rafmacalaba/datause-encoder-data huggingface dataset:
hypertension during pregnancy in Dacope Upazilla, Khulna. The study infers the impact of salinity on maternal health from three information sources: monthly measurements of salinity in shallow and deep groundwater tubewells at various sites in the Khulna region and along the Passur River during the period 1998-2000; urine samples and blood pressure measures for 343 pregnant Dacope women during the 2009-2010 dry season (October 2009 through March 2010); and hospital-based measures of the prevalence of hypertension for 969 pregnant women during the period July 2008 - March 2010. The study has three principal findings: (1) The salinity of tubewell water in the study area was far higher during dry season months in 1998-2000. (2) Pregnant women who drank tubewell water during the dry season of 2009-2010 had significantly higher urinary sodium than women from the sample group who drank rainwater during that period. However, the study does not establish a significant link between urinary sodium and hypertension. (3) Hospital-based blood pressure measures during wet and dry seasons during the period 2008-2009 revealed a significantly higher incidence of hypertension during the dry season.

This pioneering inferential study yields valuable insights into the relationship between salinity and maternal health in Bangladesh. However, its restricted geographic coverage, limited temporal span, and temporal mismatch of data sources have left a significant gap between its findings and the results that could be delivered by the longitudinal study that we described above.

# _<u>Extensions in This Paper</u>_

In this study, we attempt to narrow the research gap by mobilizing information that features a larger sample population, broader geographic coverage, a longer temporal span, and temporally-matched data. Monitoring household-specific drinking water and health indicators was beyond the scope of our research. Instead, we relied on (1) proxies for drinking water salinity: monthly spatially-formatted data from 41 soil salinity monitoring stations for the period 2001-2009, provided by the Bangladesh Soil 4
6 Table 2 Labor Force Participation Rates (%) of Older Workers **In** OECD Countrieh

|||M|en|Wo|men|
|---|---|---|---|---|---|
|Country|Year|Aae 55.64|Age 65+|Age 55464|Aae 65 +|
|Australia||||||
||1970|85.1|22.1|23.3|3.7|
||1975|78.8|16.7|23.7|3.9|
||1980|88.a|11.1|22.0|2.9|
||1985|60.4|8.9|19.3|2.0|
||1990|83.3|8.5|25.0|2.3|
|Austria*||||||
||1970|47.2|9.7|14.9|3.4|
||1975|36.8|7.0|13.0|2.8|
||1980|34.5|4.4|14.5|3.0|
||1985|19.1|3.7|8.7|1.6|
||1990|N/A|N/A|N/A|N/A|
|Canada||||||
||1970|84.2|22.6|29.8|5.0|
||1975|79.4|18.5|30.8|5.0|
||1980|76.2|14.7|33.7|3.3|
||1985|70.2|12.3|33.8|4.2|
||1990|64.9|11.4|35.7|3.9|
|Finland||||||
||1970|71.1|19.0|46.3|4.4|
||1975|62.3|10.3|44.4|2.8|
||1980|57.3|17.0|43.0|6.0|
||1985<br>|57.8<br>|10.6<br>|46.2<br>|4.8<br>|
||1990|45.4|7.9|39.7|2.9|
|France||||||
||1970|75.4|19.5|40.0|8.6|
||1975|68.9|13.9|35.9|5.8|
||1980|68.5|7.5|39.7|3.3|
||1985|50.1|5.3|31.0|2.2|
||1990|45.8|3.7|31.3|1.5|
|Germany||||||
||1970|82.2|19.9|29.9|6.5|
||1975|68.1|10.8|24.8|4.5|
||1980|65.5|7.0|27.2|3.1|
||1985|57.5|5.2|23.9|2.5|
||1990|N/A|N/A|N/A|N/A|
|Ireland||||||
||1970|91.0|44.0|21.3|11.3|
||1975|83.8|28.2|20.9|7.2|
||1980|79.1|23.7|19.5|4.8|
||1985|77.7|19.0|18.3|3.6|
||1990|N/A|N/A|N/A|N/A|
|Italy*||||||
||1970|48.2|12.9|10.6|2.6|
||1975|42.4|10.4|8.5|2.1|
||1980|39.6|12.6|11.0|3.5|
||1985|38.2|8.9|10.5|2.1|
||1990|35.9|8.0|10.1|2.2|
|Table B.9: Fiscal (pro)cyclicality of VAT rates, whole sample|(2000-2016): Impacts of rigidity, fiscal spa|ce and business cycle||
|---|---|---|---|
|Dependent variable: Individual income tax|(1)|(2)|(3)|
|Cyclicality of real GDP|-0.105***|-10.364|-0.776|
||(0.036)|(10.232)|(0.848)|
|**Business cycle**||||
|Crgdp*good times|6725549.158*|8.833e+08|58844199.784|
||(3520752.871)|(1.607e+09)|(83654229.792)|
|Crgdp*good times*rigidity||-0.190||
|||(0.302)||
|Crgdp*rigidity||2,166.815||
|||(2,028.945)||
|Rigidity of public spending||0.004||
|||(0.011)||
|Crgdp*good times*fiscal space|||-0.003|
||||(0.004)|
|Fiscal space|||33.016|
||||(42.190)|
|Crgdp*fiscal space|||0.000|
||||(0.000)|
|good times|0.033|0.001|0.062|
||(0.123)|(0.151)|(0.125)|
|Constant|-0.293|20.710***|-0.411|
||(0.270)|(0.695)|(0.320)|
|Adjusted R-squared|1.000|1.000|1.000|
|Rmse|1.009|0.868|1.008|
|Joint significance (p-value)|0.012|0.413|0.000|
|CD test (p-value)|0.000|0.000|0.000|
|Order of integration|I(0)|I(0)|I(0)|
|Cluster|country|country|country|
|Covariates|Yes|Yes|Yes|
|Observations|823|410|820|
|# countries|50|26|50|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

