rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1 Locations of surveyed villages Table 1 Average land size (ha) in 2007

|<br>|Crop income>0<br>in2007 and 2010|
|---|---|
|All sample provinces|1.088 (1.429) 1.170 (1.417)|
|Lampung|1.324 (1.256) 1.342 (1.213)|
|Central Java|0.366 (0.448) 0.401 (0.535)|
|East Java|0.519 (1.499) 0.471 (0.410)|
|NTB|1.005 (1.125) 1.120 (1.271)|
|South Kalimantan|1.249 (1.264) 1.267 (1.024)|
|South Sulawesi|1.063 (1.068) 1.111 (1.098)|
|North Sulawesi|1.626 (2.472) 2.201(2.816)|

Numbers in parentheses are standard deviations.

17
- Andres, L., Biller, D. and Dappe, M.H. (2013a). Reducing Poverty by Closing South Asia’s Infrastructure Gap, The World Bank, Washington, DC.

- Andres, L., Biller, D. and Dappe, M.H. (2013b). Infrastructure Gap in South Asia: Infrastructure Needs, Prioritization, and Financing, The World Bank, Washington, DC..

- Belyaev, L.S. (2011). Electricity Market Reforms: Economic and Policy Challenges, Springer.

- Bhattacharya, S. (2007). Power Sector Reform in South Asia: Why Slow and Limited So Far?, _Energy Policy_ , Vol. 35(1), pp. 317-332.

- BPDP (2014). Annual Reports 2012-2013, Bangladesh Power Development Board, Bangladesh.

- Brady, D. (2014). Completion of the First Cross-Border Power Interconnection between Bangladesh and India, February Press Releases, SIEMNES, Germany.

- CBSL (2013). Economic and Social Statistics of Sri Lanka 2013, Central Bank of Sri Lanka, Sri Lanka.

- CEA (2014). Executive Summary: Power Sector March 2014, Central Electricity Authority, India.

- Chaturvedi, S. (2013). India Signs Pact to Build Power Plant in Sri Lanka, India News, The Wall Street Journal, October.

- Cropper, M. Malik, K. Limonov, A.  and Singh, A. (2011). Estimating the Impact of Restructuring on Electricity Generation Efficiency: The Case of the Indian Thermal Power Sector, NBER Working Paper No. 17383

- Dossani, R. (2004). Reorganization of the Power Distribution Sector in India, _Energy Policy_ , Vol. 32, pp. 177-189.

- Dubash, N. and Rao, D.N. (2008). Regulatory Practice and Politics: Lessons from Independent Regulation in Indian Electricity, _Utilities Policy_ , Vol. 16, pp. 321-331.

- Ebinger, C. K. (2010). Energy and Security in South Asia: Cooperation or Conflict?, Brookings Institution, Washington DC.

ERLD (2014) Annual Report 2013-14, Eastern Regional Load Despatch Centre, Kolkata

- ESMAP (2010). Regional Power Sector Integration: Lessons from Global Case Studies and a Literature Review, Energy sector Management Assistance Program, Briefing Note 004/10, The World Bank, Washington, D.C.

- Ghosh-Banerjee, Sudeshna, Douglas Barnes, Bipul Singh, Kristy Mayer, and Hussain Samad. (2015). Power for All: Electricity Access Challenge in India. A World Bank Study, World Bank, Washington, DC.

- Glachant, J.M. and Levque, F. (2009). Electricity Reform in Europe: Towards a Single Electricity Market, Edward-Elgar Publishing.

GoB (2004). Bangladesh Economic Review 2004. Government of Bangladesh, Dhaka.

