rafmacalaba/datause-encoder-data huggingface dataset:
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NEERI. 2010. “Air Quality Monitoring, Emission Inventory, and Source Apportionment Studies for Delhi.” National Environmental Engineering Research Institute, Nagpur; December 2010.

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WHO. 2000. “Air Quality Guidelines for Europe.” World Health Organization, 2nd Edition, WHO Regional Publications, Copenhagen; European 35
TABLE 4: ECONOMIC LOSSES FROM PEST ATTACKS IN COTTON IN GUNTUR.

|YEARS|ACTUAL<br>PROD.|--- DALES--<br>PREDICTED<br>PROD.|-<br>PROD.<br>LOSS|LINT<br>PRICE|LINT<br>LOSS|_SEED_<br>PRICE|(RS.<br>SEED<br>LOSS|CR8.)<br>TOTAL<br>LOSS|LOSS AS<br>OOF POT<br>-ENTIAL|REASON<br>-FOR<br>DAMAGE|
|---|---|---|---|---|---|---|---|---|---|---|
|1970/71|6211|3877|||||||||
|1971/72|34088|34686|||||||||
|1972/73|24738|37804|||||||||
|1973/74|126019|113286|||||||||
|1974/75|178851|<br>167934|||||||||
|1975/76|103790|<br>98248|||||||||
|1976/77|119688|<br>174992|||||||||
|1977/78<br>1978/79|<br>50201<br>117114|<br>196135<br> <br>112641|145934|<br>1531|<br>38.0|156.3|<br>7.8|45.7|<br>74.4|CYCLONE<br>DAMAGE|
|1979/80|161823|<br>148134|||||||||
|1980/81|246386|<br>215664|||||||||
|1981/82|302160|295588|||||||||
|1982/83|324071|<br>314591|||||||||
|1983/84|567212|<br>378059|||||||||
|1984/85|495535|<br>474744|||||||||
|1985/86|294166|<br>527040|232874|<br>1460|<br>57.8|314.8|24.9|82.7|<br>44.2|WHITEFLY|
|1986/87|350000|383114|33114|1884|10.6|314.4|3.5|14.1|<br>8.6|ATTACK|
|1987/88|224000|540247|316247|3035|163.2|411.3|44.2|207.4|58.5|HELIOTHIS|
|1988/89|260000|<br>526965|266965|<br>2953|134.0|430.0|39.0|173.1|<br>50.7|ATTACK|
|1989/90|383000|<br>501075|||||||||
|1990/91|600000|517948|||||||||
|1991/92<br>---------------------------|585000<br>----------------------------|<br>564150<br>--------||||||__---|---------__----||

NOTESs 1) The best fit trend line gives the potential output for abnormal years.

2) Cotton lint and cottonseed prices from Gulati, Kishor and Pursell study.

3) Production figures for cotton from official AP agricultural publications.

I ,
where i∗ is the AEE for household _i_ in survey _j_ , which is an adjusted version of household expenditure conditional on the number of adults yy i and children i (we suppress the country and FHH type indexes for less cluttered notation). The smaller aa is, the smaller the relative weight of kk children; the higher is , the smaller the degree of economies of scale assumed.αα<sup>9</sup> We construct several different AEE levels for each household based on this method, using different values for δδ the weight of children ( ) and degree of economies of scale ( ) and show the results in Figure D.1, Appendix D. The relationship between household size and poverty dynamics reveals varying αα δδ scenarios for FHHs, with FHHs generally having a higher likelihood of escaping poverty than nonFHHs when assessing consumption on a per capita basis.<sup>10</sup>

# **3.3. Descriptive statistics**

Table 1 presents some key sample statistics on the prevalence of FHHs defined according to our proposed typology (Section 2.1) across the six Arab countries and different (survey) years. The four main types of FHHs are shown in bold while the alternative sub-types are shown in regular font. The shares of self-reported FHHs remain relatively stable over time in most countries, except for Mauritania. In recent years, this share hovers from around 10 percent (Iraq, the West Bank and Gaza) to 13 percent (Jordan) and 18 percent (Egypt, Tunisia).<sup>11</sup> Mauritania has the largest share of self-reported FHHs, which has almost doubled from 18.9 percent in 2004 to 36.6 percent in 2019. The shares of majority-female-adult FHHs are significantly higher in all countries,

> 9 When = 1 and = 1, we have per capita expenditure, which assumes no economies of scale and an equal weight for children and adults in the household. 10 These results are consistent with Abanokova δδ αα _et al.’_ s (2022) finding regarding the sensitivity of income dynamics to scale parameters, showing persistent upward mobility when income is evaluated on a per capita basis for the Russian Federation.

