rafmacalaba/datause-encoder-data huggingface dataset:
today’s US income distribution). Yet, despite these incomes being extravagantly high, this is only a part of the story since Gini without the British is still at a rather high level of 45 (as opposed to 48-49 with them). Consequently, the main cause of the very high inequality is a very low income level of the poor classes (a point discussed in the main text with regard to the appropriateness or not of using the subsistence minimum of $PPP 400).

One can also compare the without-the-British inequality in India in 1947 to the inequality results derived from the first Indian National Sample Survey (NSS) conducted in 1951. The expenditure-based NSS Gini is only 36.<sup>25</sup> So—(1) are expenditures significantly more equally distributed, compared to income, than we would expect (a conventional adjustment, suggested by Li, Squire and Zou (1998), is 5 to 6 Gini points while here the difference is 9 Gini points),<sup>26</sup> or (2) is Maddison overestimating India’s 1947 inequality; or (3) is he underestimating income of India’s poor, or (4) did inequality go down by several Gini points between the end of the British _raj_ and 1951?

**Population and area.** The Indian population in 1750 is estimated from Maddison (2003: appendix HS-8, Table 8a, p. 256). Interpolation based on the data for 1700 and 1820. The population for 1947 is taken directly from Maddison (2003). For both dates, the area includes the entire Indian subcontinent (today’s India, Pakistan and Bangladesh).

**Urbanization rate.** For 1750, from Bergier and Matthieu (2002: Table 1, original sources given there).  Obtained by interpolation from the urbanization rates of the Indian subcontinent of  11-13 % in 1700 and  9-12% in 1800. The paper available at = <u>http://eh.net/XIIICongress/cd/papers/33BergierMathieu422.pdf#search %22urbanization %20rate%20british%20india%22.  For 1947, obtained as interpolation between the</u> urbanization rate of 14.1% in 1941 and 17.6% in 1951; see Mohan (1985: Table 1, p. 621).

**Mean income in $PPP.** From Maddison (2004), “World population, GDP and per capita GDP, 1-2000 AD”, available at <u>http://www.ggdc.net/Maddison/content.shtml. For around</u> 1750, we assume the same income as in 1820 (the first year in Maddison’s series). For 1947, the value is taken directly from Maddison (2004).

# REFERENCES

Bergier, Jean Francois and Jon Matthieu (2002), “The mountains in urban development,” paper presented at the XIII World Congress, Buenos Aires, July 2002.

- Li, H., L. Squire and H.-f. Zou (1998), “Explaining international and intertemporal variations in income inequality,” _The Economic Journal_ , vol. 108, 26-43.

> 25 See WIDER data set available at <u>http://www.wider.unu.edu/wiid/wiid.htm, available also at http://econ.worldbank.org/projects/inequality</u> (all the Ginis dataset).

> 26 And it could easily be argued that the difference ought to be less since data from social tables are very rough in that they assign the same income for an entire class of people and do not allow for the fact that some people from a mean-poorer class may have higher incomes than some people from a mean-richer class.

56
assigned to either receive text messages focused on (i) early childhood stimulation and the home environment; (ii) nutrition and preventive health care; or (iii) a combination of topics.<sup>5</sup> The household level randomization was stratified on the level of education of the main caregiver (in most cases the mother of the child) distinguishing those with less than 4 grades, 4-6 grades and more than primary; and on whether there is a male caregiver (typically the father) in the household.<sup>6</sup> Among the 92 villages with at least one opinion leader, a village-level randomization determined whether household with opinion leaders received no message (27 villages), whether both the men and women in the leader households received messages (27 villages), or whether only the women (27 villages) or only the men (11 villages) in the leader household received text messages.<sup>7</sup> The village-level randomization of the “leader” treatment was stratified on the average level of education of the main caregiver in households with leaders (specifically a variable capturing the tercile in the education distribution).<sup>8</sup> A registration assembly was organized in each village in December 2014. All households from the treatment and control groups were invited. They all received a basic cell phone labeled as being assigned to the mother or the father of the target child (as per the randomization). This was done because a relatively large share of parents did not have cell phones. Households randomly assigned to have both the mother and father figure receive text messages were given two cell phones. Cell phones were also distributed to control households in order to rule out any effect resulting from an increase in cell phone access. Households to treatment were then assisted to activate the text mes- assigned sage intervention. This was done by sending a text message to a central server, with only treated cell phones able to register. Once activated, cell phones started receiving daily messages. Treatment households that did not participate in the assemblies received the

> 5Table A1 provides examples of text messages sent to parents of children 7-12 months old. Note that parents who received both stimulation and nutrition messages did not receive more messages. They also received one message daily, but the theme of the messages varied.

