rafmacalaba/datause-encoder-data huggingface dataset:
# **7. References**

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- Alan. S., S. Ertac, and I. Mumcu. 2018. "Gender Stereotypes in the Classroom and Effects on Achievement." _Review of Economics and Statistics_ 100(5): 876–890.

- Alesina, A., M. Carlana, E. La Ferrara, and P. Pinotti. 2018. “Revealing Stereotypes: Evidence from Immigrants in Schools.” _Revise and Resubmit at the American Economic Review_ . December 2018 version (NBER Working Paper 25333) available at: https://bit.ly/3kFNqVI

- APA (American Psychological Association). 2015. _The Road to Resilience_ . Online article available at: -

- <u>www.apa.org/helpcenter/road resilience.aspx</u>

- Appadurai, A. 2004. “The Capacity to Aspire: Culture and the Terms of Recognition.” in V. Rao and M. Walton (eds.). _Culture and Public Action_ . pp. 59–84. Stanford, CA: Stanford University Press.

- Avitabile, C., and R. de Hoyos. 2018. “The heterogeneous effect of information on student performance: Evidence from a randomized control trial in Mexico.” _Journal of Development Economics_ 135: 318–348.

- Baez, J. E., and A. Camacho. 2011. “Assessing the long-term effects of conditional cash transfers on human capital: evidence from Colombia.” _World Bank Policy Research Working Paper_ 5681. Washington, DC: World Bank.

- Bandiera, O., N. Buehren, R. Burgess, M. Goldstein, S. Gulesci, I. Rasul, and M. Sulaimany. 2020. “Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa.” _American Economic Journal: Applied Economics_ 12(1): 210–259.

Bandura, A. 1977. _Social learning theory_ . Englewood Cliffs, NJ: Prentice Hall.

- Bandura, A., C. Barbaranelli, G. V. Caprara, and C. Pastorelli. 2001. “Self-efficacy beliefs as shapers of children's aspirations and career trajectories." _Child Development_ 72(1): 187–206.

- Baumeister, R., and M. Leary. 1995. “The Need to Belong: Desire for Interpersonal Attachments as a Fundamental Human Motivation.” _Psychological Bulletin_ 117(3):497-529.

- Beaman, L., E. Duflo, R. Pande, and P. Topalova. 2012. “Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India.” _Science_ 335(6068): 582–586.

- Behrman, J. R., S. W. Parker, and P. E. Todd. 2011. “Do Conditional Cash Transfers for Schooling Generate Lasting Benefits? Five-year Follow-up of PROGRESA/Oportunidades.” _Journal of Human Resources_ 46(1): 93–122.

- Bell, A., R. Chetty, X. Jaravel, N. Petkova, and J. Van Reenen. 2019. “Who Becomes an Inventor in America? The Importance of Exposure to Innovation.” _Quarterly Journal of Economics_ 134(2): 647–713.

- Bernard, T., S. Dercon, K. Orkin, and A. S. Taffesse. 2014. “The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia.” _CSAE Working Paper_ 2014–16. Oxford: Centre for the Study of African Economies, University of Oxford.

27
**Table A.4: Responses to local budget questions by oblasts**

_Percentage of positive responses_

|**Indicator**|Issyk-<br>kul|Jalalabad|Naryn|Batken|Osh|Talas|Chui|
|---|---|---|---|---|---|---|---|
|Informed about the local budget|53.9|40.2|41.7|44.9|29.9|48.9|19.9|
|Interested in how the local budget is<br>spent|75.5|52.4|47.2|50.1|37.8|82.4|56.4|
|Knows that local government should<br>provide info about local budget|53.7|36.7|31.7|50.1|18.2|54.9|24.1|
|Information on local budget is<br>accessible|56.8|49.4|43.8|52.5|35.4|68.2|32.0|
|An ordinary citizen can influence<br>budget process|19.7|16.1|25.2|14.8|4.0|14.2|11.1|
|Me/my household members tried to<br>influence local budget process in the<br>last 12 months|3.5|5.0|14.1|4.3|1.4|3.0|2.6|
|Knows the date of the last budget<br>hearing in the local government|5.9|6.6|12.4|10.3|3.5|15.9|2.9|
|Participated in budget hearing of<br>Aiyl Aimak during the last 12<br>months|2.8|4.4|13.1|8.4|1.9|8.6|2.3|
|No. of observations|458|946|290|419|1,705|233|1,094|

# Source: LiK 2012

The mean differences in responses of North and South respondents are tested using t-test. Significant differences are indicated by * p<0.1, ** p<0.05, *** p<0.01.

