rafmacalaba/datause-encoder-data huggingface dataset:
# Effects of Early-Life Exposure to Sanitation on Childhood Cognitive Skills: Evidence from India’s Total Sanitation Campaign

Dean Spears and Sneha Lamba<sup>_∗_</sup> October 11, 2013 Keywords: sanitation, cognitive achievement, human capital, early-life health, India JEL codes: I25, I15, O15

> _∗_ Spears is corresponding author: dean@riceinstitute.org. Affiliations: Spears: Centre for Development Economics, Delhi School of Economics, Delhi, India and r.i.c.e. (www.riceinstitute.org); Lamba: Barcelona Graduate School of Economics. We deeply appreciate the outstanding research assistance of Aashish Gupta. We appreciate helpful comments from Anne Case, Diane Coffey, Juan Costain, Janet Currie, Aashish Gupta, Jeff Hammer, Payal Hathi, Avinash Kishore, Manisha Shah, and Sangita Vyas. We are grateful to Pratham and the ASER Centre for making the ASER data available for this project; we particularly thank Natasha Arora, Ranajit Bhattacharyya, and Sushmita Das. Dean Spears acknowledges with thanks support for the research program of which this paper is a part from the World Bank Water and Sanitation Program (WSP) and the Bill and Melinda Gates Foundation. All conclusions and any errors are our own.

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of the vehicles registered in the southwest region and only six percent in the north region (Senatran 2021). In other words, an increase in the automotive fuel price through tax would have less adverse impact on the more vulnerable population that does not own a vehicle.

II.3. External Factors: Technologies and Policies Beyond price, development of biofuels is also enabled by technological development and incentivized by government policies including through fiscal instruments. On the production side, sugarcane is one of the oldest crops in Brazil. It has been cultivated for sugar since the 16<sup>th</sup> century, and ethanol is produced as a byproduct during that process. Nowadays, distillers in Brazil could alter the relative production of sugar and ethanol, increasing the flexibility of ethanol production in response to the market. On the demand side, the introduction of automobiles in the early 20<sup>th</sup> century opened the opportunity for the usage of ethanol fuel, which was encouraged by a few local governments. It was not until the first oil crisis, and the launch of the first national program promoting ethanol ( _i.e._ , “Pró-Álcool”) in 1975, when Brazil saw the first wave of ethanol fuel production. Innovation and tax exemption on vehicles that could run on pure hydrous ethanol, and policy mandate on mixing anhydrous ethanol into gasoline, are all key factors in driving the growth of ethanol through increasing demand. The volume of ethanol fuels reached a similar level as that of gasoline towards the end of the 1980s, though it was outpaced by faster growth of gasoline thereafter. The flex-fuel technology introduced in 2003 that allows a vehicle to consume a blend of gasoline and ethanol at different proportions, tax differentials between fossil fuels and biofuels, and the mandate of mixing biodiesel into diesel through the National Program of Production and Use of Biodiesel (PNPB) have helped explain recent growth of biofuels. The availability of distribution infrastructure across Brazil is also an enabling asset for the biofuel market.

The recently established Brazilian Biofuel Policy (RenovaBio), in particular, targets carbon intensity of the fuel matrix. As an integral part of the national energy policy, RenovaBio (Law No. 13576) was passed at the end of 2017 to promote biofuels (with emphasis on reliable supply) and reduce emissions from fuels for an initial period of 10 years. In achieving these goals, the policy relies on a combination of instruments. Mandatory annual targets for reducing emissions of the fuel matrix would be imposed on fuel distributors. The compliance of the targets would be verified by the number of Decarbonization Credits which are partially based on the amount of biofuel produced/imported/traded and could be traded in organized markets. For producers/importers of biofuels, certification of efficient production based on life-cycle assessment should be issued. Certain revenues also enjoy income tax benefits/exemptions.

