rafmacalaba/datause-encoder-data huggingface dataset:
Peralta Quiros, T., Kerzhner, T., & Avner, P. (2019). _Exploring Accessibility to Employment_ _Opportunities in African Cities: A First Benchmark_ (SSRN Scholarly Paper No. 3434753).

https://papers.ssrn.com/abstract=3434753 Picarelli, N., Jaupart, P., Chen, Y., & Moreno-Monroy, A. (2017). _Unequal Commutes. Job_ _Accessibility & Employment in Accra_ (C-33401-GHA-1; IGC Working Paper, p. 52).

Rosenthal, S. S. (2014). Are Private Markets and Filtering a Viable Source of Low-Income Housing? Estimates from a “Repeat Income” Model. _American Economic Review_ , _104_ (2), 687–706. https://doi.org/10.1257/aer.104.2.687 Safetipin. (2021). _Safety Audit Report, Colombo_ . https://safetipin.com/wpcontent/uploads/2021/06/Safety_audit_report_colombo_Safetipin.pdf Seedhouse, A., Johnson, R., & Newbery, R. (2016). Potholes and pitfalls: The impact of rural transport on female entrepreneurs in Nigeria. _Journal of Transport Geography_ , _54_ , 140–147. https://doi.org/10.1016/j.jtrangeo.2016.04.013 World Bank. (2021). _Labor force participation rate, female (% of female population ages 15+)_ _(modeled ILO estimate)—Middle East & North Africa_ . The World Bank - Data.

https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS?locations=ZQ 41
-11 -

creating the conditions within which a more analytical, problem-solving and creative approach to learning could be possible. But current policies constitute a courageous reversal of at least twentyfive years of unbridled, ill-conceived university expansion.

# III. PERFORMANCE AND ITS DETERMINANTS

## Ouantities

### 37.

37. Total university enrollments during the past 15 years are shown in Table 1. Several basic facts are immediately apparent. 1) University enrollments exploded during the period 19711977, growing at a compound rate of over 14% per year, with the number of students more than doubling. 2) Growth decelerated during the following seven years, 1977-1984, with enrollments expanding at about '.4% per year; by 1984 university enrollments were more than three times as large as they had been only 13 years earlier! By the mid-1980s, the number of students was five times larger than the designed capacity of the universities' facilities. 3) 1984 was a water-shed year, in which fundamental policy decisions were taken and began to be implemented: university enrollments have fallen in every year since then, at a compound rate of about -3% per year, by 1989 the total number of university students had been reduced by nearly 100,000, a decline of some 14%.

### 38.

38. Most of the expansion in university enrollments has been in the humanities: the share of science students in total enrollments fell from over half (55%) in 1971 to just over one-quarter (26.3%) in 1984 (Table 1). This decline in the percentage of science students has not been reversed since the policy reforms of 1984: the decline in enrollments has been roughly equally apportioned between sciences and humanities. Indeed, the percentage of science students in total enrollments actually fell slightly from 1984 to 1989, to 25.3%.

### 39.

39. Several simple observations may be made on these trends. First, the explosion of enrollments, encouraged by the absence of charges for university admission and by the govemment job guarantee, had serious implications for the quality of education. This was especially true during the 1970s, when large numbers of faculty members sought, often successfully, to obtain employment in the capital-surplus oil exporting countries. Second, such rapid expansion seriously exacerbated the difficulties of the labor market: the number of graduates rose nearly four-fold between 1973 and 1986 (see Table 2). The fact that an incr-asing percentage of these graduates were humanities graduates did not improve their job prospects.

### 40.

40. Part of the response to the situation was the attempt to channel secondary school graduates either into the job market directly, or into two year technical training institutes. These were first established in 1973; enrollments expanded from an initial 20,805 in 1973 to 105,360 in 1989. Unlike university enrollments, these expanded steadily throughout the two decades (see Table
Finally, we offer robustness checks indicating that our results are not driven by a single region, and that improvements in financial indicators did in fact occur after the inception of adjustment lending, even when we control for the adverse selection effects associated with repeated lending to the same country. Taken in their entirety, these results suggest that the World Bank adjustment loans studied here did have a positive effect on financial sector outcomes. At the least, it would be difficult to conclude that such involvement was a net detriment for borrowers.

