rafmacalaba/datause-encoder-data huggingface dataset:
Note that electricity prices do not figure in this expression, because they are assumed to be same for each project.<sup>12</sup> Using data from EC (2008) and IEA (2005), Table 7 presents mean estimates of the switching prices between the different technologies, including renewables – biomass, hydro, wind and solar.<sup>13</sup>

**Table 7: Mean estimates of switching prices (two most comprehensive studies)**

|Technology<br>from<br>to||Switching price<br>$/t CO2||
|---|---|---|---|
||Pulverized coal|IGCC|Gas|
|Pulverized coal|.|.|.|
|IGCC|17|.|.|
|Gas|25|29|.|
|Biomass|91|104|145|
|Onshore wind|69|78|104|
|Offshore wind|84|96|132|
|Hydro|121|139|198|
|Solar PV|1174|1373|2096|
|Solar thermal|273|317|472|

<mark>Authors' calculations based on data from EC (2008) & IEA (2005).</mark> Table 7 shows that the mean values of the switching prices for the renewable technologies versus the fossil fuel technologies lie well above the mean and median values of the fitted distribution of the social cost of carbon in Tol (2009), as well as the 2010 mean values in the Interagency Working Group (2010). However, if precautionary (95<sup>th</sup> percentile) values of the social cost of carbon for a pure rate of time preference of 1% are chosen from the fitted distribution in Tol (2009), then biomass, wind and hydroelectric technologies are preferred (from the world perspective) to the coal-based technologies.

> 12 This is usually a reasonable assumption in the context of a single project, but it is a very strong assumption when one considers larger portfolios of projects to lower national emissions volumes or their growth rates. 13 Using the mean values hides considerable total variation, however. If the least costly renewable technologies are compared with the most costly fossil fuel technologies, then biomass, wind and hydro technologies have 0 or negative switching prices versus the two coal technologies, indicating that these technologies would be preferred (from the world perspective) even for a $0 social cost of carbon. Note as well that the levelized cost methodology masks the sensitivity of switching prices to discount rates in the case of capital-intensive renewable technologies, as shown in expression (10).

22
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Page 35 their current recommended daily requirements than men (Bleiburg and others 1980; Florencio and Aligaen 1980).

83. Protein intake appears to be more adequate than caloric intake in most countries. But figures reported do not take into account that when calories are deficient, the body will use protein to compensate and produce energy. Moreover, the quality of protein is seldom reported. "Incomplete" protein, which lacks some of the amino acids that the body cannot synthesize itself, is of lower quality. Women often consume lower quality vegetable protein, while men receive the larger share of whatever (higher quality) animal protein is available. A study from Burkina Faso, for example, shows women consuming only .8 grams of animal protein daily compared to men's 10.3 grams. (Women also consumed less of all kinds of protein.) Women's vitamin and mineral intakes show similar absolute and relative shortfalls relative to men's (Hamilton and others 1984).

84. AnthroDometric data. Studies are rare but suggest that women's weights are generally more adequate than their dietary intakes -- but still far below WHO standards. Women in Africa and Asia are particularly short. Shortness and deficient stores of fat reflect their poorer diets. Weight gain during pregnancy is a critical indicator of pregnancy outcome, particularly the baby's birth weight. The average pregnant woman in Europe and thie United States gains 10 to 12 kilograms. Studies in several low-income countries report average pregnancy weight gains of only 2 to 7 kilograms (Hauck 1963; Thomson and others 1966; Venkatchalam and others 1960).

# Promising Approaches in Women's Nutrition

85. Creating demand. To improve women's nutritional status, women themselves must be convinced of the need. In many circumstances, creating demand is as important as providing service.

