rafmacalaba/datause-encoder-data huggingface dataset:
<mark>the Jewish infants in New York City it was shown that diluting milk formula with tap water containing a high concentration of sodium will result in the infant being fed a high-salt diet and a spike in blood pressure (Pomeranz et al., 2002). A meta-analysis of several studies linked salt in drinking water to hypertension (Talukder et al, 2017). Overall globally, climate change is likely to increase salinity in drinking water and adversely affect the health of the population in developing countries. It is also likely to negate all the gains made in recent years (Chong et al., 2014). As mentioned earlier, studies on the impact of salinity on infant and neonatal mortality in Bangladesh are virtually non-existent and this study intends to fill this gap.</mark>

# **III. Data Sources**

# _(i) Maternal and child health outcomes_

Data on maternal and child health outcome indicators were obtained from the 2000, 2004, 2007, 2011 and 2014 Demographic and Health Surveys (DHS) of Bangladesh, which are nationally representative covering a wide range of indicators on health and nutrition. In this study, we focused on two primary maternal and child health outcomes: infant mortality and neonatal mortality. Clusters represented the primary sampling unit for each BDHS year from which representative households were selected for interviews. Women between 12 and 49 years old from the selected households were interviewed for live birth and infant and neonatal mortality. The study units for this paper were children who were born within the three-year window prior to the interview year. Infant death is defined as a baby who died within 12 months after birth, and neonatal death is defined as a baby who died within two months after birth.

_(ii) Relevant covariates_ Women were also interviewed for the household socioeconomic characteristics of infant and neonates who died, birth order and maternal age during the time of delivery. Some sociodemographic variables based on a priori causal knowledge were included as covariates in this study, such as educational status of both parents, rural or urban residence of households and asset wealth indices calculated by principle component analysis. Other relevant information collected from each BDHS household survey were primary source of drinking water, sanitation, geographical division and river basins for the BDHS clusters.

# _(iii) Salinity and drinking water_

Groundwater EC point data were collected from two sources. First, we collated groundwater EC data from a monitoring network of 461 boreholes under a regional-scale hydrogeology study conducted in 19 coastal areas by the Bangladesh Water Development Board (BWBD) (Zahid et al., 2013; Zahid et al., 2016). Secondly, we digitized and georeferenced the contoured map of groundwater EC (Rahman and Ravenscroft, 2003) for shallow aquifers (<150 m bgl) and extracted EC point data at 102 locations predominantly in the northern part of Bangladesh. We then interpolated the point EC data (n=563) for the entire country using the Inverse Distance Weighting (IDW) algorithm in the ArcGIS environment, after 7
Moreover, the importance of unpaved roads should ,rot be underestimated. For example, in many agricultural areas, unpaved roads are the feeder system critical to the marketing of the agricultural surplus which indirectly supports the higher productive urb. l economy. The density of unpaved roads was not included in the above equations because of its high correlation with the density of paved roads. It seems therefore prudent to interpret LPR as a proxy for a countty's road stock, both paved and unpaved.

# Time-Series Analysis of U.S. Data

A vast amount of historic data is available on the road network and economy of the United States (10, 11). By carrying out a time-series analysis of U.S. data from 1950 to 1988, we found a significant positive relationship between per capita GNP (PGNP, in $1,000/inhabitant, using 1982 constant dollars) and density of paved roads (LPR, in km/i,000 inhabitants):

# PGNP = -3.39 + 1.24LPR

with an R squared value of 0.93; the number of degrees of freedom is 37, and the<sup>t-</sup> statistic of the coefficient is 21.4 (Figure 3). The intercept (i.e., -3.4) in the above equation is difficult to interpret. However, a null GNP is well beyond the inference space. Moreover, if we force the equation through the origin, the resulting regression equation is still signiiicant: PGNP = 0.97LPR, with an R squared of 0.88.

An interesting exercise consists in running regressions between PGNP and LPR using different time lags: we found the highest correlation existed when PGNP for a given year was associated with LPR four years earlier (Figure 4). This seems to indicate that paved roads had an effect on GNP, but there was a time lag of about four years between construction and ultimate impact. This four-year time lag<sup>is</sup> in broad agreement with the "half a decade" lag period observed by Aschauer (16). Aschauer has shown that productivity (i.e., output per unit of private capital and labor) is positively related to government spending on infrastructure, including roads. Analyzing data from the United States for the period 1949 to 1985, he observed<sup>that</sup> underinvestment in infrastructure started in about 1968, and the effects of deterioration became evident half a decade later, when a productivity slump<sup>began in</sup> the U.S.

