rafmacalaba/datause-encoder-data huggingface dataset:
11

# C) _Me!iirem&'t_ errnr

The zero result may just reflect pure measurement error of educational capital. By "pure" measurement error I intend to distinguish two types of measurement problems. The first, which I call "pure" measurement error, is that the estimates of years of schooling of the labor force are bad estimates of the years of schooling of the labor force. The other possibility is that the data are perfectly acceptable measures for the years of schooling of the labor force, but years of schooling of the labor force is a very bad proxy for educational capital, perhaps because poor quality schooling does not raise cognitive skills. I address this second type of measurement error below.

There are three defenses to the accusation that the finding of a negative coefficient on educational attainment is simply the result of pure measurement error. The first is simply that the results are robust across various, independently constructed measures. I present results above using two different measures and Spiegel (1994) reports similar negative estimates using a third. One check on the magnitude of measurement error is the correlation amongst various measures. The correlation in levels between the B-L and N-S-D years of schooling estimates in 1985 is .9119. More importantly, the correlation of the growth rates of the two educational attainment series is .67 (table 7). If the measurement errors of the two methods were uncorrelated (which they won't be because they both use enrollment rate data) this would suggest that the magnitude of measurement error bias would reduce the education coefficient by about an equivalent amount (e.g about .7).20 19 Of course the correlation in levels is a rather weak criteria for judging cross national data quality as most of the variation is between rich countries with a high level and poor countries with a low level. As an illustration I asked my colleague in the office next door, Peter Lanjouw, a poverty specialist with no emphasis on the economics of education and who had not seen the data, to guess the years of schooling in 1985 for each country based only on the minimum, naximum and mean of the data. His guessing took about 15 minutes. The correlation between the levels of years of schooling N-S-D data and Pete's guesses is .88 while the correlation of the B-L data and Pete's guess/data is .86.

20 The simple calculations is the "true" variable is x* and the observations are x* + v,2), where v1n) are the measurement error in the first(second) observation. The expression for OLS bias is:
_Cost-recovery tariffs do not look to be affordable for the majority of the population in Uganda; but more so for the relatively affluent minority with access to services._ With a cost-recovery tariff of $0.24 per kWh and a monthly subsistence consumption of 50 kWh, the associated utility bill would come to $12 per month. Based on the distribution of household budgets in Uganda, monthly utility bills at these levels would be affordable by only 10 percent of the population (figure 31). Similarly, a bill for subsistence consumption of 10 m<sup>3</sup> of water would come to $12 per month. A more limited level of subsistence consumption of 25 kWh per month for power and 4 m<sup>3</sup> per month for water—which is capable of meeting the most basic needs—would cost $6 and $5 per month respectively, but would still only be affordable to barely half of the population. Nevertheless, it is important to recall that access to power and piped water services in Uganda at present is very limited and largely confined to the top quintile of the budget distribution. As a result, among those that have access to the service today the majority should be able to afford a subsistence level of consumption of these services. Affordability will become a serious issue as Uganda accelerates coverage of utility services, however. (Given that this analysis is based on the 2002 Household Expenditure Survey, the affordability situation is likely to have improved significantly in the meantime given sustained economic growth.)

**Figure 31. Affordability in Uganda worse than in other low-income countries, 2002**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Uganda<br>LIC<br>Average cost of power is 6 USD/Month per 25 kwh or 12 USD/Month per 50 kwh subsistence<br>consumption<br>Average cost of water is 4.88 USD/Month per 4 m3 or 12.2 USD/Month per 10 m3 subsistence<br>consumption<br>Power cost‐recovery tariff is 0.24 USD/kwh<br>Water cost‐recovery tariff is 1.22 USD/m3<br>than 5 percent of the monthly household budget<br>Share of urban households whose utility bill would be less<br><!-- End of picture text --> _Source:_ Banerjee and others 2009. _Note:_ LIC = low-income country; kWh = kilowatt-hour.

_Operational inefficiencies of power and water utilities cost Uganda a further $155 million a year, equivalent to 1.11 percent of GDP._ The annual value of inefficiencies in the power sector, at $139 million, is substantially higher than for the water sector, at $16 million. The burden of utility inefficiencies in Uganda is somewhat higher than for the benchmark countries in the power sector, but lower in the water sector (figure 32). The Uganda Electricity Distribution Company Limited (UEDCL) suffers from high system losses and low collection efficiency. Uganda’s 2009 distributional losses of 39.8 48
# **Notes**

1 World Bank 2009. 2 See Rosenthal and Strange (2004) and Puga (2010) for reviews of magnitudes. 3 Lucas 2004; Henderson and Wang 2005.

