rafmacalaba/datause-encoder-data huggingface dataset:
2:;!

A second technique to evaluate the halo effect is to examine the impact of participation on project performance indicators that vary in their objectivity. In addition to OPE several more objective indicators of project success were coded, including 'percent of water system in good condition," or "percent of population target reached." To the extent that these more objective phenomen **a** re relatively less susceptible to halo overestimation, the possible halo effect bias should be reduced.

If the true coefficient were equal in the two models (which is not clear-see below), the more objective indicator should be systematically lower than the upwardly-biased subjective indicator. Table 6 presents these results. There is no evidence that the more subjective indicators (such of OPE) have systematically larger estimated impacts.

Table 6: Impact of participation by various indicators of perfonrance

||Overall Project<br>Effectiveness|Objective<br>Value of<br>Benefits|% of Water<br>System in Good<br>Condition|% of Target<br>Populaion<br>Reached|
|---|---|---|---|---|
|Bivariate||X|||
|/3 on OBP|0.62|0.53|0.54|0.29|
|t-statistics|10.6|10.3|6.4|5.3i;|
|N|121|120|98|118.00|
|R<sup>2</sup>|0.49|0.47|0.29|0.19|
|Full multivari|ate||||
|_,B_on OBP|0.24|0.27|0.29|0.25|
|t-statistic|3.8|3.6|2.4|2.50|
|N|68|68|60|68.00|
|R<sup>2</sup>|0.89|0.79|0.77|0.47|
Finally, embedded in the above, is the notion that how institutions function depends very much on the incentives provided to the relevant agents. People’s incentives depend on the whole institutional system; to understand how various institutional features work together, it is necessary to understand the details of each institution.

This paper starts with a general introduction which reviews some of the existing empirical evidence on the importance of formal courts to economic development. The second part of the introduction discusses some examples of the process of change drawing on the limited literature on this subject.<sup>6</sup> The second section is devoted to a discussion of the various institutional features that must be considered when designing court reform. A final section of the paper is devoted to one particular type of institution - procedural law. It draws on a recent project conducted jointly by Harvard University, Lex Mundi and the World Bank, that looks at examples of procedural law in countries around the world. The purpose of the discussion is to illustrate , in light of what recent studies have shown, and the lessons mentioned above, how countries may think about judicial reform going forward.

The paper also highlights systematic differences among different regions of the world. This type of regional classification may be a purely spurious one—that is one might argue that contiguity does not necessarily imply similarity of either outlook, culture, or experience. However, it is almost universally true that countries are keen to learn about institutions in neighbouring countries. A regional perspective can highlight in what way neighbouring countries may learn from each other but also the limitations of merely looking regionally.

> 6 This discussion is based on World Bank, 2001, and background work done for the report.

5
34 Effbcts ofEuery Prce Clma_u _Diesel Price Incrases 1984-85: 7he Indurtrial Sector_ Given thWeimp c of diesel to the industrial sector, what was the impact of 1984-85 diesel price increase for this sector? Industrial subsectors may differ both with respect to the importance of energy inut in dw production process and the ease of substitution between energy and other priay inputs and substituion between differet fuels Table 2.2 shows ihe structure and growti of production in the industrial sector.

As table 22 shows, advities that dominate the Malaysian industrial sector are not pariculry aw naDivec ITh tare of en cost in ihe total cost of production in dif&erent subsectors lies between 0.7-1.9 pern, with the exception of non-metallic mineral products such as ceramic and glass industries whero energy cost share is 6.2 percent The impact of an increase in diesel prices on industral output depends on the share of diesel in the cost of production, interfuel substitution possibilities and the demand elasticity for output of these sectors. Given the low cost share, the effect of a diesel price increase on industi output would be laively small. For sectors where output demand is inelastic the effect of the pnce icrease on eutput would be fiurher reduced. The lack of a significant output loss during 1984, as reported above, is to be expected. However, one observes a fail in mau g output during 1983, mainy in wood and rubber products and non-metallic mineral products, subsectors where energy cost hares Hlie between 1.4 and 6.2 percent _Table 2.2: Maladia Index ofIdustrial Production (1981 = 100) and Energy Cost Shares_

