rafmacalaba/datause-encoder-data huggingface dataset:
responsiveness to consumer and business needs, and increased investment driven by market incentives rather than bureaucratic preference (Gassner _et al_ 2008). However, there are also concerns that are increasingly being expressed about the distributional consequences of these reform programs--especially their impacts on the affordability of basic service provision to poor households and other disadvantaged groups.

# **Defining and Measuring Affordability**

There is a vast and growing literature evaluating the changes in consumer welfare due to innovation and restructuring in the supply of public utility services. Foster, Tiongson and Laderchi (2005) review the different strategies of analyzing the social impacts of utility market reforms. The approaches are remarkably heterogeneous both in terms of their analytical foundations and data requirements. The welfare impact assessment can rely upon standard neoclassical economic analysis and therefore it may provide estimates of the changes in consumer surplus induced by the joint changes in the price of, and access to, utility services (Lampietti 2004; McKenzie and Mookherjee 2003). Alternatively, it is based on the calibration of computable general equilibrium models estimating the impacts of price adjustments on the poverty and inequality among different household groups (Jensen _et al_ 2008; Boccanfuso _et al_ 2006 and 2007; Chisari _et al_ 1999). Such an assessment could also rely on the willingness to pay for utility services estimated from discrete choice household surveys (Wang _et al_ 2008) or stated preference conjoint surveys (Yang _et al_ 2006). Finally, many social impact assessments refer to some affordability concept usually based on a “burden-threshold” of the household’s total income [Barrantes and Galparin 2008 (telecommunications); Estupinan _et al_ 2007 (urban transportation); Fankhauser and Tepic 2006 (energy and water); Carruthers _et al_ 2005 (transportation); Navas-Sabater _et al_ 2002 (telecommunications); Foster and Tre 2000 (energy); Milne 2000 (telecommunications)].

Changes in prices are often associated to changes in the quality of the services provided, but the potential benefits of improved quality are difficult to estimate mainly due to measurement problems, and are thus rarely considered (Klytchnikova and Lokshin 2007). Improved access to good quality utility services may significantly impact on many determinants of some key non-monetary dimensions of well-being (e.g.

9
Cleves, Mario, William W. Gould, and Roberto G. Gutierrez. 2002. _An Introduction to Survival Analysis Using Stata_ . College Station, TX: Stata Press Publication.

- Currie, Janet, and Enrico Moretti. 2003. “Mother’s Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings.” _Quarterly Journal of Economics_ 118(4): 1495-1532.

- Cutler, David M., and Srikanth Kadiyala. 2003. “The Return to Biomedical Research: Treatment and Behavioral Effects.” In Kevin Murphy and Robert Topel, eds., _Measuring the Gains from Medical Research. An Economic Approach._ Chicago, IL: University of Chicago Press.

- Cox, D. R. 1972. “Regression Models and Life-Tables.” _Journal of the Royal Statistical Society B_ 34: 187-202.

Cox, D. R. 1975. “Partial Likelihood.” _Biometrika_ 62: 269-76.

- Deaton, Angus, and Christina Paxson. 2003. “Mortality, Income, and Income Inequality Over Time in Britain and the United States.” In David A. Wise, ed., _Perspectives on The Economics of Aging,_ vol. 8. Chicago, IL: University of Chicago Press for NBER.

- De Walque, Damien. 2003. “How Do Education and Information Affect Health Decisions? The Cases of HIV/AIDS and Smoking.” Ph.D. Dissertation, University of Chicago, Department of Economics.

- De Walque, Damien. 2004. “How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment. Evidence from Rural Uganda.” Policy Research Working Paper 3289. World Bank, Washington DC.

- Farell, Phillip, and Victor Fuchs. 1986. “Schooling and Health: The Cigarette Connection.” In Victor R. Fuchs, ed., _The Health Economy._ Cambridge, Mass. and London: Harvard University Press.

- Ferrence, Roberta G. 1989. _The Rise and Fall of Cigarette Smoking in North America._ New York, London: Garland Publishing.

