rafmacalaba/datause-encoder-data huggingface dataset:
44 Table **9** (Cont): Incidence of Import and Export Taxes in Selected Developing Countries

|/1|Country||ImportDuties<br>**%**Imports**GNFS**|ExportDuties<br>**%**Export**GNFS**|TotalDuties<br>**%**Total**GNFS**|
|---|---|---|---|---|---|
|**X**|Nicaragua||na|na|na|
|**X**|Pakistan||**_29.5_/1986**|**1.6/1986**|**17.7/1986**|
|**X**|Panama||**11.8/1986**|**1.0/1986**|**_5.3_/1986**|
|**X**|Paraguay||**3.4/1986**|**1.0/1986**|2.1**/1986**|
|**X**|Peru||**21.7/1986**|**2.7 /1986**|**10.5/1986**|
|**X**|Philippines||**12.0/1986**|**1.0/1986**|**4.9/1986**|
|**GFS**|Poland||**14.8/1987**|**1.1/1987**|**6.4/1987**|
|**GFS**|St.Lucia||**14.9/1986**|**1.0/1986**|**7.6/1986**|
|**GFS**|St.Vincent||**16.6/1986**|**1.0/1986**|**8.6/1986**|
|**GFS**|Senegal||**15.8/1984**|1.0/1984|**8.8/1984**|
|**X**|SierraLeone||**28.9/1987**|**1.0/1987**|**13.2/1987**|
|**_X_**|Somalia||**17.8/1978**|**1.1/1978**|**12.5/1978**|
|**X**|SouthAfrica||**3.6/1985**|**1.0/1985**|**1.4/1985**|
|**GFS**|SriLanka||**21.1/1988**|**3.9 /1988**|**10.7/1988**|
|**X**|Sudan||**25.1/1981**|**_5.8_/1981**|**17.5/1982**|
|**X**|Suriname||**16.8/1986**|**1.0/1986**|**8.5/1986**|
|**GFS**|Swaziland||**14.8/1986**|**1.0/1986**|**7.9/1986**|
|**X**|Syria||**8.9/1987**|**1.0/1986**|**5.8/1986**|
|**X**|Thailand||**11.3/1987**|**1.0/1987**|**_5.3_/1987**|
|**GFS**|Togo||**19.5/1986**|**1.0/1986**|**11.0/1986**|
|**X**|Trinidad||**7.8/1981**|na|**_3.5_/1981**|
|**GFS**|Tunisia||**23.3/1984**|1.0/1984|**13.1/1984**|
|**X**|Turkey||**5.9/1987**|**1.0/1987**|**3.1/1987**|
|**GFS**|Uganda||**3.1/1986**|**32.6/1986**|**16.0/1986**|
|**X**|Uruguay||**18.1/1987**|**1.0/1987**|**7.5/1987**|
|**X**|Vanuatu||**18.2/1986**|**1.6/1986**|**12.5/1986**|
|**GFS**|Venezuela||**25.0/1986**|na|**12.5/1986**|
|**X**|Yemen AR||**14.8/1987**|na|**13.2/1987**|
|**X**|YemenPDR||na|na|na|
|**X**|Yugoslavia||**13.2/1987**|na|**6.5/1987**|
|**GFS**|<br>Zaire||**19.7/1986**|**_5.1_/1986**||
|**X**|Zambia||**24.0/1988**|**7.4/1988**|**8.5/1987**|
|**X**|Zimbabwe||**16.4/1985**|na|**8.0/1985**|
|||Total|**81**|||
||||X**=56**|||
||||**GFS= 25**|||

Sources: IMF, Annual Report on Exchange Arrangements and Exchange Restrictions, **1989;** Import and exports duties are from the IMF file on Government Finance Statistics.

**1/ "** X "denotes countries identified in the Fund's Annual **Report on Exchange Arrangements and Exchange** Restrictions with "cost-related import restrictions" (import surcharges and advance import deposits) as features of the trade and exchange system.

**" GFS "** denotes other countries not included in the X list with import and export duties of at least **10** of imports and exports of **GNFS,** respectively
**Figure 5. Concentration Curves for Delivery in Hospital or Other Health Facility**

<!-- Start of picture text --> Inst. delivery (cons.) Inst. delivery (asset indx.)<br>45 degree line<br>1<br>0<br>0 1<br>Women who delivered child in last 5 years, poorest to richest<br>Cumulative proportion of inst. deliveries<br><!-- End of picture text -->

# **References**

The word “processed” describes informally reproduced works that may not be commonly available through library systems.