86. Women's lack of self-confidence is a major impediment to the success of maternal and child health programs (Griffiths 1988). It often shows up as "silence" or extreme denial of self and dependence on external authorities for direction (Belenky and others 1986). Women fee reluctant to seek help for themselves or their children -- whether prenatal care, family planning, health care, education, or wages. In some sociaties where women are not encouraged to think for themselves, authority figures have helped persuade women to seek health or family planning services, continue breastfeeding, and so on. Women with more self-confidence have been more willing to take the steps needed to improve child nutrition and find effective treatment of acutely undernourished or malnourished children (Griffiths 1988).

87. Feeding programs. Supplementary feeding programs in India, the Philippines, the Gambia, Guatemala, Canada, and the United States have successfully raised birth weights of infants born to high-risk women. Presumably they also improved maternal nutrition, though that question has seldom been addressed. To improve the odds of success, the food supplement should be provided conveniently and close to home. It should cost little in time or money and should accommodate traditional beliefs. To supplement the
_A_ it is TFP which represents the efficiency of the firm in transforming inputs into output.

Using ordinary least squares (OLS) to estimate the production function coefficients (β _L_ , β _M_ β _K_ ) assumes that input choices are exogenous. However, firm input choices are endogenous. For example, the number of workers hired by a firm and the quantity of materials purchased may depend on unobserved managerial ability, which is part of TFP known to the firm but not observable by the researcher. Since input choices and productivity are correlated, OLS production function estimates are biased.<sup>10</sup> This endogeneity bias can be partly corrected using fixed effects estimation for the production function, which eliminates unobservable _fixed_ firm characteristics that may affect simultaneously input choices and TFP. However, there may still be unobserved _timevarying_ firm characteristics affecting simultaneously input choices and TFP. We follow the estimation methodology proposed by Olley and Pakes (1996) to correct for the potential simultaneity bias generated by such firm time-varying unobservables. The main idea behind the methodology is that an observable firm characteristic - investment - can be used to proxy for the unobserved firm productivity and estimate unbiased production function coefficients. The Appendix describes the methodology in detail.

The survey includes information on output and inputs, summarized in Appendix Table 2, needed for the production function estimation. Labor is measured by the total number of workers. The capital stock is obtained by cumulating deflated flows of investment using the perpetual inventory method formula.<sup>11</sup> Output is measured by deflated sales. An important advantage of our measure relative to those used in other studies is that we deflate nominal sales by _firm-specific_ output deflators, which are based on a survey question on annual changes in the price of the firm’s main product. Materials is measured by deflated material costs. Again, our measure has the advantage of being deflated by _firm-specific_ materials deflators, which are based on a question on annual changes in the price of the firm’s main materials. Previous studies have used industryspecific deflators to obtain the output and materials measures. The problem with that approach can be understood by the following example. Large firms are likely to have market power in product markets and monopsony power in input markets and may thus

> 10 See Levinsohn and Petrin (2003) for more details on the bias of OLS production function estimates.

> 11 Details on the construction of the capital stock are provided in the Appendix.

6
to speak. Such freedom has the tendency to spill over outside the GS into everyday life. Therefore, GSs are troublesome for local elites who at times try to ensure that they are not held. In 2001 twenty-five per cent of GPs did not hold even one GS over a year-long period (Besley, Pande and Rao, 2005).

# **5. DATA AND METHODS**

Data for this study are drawn from tape-recordings of 290 GS meetings in four South Indian states: Andhra Pradesh (AP), Karnataka (KN), Kerala (KL), and Tamil Nadu (TN). The tape recordings were conducted by one or two field investigators who were instructed to dress in a simple manner and locate themselves in an unobtrusive spot at these meetings, after having taken the permission of the GP president. The recordings were then transcribed into the corresponding local language, and then translated into English to facilitate coding. Each transcript also includes information on attendance, and the caste, gender, official designation, and social position of speakers (elected representative, school principal, villager, etc.).