- Finkelstein, Amy, and James Poterba. 2004. “Adverse Selection in Insurance Markets: Policyholder evidence from the U.K. Annuity Market.” _The Journal of Political Economy_ 112 (1,1): 183-208.

- Fuchs, Victor. 1982. “Time Preference and Health: An Exploratory Study.” In Victor Fuchs, ed., _Economic Aspects of Health_ . Chicago: University of Chicago Press.

- Grimard, Franque, and Daniel Parent. 2003. “Education and Smoking: Were Vietnam Draft Avoiders Also More Likely to Avoid Smoking?” Scientific Series 2003s-44, Centre interuniversitaire de recherché en analyse des organisations (Cirano), Montréal.

- Grossman, Michael. 1972. “On the Concept of Health Capital and the Demand for Health.” _The Journal of Political Economy_ 80(2): 223-255.

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_Measures of food security_ ―Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life‖ (Food and Agricultural Organization, 2008, p. 1). It includes four main factors: availability, access, utilization, and stability. Availability refers to the physical existence of food, which relates to production, stocks and trade. Access refers to a household‘s ability to obtain food, which depends on income, prices, and market access. Utilization refers to an individual‘s ability to process nutrients and energy from food; many factors contribute to food utilization, including dietary diversity and nutrient absorption, intra-household allocation of food, and hygienic preparation. The final factor refers to the stability of the other three factors over time (Food and Agricultural Organization, 2008).

We construct three measures of household food security to be used as dependent variables in the regression analysis: the value of per capita monthly food consumption; per capita daily caloric intake; and household dietary diversity. The first two relate to the access to food and the last one relates to the utilization of food. The value of food consumption is a core component of poverty indictors. Caloric intake is a widely used measure of health and undernutrition; however recent literature suggests that it is not a sufficient indicator of nutritional status, therefore we also include a measure of dietary diversity (Deaton & Dreze, 2009). Recent research highlights the relationship between child and adult nutritional status and household dietary diversity (Ruel, 2003).

The value of monthly food consumption (in Afghani) is constructed by mapping district price data to food quantity data.<sup>3</sup> Since not all food items were available in all district markets at all times of the year, we imputed the missing elements to obtain a complete price matrix, which provides prices for those items that households may have produced at home or those that households may have obtained from more distant markets.<sup>4</sup> The survey includes questions on the percentages of imported wheat and rice the household consumes;

> 3 Households are asked for the quantity of foods consumed over the past seven days; these quantities are multiplied by 4.2 to get monthly values.

> 4 The imputation process filled in missing values using the first feasible methodology according to the following order: 1) median of that month of the 20 nearest neighbors (weighted by inverse distance); 2) province median of that month; 3) national median of that month; 4) median price of 20 neighboring districts of the quarter (weighted by inverse distance); 5) province median of that quarter; and 6) national median of that quarter.

6
||**Table 2.**<br>**Example Choice Scenar**|**io**|
|---|---|---|
||**JOB A**|**JOB B**|
|Written Contract|6 months initially|1 year initially|
|Termination Notice|15 days|15 days|
|Working hours|30-45 hours per week|45-60 hours per week|
|Amount of paid leave<br>(not including major<br>government holidays /<br>festival leave)|10 days|10 days|
|Provident Fund|No|No|
|Monthly income|20% higher than your current<br>monthly income from main<br>economic activity|10% higher than your current<br>monthly income from main<br>economic activity|

_Source_ : Authors’ selection of levels from choice experiment used in survey.

_Note_ : Example of a choice set presented to respondents during the survey.

40
potential for climate variables exceeding key thresholds which might give rise to sharp drops in the productivity for some cropping activities.

While the effects of adverse climate shocks on the poor have received relatively more attention in the literature, it is entirely possible that the poverty impacts of attempts to mitigate climate change could have a much greater effect on the poor in some developing countries.

Here, preliminary evidence suggests that both the direct effects of payments to sequester carbon, as well as the indirect effects of mitigation activities on wages and food prices, could have important poverty impacts. Of course, realizing potential poverty benefits of mitigation programs depends on both the extent that agriculture and forestry offer low cost mitigation opportunities, and the extent to which governments are able to effectively mobilize resources to slow the rate of GHG emissions.

