rafmacalaba/datause-encoder-data huggingface dataset:
(1979) and 14.1 MW plant at Devi Ghat (1983). By 1971, Nepal and India had signed a Power Purchase Agreement with limited low-capacity exchange at various locations along the Nepal-India border. Under this border town exchange program, electric utilities in Nepal and India (Bihar) provide access to electricity to the towns that are accessible easily from across the border through a number of 11 kV and 33 kV crossborder interconnections.

|**_Participants_**|**_Cross-border electricity trade_**|
|---|---|
|**_India – Nepal_**|Nepal imported 793GWh electricity in 2013 from India over multiple<br>interconnections.|
|**_India-Bhutan_**|Electricity import from Bhutan to India was 5556 GWh in 2013-14<br>(4627 GWh in 2012-13) from Hydro power stations at Tala, Chukha and<br>Kurichu with a total export led capacity of 1416 MW.<br>As per an umbrella agreement between the two countries, India assures<br>a minimum of 5000 MWelectricity import by 2020.|
|**_Pakistan-Iran_**|Pakistan imported 419 GWh electricity in 2014 from Iran, up from 375<br>GWh in thepreviousyear.|
|**_Afghanistan-_**<br>**_Central Asia_**|Import of 2,246.2 GWh electricity from Iran, Uzbekistan, Turkmenistan,<br>and Tajikistan in 2011. CASA-1000 expected to enhance this trade.|
|**_Pakistan-India_**|Pakistan has submitted a draft MoU to India on importing electricity<br>using a 1200 MW interconnection. There are also possibilities of CASA<br>to be extended to India.|
|**_India-Sri Lanka_**|Feasibility studies for a 400-kV India-Sri Lanka have been conducted to<br>support import of up to 1000 MW electricity from India.|
|**_India-Bangladesh_**|In 2013, power systems of India and Bangladesh were interconnected<br>through a HVDC line that can support electricity export of up to 500<br>MW (expandable to 1000 MW in future) from India to Bangladesh<br>based on negotiated price andmarket based price.|

Table 1: Existing and some proposed cross-border electricity trade arrangements in South Asia Source: Compilation from Singh et al. (2013), NTDC (2014), ERLD (2014) The two countries signed a treaty in 1996 for integrated development of the Mahakali River basin, including Sarada barrage, Tanakpur barrage and Pancheshwar projects. In July 2001, the GoI nominated the Power Trading Corporation (PTC) as the nodal agency to deal with matters relating to power exchange with Nepal (Mittra, 2012). PTC is also the sole agency from the Indian side for finalizing commercial and technical arrangements/systems with the Nepal Electricity Authority (NEA) and coordinating with associated Indian agencies. A power trade agreement was signed between Nepal and India in October 2014 to expand the bilateral electricity trade between the two nations. The completion of two double-circuit transmission corridors 5
6 (Y). Points A, B, C, D and E represent firms in a given sample. Without the variable returns to scale (VRS) requirement, efficiency of firm E is compared to a synthetic firm G, constructed by simply expanding the present scale of B, the firm with the highest average product. Incorporating the VRS requirement restricts the synthetic firm to be a convex combination of firms A, B, C and D. Note that average product, the slope of a ray from the origin to a production point, increases from A to B (increasing returns to scale) and decreases from B to C to D (decreasing returns to scale). Efficiency of firm E is compared to the synthetic firm F rather than G. This measure is preferred when simple expansion of the high productivity firm (B) is unrealistic. In short, the Banker, Chaeres and Cooper (1984) modification allows for more flexible production relationships and further benefits the firm to be evaluated.

With this methodological background in mind, we present the summary of the data on the classrooms and classes in Bulgarian municipalities. Classrooms are fixed inputs, and classes are outputs. There are no standardized tests in Bulgaria which would enable us to use a more appropriate measure of output in terms of learning (Lockheed and Hanushek, 1994). Therefore, the fmcus on the technical relationship between classrooms and classes should capture ordy one, albeit important, dimension of technical efficiency in Bulgarian general education.