38
1

# I. Introduction

Tnere is by now agreement among a large proportion of the economics profession that countries that have relied on "outward oriented" development strategies have done better over the medium and longer run than those countries that have adopted "inward looking" strategies. Recently, and to the surprise of many, even ECLA/CEPAL -- not exactly known for its endorsement of outward policies -- has recognized that the excesses of import substitution have been very costly for Latin America; some of its senior staff members have recommended that in the future export promotion should play a more central role in Latin American development policies (see Bianchi et al. 1987).

There seems to be relatively less agreement, however, on whother "trade liberalization" packages have played an important role in the performance of the outward orienred economies. For example, in a recent paper presl3nted at the World Bank-IMF Conference on adjustment with growth, Sachs (1987:' questioned the idea that trade liberalizations are indeed a required component of successful outward oriented strategies. Making reference to the experiences of the East-Asian countries -- Japan, Korea, Singapore, l'aiwan, and Hong Kong -- Sachs argues that these countries' success was to a large extent due to an active role of government in promoting exports in ar. environment where imports had not yet been fully liberalized, and where macroeconomic (and especially fiscal) equilibrium was fostered (see also Sachs, 1988).

Whether one agrees with Sachs depends on how outward orientation, export promotion and trade liberalization are defined. In fact, in crder to have a meaningful discussion about the role of trade regimes and commercial policies it is crucial to know exactly what we mean by each of these terms.
find that the elasticity of trade with respect to income and distance increases after disintegration. For example, the coefficient on distance more than doubles in magnitude from -0.42 to -1.16. This change is hardly surprising - pre-reform trade over long distances was heavily subsidized. The coefficients in the reform period are much closer to the coefficients typically found on estimates of the gravity model.<sup>4</sup> This suggests that gravity has set in and that trade patterns in the FSU are now determined in a similar way to the rest of the world.