Because there has only been modest progress to date for mitigation efforts, policy-makers should also consider ways to support the poor in adapting to climate change. As pointed out by Adger (2003; p. 388), adaptation is not a new phenomenon: “It is clear that individuals and societies have adapted to climate change over the course of human history and will continue to do so.” We can learn a great deal from examining studies of the poverty impacts of historical climate events, which highlight these events as one of the most important reasons for households falling into or remaining trapped in poverty in rural areas. Because these events hit the entire population at once, traditional methods of risk sharing which work well for idiosyncratic events are no longer adequate. As a consequence, poor farming households are prone to modifying behavior in anticipation of such shocks, and these _ex ante_ behavioral changes can cost them quite a bit in terms of future earnings.

44
S= zwS<sup>RP</sup> + z(1 −w)S<sup>RU</sup> + (1 −z)wS<sup>CP</sup> + (1 −z)(1 −w)S<sup>CU</sup> (7) where S<sup>RP</sup> and S<sup>CP</sup> have a success probability of zero (principled individuals never do any sensitive acts and so never say "Yes", whether they are reticent or candid); and S<sup>RU</sup> and S<sup>CU</sup> have success probabilities of 1 −q and 1 , respectively. (Unprincipled respondents are always guilty, but only a fraction 1 −q of the reticent ones admit to it.) We can similarly write the number of "Yes" responses on the RRQ battery as a mixture of four binomial random variables corresponding to the four types of respondents: (8) where X<sup>RP</sup> and X<sup>CP</sup> have a success probability of 0.5(1 −q) and 0.5 respectively. (Principled individuals never do any sensitive acts, and so the candid ones answer "Yes" only when the coin comes up heads, while the reticent ones do so only when the coin comes up heads _and_ they choose not to behave reticently.) X<sup>RU</sup> and X<sup>CU</sup> have success probabilities of 1 −q and 1 respectively. (Unprincipled individuals are guilty of all of the sensitive acts, and so they should say "Yes" whether the coin comes up heads or tails, but the reticent ones do so only with probability 1 −q .) With this notation it is straightforward to calculate the average number of "Yes" responses on the CQ and RRQ battery as before: (9) and (10) E[X/n] = (1 −p)(1 −(r+ ε)q) + p0.5(1 −(r−ε)q) These expressions are the natural generalizations of Equations (3) and (4) in Section 3. First, note that they are exactly equivalent to (3) and (4) when ε= 0 , after making the substitution (1 −p) = g . Hence, in the benchmark case of zero correlation between reticence and guilt, the ratio of these two moment conditions identifies 1 −p and given this estimate of p , we can retrieve an estimate of rq from one of the two means. Finally, we can separately identify r and q from the variance of the RRQ, as we did in the previous model. There is however one difference here even in the case where ε= 0 . In this generalized version of the model a fraction 1 −p of respondents are always guilty, and so if they were candid they would be answering "Yes" to all of the questions in the RRQ battery, thereby increasing the variance of X . Since not many respondents answer in this way, matching the variance of X entails inferring that there is a smaller proportion of candid respondents in the sample.

This is precisely what we find in Table 7, when we return to the Peruvian data. In the first column, we consider the basic case where ε= 0 , i.e. reticence and guilt are uncorrelated. We find that the proportion of principled respondents is 0.63, implying a proportion of unprincipled respondents, 1 −p= 0.37 , which is the same as the estimated rate of guilt from our basic model in Sections 3 and 4.

17
were awarded to U.S.-born females and only 23 percent of doctoral recipients were foreign-born. The situation was reversed by 2000, when only 36 percent of doctoral recipients were U.S.-born males, 25 percent were U.S.-born females and 39 percent were foreign-born. Contradicting Borjas (2004), the authors found that foreign students were not substituted for domestic students. The number of PhDs granted to undergraduates from U.S. institutions, most of whom were U.S. citizens, did not change much during this period, while there was substantial growth in the number of foreign bachelor’s graduates obtaining U.S. doctorates. Thus the change in proportion is mostly due to the expansion of PhD programs, with a majority of the new slots being taken by foreign students rather than through substitution.