Acheson, D. 1998. “Independent inquiry into inequalities in health report.” HMSO: London.

Ahmed, A., and H. Bouis. 2001. “Identifying the needy: Proxy means tests for targeting food subsidies in Egypt.” IFPRI, Washington, D.C.

Baker, J. L., and J. van der Gaag. 1993. “Equity in the Finance and Delivery of Health Care: Evidence from Five Developing Countries.” In Doorslaer, Wagstaff and Rutten, eds., _Equity in the Finance and Delivery of Health Care_ . London: Oxford University Press.

Bollen, K. A., J. L. Glanville, and G. Stecklov. 2001. “Socioeconomic Status and Class in Studies of Fertility and Health in Developing Countries.” _Annual Review of Sociology_ 27: 153-85.

Castro Leal, F., J. Dayton, L. Demery, and K. Mehra. 2000. “Public spending on health care in Africa: do the poor benefit?” _Bulletin of the World Health Organization_ 78(1): 66-74.

Datt, G., K. Simler, S. Mukherjee, and G. Dava. 2000. “Determinants of poverty in Mozambique: 1996-97.” FCND discussion paper 78. IFPRI, Washington, D.C.

21
# **2.3) Nuclear energy: an unsurprising downward reassessment**

Let us now turn to nuclear energy, which was viewed in the early 1970s as the most likely large scale substitute to oil.

Both figures 9 and 10 confirm the technological optimism of the 1970s about nuclear energy. The early US forecasts may seem extreme but the forecasted nuclear share is also quite high for worldwide energy studies of the same period: IIASA and OECD projected a median share of nuclear energy in 2010 of 40% and 25%, respectively, and though for t+30 the shares forecasted by IIASA and OECD are far lower than the US studies’, they are still quite high. This optimism for nuclear energy gradually fades during the 1980s, stabilizing during the 1990s at around 5% of primary energy. Whereas we just saw that there is large variation between 2010 and t+30 for fossil and renewable energy, we can note for nuclear a remarkable convergence of the forecasted shares: from 1990, a consensus emerges around 5% for both 2010 and t+30.

The linear regression gives a reasonable fit but which is less informative than the Tukey smoother indicator which shows out, unsurprisingly for those who know about the history of the debates about the non-military uses of nuclear energy, two distinct periods: the first period encompasses the 1970s and 1980s, when the projected shares of nuclear fall sharply. The second period starts at the end 1980s when the trend is totally flat, i.e., around a stable consensus level which only translates the feeling of a continued ban of this energy in many parts of the world.

The same conclusions hold for t+30 with the notable difference that the linear fit is less steep due to a greater dispersion in this scatter plot. The two periods described in figure 11 seem to be magnified in this figure: there is more dispersion in the first period and less in the second. The decreasing slope of the linear regression lines is thus less in absolute value: 0.71 vs. 0.81 in figure 11. The fit is moderate in both figures (R2 = 0.64 and 0.63, respectively) but acceptable.
**Table 4: Summary of the number of gauging stations for which flow data were provided by the country teams**

|**Country**|**Number of gauging**<br>**stations for which data**<br>**were provided**|**Supplementary**<br>**data from the**<br>**FRIEND**<br>**database**<sup>*****</sup>|
|---|---|---|
|Burkina Faso|19||
|Cameroon|||
|Egypt|3||
|Ethiopia|||
|Ghana|29||
|Kenya|||
|Niger|6||
|Senegal|13||
|South Africa|29|21|
|Zambia|1|20|
|Zimbabwe|32|21|

* The Southern Africa FRIEND database comprises flow for SADC countries only (UNESCO 1997)

40
**Table 3 – Balance Check with Multiple Bandwidth Sizes (Excludes Firms Created in October 1996)**

