rafmacalaba/datause-encoder-data huggingface dataset:
The SLQAT pass rate _z_ might be correlated with our outcomes of interest. Thus, the treatment assignment mechanism is clearly not random and comparing schools that receive the treatment ( _di_ <sup>_z_</sup> _i_  1) to schools that do not ( _di_ <sup>_z_</sup> _i_  0 ) will yield a biased estimate of the ATT. If, however, we consider schools with pass rates near the cutoff to be comparable, then the treatment assignment mechanism in the neighborhood of the cutoff can be viewed as if it was ―almost random‖.

More formally, let _e_  0 denote an arbitrarily small number. Comparing the outcomes of schools with pass rates just below the cutoff (marginal failers) with the outcomes of schools with pass rates at or just above the cutoff (marginal passers), yields

> _E_  _y_ 1 _i_ | _zi_  _c_  _e_   _E_  _y_ 0 _i_ | _zi_  _c_  _e_   _E_  _i_ | _zi_  _c_  _e_   _E_  _y_ 0 _i_ | _zi_  _c_  _e_  _E_  _y_ 0 _i_ | _zi_  _c_  _e_  .(4)

> Under the assumptions that (1) the limit lim _E_  _y_ 0 _i_ | _zi_  _c_  _e_  is well defined; (2) _E_  _y_ 0 _i_ | _zi_  _c_  _e_  0 is continuous in the assignment variable _z_ at the cutoff (i.e., the conditional expectations of the outcome variable exhibits local smoothness at the cutoff in the absence of the treatment); and (3) the density of the assignment variable _z_ is positive in the neighborhood of the cutoff, the difference in the mean outcomes between marginal passers and marginal failers identifies

This object is interpreted as the ATT at the cutoff (Hahn et al. 2001; Todd 2007).

A sharp RD design neatly fits the phase-4 SLQAT taker data, as the school’s pass (eligibility) versus fail (ineligibility) status remains fixed given that phase 4 was the last entry phase before the follow-up data collection. A variant of the above framework will however be required for phase-3 SLQAT takers given that some phase-3 failers reapplied to the program in phase 4 and passed the SLQAT at that time. As a result, our set of phase-3 SLQAT takers can be 18
Page 51

# **Table V. 4 Estimates of Populations Unsuitable for In-Situ Upgradation**

|**Households Potentially Unviable for Upgradation**<br>**Unsuitable for**<br>**Upgradation**<br>**Area (Ha)**|**# of**<br>**Households**|
|---|---|
|1 Pavement Dwellers<br>-|3,406|
|2 Flood Prone Areas<br>2.31|577|
|3**_Infrastructure Upgrades_**<br>**_6_**|**_1,500_**|

|**Flood Prone Househ**<br>**Lake name**|**olds in Slums Near Lake**|**s**<br>**# of**<br>**Households**|
|---|---|---|
|Chandola lake|Total Population|3,930|
||Relocation HH|442|
|Thaltejlake|Slum Pocket|1,369|
||Relocation HH|101|
|Isanpur lake|Slum Pocket|1,255|
||Relocation HH|34|
|TOTAL||6,554|
|TOTAL After Lake Pro|tection|577|

Slum and chawl households in the path of important infrastructure development (e.g. new roads or widening of existing roads identified in the development plan) may be better served by relocation than upgradation, because their future in situ is uncertain. However, not all road projects come to fruition as planned, and the land cleared by relocation for infrastructure improvement is prone to further encroachment until the proposed infrastructure improvement is completed. This implementation timeline for such infrastructure projects is not always assured as it depends on various factors like availability of appropriate funding etc. It makes no sense to relocate households until such funding is available. Furthermore, the relocation of any households displaced by the infrastructure should be paid for as part of the project costs.

