rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 2: Self-evaluation about the impacts of training received from TESDA_ <!-- Start of picture text --> Trust from employers & peers<br>Work attitude<br>Job mobility and promotion<br>Skills relevant to job<br>Self confidence<br>DTS<br>Dealing with othe rpeople RP<br>Job performance<br>Income<br>Employment opportunity<br>3.2 3.4 3.6 3.8 4 4.2 4.4<br>Level of effectiveness (1-5 rating scale)<br><!-- End of picture text --> Source: _DTS Tracking Survey Data, 2015, World Bank; World Bank calculations._ Note: _These self-evaluated impacts were reported in a 5 scale of effectiveness and these differences between DTS and RP graduates are all statistically significant at 5%._ 18
Three factories have been sold so far, aside from Government shares in SOWARTHE which were sold in 2003. SORWATHE<sup>6</sup> , has always been under private control since its establishment in 1975. In February 2003, the Government sold its share of 23.54 percent, to the private company (13.54 percent) and to the association of tea growers (10 percent). A qualitative study conducted by the World Bank and the Government of Rwanda (World Bank 2003) to assess the likely poverty and social impacts of tea sector reforms noted that the yield of SORWATHE’s plantations is about two and a half times higher than the average yield on state-owned estates (excluding Nshili-Kivu)<sup>7</sup> . Also, yields for the independent growers associated with the private estate, SORWATHE, are believed to be twice as high as the average from public estates. These observations provide a _working hypothesis_ for our analysis, namely that _outcomes are expected to be better in the private sector than in the public sector_ .

# **3. Accounting for Heterogeneity in Sectoral Outcome Comparison**

To make meaningful comparisons of outcomes across sectors, we frame the analysis within the logic of _causal inference_ . Indeed, the effect of a cause can be understood only in relation to another cause (Holland 1986). This idea is akin to that of assessing the return to a resource engaged in one activity relative to its opportunity cost, i.e. what the resource would have earned in the next best alternative use. In particular, for a tea household engaged in the private sector, we cannot assess the worth of the observed outcome without some information on the _counterfactual_ i.e. what the household would have experienced had it been engaged instead in the public sector. Since we cannot observe a tea household engaged simultaneously in the private and public sectors, we construct the needed counterfactual from the information on the tea households engaged in the public sector. These counterfactual outcomes are constructed,

> 6 SORWATHE stands for _Société Rwandaise du Thé_ .  The local name of the estate is Cyohoya-Rukeri.  It was founded and is still owned by an American company, Tea Importers, Inc. of Westport Connecticut.  Its plantations cover about 2 percent (or 252 ha) of the total area under tea cultivation.  It is reported that this estate and the associated growers apply substantially more fertilizer than other estates.

> 7 State ownership is managed by the Tea Board known as OCIRTHE an off-shoot OCIR ( _Office des Cultures Industrielles du Rwanda_ ) which used to cover both tea and coffee.

4
|**Beritella**<br>**_et._**<br>**_al.,_**<br>**2005a**|<br>Determined by savings and<br>distributed to equalize the<br>expected future rates of return<br>for all regions|Capital,Labor,Land,Water|Capital and labor are perfectly<br>mobile<br>domestically,<br>immobile<br>internationally.<br>Land and natural resources are<br>sector<br>specific,<br>Water<br>is<br>mobile<br>within<br>agricultural<br>sectors|17 Sectors 6 of which are<br>related to agriculture<br>includes a water distribution<br>sector|17 Types,one for each region|16 Regions|
|---|---|---|---|---|---|---|
|**Model **|**Investment**|**Factors of Production **|**Assumptions about Factors**|**Sectoral Detail**|**Household Detail**|**Coverage**<br>**and**<br>**Regional**<br>**Detail**|
|**Beritella**<br>**_et._**<br>**_al.,_**<br>**2005b**|<br>water taxes reduce water use,<br>and<br>lead<br>to<br>shifts<br>in<br>production, consumption, and<br>international trade patterns.<br>Countries that do not levy<br>water taxes are affected by<br>other countries’ taxes. A<br>water<br>tax<br>on<br>production<br>would have different effects<br>than would a water tax on<br>final cons.||||||
|**Beritella****_et. al.,_2006**|Project<br>would<br>stimulate<br>China’s<br>economy<br>and<br>increase welfare. The payback<br>periods<br>of<br>different<br>alternatives are between 1 and<br>3 years. Benefits of additional<br>water supply are positive. If<br>the<br>water<br>transferred<br>is<br>allocated to industry and<br>households the benefits are<br>halved.||||||
|**Smajgl****_et. al.,_2005**||||||Great Barrier Reef Region of<br>Australia|
|**Letsoalo****_et. al._. 2005**|Exogenous|Labor,Capital,Land|The capital is assumed to be<br>fixed, while the rate of return<br>on capital is allowed to<br>change.<br>Labor<br>supply<br>is<br>perfectly<br>elastic. The supply of land is<br>also assumed to be inelastic.|27 Sectors,<br>energy and water intensive<br>sectors are further split into 39<br>sectors|12 Income,4 ethnicgroups|South Africa|