23
**Table 3.3.Share of regional production value in national production value, 2003 (Agriculture only)**

||**WEST**|**CENTRAL**|**EAST**|**SE_ANATOLIAN**|**LSCB**|
|---|---|---|---|---|---|
|TOTAL CROPS|0.51|0.25|0.08|0.11|0.05|
|FIELD CROPS||||||
|Wheat|0.34|0.36|0.07|0.15|0.08|
|Barley|0.30|0.40|0.10|0.19|0.01|
|Maize|0.41|0.14|0.09|0.05|0.30|
|Other grains|0.52|0.42|0.04|0.01|0.01|
|Pulses|0.28|0.35|0.07|0.29|0.01|
|Tobacco|0.73|0.15|0.04|0.09|0.00|
|Sugar beet|0.23|0.63|0.13|0.00|0.00|
|Cotton (raw)|0.47|-|0.00|0.46|0.07|
|Oil seeds|0.77|0.08|0.01|0.02|0.13|
|Potatoes|0.29|0.53|0.16|0.00|0.01|
|VEGETABLES|0.65|0.20|0.03|0.07|0.04|
|FRUITS|0.64|0.16|0.09|0.07|0.04|
|NUTS|0.31|0.18|0.30|0.21|0.00|
|OTHER CROPS|0.46|0.44|0.02|0.06|0.02|
|ANIMAL PROD.|0.51|0.25|0.17|0.05|0.02|
|Milk|0.38|0.28|0.26|0.06|0.02|
|Cattle meat|0.50|0.27|0.17|0.06|0.00|
|Other big animal meat|0.55|0.18|0.08|0.18|0.02|
|Poultry and eggs|0.74|0.22|0.03|0.01|0.01|
|Otheranimalprod.|0.38|0.19|0.34|0.06|0.03|

_Source: TurkSTAT (2003)_ The institutions sector (or, block) of the economy includes households, the government and the Water User Associations (WUA). Households are disaggregated into rural and urban households. Rural households are further disaggregated among each other according to their geographical location. Hence in TACOGEM-W, there are five rural household and one urban household type (urban household defined at the national level). Households earn income from factors of production (land, labor, capital), engage in expenditures on various commodities, save, and also pay taxes to and receive transfers from the government. The identification of rural household types links to the variation in resource endowments common to agriculture.

25
Equation (5) shows that, once we control for the share of materials in total revenue P<sup>M</sup> Mi=PYi, factors that do not a¤ect the share of imported materials in total materials will not a¤ect a ...rm’s DVAR. This is an accounting identity, independent of the choice of production functions. It highlights that in order to understand a ...rm’s DVAR, one should focus on the determinants of the share of imported materials in total materials. In Section 5, we will develop a simple but general model that features a translog cost function to formally study these determinants.<sup>19</sup>

# 3 From Firm DVAR to Industry and Aggregate DVAR

Inferring the DVAR of an industry or aggregate exports from ...rms’DVAR is straightforward. If ...rms only engage in direct trading (i.e. do not import or export for other ...rms) and only produce in one industry, then we can compute the DVAR of industry j as follows: where �j is the set of ...rms in industry j. Industries are de...ned according to the industry classi...cation by the United Nations.<sup>20</sup> By construction, the DVAR of industry j is a weighted average of the DVAR of all ...rms in industry j with weights equal to the export shares of the ...rms. Likewise, we can sum up all industry imports and exports ...rst and then compute

> counting issue when we measure DVA for non-processing and aggregate exports.

> 19To show that our main theoretical results are not speci...c to the functional form choice, we also solve for a model that features a Cobb-Douglas production function in the Appendix.

> 20See http://unstats.un.org/unsd/tradekb/Knowledgebase/HS-Classi...cation-by-Section for the UN industry classi...cation. There are originally 20 sectors in the UN list. Sectors 1-3, which are agricultural sectors, are excluded since we cannot match most of the transactions to the manufacturing survey data. Sector 5 - Mining and Sector 19 - Arms and Ammunition are excluded for the same reason. Examples of a sector include Chemical Products (HS2 = 28-38), Textiles (HS2 = 50-63), Footwear and Headgear, etc. (HS2 = 64-67), and Machinery, Mechanical, Electrical Equipment (HS2 = 84-85).

9
production of cut flowers in 1975, the last year concantration data were reported. Thus, from the 19509 through the mid-1970s, a few large establishments, particular.y in California, increasingly displaced the small, family-operated establishments in the North.