|_Bandwidth_<br>_size in months_|Obs|Primary<br>school|High<br>school|College|Gender|Skin<br>color|Household<br>size|<br>Age|Start<br>working<br>age<br>|Firms’<br>location (=1<br>if outside<br>home)|<br>Firms’ location<br>(=1 if<br>exclusively<br>outside home)|<br>Borrowed<br>money from<br>relatives to start|Used owned<br>resources|Asset (=1<br>for owned<br>dwellings)|<br>_Joint test_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|3|798|0.042|-0.046|0.042|-0.082|-0.22|0.060|-2.79|3.72*|0.27|0.27|-0.17|-0.24|0.10||
|||(0.22)|(-0.26)|(0.52)|(-0.43)|(-1.10)|(0.088)|(-0.59)|(1.91)|(1.41)|(1.43)|(-1.36)|(-1.29)|(0.59)|[0.40]|
|4|1070|0.12|-0.098|-0.060|-0.023|-0.29**|<br>0.29|1.02|0.062|0.17|0.18|-0.049|-0.19|-0.022||
|||(0.97)|(-0.88)|(-1.31)|(-0.19)|(-2.34)|(0.66)|(0.35)|(0.047)|(1.37)|(1.55)|(-0.64)|(-1.57)|(-0.19)|[0.002]|
|5|1436|0.061|-0.064|-0.034|-0.050|-0.095|0.37|1.09|-0.30|0.026|0.061|-0.0011|-0.086|-0.068||
|||(0.66)|(-0.75)|(-0.90)|(-0.54)|(-1.00)|(1.12)|(0.50)|(-0.27)|(0.29)|(0.68)|(-0.019)|(-0.94)|(-0.79)|[0.001]|
|6|1749|0.019|-0.073|-0.020|-0.028|-0.11|0.23|1.49|-0.64|0.046|0.041|0.0060|-0.040|0.033||
|||(0.25)|(-1.06)|(-0.64)|(-0.37)|(-1.44)|(0.83)|(0.86)|(-0.73)|(0.62)|(0.57)|(0.13)|(-0.53)|(0.50)|[0.000]|
|7|2333|-0.031|-0.011|0.031|-0.029|-0.051|0.25|1.16|-0.077|-0.013|-0.0070|0.0050|0.0035|0.045||
|||(-0.53)|(-0.21)|(1.21)|(-0.52)|(-0.87)|(1.20)|(0.88)|(-0.11)|(-0.24)|(-0.13)|(0.15)|(0.062)|(0.89)|[0.000]|
|8|2724|-0.043|0.0050|0.036|-0.053|-0.033|0.31|1.01|0.16|-0.041|-0.039|0.024|-0.0019|0.042||
|||(-0.78)|(0.099)|(1.41)|(-0.98)|(-0.60)|(1.51)|(0.79)|(0.25)|(-0.75)|(-0.74)|(0.74)|(-0.034)|(0.85)|[0.000]|

Note: ***, **, * statistically significant at 1, 5, and 10 percent. Robust t-statistics in parenthesis. The coefficients are from a linear regression of each covariate on a constant, a (treatment) dummy that is 1 for firms created after the threshold and 0 otherwise, and the assignment variable Z. Robust t-statistics in parentheses. P-value of the joint test in brackets.

13
<!-- Start of picture text --> Design:<br>Baseline: Employment R Incentive —+| Administrative Data Endline; 1 Endline, 2<br>survey workshop Wage (in-person) (SMS)<br>Incentive<br>Timeline:<br>April / May May June 2019 — Jun/July = Oct/ Nov<br>2019 ay 2019 May 2021 2021 2021<br><!-- End of picture text -->
- 38 -

Proposal 7 A "Rolling Settlement" **system** should be adopted by all markets. Final settlement should occur on T + 3 (as an intrim target while systems are upgraded, final settlement should occur on T + 5).

In a rolling settlement environment, trades settle on all business days of the week. This process limits the number of outstanding trades, thus reducing market exposure. Rolling settlement, even of small volumes, is facilitated by simple automation.

Tlne primary objective is to shorten the delay between trade date and settlement date; the secondary objective is to standardize settlement time fr2mes throughout international markets. These two goals are best achieved through effic.ent automation of the settlement process. In fact in an environment which is totally centralized and automated, same-day settlement is feasible as is demonstrated in Mexico and Korea.

Proposal 8 Securities lending and borrowing should be encouraged as a method of expediting the settlement of securities tractions. Existing regulatory and taxaion barriers that inhibit the practice of lending securities should be removed.

Despite all efforts made to clear and sete securities transactions in a time frame consistent with the recommended standards, failures to deliver can occur if securities are not available. Securities borrowing has become an effective tool used by market participants to satisfy their obligation to deliver to a trading counterparty in this situation. At the same time, these borrowers incur a temporary collateralized obligation to a lending party.