These same trends explain the fact that the proportion of foreign-born faculty with U.S. doctoral degrees at U.S. universities has gone up sharply during the past three decades, from 11.7 percent in 1973 to 20.4 percent in 1999. For engineering it rose from 18.6 percent to 34.7 percent in the same period.<sup>14</sup> In the last few years, however, there has been a steep decline in foreign student applications for admission into U.S. universities and a corresponding increase in applicants to universities in Asia, Australia and New Zealand.<sup>15</sup> This is due both to difficulties in obtaining U.S. visas since 9/11 and to the fact that some countries are catching up to the United States with regard to attracting foreign students and skilled labor from abroad (Hira, 2003). Recent evidence also suggests that collaboration between foreign and US universities has shown marked increase during the past two decades and increasingly research activities are being “dispersed” abroad, particularly to

> 13 <u>http://nces.ed.gov/pubs99/1999081.pdf</u>

> 14 <u>http://www.nsf.gov/sbe/srs/seind02/append/c5/at05-24.xls</u>

8
31

# Solar Water Heating

_75._ A recent ESMAP study of the Potential for Solar Water Heating in Kenya provides a unique case study on the impact of lower oil prices on the economic and financial viability of solar water heating (SWH) applications. The study originally assessed SWH systems using October, 1985 fuel prices and was revised using August 1986 prices. Between October 1985 and August 1986, petroleum product border prices in Kenya declined on average 32% and nominal retail prices were reduced on average 20%.'l Economic and retail fuel prices as of October 1985 (used in original study) and August 1986 (used in revision) are presented in Table Al.

TABLE Al

|*~<br>~<br>~<br>-<br>|<br>!FUEL|EOOI<br>I<br>| ECONOMI<br>|PRICE 85<br>|$US/TOE|C ECONOMI<br>IPRICE 86<br> !<br>$US/TOE|**i**<br>CiRETAIL<br>IPRICE 85<br> I$US/TOE|**I**<br>I<br> iRETAILI<br>IPRICE 861<br> |$US/TOEI|
|---|---|---|---|---|
||Gas Oil<br>|| 260<br>|| 140<br>|| 405|| 363 ||
|<br>| Fuel Oil 1000 sec<br> <br>|<br> <br>120<br>|<br>| 81<br>|<br>| 144<br>|<br>| 108 |<br>|
||<br>2500 sec<br>|| 143<br>|| 107<br>|167<br>|| 134 |<br>|
||LPG<br>|| 321<br>|| 276<br>|| 410<br>|| 347 |<br>|
|| Kerosene<br>|| 321<br>|| 193<br>|| 320<br>|| 240 |<br>|
|| Charcoal<br>|| 151*<br>|| 161*<br>|<br>| 151<br>|<br>| 161 |<br> <br>|
||Wood<br>|| 46*|| 48*||<br>46||<br>48 ||
||Electricity<br>|<br>| 222|<br>| 178<br>|<br>l<br>|<br> <br>l<br>l<br>|
|I<br>Residential<br>A<br>|l|I|| 251<br>|| 267 |<br>|
|I<br>Residential<br>D<br>|l|l<br>|| 145<br>|| 154 |<br>|
||Commercial|l|I|<br>| 166|<br>| 176 ||
|i<br>Industrial<br>l<br>_<br>_<br>_|l<br>l__<br>1<br>_<br>_<br>_|l<br>1<br>_<br>__|<br>| 142<br>l<br>_|<br>| 151 |<br>l_<br>l<br>_<br>_|

- Retail prices assumed as economic prices.

1985 prices @ 17 KSH - $US1; 1986 prices @ 16 KSH - $US1.

76.

