rafmacalaba/datause-encoder-data huggingface dataset:
A question that remains to be answered in the literature on PWPs is what happens when given budget constraints these programs are oversubscribed and the somewhat “fixed” wage rate is unable to ration the distribution of the resources across those who want to participate in the PWP. In such situations in which PWP resources essentially become discretionary at the local level, this paper argues that _local politicians_ determine who benefits from these programs. The theory that vote-maximizing behavior of politicians influences the allocation of discretionary social assistance is well established and empirically shown in the literature on “pork barrel” politics (Cox and McCubbins, 1986; Lindbeck and Weibull, 1987; Dixit and Londregan, 1996; Schady, 2000; Case, 2001; Dahlberg and Johansson, 2002; Sharif, 2011). A "pork-barrel" project is a publicly funded project promoted by a politician to bring money and jobs to his or her own district. The "pork" is allocated not on the basis of need, merit or entitlement; it is solely the result of political patronage, the desire of politicians to promote the interests of their own district, and thereby build up their local support or “vote-banks.” There is also a growing literature in political science on patronage and clientelism in Latin America (Diaz-Cayeros et al., 2006; Calvo and Murillo, 2004; Stokes, 2005; Weitz-Shapiro, 2005) that shows political parties may use discretionary access to state programs as a form of patronage, even if they do not generally operate major welfare networks independently from the state.

The role of politicians in determining participation in PWPs is not widely studied.<sup>2</sup> Analyses on the role of politicians in determining who gets to join PWPs, and the subsequent impact on household poverty can provide useful insights on ways in which these programs can function more effectively as safety nets. This is an important policy question given that public works are a relatively expensive way to transfer income to the poor.<sup>3</sup> Using survey data on the largest PWP in Bangladesh – the Employment Generation Programme for the Poorest (EGPP) – this paper addresses two questions: one, whether household access to

> 2 There are many studies that have looked at the determinants of participation in means tested benefit programs (Blundell et al, 1988; Chraig, 1991; Stark & Fry, 1993; Hoynes, 1996;  Walter & Bengley, 1997; Blundell et al, 2000; Micklewright et al, 2004; Parker et al, 2005) but few shed light on the role of politicians.

> 3 If poverty alleviation is the main objective, then a simple cash transfer would be less costly given lower administrative costs.

4
does not appear to support the view that quotas are driving our results. According to that hypothesis, quotas limiting the intake of Part I men and, in particular US men, together with those favoring higher Part II women hires, have led on average to higher quality Part I men hires and lower quality Part II women hires, and explain the large differences in salary and grade at entry not explained by our models. We have already shown that a substantial amount of the variation in rewards is explained by our model when it is run on the YP sample. We have also argued that for binding quotas to be responsible for the results, the omitted quality variables must favor Part I men most and Part II women least. We believe that for both these reasons, the quota story is not a compelling explanation for our results. The comparison of pre- and post-quota staff cohorts only reinforces this view. While the quotas came into force in the 90s, over time, between-group differences in entry salary and grades have diminished as has the component of that difference attributable to structure.

# **G. Conclusions**

Our study has found differences in salaries and grades between men and women and between Part I and Part II staff in the Bank which are not readily accountable to differences in the attributes that one would expect to influence productivity. The difference in mean current salaries between men and women (holding nationality group constant) is 14 percent, almost half of which is structural in that it cannot be attributed to differences in observed characteristics such as age, education, and previous work experience. The differences in mean salaries between Part I and Part II staff (independently of gender) are much smaller-5 percent-and are largely attributable to structure rather than to differences in characteristics.

_32_
# - 17 -

however, routinely collude with landlords to understate rents, fabricating false receipts. The valuer then has two choices: he can accept a declaration he knows to be false, or he can make a counterproposal. While the law gives the valuer the authority to propose his own estimate of a property's rental value--if ite disbelieves the rental evidence provided to him--the incentive structure discourages this. In the absence of a credible basis for preparing a counterproposal, a valuer is in a poor position to defend a higher valuation. Faced with the prospect of lengthy court battles, local authorities prefer to negotiate valuation down to a level satisfactory to taxpayers. Reliance on direct market data therefore yields a pattern of undervaluation; the disparities between actual rents and reported rents reflecting the skills and rewards of the two negotiating parties.