However, in markets where the practice of securities lending and borrowing is new, precautions must be taken to ensure that borrowers and lenders protect themselves from undue risk in these transactions by instituting careful credit standards, contract terms, and practices. Emerging market regulators should carefully consider the risks associated with securities lending and permit its use only under controlled conditions."0

> 1 See Group of Thity, Clearance and Settlement, pp. 47-49 and 58 for a discussion of the risk of securities lending and concoiutant safeguards required.
One drawback of the aforementioned studies using nightlights on the economic impact of tropical cyclones is that they have largely been restricted to using annual data, where for nightlights, in part because the publicly available nightlight data are at annual frequency. In reality, much of the damages, direct and indirect, are likely to take place at a much higher temporal frequency. As a matter of fact, a recent study by Ishizawa et al (2017) of six Central American countries, using monthly versions of the popular available annual nightlight images, shows that there is considerable heterogeneity of impact even within the year of a hurricane strike and that this is masked in annual data. This result is echoed by Mohan and Strobl (2017) using a differently monthly nightlight satellite data source to investigate the impact of Typhoon Pam in the South Pacific.

In this study, we use monthly nightlight composites to examine the short-term local impact of hurricanes on the Dominican Republic since 1992. To this end, we construct local maximum wind speeds for damaging storms for every pixel in the nightlights data using a wind field model and best track data. These are then inserted into a stylized damage function and used to econometrically estimate the impact of the storms on local monthly nightlight intensity. Our estimated luminosity impact is then translated into monetary values by relating quarterly nightlight values to quarterly national GDP.

Our analysis finds that there is a negative impact of tropical storms on local nightlight intensity and that this lasts up to 15 months after the strike, with the largest effect observed 9 months after the storm strikes. The estimates suggest that there were 19 damaging storms observed over the 22-year sample period, which induced on average a 2.1 percent fall in nighttime brightness. Overall, translating nightlight units into monetary values using quarterly GDP suggests that on average the storms reduced GDP by about USD 1.1 billion.

5
At the bottom of Table 4, we document averages for leading and lagging states. We define leading states to be those with above average GDP per capita across 1989-2010. The unweighted series take state-level averages directly, and the two groups appear mostly similar. When using employment weights to take into account the size of the manufacturing sector in Indian states, we find that the 1-person firm growth is stronger in leading states, perhaps twice the rate of that observed in lagging states.

These state-level differences between leading and lagging regions are important to note, but we also need to place them into context. First, the initial data column shows that the weighted overall employment growth is three-times higher in the leading states, so the 1-person establishment growth is not out-pacing the aggregate trend for these groups. If anything, the development of 1-person firms is helping lagging states to not lose ground. Second, the variation across states in these growth rates is much less than the differences observed for tradables vs. non-tradable industries at the bottom of Table 3. Thus, the state-level variation is material, but second-order to that observed on other dimensions. This provides prima facie evidence that statelevel factors like labor regulations or regional development levels will play a small role in the development of 1-person establishments.

# **4. Explanatory factors for the development of 1-person establishments**

This section analyzes factors that might explain the development of 1-person establishments, including district traits, industry traits, and phenomena important to India’s recent development (e.g., urbanization and rising female labor force participation). These explorations are not causal in nature, but they seek to isolate important influences for future work.

## _Correlations to district traits_

Table 5 documents univariate correlations between district traits and the battery of outcomes considered. District traits are taken primarily from the 2001 Population Census, with measures of state labor regulations taken from Ahsan and Pages (2007). We winsorize variables at their 1%/99% values to guard against outliers and transform non-logarithm explanatory 11
23

# Table 4: SECTORAL RESULTS (COD .10) (Percent Change)

|||CRTS|Mark<br>Pric|-up<br>ing|Con<br>No Ent|jectural<br>Va<br>ry/Exit|<br>riations<br>Entr|y/Exit|
|---|---|---|---|---|---|---|---|---|
|Column||(1)|0ao<br>(2)|*&lo<br>(3)|t0<br>(4)|ul*0<br>(6)|ea0<br>(C)|io10<br>(7)|
|Consumer**Goods**|X|12.4|19.0|81.7|10.0|9.4|6.9|227|
||E|25.1|34.9|S7.9|20.7|21.3|17.4|453|
||SE||1.0|1.9|0.3|0.3|-1.8|-1|
||c||||0.9|0.9|.8|_LA_|
||0||||||0.2|_OA_|
||N||||||26.8|238|
|Producer Goods|X|12.9|17.2|26.5|12.9|10.9|10.1|183|
||E|40.1|43.2|69.9|39.9|37.8|86.6|SS1|
||SE||1.6|2.0|0.6|0.4|-1.1|_-A_|
||||||0.2|0.3|.2|2|
||O||||||0.2|_03_|
||N||||||21.6|241|
|Heavy Industry|X|-1.7|-5.1|8.4|-8.3|-1.7|-S.S|4.3|
||E|9.7|2.8|23.8|6.9|10.8|4.6|19S|
||SE||-. 6|.7|-. 1|0.0|-1.8|_-10_|
||c||||2.6|2.6|2.5|81|
||0||||||0.2|03|
||N||||||10.7|170|

