rafmacalaba/datause-encoder-data huggingface dataset:
We also employ some ancillary data on production subsidies from the 1990 Input-Output (IO) table provided by the State Institute of Statistics [19941.2 Import tariffs and export subsidies are calibrated to collected rates in 1993, the most recent year for which we have consistent data. We also use estimates of collected VAT rates for 1990.

Table Al lists the names of our 54 production sectors, along with a 3-letter acronym that we will use throughout. This level of disaggregation provides excellent detail on the effects of tax and subsidy distortions, although there is some aggregation in the area of transportation sectors (TRN) which would be useful to have given the level and variation of SOE activity.<sup>3</sup> Table A2 lists the names of the primary factors of production identified in the SAM, along with the acronym to be used for each. Some of the occupational distinctions (e.g., L_PROF and L_ADMN) are likely to be important for understanding the impacts of reforms on the distribution of welfare across household types to the extent that we expect some correlation between household income levels and occupation. In Table A2b, we list the percent of valueadded in the sector that is accounted for by the specific labor type. If we sum across a row in Table A2a, we have the percent of value-added due to all labor types. The decomposition of labor's share into labor type is derived from De Santis and Ozhan (1994, Table B8). In addition to the Household Labor Force Survey, De Santis and Ozhan used the Census of Population (which provides data on the population according to economic activity and occupation), and the 2 The SAM only report net indirect taxes, whereas the IO table breaks out production subsidies and indirect taxes by sector.

I This aggregation is relative to the 64 sectors available in the 10 table. The IO sectors are Railway Transport (#56), Other Land Transport (#57), Water Transport (#58) and Air Transport (#59). By far the largest of these in terms of value added in 1990 was Other Land Transport, with a Gross Value Added (at purchasers prices) of 42.5 billion TKL. Water Transport is next largest, with a value added of only 3.4 billion TKL. On the other hand, Railway Transport receives the largest direct production subsidy of 331 million TKL on a value added of only 457 million TKL.

A-4
**Table 9 continued: Principal Components Analysis of Level 2 Variables**

||Sum|mary Stati|stics|Coefficients for fir|st five principal com|ponents||||
|---|---|---|---|---|---|---|---|---|---|
||Eigen-|Diff-|Prop.|Variable|Comp1|Comp2|Comp3|Comp4|Comp5|
|**CLASS**||||||||||
|Comp 1|7.06|5.58|0.59|Positive Climate|**0.31**|0.19|0.07|-0.32|-0.03|
|Comp 2|1.47|0.54|0.12|Teacher Sensitivity|**0.31**|0.19|0.14|-0.13|-0.61|
|Comp 3|0.93|0.21|0.08|Regard for Adolescent Perspective|**0.31**|-0.12|0.25|-0.01|-0.39|
|Comp 4|0.72|0.33|0.06|Behavior Management|0.00|**0.62**|-0.37|**0.63**|-0.17|
|Comp 5|0.39|0.03|0.03|Teacher Productivity|0.29|0.29|-0.08|-0.26|**0.50**|
|||||Negative Climate|-0.10|**0.33**|**0.87**|0.26|0.22|
|||||Instructional Learning Format|**0.34**|0.17|-0.02|0.00|0.02|
|||||Content Understanding|**0.33**|0.09|-0.06|-0.06|0.11|
|||||Analysis and Inquiry|0.27|-0.38|0.01|**0.46**|0.26|
|||||Quality of Feedback|**0.33**|-0.24|0.00|0.19|0.03|
|||||Instructional Dialogue|**0.31**|-0.28|0.07|0.30|-0.06|
|||||Student Engagement|**0.33**|0.13|-0.10|-0.05|0.27|
|**Teach**||||||||||
|Comp 1|2.04|0.56|0.20|Share of time teaching and learning|0.11|0.13|**0.31**|**0.59**|0.20|
|Comp 2|1.48|0.17|0.15|Supportive Learning Environment|**0.46**|-0.39|-0.03|0.00|**0.31**|
|Comp 3|1.31|0.22|0.13|Positive Behavioral Expectations|-0.13|0.06|**0.31**|**0.64**|-0.12|
|Comp 4|1.09|0.14|0.11|Lesson facilitation|**0.36**|-0.17|**0.52**|-0.09|0.02|
|Comp 5|0.95|0.09|0.10|Checks for understanding|**0.38**|**0.39**|-0.12|0.01|-0.44|
|||||Feedback|0.27|**0.51**|-0.16|0.07|-0.07|
|||||Critical Thinking|0.28|**0.43**|0.26|-0.26|0.09|
|||||Autonomy|**0.52**|-0.30|0.04|-0.01|-0.12|
|||||Perseverance|0.19|0.17|-0.47|0.22|**0.66**|
|||||Social and collaborative skills|0.18|-0.28|-0.46|**0.34**|-0.45|

