rafmacalaba/datause-encoder-data huggingface dataset:
year to the next. We strongly suspected coding errors or measurement errors<sup>as the</sup> strange numbers at the three digit level could always be<sup>traced to strange numbers at</sup> the four digit level. When this occurred, we deleted the subsector<sup>at the four digit level</sup> and recalculated three digit level price indices based on the<sup>remaining four digit</sup> subsectors. An adjustment was made in this fashion in 1981<sup>for sub-sectors 80, 120, and</sup> 212; in 1982 and 1983 for sub-sectors 100, 155, and 212; and<sup>in 1984 for sub-sectors 155</sup> and 212. Sous-branche 19 was dropped completely from the<sup>analysis as there were no</sup> subsectors that could be used to modify the price data.

The import price index presented the same kind of problems.<sup>The import prices</sup> suspected of measurement error were dropped. This was done in 1981<sup>and 1982 for sub-</sup> sectors 131, 161, and 163; and in 1984 and 1985 for sub-sector 132.

Finally, there were some sub-sectors of the BDF data for which there<sup>was no</sup> corresponding subsector in the price data from the Ministere du Plan et de<sup>l'Industrie.</sup> These subsectors were dropped from the analysis. After all the modifications,<sup>we were</sup> left with 139 (sub-sector , year) observations in the analysis. The<sup>sub-sectors were as</sup> folows:

- 61: Decorticage du cafe

- 63: Biscuiteries

- 74: Conserves de poissons

- 76 : Produit Derives du Cafe et du Cacao

- 80 : Fabrication de Boissons et Glaces Alimentaires

- 90 : Industries des Corps Gras Alimentaires

- 100: Autres industries alimentaires

- 111: Egrenage du Coton

- 113: Filature, Tissage

- 116: Sacherie, Ficelerie

- 117: Fabrication d'articles d'habillement

- 119: Fabrication d'autres articles textiles

- 120: Industrie du cuir et autres articles chaussants

- 131: Premiere Transformation du Bois

- 132: Menuiserie

- 133: Fabrication de meubles

- 152: Fabrication d'engrais, pesticides, insecticides

- 155: Fabrication de peintures, vernis, laques, cosmetiques, parfums

- 33 -
Table 2 Attrition by Treatment

|||_Dependent _<br>|_variable:_<br>||
|---|---|---|---|---|
||Surveyed<br>(1)|Employed<br>(2)|Surveyed<br>(3)|Employed<br>(4)|
|Bank or mobile payroll|-0.002|-0.020|-0.004|-0.026|
||(0.017)|(0.021)|(0.017)|(0.021)|
|Bank or mobile account|-0.011<br>(0.022)|-0.001<br>(0.026)|-0.011<br>(0.022)|-0.003<br>(0.026)|
|Baseline controls|||X|X|
|Observations|2,850|2,850|2,850|2,850|
|R-squared|0.005|0.008|0.052|0.069|
|Mean control [follow-up]|0.840|0.719|0.840|0.719|

Notes: Tests for balanced attrition by treatment. The dependent variable in columns (1) and (3) is a dummy equal to 1 if a participant is in the data at the follow-up and end-line surveys, respectively. The dependent variable in columns (2) and (4) is a dummy equal to 1 if a participant was employed at the partner firm at the time of the follow-up and endline surveys, respectively. Controls include stratification dummies, marital status, number of dependents, dummies for savings, formal savings, years of work experience, and years with current employer. Robust standard errors in parentheses. Sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.

42
source provides target yields of 90 and 94 gallons (341 and 356 liters) of ethanol per ton of cellulosic feedstocks by 2012 and 2020 respectively.

# **Table 3: Composition of forest residues feedstocks (% dry mass)**

||Hardw<br>|ood<br>|Softw<br>|ood<br>|Switchgrass|
|---|---|---|---|---|---|
||Black Locust|Hybrid<br>Poplar|Eucalyptus|Pine||
|Cellulose|41.61|44.70|49.50|44.55|31.98|
|Hemicellulose|17.66|18.55|13.07|21.90|25.19|
|Carbohydrates (%)|59.27|63.25|62.57|66.45|57.15|
|Lignin (%)|26.70|26.44|27.71|27.67|18.13|
|Ash|2.15|1.71|1.26|0.32|5.95|
|Heating value (GJ/ton)|19.5|19.6|19.5|19.6|18.6|
|Theoretical ethanol yield|390|416|411|436|377|
|(liters/dry ton)||||||

Source: Hamelinck, Hooijdonk, and Faaij (2005). Potential biofuel yields are calculated using the “theoretical ethanol yield calculator” (US DOE 2008b).