Note: X _=_ Gross output; E a Exports SE _=_ Scale efficiency massure **(sm** table 8) **expressed as a** percent of sectoral **sales at** current prices. N _=_ Number of fires (initially **set** equal to 1) c * Elasticity of demand (defined In equation 3). 0 , Calibrated conjecture.
<!-- Start of picture text --> ° (a): National Employment Expenditure (b): National Employment Generated<br>R3<br>°a 3°8<br>ag<br>8 58<br>ay £8<br>Be 8<br>g3 Bo26<br>3g 8<br>ae© $8<br>= zs<br>a8<br>8a<br>° °<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Year Year<br>ME scry = () reca<br><!-- End of picture text -->
IMF (International Monetary Fund) and World Bank. 2017. Review of the Debt Sustainability Framework in Low-Income Countries: Proposed Reforms. International Monetary Fund, Washington DC.

Kaiser, J. 2013. “One Made it Out of the Debt Trap. Lessons from the London Debt Agreement of 1953 for Current Debt Crises.” International Policy Analysis, Friedrich Ebert-Stiftung, Bonn.

Kaufmann, D. A. Kraay, and M. Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” Policy Research Working Paper 5430, World Bank, Washington, DC.

Kilic Celik, S., M. A. Kose, and F. Ohnsorge. 2020. “Subdued Potential Growth: Sources and Remedies.” In Growth in a Time of Change: Global and Country Perspectives on a New Agenda, edited by H.-W. Kim and Z. Qureshi. Washington, DC: Brookings Institution.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. S. O. Nagle, F. L. Ohnsorge, and N. Sugawara. 2020. “Can This Time Be Different? Policy Options in Times of Rising Debt.” Policy Research Working Paper 9178, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2021. Global Waves of Debt: Causes and Consequences. Washington, DC: World Bank.

Kose, M. A., and F. Ohnsorge. 2019. A Decade after the Global Recession. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, and N. Sugawara. 2021. “A Mountain of Debt: Navigating the Legacy of the Pandemic.” Policy Research Working Paper 9800, World Bank, Washington, DC.

Kose, M. A., N. Sugawara, and M. E. Terrones. 2020. “Global Recessions.” Policy Research Working Paper 9172, World Bank, Washington, DC.

Kubota, M., and A. Zeufack. 2020. “Assessing the Returns on Investment in Data Openness and Transparency.” Policy Research Working Paper 9139, World Bank, Washington, DC.

Laeven, L., and F. Valencia. 2020. “Systemic Banking Crises Database II.” IMF Economic Review 68 (2): 307-361.

Lang, V., D. Mihalyi, and A. Presbitero. 2021. “Borrowing Costs after Sovereign Debt Relief.” CEPR Discussion Paper 15832, Centre for Economic Policy Research, London.

Lian, W., A. F. Presbitero, and U. Wiriadinata. 2020. “Public Debt and r - g at Risk.” IMF Working Paper 20/137, International Monetary Fund, Washington, DC.

Mbaye, S., M. M. Moreno-Badia, and K. Chae. 2018. “Bailing Out the People? When Private Debt Becomes Public.” IMF Working Paper 2018/141, International Monetary Fund, Washington, DC.

Melecky, M. 2021. Hidden Debt: Solutions to Avert the Next Financial Crisis in South Asia. Washington, DC: World Bank.

OECD (Organisation for Economic Co-operation and Development). 2021a. OECD Economic Outlook. May. Paris: Organisation for Economic Co-operation and Development.