||Weig|ht -1992|1983|1994|1985|1986|Enem cost share|
|---|---|---|---|---|---|---|---|
|Mining|_304_|105.4|132.0|165.4|166.4|192.4|-|
|<br>Manufacturing|65.7|103.6|112.6|125A|117.6|126.1|1.4|
|<br>Food manufag|10.6|109.0|109.5|122.9|134.3|154.7|0.7|
|Wood,woodandcorkproducts|_5.5_|108.4|121.4|99.9|89.0|90.1|1.9|
|Industial chemicals|0.9|86.4|88.7|101.4|106.8|99.5|1.3|
|Rubberppmduct|1.7|105.7|108.4|119.2|112.9|124.4|1.3|
|No_uinctaicmidrproducts|3.5|94.8|99.9|111.3|_99.4_|58.2|6.2|
|Electicl mac<br>y|9.8|126.7|148.5|201.3|151.9|-|0.9|
|Trnspotcquipmcnt|2.0|96.0|111.1|119.7|120.2|72.3|1.3|

**_Nt_** _Enow._ cot **are** Is **defined a.** **_amd cds_ a perlag** _ofoupuL_
classifications commonly used in trade analysis. For textiles and clothing, the ICS is relatively imprecise in its classifications: heading 61.020 “clothes” is distinguished from 61.040 “headgear” and 61.060 “footwear”, but it is not possible to drill down to any lower level of disaggregation. We therefore have to rely both on the ICS classification and on verbal descriptions to manually map standards to Harmonized System products.

The data collection process for the World Bank database works as follows. First, Perinorm is searched for EU standards (coded as “EN”), and basic information is extracted manually from individual records. We limit attention to those documents identified as “standards” by Perinorm, and exclude all other document types included in the database. (Moenius, 2000, identifies 39 partially overlapping document types in Perinorm.) Data captured for each standard include the dates of entry into force and withdrawal, and a 1-0 dummy variable indicating harmonization with an ISO standard. That variable is coded according to whether or not Perinorm includes an ISO standard in its list of linked standards within each record, along with a code indicating that it is “equivalent” or “identical”. In the second stage, each standard is mapped to one or more HS 4- digit products using the short, verbal description provided by Perinorm, as well as its ICS code. Next, we cross-check all of the above information against CEN’s online standards catalogue (http://www.cen.eu/catweb/cwsen.htm). Then finally, we produce simple counts of the number of standards affecting each HS 2- and 4-digit product category over the period 1995-2003. A standard is considered to be in force for a given year if it came into force before or during that year. If it is withdrawn at some point during the year, it is still assumed to be in force for the entire year. Amendments to existing standards are counted as additional standards.

It is useful to provide a simple example of the above process. Standard number EN 20105 provides a series of tests for determining the color fastness to washing of textiles. It is typical of

- 10 -
Bank.<sup>5</sup> These mobility restrictions serve to safeguard Israeli civilians who have settled beyond the wall inside the West Bank: _Israel’s primary justification for the movement restrictions is that they are necessary to protect Israelis within its jurisdiction and Israelis living in the West Bank or traveling on West Bank roads._ _...the settlement enterprise, including the roads built for it, was one of the primary factors in shaping the restrictions regime that Israel has forced on the Palestinians since the beginning of the Second Intifada._ B’Tselem (2007) The ethos behind obstacle employment reduces concerns about endogeneity: mobility restrictions served security objectives and did not target market access nor economic performance. We worry that Israeli security objectives accidentally and indirectly related to Palestinian economic conditions, but we do not worry that they are deliberately or directly related.<sup>6</sup> Due to a lack of local GDP data, we use nighttime lights as a proxy for local economic output. In theory, Israel could control lights in the West Bank by regulating the supply of electricity to Palestinian localities. If cutting of electricity were used as a punishment device, then this “unobserved variable” could correlate both with NTL and the deployment of road closure obstacles. There is no documented (or anecdotal) evidence however that Israel is manipulating electricity supply as a penalty device in the West Bank. Israel has on occasion halted the supply of electricity but in all cases this has been for commercial reasons, i.e. in response to overdue electricity bills. Thus, there is no reason to believe that this will bias our results.

Another potential cause for concern is that Palestinian firms may have relocated in response to loss of market access caused by obstacle deployment. We expect firm

> 5According to UNOCHA, decisions regarding the placement of obstacles in the West Bank are made by the brigade commander of the Israeli Defense Forces assigned to the location or governorate of interest, often in consultation with the Israeli security forces. The decision to introduce or replace an obstacle is motivated by safety concerns, not by economic considerations.