Table 1 provides summary information from the transcript data. The average GS lasts about 84 minutes and is held about an hour after the scheduled time (which is not untypical of public functions in India). About 83 people attend on average, a tiny fraction of the village population which ranges from 2000 to 10,000 depending on the state. Besley, Pande and Rao (2005) report results from a regression analysis of household survey data from the same sample and show that, after controlling for household characteristics and village fixed-effects, illiterate individuals, SCs, the landless and, and the less wealthy are more likely to attend the gram sabha, while women are less 17
Table A3: Balance check: Follow-up 2

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Con|trol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Socio-Demographic**||||||
|Female|0.51|0.50|0.50|0.50|0.32|
|Married|0.12|0.32|0.14|0.35|0.37|
|Has Children|0.13|0.33|0.12|0.32|0.72|
|Age|27.51|2.84|27.60|3.09|0.61|
|Province: East|0.08|0.27|0.08|0.28|0.87|
|Province: Kigali|0.49|0.50|0.44|0.50|0.15|
|Province: North|0.14|0.35|0.17|0.37|0.33|
|Province South|0.16|0.37|0.20|0.40|0.05|
|Province West|0.13|0.34|0.11|0.31|0.26|
|Mother is employee|0.04|0.20|0.05|0.22|0.51|
|Father is employee|0.06|0.24|0.07|0.25|0.69|
|Wealth Index|-0.10|1.41|0.04|1.42|0.15|
|**Panel B. Education**||||||
|Years since graduation|1.92|0.92|1.86|0.95|0.33|
|High Education Diploma|0.21|0.41|0.20|0.40|0.72|
|Bachelor Degree|0.77|0.42|0.77|0.42|0.78|
|Masters Graduate|0.02|0.14|0.02|0.15|0.85|
|Public School|0.74|0.44|0.76|0.43|0.46|
|Good English Language|0.97|0.18|0.97|0.16|0.72|
|Good French Language|0.43|0.50|0.43|0.50|0.83|
|Good Swahili Language|0.14|0.34|0.13|0.33|0.65|
|**Panel C. Employment**||||||
|Has ever worked|0.84|0.37|0.86|0.35|0.43|
|Not paid job last month|0.60|0.49|0.60|0.49|0.73|
|Works as employee|0.10|0.30|0.09|0.28|0.36|
|<br>Works as self-employed|0.05|0.21|0.06|0.24|0.31|
|<br>Works as family employee|0.08|0.27|0.08|0.27|0.93|
|Works as casual employee|0.18|0.38|0.17|0.37|0.68|
|Has formal job|0.03|0.18|0.02|0.14|0.27|
|Job accordance to degree|0.32|0.47|0.27|0.45|0.11|
|Labor earnings (monthly USD)|43.14|81.93|35.12|78.29|0.13|
|<br>**Panel D. Training**||||||
|<br>Willingness to pay for course (USD)|174.95|992.66|140.98|338.18|0.50|
|Training: get job faster|0.59|0.49|0.57|0.50|0.61|
|Training: get higher pay job|0.30|0.46|0.27|0.45|0.41|
|Training: get better job|0.34|0.47|0.39|0.49|0.20|
|<br>Prefers to attend Kigali|0.67|0.47|0.66|0.47|0.45|
|Prefers to attend North|0.19|0.39|0.18|0.39|0.16|
|Prefers to attend South|0.15|0.35|0.16|0.36|0.79|
|**Panel D. Joint test for panels A-D**<br>||||||
|p-value (joint F-test)|||||0.46|
|Observation|430||435||865|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. We do not test for differences in means for females since the lottery was randomized for each gender separately. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

28
there is a non-zero correlation betwteen the training variable and the earnings error. In order to account for this latter source of bias in tie wage estimates while accounting for labor force participation, one could use instrumental variables for training (Heckman 1977). The predicted probabilities of training participation estimated by the training probit could be used instead of observed values of the training variable in the wage equation 41.