It is useful to note that the changes in the U.S. cut flowers industry from the 1950. to the early 1970s occurred prior to the emergence of any import competition. The first imports occurred in 1966, but by 1970 their share of the U.S. market was only 1.1 percent. We now turn to the emergence of import competition.

The rise of Colombian growers<sup>5</sup> During the late-1960s, cut flower production shifted location to Colombia. The shift was an extension of the regional shifts that had occurred in the U.S. The development of commercial air transportation opened up Colombia's attractive environmental and labor conditions to flower growing. The shifts were so related that one of the first Colombian cut flower enterprises was started by four Americans, one of which was a California grower concerned with rising land and fuel costs in the United States. Even the order in which flower types were introduced into Colombia was the same. Carnations and chrysanthemum, flowers which are sturdier and technically less demanding to produce, were introduced first and later followed by roses, which are more fragile and more complex to produce.

# _A. The right combination of factors_

Colombia was an ideal location for the growing of cut flowers. It had near perfect environmental conditions and an abundance of le.nd and unskilled labor. The climate in the plateau region surrounding Bogota (Savana de Bogota) has year-round moderate and unvarying temperatures, 12-nour days, and high light intensity. As a result, high quality flowers can be grown yearround without expensive greenhouses and without incurring costs for heating, 6
# **2.2 A New Policy Database**

## **2.2.1 Information on Policy Barriers to Trade in Services**

Borchert, Gootiiz and Mattoo (2014) describe a project to collect primary data on policies affecting international trade in services. The resulting Services Trade Restrictions Database contains information on legal provisions affecting services trade and investment, including by establishing a commercial presence abroad. It is thus ideally suited to be matched with data on actual cross-border investment flows, variation in which could be expected to reflect the impact of policy barriers.

The new database covers the following five major services sectors: financial services (banking and insurance), telecommunications, retail distribution, transportation<sup>5</sup> and professional services, with each of these broad sectors further disaggregated into subsectors. It covers a total of 103 economies, of which 79 are developing countries and 24 OECD countries, representing all the world’s regions and income groups. First-hand information from developing countries was collected by administering a survey instrument whereas information for OECD countries was obtained from publicly available sources.<sup>6</sup> To the best of our knowledge, no other data source provides comparable information on barriers to services trade in a consistent manner for such a wide range of services sectors and countries.

The primary focus of the database is to gather information on policies and regulations that potentially constitute a discriminatory barrier for foreign services providers, as well as certain key aspects of the regulatory environment.<sup>7</sup> Regulatory measures affecting foreign investment are organized along the following broad categories:

> 5Regarding policies governing cross-border trade in international air passenger transportation services, the Database draws on the WTO’s QUASAR database since it represents the most comprehensive source currently available on bilateral air services agreements, covering over 2000 agreements.

> 6To ensure data accuracy, all policy information has been reviewed by government officials, though not all countries eventually responded to the vetting request.

> 7For every service sector included, the database covers the most important mode(s) of supplying that particular service, i.e. cross-border delivery or the movement of a natural person, in addition to establishing commercial presence. In this paper we focus predominantly on measures affecting foreign investment and include other measures only to the extent that they can be expected to have a bearing on M&A capital

7
Our cross-section results are not as robust as Rodrik’s, as we find that trade openness and external risk are positively associated with government employment in only two of the six specifications, while the GDP per capita is the only significant variable, in addition to regional dummies (Table 4).

