|---|---|---|---|
|_Year_|_Modern_<br>_Methods_|_Modern_<br>_Methods_|_Modern_<br>_Methods'_|_All_<br>_Methods_|
|1975|3.0|3.0|4.5|7.7|
|1976|_9.5_|9.5|-|-|
|1977|9.0||||
|1978|25.0|--||-|
|1979|32.4|-|8.9|12.7|
|1980|32.4|--||-|
|1981|32.9|-|10.9|18.6|
|1982|34.7|-|||
|1983|38.7|-|13.8|19.1|
|1984|43.2|11.0|-|-|
|1985|45.6|-|22.9|29.8|
|1989|-|-|25.8|32.8|
|1990|57.0|-|||

- a. Figur **rpent** original or reconsidued **data** for Matab; - indicats non-availability of **data.**

- b. Estimated on **th** basis of ervic, **_utatw_** as wai a data **obtaind from smuveys** conducte in

- 1978 **and 1984** in Pjmect Ara; **figur** for 1990 **based** on personal conmunication withf **Michael** A. Koenig.

- a. Eftimaed on the basis of governmnt satstics available at the local upazila level and at the nainal MIS office as weU as on the data obtained from 1978 and 1984 surveys in the Projec Area.

- d. PigR repent the findings of the 1975 Bangladesh Pertility Survey (BPS) associated with World Petlity Survey (WFS) and the Contraceptive Prevalnco Surveys (CPS) of 1979, 1981, 1983, 1985, and 1989. They include rural and urban areas and the areas saved by _NGO_ progrUaS.

- a. A methods include modern methods (injoctable, pil, condom, IUD, tubectomy, vasetomy, and foam) and traditional methods (safe period, withdrawal, abstinence and others).

Sources: Balk et al. (1988:41,52); Mitma(1987:186); Mitra **t** al. (1990:98).

The Matlab Project had a pronounced effect on contraceptive prevalence in the Treatment Area immediately after its initiation in October 1977. Table I shows that the pre-PrGject prevalence rates of modern contraceptives in the Comparison Area was similar to that of the Treatment Area and the difference became increasingly sharper as the Project progressed. The yearly mean contraceptive prevalence rate for modern methods rose from 9.0 percent in 1977 to 25.0 percent in 1978. After rising up to 32.4 in 1979, its rate of increase slowed down during 1980-81. Since 1982 again it started rising rapidly until it reached 45.6 percent in 1985. No published figures are available for subsequent years but it is reported that the rate reached a level
# **2 Literature Review**

The strongest predictor of uneven sex ratio for a given parity is the sex composition of previous children (Retherford and Roy, 2003; Jha et al., 2006; Abrevaya, 2009). For families without a son, the higher the parity is, the higher is the probability of having a son as the next birth. The propensity to use sex selection increases with socioeconomic status, especially education, and the proportion of males to females is larger in cities than in rural areas (Retherford and Roy, 2003; Jha et al., 2006). There is substantial disagreement on whether sex-selective abortion is used for the first birth. Using the Special Fertility and Mortality Survey Jha et al. (2006) found that in both urban and rural areas there were 54.4 percent boys born among first-borns, while Retherford and Roy (2003) using the first two rounds of the National Family and Health Survey find little or no evidence of sex selection on the first birth. Although the Special Fertility and Mortality Survey covers a much large number of households than the three rounds of the NFHS combined, Jha et al. (2006) only use births that took place in 1997 making their sample sizes by parity smaller than what is used here. In addition, there are serious questions about the quality of the data, especially the possibility of systematic recall error for girls (George, 2006; Bhat, 2006).

Despite a large demographic literature on the relation between son preference and fertility stopping behavior (see, for example, Clark, 2000), there is little formal analysis of the link between fertility and sex selection (Park and Cho, 1995). For India, it has been argued that falling fertility increases the bias against girls (Das Gupta and Bhat, 1997), but the stated preference for sons also appears to decline with lower desired fertility (Bhat and Zavier, 2003). In the Republic of Korea simulations suggested that introduction of sex selection changed family size little, but did result in abortions of female fetuses equal to about five percent of actual female births (Park and Cho, 1995). For China allowing a three-child policy has been predicted to increase the fertility rate by 35 percent, but also reduce the number of girls aborted by 56 percent (Ebenstein, 2011).<sup>6</sup> Most of the sex selection in China is due to parents with low levels of education (Ebenstein, 2011).