44
In order to study the effects of alternative regulatory regimes on market risk, it is necessary to control for all other factors that may influence beta values. However, it is not feasible to find two companies that are similar in every respect apart from their regulatory systems and such a straightforward comparison cannot be made. A study involving a large number of companies, operating in environments that are similar in some respects but different in others, should allow the effects of various factors to be disentangled so that the influence of regulation on the cost of capital may be inferred. This paper considers evidence from a large number of countries, covering Europe, North and South America, and the Pacific region.

Linked to the above question of considering the whole regime, is the problem of focusing on just one specific element of what is a complete financial package. Betas cannot be studied in isolation from the determination of the rate base, the way in which investment is treated, and several other financial factors. This analysis focuses on one specific element of regulation: namely, the systematic risk of utilities operating under different regulatory regimes.

Another benefit of this approach, aside from the US/UK studies, is that the UK can be compared with other newly-established systems of regulation. Even if the higher beta values of UK compared with US utilities can be attributed to regulatory risk, this may be because the US system is long established and enshrined in case law whereas the UK regime is new and subject to change (particularly by political influences), rather than the difference between rate-of-return and price-cap regulation per se. A comparison of recently privatised companies under alternative systems of regulation will allow a more accurate assessment of the effect of regime differences on the cost of capital.

There are also a few cases where a shift of regulatory regime occurs within a country. Such an example is useful as it automatically controls for market factors and should therefore provide an accurate picture of the influence of the regulatory regime on the cost 2
Table 2: Conflict Incidence and Country Economic Performance: Ethnic-level data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||light per capita growth|light per capita growth|light per capita growth|light growth|population growth|
|Confict Incidence|-0.074**|-0.069**|-0.063**|-0.077***|-0.003**|
||(0.029)|(0.034)|(0.031)|(0.029)|(0.001)|
|N. of groups in confict in the country|||-0.0078*<br>(0.00424)|||
|Group FE<br>15|YES|YES|YES|YES|YES|
|Year FE<br>|YES|YES|YES|YES|YES|
|Lagged growth control|NO|YES|NO|NO|NO|
|Observations|7,949|7,425|7,949|7,949|7,949|
|R-squared|0.268|0.473|0.268|0.267|0.045|
|Number of groups|502|499|502|502|502|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Errors in column (3) are clustered at the country level. We use data over the period 1992-2013. Unit of observation are ethnic groups. “Light per capita growth” and “Population growth” are the log difference of the variable to the previous year. “Conflict incidence” is a dummy defined through more than 25 fatalities in ethnic conflicts. “Number of group in conflict” counts the number of ethnic group involved in conflict episodes in a given country during year _t_ .
# Table A.3: Election and District Characteristics (DLHS samples)

||N|Mean|S.D.|
|---|---|---|---|
|**Panel A: Election data**||||
|Share of constituencies won by women in a district (F)|245|0.05|0.09|
|Share of male-female close elections won by women in a district (FC)|245|0.01|0.04|
|Share of close male-female elections in a district|245|0.02|0.06|
|Whether the district had at least one seat won by a woman|245|0.33|0.47|
|Whether the district had at least one male-female election|245|0.56|0.50|
|Whether the district had at least one male-female close election|245|0.16|0.36|
|Whether the district had at least one male-female close election won by a woman|245|0.07|0.25|
|**Panel B: Census data**||||
|Fraction of female population|245|0.49|0.02|
|Fraction of urban population|245|0.33|0.21|
|Fraction of SC/ST population|245|0.34|0.25|
|Female literacy rate|245|0.63|0.11|
|Male literacy rate|245|0.74|0.08|
|**Panel C: Village data (DLHS)**||||
|Any sub-center|7,721|0.55|0.50|
|Any primary health center|7,721|0.23|0.42|
|Any block primary health center|7,722|0.06|0.24|
|Any community health center|7,718|0.07|0.25|
|Any government hospital|7,722|0.06|0.23|
|Any government dispensary|7,713|0.11|0.32|
|Connected to a sub-center|7,726|0.86|0.34|
|Connected to a primary health center|7,726|0.81|0.39|
|Connected to a block primary health center|7,702|0.55|0.50|
|Connected to a community health center|7,725|0.72|0.45|
|Connected to a government hospital|7,725|0.75|0.43|
|Any accessible auxiliary nurse midwife|7,723|0.65|0.48|
|Any accessible female doctor|7,722|0.22|0.41|
|Janani Suraksha Yojana implemented|7,726|0.92|0.27|
|Janani Shishu Suraksha implemented|7,726|0.63|0.48|
|Kishori Shakti Yojana implemented|7,725|0.50|0.50|
|Balika Samriddhi Yojana implemented|7,725|0.42|0.49|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The table presents the number of observations, the means, and standard deviations (S.D.) for district-level characteristics of the election data and Census data matched to DLHS data in Panels A and B, and village-level public health infrastructure data from DLHS data in Panel C.