23
**Figure 1b: Progress toward the health MDGs for the entire population – common growth rate scales**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

26
# **Table 14. Estimated Effects of the Uruguay Round**<sup>**Tariff Cuts on Middle Eastern Countries Exports.**</sup>

||199|2 Exports to ($ milli|on)|Projec|ted UR Trade Effec|ts (%)||
|---|---|---|---|---|---|---|---|
||||||||Overall Export<br>|
|Exporting Country|EU|Japan|USA|EU|Japan|USA|Change ($ million)|
|Bahrain|172.9|304.1|71.2|1.12|0.42<br>|7.42<br>|8.5<br>|
|Cyprus|422.2|2.1|11.7|0.21|-1.74<br>|4.54<br>|1.4<br>|
|Egypt|2,893.4|91.0|465.6|0.30<br>|1.42<br>|2.41<br>|21.2<br>|
|Iran|6,649.1|2,604.5|0.8|0.02|2.73<br>|0.49<br>|72.4<br>|
|Israel|3,731.3|694.6|3,903.3|-0.14|-0.81<br>|-0.16<br>|-17.1<br>|
|Jordan|8..2|24.1|18.6|_-0.55_|0.76<br>|2.30<br>|0.1<br>|
|Lebanon|96.8|2.2|28.5|7.92|-0.11|-0.16<br>|7.6<br>|
|Libya|7,021.0|0.7|0.0|-0.13|-1.14|0.00<br>|-9.1<br>|
|Oman|165.7|1,962.4|207.3|0.88|3.04|0.93<br>|63.0<br>|
|Qatar|55.8|2,175.5|75.9|--|2.94|1.30<br>|64.9<br>|
|Saudi Arabia|11,150.4|10,181.1|11,285.7|-0.08|2.40|0.46|287.3<br>|
|Syria|1,225.1|8.5|45.8|-0.20|0.12|0.33<br>|-2.3<br>|
|Turkey|4,208.4|202.2|1,183.4|-0.08|0.98|3.06<br>|34.8<br>|
|UAE|1,713.7|9,733.3|871.0|0.95|2.46|1.56|269.3|
|ALL MIDDLE EAST|39,594.0|27,986.3|18,168.8|0.02|2.42|0.64|802.2|

Source: World Bank-UNCTAD SMART System.

**(1In**
table 7, the model is estimated by maximum likelihood, on the formal sector employees sub-sample of the 2004 wave that were also covered in the 2000 wave. The earnings gap that enters the equation is obtained from sector specific earnings equations that we report in the first (public sector) and the second (private sector) columns of the same table. These are obtained by applying OLS to the specifications given by equations 9b and 10b, respectively. The selectivity terms of these columns are obtained from a binary dynamic public sector choice model of the same specification as column 6 of table 4-that is, as given by equation 5- but this time on data points of wage workers only. In the third column of the table we estimate a single earnings equation for both private and public sector workers having included a public sector dummy. Since the dummy is assumed to be endogenous in this context, we include the generalized residual of equation 5 as a selectivity correction term in this column.

Tables 8 and 9 are in similar to table 7 in content, in terms of underlying specification and identification and estimation methods. Table 8 reports results of estimation on data on wage earners in the 2000 survey sample who were also covered in the 1994 survey. Table 9 relates to wage earners of the 1997 wave who were also covered in the 1994 wave.

In table 10 the focus is on the estimation of a structural model of the sorting of workers between formal sector wage employment and informal employment. Here too the assumed structure is given by equation 8b. Since there are practically no earnings data for informal sector wage workers, we have been forced to confine the analysis to data on formal sector wage workers and own account workers reporting business sales revenue. We use the latter as a proxy for earnings from self employment. What we refer to in the table as self-employment earnings premium is in fact the log difference between annual business revenue and the counterfactual annual wage, which we assume to be monotonically increasing in the true gap between earnings from self-employment and wages. The equations we estimate in table 10 are very similar to those of tables 7 to 9 except that we do not include a common earnings equation across the two divides in the case of table 10. The sector-specific earnings equations are identified and estimated in the same way in both sets of tables, as are the underlying reduced form selection equations.

22
# **Figure 15. Countries approved to export fish and fishery products to the European Union by income group, December 2003:**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Low income Lower middle-income Upper middle-income High-income OECD High-income non-<br>OECD<br>Part I Part 2<br><!-- End of picture text --> A number of countries have been excluded from Part II, and thus are prohibited from exporting to the EU, following inspection by the European Commission. Examples are St Vincent and the Grenadines and Cape Verde. Others, including Guinea-Bissau, St Lucia, Congo Brazzaville and the Former Yugoslav Republic of Macedonia, were not able to demonstrate equivalent conditions when the provisions for bilateral trade with individual EU Member States (List II) were removed at the end of 1999, and have still not achieved even Part II status.

Other countries have faced temporary restrictions on exports of fish and fishery products to the EU. These include India, Bangladesh, Vietnam, Mozambique, Tanzania, Uganda and Kenya. In certain cases major non-conformities have been identified during inspections by the European Commission. In others, microbiological contamination had been detected through border inspection.