The source of the data is the Bulgarian National Statistical Institute publication on general education (National Statistical Institute, 1994), supplemented by the general demographic data, and cover 199 of the total of 259 Bulgarian municipalities. The difference, 60 observations, had to be dropped from the analysis due to incomplete and/or inconsistent infbrmation.
27

- , Daniela Klingebiel and Luc Laeven. 2001. "Financial Restructuring in Banking and

- Corporate sector crises: What Policies to Pursue?" National Bureau Of Economic Research. Working Paper Series, No. 8386:1-36. July.

- Drees, Burkhard and Ceyla Pazarbasioglu. 1998. "The Nordic Banking Crisis. Pitfalls in Financial Liberalization?" International Monetary Fund Occasional Paper, No. 161. Washington, D.C.

- Dziobeck, Claudia and Ceyla Pazarbasioglu. 1997. "Lessons from System Bank Restructuring: A Survey of 24 Countries." International Monetary Fund Working Paper, No. 161. Washington, D.C.

- European Bank for Reconstruction and Development. 1998. _Transition Report 1998: Enterprise Performance and Growth._ London.

- Foley, Fritz. 2000. "Going Bust in Bangkok: Lessons from Bankruptcy Law Reform in Thailand." Harvard Business School, Cambridge, Mass.

- Galvez, Julio, and Tybout, James. 1985. 'Microeconomic Adjustments in Chile during 1977-81: The Importance of Being a Grupo." _World Development_ 13, 969-994.

- Giorgio, Luis A. and Silvia B. Sagari. 1996. In Sheng, Andrew, ed. _Bank Restructuring. Lessonsfirom the 1980s._ World Bank. Washington, D.C.

- Goldman Sachs. 2002a. _Just Say No (to Public Money for Major Banks): Banks._ Goldman Sachs Global Equity Research. January 9. Tokyo.

- Goldman Sachs. 2002b. _Update on Disclosed NPLs: Banks._ Goldman Sachs Global Equity Research. February 13. Tokyo.

- Goldman Sachs. 2002c. _Public Money and Deflation Policy: Banks._ Goldman Sachs<sup>Global</sup> Equity Research. February 19. Tokyo.

- Gray, Cheryl W., and Arnold Holle. 1996a. "Bank- Led Restructuring In Poland: An Empirical Look At The Bank Conciliation Process." World Bank Policy Research Working Paper, No. 1650. Washington, D.C.

- . 1996b. "Bank-led restructuring in Poland: Bankruptcy and its Alternatives." Policy

- Research Working Paper, No. 1651. Washington, D.C.

- Hagan, Sean. 2000. "Promoting Orderly and Effective Insolvency Procedures." _Finance_<sup>_and_</sup> _Development_ 37,No. 1:50-52.

- Hausmann, Ricardo, and Liliana Rojas-Suarez. 1996. "Banking Crises in Latin America". Published by the Inter-American Development Bank. Distributed by the John Hopkins University Press. Washington, DC.
# **Table 3.2: Trade Control Measures Reported by Global Trade Alert, 2008-2011;** **<u>Sorted by Formal versus Informal Process for Application/Administration</u>**

|Country|PERCEN<br>IMPORT<br>MEA<br>Formal<br>Process|TAGE OF<br>CONTROL<br>SURES<br>Informal<br>Process|Import<br>Control<br>Measures –<br>Formal and<br>Informal|NUM<br>Export<br>Support<br>Measures|BERS<br>Export<br>Restriction<br>Measures|Support<br>Measure for<br>Domestic<br>Production|
|---|---|---|---|---|---|---|
|Argentina|40|60|108|1|7|10|
|Indonesia|40|60|20|0|5|7|
|Ukraine|56|44|9|0|2|3|
|Thailand|60|40|5|1|1|0|
|Japan|64|36|11|8|1|7|
|China|67|33|46|7|10|11|
|United States of America|76|24|17|2|0|13|
|Mexico|85|15|13|0|0|0|
|Russian Federation|85|15|68|3|13|61|
|India|86|14|50|13|10|2|
|South Africa|90|10|20|0|0|4|
|Brazil|91|9|46|8|1|6|
|European Communities|94|6|32|4|1|1|
|Turkey|100|0|11|0|0|1|
|Viet Nam|100|0|8|1|3|0|
|Peru|100|0|7|0|0|0|
|Australia|100|0|6|0|0|7|
|Republic of Korea|100|0|5|9|0|5|

Notes: a. Countries listed are those with 5 or more import control measures listed in the source table.

b. Data included are for “Implemented” measures coded “Restrictive,” i.e., “amber” or “red.”

