rafmacalaba/datause-encoder-data huggingface dataset:
Table A14. Regional emissions intensity index of non-CO2 emissions, Far Eastern Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Far Eastern**<br>**FD**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile**<br>**organic**<br>**components**|
|---|---|---|---|---|---|---|
|**Row Labels**|**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|A||29,05||120,80|||
|CA_col|111,59|19,27|8,56|169,28|||
|CA_gas||154,67||100,58|767,10||
|CA_oil||2 253,81|67,57|485,26|859,58|907,98|
|CB||594,97|90,28|495,63|210,19||
|DA||62,43|20,74|117,14|||
|DB||144,56|51,25|260,12|||
|DC||||400,08|251,98||
|DD||396,46|46,20|301,20|||
|DE||171,20|95,91|445,68|214,12||
|DF||143,96|28,33|103,75||198,97|
|DG||324,34|87,33|315,78||138,74|
|DH|||13,12|66,61||78,38|
|DI||358,21|178,02|835,34|||

114
**Table 2. Pre-treatment characteristics**

|||All simpl|e|Control Gro|up<br>|Sequence|Group|Coache|s Group|
|---|---|---|---|---|---|---|---|---|---|
||N|Mean|Sd|Mean|Sd|Mean|Sd|Mean|Sd|
|**_Student-level variables_**||||||||||
|Percent female|2359|0.49|0.50|0.51|0.50|0.48|0.50|0.49|0.50|
|Age|2346|12.19|0.52|12.17|0.49|12.22|0.55|12.18|0.52|
|Percent of Argentines|2341|0.86|0.34|0.86|0.35|0.87|0.34|0.86|0.35|
|Mother or father education (secondary)|1858|0.71|0.46|0.72|0.45|0.71|0.45|0.69|0.46|
|Have internet in their home|2279|0.90|0.31|0.91|0.29|0.89|0.32|0.89|0.31|
|Have air conditioning in their home|2130|0.59|0.49|0.60|0.49|0.60|0.49|0.58|0.49|
|Have at least one car in their home|2162|0.64|0.48|0.64|0.48|0.66|0.48|0.62|0.49|
|At most,missed one classper month|2288|0.65|0.48|0.66|0.48|0.64|0.48|0.64|0.48|
|**_Teacher-level variables_**||||||||||
|Percent female|91|0.88|0.33|0.85|0.36|0.89|0.32|0.90|0.31|
|Age|90|41.52|8.75|39.59|8.69|42.64|9.46|42.75|7.94|
|Percent with Post-Graduate Certificate|91|0.43|0.50|10.42|6.67|12.68|6.49|12.26|8.36|
|Percent with University degree|91|0.10|0.30|5.81|5.58|7.04|5.62|6.75|7.79|
|Seniority in teaching (in years)|91|11.70|7.19|3.52|3.28|4.09|3.49|3.23|4.29|
|<br>Seniority in teaching Science (in years)|88|6.48|6.33|0.35|0.49|0.50|0.51|0.45|0.51|
|Percent of teachers that participated in trainings|91|0.90|0.30|0.91|0.29|0.96|0.19|0.83|0.38|
|Percent of teachers that used a teachingsequence|91|0.55|0.50|0.62|0.49|0.46|0.51|0.55|0.51|
|**_School-level variables_**||||||||||
|Students per school|70|301.20|132.10|316.50|139.60|<br>289.60|127.70|296.80|132.80|
|<br>Students of 7th grade|70|42.36|19.42|45.25|22.16|39.87|16.85|41.83|19.21|
|<br>School promotion rate (%)|70|0.97|0.02|0.98|0.03|0.97|0.02|0.97|0.02|
|School drop-out rate (%)|70|0.00|0.01|0.00|0.00|0.00|0.01|0.00|0.00|
|School over-aged rate (%)|70|0.15|0.07|0.15|0.08|0.16|0.08|0.14|0.06|

40
enterprises exit. Management services definitely support and help rural entrepreneurs to stay in their businesses. If there are natural disasters, the household enterprises are more likely to close their operations. The household businesses are less likely to close their operations in the community with lower price of rice, where the cost of living is lower. Regional variations are also observed.