Several factors restrict the potential use of forest residues for biofuel production (Perlack et al. 2005). The first factor is the economic costs of transportation. Limited accessibility largely increases operation costs of logging/collection activities. Another factor is a potential reduction of recoverability in harvest areas due to environmental considerations (Richardson 2008).

# _2.1.3 Biofuel crops_

Dedicated energy crops represent an additional potential source of feedstock for biofuel production. Biofuel crops can be broadly classified between grassy (herbaceous or forage) and woody (tree) crops. The former are described in the next section. Woody biofuel crops and their potential contribution are discussed in Section 2.1.3.2.

# _2.1.3.1 Perennial forage crops_

Perennial forage crop species are a promising source of feedstock for second-generation biofuels. Swithgrass (panicum virgatum L.) is frequently mentioned because of its relatively low water and nutrition input and requirement costs, positive environmental impact, and adaptability to low quality land (Keshwani and Cheng 2009). Other perennial forage crops such as alfalfa (Medicago sativa L.), reed canarygrass (Phalaris arundinacea L.), napiergrass (Pennisetum purpureum Schumach.), and bermudagrass (Cynodon spp.) 8
27

- DemirgUt-Kunt, Asli, and Harry Huizenga,<sup>1992. "Barriers to Portfolio Investments in</sup> Emerging Stock Markets," Washington:<sup>The World Bank.</sup>

- Eisenbeis, Roben A., 1992. "Seminar<sup>on Banking Reform in Chile," Aide-Memoire</sup> Prepared for the Inter-American Development<sup>Bank (Nov. 30).</sup>

- Germides, Dimitri, Dennis Kessler, and<sup>Rachel Meghir, 1991. Financial Sstemsan</sup> Develupment: What Role for the Formal<sup>and InforMal Financial Sectors?,</sup> Development Centre Studies, Paris: OECD.

- Gutierrez, Joaquin, Vincent Polizatto,<sup>David Scott, Andrew Sheng, and Samuel Talley,</sup> 1991. "Bank Supervision: Suggested<sup>Guidelines --Models of Bank Supervision,"</sup> Washington: The World Bank, Financial<sup>Policy and Systems Division.</sup>

- =__ 1992. "Bank Supervision Guidelines:<sup>Capital Adequacy," Washington: The</sup> World Bank, Financial Policy and Systems<sup>Division.</sup>

- Hall, Maximilian J.B., 1990. "Officially Imposed<sup>Impediments to a Level Playing Field</sup> in International Banking: A Comparative<sup>Study of the UK, US, and Japanese</sup> Approaches Adopted Towards the Implementation<sup>of the BIS 'Rule' on Capital</sup> Adequacy Assessment." (Nuffield University,<sup>unpublished).</sup>

- Kane, Edward J., 1985. The Gathering<sup>Crisis in Federal Deposit Insurance. Cambridge,</sup> MA: MvIT Press.

- _______ 1988. "Hcw Market Forces Influence the<sup>Structure of Financial Regulation," in</sup> W. Haraf and R.M. Kushmeider (ed.), Restructuring<sup>Banking and Financial</sup> Services in America, Washington: American<sup>Enterprise Institute for Public Policy</sup> Research, pp. 342-382.

- Kraus, James R., 1992. "Developing<sup>Nations Offer a Hot Market," Amerrican anker</sup> (December 22).

- Mayer, Martin, 1993. "Testimony," U.S.<sup>Senate Committee in Banking, Housing, and</sup> Urban Affairs, The State of the Banking<sup>Industrv and the Bank Insurance Fund,</sup> Hearings (June 9-10 and 17, 1992; July<sup>29, 1992; October 26, 1992), Washington:</sup> Government Printing Office, pp. 809-817.

- Polizatto, Vincent P., 1990. "Prudential<sup>Regulation and Banking Supervision: Building</sup> an Institutional Framework for Banks,"<sup>Policy, Planning, and Research Working</sup> Paper, Washington: The World Bank.

- Schuyer, Jan, 1992. Banks Under Stress.<sup>Paris: OECD.</sup>
The estimation of these structural parameters requires survey data with sufficient price variation at the household level. This is rarely the case. One possibility is to combine household surveys with official price information. Some studies exploit time and regional variation in official prices (Deaton, 1997; Porto, 2003; Ravallion, 1990; Wolak, 1996); others use community price questionnaires (Edmonds and Pavcnik, 2004a; 2004b). In this paper, I use unit values, the ratio of reported expenditures and quantities, as measures of prices.