30
Gulati , Ashok and Bhide, Shashanka (1993) Structural Adjustments and Agriculture. November. National Council of Applied Economic Research New Delhi . Working Paper no. 44.

Gulati , Ashok and G.D. Kalra (1992) "Fertiliser Subsidy: Issues Related to Equity and Efficiency". _Economic and Political Weekly,_ March. Also published in Fertiliser Pricing: Issues Related to Subsidies. Vidyasagar Institute of Development Studies , Jaipur, 1993.

Gulati, Ashok and Katula, Rajni (1991) "Electricity Subsidy to Indian Agriculture." Mimeo. November.

(1992) Input Subsidies in Indian Agriculture: Concepts and Quantification. Mimeo. March.

Gulati, Ashok and Pursell, Garry (1996) Trade Policies, Incentives and Resource Allocation in Indian Agriculture. (Draft Manuscript, Chapters 2-6).

Gulati, Ashok and Sharma, Pradeep K. (1990) "Prices, Procurement and Production: an Analysis of Wheat and Rice." _Economic and Political Weekly,_ March 31.

(1990) "Employment, Foreign Exchange and Environment: Implications for Cropping Pattern." _Economic andPolitical Weekly_ (Review of Agriculture). XXV (13). March 31.

(1990) "Fertilizer Pricing and Subsidy: An Alternative Perspective." Paper presented at the National Workshop on Agriculture Input Marketing, Indian Institute of Management, Ahmedabad, February 15-16. Published in: S.P. Seetharaman, Agricultural Input Marketing Oxford & IBH Publishing Co., New Delhi 1992.

(1991) "Intervention and Subsidies in Indian Agriculture during 1980s." Background paper for the World Bank's 1991 Country Economics Memorandum on India. pp. XI + 129. January.

(1991) "Government Intervention in Agricultural Markets: Nature, Impact and Implications." _Journal of Indian School of Political Economy._ (April-June).

(1991) "Indian Agriculture: Protection, Subsidies and Resource Allocation." pp. XI + 132, Mimeo. July.

(1991) "Cereals Economy of India: Protection and Efficiency" Paper presented at the Workshop on Trade Policy and Resource Allocation in Indian Agriculture organised by the World Bank at New Delhi, 25-26 November 1991.

xxxv
To determine the impact of the fiscal system on either poverty or inequality, we estimate the difference between its preferred measures of poverty or inequality over the pre- and postfiscal distributions. Naturally, the extent of the fiscal system under consideration limits the team’s choice of the postfiscal income concept. The impact of a fiscal system that includes only two elements must be estimated over a postfiscal income concept that includes only these two elements.

To determine the impact of single tax or transfer (or a subset of taxes and transfers), we take the difference in inequality (or poverty) at the postfiscal income concept _excluding_ the item in question (but including everything else in the team’s fiscal system) and the postfiscal income concept _including_ the item in question (and also including everything else in the team’s fiscal system).

A single tax or transfer (or a fiscal system) is _inequality reducing_ when the addition of the fiscal item in question to an income concept reduces measured inequality. A transfer is _absolutely progressive_ if, when households are ranked by prefiscal income levels, the cumulative household shares of the transfer are greater than cumulative population shares. In a Lorenz curve figure, an absolutely progressive transfer’s concentration curve would lie above and to the left of the 45 degree line (Figure 8). A transfer (tax) is _relatively progressive_ if, when households are ranked by prefiscal income levels, the cumulative household shares of the transfer (tax) are greater (less) than the cumulative household shares of prefiscal income. In a Lorenz curve figure, a relatively progressive transfer’s (tax’s) concentration curve would lie above and to the left (below and to the right) of the Lorenz curve for prefiscal income.

**Figure 8. Diagram Representing the Progressivity of Taxes and Transfers**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative proportion of population (ordered by market income)<br>Transfer: regressive ; Tax: relatively progressive<br>Market Income Lorenz curve. Transfer or tax: neutral in relative terms if on this line<br>Transfer: progressive in relative terms. Tax: regressive<br>45 degree line; population share. Transfer: neutral in absolute terms; Tax: upper bound of regressive<br>Transfer progressive in absolute terms<br>Cumulative proportion of benefits, taxes, or income<br><!-- End of picture text --> _Source:_ Adapted from Lusting (2018).