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has during the past three decades grown very fast, converging on the rich world. Until recently, it was China alone that had been preventing a rise in global inequality as measured by Concept 2. But now it has “support” from India which is also registering high rates of growth, and is also starting from a very low baseline. High rates of growth of these two countries are thus the major factor underlying the downward trend of Inequality 2.

Inequality 3 can be calculated, as mentioned before, only from the mid-1980s because we do not have household surveys going further back in time. Figure 2 shows that Inequality 3 is higher than Inequality 2. This is true by definition because in Inequality 3 people enter the calculations with their actual incomes, not with country averages. A quick glance at Figure 1 shows that the variability of heights is greater in the third row than in the second. Averaging-out reduces measured inequality.

To calculate “true” global inequality or Concept 3 inequality, we have to adjust people’s incomes with the price levels they face and which, of course, differ between countries. We are interested in real welfare of people and those living in “cheaper” countries will get a boost in their incomes compared to what they make in nominal dollar terms. The currency we use is international (or “PPP” for purchasing power parity) dollar with which, in principle, one can buy the same amount of goods and services in any country of the world. Indeed, if we were not to adjust for the differences in price levels, and were to use nominal dollars, global inequality would have been even higher. This is because price levels tend to be lower in poorer countries, and income of people living in poorer countries thus gets a significant “boost” when we use PPP dollars.

Often, a key issue of concern regarding global inequality is not only its level, but its trend: has it been going up or down during the globalization era? Global inequality is calculated at approximately five-year intervals, from 1988 (the first dot on the left) to 2008 (the dot on the right). If we compare this last dot with a couple of dots for the earlier years, we see something that may be historically important: perhaps for the first time since the Industrial Revolution, there may be a 7
constitute evidence that microfinance expansion had an _impact_ on poverty reduction given our lack of household level microfinance access data.

# **IV. Changes in poverty over time: results from a decomposition analysis**

A comparison of the regression results from HIES datasets of 2000 and 2005 can be useful to identify the factors that were largely responsible for the reduction in poverty during this period. We can compare our results with Wodon (1999) who uses the same methodology. This exercise involves decomposing the growth in mean per capita real consumption – using the Oaxaca-Blinder method (see Oxaca 1973) between 2000 and 2005 into growth due to changes in (i) household and location _endowments_ and (ii) _returns_ to these endowments.<sup>8</sup> More specifically, we specify the linear regression equation (specified in section III above) for two different time periods, t and t+1, and then subtract the latter from the former to obtain: t+1 t In most cases these two ways to decompose log(yi ) - log(yi ) will give similar decomposition results, and this is borne out by the empirical results.<sup>9</sup> In each version, the first term on the right hand side represents the effect of changing returns over time (holding characteristics constant) and the second term represents the effect of changing household characteristics (holding returns constant).

> 8 See Glewwe et al (2002) for a similar decomposition exercise, with household survey data from Vietnam. 9 The standard index number problem, where two possible ‘weights’ (coefficients in this case) can be used for decomposition purposes is the reason for the two equations presented in this paper.

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Why South Africa Is Cheap for the Rich and Expensive for the Poor: Reconsidering the Balassa-Samuelson Effect<sup>_∗_</sup> Vincent Dadam,<sup>_†_</sup> Marek Hanusch,<sup>_‡_</sup> Nicola Viegi<sup>_§_</sup>

> _∗_ The authors would like to thank Sebastien Dessus, Ha Nguyen, and Max Rudibert Steinbach for valuable comments.