It should be noted, however, that the above result was obtained for only one country, and using only one independent variable in the equation. This<sup>is an area</sup> specifically recommended for further research, in that similar exercises could be carried out for other countries with inclusion of additional explanatory variables<sup>in the</sup> equations.

6
_For labor or for divorce?_ positive legislation reform on women’s labor outcomes (Uribe et al., 2019). For this reason, we view the overall legislative framework in conjunction with the introduction of unilateral divorce provisions. Figure B.1. in the Appendices, illustrates the relationship between legal gender discrimination, measured by the World Bank Women, Business, and the Law index (WBL), and female labor force participation rate around the (a) World and in (b) MENA region countries. While in panel (a) this relationship is upward slopping (higher WBL index score has a positive relationship with FLFP rate), this relationship resembles an inverted U curve for MENA countries (panel b). This finding is consistent with literature investigating the effect of increasing mandated maternity leave on the employment and wage penalty women pay (Pettit & Hook, 2005; Akgunduz and Plantenga, 2013; Budig, Misra, and Boeckmann, 2012). It shows that the unintended results of reducing legal gender discrimination are explained by cultural resistance, particularly from husbands who see their utility cost increase from their wives’ labor, but also by the wage costs employers incur (Espino and Salvador, 2014). This further reinforces the argument that passing legislation targeting several areas of gender-based discrimination increases the chances of changing cultural norms and inching towards desired outcomes. Thus, to include a more accurate and context specific dimension for legislative effects, a variable WBLaw_index is taken into account that takes the value of the calculated score of the World Bank’s Women, Business, and the Law Index for corresponding year observations in our samples.

The third and sixth columns of each table below report coefficient estimates from specifications that include these controls as well as the above-mentioned time trends. As for the case of Morocco, the results indicate positive and statistically significant results in the first period (1992 to 2007) but negative effects of the legislation in the second period (1992 to 2014). These results may be backed by two mechanisms. The first pertains, on one hand, to what Klasen (2019) described as the combination of strong stigma from educated women against working jobs below white collar services. The second, on the other hand, completes the first with an added layer of transfer of bargaining power to women (Stevenson and Wolfers, 2007) favoring their position within the marriage. Combining these two mechanisms, we conclude that if labor market conditions are not favorable, especially to educated women, marriage can become a more desired option (Verme et al., 2016). Considering these results, it can be stated that while the legislation reform might have had a positive effect on women’s labor outcomes initially, as was also measured by HassaniNezhad and Sjögren (2014), this effect dissipated over time, for the case of Morocco. Further 23
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19
**<u>Table A.5. RDD regressions of households’ income and poverty using VHLSSs 2004 to 2008</u>**

|||||Dependent<br>|variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|Poor<br>households<br>(using<br>constant<br>povertyline)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program|-0.1970**|-0.9491|-0.6676|0.1892|-0.4467|-0.5750**|-0.2603|0.1224*|
||(0.0931)|(0.5946)|(0.4126)|(0.2526)|(0.4421)|(0.2485)|(0.2403)|(0.0644)|
|(Poverty rate – 50)|0.0050|0.0938*|0.0168|-0.0284|-0.0102|0.0509*|0.0047|-0.0012|
||(0.0101)|(0.0558)|(0.0498)|(0.0337)|(0.0494)|(0.0264)|(0.0267)|(0.0065)|
|Program * (Poverty rate – 50)|-0.0083|-0.0709|0.0123|0.0249|0.0300|-0.0369|-0.0156|0.0039|
||(0.0108)|(0.0626)|(0.0525)|(0.0341)|(0.0535)|(0.0295)|(0.0283)|(0.0072)|
|Age of household heads|0.0210***|0.0927***|0.0549**|0.1233***|-0.0396***|-0.0263**|-0.0260|-0.0140***|
||(0.0041)|(0.0239)|(0.0223)|(0.0191)|(0.0150)|(0.0126)|(0.0207)|(0.0026)|
||-0.0002***|-0.0013***|-0.0007***|-0.0013***|0.0006***|0.0005***|0.0007***|0.0001***|
|Age squared of household heads|(0.0000)|(0.0002)|(0.0002)|(0.0002)|(0.0002)|(0.0001)|(0.0002)|(0.0000)|
|Gender of household head|-0.0382|-0.4097***|0.3645**|0.5771***|-0.4978***|-0.1663*|-0.0721|0.0338*|
|(male=1, female=0)|(0.0260)|(0.1360)|(0.1721)|(0.1710)|(0.0909)|(0.0942)|(0.1566)|(0.0173)|
|Ethnic minorities (yes=1,|-0.3843***|-0.5678*|-1.1928***|0.7370***|-0.8648***|0.2361**|-1.0454***|0.2432***|
|Kinh=0)|(0.0561)|(0.3085)|(0.2192)|(0.1521)|(0.1921)|(0.1195)|(0.1267)|(0.0360)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.7636***|3.3031***|1.3617**|4.2311***|5.2798***|1.1064***|1.8050***|0.7455***|
||(0.1263)|(0.8451)|(0.5453)|(0.5251)|(0.4835)|(0.3660)|(0.4543)|(0.0861)|
|Observations|12,154|12,154|12,154|12,154|12,154|12,154|12,154|12,154|
|R-squared|0.178|0.032|0.039|0.102|0.059|0.039|0.111|0.132|