48
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**Table 6: Goodness of Fit: Dropout Rates by Income and Ability.**

|||||||Ability|quintiles||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Income||O|bserv|ed valu|es|||P|redict|ed valu|es||
|Bracket|1|2|3|4|5|Total|1|2|3|4|5|Total|
|5+ MW|81.4|65.8|61.5|52.1|39.1|44.7|84.4|67.8|63.4|51.9|40.7|46.7|
|3-5 MW|74.2|69.4|62.2|57.9|43.8|51.3|81.6|68.3|62.2|57.3|44.4|52.5|
|2-3 MW|68.5|67.6|63.7|57.7|46.5|55.1|70.8|67.5|61.9|60.1|44.4|55.6|
|1-2 MW|71.6|66.6|62.2|57.7|50.6|57.8|69.9|66.5|60.9|56.2|46.9|56.7|
|_<_1 MW|69.0|67.9|61.3|55.9|50.3|58.7|69.6|66.3|58.6|53.7|46.2|57.5|
|Total|71.0|67.4|62.4|60.0|45.8|54.4|71.3|66.9|61.0|56.6|44.8|55.0|

_Source_ : Source: SPADIES for observed data; fitted values for predicted data. _Notes_ : Values are expressed in percentages (%). Income is reported in brackets; MW = monthly minimum wage. Ability is reported in quintiles of standardized Saber 11 scores. Quintile 1 is the lowest.

**Table 7: Goodness of Fit: Transitions Among Tiers of Cumulative Classes Completed.**

|||Observe|d values|||Predicte|d values||
|---|---|---|---|---|---|---|---|---|
||Year 2|Year 3|Year 4|Year 5|Year 2|Year 3|Year 4|Year 5|
|_Persistence_|||||||||
|Tier 1|64.33|79.78|86.98|79.89|63.70|75.80|75.10|74.30|
|Tier 2|28.67|39.23|51.47|57.76|22.80|38.40|53.70|60.10|
|Tier 3|34.94|49.44|60.94|69.04|35.10|59.20|71.20|78.80|
|Tier 4|29.67|45.75|57.87|58.59|37.10|42.50|60.70|76.00|
|_Dropout rate_|||||||||
|Tier 1|12.16|4.03|1.75|0.71|11.60|4.20|3.00|2.20|
|Tier 2|14.43|6.50|3.25|1.52|16.20|6.40|4.60|3.00|
|Tier 3|23.02|12.30|7.29|4.02|25.10|9.20|7.00|5.20|
|Tier 4|54.23|41.95|28.24|24.24|55.20|50.70|35.20|21.40|
|_Prob. of Catch up_|||||||||
|Tier 3 to Tiers 1 & 2|18.10|20.42|20.61|19.63|26.60|20.30|13.10|9.40|
|Tier 4 to Tiers 1 & 2|4.26|0.55|0.22|0.14|0.82|0.00|0.00|0.00|
|_Prob. of Fall behind_|||||||||
|Tier 1 to Tiers 3 & 4|11.01|5.41|2.54|1.66|4.30|1.20|0.30|0.00|
|Tier 2 to Tiers 3 & 4|36.8|28.77|17.70|18.40|26.00|25.60|22.80|24.40|

_Source_ : SPADIES for observed data; model simulations for predicted data. _Notes_ : Values are expressed in percentages (%).

