rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6. Countries with High Rural Low Elevation Coastal Zone Poverty, 2000**

|**Area**<br>**Country**|**s of High Inf**<br>**Rural**<br>**LECZ**<br>**Population**<br>**in Areas of**<br>**High Infant**<br>**Mortality**<br>**('000)**|**ant Mortalit**<br> <br>**Share (%)**<br>**of Rural**<br>**LECZ**<br>**Population**<br>**in Areas of**<br>**High Infant**<br>**Mortality**|**y**<br>**LECZ**<br>**Share (%)**<br>**of Rural**<br>**Population**<br>**in Areas of**<br>**High Infant**<br>**Mortality**|<br> <br> <br> <br>**Country**|**Areas of Malno**<br>**Rural LECZ**<br>**Population in**<br>**Areas with**<br>**Malnourished**<br>**Children**<br>**('000)**|**urished Childr**<br> <br>**Share (%) of**<br>**Rural LECZ**<br>**Population in**<br>**Areas with**<br>**Malnourished**<br>**Children**|**en**<br> <br> <br>**LECZ Share**<br>**(%) of Rural**<br>**Population in**<br>**Areas with**<br>**Malnourished**<br>**Children**|
|---|---|---|---|---|---|---|---|
|India|19,419.9|55.4%|3.3%|Bangladesh|19,092.3|46.4%|33.2%|
|Bangladesh|19,143.5|46.5%|27.1%|India|14,635.0|41.7%|4.3%|
|Myanmar|7,791.0|99.7%|21.1%|Vietnam|10,764.0|33.0%|53.7%|
|Cambodia|3,306.5|99.5%|30.6%|Indonesia|4,466.6|26.3%|12.8%|
|Nigeria|2,175.1|100.0%|2.7%|Myanmar|3,001.2|38.4%|22.9%|
|Pakistan|1,694.1|99.5%|1.8%|Philippines|1,564.4|27.9%|10.5%|
|Iraq|1,324.3|99.3%|11.9%|Cambodia|1,384.5|41.7%|27.8%|
|Mozambique|1,041.2|100.0%|7.8%|China|1,117.5|2.3%|5.1%|
|Senegal|802.9|100.0%|14.0%|Thailand|955.0|18.6%|11.8%|
|Brazil|759.6|38.6%|4.0%|Pakistan|842.9|49.5%|2.3%|
|China|624.7|1.3%|0.6%|Nigeria|450.5|20.7%|1.9%|
|Indonesia|416.0|2.4%|13.5%|Mozambique|265.7|25.5%|6.6%|
|Philippines|359.6|6.4%|10.5%|Iraq|211.2|15.8%|12.0%|
|Vietnam|314.5|1.0%|24.7%|Senegal|194.7|24.2%|13.2%|
|Thailand|9.4|0.2%|0.4%|Brazil|127.4|6.5%|3.1%|
|**Total 15**|**59,182.3**|**28.9%**|**5.7%**||**59,072.9**|**28.8%**|**10.2%**|
|**All Developing**|**64,335.1**|**27.2%**|**5.0%**||**62,360.1**|**26.4%**|**9.2%**|

The low elevation coastal zone (LECZ) is the contiguous area along the coast that is less than 10 m above sea level.

High infant mortality is 50 or more deaths within the first year of life per 1,000 births (or 500 deaths/10,000 live births). The above 15 countries account for 92.0% of all rural LECZ populations with high infant mortality in developing countries.

Child malnutrition counts are the product of malnutrition rates and population counts. Malnutrition rates are based on the percentage of children with a weight‐for‐age z‐score more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. The above 15 countries account for 94.7% of all rural LECZ populations with malnourished children in developing countries.

See the appendix for details on the spatial methods in calculating these coastal population distributions.