Note: This table reports district fixed-effect RDD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

55
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24
came from in-budget revenues, from transfers and the expanded agricultural tax surcharge. All the rest – nearly RMB 60 – came from fees and rental incomes from collective assets. In 2000, before the rural fee reform abolished the _tiliu_ (village fee assessment), the portion of nonbudgetary revenues was even higher.

**Table 15.  Village Revenue by Categories, 2000 and 2004**

|in|RMB per capita|2|000|2|004|
|---|---|---|---|---|---|
|||Mean|% of total|Mean|% of total|
|1|Transfers from above|4.6|5.9|17.8|21.7|
|2|Regular fee assessments(_tiliu_)|30.0|38.1|0.2|0.2|
|3|Surcharges on agricultural tax|0.2|0.2|4.7|5.7|
|4|Contractpayments for land|8.1|10.3|12.0|14.6|
|5|Contractpayments for enterprises|5.8|7.4|7.9|9.6|
|6|Land and asset sales|15.8|20|23.5|28.6|
|7|Other revenues|14.3|18.2|16.0|19.5|
|8|Total revenues|78.7|100|82.1|100.0|

Note: “Other revenues” include income from: administration fees charged to village enterprises; shared profits with village enterprises; fines for violating the birth control policy; fines for other penalties; interest, rents, and repayment of credits; collective operations; etc. Source: 2005 Village survey data.

Villages vary tremendously in terms of their financial strength, investments, and provision of public goods and services. This is shown in Table 16. In 2004 per capita village revenues were highest in Jilin and Jiangsu, with RMB 156 and RMB 110, respectively. Villages in these two provinces also received the highest transfers. Transfers to other provinces were much smaller, with total village revenue in Sichuan amounting to only RMB 23 per capita. Similarly, per capita capital investments in villages ranged widely, from RMB 353 in Jiangsu to only RMB 63 in Hebei, and per capita village debt was as large as RMB 542 in villages in Shanxi and only RMB 91 in villages in Sichuan.

30
the U.S. manufacturing producer price index. The results show persistent effects of shocks to world grain prices on cereal price inflation as illustrated by the positive and significant coefficients on the fourth and twelfth lags. The results also show a positive effect of a change in U.S. manufacturing producer price on non-food price as well as a positive effect of a change in world food price on overall CPI.

Looking more specifically at oil price impacts, an increase in the oil price affects non-food prices and overall CPI in the 11<sup>th</sup> month after it has taken place on international markets. This long delay may be explained by the fact that retail oil prices in Ethiopia are controlled by the government, and price increases may not be immediately transmitted to consumers. For example, during the COVID-19 crisis, retail fuel prices in Ethiopia were maintained at the same level even though international oil prices collapsed, with the gains accruing to the public stabilization fund. As prices started to rebound during the last months of 2020, authorities increased retail prices in January and February 2021 by about 20 percent. This mitigates the direct link between the domestic and international oil markets. Keeping the exchange rate constant, a 1 percent increase in the international price of oil leads to a 0.01 percent rise of the non-food price and a 0.02 percent increase of the all-items CPI.