> 6Figure A2 illustrates the household-level randomization design. Note that the number of households in each treatment variation varies slightly due to stratification.

> 7Figure A3 illustrates the village-level randomization design.

> 8Randomization of the content of the messages among leader households was also stratified on access to electricity at home. This is only done for leaders because access to electricity is highly correlated with education among non-leaders.

8
Table 4: The role of bank branches from the trade-partner country: before and after the crisis

|Crisis? (Yes=1)|-0.859***|-1.033***|-1.020***|-0.925***|-0.903***|
|---|---|---|---|---|---|
||(0.317)|(0.307)|(0.311)|(0.315)|(0.318)|
|Financial<br>|0.992***|||1.000***|0.756***|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup>|(0.205)|||(0.205)|(0.209)|
|<br>Financial|-0.354|||-0.348|-0.101|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup> <sup>_×_ Crisis?</sup>|(0.390)|||(0.390)|(0.391)|
|<br>TP bank? (Yes=1)||0.340***<br>(0.090)||0.338***<br>(0.090)||
|TP bank? _×_||-0.470***||-0.472***||
|Crisis?||(0.072)||(0.072)||
|Share(TP branches)|||0.078***<br>(0.007)||0.078***<br>(0.007)|
|Share(TP branches)|||-0.047***||-0.046***|
|_×_ Crisis?|||(0.004)||(0.004)|
|Log(Sub-regional|0.120|0.227***|-0.036|0.143*|-0.097|
|GVA)|(0.086)|(0.085)|(0.090)|(0.086)|(0.091)|
|Log(Importer GVA)|0.653***<br>(0.074)|0.620***<br>(0.074)|0.698***<br>(0.076)|0.595***<br>(0.073)|0.678***<br>(0.075)|
|Log(Distance)|-1.313*** <br>(0.161)|-1.309*** <br>(0.161)|-1.296*** <br>(0.160)|-1.309*** <br>(0.161)|-1.296***<br>(0.160)|
|Log(Sub-regional|0.112***|0.121***|0.127***|0.111***|0.116***|
|GVA) _×_ Crisis?|(0.020)|(0.019)|(0.019)|(0.020)|(0.020)|
|Log(Importer GVA)|0.071***|0.089***|0.070***|0.089***|0.070***|
|_×_ Crisis?|(0.007)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log(Distance)|-0.141***|-0.153***|-0.136***|-0.153***|-0.136***|
|_×_ Crisis?|(0.020)|(0.021)|(0.021)|(0.021)|(0.021)|
|Regional (exporter) FE|Yes|Yes|Yes|Yes|Yes|
|Country (importer) FE|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.99|0.99|0.99|0.99|0.99|
|R<sup>2</sup>|0.549|0.549|0.551|0.549|0.551|
|N|42120|42120|42120|42120|42120|

The dependent variable is the _Log_ ( _Exportit_ ) expressed in 100,000 constant USD. Robust standard errors clustered at the pair level are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level.

13
**Table 3: Sensitivity analysis results: biodiesel production costs (US$/liter)**

||Ara|ble Lands<br>||Low qualit|ylands|
|---|---|---|---|---|---|
||10t/ha|5t/ha|3t/ha|2t/ha|1t/ha|
||||Main case|||
||0.83|1.20|1.68|1.07|1.67|
|||S<br>|ensitivitycases<br>|<br>||
|Increasing the social discount rate from<br>6.5%to 10%|0.87|1.25|1.75|1.15|1.79|
|Increasing wage rate by||||||
|15%|0.87|1.24|1.73|1.13|1.75|
|50%|0.97|1.35|1.85|1.27|1.95|
|Increasingland rent by15%|0.87|1.26|1.79|1.12|1.77|
|Decreasing labor productivity of<br>jatropha seeds harvesting to 45 kg per<br>dayform 60 kg per day|0.91|1.28|1.76|1.15|1.75|
|15%increased collection cost|0.89|1.29|1.82|1.15|1.81|
|Decreasing economic life of jatropha<br>plantation from30to 20 years|0.88|1.29|1.83|1.23|2.00|
|Decreasing oil contents of jatropha<br>seeds from36%to 25%|1.10|1.62|2.31|1.44|2.30|
|Increasing capital costs of biodiesel<br>extractionplant by15%|0.85|1.21|1.69|n.a.|n.a.|

**Increasing the social discounting rate from 6.5% to 10%** : The discount rate is always an uncertain variable. Based on Nepal’s Central Bank’s data we used 6.5% real discount rate in our main analysis. This rate is increased to 10% in the sensitivity analysis. The increase of discount rate to 10% from 6.5% causes the production costs of jatropha to increase by 4.2%, from US$1.2/liter to US$1.25 per liter under the 5 tonnes per hectare yield case. This implies that increased discount rate would lower the competitiveness of jatropha with diesel.