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adverse environmental news.<sup>2</sup> The impact of firm-specific environmental news on market value may work its way through various channels: a high level of pollution intensity may signal to investors the inefficiency of the firm’s production process; it may invite stricter scrutiny by environmental groups and/or facility neighbours; it may result in the loss of reputation, goodwill, etc. On the other hand, the announcement of a good environmental performance or of the investment in cleaner technologies may have the opposite effect: lesser scrutiny by regulators and communities (including the financial community), and greater access to international markets among other benefits.<sup>3</sup> Studies of this nature in developing countries have been very limited in numbers. In a recent paper, Dasgupta et al. (2001) have shown that capital markets in Argentina, Chile, Mexico, and the Philippines do react negatively (decrease in firms’ value) to citizens’ complaints targeted at specific firms, and positively to the announcement of rewards and recognition of superior environmental performance. These results suggest that environmental regulators in developing countries may explicitly harness those market forces by introducing structured programs of information release on firms’ environmental performance, and empower communities and stakeholders through environmental education programs.

Numerous countries, both developing and developed have in fact implemented such programs. An increasing number of environmental regulators around the world have

> 2 In the United States, these studies include analyses of the reaction of capital markets to releases of the _Toxics Release Inventory_ (Hamilton (1995), and Konar and Cohen (1997)). Lanoie and Laplante (1994) analyze the reaction of capital markets to environmental news in Canada. For a survey of these studies, see Lanoie, Laplante and Roy (1998).

> 3 See Porter and Van Linde (1995), and Konar and Cohen (1997) for a more detailed discussion.

4
allowances from its account to cover its emissions at the “true‐up” date. Title IV was implemented in two phases, with the first round of emissions regulation taking effect in 1995 and a more stringent regulation starting in 2000.

Most of the units affected by Title IV are coal‐based power plants. Consider a risk‐neutral firm that uses adjustable levels of low‐ and high‐sulfur coal ( _l_ and _h_ ) to generate electricity. The output is given by _g_ ( _l,h_ ), which is assumed to be quasi‐concave and increasing in both arguments. Some previous studies assume that low‐ and high‐sulfur coals are perfect substitutes that differ only in the sulfur contents. For example, Arimura (2002) uses a linear function to describe the production technology with low‐ and high‐sulfur coals as the factor inputs. In reality, low‐ and high‐ sulfur coal cannot so easily be substituted for each other. Typically, power plants are designed for a particular type of coal. Deviations of coal properties from the initial design may result in reduced efficiencies, impaired plant performance, or even serious operating problems, and the damage increases as the deviations multiply.<sup>4</sup> As a by‐product of coal combustion, SO2 is produced and emitted to the air. Denote the emission function as _e_ ( _l,h_ ) = _μll_ + _μhh_ , where _μl_ and _μh_ ( _μl < μh_ ) are the sulfur content of low‐ and high‐sulfur coal, respectively.

Since the majority of coal purchases are locked into long‐term contracts, we assume that the price of low‐ and high‐sulfur coal ( _Pl_ and _Ph_ ) is constant, while the price of a permit ( _Pa_ ) evolves stochastically. Price uncertainty manifests both in fluctuations in the trend and in the standard deviation from the trend. In this paper, we focus on the impact of price volatility around an expected value by assuming that the permit price evolves following a mean‐preserving process. Specifically, let _Pa_ be defined by _Pa_ = �<sup>�</sup> �+ _ε_ , where �<sup>�</sup> �is a constant and _ε_ is a random

> 4 For more details on the impact of blending high‐sulfur coal with low‐sulfur coal on production performance, refer to International Energy Agency Fuel Research (1993).

6
at risk for depression: a prediction score performance in cohorts based in 3 different continents. _Journal of the American Academy of Child & Adolescent Psychiatry_ , 60(2).

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- Rossell ́o, J., Bernal, G., and Rivera-Medina, C. (2012). Individual and group CBT and IPT for Puerto Rican adolescents with depressive symptoms. _Journal of Latinx Psychology_ , 14(3):36–51.