Overall, the results from the inflation models including the fiscal sector confirm the existence of statistically significant long-run effects of disequilibria in the fiscal sector on all inflation measures except non-food prices. Moreover, government borrowing has inflationary short-run effects (except for non-food inflation). This implies that fiscal imbalances as well as the way fiscal deficits are financed need to be considered in any attempt to understand the drivers of inflation in Ethiopia.

In summary, the models specified in this study perform well in explaining short-run and long-run inflation dynamics in Ethiopia over the sample period. The key regression results hold when we substitute the monetary sector by the fiscal sector to explore the impact of budget deficits and public sector borrowing on inflation. The estimated models explain over 80 percent of the variations in food inflation and overall inflation as shown by the R-squares. The explanatory power is lower for the cereal inflation model, with R-squares of 51-57 percent. This is probably because cereal inflation is more exposed to shocks, and the models have not been able to sufficiently capture the impact of these shocks even with the range of dummies included for this purpose. Overall, the behavior of non-food inflation has proven to be more difficult to model as exhibited by the low R-square and fewer significant explanatory variables, which is consistent with the observed smoother pattern over the sample period, for reasons discussed earlier.

22
**-** **<u>Table 7: Placebo regressions estimated program effects on area cultivated with winter crops</u>**

||<br>**Overall effect**|<br>**Heterogeneity by**<br>**Front distance**<br>**Farm size**|**Formality**|
|---|---|---|---|
|PSG|0.0848|||
||(0.1118)|||
|PSG * < 50 km to front||-0.0845||
|||(0.1800)||
|PSG * 50 - 100 km to front||-0.0364<br>(0.1928)||
|PSG * > 100 km to front||0.2243<br>(0.1447)||
|PSG * farm size < 20 ha||-0.0028||
|||(0.0370)||
|PSG * farm size 20 - 50ha||0.2114<br>(0.3382)||
|PSG * farm size > 50ha||0.3757<br>(0.6576)||
|PSG * Individual|||0.0344|
||||(0.0774)|
|PSG * FOP|||0.0354<br>(0.2457)|
|PSG * Legal entity|||0.2859<br>(0.2803)|
|No. of obs (farms)|201,295|201,295<br>201,295|<br>201,295|
|<br>R-squared|0.6940|0.6941<br>0.6941|0.6941|
|Mean of dep. var.|5.3094|5.3094<br>5.3094|5.3094|
|SD of dep. var.|10.2827|10.2827<br>10.2827|10.2827|

_Note:_ Results are based on weights derived using entropy matching. Dependent area is the area cultivated with summer crops in panel A and the area cultivated with winter crops in panel B. For summer crop regressions in panel A, the level and square of weather variables, in particular GDD (>5°C), total rainfall, and the number of zero precipitation days during the planting and growing seasons, winter crop area, and a constant are included but not reported. In panel B, the cut-off for GDD is >0°C and all weather variables (and their squares) are for planting, dormancy, and growing seasons. Robust standard errors clustered at rayon level in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

23
<!-- Start of picture text --> 7,000 S 450<br>$ 400<br>6,000<br>$350<br>5,000<br>S 300<br>4,000 $ 250<br>GWh USD/MWh<br>3,000 $ 200<br>$150<br>2,000<br>S 100<br>1,000 $50<br>- S-<br>12 3 4 5 6 7 8 9 10 1112 12 3 4 5 6 7 8 9 10 11 12<br>2015 2016<br>Ms Hydro) 06 Mm Gas) Mm Liquidfuels MmmmCoal —@®—Averagespot electricity price (USD/MWh)<br><!-- End of picture text -->
**Figure 5-B. Explanation of Differences in Informality, LAC Countries and Chile** Heritage Foundation Informal Market index (range 1-5: higher, more informality)

<!-- Start of picture text --> ARGENTINA BOLIVIA BRAZIL<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>COLOMBIA COSTA RICA DOMINICAN REPUBLIC<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>ECUADOR EL SALVADOR GUATEMALA<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>GUYANA HAITI HONDURAS<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>JAMAICA MEXICO NICARAGUA<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>PANAMA PARAGUAY PERU<br>(%) (%) (%)<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>URUGUAY VENEZUELA, RB Legend<br>(%) (%)<br>80 80 Law and Order<br>60 60<br>40 40 Regulatory Freedom<br>20 20 Education<br>0 0 Sociodemographic Factors<br><!-- End of picture text --> 28