Since GNP is not exogenous with respect to trade we also use population to instrument for GDP. The results are shown in Table 2. The coefficients on income are higher. The reorientation within Russia is still evident from the change in significance of the Russia dummy over time. Russian regions are significantly less likely to trade with one-another in the pre-reform period and this bias disappears in the reform period.

# 3.3 Allowing **for heterogeneity across countries**

Non-tariff barriers, currency variability, and differences in wages and prices across regions and republics may affect trade. To allow for this possible heterogeneity, we split the sample into two panels-pre-reform and reform-and use country fixed effects in the estimation. We exclude interrepublic trade from the sample and estimate equation (2) separately for the pre-reform and reform periods,

## _TRADE,j,t = at_ + _I3yij + 3lYit + 32Yjt + / 3 DISTij + uij,_

where _-yij_ is a country dummy. The dummy for Armenia, for example, is one when Armenia is either an exporter or an importer. Since inter-republic trade is excluded and all republics are dummied 4 Most comparable is the Canada U.S. study since trade in North America is also land-based trade. McCallum 1995 and Helliwell 1998 find the elasticity of trade to distance on Canada-U.S. data is between -1.23 and -1.62. Helliwell finds the elasticity on OECD trade to be between -0.87 and -1.02 in 1992.

11
Fifty-five percent of initiations in Mexico took place in only four years, from 1991 to 1994, the most intense period of trade negotiations and economic reform. In that period, Mexico became one of the five most active countries in the use of antidumping instruments in the world, together with the United States, Australia, Canada and the European Union.

**Figure 1 - Initiations in Mexico, 1987-2003**

<!-- Start of picture text --> 83<br>26 26<br>18<br>11 12 11 11 12 11<br>9<br>7 7<br>4 4 5 5<br>0<br><!-- End of picture text --> Source: 1986-2002, UPCI; 2003, DOF.

Figure 1 shows that antidumping proceedings in 1993 reached an unprecedented figure, never recorded again in the country. Of the 83 initiations reported in that year, 53% corresponded to two investigation groups or packages: the first one against imported goods from China, the socalled “China Package”; the second one, against steel products from several countries, proceedings known as “multi-country/multi-product steel cases.” Between 1992 and 1993, the pressure exerted upon the Mexican government by sectors significantly affected by the trade liberalization process was so strong that it began to jeopardize the credibility of the new trade defense institutions. This was threatening the national consensus reached for the conclusion and approval of the NAFTA, other international negotiations, as well as several economic reforms underway.

In its first activity report, the UPCI explained that the behavior of the economy had a direct impact upon the evolution and development of the system. “The most active years were those in which the reduction of the real exchange rate of the peso as well as the economic growth led 18
Transitions in and out of poverty are a function of individual and household characteristics and often reflect policies which support and protect households from falling below a certain welfare level when exposed to a variety of shocks. The following section looks into heterogeneity across households and describes patterns of economic mobility for different subgroups of the population.

# Structure of economic mobility

Transitions in and out of poverty show stark disparities for different groups of the population. Circumstances such as the place of residence, and the endowment including education determine the access to opportunities and thereby shape the probability of escaping poverty or remaining trapped in it. The following analysis looks into differences in patterns of economic mobility based on the characteristics of the household head.

Patterns of economic mobility show a divide between the capital city Yerevan, urban and rural areas in the country. The share of chronically poor households is highest in other urban areas outside the capital city Yerevan (Figure 7). Patterns of economic mobility for the three locations suggest that the distribution of households in chronic poverty is broadly similar to the distribution of poor households in the country.<sup>15</sup> As poverty has reduced for all three locations in the country between 2010 and 2015, net changes in poverty mask significant churning. Findings highlight the unstable path of poverty reduction as some households move above the poverty line whereas others fall behind.