# The Data

As stated earlier, this analysis uses the PLSS data set. In this paper, women aged 15 to 65 were classified first as labor force participants uif they "worked" one hour or more during the seven days or twelve months prior to the survey, or were looking for work, and as non-participants if they reported no work activities in either reference period U. Second, any woman in the labor force was then classified according to the activity she reported as the "main job" In the seven days prior to the survey. If she did not work 1/ To determine whether to use instrumental variables or observed values for training, a test for the correlation between training and the error term will be used. The test includes both the instrumental and the observed training variables in the wage equations. If there is correlation between the probability of entering training and the error term, the coefficient of the instrumental variable will be significant. In this situation, the use of instrumental variables would be appropriate. If there is no correlation between the training variable and the error term, the coefficient of the predicted probabilitiea would be insignificant, and the use of the observed training variable would be appropriate. See Wu 1973, Hausman 1976, Kiefer 1979.

1 There were about 300 women who were out of the labor force during the seven days prior to the interview, but reported a job and hours of work during the last twelve months.

9
# **_Subjective preferences for redistribution_**

The analysis focuses on a set of questions in the LiTS survey aimed at eliciting the respondent’s views on economic inequality, government’s policy priorities, and on the importance of support (both personal and by the government) to the poor and various groups. Taken together, responses to these questions can be viewed as a proxy for unobserved preferences over redistributive policy in the region. Two questions elicit the degree of inequality aversion of the respondent:

- (i) Respondents were asked to what extent they agreed or disagreed with the statement: _The gap between the rich and the poor in our country should be reduced._

- (ii) Respondents were asked to indicate whether they thought that _“Incomes should be made more equal”_ or that _“We need larger income differences as incentives for individual effort”_ .

The analysis of the above measures is supplemented by the following measures eliciting the respondent’s views on government support to the poor:

- (iii) Respondents were asked to identify what should be the first and second priorities for extra government spending (choosing between education, healthcare, housing, pensions, assisting the poor, the environment, public infrastructure, etc.).

- (iv) Respondents were asked whether they would be willing to give part of their income or pay more taxes for various public policy objectives, including to “help the needy”.

- (v) Finally, support for redistribution for specific groups was elicited by asking “which groups of citizens deserve support from the government”, possible answers including: elderly, disabled, families with children, war veterans, working poor, unemployed, or nobody.

While the literature refers to both questions (i) and (ii) as measures of inequality aversion (mostly without distinguishing between them), they may reflect and be driven by very different factors. The correlation between responses to question (i) and (ii) is weak. On average, the individual correlation coefficient between the 1-10 step inequality measure and the 5-step inequality measure is about 13 percent, with the highest correlation in the countries of Western Europe.<sup>7</sup> Quite likely, the important difference between the two questions is the fact that the former is framed in terms of helping the poor catch up and the latter suggests that higher inequalities can have beneficial effects by incentivizing effort, and also refers to “making

> 7 The countries with highest correlation are Germany (42%), Czech Republic (35%), France (31%), Sweden (31%) and the UK (30%).

7
by higher demand is directly related to _βS_<sup>_′_,thesizeofthissectorrelativetononagriculture</sup> as a whole.

If the demand for services is unresponsive to changes in income induced by higher agricultural prices, as I have suggested might be the case in the short-run, then the segmented model delivers a very sharp prediction. In this case, _δ_<sup>_′_</sup> = 0 and, consequently, _φ_<sup>_SR_</sup> = 0 _._ In other words, until (permanent) income effects kick in, there are zero inter-sectoral price spillover effects in a segmented economy.