**Table 4.1:** **<u>Nonfarm Enterprise Dynamic: Probit Model for Entry and Closure</u>**

|Model|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Independent Variables|Entry|Entry|Closure|Closure|
|**Household Characteristics (H)**|||||
|ln (# of HH member)|**0.452*****|**0.426*****|0.720<br>|**1.367*****|
||**[0.151]**|**[0.152]**|[0.450]|**[0.505]**|
|ln (head’s years of education)|0.001|0.012|0.144|**0.443****|
||[0.061]|[0.062]|[0.166]|**[0.212]**|
|ln (head’s age)|-0.112|-0.007|-0.021|-0.114|
||[0.248]|[0.252]|[0.580]|[0.660]|
|ln (total agriculture income)|0.005|0.007|-0.032|**-0.068****|
||[0.009]|[0.009]|[0.024]|**[0.032]**|
|ln (other income)|**-0.028*****|**-0.029*****|-0.018|-0.039|
||**[0.009]**|**[0.009]**|[0.022]|[0.025]|
|**Community RIC (C)**|||||
|ln (hours to markets)||-0.197||-0.053|
|||[0.289]||[0.764]|
|ln (% of electrified)||-0.009||**-0.350*****|
|||[0.043]||**[0.103]**|
|ln (% water available)||**0.064***||-0.114|
|||**[0.039]**||[0.080]|
|ln (% high school educated)||**-0.204***||**0.676****|
|||**[0.110]**||**[0.322]**|
|Dummy: physician’s availability||-0.030||-0.330|
|||[0.135]||[0.358]|
|Dummy: management service||**0.461****||**-1.223***|
|||**[0.195]**||**[0.670]**|
|Dummy: formal fin inst.||-0.260||**1.189****|
|||[0.168]||**[0.483]**|
|Dummy: theft security||0.193||-0.550|
|||[0.167]||[0.454]|
|Dummy (disaster)||-0.099||**1.053***|
|||[0.213]||**[0.552]**|
|ln (rice price)||0.253||**-6.943*****|
|||[0.485]||**[1.267]**|
|**Dummy**<br>Rural|0.186|**0.406****|-0.182|-0.622|
||[0.136]|**[0.174]**|[0.356]|[0.491]|
|Observations|663|663|221|221|

_Source:_ 2010 Yemen RIC Survey; Notes: Sampling weights are applied; Standard errors are in parentheses; *, **, and *** denote significant levels at the 10, 5, and 1% respectively, based on standard errors; The dependent variable for entry is a dummy of a household member’s success in starting nonfarm business. The dependent variable for closure is a dummy of a manager’s decision to close after business started within the past three years.

The unexpected signs of the results are the availability of formal financial institutions and community level of high-school education. Many rural entrepreneurs are not using the formal 13
**Table 5:** **_Private Investors Favor Energy and Telecom_**

_(Total Private Sector Investment Commitments in current US$ Mil (1990-2012)_

|Country|_Water &_<br>_Sewerage_|_Transport_|_Energy_|_Telecom_|_Total_|_% of total_<br>_PPI_|_% of GDP_<br>_(2007-12)_|
|---|---|---|---|---|---|---|---|
|India|470|81,098|135,703|89,054|306,325|85.49|2.57|
|Pakistan|0|2,555|13,416|17,090|33,061|9.23|1.59|
|Sri Lanka|0|740|1,438|3,003|5,181|1.45|1.04|
|Nepal|0|0|997|135|1,132|0.32|0.66|
|Maldives|0|478|0|84|562|0.16|4.09|
|Bhutan|0|0|201|18|219|0.06|2.65|
|Bangladesh|0|0|3,285|6,855|10,140|2.83|1.14|
|Afghanistan|0|0|2|1,683|1,685|0.47|0.01|
|SAR|470|84,871|155,042|117,922|358,305|100|2.34|

_Source: World Bank Private Participation in Infrastructure Database. Note:_ % of GDP was computed as a simple average across the specified period. Afghanistan % of GDP is from 2001-2011 due to a lack of data.