76. The technical performance of optimal solar water heating systems was simulated in forty five sites. The average installed system cost was determined to be US$ 126/m<sup>2</sup> while the simpler systems used in industrial applications averaged US$ 103/m<sup>2</sup> of collector area. A sample of the original and revised economic evaluations are summarized in Table A2. The financial net present worth of each installation in the revised analysis is presented as an indication of the impact of lower retail oil prices on the viability of SWH systems from the consumer's perspective. In the original report (1985), all applications had a positive net present worth.

27/ **Real** petroleum product retail **price** reductions averaged 21%. These **prices are** peculiar to Kenya and the following summary is not intended as a **general** model of the impact of lower oil **prices** on the economic viabitity of solar water heating.
categorize each term into the left or right column on a computer monitor. Sometimes the categories would be grouped congruently with gender stereotypes— Woman Man Domestic tasks Leadership activities and sometimes incongruently: Woman Man Leadership activities Domestic tasks The difference in accuracy and speed between the two conditions is a measure of the strength of the stereotype. Exposure to female leaders through reservations weakened the stereotype. That is, it increased the ability of men to associate women with leadership activities.

A third measure to evaluate the impact of political reservations for women was villagers’ evaluation of the _pradhan_ of their own villages. For villagers who were exposed for the _first_ time to a woman _pradhan_ , evaluations were less favorable than evaluations by villagers with a male _pradhan_ . But for villagers exposed for the second time to a woman _pradhan_ , evaluations of the woman _pradhan_ were no less favorable than evaluations of a male _pradhan_ . This finding is consistent with the implication of the Goldberg and IAT experiments— seven years of exposure to a woman _pradhan_ had changed villagers’ perceptions of women as leaders. But it does not prove that, since the second-term women could have been more qualified than the firstterm women.

The strongest evidence that exposure to women _pradhans_ changed cultural categories in a way that reduced the cultural barriers to women to exercise their legal right to hold political office is that in free elections following a period of political reservations, women were more likely to run for office and female candidates were more likely to win. The rational actor model 25
**_Table 8: Labor productivity convergence regressions conditioning on initial poverty level, All sectors_**

|||Overall<br>Disaggregated||
|---|---|---|---|
||Total<br>aggregation|<br>by Primary,<br>Secondary &<br>Tertiary|Disaggregated<br>by all sectors|
||(1)|(2)|(3)|
|Dependent variable:||||
|Growth of VAper worker over a decade||||
|Log initial VA per worker|‐0.040***|‐0.023***|‐0.020***|
||(0.008)|(0.007)|(0.006)|
|Log initial poverty level|‐0.051***|‐0.031***|‐0.014|
||(0.014)|(0.011)|(0.012)|
|Constant|0.360***|0.220***|0.200***|
||(0.066)|(0.060)|(0.049)|
|Observations|24|72|255|
|R‐squared|0.541|0.283|0.110|
|Stardard errors in parenthesis.||||
|*** p<0.01, ** p<0.05, * p<0.1||||

# **4.5 Convergence in department GDP (value added) per capita**

Value added per capita also appears to be converging across Peru’s departments. Table 9 presents the regression results. The convergence in output per capita is not surprising in light of the convergence in output per worker. However, the estimated rate of convergence is lower, and less statistically significant. Also, this finding is sensitive to the choice of initial year (whether 2001 or 2002). How do we interpret this result? It is possible that changes in the ratio of the employed population to total population vary across departments in such a way as to increase the variance in output per capita growth rates and dampen the effect of the convergence in labor productivity. Specifically, our interpretation is that some of the regions experiencing a faster (resp., slower) increase in labor productivity are also experiencing a relative decline (resp., increase) in the ratio of the employed population to total population.

**_Table 9: Value added per capita convergence regressions_**

|Dependent variable:|(1)|(2)|
|---|---|---|
|Growth of VAper capita over a decade|||
|Log initial VA per capita|‐0.016**|‐0.040***|
||(0.007)|(0.009)|
|Log initial poverty level||‐0.053***|
|||(0.015)|
|Constant|0.176***|0.334***|
||(0.057)|(0.063)|
|Observations|24|24|
|R‐squared|0.199|0.505|
|Stardard errors in parenthesis.|||
|*** p<0.01, ** p<0.05, * p<0.1|||

18