4 Baum-Snow and Pavin 2012; Moretti 2004.

5 World Bank 1999, 2000; Fay and Opal 2000; Collier and Venables 2007; Barrios, Bertinelli, and Stobl 2006. 6 Johnson, Ostry, and Subramanian 2009. 7 Binswanger and Townsend 2000. 8 For example, Alexandersson (1959) and Henderson (1988); Henderson also looks at city types in Brazil. 9 Baum-Snow and Pavin 2012; Wang 2012. 10 Holmes 2005. 11 Glaeser and Gyourko 2005. 12 Rauch 1993. 13 Duranton 2012. 14 Foster, Haltiwanger, and Krizan 2008. 15 Duranton 2012. 16 Henderson, Lee, and Lee 2001. 17 World Bank 2012b. 18 Kahn 2006. 19 Naughton 2007. 20 Renaud 1981. 21 Ades and Glaeser 1995. 22 See Feler (2012) on Brazil. 23 Kwong 2004. 24 Henderson and Wang 2007. 25 Gallinai and Schargrodsky 2010; Field 2007. 26 Henderson and Wang 2007. 27 Henderson and Venables 2007. 28 World Bank 2009, 2012b.

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In our analysis, we show that a consistent approach to global poverty estimation that includes incorporating the 2011 PPP data leads to poverty estimates that are close, but slightly higher, than those based on 2005 PPP extrapolations. The analysis in this paper suggests that if one accepts that the 2011 ICP have reduced the urban bias believed to exist in the 2005 PPP data, then the estimates suggest a relatively smooth transition between the two indices for global poverty estimation. Following the approach considered in this paper of using the 2011 PPP data at face value and a poverty line of $1.82, the global headcount increases by 0.4 percentage point (or less than 3 percent) and the changes in the regional profile of poverty are substantially less than the changes introduced with the 2005 PPP data.

In addition to having a relatively small impact on global poverty estimates for 2011, using the 2011 PPP data (either at face value or with the 2005 urban-bias adjustments) has a similarly small effect on poverty projections for 2030. Analysis based on assuming that countries grow at a rate equal to country-specific average growth rates from the last 20 years projects a poverty rate of 6.6 percent when using the 2005 PPP data. Our analysis based on the face value 2011 PPP index and the $1.82 line projects a poverty rate of 5.5 percent in 2030, well short of ‘ending’ extreme poverty. Our analysis also reveals that poverty projections to 2030 look very similar whether based on the adjusted or face value 2011 PPP index. Based on the more optimistic 10year average growth rates, poverty is projected to be 4.3 percent based on the $1.82 line and the 2011 PPPs (used at face-value).<sup>34</sup> When considering the same growth scenario, but using the $1.25 line and the 2005 PPPs, poverty is projected to be 4.7 percent. Similarly, when considering the higher $1.92 line based on the LIC country status and the 2011 PPPs, poverty is projected to

> 34 Despite there being a very large difference in poverty rates for 2011 between using PPPs at face value or with the adjustments, by 2030 this differences essentially disappears. The 2030 poverty projection based on the adjusted PPPs is 4.2 percent.

30
Policy Research Working Paper 6255

# **Abstract**

In 2009, buffeted by the great recession, Thai gross domestic product fell by 2.3 percent. Using monthly data from the socio-economic surveys of 2007–2010, this paper finds, after controlling for household variables, that real consumption per capita rose in 2009 relative to 2008 for most groups, including the poor, urban and rural households, men, women, and children. The losers were residents of Bangkok, especially those aged 20–29, and those working in sales and services. During the recession year of 2009, school enrollment rates did not fall, and durable goods purchases actually rose; households probably reduced their savings, and also benefitted from the lower food prices that prevailed in 2009. A simulation exercise based on the slowdown in growth of gross domestic product would have missed these effects, as would models based solely on readily-available data series. This points to the importance of country-specific policy analysis, rooted in timely local evidence, including household survey data.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jhaughton@suffolk.edu and skhandker@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
et al (2007) draws on NSS data in combination with total factor productivity data from other sources. They find that agricultural wage growth during the period 1983-1999 has been driven for the greatest part by changes in agricultural productivity and that the contribution of the non-farm sector to real wage increase is likely to have been no greater than a quarter.