> _†_ University of Pretoria email:dadamvince@yahoo.fr

> _‡_ World Bank, email:mhanusch@worldbank.org

> _§_ University of Pretoria and ERSA, email: nicola.viegi@up.ac.za
# **Table 23d.  Piecemeal Sensitivity Analysis – Nitrogen Oxide Emissions by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|2.8|3.0|2.9|3.7|2.8|3.6|2.4|1.7|4.6|2.8|6.8|
|**esubc = 0.5**|2.8|3.0|3.0|3.8|2.8|3.6|2.4|2.2|3.4|2.8|6.8|
|**esubc = 1.5**|2.8|3.0|2.9|3.7|2.8|3.6|2.4|1.5|4.6|2.8|6.8|
|**esub = 2**|2.4|2.6|3.1|3.6|2.4|3.1|1.8|1.2|4.6|2.6|6.7|
|**esub = 4**|2.9|3.0|2.7|3.7|2.9|3.7|2.6|1.8|4.5|2.8|6.8|
|**esubs = 0.5**|1.5|1.7|0.8|1.8|1.7|2.1|1.4|0.7|2.1|1.6|4.5|
|**esubs = 2.0**|4.8|5.2|6.5|7.0|4.7|6.0|4.0|3.3|6.9|4.8|11.1|
|**esubva = 0.5**|2.8|3.0|2.9|2.6|2.8|3.7|2.5|1.7|4.5|2.9|6.6|
|**esubva = 1.5**|2.8|3.0|2.9|3.7|2.8|3.6|2.4|1.7|4.7|2.8|6.9|
|**etad = 10**|2.7|3.0|2.6|3.5|2.8|3.5|2.3|1.8|4.8|2.6|6.9|
|**etad = 5**|3.0|3.0|3.3|4.0|2.9|3.8|2.7|1.6|4.6|3.2|6.8|
|**etadx = 3**|2.7|2.9|2.8|2.5|2.8|3.4|2.4|1.8|4.5|2.9|6.7|
|**etadx = 7**|2.9|3.0|3.0|3.8|2.9|3.8|2.5|1.7|4.8|2.8|6.9|
|**etaf = 12.5**|2.4|2.5|2.7|3.4|2.5|3.1|2.0|1.4|4.1|2.4|6.2|
|**etaf = 17.5**|3.2|3.4|3.0|4.0|3.1|4.0|2.9|2.0|5.1|3.2|7.3|
|**sigmadm = 4**|2.9|3.1|2.8|3.7|3.0|3.7|2.6|1.8|4.7|2.9|7.2|
|**sigmadm = 2**|2.4|2.5|2.9|3.6|2.4|3.1|1.8|1.3|4.4|2.6|6.3|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys centra|l values for a|llparamete|rs(see belo|w)other than|the testedp|arameter.|||||

|**Source: Authors' calculations.**<br>**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys cen|tral values for allparameters(see below)other than the testedparameter.|
|---|---|---|
|**Parameter**|**Central**|**Definitions of theparameter(see figures, especially figure 5)**|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

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# Table 6: Access to infrastructure

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper capita incom|e(1996-2011)||||
|Infrastructure variable:|Electricgrid|No Refuse|Water|All|
|Homeland (=1)|1.422**|0.866|1.098**|1.371**|
||[0.635]|[0.545]|[0.535]|[0.632]|
|Share Employed mining1996|-0.684***|-0.552**|-0.583**|-0.690***|
||[0.228]|[0.245]|[0.246]|[0.225]|
|Homeland*Share Employed|1.072***|0.942***|0.974***|1.076***|
|mining1996|[0.257]|[0.273]|[0.276]|[0.257]|
|Share_Infrastructure1996_|-0.007|-0.025|-0.153||
||[0.088]|[0.156]|[0.100]||
|Homeland*Share_Infrastructure1996_|-0.013|0.135|0.017||
||[0.095]|[0.162]|[0.105]||
|Share_Infrastructure2011_|0.670***|-0.668***|-0.069||
||[0.124]|[0.222]|[0.098]||
|Homeland*Share_Infrastructure2011_|-0.722***|0.751***|0.046||
||[0.132]|[0.228]|[0.102]||
|Observations|9,562|9,562|9,562|9,562|
|R-squared|0.641|0.637|0.636|0.642|
|1996 Controls|Yes|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). Column 4 includes all infrastructure variables (coefficients for these not presented). The dependent variable is the difference in log per capita income for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
|Observations|1,235,877|8,308,722|
|---|---|---|
|R‐squared|0.669|0.627|