Figure 7: Patterns of economic mobility for different locations in Armenia <!-- Start of picture text --> Panel A: 2004 to 2008 Panel B: 2010 to 2015<br>60% 70%<br>50% 60%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10% 10%<br>0% 0%<br>Yerevan Urban Rural Yerevan Urban Rural<br>poor-poor poor-nonpoor poor-poor poor-nonpoor<br>nonpoor - poor nonpoor - nonpoor nonpoor - poor nonpoor - nonpoor<br><!-- End of picture text --> _Source: Authors’ calculations based on ILCS 2004, 2008, 2010 and 2015._ 15 The household survey for Armenia (ILCS) is by design representative on the level of 10 marzes and Yerevan. Small area estimates of poverty produced for the poverty map in Armenia highlight the pronounced spatial dimension of poverty in the country and link some of these disparities to differences in endowment and physical access to opportunities across regions.

13
ble 5). We allow sequencing to occur over the 12-year period over 4 three-year liberalization steps. In the first step, tariffs on EU products are capped at 20%. The cap is reduced to 10% in the second step, 5% in the third, and in the final step, tariffs are eliminated completely.<sup>22</sup> We also assume the absence of an exclusion list at this point; the numbers shown should therefore be interpreted as an estimated upper bound on the projected impact of the agreement. It is important to remember, however, that TRIST has been designed with the specific task of providing policy makers with important insights into the short-term effects of trade reform. It has not been designed for making longer-term predictions about the economy wide impact of trade reform; TRIST only looks at the import side of the economy whereas trade reform will also have an impact on exporting sectors by reducing bias against exporting. For the sequenced scenario in particular, this implies that TRIST does not project the actual revenue impact throughout the implementation horizon. It rather shows how different steps during the phase-in period of the trade agreement compare in terms of short term revenue and import volume impacts.

The aggregate impact of the sequential implementation of the EUSAA on tariff revenue and overall trade tax revenue is very similar to the impact of the static scenario discussed previously, although we would point out that the final liberalization step generates, by far, the largest losses in revenue: This step leads to a tariff revenue reduction of 11.6% and a trade tax revenue fall of 6.5%, with the average collected tariff rate falling from 5.5% to 4.9%.

# **3.2.2 Changes to the import regime**

Having investigated the impact of the EUSAA, we now move to a broader analysis of the Syrian import regime which has undergone significant reform over the past decade. As a first step, we investigate the revenue implications of redesigning the tariff band structure. We assume that the new bands are defined in terms of statutory rates, with no tariff exemptions granted post-reform.<sup>23</sup> There are several options for tariff band structures that would reduce the number of tariff bands. We consider three possibilities. Our first approach collapses the existing bands into four bands by fixing all rates above 40% at 35%, all rates above 20% and below or equal to 40% at 30%, raising bands above 10% (5%) and below or equal to 20% (10%) to 20% (10%), and eliminating all nuisance tariffs completely; we denote this _Option A_ . The second approach likewise introduces four bands. It differs from the first option only in its treatment of tariffs lesser than or equal 20%.

> 22 Since we assume that collected tariffs are capped at these thresholds, this also implies that the caps already take tariff exemptions into account. 23 Alternatively, depending on the likely volume of tariff exemptions, post-reform tariff bands could also be set higher to obtain the same revenue impact.

17
dealing more directly with the farmers could eliminate a number of these problems. The e-Choupal was designed to facilitate this more direct connection.