In Section V below we shall revisit the arguments offered by Foster and Rosenzweig (2003 and 2004) and those by Eswaran et al (2007), regarding the role of agricultural wage labor markets, and the farm versus the non-farm sector, in understanding rural poverty reduction. We turn first to descriptive analysis of NSS data in order to gain a sense of overall dimensions of the non-farm sector.

# _Non-Farm Employment Shares_

Non-farm employment is divided into three categories: regular employment (generally salaried), casual employment (daily wage) and self-employment/own enterprise activities. This distinction is intended to reflect to some extent the very different characteristics of non-farm activities in rural areas – characteristics that are important in terms of defining the desirability of such jobs. A general typology that appears to resonate with findings from many village studies is that regular non-farm employment is typically highly sought-after in rural areas as it is associated not only with high incomes, but crucially also with a degree of stability. Non-farm self-employment activities can be both residual, last resort options as well as high return, productive, activities, but whether they are of the former or latter variant generally depends on the amount of capital resources that can be brought to the activity. Casual non-farm wage employment is generally thought to be less demeaning to a worker than agricultural wage labor, but returns may be only marginally higher and the nature of the work may be both physically demanding as well as hazardous (construction, rickshaw pulling, industrial workshops, etc.).<sup>7</sup>

> 7 Note, there is potential overlap in our categories of casual, self-employment and regular non-farm employment – a person may be doing more than one type of activity in the course of the year.  In our econometric analyses below we classify individuals on the basis of Principal Usual Status, the activity they are engaged in for the major part of the year preceding the survey, including both their principal and main subsidiary activity.

10
Policy Research Working Paper 9294

# **Abstract**

Nearly one in three children under age five in the Philippines is stunted, a key marker of undernutrition. This rate is high for the country’s level of income. This paper provides the first detailed multivariate analysis of potential drivers of stunting in the Philippines, using data from the 2015 National Nutrition Survey. Potential drivers are analyzed individually and grouped in major categories. The analysis finds that stunting between 24–60 months is principally associated with suboptimal prenatal conditions and inadequate food security and diversity. If the results are given a causal interpretation, they imply that if all Filipino newborns had adequate prenatal conditions, the fraction stunted at age 24–60 months would fall by 20 percent. Similarly, providing adequate food security and diversity to all Filipino children would reduce stunting by 22 percent. Other factors—including access to water, sanitation, and environmental conditions—have less strong associations with stunting. The results point to a series of policy priorities to reduce stunting: supporting the nutrition and health of expectant mothers, ensuring access to contraception to reduce adolescent pregnancy, and ensuring that children consume a variety of healthy foods, including protein-dense foods such as milk, meat, and eggs.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gdemombynes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
merged dataset with real sector variables available only at the Central Bank of Chile, we cannot use our baseline predicted default risk measure, which relies on sales, employment, and value added. Instead, we proxy firm risk using the loan spread, a forward-looking financial variable observed in the CMF data, though only for a subset of firms with prior borrowing.

While the triple interaction term is not statistically significant for either program, the previous results hold (Appendix Table 17, Panel A). That is, within firms under a lockdown, risk does not appear to influence participation in the credit or employment programs. Credit program take-up is broad and largely unaffected by lockdowns. In contrast, employment program take-up responds to real-side shocks (e.g., sales declines or closures), not credit risk, suggesting that lockdowns affect participation mainly through real disruptions. The triple interaction remains insignificant for firms near municipal borders (Appendix Table 17, Panel B). However, the lockdown effect for the employment program, though similar in size, loses statistical significance due to the limited number of firms with spread data.

# **5 on Firm Indebtedness**

We next study the effects of using the credit program on debt at the firm level. To do so, we estimate the following cross-sectional regression: ∆ _Debti_ is the growth in (net) outstanding bank debt during the entire 2020, normalized by sales in 2019. This ratio focuses on the change in indebtedness, holding constant sales and thus abstracting from the sales decline in 2020.<sup>52</sup> The dummy _Program Usei_ is defined as previously reported. _Riski_ corresponds to the fitted default probability value derived from the firm-level default regression estimates and used in Section 4.

Table 5 presents the results.<sup>53</sup> Firms with and without previous loans that use the credit program increase their indebtedness by 14.5 and 13.0 percentage points, respectively,

> 52We normalize the debt change by sales instead of assets because sales are more accurately measured and audited by the tax authority than assets. However, our results are robust to normalizing the change by 2020 sales (Appendix Table 18 and Appendix Table 19). Appendix Table 20 shows a version of this regression where the outcome is in levels, controlling for lagged firm indebtedness on the right-hand side. The main conclusions hold.