# **7.5 The Role of**

Since our estimates show that class completion is much more responsive to effort than ability, we now examine the role of

## **7.5.1 Average effort over time.**

We begin by looking at average student effort by year (Figure 11). On average, effort is relatively high in years 1 and 4. In year 1, students work hard to complete classes and mitigate dropout 26
subsequent, increasing payments over a specified time frame (e.g. every six months), where payments are contingent on verification by the buyer and sellers (Seller Aggregator 2009).

Sandker et al. (2010) model different scenarios for potential REDD contracts in Ghana, where the probability of forest conversion to cocoa plantations is high. The scenarios include business as usual, payments for avoided deforestation of old-growth forests, and payments for avoided deforestation of all forests. The scenario focusing on old-growth forest is the most attractive to farmers, where the contract is for a 20-year period and REDD payments amount to US$ 2.50 per tonne CO2 reduced.<sup>11</sup> The contract involves a large upfront payment, which makes the REDD project more appealing for some farmers initially, since cocoa revenues would not be realized for 8 years. In the simulation, Sandker et al. (2010) find that farmers would renege on their contract after 5 years, and start converting land to cocoa at that time. To prevent farmers from breaking the contract, a carbon price of US$ 55-60 per tonne CO2 would be required. Given this price, the authors then recommend that payments to farmers extend beyond the 20year period to keep the forest area intact. However, they also acknowledge that the high variability in prices for agricultural commodities in tropical countries makes it difficult to predict Jindal et al. (2008) examined forest carbon sequestration programs in Africa and recognized the importance of “developing effective contractual arrangements with farmers” as part of a successful forest carbon sequestration project. Some key contractual aspects include who to pay (landowners, local government, intermediaries, etc.) and what the payment method and schedule should look like (cash vs. in kind and up front vs. periodic payments). Social and

> 11 This is based on a carbon price of US$ 10/tonne CO2 and transaction costs accounting for 75% of the carbon payment.

33
**Table 6: Relationship between the formal employment rate of older persons (55-64) and formal employment rate of youth (1524) and prime-aged persons (25-54) by sector**

||(1)<br>Youth<br>Agriculture|(2)<br>Youth<br>Industry|(3)<br>Youth<br>Services|(4)<br>Prime-aged<br>Agriculture|(5)<br>Prime-aged<br>Industry|(6)<br>Prime-aged<br>Services|
|---|---|---|---|---|---|---|
|Formal employment rate of old|0.460|0.878***|0.224***|1.037***|0.817***|0.600***|
|persons in the relevant sector|(0.691)|(0.184)|(0.054)|(0.344)|(0.103)|(0.059)|
|Natural log of hourly income of|0.592|7.313*|12.003***|1.554|2.375|13.054***|
|younger persons (15-24/25-54)|(1.723)|(4.363)|(1.922)|(2.355)|(3.309)|(2.471)|
|Poverty rate|-0.401***|0.124|0.010|-0.359***|0.036|0.309***|
||(0.108)|(0.178)|(0.074)|(0.065)|(0.112)|(0.060)|
|Share of employment in the|0.006|1.295***|0.420***|0.092***|0.738***|0.114*|
|relevant sector|(0.063)|(0.171)|(0.059)|(0.031)|(0.094)|(0.062)|
|Share of population of younger|0.412**|-1.306***|-0.749***|0.048|0.839**|-0.059|
|persons (15-24/25-54)|(0.209)|(0.431)|(0.191)|(0.229)|(0.339)|(0.188)|
|Share of population with a primary|0.116**|-0.138|0.198***|-0.035|-0.083|0.011|
|education or less|(0.048)|(0.105)|(0.065)|(0.039)|(0.072)|(0.062)|
|GRP per capita (constant 2010 IDR)|6.185***|9.455***|1.919**|3.799***|8.834***|-0.013|
||(2.124)|(2.471)|(0.886)|(1.043)|(1.467)|(1.000)|
|Growth in GRP per capita|-0.113|-0.137|0.159|-0.154**|-0.190|0.050|
||(0.097)|(0.205)|(0.120)|(0.071)|(0.128)|(0.079)|
|Observations|237|238|238|238|238|238|
|R-squared|0.309|0.721|0.677|0.580|0.820|0.718|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1 22
# **Table A.6b: Sustainability of Reform: Full Difference-n-Difference Estimates** **<u>(Margins Based on 2 Week Lagged World Price of Crude Palm Oil)</u>**