38
# **D.3 Non-response**

The phone survey after 3-4 months is our main source of endline data. We use a text message survey after 2-3 days only to measure beliefs about numeracy and self-esteem. The response rates for the text message and phone surveys are respectively 83 and 96%. Non-response does not differ by treatment arm (Table D.5). Non-response does not differ over most baseline characteristics (Table D.6). Men are less likely to respond in both surveys. Higher numeracy and concept formation scores predict higher response rates in the text message survey. Higher grit predicts lower response rates in the endline survey.

Table D.5: Non-response by Treatment Group in Each Post-Treatment Survey Round

||(1)<br>Text Message Survey|(2)<br>Endline Phone Survey|
|---|---|---|
|Control|0.170|0.040|
||(0.013)|(0.006)|
|Public|0.177|0.039|
||(0.011)|(0.004)|
|Private|0.182|0.044|
||(0.010)|(0.004)|
|Placebo|0.142|0.047|
||(0.032)|(0.026)|
|p: Control = Pvt.|0.481|0.632|
|p: Control = Pub.|0.670|0.855|
|p: Pvt. = Pub.|0.785|0.388|
|p: Control = Pvt. = Pub.|0.778|0.681|
|p: Control = Plc.|0.414|0.787|
|p: Pvt. = Plc.|0.238|0.888|
|p: Pub. = Plc.|0.297|0.746|
|p: Control = Pvt. = Pub. = Plc.|0.641|0.841|
|# observations|6891|6891|
|# clusters|84|84|

Coefficients show the fraction of each treatment group that does not complete each follow-up survey round. Heteroskedasticity-robust standard errors clustered by treatment date are shown in parentheses.

52
# **ONLINE   APPENDIX**

**Online   Appendix   Table   IV:   Fees   -­‐   Impact   by   Provider   Quality   using   Different   Definitions   of   Quality**

|||**Village,p60**||**Vi**<br>**F**|**llage Media**<br>**ees(Year 2**|**n**<br> **)**|**Sa**|**mple Medi**|**an**|
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)<br>|(9)<br>|
|Report Card (RC)|-164.1|-92.24|-78.79|-60.56|-49.93|-40.89|-124.5|-78.54|-64.98|
||(106.8)|(77.87)|(74.62)|(113.5)|(79.74)|(81.92)|(133.4)|(112.9)|(117.0)|
|High*Private|371.0***|142.8|129.4|489.3***|221.2**|212.4**|479.7***|179.1|175.5|
||(127.2)|(109.7)|(103.6)|(123.2)|(103.6)|(97.93)|(119.6)|(141.0)|(140.9)|
|RC*High*Private|-344.0**|-277.7**|-276.2**|-426.9***|-282.7**|-277.5**|-313.3*|-224.6|-226.1|
||(143.9)|(129.0)|(125.4)|(157.4)|(119.7)|(117.3)|(174.4)|(145.8)|(147.5)|
|Baseline Fee||0.685***|0.699***||0.674***|0.685***||0.679***|0.690***|
|||(0.111)|(0.113)||(0.113)|(0.116)||(0.117)|(0.120)|
|Village Controls|None|Baseline|Village|None|Baseline|Village|None|Baseline|Village|
|Observations<br>|276|274|274|276|274|274|276|274|274|
|R-Squared|0.254|0.581|0.583|0.265|0.581|0.583|0.261|0.578|0.581|
|SUBGROUP POINT ES<br>|TIMATES,F<br>|-TESTp-VAL<br>|UES IN BRA<br>|CKETS<br>||||||
|Low Private School|-164.1|-92.24|-78.79|-60.56|-49.93|-40.89|-124.5|-78.54|-64.98|
||[0.128]|[0.239]|[0.294]|[0.595]|[0.533]|[0.619]|[0.353]|[0.488]|[0.580]|
|High Private School|-508.1|-369.9|-355.0|-487.5|-332.6|-318.4|-437.8|-303.2|-291.1|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|

_Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level

This  table  runs  robustness  checks  on  the  heterogenous  fee  results  in  Table  V  by  considering  different  definitions  of  high  and  low-­‐achieving schools  and  a  series  of  controls.  Columns  1  to  3  define  high  as  top  40th  percentile  within  the  village.  Columns  4  to  6  define  high  as  above the  village  median.  Columns  7  to  9  define  high  as  above  the  median  of  the  sample.  Columns  1,  4  and  7  include  no  controls;  Columns  2,  5 and  8  include  baseline  fee  as  a  control;  and  Columns  3,  6  and  8  includes  the  same  village  controls  as  in  previous  tables  in  addition  to baseline  fee  control.  Regressions  include  district  fixed  effects  and  cluster  standard  errors  at  the  village  level.  We  have  fewer  observations than  the  total  number  of  private  schools  in  our  sample  due  to  private  school  closure  and  missing/inconsistent  data  in  rounds  1  and  2.  The lower  panel  displays  the  estimated  coefficients  and  p-­‐values  [in  square  brackets]  for  relevant  subgroups  obtained  from  the  coefficients estimated  in  the  top  panel.
of 2004. To complete the GQ upgrades, 128 separate contracts were awarded. In total, 23% of the work was completed by the end of 2002, 80% by the end of 2004, 95% by the end of 2006, and 98% by the end of 2010. Differences in completion points were due to initial delays in awarding contracts, land acquisition and zoning challenges, funding delays,<sup>6</sup> and related contractual problems. Some have also observed that India’s construction sector was not fully prepared for a project of this scope. As of August 2011, the cost of the GQ upgrades was about US$6 billion (1999 prices), about half of the initial estimates.