3.37 A few ARV countries have found solutions to this problem, by exploiting alternative sources of accurate rental information or providing landlords or tenants with incentives to provide accurate data. In Jordan and Cote d'Ivoi7e, registered rental contracts are used as the basis for determining actual rents. (In these countries, government agencies register rental contracts, as a means of providing an official imprimatur to private agreements.) As the landlord and tenant have a financial interest in recording their agreement accurately, these contracts serve as an reliable source of rental data. But the coverage of legally registered rental agreements is limited. Even in Amman and Abidjan, the majority of rental &greements take the form of undocumented understandings between landlord and tenant. In Jordan, the government also employs the income tax law to provide tenants with an incentive to reveal accurate rents directly to the valuer: rent payments are deductible from the personal income tax. Again, the limited coverage of the personal income tax system limits the coverage of this enforcement technique.

3.38 Direct market information has a second limitation, in that it is applicable only to properties on the market. As a result, its use in ARV countries is limited to renter-occupied property. It has no application in countries valuing on the basis of capital value: as only a small propo.tion of properties are actually sold in any given year, few direct market observations of capital value are e-ier available at any given time.<sup>2</sup> 3.39 The more common approach to valuation is mass appraisal. Valuation by mass appraisal is accomplished in three steps:

- (a) First, a sample of recent property transactions (sales or rents) or construction data is analyzed to identify the physical characteristics that appear to determine a property's value;

- (b) The contribution of each of the prircipal determinants of value is then quantified and reduced to a formula (or more commonly, a table);

12./ Some cases also exist in which property sales are used as the basis for valuations. In the State of Tabasco, Mexico, property is revalued only when it is sold, and it is then valued at the reported sales price.
Table 9: Under-5 mortality: Heterogeneity by socioeconomic status

||**Moth**|**er’s Education**||**Weal**|**th**||**Mother’s **|**Employment**||
|---|---|---|---|---|---|---|---|---|---|
||Illiterate|Literate||Bottom 40%|Top 20%||Paid employment = 0|Paid employment =|1|
||(1)|(2)|(1)-(2)|(3)|(4)|(3)-(4)|(5)|(6)|(5)-(6)|
|_Firstborn girl_ * _Female_|3.455***|1.679**|1.777|3.279***|1.022|-2.258|2.993***|2.392***|-0.6011|
||(0.830)|(0.774)|(1.175)|(1.143)|(0.752)|(1.449)|(0.753)|(0.725)|(0.908)|
|_Firstborn girl_ * _Female_ * _Post_1|-1.986*|-0.789|-1.198|-1.622|0.386|2.008|-1.824*|-1.039|0.7851|
||(1.077)|(0.992)|(1.435)|(1.542)|(1.090)|(2.047)|(0.971)|(1.026)|(1.279)|
|_Firstborn girl_ * _Female_ * _Post_2|-2.320*|-1.232|-1.089|-2.177|0.500|2.676|-2.463**|-1.360|1.103|
||(1.244)|(1.031)|(1.845)|(1.723)|(1.506)|(2.981)|(1.113)|(0.870)|(1.396)|
|N|137,681|89,448||96,762|41,745||131,325|95,657||
|Baseline mean|15.41|7.12||16.91|6.04||11.69|13.41||
|||**Caste**||**Rural**|**ity**|||||
||SC|General/OBC/ST||Rural|Urban|||||
||(7)|(8)|(7)-(8)|(9)|(10)|(9)-(10)||||
|_Firstborn girl_ * _Female_|1.707|2.816***|-1.109|3.718***|0.315|3.403***||||
||(1.547)|(0.555)|(1.439)|(0.776)|(0.946)|(1.267)||||
|_Firstborn girl_ * _Female_ * _Post_1|-0.863|-1.525*|0.6613|-2.522**|1.086|-3.608**||||
||(2.033)|(0.774)|(1.983)|(1.055)|(1.035)|(1.522)||||
|_Firstborn girl_ * _Female_* _Post_2|-0.683|-2.100**|1.417|-3.179***|0.833|-4.012**||||
||(2.483)|(0.787)|(2.619)|(1.072)|(1.313)|(1.936)||||
|N|37,912|189,217||156,311|70,818|||||
|Baseline mean|15.90|11.80||14.15|8.15|||||