**Increased wage rate** : Although the average wage rate for agricultural labor in Nepal is one of the lowest in the world it is increasing rapidly in the recent years. The trend for rural to urban migration has been accelerated and the trend to go abroad (Middle East, Malaysia, the Republic of Korea) for unskilled jobs has increased greatly in the recent past. The trend is such that it is difficult to find agricultural labor, especially youth, in many villages in Nepal. We used current wage rate in the Chitwan district, the case study site, in the main analysis. The wage rate is US$4 (NRs 400) per day (not per hour). We carried out two sensitivity analyses on this wage rate, 15% increase and 50% increase. If wage rate increases by 15%, production cost of biodiesel increases by four to eight cents per liter (or 3% to 6%) depending on jatropha seed yields. If the wage rate increases by 50%, jatropha

22
19

**Figure 15:  Financing Structure of Chilean Firms by Size (2003)**

<!-- Start of picture text --> LT financing structure ST financing structure<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Mega Large Medium Small Micro Mega Large Medium Small Micro<br>% bank loans % public securities % bank loans % public securities<br>% ow n funds % supplier credit % supplier credit % intra company loans<br>% intra company loans % other LT fin. sources % other ST fin. sources<br>Source: FECUs<br><!-- End of picture text -->

**Figure 16:  Average Financing Structure of US Firms (2004)**

<!-- Start of picture text --> Financing structure, US Firms<br>100%<br>Other current liabilities<br>90%<br>80% Accounts payable<br>70% ST bank loans<br>60%<br>Other LT liabilities<br>50%<br>40% Inter company loans<br>30% Net Equity<br>20%<br>LT bank loans and bonds<br>10%<br>0%<br>LT liabilities ST liabilities<br>Source: Bizstats<br><!-- End of picture text --> However, Chilean banks are growing rapidly their business lines dedicated to SMEs (factoring and leasing), although these still account for a small share of total lending volumes. Factoring and leasing are the only lines of lending products dedicated mostly to medium and small enterprises (65 percent for factoring and 55 percent for leasing). Factoring and leasing account for only 5 percent of the total loan market however.
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|---|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|Pakistan: Second Partnershipfor Polio Eradication Project<br>Inaam Ul Haq|
|StrengtheningImplementation,Monitoring& Evaluation of the ImpAhsan Ali|
|StrengtheningImplementation,Monitoring& Evaluation of the ImpAhsan Ali|
|Pakistan NWFP Development PolicyCredit Two<br>Harsha Aturupane|
|Pakistan NWFP Development PolicyCredit Two<br>Harsha Aturupane|
|ID CommunityRecoveryThrough the KDP<br>Sentot Surya Satria|
|ID CommunityRecoveryThrough the KDP<br>Sentot Surya Satria|
|ID CommunityRecoveryThrough the KDP<br>Sentot Surya Satria|
|ID CommunityRecoveryThrough the KDP<br>Sentot Surya Satria|
|ID CommunityRecoveryThrough the KDP<br>Sentot Surya Satria|
|NWFP: Support to Strengtheningof Procurement RegulatoryFrame Uzma Sadaf|
|NWFP: Support to Strengtheningof Procurement RegulatoryFrame Uzma Sadaf|
|NWFP: Support to Strengtheningof Procurement RegulatoryFrame Uzma Sadaf|
|NWFP: Support to Strengtheningof Procurement RegulatoryFrame Uzma Sadaf|
|NWFP: Support to Strengtheningof Procurement RegulatoryFrame Uzma Sadaf|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|UY Institutions BuildingTAL<br>Daniel Alvarez Estrada|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
|ID SPADA in Aceh and Nias<br>Natasha Hayward|
until the last quarter of **1997,** during the Asian crisis. Redemptions continued during **1998,** increasing during the Russian crisis, and reaching 20 percent in late **1998** and early **1999.** As depicted in the graph, the fluctuations in injections/redemptions influence the funds' net asset values, which are also determined **by** movements in the underlying stock prices.