> 11 These shares are lower than the corresponding figure of 26 percent for African households observed in Milanzo and van de Walle (2017).

18
**Pep31**

1991 prices),O and with the revaiuation of the J$ that reduced (at least transitorily) the real value of its foreign debt Nevertheless, as Figure 14 shows, the operating return on fixed assets did not exceed the real cost of its long term debt Thus. CTC's ditficulties in attracting capital were not imaginary. The company was not sufficiently prolitable to attract private investors, neither Jamaican or foreign. By 1971 CTC was already asking for direct govemrnment investrnent in JTC. A first step was taken, as mentioned, when the governmen **t** ook a 10% stake in 1973.

56. The government's decision in 1973 to support JTC, though, did not go far enough. First the subsidy originating from the stamp-duty tax was to end in June 1974. Furthermore, the JPUCs attitude towards JTC did not change. Figure 3 shows the evolution of real prices for domestic calls. Through the 197111979 period real prices increased in steps, with nominal price increases, granted by the JPUC until 1976 and by the Ministry of Public Utilities since then, compensating for past domestic inflation. The 1971/1975 does not seem to be too different from the 1975/19. 9 period in the evolution of prices. In 1974, though, the PNP govemment made pubic its change of policy towards a socialist economy, which included govemment ownership of the main enterprises. This change in govemment atfitude provided JTC with an opportunity to negotiate a takeover by the GOJ. In September 1975 CTC agreed to the sale of its holdings in JTC to the govemment 57. The final event on the takeover of JTC was the dismantling of the JPUC in 1976, and the transfer of regulatory responsibilities to the Ministry of Public Utilities and Transport, even though the PUC Act remained in the books. Following the takeover, the financial situaton of JTC did not improve, with JTC showing an economic loss in 1977, after essentially breaking even since 1973 (see Figure 11). The GOJ pricing policy until 1979, though, did not differ substantially from that of the JPUC. Figure 3 shows no break in pricing around late 1975-1976. Furthermore, th.l expansion in the network

- **The** **_issue_** _of_ now **stock to** CTC **did** not **involve** a **new** inflow **of** funds **to the company,** but rather **a** cancellaton of a **shout oem** bond to **CTO.**
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17
3 FOREIGN DIRECT INVESTMENT IN KENYA <!-- Start of picture text --> 750<br>500<br>250<br>0<br>1980 1985 1990 1995 2000 2005 2010 2014<br>Year<br>FDI (USD Millions)<br><!-- End of picture text --> Figure 14: Kenya’s FDI is low. Trend in red (1980-2014) Source: World Development Indictors World Bank 2015 <!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1980 1985 1990 1995 2000 2005 2010 2014<br>Year<br>FDI (% of GDP)<br><!-- End of picture text --> Figure 15: Kenya underperforms in attracting FDI relative to potential. Trend in red (1980-2014) Source: World Development Indictors World Bank 2015 19
unobservable characteristics of a child and his/her family. As in the case of calendar month-of-birth, the ‘nutrition-disease’ hypothesis seems to be most plausible in explaining the variation in health outcomes by month-of-birth relative to the beginning of the monsoon.

We also assess how our results would change if we use trimesters, quarters and semesters of birth instead of the month of birth. In addition to calendar seasons, we define three subtropical seasons, and the hungry and harvest seasons, relative to the start of the monsoon in a particular area (as in Moore _et al._ , 2004). The three subtropical seasons are: the monsoon season (June/July-September/October), the cool-dry winter season (October/November-February/March) and the hot-dry season (March/AprilMay/June). The hungry season starts in June/July and ends in December/January. Our main results hold when we use these different timeframes. Expectedly, the larger the time span, the weaker the impact of the season on health outcomes--in other words, we find that trimestral variations in WAZ and HAZ are larger compared to quarterly and halfyear variations.

Another serious concern about the stability of our results is the impact of the sharp deterioration in health outcomes of children after they are switched from breast milk to solid food (in India this happens 4 to 6 months after birth) (for example, Barrera 1990; Olango and Aboud 1990). To address this concern, we repeat our analysis and test our hypotheses on a sample of children older than 12 months. Again, our main conclusions remain the same: the environmental conditions around the date of birth of a child in India play an important role in determining his/her health outcomes later in life.