||Trading|Margins|
|---|---|---|
||Wholesale-World Price|Retail-World Price|
|**Annoucement Effects**|||
|Announcement Dummy|-3.288***|-4.247***|
||(-5.329)|(-5.751)|
|**Announcement*Palm**|**32.28***|**11.35**|
||(1.812)|(0.480)|
|**Announcement*Palm*World Price**|**-0.310**|**-0.0454**|
||(-1.389)|(-0.153)|
|Effects of Policy Implementation|||
|**Effects during first 6 months**|||
|Policy Implemt.|-0.306|-1.993**|
||(-0.492)|(-2.427)|
|**Policy*Palm**|**35.13*****|**46.72*****|
||(4.698)|(6.089)|
|**Policy*Palm*World Price**|**-0.382*****|**-0.502*****|
||(-3.752)|(-4.896)|
|**Effects after first 6 months**|||
|Policy Implement.|-0.888|-0.311|
||(-0.784)|(-0.314)|
|**Policy Implement.*Palm**|**11.68****|**13.10*****|
||(2.048)|(2.846)|
|**Policy*Palm*World Price**|**-0.107**|**-0.0944**|
||(-1.494)|(-1.616)|
|World Price*palm|0.0279|0.107|
||(0.377)|(1.313)|
|World Price|-0.369***|-0.464***|
||(-5.451)|(-6.237)|
|Diesel Price|25.33***|25.32***|
||(6.303)|(7.073)|
|Dummy if World Price was rising (DW)|-0.0635|0.0210|
||(-1.432)|(0.379)|
|DW*palm|-0.214|-0.480|
||(-0.155)|(-0.350)|
|Palm|<br>-1.389|<br>-0.691|
||(-0.656)|(-0.377)|
|Relative world prices of palm-soy/wheat-rice|<br>-2.382|<br>-2.919*|
||(-1.543)|(-1.901)|
|Relative world prices of palm/wheat|<br>0.757***|<br>0.619*|
||(2.992)|(1.939)|
|Intercept|<br>18.04***|<br>20.13***|
||(5.493)|(6.745)|
|Observations|1,292|1,292|
|Controls|||
|Year, Quarter, Product Fixed effects|Yes|Yes|
|Ramadan Dummies|Yes|Yes|
13 For Karnataka, this includes women identified by the field observer in the transcript as SHG members and women who were not identified in the transcript as such (because of variation in field observer transcribing strategy) but demonstrated direct affiliation to SHGs by mentioning their SHG or SHG-related demand in the content of their speech (coded as ‘SHG mention’). We can safely assume that women mentioning their SHG or a related demand (like a building or finances for the group) belong to SHGs.

14 The literacy data have been computed from the 2001 census of the Govt. of India (GOI). It relies on the government definition of literacy, which is simply the ability to write one’s own name. It is a very basic measure and quite possibly inflated.

15 In Karnataka, the Stree Shakti program at the time was relatively weaker due to weak implementation in the first three to five years of the program.

> 16 In some of the fifty-two villages in TN, _gram sabhas_ in two different cycles were observed and recorded, such that we have eighty-nine _gram sabhas_ in the data set.