25
Page 30 Clearly many parcels have been built nonetheless. Many of these may be the parcels that got approvals and rural urban conversion early on or those whose new boundaries have shrunk but do not overlap with the old boundaries of other landowners. In the latter circumstance, the risk of a boundary dispute is low and owners may choose to invest even before full formalization. However, for other plots whose boundaries are significantly displaced and overlap with the old boundaries of other properties, the newly created parcels remain in limbo as it takes years to complete the approval of the new boundaries under the scheme. Until those new boundaries are approved, the risks of any substantial investment in housing are very high, probably too high for any but the largest and most powerful investors.

# **_Figure IV.3:  Process for Conversion of Rural Land already Zoned for Urban Development_**

<!-- Start of picture text --> Agricultural Developed<br>Land Property<br>7 8 10 11 13<br>Plot by plot Plot by plot Plot by plot Plot by plot Property by<br>upgrading of grant of NA registering of land updating of property<br>tenure permission transaction and land cadastre registration and<br>levy of stamp duty levy of stamp duty<br>2 5<br>Sanction of DP Sanction of TPS<br>12<br>Plot by Plot development of<br>1 3 4 6 real estate<br>Building permission<br>Zoning of land for Building city level Detailed area planning – Building TPS level Construction monitoring<br>development infrastructure TPS infrastructure Building use permission<br>in DP<br>9 14<br>Purchase of land Use of<br>for development Property<br>Revenue Dept.<br>Urban DevelopmentDept.<br>Dev. Authority/Local Govt.<br>Private Sector<br><!-- End of picture text -->
<!-- Start of picture text -->
* Non-oil exporters °¢ Oilexporters °* SSA OLS fit<br><!-- End of picture text -->

<!-- Start of picture text --> A. Impact on Revenue of a 100% Increase in Commodity Prices<br>Ss<br>oa o 4<br>& uw ALL: OLS slope = .92 (t-stat = 16) _- =<br>x 2 SSA: OLS slope = 1.01 (t-stat = 10) _ 2 ShU oT<br>io ecoa 7 FONN<br>2 8 27 AGO<br>Om _ SARE —<br>@ 6 eYEM = “®DZA<br>= 9° _—*BHR eAZE<br>SN e _ ac -<br><2 _& = © QnEr0<br>£° ° os * ®QAT<br>5 ~ ee Pa : : ° Lt<br>‘OoS te)oO thtbd ee e<br>x 0 5 10 15 20 25 30 35 40 45 50<br>Resource revenue, % of 2010 GDP, 2008 - 2012 average<br><!-- End of picture text --> <!-- Start of picture text --> B. Impact on Revenue of a 100% Increase in Commodity Prices<br>(Displaying only moderate commodity exporters, Revenue < 15% of 2010 GDP)<br>3<br>x<br>ay 0 SINOR<br>io” ao @KAZ<br>a2 °CMR eMNG_-S2I— —<br>fa -2>- RUS<br>S aoe ®BOL<br>2 ®NER eCHL one e IRN<br>5 © eVEN ePER _--eNEX SBWA<br>> “2<br>SeZaCc _ 2 -—<fiat 22eee RR eEGY eMyYS oan<br>2 wes e7MBeCOL<br>So ec? BHA<br>xe<br>0 2 4 6 8 10 12 14<br>Resource revenue, % of 2010 GDP, 2008 - 2012 average<br><!-- End of picture text -->
these channels are relevant.<sup>30</sup>

# **4.3 Placebo tests**

The fundamental identifying assumption of the main analysis requires that there is no unobserved variable correlated with fluctuations in the quota price that also has a disparate impact across areas with different climatic conditions. A useful test of this assumption is to evaluate whether trends in major economic outcomes are parallel between those areas in a period without major changes in the quota price.

Between 1986 and 1993, the quota price, again defined as the unexplained residual in a regression of market on quota price, showed no major fluctuations; this is evident in Figure 1b. Accordingly, it is feasible to evaluate whether parallel trends are observed across areas with different climatic conditions in this period, using a more limited set of outcomes reported in these earlier surveys.<sup>31</sup> Two sources of evidence are presented on this point. First, the primary specification, equation (16), is re-estimated using the earlier data. Referring to the years in which the dependent variables are observed, the primary analysis examines the years 1995 to 2002, and the placebo analysis examines the years 1986 to 1993; again, quota income is measured in the year prior to the dependent variables of interest. The results can be found in Panel C of Table 4. It is evident that there is no significant first stage in the pre-period, and the second-stage results are likewise small in magnitude and generally statistically insignificant; in many cases, the coefficients are opposite in sign relative to

> 30These results are reported in Section S5 of the appendix.