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- Salomon, J. A., Vos, T., Hogan, D. R., Gagnon, M., Naghavi, M., Mokdad, A., Begum, N., Shah, R., Karyana, M., Kosen, S., et al. (2012). Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. _The Lancet_ , 380(9859):2129–2143.

- Saxena, S., Thornicroft, G., Knapp, M., and Whiteford, H. (2007). Resources for mental health: scarcity, inequity, and inefficiency. _The lancet_ , 370(9590):878–889.

- Schwarzer, R. and Jerusalem, M. (1995). Generalized self-efficacy scale. In Johnston, M., Wright, S., and Weinman, J., editors, _Measures in Health Psychology: A User’s Portfolio. Causal and Control Beliefs_ , volume 35, page 37.

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# **4.2.1 Differences between EGPP beneficiaries and eligible non-beneficiaries**

Columns1-5 of Table 1A compare the socio-economic and demographic characteristics between EGPP beneficiary and non-beneficiary households in the baseline survey and show that the two groups had similar socio-economic characteristics. The EGPP and non-EGPP households appear to have no significant differences in their value of assets, and other poverty correlates such as housing characteristics, access to electricity and other services. There are also no significant differences in monthly per capita food (BDT1071.44 and BDT1085.25) and non-food consumption (BDT578.9` and BDT571.15), and the average poverty rates in the corresponding _Upazilas_ (34.25 and 33.15 percent) they live in. There are also no significant differences in the ownership of common assets except for cassette player. Landlessness patterns are also similar: 37% of the beneficiary households and 36 percent of the comparison households are landless in the baseline sample. We find that on average an EGPP beneficiary households have larger household sizes with older household heads. A significantly lower percentage of EGPP household heads are male (88 percent) compared to non-EGPP households (91 percent).

The data shows no significant differences in housing conditions between the two groups. Only 4.99 percent of the program households have a separate dining room, 63.51 percent possess a separate kitchen, 40% live in houses that have walls made of corrugated sheet or wood while 85 percent have roofs made of corrugated sheet. The corresponding figures for the comparison households are 4.04, 60.20, 35.85 and 83.32 percent. 51 percent of the EGPP participants use sanitary (flush, water seal and pit) latrine, 95 percent have access to tube-well water, 21.15percent households have electricity. Among the comparison households 52 percent use sanitary latrine, 94.64percent have access to tube-well water and only 18.41 percent households have electricity. However, the beneficiary households have on average significantly more rooms in the households (1.57) than the non-beneficiary households (1.48).

Both the program and comparison households use similar energy sources for cooking and lighting. 21 percent of the beneficiary households use wood, 8.72 percent use dung, 0.03 percent use solar energy for cooking. Among the non-beneficiaries, 21.15 percent use wood, 8.39 percent use dung and 0.04 percent use solar energy for cooking. Of the program households, 79.09 percent use kerosene and 19.39 percent 17
sown or area insured was not available so all districts have been weighted equally, and within each district each insurance unit has been weighted equally.

Table 1. Groundnut: weighted average yield by district

|**State**|**District**|||**Wei**<br>|**ghted Av**<br>|**erage Y**<br>|**ield(kg/**<br>|**ha) for**<br>|**year**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|[11]|[12]|
|AP|AP1|1,428|1,994|2,062|1,679|2,618|2,130|2,389|2,669|2,967|-|
|AP|AP2|1,164|1,449|1,098|1,735|1,770|2,471|2,719|2,121|2,771|-|
|AP|AP3|1,465|1,288|1,150|1,394|1,339|1,773|1,627|1,732|1,627|-|
|GA|GA1|1,957|1,535|1,959|1,796|1,436|1,949|1,483|1,617|2,277|3,126|
|GJ|GJ6|1,094|942|1,399|636|1,424|1,556|1,826|1,703|1,706|1,668|
|GJ|GJ13|1,243|1,124|1,361|1,280|1,488|2,007|1,123|1,444|1,220|1,367|
||All districts|1,363|1,548|1,425|1,598|1,857|2,121|2,215|2,126|2,426|2,347|

Second, at both the Threshold Yield Collective level and the crop level, calculate the best estimate linear trend in weighted average yield and the p-value of a two tailed t-test with null hypothesis that there is no trend.<sup>3</sup> Table 2 lists best estimate linear trends and p-value of trends for groundnut. As can be seen, the best estimate trends for groundnut vary from 14 kg/ha/year to 207 kg/ha/year for the six districts considered.