Note: Standard errors in parentheses are clustered by exporter‐importer pair. *** p<0.01, ** p<0.05, * p<0.1 v. Complementarity between infrastructure improvements and trade reforms In this sub‐section, we assess the complementarity of trade policy reforms (deepening trade agreements and improving market access) on the one hand and infrastructure improvements in the BRI region on the other hand. To do this, we introduce an interaction term between the GIS variable of shipment times across countries and the policy variable ( Policy�� ), capturing respectively, the level of depth of agreements signed between a pair of countries _ij_ or the average preferential tariffs that the importer _j_ is imposing to _i_ in sector _k_ : X��� �exp �β� �β�GisTime�� �β�Tariff��� �β�PTAdepth�� �β�Policy�� ∗GisTime�� � Gravity�� �λ�� �γ����ε��� (3) The results presented in Table 7 highlight the complementarity between infrastructure improvements and trade reforms.<sup>13</sup> The positive impact of a reduction in trading times is magnified for country‐pairs that are involved in deeper trade agreements (e.g. countries that have better institutions and that share a common regulatory framework in trade related disciplines such as investment, intellectual property rights, competition policy and technical barriers to trade among others) as opposed to country pairs that have shallow agreements (see columns 1 and 2). Specifically, the positive impact of a one‐day reduction in trading times is 1.1 percent higher (6.3 percent) for country‐pairs that are part of the European Union, the deepest agreement in the BRI sample of countries, compared to countries that are part of very shallow agreements (agreements including only one provision).

In terms of market access, the positive impact of reducing trading times is dampened for country‐ pair‐products with lower levels of tariff liberalization (see columns 3 and 4). Whereas a one‐day reduction in trading times without reductions in tariff rates will increase average exports of

> 13 The IV regressions are excluded from this exercise given the complexity to instrument and interpret the interaction between endogenous variables.

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et al., 2021; Arag ́on et al., 2021; Tesfaye et al., 2021), and extreme weather impacts (Wineman et al., 2017; Michler et al., 2019; McCarthy et al., 2021). Our findings suggest that economists should exercise caution when seeking to combine EO data with socioeconomic survey data, as the selection of different EO sources may produce very different results, thus contributing to this literature on the use of EO data in economic applications.

Our paper also contributes to the nascent, but growing, literature on the economic implications of measurement error. Much of the research in this stream has focused on mismeasurement in the context of agricultural production and focuses on mismeasurement in self-reported data (Carletto et al., 2017; Abay et al., 2019; Gourley et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Abay, 2020; Abay et al., 2021; Gollin and Udry, 2021; Kilic et al., 2021; Michler et al., 2022). A related literature focuses on mismeasurement in everything from prices (Fitzpatrick, 2023), to consumption (Abate et al., 2023), to time use (Field et al., 2023), to willingness to pay (Jack et al., 2022). A smaller literature has begun to document, for economists, the presences of measurement error in EO data (Gibson, 2021; Gibson et al., 2021; Alix-Garcia and Millimet, 2023), an issue long known and discussed in GIS, remote sensing, and geophysical science (Wilkinson, 1996). We add to this literature as the first attempt, to our knowledge, at understanding the impact of mismeasurement in EO products in estimating policy relevant relationships between climate and development outcomes in low- and middle-income countries.

Understanding the differences in results that arise when a researcher chooses one EO product over another also has practical consequences, due to the importance of smallholder agriculture for rural livelihoods in low- and middle-income countries. Agriculture is a source of both income and employment, and as such understanding how weather affects agricultural productivity on smallholder farms is of policy interest. Thus, these results are important for informing policies to develop and advise on improved agricultural technologies that can mitigate the risks posed by climate variability and extreme weather events, and to provide social protection measures to smallholder farmers that are exposed to climate shocks.