The main advantage of using unit values in household models is the substantial cross-sectional variability. However, unit values are not the same as prices when consumers jointly choose quantity and quality (Deaton, 1987). Since unit values combine measures of price and quality, unit values can only be used as proxies for prices. This may lead to biases in the estimation of the relevant structural parameters. Further, endogenous income responses generate additional income effects that can bias the estimates of Marshallian demand elasticities.<sup>2</sup> Building on the seminal work of Deaton (1987, 1988, 1990), I develop methods to correct the estimates of consumption and income responses.

The procedure works as follows. By modeling consumer choices of quantity and quality simultaneously, I am able to extract the right price signals from unit values, expenditures, and quality choices. Then, I use this price information to estimate the response of consumption and wage agricultural income. To do this, I develop a general equilibrium econometric model of agricultural prices, quantities consumed, unit values, and household income.

I estimate the own- and cross-price elasticities as well as the wage-price elasticities using survey data for rural Mexico. It is shown that using unit values instead of prices may indeed lead to inconsistent results, and that the corrections suggested in this paper are empirically important. On the income side, the endogeneity of unit values and measurement error lead to attenuation bias in the estimates of the wage elasticities. On the demand side, the correction for the endogeneity of household agricultural income seems to make a difference in terms of both the Marshallian and Hicksian elasticities. These differences originate in the consistent estimation of the expenditure elasticities and in the account of the “profit effect”. I also

> reforms, which are often accompanied by other simultaneous reforms, are an example. In many instances, in addition, there is an interest, or a need, to explore the effects of a policy that has not yet taken place. WTO reforms are an example. The method proposed here accommodates these cases.

> 2This is a version of the “profit effect” discussed in Singh, Squire and Strauss (1986).

2
In order to allow for more adaptation options than those considered by the simple Ricardian approach used in the estimation of the impacts of climate change on agricultural productivity, the authors consider two alternatives. First, they consider a measure of total poverty taking into account all residents in each municipality, i.e. including all urban households. This alternative measure of poverty captures the fact that some individuals might adapt to the new climate conditions changing sectors or occupations. Second, they build a migration-adjusted poverty measure.<sup>5</sup> They then compute a poverty measure using this adjusted sample for each municipality, for both urban and rural areas. After allowing for labor mobility across sectors or across municipalities, the absolute impact in rural areas reduces from 3.2 percentage points, for the case without labor mobility, to 2 percentage points.

In sum, these results suggest that climate change is likely to generate heterogeneous effects within Brazil with poverty increasing in the already poorer Northeast region and decreasing in the already richer Southern region. Moreover, the poverty impacts of climate change are likely to be lower depending on the extent to which households are able to adapt by migrating across municipalities or switching sectors of employment. However, the poverty estimates of the Assunção and Chein Feres (2009) study tend to overstate the impacts of climate change on poverty in Brazil since they do not take into account the potential increase in mean per capita income due to economic growth over the next forty years. In other words, they consider climate change as it would happen tomorrow, predicting the impact of a warming climate on the actual poverty rate and not the prevailing poverty rate by 2050 in a world without such a warming. The proper way to present poverty estimates associated with future climate change is to project both output and population growth and then use the elasticities to predict its impact on poverty.

# **_2.2 Accounting for Future Growth: Evidence from Integrated Assessment Models_**

An Integrated Assessment Model (IAM) is a general equilibrium model that relies on micro-evidence to quantify various socio-economic dimensions of climate change and then aggregate these to produce a net effect on national income. IAMs are utilized extensively in the climate change literature to model climate-economy interactions and form the basis of many

> 5 For each municipality, they consider a sample comprised of the non-migrant households and those who out-migrate to other municipalities - migrants from other municipalities are also excluded.