> 53Credit program eligibility requires firms to have prior loans (because only firms that are up to date with their debt payments at the moment of applying are eligible). As a result, the 51,729 firms without prior loans—used in the indebtedness regressions (Table 5, Columns 2, 4, and 6)—are excluded from the program participation regressions (Table 2). The sample of firms with prior loans is nearly identical across both sets of regressions: 62,894 firms in the program participation regressions and 62,950 in the indebtedness regressions.

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regarding the seriousness of climate change were varied. Next participants were exposed to a message about energy efficient light bulbs. Participants in the groups with higher levels of fear did a better job processing information about the light bulbs. “The results indicate that both moderate and high levels of fear had an impact on attitudes. Moderate fear resulted in more positive attitudes toward energy-saving bulbs, but only when strong arguments in favor of these bulbs were provided. High fear had a positive effect on attitudes, regardless of argument strength.” The authors conclude that fear regarding future climate scenarios may influence attitudes towards energy conservation both directly and indirectly.

Moser, Susanne, 2007 “More bad news: the risk of neglecting emotional responses to climate change information”in Moser, Susanne C. and Lisa Dilling. eds Creating a Climate for Change: <u>Communicating Climate Change and Facilitating Social Change. New York: Cambridge</u> University Press.

Norgaard, Kari Marie. 2006. "'People Want to Protect Themselves a Little Bit': Emotions, Denial, and Social Movement Nonparticipation." Sociological Inquiry 76:372-396.

Emotions can be a source of information and an impetus for social action, but the desire to avoid unpleasant emotions and the need for emotion management can also prevent social movement participation. Ethnographic and interview data from a rural Norwegian community describes how people avoided thinking about climate change in part because doing so raised fears of ontological security, emotions of helplessness and guilt, and was a threat to individual and collective senses of identity. In contrast to existing studies that focus on the public's lack of information or concern about global warming as the basis for the lack of public response, my work describes the way in which holding information at a distance was an active strategy performed by individuals as part of emotion management. Following Evitar Zerubavel, I describe this process of collective avoiding as the social organization of denial. Emotions played a key role in denial, providing much of the reason why people preferred to avoid information. Emotion management was also a central aspect of the process of denial, which in this community was carried out through the use of a cultural stock of social narratives that were invoked to achieve "perspectival selectivity" and "selective interpretation.".

Stern, Paul. 1992. “Psychological Dimensions of Global Environmental Change” Annual Review <u>of Psychology</u> 43: 269-302.

In this early piece on the topic, Paul Stern reviews existing knowledge from the field of psychology regarding global change mitigation, and adaptation. Stern also identifies a research agenda with emphasis on environmental attitudes, the determinants of specific human activities such as energy use, conservation and participation in the environmental movement and the ways people may perceive, respond to, or be affected by global environmental changes.

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50% of the affected population. Similar contamination of ground water with arsenic has occurred in Thailand, Nepal and Pakistan, and major problems have also been identified in some areas in China, Ghana, the USA, and South America (WHO, 2001; Misra, 2006). Therefore, it is hoped that our findings will inform government policies in other affected regions as well.

The remaining part of the paper is organized as follows. Section 2 elaborates on the study background. Section 3 discusses the data and sampling. The main results are reported in section 4. Section 5 concludes.

# **2 Background**

# **2.1 Genesis of arsenicosis in Bangladesh**

Tube wells are the single-most important source of fluid (drinking water) in Bangladesh. According to 2001 population census, 88% of rural households use tube wells as the main source of drinking water although the 1999-2000 Household Income and Expenditure Survey puts this figure to 95.7%. Nevertheless, usage of tube wells is relatively a recent phenomenon. Even in the early 1970s drinking from open water bodies was a common practice. However, surface water is often contaminated with bacteria and a major cause for waterborne diseases such as cholera, dysentery, typhoid, and diarrhoea. Unsurprisingly, these water-related illnesses in young children were the leading cause of morbidity and mortality. Therefore, for the past three decades, the government and various international organizations had promoted shallow (Tube) wells, as a course for safe drinking water from underground aquifers. Tube well construction strongly accelerated in the 1980s. Around 11 million tube wells were installed during the period, a vast majority being in private ownership (Geen et al, 2005). This initiative has helped control water-born diseases, but in many areas it has had the unintended side-effect of exposing the population to another health problem – toxic arsenic in ground water.

Pollution by arsenic occurs naturally through the dissolution of minerals and ores, and concentrations in groundwater in some areas are elevated as a result of erosion from local rocks (McArthur et al., 2001). The WHO maximum permissible level is less than 50 3