The patterns of Figures **6** and **7** are closely associated to the ones on Figure 4. During the Mexican crisis, investors pulled out of Latin American funds, and there was a large outflow from Latin American countries. Then, investors and flows returned to Latin American countries until the last quarter of **1997,** when the Thai crisis expanded to other countries. In Asia, there are no signs of fund outflows or investor redemptions during the Mexican crisis, but there are large effects during the Asian crisis. This pattern suggests that investors' decisions influence fund flows.

Now we turn to the managers. Managers cannot control the injections/redemptions of underlying investors. What they can control is the use of "cash" or "short-term positions" (e.g., **U.S.** Treasury bills). These positions help to buffer portfolios from redemptions. Holding assets that are **highly** liquid allows managers to meet redemptions without the need to sell less liquid assets. In principle, this can mute the volatility of investment outflows. However, managers can also reinforce investors actions if they increase their liquid positions in times of investor retrenchment. In this case, the volatility of flows is amplified. It is also true that-in multiple-country portfolios-the decision<sup>20 21</sup> about which country<sup>to withdraw from belongs to the managers.</sup>

> 20 Investors obviously determine the withdrawal country in the case of single-country funds.

> 21 There are two drawbacks to this data set. First, here we use data only from Latin American funds. In the future, it will be interesting to study the behavior of managers **by** considering a broader set of mutual-fund types. Second, our data do not provide a complete picture of managers' responses to liquidity squeezes because we do not have information on funds' credit lines with banks. Funds mired

> in redemptions may have resorted to using such credit lines.

21
# **<u>Table A1. Entry into Banking: Selected Responses</u>**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Licensing Requirements**|**% Yes**|**% Yes**|**% Yes**|
|Do you have the authority to take legal action against those entities that undertake<br>banking activities without a given license?|91|95|90|
|Is more than one license required (e.g. one for each banking activity, such as<br>deposit-taking, consumer lending etc.)?|16<br>(47)|16<br>(51)|16<br>(45)|
|What is legallyrequired to be submitted before issuance of the bankinglicense?||||
|Draft bylaws|94<br>(96)|92<br>(92)|95<br>(97)|
|Intended organizational chart|99<br>(99)|100<br>(95)|99<br>(98)|
|Structure of Board (composition, committees, functions)|98|100|97|
|Market / business strategy|100|100|100|
|Financial projections for first three years|99<br>(99)|100<br>(97)|99<br>(99)|
|Financial information on main potential shareholders|100<br>(98)|100<br>(95)|100<br>(99)|
|Background/experience of future board directors|99<br>(97)|100<br>(97)|98<br>(97)|
|Background/experience of future senior managers|93<br>(95)|86<br>(89)|95<br>(97)|
|Source of funds to be used as capital|97<br>(92)|92<br>(84)|99<br>(94)|
|**B.**<br>**Entry of Foreign Entities**|**% Yes**|**% Yes**|**% Yes**|
|Are foreign entities prohibited from entering through acquisition?|3<br>(2)|0<br>(0)|4<br>(3)|
|Are foreign entities prohibited from entering through subsidiary?|4<br>(2)|0<br>(0)|5<br>(3)|
|Are foreign entities prohibited from entering through branch?|14<br>(20)|0<br>(0)|19<br>(26)|
|Are foreign entities prohibited from entering through joint venture?|9<br>(5)|0<br>(0)|12<br>(7)|
|**C. Minimal Capital Entry Requirements**|**Median **|**Median **|**Median **|
|What is the minimum capital entry requirement for commercial bank operations for<br>domestic bank? (2010 Million USD)|10.0<br>(6.4)|7.2<br>(7.5)|11.5<br>(5.9)|
|What is the minimum capital entry requirement for commercial bank operations for a<br>subsidiaryof a foreign bank?(2010 Million USD)|8.1|7.1|10.5|
|What is the minimum capital entry requirement for commercial bank operations for a<br>branch of a foreign bank?(2010 Million USD)|6.4|2.4|8.1|
|**D. Rejection Rates for Entry Applications**|**ALL**|**ADV**|**EMDEs**|
|Number of domestic applications received|782<br>(321)|198<br>(109)|584<br>(212)|
|Fraction of domestic denied ( % out of # applications received)|22<br>(7)|10<br>(2)|26<br>(9)|
|Number of foreign applications received|1,141<br>(1,282)|421<br>(868)|720<br>(414)|
|Fraction of foreign applications denied ( % out of # of applications received)|7<br>(4)|3<br>(1)|9<br>(12)|
|Number of total (foreign and domestic) applications received|1,923<br>(1,603)|619<br>(977)|1,304<br>(626)|
|Fraction of total applications denied ( % out of total received)|13<br>(5)|5<br>(1)|17<br>(11)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