7
# **_Table 2: Continued_**

||||**Gini**<br>|||**P90/P50**<br>||
|---|---|---|---|---|---|---|---|
|**_Independent variable_**|_Full model,_<br>_OLS_|_Full model,_<br>_RE_|_No high_<br>_income, RE_|_No high income and_<br>_post-2000, RE_|_Full model,_<br>_RE_|_No high_<br>_income, RE_|_No high income and_<br>_post-2000, RE_|
|_Agriculture value added, percentage_<br>_over total GDP_|-0.025|-0.067**|-0.047|0.044|-0.193**|-0.145|0.002|
|_Service value added, percentage over_<br>_GDP_|0.211***|0.134***|0.162***|0.123***|0.290***|0.368***|0.283***|
|_Export of good and services, percentage_<br>_over GDP_|-1.512***|1.584**|3.934***|5.003***|-0.095|2.844|8.462***|
|_region = EAP_|0.060***|0.062***|-0.084***|-0.090***|0.088**|-0.191***|-0.199***|
|_region = ECA_|0.011**|0.023**|-0.120***|-0.116***|0.042|-0.252***|-0.227***|
|_region = LAC_|0.132***|0.151***|—|—|0.295***|—|—|
|_region = MENA_|0.026***|0.037**|-0.112***|-0.114***|0.042|-0.255***|-0.257***|
|_region = SAS_|-0.007|0.035*|-0.108***|-0.100***|0.064|-0.220***|-0.197***|
|_region = SSA_|0.216***|0.195***|0.095***|0.117***|0.650***|0.545***|0.672***|
|_Constant_|0.243***|0.242***|0.342***|0.395***|0.163***|0.379***|0.490***|
|_Number of observations_|1,529|1,529|1,026|741|1,529|1,026|741|
|_R square_||||||||

# _Source:_ World Bank.

_Note:_ OLS = ; RE = ; EAP = East Asia and Pacific; ECA = Europe and Central Asia, LAC = Latin America and the Caribbean; MENA = Middle East and North Africa, SAS = South Asia, SSA = Sub-Saharan Africa, OTH = other countries. * p<0.05, ** p<0.01, *** p<0.001.

35
# **1. Introduction**

Housing, food and water are considered to be basic requirements for daily living. Unfortunately, inadequate housing with poor water supply, sanitation, and ventilation threatens the lives and health of some 600 million urban dwellers worldwide.<sup>1</sup> For this reason, most countries in the world devote substantial resources to upgrading slum areas and improving housing quality for poor groups in the population. For example, the U.S. Government spends more on housing programs than on other better-known welfare programs, such as food stamps and temporary assistance for needy families (Olsen, 2003). In the developing world, where urbanization is strongly associated with the rapid spread of slums, and where slum dwellers account for 45% of the urban population, policies to improve the welfare of slum dwellers focus on such areas as upgrading slum housing _in situ_ and relocating slum dwellers to better-quality, low-cost housing (World Bank, 2005). Despite the importance of housing as a factor influencing well-being, little work has been done to assess the causal impact of housing and housing improvement programs on health and welfare.

In this paper, we examine one particular aspect of housing, floor quality, and its impact on the health of young children, as well as the mental health and happiness of their mothers. Specifically, we examine a large-scale effort by the Mexican Government to replace dirt floors with cement floors as a means of identifying the impact of cement floors on child health and maternal happiness. The program, called _Piso Firme_ , offered households with dirt floors up to 50 square meters (538 square feet) of concrete cement flooring.

In order to identify the effects of this intervention on child health and development, as well as maternal happiness outcomes, we take advantage of a geographic discontinuity in the implementation of the program. _Piso Firme_ was first implemented as a local program in the State of Coahuila. The program was later adopted nationally and gradually extended to other states. We compare beneficiary families in Coahuila to similar families in the neighboring State of Durango, which at the time of our survey had not yet implemented _Piso Firme_ .

> 1 See, among others _,_ the Cebu Team (1991), Galiani et al. (2005), Esrey et al (1991) and the World Bank (2005).

2
Over the three years, some household members split from the 2007 original households and became an independent family head (for marriage or other reasons). In our sample, household division occurred in 204 original households (9% of our sample). We use the 2007-survey original household as the unit for analysis to avoid bias that may arise from household splits. For instance, a new household head, who split from his original household, might share and cultivate farm lands with his parents though the land is still owned by his parents (vice versa). By aggregating original and split households in 2010, we minimize the split bias<sup>9</sup> .

The 2007 survey was designed to overlap with villages in the 1994/95 PATANAS survey conducted by ICASEPS to build household panel data. The 1994/95 PATANAS survey focused on agricultural production activities in 48 villages chosen from different agro-climatic zones in seven provinces. In 2007, we visited those villages to expand the scope of research as a general household survey under the IMDG survey. In the 2007 round, therefore, we added 51 new villages in the same seven provinces<sup>10</sup> .