The first step is identifying the location for the e-Choupal, the location acts as the hub with spokes reaching out to neighboring villages. On average 600 farmers from 10 villages within 5 km are served by one e-Choupal. Once the village is identified, a _sanchalak_ is selected, he is also a farmer (Annamalai and Rao 2003). The _sanchalak_ is the operator of the e-Choupal. The computer is placed inside the home of the _sanchalak_ and this farmer acts as the intermediary between local farmers and the e-Choupal. The _sanchalak_ is an important a vital piece that makes the e-Choupal successful who must be willing to accept the responsibility and have the entrepreneurial spirit to undertake the project. To insure their commitment the _sanchalak_ must take a public oath to serve the farming community, because of this the position the garners respect and prestige within the village (Bowonder et al. 2002).<sup>40</sup> Once installed, the _sanchalak_ accesses information from the e-Choupal regarding weather, new and best farming practices, and market price information, which is gathered from mandis. With this information the farmers are now capable of making an informed decision; they can either sell their produce to ITC or at the mandis. The price offered by ITC is based on the mandi’s closing price of the previous day, this price is the highest possible price, and it is reduced depending on produce quality. If a farmer chooses to sell to ITC he first brings a sample to the _sanchalak_ , who conducts a quality assessment using a check list (this provides transparency in pricing). The _sanchalak_ then gives the farmer a tentative price quote; from there the farmer proceeds to an ITC procurement hub with his produce. ITC’s goal is to have a hub within 30 to 40 km of every farmer. At the hub another quality test is undertaken, with price deductions resulting from the presence of foreign matter or moisture content, concepts that are well understood by the farmers (lab tests are not yet accepted by farmers). After inspection the produce is weighed using an

> 40 To install the computer in the sanchalak’s home ITC spends between $3,000 and $6,000, and about $100 per year to maintain it (Annamalai and Rao 2003). The set up includes insuring constant power supply (ITC may install solar panels if needed), telecom connectivity, and bandwidth. Along with the technical aspects, ITC also trains the sanchalak to use the e-Choupal and there is a 24 hour helpdesk available. Though the sanchalak makes a commission on every transaction processed through the e-Choupal, there are costs to the sanchalak, including power and phone lines which can run between $60 and $160 per year.

50
INTRODUCTION liii Table 10 Annual Population Increase and Growth<sup>Rate by Geographic Region and by Income Group,</sup> 198S-2100

|Region<br>or<br> <br>|Arc l p<br> <br>|opulati<br> <br> <br>|on<br>incre<br> <br> <br>|ase<br>(milt<br> <br> <br>|ions)<br>|<br>Ann<br> <br>|ual<br>grow<br> <br> <br>|th<br>rate<br> <br> <br>|(percen<br> <br> <br>|t)<br> <br>|
|---|---|---|---|---|---|---|---|---|---|---|
|income<br>group<br>|985-90<br>2|<br>000-05<br>2|<br>025-30<br>2|<br>50-75<br>21|00-25|1985-90<br>2|<br>00-05<br>2|<br>025-30<br>2|<br>050-75<br>2|<br>00-25|
|wortd|88.2|91.7|78.5|37.2|4.9|1.74|1.43|0.91|0.35|0.04|
|Less developed<br>(a)|81.7|87.4|78.2|37.7|4.8|2.11|1.70|1.08|0.41|0.05|
|More developed<br>(a)|6.5|4.3|0.3|-0.5|0.1|0.54|0.34|0.02|-0.04|0.01|
|Africa|18.2|26.0|30.0|19.4|2.3|3.02|2.79|1.81|0.77|0.08|
|East Africa|7.9|11.6|14.6|9.7|1.1|3.15|2.94|1.98|0.84|0.08|
|West Africa|6.8|10.1|11.7|7.9|1.0|3.10|2.93|1.86|0.81|0.09|
|North<br>Africa|3.5|4.3|3.7|1.8|0.2|2.65|2.21|1.26|0.47|0.05|
|America|10.9|<br>9.4|6.0|1.4|0.2|1.57|1.10|0.57|0.12|0.02|
|Latin<br>America<br>and Caribbea|n 8.5|7.6|5.7|1.5|0.2|2.02<br>|1.40<br>|0.80<br>|0.19<br>|0.02<br>|
|Northern<br>America|2.4|1.8|0.2|-0.1|0.0|0.87|0.59|0.e7|-0.04|0.00|
|Asia|55.4|53.9|41.9|16.5|2.2|1.87|1.42|0.84|0.28|0.04|
|East and Southeast<br>Asia|26.3|21.8|13.5|3.0|0.5|1.53|1.04|0.53|0.11|0.02|
|South<br>Asia|25.6|27.7|23.7|10.7|1.5|2.29|1.82<br>|1.09<br>|0.39<br>|0.05<br>|
|Southwest<br>Asia|3.5|4.4|4.7|2.7|0.2<br>|2.81<br>|2.39<br>|1.57<br>|0.64<br>|0.05<br>|
|Europe and U.S.S.R.|3.3|2.0|0.4|-0.1|0.1|0.42|0.25<br>|0.05<br>|-0.02<br>|0.01<br>|
|Oceania|0.4|0.4|0.2|0.1|0.0|1.63|1.21|0.49|0.16|0.02|
|Income group(b)|||||||||||
|Low|58.7|63.1|56.4|27.8|3.8|2.05|1.67|1.06|0.42|0.05|
|Lower-middle|13.6|14.4|12.9|5.7|0.6|2.23|1.74|1.09<br>|0.38<br>|0.04<br>|
|Upper-middLe|8.1|8.2|7.0|3.0|0.3|1.84|1.45|0.91<br>|0.32<br>|0.01<br>|
|High|4.4|3.3|0.2<br>|-0.2<br>|0.0<br>|0.57<br>|0.39<br>|0.02<br>|-0.02<br>|0.01<br>|
|;onreporting nomeffber|3.3|2.7|2.1|0.9|0.1|0.89|0.65|0.44|0.17|0.03|