NOTES: This tables reports estimates from the specification in column 3 of Table 3 for various sub-samples. Each column within a panel is a separate OLS regression. SC, ST, OBC, and General respectively denote scheduled castes, scheduled tribes, other backward classes, and upper castes. The wealth categories are based on the national household wealth distribution. Standard errors in parentheses are clustered by state. Baseline mean refers to under-5 mortality for children born in the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA Countries that recently experienced or are currently involved in armed conflict have a higher share of own generation (see table 1).<sup>16</sup> Own-generation makes up 13 percent of installed capacity in these countries, compared with less than 4 percent in nonconflict countries. The share of own generation is also about twice as high in manufacturing and about five times as high in commerce in conflict and postconflict countries than in nonconflict countries.

There is a strong relationship between own generation and industrial structure. Naturalresource industries (oil extraction and mining) account for more than 50 percent of all owngeneration capacity (figure 14). The economies of most of the countries in which own generation is prevalent are heavily dependent on natural-resource industries, whether mining (Mauritania, Niger, Guinea, Togo) or petroleum extraction and refining (Equatorial Guinea, Republic of Congo, Gabon, Nigeria). The causality of the relationship is difficult to predict. The high degree of asset specificity in mining and oil extraction industries, coupled with poor contract enforcement in Sub-Saharan

**Figure 14 Own-generation as a share of installed capacity, by sector, 2006**

<!-- Start of picture text --> Mining13% Services0.5% ManufacturingOther Commerce<br>Pulp 3% 22%<br>3%<br>Cement<br>5%<br>Fuels Sugar<br>39% 15%<br><!-- End of picture text --> Source: UDI World Electric Power Plants Data Base (WEPP).

African countries, can influence a firm’s decision to supply its own power, bundling together generation and extraction facilities (Joskow 1988). On the other hand, poor public provision affects countries’ international competitiveness and enhances their specialization in low-value-added industries (Dollar, Hallward-Driemeier, and Mengistae 2005).

Sugar processing accounts for 15 percent of all own generation, reflecting the heavy use of electricity cogeneration agro-based industries. Cogeneration offers substantial opportunities for generating electricity with limited capital investments, while avoiding the negative environmental effects of increased fossil fuel combustion (Karekezi and Kithyoma 2003). Thus, countries with a large domestic sugar industry, such as Swaziland and Mauritius, have high rates of own generation.

Own-generation plants differ from public utilities in technological aspects, such as capacity and fuel type. Public utilities are much larger than own-generation plants (figure 15). About half of the capacity of public utilities is provided by large (500–1,000MW) plants. The average capacity of own-generation plants is about ten times smaller (10–100MW). Most public utilities rely for fuel on coal (in the Southern African Development Community) or hydropower (in East and Central Africa), whereas most owngeneration plants, as already noted, are thermal (oil, gas, and biomass) (figure 16).

> 16 Information on countries in armed conflicts was taken from Ploughshares’ _Armed Conflicts Report_ (2006), available at <u>http://www.ploughshares.ca/.</u>

11
nonfarm employment increased inequality in this period, temporary migrant employment decreased it.