19
however, the highest wage differential of 76% is registered among university-educated workers, while it is only 50% among illiterate rural males. Wage differentials among illiterate urban female workers is high (about 93%), and it is similar to that of women with secondary-education levels. The lowest wage differential is registered among urban women with a middle-school diploma.

# _Estimates with sector selection bias correction (SBC)_

As Table 10 shows, on average, and for nearly all groups, the conditional public-private wage differentials estimated by the SBC is higher than the corresponding wage differential estimated by the OLS. Since the conditional differential measures the ratio of current (in public sector) to potential (in private sector) wages of public sector employees, taking into account their self-selection, this possibly reflects the self-selection of individuals in the sector that offered them a higher comparative wage advantage.

Similar to the OLS estimates, the selection bias corrected wage advantage is higher in rural than urban areas, and it is higher among women than men. Public sector wages are about 83% higher than alternative wages in the private sector for urban male employees; the ratio of public-to-private wages is even higher for rural males (2.15), and it is as high as 2.5 for female workers. Patterns of wage differential with respect to occupation are roughly similar to those estimated by OLS regressions. For urban males, the highest wage differential of 107% is registered among sales workers, followed by the differential of 92% among production workers. The wage differential among urban men in professional/technical, administrative, and clerical and service occupations ranges from 70 to 80%. In rural areas, male public sector workers in professional/technical, administrative, and production occupations enjoy wages that are more than twice as high as they would have been in the private sector. The wage advantage of rural male employees in clerical, sales, and service occupations is about 90%.

The conditional wage differential among women in service occupations is very high, reaching 289%. The private sector wage advantage of women employed in administrative and sales occupations is also higher than those of men in the same occupations. Younger men in the public sector enjoy a higher wage advantage as compared to older men. The ratio of public to private wages is close to 2.0 for male 21
|_Sample_<br>This table presents estimations country weights <br>country fixed effects. Country returns are from<br>zero otherwise. We exclude data when country <br>aggregated flow data by country. Data is from 2<br>respectively. Source: MSCI, GEM World Bank,|_Fund Detail_<br> on lagged countr<br>MSCI country ind<br> weight is equal t<br>005-November 2<br>Bloomberg, US Tr|_Fund Detail_<br>y weights and cou<br>exes. Dummy crisi<br>o zero. Fund deta<br>010. Robust stand<br>easury, EPFR, Ba<br>**Ta**<br>**Benchmarking**|_Fund Detail_<br>ntry returns for i<br>s is a binary varia<br>il indicates that w<br>ard errors in pare<br>rclays Capital, GE<br>**ble 12**<br>**in Mutual Fun**|_Fund Detail_<br>nternational mutu<br>ble that takes the<br>e use individual <br>ntheses. *, **, an<br>M World Bank, I<br>**ds**|_Aggregated_<br>al funds. All resu<br>value of 1 betwee<br>mutual fund data<br>d *** denotes sig<br>MF IFS Statistics.|_Aggregated_<br>lts are from panel<br>n March 2008 and<br>, and Aggregated <br>nificance at the 1|_Aggregated_<br> estimations with<br>March 2009 and<br> indicates we use<br>0, 5 and 1% level|
|---|---|---|---|---|---|---|---|
|||**Count**|**ry Weight**|||||
|Country Weight (t-1)|0.956***<br>(0.002)|0.956***<br>(0.002)|0.956***<br>(0.002)|0.904***<br>(0.005)|0.963***<br>(0.015)|0.96338***<br>(0.015)|0.963***<br>(0.016)|
|Country Weight (t-1)*Crisis Dummy||-0.002||||-0.00039||
|||(0.002)||||(0.008)||
|Country Returns (t-1)|0.009|-0.010|-0.021|0.061|0.012|0.00971|0.060|
|||(0.047)|(0.097)|(0.099)|(0.109)|(0.100)|(0.150)|
|Country FE|YES|YES|YES|NO|YES|YES|YES|
|Time FE|NO|NO|YES|YES|NO|NO|YES|
|Fund-Country FE|NO|NO|NO|YES|NO|NO|NO|
|Observations|94,020|94,020|94,020|94,020|1,286|1,286|1,286|
|R-squared|0.974|0.974|0.974|0.976|0.995|0.995|0.995|
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- 64 -
* 49.

# V. REFERENCES

Primary sources for this repon were interviews with 74 officials of 25 international organizations (See Methodology), written communications, and official annual or periodic reports of bilateral and multilateral organizations.

# Referenx

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