Accordingly, while noting the presence of this set of households, their relocation is not included as part of the project costs. Assuming full cost of relocation within the cost of major infrastructure projects with high economic value has become increasingly accepted through, among others, experience with IFIs that developed working models for rehabilitation of all affected households. The MMRDA in Mumbai, for example, subsequent to a World Bank urban transport project, has adopted R&R guidelines using community involvement to avoid abusive inclusion in rehabilitation schemes, NGOs that are trusted intermediaries, and abolishing punitive cut off dates for providing compensation to affected households. AMC and AUDA certainly have the capacity take up such a forward looking approach to rehabilitation in their own infrastructure projects.
in and the maximum amount of learning decreases. The opposite happens to the budget curve conditional on choosing the comparison school: the pupil-resource ratio decreases and the amount of learning per unit of schooling increases. Hence the budget constraint rotates out, and the maximum amount of learning increases. This leads to more participation in comparison schools and less participation in treatment schools than there would have been in absence of the transfers or in the case of a perfectly elastic supply curve.

The intention-to-treat estimator computed from the data of the randomized evaluation incorporate price increases in treatment schools and price reductions in comparison schools that are due to the transfers. When school meals are implemented on a general scale, we would expect fewer transfers because the budget constraint of a household would rotate out regardless of which school it chooses. Therefore we would expect the prices in the general setting to be higher in comparison schools and lower in treatment schools than in the randomized evaluation. Hence the estimator of school participation in the randomized experiment is a lower bound of what would happen in a setting where school meals are implemented on a general scale. Note that we do not need baseline children to transfer at all to obtain these general equilibrium e¤ects. It is su¢ cient that children who do not belong to the baseline but who would otherwise have attended, switch schools.

# 6.2 Empirical evidence on price e¤ects

The empirical evidence from the school meals program supports the case of an upward sloping supply of schooling, though there are puzzles remaining. The upward sloping supply curve implies that the price of schooling in treatment schools will be higher than before the introduction of the program and that the price will be higher than in comparison schools. This is supported by the empirical evidence: the price per day of participation was 58 percent higher in treatment schools than in comparison schools (Table 15, column 4).<sup>40</sup> Children from treatment school baselines paid 57 percent more per day of school attendance than children from comparison school baselines. Non-baseline children paid 66 percent more in treatment schools than in comparison schools. The di¤erence between the ...gures is statistically signi...cant at the 1 percent level for both groups (Table 15, columns 2 and 3). This con...rms the hypothesis that teachers became more stringent in fee collections. Most of the money went towards higher teacher salaries. Both in absolute amounts and

> 40 The price per day of school participation is de...ned as the total amount paid by the parents, divided by the observed average attendance of the child (on a scale of zero to one) and by the number of school days. Note that the price per unit of schooling is a function of the price per day and the amount of schooling per day. I show further in the paper that the school meals program crowded out teaching time. Therefore the di¤erence in price per day of participation between treatment and comparison schools is a lower bound on the di¤erence in price per unit of schooling.

42
- 45 -

**Table 22**

|||**Istanbul St**|**ock Exchange**||||
|---|---|---|---|---|---|---|
|_|No. of Listed<br> Companies|Market Capitalization<br>(in billion USdollar)|Trading Value<br>(in billion US dollar)|Price<br>Index*|P/E<br>Ratio|Dividend<br>Yield(-/%)|
|1986|80|0.94|0.01|132|5.1|9.2|
|1987|82|3.12|0.12|385|15.9|2.8|
|1988|79|1.13|0.12|120|_5.0_|10.5|
|1989|76|6.76|0.77|561|15.7|3.4|
|1990|110|18.74|5.85|643|24.0|2.6|
|1991|134|15.56|8.50|501|15.9|4.0|
|1992|145|9.92|8.57|273|11.4|6.4|
|1993|160|37.82|21.77|833|25.8|1.6|
|1994|176|21.78|23.00|413|24.8|2.8|
|1995|205|20.78|52.46|383|9.2|3.6|
|1996|228|30.80|37.74|534|12.2|2.9|
|1997|258|61.88|58.10|982|24.4|1.6|

*US$ base, January 1986=101

Source: Istanbul Stock Exchange Foreign Participation in Capital Markets A milestone in the Turkish financial liberalization process was the August 1989 issuance by the government of Decree No. 32 regarding capital account transactions. Under the decree, nonresidents were allowed to buy and sell Turkish securities quoted on the domestic stock exchange or government securities through intermediary institutions operating in Turkey. They were also permitted to transfer income and the sales proceeds of these securities abroad through banks and other authorized financial institutions. Residents were permitted to purchase securities issued by foreign countries through authorized financial institutions. They were also allowed to transfer the foreign exchange required to purchase such securities abroad.