> 31In this analysis, agricultural investment is constructed using principal component analysis on a restricted set of variables: sown area, agricultural labor, and the value of investment in animals and tools. Agricultural income is the value of grain and pork production. Non-agricultural investment is constructed using principal component analysis on a restricted set of variables: a dummy variable equal to one if the household reports any new cash investment in non-agricultural assets, a dummy variable equal to one if the household reports any labor invested in non-agricultural businesses, and the amount of labor reported invested in non-agricultural businesses. Non-agricultural income is income from nonagricultural household businesses and wage income. Outside labor is not reported in this period; the other variables of interest are defined identically to the primary analysis. Given that only agricultural income from a restricted set of sources (grain and pork production) is reported, total income is not calculated. In addition, given that non-grain consumption is not reported in this period, consumption results are omitted.

26
market prices. Subsequently no long-term cointegration in a model with structural break was found in Colombia. However in the period 1995-2002 grower prices were cointegrated with the world prices, indicating that the long-run relationship between the internal and the international prices has improved.

Similarly, in a number of the African countries (Togo, Cameroon and Central African Republic) no cointegrating relationship between the price time series was found during the whole period considered, but cointegration existed in the post-reform period. This means that we cannot reliably estimate an error-correction model with a structural break, and it is therefore not possible to compare the coefficients of price transmission and producer price shares before and after the reforms. However, given that the null hypothesis of no cointegration between the local and the world prices can not be rejected in the pre-reform period, but is rejected when only post-reform years are considered, the conclusion is that the reforms were successful in creating greater linkages between the internal and the external prices.

Lastly, in the case of Angola, where the reforms had no impact on the cointegration between the domestic and the world prices, it is clear that the reforms have not been far reaching enough to produce a lasting result. The domestic prices are still fixed by the government, providing little incentives for private traders to enter the market. Moreover, many remote markets are disrupted because of the war and the infrastructure has been destroyed, cutting off some of the coffee growing areas.

# **7. Conclusions**

With the help of the cointegration analysis and an error-correction model, this paper examined price transmission from the world coffee market to local markets in the producing countries before and after sector reforms. In addition, the impact of the policy changes on the share of grower prices in world prices was estimated. The results show that the share of producer prices in the world price has increased substantially in all countries except one. There is greater integration between the domestic and world markets today than prior to the reforms, and the transmission of world price signals has improved in most cases.

The impact of the liberalization process seems to have been limited if the reforms were incomplete. Administered producer prices, continuing government involvement in marketing and lack of private initiative in trading appear to be detrimental to greater integration and higher domestic prices. When the government sets the floor prices in the post-reform period, the prices tend to stay at the minimum, 17
Figure 9: TFP Gains Removing All Distortions <!-- Start of picture text --> Kenya<br>India Portugal<br>Salvador<br>Ethiopia Ghana<br>Peru Colombia<br>Bulgaria Romania Chile Belgium<br>Hungary<br>Latvia<br>SpainItalyFranceFinland<br>6 7 8 9 10 11 12<br>log GDPpc<br>1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>Y−efficient/Y−distorted 1.1<br>1.0<br>0.9<br><!-- End of picture text --> Note: The figure reports the gains in TFP resulting from dismantling _τi_<sup>_E_</sup> and _γi_ in each country and achieving the undistorted allocation. These gains are reported the log of GDP per capita, taken from Penn World Table version 9.0 for 2014.

_Model_ where � _TFPTFP{τ{i_<sup>_E_</sup> 0 _<u>,</u>_<sup>_,γ_</sup> 0 _<u>}</u>_<sup>_i}_</sup> � is the productivity gain from removing � _τi_<sup>_E, γi_</sup> � in _Data_ _<u>TFP</u>_<sup>_USA_</sup> the model, plotted in Figure 9, and � _TFPi_ � denotes the TFP gain between the US and each country in the data.<sup>34</sup> We see from Figure 10 that distortions account for a notable share of the observed TFP gaps in the data. For instance, the combined effect of distortions accounts for more than 40 percent of the gaps in countries with less than half of the US’ TFP. The distortions in our model, however, can account for only 20 percent of the gaps in countries like Ghana and Ethiopia, for which TFP

> 34TFP gaps in the data are taken from _ctfp_ in Penn World Table version 9.0 for the year 2014, with the exception of Ethiopia, Ghana, and El Salvador, for which _ctfp_ is missing. In these cases, TFP was computed as _K_<sup>1</sup><sup>_/_</sup> _<u>Y</u>_<sup>3</sup> _L_<sup>2</sup><sup>_/_3,using</sup><sup>_rgdpna_astherealGDP,</sup><sup>_rkna_asthe</sup> real capital stock, and _emp_ as the labor input.