# **5. Conclusions**

In this paper, we use data from three waves of the India NFHS to explore the relationship between the month-of-birth and health outcomes of young children in India. We demonstrate that children’s anthropometric scores (WAZ and HAZ) vary significantly with the month of birth: children born during the monsoon months have consistently worse health outcomes compared with children born in the fall-winter season. The ‘month-of-birth’ effect persists after controlling for a wide set of observable and 19
The 2018/19 NLSS was conducted between September 2018 and October 2019 and was designed to provide estimates for a wide range of socioeconomic indicators – including consumption and poverty – for Nigeria’s 36 states and the Federal Capital Territory (FCT), Abuja. The sample of around 22,000 households is representative at the national, zone, and state levels, aside from Borno state (which accounts for around 2.5 percent of the Nigerian population).<sup>9</sup> Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. The household questionnaire provides information on demographics, education, employment, food and non-food consumption, food security, shocks, safety nets, housing conditions, assets, information and communication technology, agriculture and land tenure, and other sources of household income. Overall estimates of household consumption were constructed using the modules on food and non-food consumption and spatially and temporally deflated using a price index constructed from unit prices in the food consumption module: this provides a consistent measure of welfare for the whole of Nigeria. From this, poverty estimates at the international poverty line of US$1.90 2011 PPP per person per day can be constructed by deflating over time using CPI data and converting to dollars using Purchasing Power Parities (PPP) (Atamanov et al. 2018, Lakner et al. 2018).

The backcasting exercise uses two sources of national accounts data. First, we use yearly data on sectoral (agriculture, industry, and services) GDP growth rates (available from the MFMod tool of the World Bank, see Burns et al. 2019) to backcast the 2018/19 consumption vector to 2009. Real GDP _levels_ are expressed in constant local currency units – meaning that they have already been deflated by the GDP deflator – and are shown in Table _3_ . Second, we use GDP and GNI data from the WDIs to estimate different pass-through rates between growth in the national accounts and in household consumption. These data on GDP and GNI growth rates are matched to survey-year estimates of average per-capita household monthly consumption available in PovcalNet (http://iresearch.worldbank.org/PovcalNet/home.aspx). The population data used in the main analysis, and for the purposes of global poverty monitoring, are also taken from the WDIs (available at <u>https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG).</u> However, we also provide estimates of the absolute number of poor people using NBS’ population estimates.<sup>10</sup>

> 9 Parts of Borno state became inaccessible over the course of the survey. This meant only 530 households were reached, so only 15 Local Government Areas (LGAs) were reached of 27 LGAs that were originally sampled. 10 NBS’ population estimates were used to construct the survey weights for the 2018/19 NLSS. Table 16 Table 16 shows how poverty estimates from this analysis differ when using WDI and NBS population data.

7
9 enhancement can take other forms such as the comfort to investors of having the Work Bank in a co-financing role or a guarantee from a multilateral agency.

Credit enfhncement cap be used not only to allow countries of lesser creditworthiness to tap private capital sources but can also be used by other developing countries, including India, to meet other objectives. Collateral, partial or whole, can for example be us6d t' lengthen the maturity of a borrowing beyond the normal term of a market financing. Alternatively, if the collateral were in a foreign currency or linked to a foreign currency, it could be a useful hedge against the risk of a devaluation of the dome.stic currency. These methods of credit enhancement can, of course, be used on non-securitized financings equally well.

Such innovations, however, can be introduced only if the developing countries become active participants in the international market. The innovations so far introduced have not been motivated by the special needs of the developing countries. The reasons are: developing countries have not been significant participants in the market and secondly most financial institutions are inherently conservative and therefore tend to introduce innovations mainly in there dealings with the most creditworthy borrowers-which developing countries have not been for some time.

The most interesting innovation as a result of globalisation relevant to the developing countries is the development of the swap market in both currency and interest rate swaps. A swap, as the market understands the term currently, is a medium or long term arrangement between two parties in which each party commits to service the debt obligation of the other. This has been the most radical or revolutionary innovation that has been introduced in the international market in recent years.<sup>1 3</sup> As a result, borrowers are no larger restricted only to those markets in which their name is acceptable or where their credit standing is the highest brt which do not provide the currency and type of debt they require. A borrower can tap the market for borrowing, where he is most reditworthy and Lence can get the most favourable terms and then trade or 13 A.J. Davis. "Me Banking Revolution-EnAks and Industy." **_Statc_** _BOan of_ **_India Motly Review,_** Volume 36, Number 2 (February **17).**
46

**Table 21. Magnitude of pollutants generated by automobiles under difrerent driving modes in the United States (in parts per million, ppm)**

|Driving Mode|Hydrocarbons<br>(HC)|Carbon<br>Monoxide<br>(CO)|Carbon<br>Dioxide<br>(Co2)|Oxides of<br>Nitrogen<br>(NOx)|
|---|---|---|---|---|
|Idling|1.34|16.19|68.35|0.11|
|Accelerating|||||
|0-15 mph|536.00|2,997.00|10,928.00|62.00|
|0-30 mph|757.00|3,773.00|19,118.00|212.00|
|Cruising|||||
|15 mph|5.11|67.36|374.23|0.75|
|30 mph|2.99|30.02|323.03|2.00|
|45 mph|2.90|27.79|355.55|4.21|
|60 mph|2.85|28.50|401.60|6.35|
|Decelerating|||||
|15-0 mph|344.00|1,902.00|5,241.00|21.00|
|30-0 mph|353.00|1,390.00|6,111.00|41.00|