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18
|**Table 1: Who are theparticip**|**ants in business training evaluations?**<br>**Existing**|**All microfinance/**|**Rural**|**Business**|**Selected on interest**|**Mean**||
|---|---|---|---|---|---|---|---|
|**Study**|<br>**Country**<br>**Businesses?**|<br>**bank clients?**|**or Urban**|**Sector**|**in training?**|**Age**|**% Female**|
|Berge et al. (2012)|Tanzania<br>Existing|Yes|Urban|Many|No|38|65|
|Bruhn and Zia (2012)|Bosnia-Herzegovina<br>67% existing|Yes|Urban|Many|Yes|28|35|
|Calderon et al. (2012)|Mexico<br>Existing|No|Rural|Many|No|46|100|
|De Mel et al. (2012)|Sri Lanka<br>50% existing|No|Urban|Many|No|34-36|100|
|Drexler et al. (2012)|Dominican Republic<br>Existing (a)|Yes|Urban|Many|No|40|90|
|Field et al. (2010)|India<br>24% existing|Yes|Urban|Many|No|32.4|100|
|Giné and Mansuri (2011)|Pakistan<br>61% existing|Yes|Rural|Many|No|37.6|49|
|Glaub et al. (2012)|Uganda<br>Existing|No|Urban|Many|Yes|39|49|
|Karlan and Valdivia (2011)|Peru<br>Existing|Yes|Both|Many|No|n.r.|96|
|Klinger and Schündeln (2011|) El Salvador, Guatemala, Nicaragua<br>39% existing|No|n.r.|Many|Yes|36|28|
|Mano et al. (2012)|Ghana<br>Existing|No|Urban|Metalwork|No|45|0|
|Premand et al. (2012)|Tunisia<br>No|No|Urban|Many|Yes|23|67|
|Sonobe et al. (2011)|Tanzania<br>Existing|No|Urban|Garments|No|45|85|
||Ethiopia<br>Existing|No|Urban|Metalwork|No|44|4|
||Vietnam<br>Existing|No|Urban|Rolled Steel|No|40|55|
||Vietnam<br>Existing|No|Urban|Knitwear|No|41|66|
|Valdivia(2012)<br>_Notes:_<br>n.r. denotes not reported<br>(a) 78% of sample is existing|Peru<br>Existing<br>businesses, and study does not look at business out|No<br>comes for those w|Urban<br>ho were n|Many<br>ot existing a|Yes<br>t baseline.|43|100|

27
We also notice that fluctuations in the balance sheet aggregates are sizable: movements of several percentage points even for the median of over 70 countries: intdielvlida cou ntries exper-enced much more volatility.

SO WIaL Ia.t **aU1** ULLd **UL L:Us VUlaUlILl, a.1U LIhW UV** UIhe **VaIy Ga Uy** b--el different types of banking system?

Figure 1 <!-- Start of picture text --> hban balance sheet structure<br>l. 2 i<br>--<br>0.8 M advances<br>0.6 _o0 capital<br>0.4 C3 deposits<br>* other<br>-0.2 _ l l E @foreign (net)<br>Assets. Liabilties<br>-0.2<br>(share of bills+advances)<br><!-- End of picture text --> 14
Figure 4: Long-term Trends in Student Academic Performance <!-- Start of picture text --> 0.4<br>0.3<br>0.2<br>GPA for students from non-<br>Cape Town high schools<br>0.1<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Linear prediction<br>-0.1 using 2001-2005 data<br>-0.2<br>-0.3<br>0.3<br>High schools in Cape Town<br>0.25<br>0.2<br>0.15<br>High schools not in Cape Town<br>0.1<br>0.05<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>University GPA<br>Rate of qualification for university admission<br><!-- End of picture text --> _Notes:_ The first panel shows mean GPA for first year university students from high schools outside Cape Town. The time series covers the tracking period (2001-2005) and the random assignment period (2006-2008). Mean GPA for students from Cape Town high schools is, by construction, zero in each year. Data for 2003 is missing and replaced by a linear imputation. The dotted lines show a 95% confidence interval constructed from 1000 replications of a percentile bootstrap stratifying by assignment policy and dormitory status. The bootstrap resamples dormitory-year clusters for 2004-2008, the only years in which dormitory assignments are observed. The second panel shows the proportion of grade 12 students whose score on the high school graduation examination qualified them for admission to university. The mean qualification rate for high schools in Cape Town is 0.138 in the tracking period (2001 - 2005) and 0.133 in the random assignment period (2007 - 2008). The mean qualification rate for high schools outside Cape Town is 0.250 in the tracking period (2001 - 2005) and 0.245 in the random assignment period (2007 - 2008). The second difference is 0.001 (bootstrap standard error 0.009) or, after weighting by the number of grade 12 students enrolled in each school, 0.007 (standard error 0.009).

32