a. "More developed" comprises Europe, U.S.S.R.,<sup>Northern</sup> America (United States and Canada), Australia, New Zealand, and Japan. "Less de,,-loped" comprises the rest<sup>of the world.</sup> b. Criteria for income groups are given in World Bank 1989. See listing in Exhibit 1.
While only a minority of our sample exhibits “present”-biased static preference reversals, the correlation between these reversals and revision of prior choices toward sooner is relatively large. In addition, with considerable statistical precision, we find no evidence that respondents’ revisions tend to move in the direction of their spouses’ preferences for such revision. Similarly, though with less precision, we find no evidence that mistakes or shocks are important predictors of the decision to revise prior choices about intertemporal allocations.

These results thus suggest cautious optimism about the prospects for improving the lives of the very poor in developing countries by alleviating their problems of self-control. By providing evidence of the relative importance of time-inconsistency, the results support the view that, if we privilege an individual’s preferences at moments relatively far from the present, the benefits of commitment may be substantial and the costs, in terms of reduced flexibility, may be slight. This view should be tempered, however, by two important caveats. First, our findings show that “present”-bias, as evidenced by static preference reversals, is far from ubiquitous in this population. Many of the participants in the experiment exhibited, at most, just a modest tendency to be “present”-biased. Policy design must take account of this heterogeneity; efforts to help some with their legitimate self-control problems must avoid saddling others with commitments they do not need. Second, and related, our experiment was conducted during the lean season when little income is generated. This makes ours an unlikely context for finding that income fluctuations influence revision. It is possible that income shortfalls may influence revision behavior in other parts of the Malawian agricultural year, such as in the post-harvest months. Our findings should therefore be tested for robustness using similar research designs in other time periods and contexts.

# **Appendix A: Variable definitions**

The key dependent variable we analyze is _change in sooner allocation upon revisiting (MK)_ , which is respondent’s allocation to later period (t=91) in revisit survey minus allocation to later period (t=91) in baseline survey. All other variables are from either the baseline survey, the revisit survey, or from administrative (project) data.

# A.1 Variables collected in baseline survey

_Present-biased ratio_ is fraction of choices where allocation to sooner in near period is more than 100MK higher than allocation to sooner in far period (comparing choices in near and far periods for same interest rate). In all regressions this variable excludes the implemented interest 32