> 6 This study used data on women who have completed their fertility. No allowance is made for the role of spacing and because only women who have completed their fertility are used, the method cannot be applied to those still in their childbearing years and therefore have censored spells.

4
<!-- Start of picture text --> 75% 72.5%<br>57.5% 57.4%<br>50%<br>41.1%<br>30.5%<br>28.2%5 26.6%<br>25%<br>15.9%<br>5.8%<br>0%<br>Small Medium Large<br>O Access to Internet @ Ownwebsite © Social Media<br><!-- End of picture text -->
For females, meanwhile, figure 7 there is a sizeable cohort effect between the young and the middle cohorts. At the age of about 25 , vocational graduates in the young cohort enjoys lower unemployment rate compared to general graduates, while vocational graduates in the middle cohort face the same unemployment rate as general graduates. At around thirty, however, the unemployment rate of vocational graduates in the young cohort is higher than general graduates. Looking at the age profile, it appears that general and vocational graduates over thirty years old have similar unemployment rates.

<!-- Start of picture text --> Figure 7. Effect of Public Vocational on Unemployment among Women<br>Old cohort Middle cohort Young cohort<br>0.06<br>0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>20 25 30 35 40 45 50 55<br>Average age in year<br>unemployment<br>Estimated effect on probability of<br><!-- End of picture text --> Notes: See notes to figure 4 The next two figures examine the effect of public vocational education on the probability of holding a formal job, conditional on being employed. A job is classified as formal if the worker is a salaried employee, is self-employed with permanent workers, or is self-employed with temporary workers outside of agriculture.<sup>19</sup> Formal employees tend to earn higher wages and express greater job satisfaction than informal employees, particularly casual informal workers (World Bank, forthcoming). Figure 8 shows that public vocational increases formality early in one’s career, but that the positive effect declines sharply with age. In addition, each successding cohort

> 19 This definition, which is based on employment status and sector, is 99 percent correlated with the official definition adopted by the Statistics Indonesia, which is based on employment status and occupation.

22
**Figure 21: Top 20 six‐digit products with highest HHI (most concentrated) of overall GVC Exports, 2014**

<!-- Start of picture text -->
1.2 1.2<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>851921 540320 521022 611410 570251 610321 551342 650692 910112 610311 520853 851931 630691 620910 630611 520642 600241 852010 900930 551322<br>Six‐digit products, H0 Nomenclature<br>HHI of GVC Exports EXPRELY of GVC Exports<br>Notes:  Values expressed in ratios computed from original variables expressed in thousand current USD, and calculated on the total number of 254 countries/geographies left<br>after excluding two small countries (Bermuda and Luxembourg) with normalized 2001 GDP >1 from the original dataset with 256 countries/geographies with available data on<br>at least some years along the 27‐year period spanning 1988‐2014, even though some may have missing data in some years (HS 1988/92 nomenclature, mirror data, six‐digit<br>products). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.<br>Sources:  Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).<br>World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).<br>HHI of GVC Exports<br>EXPRELY of GVC Exports<br>copying apparatus<br>without loudspeaker, textured yarn, nprs 3 or 4‐thread twill, with or fine animal hai TEXTILES FINAL: Non‐pile floor coverings of wool... APPAREL FINAL: Men's or boys' ensembles of wool or or 4‐thread twill, <85% p APPAREL FINAL: Hats and other headgear, of furskin with opto‐electronic of wool or fin or 4‐thread twill (incl. automatic record ch cotton (excl. ten and clothing acces and sunblinds, cabled cotton yarn, with <85 warp knit, of wool or fine not capable of opera ELECTRONICS FINAL: Thermo‐ 4‐thread twill, <85% poly<br>ELECTRONICS FINAL: Record‐players APPAREL INTERMEDIATE: Artificial APPAREL INTERMEDIATE: Bleached APPAREL FINAL: Garments of wool APPAREL INTERMEDIATE: Printed 3 ELECTRONICS FINAL: Wrist‐watches APPAREL FINAL: Men's or boys' suits APPAREL INTERMEDIATE: Printed 3 ELECTRONICS FINAL: Turntables with TEXTILES FINAL: Camping goods, of APPAREL FINAL: Bables' garments TEXTILES FINAL: Tarpaulins, awnings APPAREL INTERMEDIATE: Combed APPAREL INTERMEDIATE: Fabrics, ELECTRONICS FINAL: Dictating mach APPAREL INTERMEDIATE: Dyed 3 or<br><!-- End of picture text -->

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