**The telecom sector comes in second.** From 1990-2012, telecom received US$118 billion in private investments. In 2012, service providers invested a total of US$4.7 billion. The transport sector comes in third with a total investment commitment of US$85 billion for 1990-2012. In 2012, it received US$20 billion in private investments.

**The greatest interest from the private sector is in greenfield investments.** These investments have taken off since the mid-2000s, reaching about US$61 billion in 2010 before dropping off somewhat (Figure 4). For most South Asian countries these investments oscillate between energy and telecommunications projects. Concession projects have also drawn some private sector interest, almost all in transport. For the period 1990-2012, concessions account for 100 percent of transport investments in Maldives, 72 percent in India, and 32-33 percent in Sri Lanka and Pakistan. In addition, 4 out of India's 14 total water and sanitation projects are concessions.

13
The problem of moral hazard for commercial banks has been no less serious. As long as the government was willing to rescue firms, banks did not have to pay much attention to screening projects and monitoring the firrns. The government-supported firms became too large and dominant to go bankrupt. Large financial support was given to ailing firms or industrial sectors in the mid-1980s when many individual firms and business groups suffered heavy losses from excess capacity and the collapse of export markets in the aftermath of the second oil shock and the worldwide recession of the early 1980s, although detailed data on this are not readily available. These problems made it increasingly difficult for the government to break out of the vicious circle of financial repression. When the expansion of the Korean economy and increasing sophistication of the industrial structure called for a more innovative and market-oriented financial sector, the past legacy became a constraint for liberalization.

As the industrial policy emphasized the economies of scale to maintain international competitiveness, it led to overwhelming economic concentration within the Chaebols. It was not uncommon for the Chaebols to triple the number of their affiliates with new acquisitions in heavy and chemical sectors during the period of the HCI drive. In the face of growing public discontent against excessive economic concentration, the government had to redirect policies to emphasize the redistribution of income, which often involved increased regulation on the business activities of large firms.

# **D. CREDIT POLICY IN CHINA** 14

Budgetary allocation of all financial resources can be regarded as an extreme form of policy lending under the planning system. Since China relied heavily on budgetary funds to finance its investment and production before and during the early period of the recent reform, policy lending was one of the main instruments of financing China's economic growth and development.

The rise of the more market-oriented non-state economic sectors has reduced the importance of policy directed lending for China's overall economic growth and development. Policy directed lending has evolved into a vehicle for financing a few shrinking or stagnant state sectors and of supporting price control in farm and export-import products. New firms in the private sector in the economic zones have been financed by large inflows of overseas capital, especially form the Chinese diaspora.

Policy loans certainly contributed to a gradual and orderly transition of the Chinese economy. In particular, policy directed lending played an important role in cross-subsidizing key state supported investment projects and price control schemes in farm and export-import products. But, the growth and productivity performance of priority sectors were poor. Also, many policy loans were either diverted or turned into bad loans.

14 This section draws on Xiao and Xu (1993).

22
# **2 Background: The H1N1 pandemic and school closures in São Paulo**

In June 2009, a new influenza outbreak, H1N1 (also known as swine flu), was declared a pandemic by the World Health Organization (WHO).<sup>2</sup> From April to December 2009, Brazil confirmed 54,171 cases and 2,196 deaths from H1N1.<sup>3</sup> However, the number is likely to be much higher as many people with flu symptoms do not seek help, not all the ones that look for health assistance are tested, and the under-reporting of hospitalizations and deaths are publicly known.