There is no information available as to how much the outstanding Treasury bills and government bonds are now held by foreign residents. However, there has been a huge increase in the proportion of shares held by foreign residents. Chart 11 shows that, according to the Istanbul Stock Exchange, foreign holdings of the "free float" market capitalization reached more than 50% in 1997, as compared to about 33% in 1996, and less than 5% in 1990. By the end of April 1998, net equity investments by foreign investors was approximately US$7 billion.
# **What Do We Know about Poverty in India in 2017/18?**

_Ifeanyi Nzegwu Edochie, Samuel Freije-Rodriguez, Christoph Lakner, Laura Moreno Herrera, David Locke Newhouse, Sutirtha Sinha Roy,_ _Nishant Yonzan_<sup>*</sup> JEL codes: I32, C53.

Keywords: poverty, survey-to-survey imputation, India.

> * Corresponding authors: Samuel Freije-Rodriguez (sfreijerodriguez@worldbank.org) and Christoph Lakner (clakner@worldbank.org). All authors are with the World Bank. Ifeanyi Nzegwu Edochie, Samuel Freije-Rodriguez, Laura Moreno Herrera, David Locke Newhouse and Sutirtha Sinha Roy are with the Poverty and Equity Global Practice. Christoph Lakner and Nishant Yonzan are with the Development Data Group. This is a background paper for the Poverty and Shared Prosperity Report 2020. The authors wish to thank Junaid K Ahmad, Benu Bidani, Maurizio Bussolo, Andrew Dabalen, Haishan Fu, Dean Jolliffe, Aart C. Kraay, Daniel Mahler, Ambar Narayan, Pedro Olinto, Carolina Sanchez-Paramo, Umar Serajuddin and Hans Timmer for helpful comments and suggestions. Special thanks to Nobuo Yoshida, Shinya Takamatsu and Roy van der Weide for special comments and reviewing the econometric methods. Thanks go as well to members of the World Bank’s PovcalNet team and the Data for Goals team, with whom several consultation meetings took place. We gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. The findings and interpretations in this paper do not necessarily reflect the views of the World Bank, its affiliated institutions, or its Executive Directors.
**Table 11**

C6te **d'Ivoire: P0** Estimates **of Poverty with and without Household-Size Weights** **_by_ Region. 1985-88 (Poverty Line = 128,600 CFAF)**

|||1985|||1986|||1987||1988|
|---|---|---|---|---|---|---|---|---|---|---|
||A.|B.|**%**|A.|B.|**%**|A.|B.|%|A.*|
||||Diff.|||Diff.|||Diff.||
|Abidjan<br>|0.041|0.034|-17.07|0.206|0.166|-19.42|0.078|0.074|-5.13|0.139|
|Other Cities<br>|0.272|0.236|-13.24|0.277|0.223|-19.49|0.225|0.244|8.44|0.409|
|East Forest<br>|0.501|0.479|- 4.39|0.425|0.395|-7.06|0.445|0.435|-2.25|0.494|
|West Forest<br>|0.209|0.178|-14.83|0.283|0.200|-29.33|0.375|0.376|0-27|0.553|
|Savannah<br>'|0.523<br>|0.502|-4.02|0.522|0.481|-7.85|0.589|0.578|-1.87|0.652|
|Cote dlvoire|0.324|0.300|-7.10|0.346|0.299|-13.58|0.354|0.348|-1.69|0.459|

- .A. P0 computed without household-size weights.

- B. P0 computed with household-size weights.

- Since 1988 data were obtained entirely under the revised sampling frame and sampling procedures, no household-size weights are applied.