Table 2. Groundnut: Linear trend analysis by district

|**State**|**District**|<sup>**Best estimate linear**</sup><br>**trend (kg/ha/year)**|**P-value**<br>**of trend**|**Percentage of statistically**<br>**significant best estimate**<br>**trend to remove for this crop**|**Amount of**<br>**trend to be**<br>**removed**|
|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|
|AP|AP1|155|0.2%|75%|116|
|AP|AP2|207|0.1%|75%|155|
|AP|AP3|55|2.9%|75%|41|
|GA|GA1|82|14.4%|75%|-|
|GJ|GJ6|95|1.3%|75%|72|
|GJ|GJ13|14|65.7%|75%|-|
|All|districts|125|0.0%|||

Judgment should be applied to determine where, if anywhere, de-trending should be applied and the degree of de-trending, where de-trending is to be applied. The decision to de-trend data should be made with proper consideration of the impact any such decision will have on design and ratemaking. Removal of a statistically significant trend is likely to increase the threshold yield and reduce the commercial premium rate, resulting in a better value product for the farmer.

One potential approach would be to remove the same percentage of any statistically significant best estimate trend for each crop. This percentage could be set to zero for crops which do not appear to

> 3 These may be calculated using MS Excel functions as follows.  Suppose cells A1:A10 contained ten yield data years (e.g. the numbers 1998, 1999, ... , 2007) and cells B1:B10 contained weighted average yields from year 1 to year 10.   The linear trend, assumed to be calculated in cell C1, is given by the formula

> .  The two sided t-value, assumed to be calculated in cell D1, is then given by

> .  Finally, the p-value is given by

> .

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# **3 Evidence**

Proposition 1 summarizes the main theoretical results concerning the correlation between mean quality growth and variance of quality growth as well as the positive relationship between the mean and the variance of quality growth and financial development in a crosssection of countries. We now turn to the empirical evidence for these predictions.

## **3.1 Data**

We use data from disaggregated (HS10) bilateral exports to the United States for the years 1989 to 2001, sourced from the NBER Trade Data Set, a detailed description of which is provided by Feenstra, Romalis, and Schott (2002). The data sum across U.S. ports and modes of transportation. In total, there are 16,926 HS10 products and 178 countries. The number of products per country ranges from 1 to 9907, with a mean number across countries of 5385. We focus on manufacturing industries (SITC 5-8). In keeping with the spirit of the underlying theoretical analysis, we exclude “homogeneous goods” as defined by Rauch (1999a).

As is well known, the trade data in the NBER data set are quite noisy. In addition to data reporting and entry errors, there are product classification errors due underlying product heterogeneity. Further, as Besedes and Prusa (2006) note, there are changes in product categorization across time. For instance, a given HS10 product category may split into several HS10 codes over time and, alternately, multiple HS10 codes may be merged into one HS10 category, due to the evolution of new products as well as the attrition of old ones. To deal with the noise in the data, we trim the data along two dimensions. First, we exclude all observations for which the dataset reports a quantity of one unit or value less than $7500 1989 dollars. Second, as in Khandelwal (2010), we remove varieties with extreme unit values that fall below the 5<sup>th</sup> percentile or above the 95<sup>th</sup> percentile within the industry. To mitigate the effects of outlier observations on our dispersion measure, we drop those goods for which the growth rate of quality on annual basis is greater than 2 (that is, those products whose unit value more than doubled).<sup>9</sup> These filters, taken together result in a loss of 7.99% percent of trade volume. Additional filters of the data, as part of robustness analysis, are considered in the following sections.