At the end of June 2009, amid the increasing number of cases, the Health Department of the State of São Paulo, where 8% of all national cases were registered, recommended that public schools extend the winter break of July 2009 for a few weeks. The recommendation was based on the fact that children can act as vectors of transmission as they are less likely to adopt behavioral changes, such as washing hands regularly and avoiding physical contact for prolonged times (Klaiman et al. (2011)).<sup>4</sup> The state authority postponed the return to school for two weeks in all the schools under their management across the 645 municipalities of São Paulo state. However, each local authority of the 645 municipalities had the autonomy to decide whether or not to follow the state guidelines. By checking local newspapers from 2009, we found that 13 local authorities followed the guidance of the state Health Department (Figure A.1)<sup>5</sup> . These 13 municipalities postponed the winter break of their locally-managed schools for two to three weeks. These municipalities are among the one-fifth most populous in the state. On average, they concentrated most confirmed cases of H1N1 per 100,000 inhabitants between April and July in the state (10 versus 2 cases, Figure A.2). Overall, more than half of the public schools were closed, affecting about 70% of the students (Table 1).

> 2By May 2010, 214 countries had reported cases and an estimated death toll of more than 200,000 people (The World Health Organization (2010), Dawood et al. (2012)).

> 3DATASUS, 2009. Available through: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sinannet/cnv/influbr.def.

> 4Check the official statement of the state government of São Paulo in this link: https://www.saopaulo.sp.gov.br/ultimas-noticias/nota-oficial-da-secretaria-da-saude-sobre-retorno-as-aulas/.

> 5São Paulo, the state capital, Campinas, Diadema, Embu das Artes, Indaiatuba, Mairiporã, Osasco, São Bernardo do Campo, Santo André, São Caetano do Sul, Sumaré, Ribeirão Preto and Taboão da Serra

3
10 average, devaluations did not lead to sustained real depreciations. Likewise, Edwards (1989) takes a comprehensive look at the experiences with devaluation in developing countries and finds that the evidence is mixed. We perform a similar exercise here for devaluations in Latin American countries in the sixties and seventies. We use a small sample which is restriLted to countries that did not have another devaluation three years after the initial one.' This was done in order to separate the high inflation countries, where devaluations were a frequent event, from the low inflation countries, where devaluations were sporadic. The results of the eomputation are presented in table 2. As can be seen from there, in most cases devaluations have only a temporary effect on the real exchange rate. There are only four cases where the devaluations were clearly effective in achieving a real depreciation after three years: Venezuela in 1964, Costa Rica in 1964 and again in 1974, and Ecuador in 1970.

The contrast between successful and unsuccessful devaluations is illustrated in figure 2, which shows the evolution of the log of domestic prices and the exchange rate for Venezuela Argentina, and Peru. The 1964 devaluation in Venezuela was effective in changing the RER permanently. On the other hand, both the evolution of the exchange rate in Argentina and Peru displays a see-saw pattern, as governments needed to effect recurrernt devaluations to avoid a real appreciation resulting from persistent inflation.

One should not conclude from the above evidence that devaluations can not affect the RER. In fact, devaluations usually have a large effect on the RER in the short run. On the other hand, the effectiveness of devaluations on the RER in the longer term largely depends on the initial conditions -- especially the initial degree of overvaluation in relation to the fundamentals, the prevailing rate of inflation, etc.-- and the policy measures that accompany the devaluation.

' The data is obtained from the International Financial Statistics, and hence we only include official devaluations in the "primary" rate.
# **Table 6: Aggregate Decomposition of the Gender Differential in Agricultural Productivity At Selected Points of the Agricultural Productivity Distribution**

Agricultural Productivity Proxied by Log [Self-Reported Productivity (Birr/HA)]