# **2 Measuring the Truth: Gauge Station Data and Earth Observation Data in Africa**

The goal of weather data products is to measure and report on the objective fact that is the volume of precipitation and the temperature in degrees in a given location at a given time. In theory, this goal is easily accomplished using technology that has existed for centuries. One simply needs a rain gauge to collect precipitation, a mercury thermometer to measure temperature, and someone to record this data with pencil on paper. What could be simpler than measuring the weather? Humans have been reporting on the weather since the invention of writing and have been using scientific instruments to record precipitation and temperature since the 1600s (Lundstad et al., 4
# **Appendix A: Plots of temperature anomalies**

<!-- Start of picture text --> Coastal stations<br>CAMPO DE MARTE CHICLAYO<br>7<br>5<br>5<br>3<br>3<br>1 1<br>|<br>-1<br>-1<br>-3<br>-3<br>-5<br>-5 -7<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>LIMA CALLAO/ APTO. INT. JORGE CHAVEZ PISCO<br>7<br>5<br>5<br>3<br>3<br>1<br>1<br>-1<br>-1<br>-3<br>-3<br>-5<br>-5 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>PIURA SAN JUAN<br>5 5<br>4 4<br>3<br>3<br>2<br>2<br>1<br>1<br>0<br>0<br>-1<br>-1<br>-2<br>-2<br>-3<br>-3<br>-4<br>-5 -4<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 -5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Temperature Anomaly compared to Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius) average for 1960-1990 (degrees Celsius)<br>Temperature Anomaly compared to average for 1960-1990 (degrees Celsius) Temperature Anomaly compared to average for 1960-1990 (degrees Celsius)<br>Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius) Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius)<br><!-- End of picture text -->

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# **_2.2. Definitions of downward nominal and real wage rigidity_**

In the literature, wage rigidities are consensually referred to as (obstacles to) the speed or the amount with which wages adjust to changes in warranted real wages – real wage rigidity – and to changes in prices – nominal wage rigidity (see e.g. Blanchard, 2006). In this paper, rigidity refers to obstacles to wage adjustment, rather than to infrequent adjustment or stickiness of wages. Most often, the obstacles to wage flexibility prevent nominal or real wages from being adjusted downwards. We asked firms about wage freezes and indexation mechanisms, which we relate to downward nominal and real wage rigidity respectively, as argued below.

The measures of downward nominal and real wage rigidity used in the current study are closely related to the indicators which are derived on the basis of individual wage change distributions observed in household survey and administrative data (see e.g. Dickens et al., 2007). Our survey asked if firms have ever cut or frozen wages during the past five years. Firms were instructed to answer the wage-setting questions with reference to their main occupational group, defined earlier in the survey. Following the information on wage freezes, we regard firms that froze wages at any point as showing evidence of downward nominal wage rigidity.

We also asked firms if they had a policy that linked wage changes to inflation. Firms that replied yes to this question were further asked if the link with inflation was automatic or discretionary and whether the link was with past or expected inflation. Using information from these questions, we consider as subject to downward real wage rigidity those firms that have an automatic link between wages and past or expected inflation, i.e. who apply automatic wage indexation. The idea here is that workers not just resist nominal wage cuts but rather defend their real wages. They can do this through focusing collective bargaining on some measure of inflation, a practice that can be institutionalised by indexation mechanisms that link wages automatically to inflation.

Strictly speaking, our survey-based measures of real wage rigidity and nominal wage rigidity do not capture only downward wage rigidity. Due to various reasons mainly related to ‘menu costs’, a wage freeze can indicate upward as well as downward wage rigidity. For example, Elsby (2009) develops a model where he demonstrates that if firms are not able to cut nominal wages then they react to this constraint by compressing wage increases, i.e. downward rigidity imposes also upward rigidity in nominal wages. However, Dickens et al. (2007) show on the basis of 31 different datasets from 16 countries that a large spike at zero in the wage change 8