25
Appendix III.A Income Sources of the Elderly, Australia 1985

## Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|75.2|18.9|2.9|1.3|1.7|
|65-74 years|9.7|76.1|2.7|2.1|9.4|
|75+ years|3.2|81.1|2.5|1.4|11.9|
|65+ years|7.4|77.9|2.6|1.8|10.3|
|Quintile (65+)||||||
|1|2.2|93.8|0.3|0.0|3.7|
|11|2.4|89.0|0.4|0.0|8.3|
|III|2.1|89.2|0.7|0.0|7.9|
|IV|2.1|70.2|4.1|1.9|21.7|
|V|47.9|14.2|12.8|12.8|12.4|
|QV/QI|21.8|0.2|42.7||3.4|

Source: Calculations based on Luxembourg Income Study Data.

# Appendix **IMIB** Income Sources of the Elderly, Canada 1987

Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|78.8|3.9|2.1|9.9|5.4|
|65-74 years|4.6|33.7|0.3|21.2|40.2|
|75+ years|0.0|47.0|0.0|9.0|44.0|
|65+ years|2.8|38.7|0.2|16.6|41.7|
|Quintile (65+)||||||
|1|1.3|77.3|0.0|1.2|20.1|
|II|0.1|54.1|0.0|3.3|42.4|
|III|4.1|29.4|0.7|6.4|59.4|
|IV|4.3|9.9|0.0|31.7|54.1|
|V|_5.7_|3.8|0.3|52.3|37.9|
|QV/QI|4.4|0.0|*|43.6|1.9|

Source: See table III.A 114
28 Thaler, Richard, and H M, Shefrin 1981. "An Economic Theory of Self-Control." _Journal of Political Economy_ . 89(2): 392-406.

Thomas, Duncan. 1990. “Intra-Household Resource Allocation: An Inferential Approach.” _Journal of Human Resources_ 25(4): 635-664.

- Thomas, Duncan. 1993. “The Distribution of Income and Expenditure within the Household.” _Annales de Economie et de Statistiques_ 29: 109-136.

Villa, Kira, Christopher Barrett, and David R. Just, 2011. “Differential Nutritional Responses across Various Income Sources Among East African Pastoralists: Intra-household Effects, Missing Markets and Mental Accounting.” _Journal of African Economies_ 20(2): 341-375.
Table E2: Enrollment by Grades

||(1)<br>Lower than 1|(2)<br>1 to 3|(3)<br>4 to 5|(4)<br>6 to 8|(5)<br>9 to 12|
|---|---|---|---|---|---|
|High|3.11|2.49|1.57|1.82|1.36|
||(2.15)|(2.05)|(1.11)|(1.55)|(1.15)|
|Low Treated|6.51**|8.81***|2.85**|4.33**|3.73|
||(2.52)|(2.57)|(1.27)|(2.04)|(2.45)|
|Low Untreated|1.31|1.78|1.32|0.63|-1.29|
||(1.95)|(1.83)|(1.06)|(1.48)|(1.29)|
|Baseline|0.59***|0.73***|0.70***|0.62***|0.78***|
||(0.06)|(0.05)|(0.03)|(0.04)|(0.10)|
|R-Squared|0.38|0.54|0.59|0.57|0.65|
|Observations|3,334|3,420|3,420|3,420|3,420|
|# Schools (Rounds)|852 (4)|855 (4)|855 (4)|855 (4)|855 (4)|
|Mean Depvar|49.89|53.68|28.15|23.10|8.22|
|Test pval (H=0)|0.15|0.22|0.16|0.24|0.24|
|Test pval (_L_<sup>_t_</sup>= 0)|0.01|0.00|0.03|0.03|0.13|
|Test pval (_L_<sup>_t_</sup>=H)|0.17|0.01|0.28|0.20|0.39|

Notes: * p<0.1, ** p<0.05, *** p<0.01 a) This table disaggregates school enrollment into grade bins to examine the source of enrollment gains over the two years of the study. Data from rounds 1-4 are used since grade-wise enrollment was not collected in round 5. All grades in closed schools are assigned 0 enrollment. b) Regressions are weighted to adjust for sampling and include strata and round fixed effects, with standard errors clustered at village level. We report the number of observations and the unique number of schools and rounds in each regression; the number of unique schools may be fewer than the full sample due to attrition or missing values for some variables. The mean of the dependent variable is its baseline value. c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

39