23
<!-- Start of picture text --> Correlation between between sectoral productivity and change change in employment employment share Correlation between sectoral productivity and change and change change in employment employment share<br>2 Tanzania 2 Senegal<br>3g 1970-1980 1980-1990 g 1970-1980 1980-1990<br>B°) Bt407407 ° | Bae”” ff 8°) Be~407407 |. ae 735 ett e e<br>g== sé-- raOreOre 6ii rratyOnOn gSS we-- eS heGROGRO -oo raeOROR ~ Oy<br>245 af ° 05 “15 at =.05 05 “a 05“ 0 05 at \os_ 7 7 ° 05<br><!-- End of picture text -->

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-40 -

Bulow, Jeremy and Kenneth Rogoff<sup>(1990): "Cleaning up Third World Debt Without Getting</sup> taken to the Cleaners", Journal of Economic<sup>Perspectives, 4(1), 31-42.</sup>

- Campos, iose Edgardo (1989): "Legislative<sup>Institutions, Lobbying, and the Endogenous Choice</sup> of Regulatory Instruments: A Political Economy<sup>Approach to Instrument Choice", Journal of</sup> Law. Economics. and Organization, 5(2),<sup>333-353.</sup>

- Chikulo, B.C. (1985): *Reorganisation<sup>for Local Administration in Zambia: An Analysis of the</sup> Local Administration Act, 1980",Public<sup>Administration and Development, 5, 73-81.</sup>

- Coase, Ronald (1960): "Te Problem of Social<sup>Costs",The Journal of Law and Economics, 3,</sup> 1-44.

- Conyers, Diana (1984): "Decentralization<sup>and Development: a Review of the Literature", Public</sup> Administration and Development, 4, 187-97.

Crampton, Peter, Robert Gibbons, and Paul<sup>Klemperer (1987): Dissolving a Partnership Efficiently",</sup> Econometrica, 55(3), 615-632.

- Demski, Joel and David Sappington (1987):<sup>"Hierarchical Regulatory Control", Rand JournalQo</sup> Economics, 18(3), 369-383.

- Downing, Paul and William Watson (1974):<sup>"The Economics of Enforcing Air Pollution Control",</sup> Journal of Environmental Economics and Management,<sup>1, 219-236.</sup>

- Eskeland, Gunnar and Emmanuel Jimenez<sup>(1991): "Choosing Policy Instruments for Pollution</sup> Control: A Review", World Bank working paper,<sup>WPS 624.</sup>

- Estache, Antonio (1991): "Municipal Environmental<sup>Issues in Brazil", mimeo, World Bank.</sup>

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1968b) carried out detailed empirical testing of competing models--accelerator, neoclassical, liquidity, and market value--at the firm-level and found the neoclassical model to be the best. The Jorgensen and Siebert sample consisted of fifteen large manufacturing firms for the 19491963 period.6 However, Elliott (1973) re-estimated the models of the original Jorgensen sample and cane to quite different conclusions. Elliott's sample consisted of 184 firms for the 1947-1963 period. In cross-section estimates, cash flow model was found to be the best, while in time-series estimates, the accelerator model was better than all others. In general, Elliott's results nullified Jorgensen's results for the neoclassical model and confirmed the need to have a more eclectic framework towards understanding investment decisions by firms.

As noted earlier, Eisner's studies consistently indicated the superiority of the accelerator/accelerator-cash flow models compared to the neoclassical model of investment. Grabowski and Mueller (1972) carried out a testing of the managerial and stockholder welfare (neoclassical) models of firm expenditures using data for 66 firms for the 1959-1966 period. Their empirical results indicated the managerial variant of the model to be far superior to the stockholder welfare maximization version.

From this point onwards, almost all the testing of alternative models of investment that has been undertaken in the literature has been confined to the level of the aggregate economy. One addition to the research agenda has been the estimation of separate models for structures and equipment. In particular, there has been an emphasis on understanding the role of tax policy 6See Jorgensen (1971) for a survey of other studies of investment up to the 1970s.

9
|Pakistan|Manufacturi<br>ng firms|Power outages<br>and<br>load<br>shedding|Calculations<br>based<br>on<br>firm<br>survey|Firms<br>experience<br>annual<br>output losses of US$ 8.4<br>billion or 3.1 percent of GDP<br>due to power outages and<br>load shedding|(Zhang, 2019)|
|---|---|---|---|---|---|
|Cameroon|Industrial<br>firms|Power outages|Descriptive<br>statistics<br>from<br>firm survey|Firms on average experience<br>total output losses of at least<br>about 31 percent due to<br>power outages|(Diboma<br>and<br>Tamo<br>Tatietse,<br>2013),<br>own<br>calculation<br>of<br>average|