The NS-EW network, with an aggregate span of 7,300 km, is also shown in Figure 1. This network connects Srinagar in the north to Kanyakumari in the south, and Silchar in the east to Porbandar in the west. The NS-EW upgrades were initially planned to begin in Phase I of NHDP along with the GQ upgrades. The scope of the first phase of upgrades was smaller at 981 km, or 13% of the total network, with the remainder originally planned to be completed by 2007. However, work on the NS-EW corridor was pushed into Phase II and later, due to issues with land acquisition, zoning permits, and similar. In total, 2% of the work was completed by the end of 2002, 4% by the end of 2004, and 10% by the end of 2006. These figures include the overlapping portions with the GQ network that represent about 40% of the NS-EW progress by 2006. Since then, the planned upgrades for the NS-EW have expanded substantially. As of January 2012, 5,945 of the 7,300 kilometers in the project have been completed, at an estimated cost of US$12 billion.

# **Section 3: Data Preparation**

We employ repeated cross-sectional surveys of manufacturing establishments carried out by the government of India. Our work studies surveys that were conducted in fiscal years 1994, 2000, 2005, and 2007 for the organized sector. In all cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This time span allows us two surveys before and after the GQ upgrades. This section describes some key features of these data for our study.<sup>7</sup>

> 6 The initial two phases were about 90% publicly funded and focused on regional implementation. The NHDP allows for public-private partnerships, which it hopes will become a larger share of future development.

> 7 For additional detail on the manufacturing survey data, see Nataraj (2011), Kathuria et al. (2010), Fernandes and Pakes (2008), Hasan and Jandoc (2010), and Ghani et al. (2011).

8
<!-- Start of picture text --> HIC UMC LMC<br>18 18 18<br>16 16 16<br>14 atest aren 14 14<br>=S42 eee Ss42 =S42<br>B102a oe oe10a 3102a<br>Qa Qa Qa<br>5 5 5<br>5° 5° 5°<br>6 6 amen hs OE el trelated 6<br>4 4 4 Neee As:<br>222<br>0() 0<br>2005 =.2010 2015 2020 2025 2000 2005 2010 2015 2020 2025 2005 =—-2010 2015 2020 2025<br>year year year<br>-* DEU FRA -®- GBR USA -- BGR CHN -®- MEX TUR -* BOL EGY -®: PHL VNM<br><!-- End of picture text -->
A number of macroeconomic and institutional factors are associated with greater levels of SME financing. Table 5 presents pairwise correlations of the ratio of SME loans to GDP and a number of macroeconomic and institutional factors.<sup>11</sup> Consistent with earlier research on SME and enterprise financing, we find a positive correlation between the overall level of economic development measured by income per capita and financial development measured by the ratio of private credit to GDP with the level of SME financing. Legal frameworks and the overall business environment are also important factors affecting the level of SME financing. For example, the ability to open and close a business is found to be an important factor associated with growth. Using information from the _Doing Business_ database, we find a negative correlation between the number of days it takes to start and close a business and the value of SME financing.

In addition, we consider a number of parameters describing financial institutions operating in a country. We do not find a statistically significant correlation between the share of foreign- or state-owned banks and levels of SME financing, which is consistent with bank level evidence in Beck _et al._ (2008b). Unlike firm level analysis in Beck _et al._ (2005b), we do not find a statistically significant level of correlation between the level of bank concentration and the ratio of SME financing to GDP. However, broader retail outlet networks measured by the number of bank branches per 100,000 adults from the _Financial Access_ database are associated with more SME financing. Countries where banks have less efficient structures measured by a higher ratio of overhead costs to total assets, higher interest rate margin and a greater cost to income ratio, tend to have lower levels of SME financing.

Even after controlling for the overall development using income per capita as a proxy, we find that the framework for starting businesses and the overall level of development of the financial market are significantly correlated with the levels of SME financing (Table 6). We also find that parameters characterizing efficiency of the banking system, including cost ratios and net interest margins, have statistically significant negative coefficients, even after controlling for the overall level of development.

> 11 We have also conducted analysis using the share of SME loans in total loans but did not find significant correlations with various macroeconomic and business environment factors, except for a negative correlation between the share of SME loans in total loans and an offshore financial sector dummy.

11