Table 4: Cross section for 2005: Rodrik’s framework

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_VARIABLES_|Central|General|Public|Central|General|Public|
||Gov.|Gov.|sector|Gov.|Gov.|sector|
|_log GDP per capita_|0.268|0.260**|0.298***|0.622**|0.258**|0.370***|
||(0.220)|(0.101)|(0.095)|(0.277)|(0.109)|(0.101)|
|_log urbanization_|-1.225|-0.150|-0.118|-1.191|-0.066|-0.135|
||(0.878)|(0.338)|(0.249)|(0.997)|(0.346)|(0.242)|
|_Trade openness_|0.006***|-0.000|0.001||||
||(0.002)|(0.001)|(0.001)||||
|_External risk_||||0.078|0.009|0.037*|
|||||(0.054)|(0.021)|(0.019)|
|_East Asia & Pacific_|0.831|-0.656***|-0.764***|0.656|-0.812***|-0.629***|
||(0.646)|(0.201)|(0.190)|(0.728)|(0.240)|(0.196)|
|_Sub-Saharan Africa_|-0.701|-0.673**|-0.569*|-0.465|-0.256|0.133|
||(0.686)|(0.283)|(0.328)|(0.812)|(0.310)|(0.421)|
|_Constant_|-0.906|-3.846***|-4.236***|-4.358|-4.217**|-4.958***|
||(3.942)|(1.159)|(0.762)|(4.832)|(1.569)|(0.842)|
|_Observations_|31|52|57|30|48|52|
|_R-squared_|0.407|0.423|0.430|0.260|0.311|0.389|
|_Standard errors in par_|_entheses_||||||
|_*** p<0.01, ** p<0.05_|_, * p<0.1_||||||

When the regression is estimated with pooled country data over time, trade openness and external risk are not robust and are only statistically significant for central government employment (Table 5).

Table 5: Pooled OLS: Rodrik’s framework

|_(1)_<br>_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|

13
2011 interview, eight subdivisions in the coastal area had positive predicted number of infant deaths. Among the 0.13 million infant deaths and 0.11 million neonatal deaths in these high salinity exposure subdivisions, 3.30% and 3.20% of them were caused by salinity on average respectively. Khulna and Patuakhali districts has the higherst with 11.93% and 8.36% infant deaths and 9.42% and 10.41% neonatal deaths respectively caused by high salinity level. During the three years prior to the interview year of BDHS 2014, six coastal subdivisions were on the list of positive predicted number of infant deaths with Barisal and Patuakhali districts having 19.76% and 11.71% of infant deaths and 15.38% and 16.33% of neonatal deaths respectively caused by high salinity exposure. The reason for the dramatic increase in some subdivisions between the 2011 and 2014 BDHS surveys needs further analysis. Overall, high salinity exposure induced mortality declined slightly from 1.59% to 1.24% for infants and 1.41% to 1.12% for neonates.

# **VI. Conclusions**

This study used non-parametric regression without assuming any functional forms to explore the relationship between drinking water salinity and infant and neonatal deaths. This was followed by three OLS approaches using salinity as continuous exposure, categorical exposure, and Restricted Cubic Splines specification. The RCS model specification explained the relationship with a concave shaped curve of marginal effects and a U-shaped curve of predicted values.

Overall, our findings indicate that both freshwater and severe-saline water are associated with higher infant and neonatal mortality. There is also a strong U-shaped relationship between salinity and infant and neonatal mortality, suggesting the harmful effects of freshwater and severe saline exposure, and protective role of mild salinity on infant and neonatal health across unadjusted and fully adjusted covariate models. Although overall effects were attenuated in full-multivariable adjustments, the coefficients of salinity from categorical and RCS models were slightly smaller for neonatal death relative to infant death, which suggests that larger harmful or protective salinity exposure was channeled through the intake of salty water by mothers during pregnancy. This may be because mothers had a lower intake of essential minerals (e.g. calcium and magnesium) through drinking freshwater whereas mothers had excessive concentrations of sodium through drinking severe-saline water. Both drinking water calcium and magnesium were associated with lower risks for infant and neonatal mortality. It is plausible that women drinking water with high EC had higher intake of salubrious essential minerals that have been transferred to the fetus before birth, or through breast milk during the nursing period. However, drinking water with high levels of salinity was associated with higher infant and neonatal mortality. The relationship between groundwater sodium and EC followed an almost linear upward trend, suggesting that severe-saline water drinkers have very high sodium intake.