# **2.3 A taxonomy of wage-price elasticities**

The theoretical framework yields wage-price elasticities as functions of input cost shares, sectoral labor shares, and other parameters, all of which can be estimated from nationally representative data collected by India’s National Sample Survey (NSS) Organization (see Appendix B for details). Table 1 summarizes the results of these calculations for 472 districts in 18 geographically contiguous states of India, containing the vast bulk of its rural population.<sup>13</sup> Columns 1-3 present wage-price elasticities implied by perfect sectoral mobility and columns 4-6 the _nonagricultural_ wage-price elasticities implied by labor market segmentation. Thus, in column 1, the _ψ_ from my baseline model, which assumes a unitary elastic demand for services and equal input cost shares across sectors, averages 1.17 across districts.<sup>14</sup> Such high elasticities reflect large values of _βA_ ; for the average rural district, around three-quarters of _manual labor days_ (adjusted for efficiency units; see Appendix B.2) are spent in agriculture. Statewise statistics on labor shares, per-capita expenditures, and _ψ_ are reported in Appendix Table C.1.

> 13Excluded are the peripheral states of Jammu/Kashmir in the far north and Assam and its smaller neighbors to the north and east of Bangladesh. Included states, organized into five regions, are _North_ : Harayana, Himachal Pradesh, Punjab, Uttar Pradesh, and Uttaranchal; _Northwest_ : Gujarat and Rajastan; _Center_ : Chhattisgarh, Madhya Pradesh, Maharashtra, and Orissa; _East_ : Bihar, Jharkhand, and West Bengal; _South_ : Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu. See Appendix Table C.1.

> 14By contrast, the smattering of aggregate time-series evidence that exists for other countries suggests a wage-food price elasticity somewhat less than unity (see Lasco et al., 2008, for a review).

8
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countries were trapped in a low-level equilibrium. Minor conflict countries have a substantially higher probability of a growth deceleration than the average and are much more likely to experience bad times than good times.

**<u>Table 5: Frequency of growth acceleration and deceleration by country category</u>**

||**Growth a**<br>|**cceleration**<br>|**Growth d**<br>|**eceleration**<br>|
|---|---|---|---|---|
|**Country category**<br>|**Frequency**<br>**(country-years)**|**Above/below all**<br>**countries' mean**|**Frequency**<br>**(country-years)**|**Above/below all**<br>**countries' mean**|
|All countries' mean|0.25|-|0.22|-|
|Coastal|0.26|Above|0.22|Equal|
|Landlocked|0.23|Below|0.22|Equal|
|Coastal without resources|0.24|Below|0.23|Above|
|Landlocked without<br>resources|0.22|Below|0.22|Equal|
|Oil exporters|0.29|Above|0.23|Above|
|Non-oil exporters|0.24|Below|0.22|Equal|
|Resource countries|0.30|Above|0.21|Below|
|Non-resource countries|0.23|Below|0.23|Above|
|Major conflict|0.16|Below|0.17|Below|
|Minorconflict<br>Source: Arbache and Page(200|0.19<br>7b).|Below|0.32|Above|

Table 6 shows the unconditional probabilities of growth acceleration and deceleration at the country level and the growth rates during these episodes. The gaps between growth rates during accelerations and decelerations at the country level tend to be high, generating the high growth volatility observed in Africa. The high average growth rates observed in many economies during acceleration episodes also show the resilience and capacity of the region’s economies to grow when economic and political conditions favor growth. The magnitude of economic contractions during deceleration episodes similarly indicates the severity of the consequences when economic and political conditions are unfavorable.

Sixteen countries in our sample have avoided growth decelerations altogether. Many—Botswana, Cape Verde, Equatorial Guinea, Lesotho, Mauritius, Mozambique, and Uganda—are among the region’s top performers in per capita income growth over the three decades, but not all. Burkina Faso, Guinea, Namibia, São Tomé and Príncipe, and Swaziland are not among the region’s growth leaders. Avoiding growth collapses is important for long-run success at the country level, but it is not the only factor contributing to robust long-term growth.

Seven countries—the Democratic Republic of Congo, Eritrea, Gabon, The Gambia, Madagascar, Mauritania, and Niger—have never had a growth acceleration. Of those seven, only Eritrea shows good long-term per capita income growth. Four of the seven had long-run declines in per capita income.

15