- c. “Formal” in this tabulation includes Trade Defense Measures and Tariff Measures.

d. “Informal” in this tabulation includes Non-tariff measures (not otherwise specified), Technical barriers to trade, Sanitary and phyto-sanitary measures, import quotas, and import bans.

e. “Other support measures for domestic production” includes Bail out / state aid measures, Public procurement, Local content requirement and support for state-controlled companies.

f. “Export support measures” include Export subsidies and support for export finance.

g. “Export restriction measures” includes Export taxes and quantitative restrictions on exports.

Source: Tabulated from Global Trade Alert data “Implementing Country and Measure Type,” accessed of January 2012 18
(alternatively measured by employment share or fraction of facilities) of these six sectors in each Brazilian municipio is used as a proxy for the concentration of dirty industry.

The Brazilian data suggest that the share of these six dirty industries -- no matter whether based upon - share of employment or share in number of enterprises declines smoothly across deciles. This may be seen in columns 1 and 2 of Table 2, which show the share of the dirty six industries falling from about 55% in the poorest regions to 30-35% in the richest.

# T'able 2: **Pollution Intensity and** Plant **Scale by Income DeCile**

<!-- Start of picture text --> Dirty Industry Small Plant Large Plant<br>________________ Share Share Share<br>No. Employ No. Employ No. Employ<br>Plants ment Plants ment Plants ment<br>Income<br>Decile N6/NT E6/ET Ns/NT ES/ET NU/NT EL/ET<br>1 0.56 0.55 0.90 0.83 0.06 0.10<br>2 0.55 0.56 0.85 0.71 0.08 0.16<br>3 0.51 0.51 0.85 0.67 0.05 0.16<br>4 0.45 0.46 0.84 0.63 0.06 0.18<br>5 0.44 0.46 0.83 0.60 0.06 0.21<br>6 0.43 0.44 0.82 0.55 0.06 0.23<br>7 0.37 0.39 0.80 0.46 0.06 0.28<br>8 0.34 0.37 0.75 0.35 0.08 0.36<br>9 0.33 0.40 0.74 0.31 0.09 0.45<br>10 0.30 0.35 0.63 0.19 0.16 0.62<br>E6 = Employment in 'dirty six' plants<br>ET = Employment in all plants<br>Es = Employment in small plants<br>EL = Employment in large plants<br><!-- End of picture text --> N6 = Number of. 'dirty six' plants NT= Total number of plants NS = Number of small plants NL= Number of large plants Columns 3-6, in Table 2, present shares by decile for small, medium and large plants again based 11
36 1987 is am indicated by the 1990 SUSENAS<sup>health module (see Table 17).</sup> The overall subsidy is found to be mildly prograssive<sup>in that the</sup> subsidy as a percentage of household consumption<sup>tends to be higher for the</sup> poor. Absolute levels tend to increase<sup>with the levels of per capita</sup> expenditures, but decline as a proportion<sup>of household per capita</sup> expenditures. Hence, they are inequality<sup>reducing. However, from the</sup> evidence, it cannot be argued that the<sup>programs are particularly well</sup> targeted. Indeed, uniform (untargeted)<sup>provision of lump-sum transfers would</sup> be much more progressive. The magnitude<sup>of the hospital subsidy tends to</sup> increase much more with per capita expenditures<sup>than that of the health center</sup> benefits. The latter are generally much flatter<sup>across deciles, though they</sup> tend to increase for the top 3 rural deciles<sup>and to decrease for the top 3</sup> urban ones. This result is in line with<sup>Section 4's findings that in rural</sup> areas utilization of public health centers<sup>is not limited to poor households</sup> but that the oppo ite tendency is true in urban<sup>Indonesia.</sup> Table 19 summarizes Indonesia's "household<sup>health account" in 1987</sup> in a similar way to that done for education<sup>in Table 15. Total per capita</sup> spending on health care is found to generally<sup>increase (though withsomeups</sup> and downs) the higher the decile. Both<sup>public and private expenditures follow</sup> a similar upward trend, though public<sup>exceed private outlays for most deciles.</sup> The exceptions occur for the tenth decile<sup>for the all Indonesia and rural</sup> Indonesia distributions. In urban Indonesia,<sup>household spending surpasses</sup> that of the government starting with the seventh<sup>decile. Again we find that</sup> public provisioning is relatively more<sup>important than private provisioning for</sup> the poor.