> 6One conceivable endogeneity concern, for example, is that in addition to obstacle deployment, the Israeli army performed other security measures such as raids, arrests or curfews, which could be correlated with both obstacle placement and local economic prospects. Though we attempt to assuage these and other endogeneity concerns later by using an instrumental variable approach, an important argument against this concern is that obstacles were essentially a defense security measure, and affected Palestinian market access only as an afterthought. By contrast, raids, arrests, and curfews were all offensive (proactive) security measures taken deliberately against specific Palestinian locations. We do not expect defensive and proactive measures to correlate spatially. For a more in-depth contextual overview we refer the interested reader to Abrahams (2018b).

6
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# 2.9.4 Student and teacher sampling

A number of outcomes of interest, including test scores, are drawn from a sub-sample of students and teachers that are interviewed and tested as part of MLSS. For teachers, the MLSS samples 10 teachers per school at baseline, all of which are re-surveyed at endline if eligible, with additional teachers added to replace those which become ineligible for interview and testing (for example, having left teaching). See section A.0.2 for details.

For students, the study uses a longitudinal sample. At baseline, a gender-balanced random sample of 25 Grade 4 students was sampled in each school. At endline, a subset of 15 students were randomly selected for re-survey at endline.<sup>24</sup> Students who had transferred to new schools were tracked to the new schools, and students who had dropped out were tracked to their homes where possible. The longitudinal sample enables us to measure the impact of the intervention on individual students’ learning trajectories, measured through endline test scores. The median student from the longitudinal sample was in Grade 7 at endline.<sup>25</sup>

# 2.9.5 Power Calculations

We follow convention in the social sciences for power calculations, using a significance level (probability of Type I error) of 0.05 and power (probability of avoiding a Type II error) of 0.8.

We are interested in calculating the endline sample size necessary to detect an effect of _∼_ 0.2 S.D. in student learning, our main outcome of interest, in each of the two specifications.

Our randomization takes place at the school level. We use the mean score for the baseline control group of 500, S.D. of 115, cluster size of 15 students per school. The intra-cluster correlation is 0.28. The required sample size to achieve this effect is 206 (103 in the treatment group and 103 in the control group).

For the identification of the effects of the intervention, our MLSS overlap exceeds the necessary sample size for both treatment and control. We are reasonably powered for subgroup analysis.

> 24 A reserve sample of two additional students from the baseline sample was used to replace students who were ineligible for the interview and testing (for example, who had died) or were untraceable.

> 25 At endline, in addition to the tracking and re-testing of the longitudinal sample, a new gender-balanced random sample of 15 Grade 4 students was sampled. Findings for this Grade 4 sample are presented in Appendix B.

19
experience is much lower, driven by lower average level of labor supply and by interruptions due to maternity. Reducing the length of such interruptions through child care services should be a top priority for policy makers aware of the drivers of gender differences.

Another important field of intervention is granting equal pay at work. In the case of Azerbaijan, the gender gap in earnings is mainly due to the way the market rewards the same characteristics differently for men and women. The results of the Oaxaca and Blinder decomposition analysis show that both their human capital and their hours of work are rewarded more in the case of men. Discrimination against women is confirmed by the size of the so-called sticky floor and glass ceiling effect based on the Machado and Mata decomposition analysis. Women are paid much less than men both at the bottom and at the top of the wage distribution.

20
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**Table 3. Average landholding size (acre) and MPCE of farmers with different types of irrigation pump**

|**Farmer Type**|**Land Holding (Acre)**|**Monthly Per Capita Expenditure**<br>**(In Rs.)**|
|---|---|---|
|Don’t use a pump|1.9|738|
|Own a diesel pump|2.8|808|
|Rent in a diesel pump|2.1|690|
|Own an electric pump|3.3|889|

_Source: Created by authors using primary data collected from farmers_ Access to grid electricity in Jharkhand has improved rapidly over the last few years. The percentage of households with electricity connections increased from 32 percent in 2011 to 87 percent in 2019. The share of rural households using electricity as the primary source of light—as opposed to kerosene—increased from 20 percent in 2015 to 84 percent in 2019. Even as access to electricity increased, the duration and the quality of the power supply also improved. Between 2015 and 2018, about one-fifth of the rural households in Jharkhand moved from consuming electricity for 0-4 hours/day (tier 0) to 4-8 hours/day (tier 1).<sup>4</sup> Encouraged by the recent improvements in power supply, many farmers in Jharkhand are investing in electric irrigation pumps. Because the electricity consumption for irrigation is heavily subsidized in the state, cheap irrigation from the growing number of private electric pumps can make the community irrigation systems promoted by JSLPS and other institutions less attractive to farmers. These implications are discussed in more detail in the final section of the paper.