# **c. Elasticity of firm productivity with respect to power supply quality**

|**Country**|**Type**<br>**of**<br>**firms**|**Power**<br>**supply**<br>**quality variable**|**Method**|**Effect**<br>**of**<br>**low**<br>**electricity**<br>**supply quality on firms**|**Source**|
|---|---|---|---|---|---|
|26<br>African<br>countries|World Bank<br>Enterprise<br>survey|Number<br>of<br>power outages<br>in<br>a<br>typical<br>month|Regression|An increase in the number of<br>power outages in a typical<br>month<br>by<br>one<br>reduces<br>productivity<br>by<br>0.1–0.2<br>percentage points|(Bbaale, 2018)|
|15<br>Sub‐<br>Saharan<br>African<br>countries|World Bank<br>Enterprise<br>survey|Outage<br>frequency|IV regression|A one percentage increase in<br>outage frequency is<br>associated with a decline in<br>firms’<br>productivity<br>by<br>between<br>0.6%<br>and<br>1.1<br>percent<br>(Angola<br>1.85%,<br>Burkina<br>Faso<br>1,36%,<br>Cameroon 2,52%, DR Congo<br>2,51%, Ghana 2,22%, Kenya<br>1,50%, Mali 1,33%, Malawi<br>2,09%, Nigeria 3,46%, South<br>Africa 1,35%, Rwanda 2,40%,<br>Tanzania 2,01, Uganda 2,38%,<br>Zambia 2,51%|(Mensah, 2016)|
|23<br>African<br>countries|World Bank<br>Enterprise<br>survey|Outage<br>frequency<br>per<br>month|IV regression|A one percent increase in<br>outages leads to a 3.5 percent<br>loss in firms’ total factor<br>productivity|(Mensah, 2018)|

# **d. Elasticity of firm production with respect to power supply quality**

|**Country**|**Type**<br>**of**<br>**firms**|**Power**<br>**supply**<br>**quality variable**|**Method**|**Effect**<br>**of**<br>**low**<br>**electricity**<br>**supply quality on firms**|**Source**|
|---|---|---|---|---|---|
|23<br>African<br>countries|World Bank<br>Enterprise<br>survey|Outage<br>frequency<br>per<br>month|IV regression|A<br>one<br>percentage<br>point<br>increase in the frequency of<br>power<br>outages<br>decreases<br>firm‐level<br>output<br>by<br>3.3<br>percent; this increase in<br>outage frequency also results<br>in a 2.7 percent loss in firm<br>revenue|(Mensah, 2018)|
|India|Micro,<br>Small,<br>and<br>Medium<br>Enterprises|Power outages<br>and<br>load<br>shedding|IV regression|A<br>one<br>percentage<br>point<br>increase<br>in<br>shortages<br>increases production costs of|(Zhang, 2019)|

23
year old from 16 percent to 12.5 percent. Further two years of study would shave additional 2.5 percent off to 10 percent. For primary education (not showed), the extra study time costs 3 percent points, and for tertiary education (not showed) close to 5 percent points. For comparison, additional time to complete a degree affects returns more than the difference in re-entry age to the education system from 20 to 30 years of age.

**Figure 6 - How study completion time affects returns to secondary education**

<!-- Start of picture text --> 20%<br>18%<br>16%<br>14%<br>12% re-entry at age 20<br>10% re-entry at age 30<br>8% re-entry at age 40<br>6%<br>4%<br>2%<br>0%<br> 6 8 10<br>(ordinary) (base scenario)<br>Completion time of secondary education (years)<br>Rate of return<br><!-- End of picture text --> Source: Authors’ calculation based on _Encuesta de Hogares_ 1995-2000 Note: Secondary education ordinarily consists of 6 years of fulltime classes _<u>Part-time work</u>_ <u>. The alternative scenario that adult learners do not work part time—and</u> complete the studies on time—reveals the decisive importance of foregone income. For primary education, the “breakeven age”—the age where the return is zero—falls by almost 15 years to the age of 30, Figure 7. It becomes unprofitable for a 30 year-old to complete primary education if there is no possibility of working part-time. At the secondary level the breakeven age declines by ten years to 40, while for tertiary education, the break-even age only decreases by 5 years to the age of 50.

16