Under central planning, China was characterized by a severe rural-urban divide. This divide was not reduced by the reform and marketization of the economy; the ratio of urban to rural household income per capita was greater than ever in 2007, at 4.10 according to the CHIP survey. However, it decreased to 2.91 after adjustment for spatial differences in prices. The corresponding CHIP ratios in 2002 were 3.35 and 2.28 (Li et al. 2013: table 2.8). Including various disguised subsidies (for health care, education, and pension contributions), the 2002 ratios were 4.35 and 3.10, respectively (Li and Luo 2010: 119). Rural-urban differences in the cost of living were offset by subsidies to urban people. The explanation for the high ratio is the underlying political economy that favors urban dwellers and the control of migration (Knight and Song 1999). The contribution to overall inequality made by the mean difference in rural and urban incomes rose from 37 percent in 1988 to 54 percent in 2007. Even adjusting for spatial price differences, which reduces the 2007 figure to 41 percent (Li et al. 2013), this is far higher than that in most other developing countries. Much of China’s income inequality would vanish if mean income per capita in rural and urban China were equal.

# <<A>>Regional Inequality

It is inevitable that a country as large as China will have large spatial or geographical differences in income levels. The more interesting question is whether there is 10
|**Table A1: Sample difference in household c**|**haracteristics between**|**those reporting owni**|**ng land and those**|**of non-response**|
|---|---|---|---|---|
||**Total**|**Reported**|**No**|**Difference**|
|||**own/use land**|**response**||
|Household size|5.167|5.341|4.787|***|
|No. of adults|2.441|2.556|2.186|***|
|Share completed high school|0.334|0.343|0.312|***|
|Share working for salary|0.252|0.283|0.182|***|
|Share self-employed|0.231|0.245|0.201|***|
|Share unemployed|0.277|0.275|0.282||
|Dwelling: Traditional hut|0.436|0.396|0.522|***|
|Dwelling: Traditional house|0.280|0.271|0.298|**|
|<br>Dwelling: Detached house|0.253|0.295|0.161|***|
|Dwelling: Flat/apartment|0.032|0.037|0.019|***|
|No. of rooms|3.050|3.151|2.827|***|
|Electricity access|0.156|0.192|0.079|***|
|<br>Access to tap water|0.165|0.202|0.084|***|
|Own dwelling|0.968|0.953|1.000|***|
|**Household head characteristics:**|||||
|Male|0.717|0.726|0.699|**|
|Age|45.203|46.739|41.829|***|
|<br>Married|0.704|0.707|0.698||
|Separated|0.111|0.104|0.127|***|
|Widowed|0.151|0.163|0.126|***|
|No education|0.173|0.182|0.155|***|
|Completed primary school|0.264|0.256|0.283|**|
|Completed high school|0.433|0.418|0.468|***|
|College or higher|0.129|0.145|0.094|***|
|<br>Self-employed|0.641|0.596|0.739|***|
|... self-employed: farming|0.699|0.697|0.702||
|... self-employed: non-farming|0.301|0.303|0.297||
|Work for salary|0.359|0.404|0.261|***|
|**Rural/Urban and provinces:**|||||
|Rural|0.779|0.758|0.827|***|
|Central|0.084|0.075|0.105|***|
|Copperbelt|0.109|0.127|0.069|***|
|Eastern|0.123|0.156|0.050|***|
|Luapula|0.095|0.050|0.193|***|
|<br>Lusaka|0.083|0.100|0.045|***|
|Muchinga|0.095|0.091|0.106|**|
|Northern|0.114|0.107|0.127|**|
|North Western|0.091|0.078|0.120|***|
|Southern|0.107|0.116|0.087|***|
|Western|0.100|0.100|0.098||
|Number of households|7,113|4,888|2,225||

_Notes_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Stars indicate t-tests for significance of differences in mean values for the two categories: * for sig. at 10%, ** for sig. at 5% and *** for sig. at 1%. To deal with non-response bias, we exclude the households that are not reporting owning or using land and are renters or obtaining free dwelling from companies.