||_Mean_|_10_<sup>_th_</sup>_Percentile_|_20_<sup>_th_</sup>_Percentile_|_30_<sup>_th_</sup>_Percentile_|_40_<sup>_th_</sup>_Percentile_|_50_<sup>_th_</sup>_Percentile_|_60_<sup>_th_</sup>_Percentile_|_70_<sup>_th_</sup>_Percentile_|_80_<sup>_th_</sup>_Percentile_|_90_<sup>_th_</sup>_Percentile_|
|---|---|---|---|---|---|---|---|---|---|---|
|**_A. Gender Differential_**|||||||||||
|Male Manager Value|8.502***<br>(0.0635)|7.196***<br>(0.0971)|7.686***<br>(0.0997)|8.070***<br>(0.0834)|8.345***<br>(0.0789)|8.628***<br>(0.0702)|8.803***<br>(0.0655)|9.067***<br>(0.0676)|9.311***<br>(0.0606)|9.689***<br>(0.0672)|
|Female Manager Value|8.268***|7.078***|7.525***|7.815***|8.126***|8.373***|8.549***|8.858***|9.106***|9.501***|
||(0.0935)|(0.1447)|(0.1396)|(0.1420)|(0.1179)|(0.1021)|(0.1007)|(0.1177)|(0.1088)|(0.1393)|
|Gender Differential|0.234***|0.118|0.160|0.254*|0.219*|0.255**|0.254**|0.210*|0.205*|0.188|
||(0.0889)|(0.1439)|(0.1383)|(0.1379)|(0.1185)|(0.1022)|(0.0991)|(0.1190)|(0.1068)|(0.1422)|
|**_B. Aggregate Decomposition_**|||||||||||
|Endowment Effect|0.101|0.208*|0.137|0.0900|0.0543|0.110|0.122*|0.104|0.0286|0.0623|
||(0.0690)|(0.1174)|(0.1116)|(0.0963)|(0.0920)|(0.0760)|(0.0733)|(0.0803)|(0.0811)|(0.1060)|
|Share of the<br>Gender Differential|43.2%|176.3%|85.6%|35.4%|24.8%|43.1%|48.0%|49.5%|14.0%|33.1%|
|Male Structural Advantage|0|0|1.78e-15|0|-1.78e-15|0|-1.78e-15|1.78e-15|0|0|
||(0.0065)|(0.0105)|(0.0092)|(0.0087)|(0.0085)|(0.0089)|(0.0078)|(0.0091)|(0.0090)|(0.0157)|
|Share of the<br>Gender Differential|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Female Structural Disadvantae|0.134|-0.0905|0.0237|0.164|0.165|0.145|0.132|0.106|0.176*|0.125|
|g|(0.0826)|(0.1798)|(0.1531)|(0.1282)|(0.1183)|(0.1032)|(0.0939)|(0.1025)|(0.0950)|(0.1349)|
|Share of the<br>Gender Differential|56.8%|-76.3%|14.4%|64.6%|75.2%|56.9%|52.0%|50.5%|86.0%|66.9%|
|**Observations**|||||1|,518|||||

Note: Clustered Standard Errors at EA (Enumeration Area) Level in Parentheses. *** p<0.01, ** p<0.05, * p<0.1 29
attempts to systematically organize what can be learned from the existing empirical literature according to this framework.

# **3. The Empirical Evidence**

This section reviews a sample of 80 different specifications from 30 macro level studies, realized between 1989 and 2006, that include some measure of infrastructure as an independent variable and some measure of economic performance (output level or growth, productivity level or growth) as dependent variable.<sup>15</sup> These studies were nonrandomly selected as the result of skimming through and reading the literature, so the sample can be considered to be a subset of the most often quoted references.<sup>16</sup> Sixteen of the 30 studies have been published in peer-reviewed outlets like the American Economic Review, the Journal of Monetary Economics or the Journal of Development Economics, others as book chapters or working papers.

A majority of these papers uses cross country data, the rest being either cross-state, crossregion or time series based. Their data sets cover periods that go as far back as 1949, up to 2003, and sample sizes (when not single country time series) vary from 8 to 121 countries/states. In terms of level of development, we find a relatively balanced composition between studies looking either at developed, developing or mixed settings.

In terms of technical characteristics, there is also a lot of variation. Looking at the whole set of specifications, we see that 66% use panel data, and fixed effects are included in 35% of them. The underlying theoretical framework is either a production function (58%), cross country regressions (37%), cost function (1%) or growth accounting (4%).

> 15 In a given paper, specifications testing the effect of different types of infrastructure services (telecom, transport, electricity, etc.) are considered separately, and so are estimations using different techniques (e.g. panel data, then collapsed cross-country) or those testing the effect on different dependent variables. The list of studies included, classified by type of data and techniques used is in the Appendix.