In the revisited villages in 2007, we re-sampled 20 households per village from the 1994/95 sample and followed the split households. In the new villages, we sampled 24 households from two main hamlets in each village. Since one of the 48 villages in the 1994/95 PATANAS was 9 It is possible that the 2007-08 food price crisis affected household split decisions, which will potentially cause an additional bias if we omit split households or do not aggregate original and split households. In this paper, we only report the results using the aggregated households. However, even when we use only households which did not experience split (excluding 204 split households), our empirical results remain the same.

10 These new villages were selected with the following criteria. First we chose the same districts where PATANAS villages are located. We list villages that had received relatively large amounts of government infrastructure projects during the period of 1995 to 2005, funded by either the Japan Bank for International Cooperation or the World Bank. Finally, the new villages were randomly sampled from the list.

15
**Table 11.D. Model comparison: Diebold and Mariano (1995) test for lead prices**

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus<br>|3<br>|...<br>*|||**<br>**||***|
|Futures|3||...|||||
|Bivariate correlations|3|*||...|**||***|
|BVAR|3||||...|||
|OEM|3|†|||*|...|**|
|Machine learning|3||||*||...|
|Consensus|6|...|||**|||
|Futures|6|†|...||**||**|
|Bivariate correlations|6|||...||||
|BVAR|6||||...|||
|OEM|6||||**|...|†|
|Machine learning|6||||*||...|
|Consensus|9|...|||**|||
|Futures|9|†|...||***||†|
|Bivariate correlations|9|||...||||
|BVAR|9||||...|||
|OEM|9|*|||**|...|*|
|Machine learning|9||||**||...|
|Consensus|12|...|||**|||
|Futures|12|*|...|†|***|†|†|
|Bivariate correlations|12|||...||||
|BVAR|12||||...|||
|OEM|12||||*|...|*|
|Machine learning|12||||†||...|
|Consensus|15|...|...|...|**||*|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15||...|...||||
|BVAR|15||...|...|...|||
|OEM|15||...|...|*|...||
|Machine learning|15||...|...|||...|
|Consensus|18|...|...|...|***||***|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations<br>|18<br>||...|...||||
|BVAR|18||...|...|...|||
|OEM|18||...|...|***|...||
|Machine learning|18||...|...|||...|
|Consensus|21|...|...|...|***||***|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|BVAR|21||...|...|...|||
|OEM|21||...|...|***|...||
|Machine learning|21||...|...|||...|
|Consensus|24|...|...|...|***||***|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24||...|...||...||
|Machine learning|24||...|...|||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

55
Weiskel, Timothy 2005. “From Sidekick to Sideshow -- Celebrity, Entertainment, and the Politics of Distraction: Why Americans Are "Sleepwalking Toward the End of the Earth" <u>American Behavioral Scientist</u> 49(3): 393-409.

Timothy Weiskel describes a “politics of distraction” in the US media which he argues accounts for at least part of why the public fails to respond to climate change. Weiskel describes the movement of celebrities into the White House as well as increased ties of the news media to the entertainment industry.

# **Political Economy**

Dispensa, Jaclyn Marissa and Robert J. Brulle 2003. “Media’s Social Construction of Environmental Issues: Focus on Global Warming – A Comparative Study” International Journal <u>of Sociology and Social Policy. 23(10): 74-105.</u> In order to address whether the US media present a biased view of global warming, the authors examine media coverage of global warming from a content analysis of three countries' newspaper articles and two international scientific journals in 2000. They compare media presentations in the United States, New Zealand and Finland with the country’s economy, industry and environment. Dispensa and Brulle conclude that in the US the fossil fuel industry has a significant impact on the media coverage of global warming in comparison to New Zealand & Finland where it does not. US media portray global warming as controversial whereas media portrayals in Finland and New are on par with presentations commonly found in the international scientific journals.

Jacques, Peter, Riley Dunlap and Mark Freeman 2008 “The organisation of denial: Conservative think tanks and environmental skepticism” Environmental Politics 17( 3): 349-385 The authors conduct a quantitative analysis of 141 English-language environmentally skeptical books published from 1972 to 2005. They find that over 92 percent of these texts are associated with conservative think tanks. The authors also examine those conservative think tanks who produce information on the environment, finding 90 per cent of such think tanks espouse environmental skepticism. The authors describe skepticism as a tactic of an elite-driven countermovement that is designed to combat environmentalism. They further claim that the successful production of skepticism regarding scientific certainty has contributed to the weakening of U.S. commitment to environmental protection at the political level.

Michaels, David and Celeste Monforton 2005. “Manufacturing Uncertainty: Contested Science and the Protection of the Public’s Health and Environment” American Journal of Public Health 95(S1): S39-S48.

67