29
# **Table 15: Impact of “Cap and Trade” Market Based Emission Control System to Reduce Carbon Emissions by 20 Percent on Regional Markets, with decomposition of carbon emission effects. (Results are Percentage Change from Base Year.)**

||**Overall**||**St. **||**North-**||||||**Far **|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average **|**Moscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare (EVas % ofconsumption)|-1.1|-1.8|-1.4|-0.2|0.5|-1.2|-0.1|-1.3|-0.5|-0.6|-1.7|
|Welfare (EVas % ofGDP)|-0.5|-1.0|-0.6|0.0|0.3|-0.5|-0.1|-0.6|-0.2|-0.3|-0.8|
|**Carbon Dioxide Emissions and Deco**|**mposition**|||||||||||
|CO2price (ruble pertonofCO2)||96.0|96.0|96.0|96.0|96.0|96.0|96.0|96.0|96.0|96.0|
|CO2emissions, decomposedinto:|-20.0|-18.3|-18.9|-21.9|-21.7|-18.9|-20.3|-18.3|-20.5|-20.7|-19.1|
|Output effect (% ofCO2)|-1.0|-1.0|-0.9|-2.3|-1.0|-1.0|-0.7|-1.0|-0.9|-0.8|-1.0|
|Compositioneffect (% ofCO2)|-3.1|0.1|-0.8|-9.9|-4.3|-2.5|-2.4|-1.2|-2.5|-4.7|-2.3|
|Technique effect (% ofCO2)|-16.3|-17.5|-17.5|-10.5|-17.3|-15.8|-17.6|-16.3|-17.6|-16.0|-16.2|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|-0.1|-2.7|-0.6|-5.2|-12.9|-0.2|-9.1|-5.3|-3.6|2.1|0.7|
|NitrogenOxide|-3.3|-3.9|-2.9|-3.6|-5.0|-3.1|-3.9|-4.1|-3.3|-2.3|-3.0|
|Hydrocarbons|-4.2|-2.4|-2.4|-2.1|-3.2|-5.8|-3.5|-2.8|-2.1|-10.6|-19.1|
|ParticulateMatter|-2.9|-4.0|-3.6|-2.7|-4.4|-2.7|-3.8|-4.1|-3.5|-1.6|-2.2|
|Volatile Organic Components|-1.5|-3.8|-1.5|-1.6|-0.4|-1.2|1.2|-1.4|-1.4|-2.9|-0.6|
|Carbon Monoxide|-1.7|-1.0|-0.9|-1.8|-12.8|-0.6|-13.0|-2.4|-2.3|1.1|-0.1|
|**Aggregate trade**||||||||||||
|Regionalterms oftrade (% change)|-0.1|1.5|1.3|0.5|0.8|1.4|1.3|1.3|1.2|1.7|1.5|
|Regionalexports (% change)|-1.2|-1.5|-1.3|-1.8|-1.0|-1.4|-0.6|-0.9|-0.8|-1.8|-1.5|
|Realexchangerate (% change)|0.4|0.4|0.3|-0.3|0.9|0.3|0.8|0.4|0.5|0.3|0.2|
|Internationalexports (% change)|0.4|3.6|0.3|1.1|-3.9|1.2|-5.5|1.3|0.2|-0.1|2.4|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|-1.7|-1.4|-1.6|-2.1|-0.7|-1.9|-1.3|-1.8|-1.9|-2.1|-1.9|
|Skilledlabor|-2.6|-1.4|-1.6|-4.9|-2.1|-3.2|-1.9|-2.2|-2.1|-4.4|-3.4|
|Nationalcapital|-1.7|-1.7|-1.9|-2.4|-1.3|-1.9|-1.4|-1.8|-1.6|-1.9|-2.0|
|Regional mobile capital|-1.8|-2.1|-2.0|-0.2|-2.9|-1.6|-2.4|-1.8|-1.9|-1.5|-1.5|
|Crude oil resources|-0.6|||-1.3|1.4|0.1||0.5|0.7|0.1|-0.1|
|Naturalgasresources|-32.9|||-34.6|-2.2|-19.2||-16.2|-13.6|-19.5|-20.7|
|Coal resources|-13.6|||||-12.9|-11.0|-12.1|-13.4|-13.8|-13.3|
|Specific capital indomesticfirms||-0.4|-0.6|-1.3|-1.4|-1.2|-0.9|-1.2|-1.8|-0.3|-0.9|
|Specific capital in multinational firms||0.1|0.9|3.8|0.7|1.8|2.4|2.6|3.9|3.7|2.1|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|0.9|0.6|0.6|1.2|2.0|0.8|1.0|0.5|0.9|1.3|1.0|
|Skilled labor (% changing sector)|1.5|0.6|0.8|2.1|2.5|1.8|1.3|0.7|1.2|3.3|2.2|
|**Source: Authors' estimates**||||||||||||