17 Physical infrastructure for community use such as road repairs, installation of streetlights, water pipelines & borewells, construction of public-use buildings and sanitation facilities 18 The percentages report the frequency with which an issue was raised as a percent of all issues raised by women in Tamil Nadu.

19 A key observation for most women in Tamil Nadu is that a problem is rarely structured in individualized terms using singular pronouns of 'I,' and 'me,' but done so using plural pronouns of 'we' and 'us' when referring to those affected by a problem. This statewide observation, encompassing both SHG and non-SHG members, indicates the standard norm is to speak in collective rather than individualistic terms. This could be an effect of the more than two decade long SHG movement or a predisposition due to the cultural speech norms of the region.

47
presumption that these officials are the ones worth retaining—would weaken incentives faced by qualified staff to defect to aid agencies or other employers.

A range of other reforms could also contribute to the resolution of these problems. Agreements between government and donors are needed on codes governing recruitment, salary and benefit levels, and the use of government officials for part-time consultancy work (Fallon and da Silva 1994: 101). Cohen and Wheeler (1997b: 318) recommend finding ways to tailor training to enhance carefully specified skills directly related to responsibilities within a ministry, without granting academic credentials that facilitate mobility. They also suggest reducing pay disparities by reducing overstaffed lower grades, and using savings to increase upper level salaries. They recognize, however, the potential for political resistance as overstaffing lower grades is a prime source of patronage (Cohen and Wheeler 1997b: 320–21).

The analysis here also underscores the critical importance of organizational capacity building. In particular, multilateral and other agencies under fewer political constraints to demonstrate visible, attributable results should take on increased responsibility for technical assistance in upgrading policymaking, budgeting, and related administrative systems that tend to be victimized by collective action failures among donors. As capacity is improved, the plausibility of any efficiency rationale for project aid (by enabling the tracking of funds) diminishes, and donors may grudgingly accede to growing pressures from the DAC, UN and other international agencies to reduce the emphasis on “their” projects in favor of pooling funds in the form of budget support. Successful implementation of high-level decisions to employ more development-friendly aid modalities will require changing incentives for staff within donor agencies, for example by focusing more on output and country-level indicators, and less on input and project-level indicators. Staff performance appraisals could also take into account written evaluations of job performance from other donor staff, and from government counterparts (Whittington and Calhoun 1988: 307).

Additional reforms could address other aspects of donor coordination problems that detract from the developmental impact of aid. Ongoing efforts by aid agencies to improve donor coordination have focused to date on reducing transactions costs by harmonizing operational policies and procedures, such as standardizing reporting and monitoring systems,<sup>29</sup> and establishing a Web site to disseminate information on completed and planned country analytic work<sup>30</sup>

> 29 See “Rome Declaration on Harmonization,” February 25, 2003.

> 30 See www.countryanalyticwork.net.

25
**Table** 1 Unit Roat Tat. - Debt Partlollo Flow.

# 1. Test for Indivdual Country Seris Levols

|Ho: p. I|Augrmnted Dickey.<br>Fuller (ADF)<br>Tint Statistlo|Lovels of Slgnificnce<br>5%<br>10%|Stati<br>5%|onary<br>at:<br>10%|
|---|---|---|---|---|
|ARGENTINA|-1.40|do not rejct do not reject|no|no|
|BRAZIL|.2,37|<br>do not roject do not reject|no|no|
|CHILE|near mingulur<br>matrix||||
|MEXICO|<br>.1.16|do not reject do not reject|no|no|
|INDIA|-<br>.3.73|<br>do not reject reject|no|yes|
|INDONESIA|.241|<br>do not reject do not reject|no|no|
|KOREA|2.30|<br>do not rejct do not reject|no|no|
|THAILAND|-2.19|<br>do not reject do not reject|no|no|
||MacKlnnon<br>crItical values|<br>-3.7511<br>.3.3228|||