Variations in household per capita expenditures<sup>across quantiles</sup> are the result of various factors. Specifically,<sup>spending per individual can</sup> be interpreted to be the product of the<sup>number of illnesses reported per</sup> person, times the proportion of total reported<sup>illnesses which are treated,</sup> times the level of expenditures per treatment.<sup>Table 20 presents the results</sup> of this decomposition. Clearly, the observation<sup>that private per capita</sup>
**Proposition 7** **_State-Level Effects of Population Size:_** _The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects._

# **3 Research Design**

# **_3.1 Unit of Analysis_**

To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8.6 km x 8.6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope' and `location' variables in the Uppsala/PRIO dataset. Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict.

# **_3.2 Disaggregated Dependent Variable: ACLED_**

The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO/Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths.<sup>3</sup> ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars.

> 3See the PRIO/Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004).

13
in more detail below). With nearest neighbor matching one chooses the _m_ closest comparators; we follow the common practice of using _m_ =1. As a robustness check, we also use kernel matching, which puts more weight on closer comparators than those that are more distant. Dehejia and Wahba (2002) argue that the choice of matching mechanism is not as crucial as the proper estimation of the propensity scores, but this is not a settled issue.

In practice, the plausibility of propensity score matching depends on ensuring “common support” and “balancing.” Only in the area of common support is it possible to make comparisons that allow us to make inferences about causality (Rubin and Waterman 2006), so our comparisons need to be confined to this area, and an impact evaluation is not possible unless there is an area of common support (Imbens 2004, p.7). The area of common support occurs where the densities of the estimated propensity scores for participants and for non-participants overlap, as shown in Fig. 1. Above point B there are no comparators for borrowers, so matching is not possible in this zone; below point A there are no borrowers that need to be matched. In all the results reported below there was an acceptably broad region of common support.

<!-- Start of picture text --> Region of common support<br>Density<br>for non-<br>participants<br>Density for<br>participants<br>0 A .2 .4 .6 B .8 1<br>3<br>2<br>1<br>0<br><!-- End of picture text -->

**Fig. 1. Illustrating the Region of Common Support.**

In addition, for propensity score matching to work, the treatment and comparison groups need to be “balanced.” A treated unit and matched comparator might both have essentially the same propensity scores, but this does not guarantee that they are similar in a relevant way: one village might be well educated but ageing, while another (with a similar propensity score) might be poorly educated but young.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 9 of 36_
# **1. Introduction**

Productivity is a key determinant of economic growth, but its empirical measurement remains a challenge. Measures of labor productivity at the level of the individual worker or firm are rare and, when they occur, often noisy and/or biased.<sup>1</sup> Individual labor productivity is especially difficult to measure outside a piece rate setting, where most labor activity is located. Effort and time on the job are typically unobserved by a third party, difficult to recall after the fact by the respondent, and hard to link to firm or farm output. A proposed alternative measure of worker productivity is the direct measurement of physical activity, which can be a proxy for worker effort especially for physically arduous tasks. However, the validation of physical activity measures and their consistency as a proxy for worker productivity have not been well established (Bort-Roig et al. 2014).

This paper builds on previous work on the productivity costs of malaria infection conducted at a large sugarcane plantation in rural Nigeria that pays workers piece rate wages. Using the piece rate daily earnings as the direct observation of worker productivity, the previous work estimates the high economic cost of malarial infection – the offer of a workplace based malaria testing and treatment program increases worker earnings by approximately 10% over the weeks following the offer (Dillon et al. 2014). This study, conducted in a subsequent harvest season, posits physical activity measured by accelerometer as a proxy for worker productivity, and investigates the impact of access to malaria treatment on work and productivity as well as on the proposed proxy for productivity.