> 16 A similar “meta-analysis” exercise on a much larger sample of studies is currently being undertaken and will be the object of a forthcoming paper. The insights from the present sample should be considered as illustrative only.

14
# Poverty Comparisons with Non-Compatible Data:

# Theory and Illustrations

Jean Olson LanjouwYale University Peter Lanjouw The World Bank We are grateful for commnents to Gaurav Datt, Francisco Ferreira, Gary Fields, Emmanuel Jimenez, Jesko Hentschel, Stephen Howes, Martin Ravallion and participants at the conference entitled "Economic Growth and Poverty: The Roles of the Market, Public Action and the Community" held at the University of Namur, July 6-13, 1996.

* Corresponding author; Dept. of Economics, Yale University, 37 Hillhouse Avenue, New Haven Connecticut 06511, U.S.A. Tel: (203) 432-3568; Fax: (203) 432-6323; E-mail: Lanjouw@econ.yale.edu. c:\wp5 l\docs\lanjouw\engelpap.wp I

## **JEL: D63, 132**

Keywords: Poverty Measurement, Poverty Lines, Welfare Indicators, Measurement Error.
# SUPPLEMENT REFERENCES

- Abadzi, Helen. “Efficient learning for the poor: New insights into literacy acquisition for children.” _International Review of Education_ 54, no. 5-6 (2008): 581-604.

- Altinok, Nadir, Noam Angrist, and Harry A. Patrinos. _Global Dataset on Education Quality 19652015_ . World Bank Policy Research Working Paper No. 8314, 2018.

- Barro, Robert J., and Jong Wha Lee. “A new data set of educational attainment in the world, 1950– 2010.” _Journal of Development Economics_ 104 (2013): 184-198.

- Caselli, Francesco. “Accounting for Cross-country Income Differences.” _Handbook of Economic Growth_ , 1 (2005), pp.679-741.

- Caselli, Francesco and Antonio Ciccone. “The Human Capital Stock: A Generalized Approach. Comment.” _American Economic Review_ , 109, no. 3 (2019): 1155-74

- Caselli, Francesco and John Coleman. “The World Technology Frontier.” _American Economic Review_ 96.3 (2006): 499-522.

- Das, Jishnu and Tristan Zajonc. “India Shining and Bharat Drowning: Comparing Two Indian States to the Worldwide Distribution in Mathematics Achievement.” _Journal of Development Economics_ 92, no.2 (2010): 175–187

- Dubeck, Margaret M. and Amber Gove. “The Early Grade Reading Assessment (EGRA): Its Theoretical Foundation, Purpose, and Limitations.” _International Journal of Educational Development_ 40 (2015): 315-322.

- Gneezy, Uri, John A. List, Jeffrey A. Livingston, Sally Sadoff, Xiangdong Qin, and Yang Xu. _Measuring success in education: the role of effort on the test itself_ . No. w24004. National Bureau of Economic Research, 2017.

- Gove, Amber. _Early Grade Reading Assessment Toolkit._ RTI International, USAID and the World Bank, 2009.

- Hall, Robert E. and Charles I. Jones. “Why Do Some Countries Produce So Much More Output Per Worker Than Others?” _Quarterly Journal of Economics_ , _114_ , no.1 (1999): 83-116.

- Hanushek, Eric A., and Ludger Woessmann. “Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation.” _Journal of Economic Growth_ 17, no.4 (2012a): 267321.

- Hanushek, Eric A., and Lei Zhang. “Quality-consistent estimates of international schooling and skill gradients.” _Journal of Human Capital_ 3, no. 2 (2009): 107-143.

- Hendricks, Lutz. “How Important is Human Capital for Development? Evidence from Immigrant Earnings.” _American Economic Review 92_ , no.1 (2002), 198-219.

- Hendricks, Lutz. and Todd Schoellman. “Human Capital and Development Accounting: New Evidence from Wage Gains at Migration.” _Quarterly Journal of Economics_ , _133_ no. 2 (2017): 665-700.

- Holland, Paul W. and Neil J. Dorans. “Linking and Equating.” _Educational Measurement_ 4 (2006): 187-220.

42