Source: [Bellomo and Liff 1984]

**Table 22. Representative composition of exhaust gases from heavy vehicles (in ppm by volume)**

|Pollutant|Idling|Accelerating|Cruising|Decelerating|
|---|---|---|---|---|
|Gasoline Engine|||||
|Carbon monoxide|69,000|29,000|27,000|39,000|
|Hydrocarbons|5,300|1,600|1,000|10,000|
|Nitrogen oxides|30|1,020|650|20|
|Aldehydes|30|20|10|290|
|Diesel Engine|||||
|Carbon monoxide|Trace|1,000|Trace|Trace|
|Hydrocarbons|400|200|100|300|
|Nitrogen oxides|60|350|240|30|
|Aldehydes|10|20|10|30|

Source: [Holdgate et al. 19821
other cooking fuels or purchases fuelwood. The household level of welfare is measured by the real annual per capita consumption expenditure of the household. The agricultural characteristics of the household are represented by the households that reported agriculture as the main economic activity and the household that grew tobacco in the last growing season. Area under rain-fed and dry (dimba) agriculture cultivated by the household, area owned but not cultivated by the household, area with trees owned by the household were also taken into account.

A third type of data collected by the survey relates to individual members of a household. Along with demographic characteristics of the members such as age, sex, marital status, the survey collected data on the amount of time spent by each household member above five years in various domestic and productive activities. These activities include fuelwood collection and household agriculture.

We define active men and women as those ten years and above who reported time spent on at least one of the activities for which time use data was collected. We also looked at children between age five and ten. However, preliminary analysis showed that very few children below age ten participated in fuelwood collection and agricultural production. Thus, by considering men and women above age ten we take into account most of the active labor force in the household.

We separate the households living in areas with less than 20 cubic meters per hectare of biomass (Less) and in areas with more than 20 cubic meters per hectare of biomass (More). The areas with less than 20 cubic meters per hectare of biomass are where biomass is scarce and areas with more biomass are where the scarcity is not as severe. Sixty five percent of the rural households surveyed reside in biomass scarce areas.

Along with the labor allocation data for the active men and women, we control for the following demographic characteristics: age, marital status of the active person, number of living children less than five years old, if the active person suffered from any illness or injury in the past two weeks, if the active person suffered from any chronic illnesses, and the number of other members of the households suffering from chronic illnesses.

# **3. METHODS**

<u>Biomass Assessment</u> 8
# **_I1._** _Salient Features ofAgricultural Trade_

Before doing _ex post_ and _ex ante_ analysis of the impact of regional economic integration on trade flows, let us briefly examine the salient features of agricultural trade, in comparison with manufacturing trade. As discussed in detail below, generally speaking, agricultural trade is characterized by two important features: (a) agricultural trade is subject to heavier trade restrictions (both tariff and nontariff barriers) than manufacturing trade; and (b) agricultural product is also far from homogeneous, although the degree of differentiation is generally lower than that of manufacturing trade.

# _(A) Heavy Protection_

As shown in Table 1, trade barriers imposed on manufacturing trade, perhaps with the exception of textiles and clothing, have been greatly reduced through a series of tariff negotiations under GATT, and as of 1989, the average tariffs of advanced countries on manufacturing trade (MFN tariffs) are minimal at 3-6 percent. Tariff rates imposed on Table 1: Average MFN Tariffs on Manufactured Goods (percent)

||1962|1970|1989|
|---|---|---|---|
|European Community|11|8|6|
|France|11|8|6|
|Germany|11|8|6|
|Netherlands|11|8|6|
|United States|12|9|5|
|Japan|16|12|3|

Source: PohI and Sorsa (1992), p. 13.

_5_
**Table B.3 Definition of institutional variables**

|_Variable_|_Description_|_Source_|
|---|---|---|
|Executive<br>Recruitment<br>Concept|Index of openness of executive recruitment, ranging from 1<br>(succession by birthright) to 8 (competitive election).|Polity IV Project|
|Executive<br>Constraints<br>Concept|Index of degree of constraints on the executive, ranging from 1<br>(unlimited authority) to 7 (executive parity or subordination).|Polity IV Project|
|Political<br>Competition<br>Concept|Index of degree of competition in politics, ranging from 1<br>(suppressed) to 10 (institutional electoral).|Polity IV Project|

_Note_ : For all three variables, values for interruption, interregnum, and transition periods were recalculated using interpolation.

38