Paddy is the dominant crop in the Kharif season and the largest crop across all three seasons in terms of the cropped area. All farmers in our sample grow Kharif paddy. Pulses, millets, groundnut, and maize are other common Kharif crops. Vegetables dominate the cropped area in the Rabi and the summer season. Potato, tomato, peas, onion, and cabbage are the most commonly grown Rabi crops while tomato, long beans, and squashes are common in Summer. Table A.14 in the appendix shows the cropping pattern of the farmers in our sample in all three seasons.

> 4 Data reported in this paragraph have been taken from the ‘Power for All Factsheet’ available at: <u>https://www.powerforall.org/application/files/9615/8470/4205/FS_Jharkhand_sets_to_improve_rural_electricity_service_and_pr ovide_green_jobs_with_renewables7.pdf</u>

13
the differences in the evaluation of temporal and spatial deflators, and the consistency of within‐ household spending and asset ownership trends with poverty trends. While these components will be the main focus of the report, other potential sources of divergence will also be examined, including the differences in survey design, methods of data capture (for example, diary versus recall), the length of the reference period, the number of consumption items and the degree of commodity details.

# **II. Poverty, Inequality and Distributional Pattern of Growth in HBS and NPS: Actual Situation**

This section presents the levels and trends in poverty and inequality estimated using HBS 2007, HBS 2011/12 and the three NPS waves. Differences in the levels of poverty are expected across the surveys due to differences in survey collection methods and the methodological approaches used to measure poverty, i.e., the construction of the consumption aggregates, the price deflators applied, and the poverty line. All these issues are investigated in depth in section III. Meanwhile, this section also examines the differences in the Growth Incidence Curves (GIC) and pro‐poor growth patterns between the HBS and NPS surveys.

# **A. Poverty trends**

As shown in Figure 1, the poverty rates estimated using the NPS data are significantly lower than the poverty rates measured using the HBS data. The NPS data also indicate an increasing trend in poverty between 2008 and 2013, while the HBS data reveal a declining trend between 2007 and 2012.

3
- 24 -

# Table 9: Estkiates of Production Functlons

# (Cobb-Douglas Specification)

|Input|County<br>Gongzhuling<br> <br>(N-200)|Jurong<br>(N-190)|Tal<br>(N-200)|Xlajlang<br>(N-196)|
|---|---|---|---|---|
|(a)|Land<br>.732*<br>(14.738)|.651*<br>(10.287)|.661 *<br>(7.924)|.508*<br>(7.872)|
|(b)|Capital<br>.015*|.020|-.005|.067*|
|(c)|(1.816)<br>Labor<br>.055<br>(1.279)|(1.345)<br>.2850<br>(4.735)|(.621)<br>.393*<br>(5.216)|(3.049)<br>.263*<br>(4.625)|
|(d)<br>(e)|Nitrogen<br>.147*<br>(4.074)<br>Manure<br>.029*<br>(2.592)|.086*<br>(2.691)<br>.015<br>(1.190)|.044<br>(1.364)<br>.018<br>(1.056)|.131*<br>(4.382)<br>.004<br>(.326)|
|(f)|R<sup>2</sup><br>.921|.903|.945|.900|
|(g)|Returns to<br>.977<br>scale|1.056|1.116|.975|
|(h)|t value of<br>.759<br>test for returns<br>toscale dlfferent<br>from1|2.022*|4.08*|.800|

* Slgnificant at a one-tailed 95 percent confidence level.

Notes: General: Other variables In the equation Include a constant, village dummy variables, an Indicator for production problems such as post, and human capital (age, education). The parameters are not presented.

- (a) Land Is measured as a combined area of all parcels, adjusted for quality differences through an Index based on parameters from a hedonic value equation.

- (b) Capital Is measured as the value of cropping-related equipment, machinery and draft aninals.

- (c) Labor Is measured as the actual days applied, with the weights of 1.0, .75,.5 for males, females, and children respectively.

- (d) Nitrogen Is measured as weight of pure nutrient.

- (e) Welght of manure.

- (g) Sum of coefficients (a)-(e).

- (h) Test against the null hypothesis that returns to scale are constant.