Product unit values (trade value divided by trade quantity) serve as a proxy for product quality. To make statements about a country’s innovations in product quality, we implicitly

> 9In a separate calculation, we also consider only those goods which additionally saw no downward movement in unit values as it is not clear what would cause such a downgrading of quality. Results were similar and are available upon request.

8
observations for t = -24, ... 0, I periods are used to arrive at the proxy estimate for the t = 2 period and so on. Thus, the proxy formation period for eac", subsequent test period is obtained by replacing the first observation of the previous proxy formation period by the observation corresponding to the last test period. Finally, the following OLS regression is run for eacn ith fund.

# _[(PD),] =Constant + P l(SPN), + ,B2(StUB), + P_ **_3(A_** _GB), + error term_

Cross-Sectional Tests: Seventeen of the funds in our sample have complete data beginning in Deeember 1989. The sample increases to twenty-nine funds in July 1990. Since **_6_** months (twenty-six observations) are used to calculate proxies (SPN and S0B) for each fund as described under the time-series tests above, we have a total of 57 periods (27 for a seventeen fund sample and 30 for a twenty-nine fund sample) for cross-sectional tests. The following OLS regressions are run for each of the 57 periods (weeks):

# _[(PD) -A GB] = Constant + p1(SPN)I_ + _p2(SUB)1_ + p _3(FSZ)1 +error term_

where all variables are defined as before.

Since the global country fund premium/discount is invariant across funds for a given period, we use the NIF premium/discount net of the AGB as the dependent variable as in Errunza (1991). These regressions result in weekly OLS values of the coefficients (p's) for each of the 57 periods. Note that although it would be interesting to include the access variable in these regressions, the use of two dummy variables to capture three categories would add two intercept and six slope terms (on R.H.S.). Given the very small sample sizes, we have found it appropriate to exclude this variable.

24
Figure 2. Poverty Estimates, by year and migrant status <!-- Start of picture text --> 2009<br>80<br>70<br>60<br>50<br>44.2<br>40<br>30<br>27.5<br>20 21.1<br>15.6<br>10 9.1 7.3<br>0<br>Host Community Settled Migrants Recent Migrants Host Community Settled Migrants Recent Migrants<br>(>4 years) (<=4 years) (>4 years) (<=4 years)<br>Near Syrian Border Rest of the Country<br>2013<br>80<br>70<br>60<br>50<br>46.7<br>40 40.3<br>30<br>20 17.7 17.9 16.1<br>10<br>6.6<br>0<br>Host Community Settled Migrants Recent Migrants Host Community Settled Migrants Recent Migrants<br>(>4 years) (<=4 years) (>4 years) (<=4 years)<br>Near Syrian Border Rest of the Country<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> Notes: Regionally deflated, OLS imputation model, time-invariant. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval _Source_ : LFS 2009-2014. World Bank author’s calculations.

It is important to understand which characteristics or circumstances may be driving the large differences in poverty between our six comparison groups. Changes over time are also worthy to note since the number of foreign households in the LFS increases dramatically in 2013 and 2014. Table 10 illustrates the mean summary statistics for our comparison groups in 2009, 2013, and 2014 that are located near the Syrian border.<sup>12</sup> Table 11 shows complementary statistics for households located in the rest of the country.

Recent migrant households are younger and have more children, and a higher dependency ratio. Dependency in migrant households result not from the elderly but from children. Families of recent migrants are much larger. In 2013, recent migrant households near the Syrian border had on average 3.1 adults, 2.4 children, and 0.4 elderly. The large number of children is indicative of entire families moving to Turkey from Syria. The composition is in contrast to working age individuals who are more likely to be economic migrants. These individuals would migrate without families, and send remittances home to their

> 12 The years 2009 and 2014 are the earliest and latest available surveys in the LFS that are usable for measuring migration. However, due to data availability on Syrian refugees and changes to the survey in 2014, regression analysis will use only 2011 and 2013 LFS data.

12