# 11. Test for Panel Series by Region Levels end First DIffrences

|Ho:peo|Dickey-Fuller<br>(DF) Tast<br>StatItic|Levels o<br>5%|f Significan<br>10%|ce|Stationary<br>at:<br>5%<br>10%|
|---|---|---|---|---|---|
|LAC|-0.48|do not rejec|t do not reje|ct no|no|
|ASLA|-2.67|<br>reject|<br>reject|<br>yes|<br>yes|
|D(LAC)|-11.11|reject|reect|yes|<br>yes|
|D(ASI)|-10.91|<br>reject|reject|<br>yes|<br><br> <br>yes|
||Levin and Un critical values|<br>-2.30|-1.93|||

When Unit root tests were conducted for the panels for Latin America and Asia, the forner was found to contain a unit root. This is consistent with the results obtained using the Ljung-B3ox Q tests for the individual countries in the LAC panel. That is, the series for Argentina, Brazil and Mexico were found to contain serially correlated errors. In addition they were found to be non-stationary.1 The panel of first difference of both LAC and Asia were found to be stationary on the basis of the Levin and Lin (1992) critical values.

1 Once again, the individual county results should bc treated with caution because the small number of observations (n-8) imply low power of tesis.

24
- 63 _

# **Deregulation of Trucking**

We have already discussed the general political and econiomic pressures that led to deregulation of rail and trucking carriage, including a general ideological shift favoring less government regulation, the accumulating weight of economic studies critical of regulation and its effects, and the financial crisis facing the railroads.

In the case of trucking, three other factors probably played a role as well. One was the energy inefficiencies built into the route and commodity restrictions that governed regulated carriers: in a period of great concern for energy efficiency, circuitous routes and empty backhauls simply were an embarrassment. A second was the increasing public awareness of the unsavoriness of the International Brotherhood of Teamsters' leadership: unionized truckers were strongly opposed to deregulation (as were the licensed trucking firms themselves) because it threatened the economic rents in which they shared<sup>78</sup> , and they were a large and well-organized group that had been politically effective for some time; however, the Teamsters' reputation at the time that deregulation became a salient issue made it difficult for them to rally public or legislative support. A third force favoring deregulation was the fact that independent truckers, shippers, and utilities, all supported deregulation (Asch and Seneca, 1989, p. 402) and communicated that to 78 See Thomas G. Moore, 1978, pp. 327-43.
two. The same parameters are used to draw the observations and to update the beliefs (parameters are assumed common knowledge); _x_ is of the good type and _y_ of the bad type.

In the LHS case, positions tend to lock in fairly quickly. In the RHS case, longer sampling is needed to tell apart the two suppliers; however, note that the difference in expected returns ( ~~~~ _G_  ~~~~ _B_ ), which is part of the value of the information, is also smaller, so the truth takes longer to appear but it matters less. Observe also that in the RHS panel, around iteration #20, supplier _x_ has accumulated so many bad draws and supplier _y_ so many good draws that the buyer is ―almost certain‖ that _y_ is of the good type, _even though this belief is false_ (observe the dotted curve ( _y_ ) approaching one between iterations #20 and #40). Going back to (11), we see that The model thus implies that concentration, when it occurs, is on high quality products. It also suggests that periods of diversification are followed by periods of re-

> 8 Note that in this setup there can be no ―informational cascade‖. An informational cascade (Bikhshandani et al. 1992) can take place when a sequence of actors make binary decisions on a singe issue (say, buying or selling a stock) based on a noisy signal about the correct decision and on the observed behaviour of past players. Each player forms his own belief based on a weighted average of his signal and past players‘ actions, with weight on the latter that increases with the number of past players. Bikhshandani et al. show that there exists a critical number _n_ such that, if _n_ players observe the wrong signal and act accordingly, the _n_ +1<sup>st</sup> will discard his own signal and follow the crowd. From then on, the herd behaviour cannot be reversed. Our setup is different because the buyer is repeatedly getting information about his supplier, whereas in an informational cascade the individual experimenter gets only one signal that he compares with the actions of other (past) players.

19
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