53
<!-- Start of picture text --> 350<br>-<br>g 300 - |<br>5& i250 |-<br>eo]— ae 1<br>£co ot oe||il<br>2. oo Cl<br>i Ue Oe Oe ee<br>‘ffo- fo Baa Ba BY<br>2002 2003 2004 2005 2006 2007 2008 2009<br>OCollection inneficiencies Losses Underpricing<br><!-- End of picture text -->
# Table of Contents

|Intro|duction|3|
|---|---|---|
|I.|Efficiency Gains and Benefits of DDSR|6|
||Efficiency Gains of DDSR<br>The Net Benefit of DDSR|7<br>12|
|II.|Financial Cost of Debt Reduction|14|
||Valuation of Country Debt and Financial Costs<br>Minimizing Fmancial Cost: The Case for Menu-based<br>Debt Reduction|14<br>17|
|M1.|<br>Experience of Brady Deals|26|
||The Extent of Debt Reduction<br>Cost Reduction in Menu-based Operations<br>Financial Gains of Commercial Banks<br>Financial Cost to Debtor Countries|26<br>29<br>32<br>34|
|IV.|Concluding Remarks|37|
|Tabl|es|39|
|Refe|rences|45|
|Ann|ex Table|47|
**Table 4.6. Average fault repair times for critical equipment (hours) at selected BAA airports. April 1995 to March 1996**

|T111<br>M O|1.52|3.18|2.31|1.92|1.79|
|---|---|---|---|---|---|
||g § 2 ! !|<br>:<br>0.14<br>2.35|<br>1.31|n.a.|3.24|
|M0111|_o.55_|g1<br>4.53|4.24|4.45|1.82|
||0.63|3.75|0.83|0.68|1.09|
||0.92|2.04|1.25|1.49|1.29|
||1.60|1.97|6.27|1.57|1.81|
|M gg<br>||0.12|1.64|0.46|n.a.|0.31|

n.a.: Not available.

Source: Monopolies and Mergers Commission (1996).

**Table 4.7. Average levels of pier service at selected BAA airports. 1995/96**

|.ncldi g<br>ChannelIsandsand<br> <br>|IArrival<br>|87<br>|9|4|
|---|---|---|---|---|
|_J_<br>~~~~~~~D|eparture|8 974|||
|ial<br>g<br>SX<br>...**.**..|Arrival|79|1 7|4|
||Departure|8C1|_15_|4|
||<br>4D|eparture 92|2||
|&;l jl;<br>11 ||Arrival|8 9|7|4|
||Departure|90|4|6|
||| Arrival|94|3|3|
|_<br>t|~~Departure|93|4|3|
|||Departure|**7**5|2 1 |4||
|=<br>||<br>~~~~~~~Arrival<br>||<br>83|31|1 4|
||<br>~~~~~~~~Dep<br> <br>|arture<br> <br>||<br>83<br>1|<br>31|14|
|*Including<br>Ihne<br>Islands and|Ireland.||||

Source: Monopolies and Mergers Commission (I1996).

42