An earlier literature has considered the relationship between productivity, effort, and physical activity. Becker (1977, 1985) models firms as choosing a time and effort ‘package’ from each worker, with effort per time unit a determinant of the worker’s wage.<sup>2</sup> Gibbons (1987) and

> 1 A large literature examines both the measurement of labor productivity for an individual firm and for an individual worker. <mark>For firm level studies see, for example, Bartelsman and Doms (2000), Asker et al. (2014), Syverson (2011), and Bartelsman et al. (2013); for individual worker studies see for instance Lazear (2000), Mas and Moretti (2009), Bandiera et al. (2010), Hermann and Rockoff (2012), Ziven and Neidell (2012).</mark>

> 2 Becker (1977) notes, “Although the word “effort” is used throughout this paper, it cannot yet be satisfactorily measured, nor adequately defined in words. Surely, “effort” includes the expenditure of physical energy....This paper sidesteps these difficulties by defining effort analytically by its role in a model of behavior.”

2
# **Notes**

1 Glaeser 2011; Moretti 2012.

2 In India, for example, Ankleshwar focuses on chemical production (see World Bank’s Growth Poles). The industrial plants in Ankleshwar process large quantities of basic chemicals, solvents, acids, and fuels to manufacture more than 25 percent of Gujarat’s (5 percent of India’s) pharmaceuticals, chemicals, pesticides, and dyes (Kathuria and Sterner 2006).

3 Neal 1995. 4 Cai, Chen, and Gong 2012. 5 Currie and Neidell 2005; Currie and Schmieder 2009; Currie 2011. 6 Henderson, Lee, and Lee 2001. 7 Jaffee and others 1995. 8 Stavins 1998. 9 Schmalensee and others 1998. 10 Metcalf 2007. 11 See Pargal and Wheeler (1996) and López, Sterner, and Afsah (2004). 12 Kahn 2002. 13 Bradsher 2012. 14 Besley and Burgess 2002; Banerjee and others 2011. 15 Olson 1965. 16 Glaeser, Kahn, and Rappaport 2008. 17 Ingram and Liu 1998. 18 Ahram Online 2011. 19 This expected growth in vehicles is calculated based on (.074) * 1.1 * 8 million – .074 * 8 million. 20 Packham and Massola 2012. 21 Cragg and others 2012. 22 Davis and Kahn 2010. 23 Randall 2012. 24 Leape 2006. 25 Leape 2006. 26 Baum-Snow 2007; Baum-Snow and others 2012. 27 Zheng and Kahn 2012. 28 The official exchange rate used here is 6.5 yuan per dollar. 29 Zheng and Kahn 2012. 30 Meyer, Kain, and Wohl 1965; Pickrell 1992.

> 31 For an excellent series of case studies documenting success stories, see Suzuki, Cervero, and Iuchi (2011).

> 32 Suzuki, Cervero, and Iuchi 2011.

33 Stavins 2005.

34 “Fuel Efficiency Automobile Test Data Center,” University of Denver, www.feat.biochem.du.edu/.

> 35 Hilton and Levinson 1998.

36 Nweke and Sanders 2011.

37 Brueckner, Thisse, and Zenou 2000; Margo 1992; Zheng, Fu, and Liu 2006.

38 In Santo Domingo’s largest slum ,45 percent of houses near a river flood when it rains. Housing prices reflect this risk with the poorest living in the lowest quality housing in the areas most at risk. In Caracas and Rio de Janeiro, poor families occupy steeply sloped land prone to landslides (Lall and Deichmann 2010).

39 Zivin and Neidell 2012; Currie 2011.

40 Glaeser, Kolko, and Saiz 2000.

41 Glaeser, Gyourko, and Saks 2005.

42 Brueckner and Sirdhar 2012.

> 43 Suzuki, Cervero, and Iuchi 2011.