44
It is surprising that income di ff erences are not signi fi cant once we control for geography, population, prices and amenities. This may be because we have not included individual-speci fi c � � income di ff erentials across districts. We therefore reestimate (3) with ( η s − η i) z<sup>h</sup> as well as � � δ s − δ i. Results are shown in column 2 of Table 4. They remain non-signi fi cant. In column 3 we replace absolute di ff erences in log income with relative di ff erences. The constructed regressor, which by construction depends only on ( �η s − �η i) z<sup>h</sup> , remains non-signi fi cant. Finally in column � � 4 we compute<sup>�</sup> δ s −<sup>�</sup> δ i and ( η s − η i) z<sup>h</sup> using answers to the question regarding the subjective adequacy of total income. Estimate coe ffi cients are signi fi cant, but with opposite signs: only � � the ( η s − η i) z<sup>h</sup> part as the anticipated positive sign.

It is conceivable that these surprising and disappointing results are driven by measurement error in income. It is indeed well known that income is notoriously di ffi cult to measure in poor, primarily self-employed populations. To investigate this possibility, we reestimate (3) using � � NLSS consumption data to construct<sup>�</sup> δ s −<sup>�</sup> δ i and ( η s − η i) z<sup>h</sup> .

Results, shown in Table 5, are more in line with expectations. Although average log con� � sumption in the district is not signi fi cant, the coe ffi cient of the ( η s − η i) z<sup>h</sup> is strongly signi fi cant, and so is the coe ffi cient of the combined<sup>�</sup> δ s −<sup>�</sup> δ i+ ( �η s − �η i) z<sup>h</sup> variable. We also fi nd a signi fi cant positive coe ffi cient when the combined<sup>�</sup> δ s −<sup>�</sup> δ i+ ( �η s − �η i) z<sup>h</sup> variable is constructed using relative rather than absolute log consumption. If we include<sup>�</sup> δ s −<sup>�</sup> δ i+ ( �η s − �η i) z<sup>h</sup> computed both from absolute and relative income, neither of them is signi fi cant, probably because they are too strongly correlated. We cannot therefore discern whether it is absolute or relative standards of living that a ff ect the choice of migration destination.

We also estimate similar regressions using subjective consumption adequacy questions to � � construct<sup>�</sup> δ s −<sup>�</sup> δ i and ( η s − η i) z<sup>h</sup> . Results, not shown here to save space, are generally nonsigni fi cant. The only exception is food consumption but, as we found in column 4 of Table 4, 25
Figure 14. Cumulative change in weighted average log labor productivity, 2014-2021 (non-mining industry, constant 2010 prices) <!-- Start of picture text --> 0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 16. Cumulative change in weighted average log labor productivity, 2014-2021 (manufacturing, constant 2010 prices) <!-- Start of picture text --> 0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 18. Cumulative change in weighted average log labor productivity, 2014-2021 (retail trade, constant 2010 prices) <!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database

- Figure 15. Cumulative change in weighted average log labor productivity, 2014-2021

- (non-industrial services, constant 2010 prices)

<!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database

- Figure 17. Cumulative change in weighted average log labor productivity, 2014-2021

- (construction, constant 2010 prices)

<!-- Start of picture text --> 0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 19. Cumulative change in weighted average log labor productivity, 2014-2021 (wholesale trade, constant 2010 prices) <!-- Start of picture text --> 0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>-1.6<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database 12
Policy Research Working Paper 6787

# **Abstract**

This paper studies the relative academic performance of students tracked or randomly assigned to South African university dormitories. Tracked or streamed assignment creates dormitories where all students obtained similar scores on high school graduation examinations. Random assignment creates dormitories that are approximately representative of the population of students.

Tracking lowers students’ mean grades in their first year of university and increases the variance or inequality of grades. This result is driven by a large negative effect of tracking on low-scoring students’ grades and a near-zero effect on high-scoring students’ grades. Low-scoring students are more sensitive to changes in their peer group composition and their grades suffer if they live only with low-scoring peers. In this setting, residential tracking has undesirable efficiency (lower mean) and equity (higher variance) effects. The result isolates a pure peer effect of tracking, whereas classroom tracking studies identify a combination of peer effects and differences in teacher behavior across tracked and untracked classrooms. The negative pure peer effect of residential tracking suggests that classroom tracking may also have negative effects unless teachers are more effective in homogeneous classrooms.

Random variation in peer group composition under random dormitory assignment also generates peer effects. Living with higher-scoring peers increases students’ grades and the effect is larger for low-scoring students. This is consistent with the aggregate effects of tracking relative to random assignment. However, using peer effects estimated in randomly assigned groups to predict outcomes in tracked groups yields unreliable predictions. This illustrates a more general risk that peer effects estimated under one peer group assignment policy provide limited information about how peer effects might work with a different peer group assignment policy.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rgarlick@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team