12
- 21

The above view about the tradeoff<sup>between aggregation and the</sup> redistributive potential of commodity taxation was<sup>confirmed by aggregating</sup> the 35-sector model into 5 sectors<sup>and observing that the sectoral rankings</sup> were almost perfectly correlated even<sup>for v-</sup> -5. This finding confirms that even moderate redistributive concerns<sup>are enough to make shifting</sup> assumptions matter, the greater the degree<sup>of sectoral disaggregation. In</sup> practice, the number of commodities<sup>and sectors is very large and the</sup> resulting classifications facing tax policy makers very disaggregated, thus highlighting the need for careful attention<sup>to the underlying determinants</sup> of shifting in incidence analysis.

Implication for Tax Policy **Analysis** The analysis with the Bangladesh<sup>tax policy model has shown that</sup> judgments on desirable directions of tax reform based on revenue-cum-incidence analysis can be quite sensitive to<sup>the modelling of</sup> the degree of substitution among<sup>factors in production and on the</sup> conditions prevailing in factor markets<sup>in the economy.10</sup> Moreover, this conclusion applies to comparisons between<sup>different model specifications</sup> that, far from being extreme, implicitly<sup>underpin the shifting assumptions</sup> actually used in partial equilibrium-based empirical work.

Given our uncertainty about the<sup>true values of some of the</sup> underlying elasticities, models such<sup>asthe one developed here should be</sup> used to identify revenue-raising tax packages that are broadly satisfactory for plausible rather than polar values<sup>of the elasticities, an approach</sup> implemented in Mitra et. al. (1989b).

10/ The demand system is kept constant<sup>in these experiments. Sensitivity</sup> of the rankings to changes in demand<sup>systems is examined in Dahl and</sup> Mitra (1990).
Solution (ORS) and for adults, sodium chloride injections, are viable rehydration treatments. The survey results indicate that 62% of facilities in Tanzania, 58% in Sierra Leone, and 39% in Kenya have stocks of both. Figure 10 indicates that hospitals are more likely than lower-level facilities to stock both types of rehydration treatment. ORS is more commonly available than sodium chloride injections, and lower-level facilities are more likely to stock only ORS than are hospitals (Appendix Table 3). The relatively low availability of these basic rehydration treatments is concerning not only from a COVID-19 preparedness standpoint, but also for readiness to treat diarrhea and other common conditions that manifest with dehydration.

**Figure 10** : Availability of rehydration supplies by facility type (hospital vs. non-hospital) and urban/rural location in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Hospital Non-hospital Hospital Non-hospital Hospital Non-hospital<br>Kenya Sierra Leone Tanzania<br>No hydration available Sodium chloride inj. only<br>Oral rehydration sol. only Both treatments available<br><!-- End of picture text --> During a pandemic, supply chain functionality is important to ensure that needed stocks of equipment, supplies, and medicines are available in facilities under conditions of high utilization. The Kenya SDI captures some information along these lines. For instance, 62% of facilities in Kenya reported that the average time between ordering new supplies and receiving them is more than two weeks. During emergencies, a more efficient supply chain may be required.

As mentioned previously, large facility surveys like the SDI do not collect information on available stocks of medicines and treatments (a facility will be recorded as having the medicine even if only one tablet is available). It is therefore not possible to say whether, for example, facilities identified as having a supply of azithromycin would have adequate stock to meet rising demand.

19
in the previous 12 months, while this figure was less than one fifth for similarly educated men (Demographic and Health Survey, 2013). Despite their relatively high education, only 53 percent of these women reported that they could decide for themselves how their earnings were used. On the other hand, among these highly educated working women, at least half of them worked in an office setting, with 52.2 percent reporting occupations that could be classified as _professional_ , _technical_ , or _managerial._<sup>_3_</sup> Slightly less than half of men in this category worked in an office job<sup>4</sup> . Therefore, this data on representation across occupations suggests that these highly educated female applicants should have had reasonably equitable chances of securing an office job using the program-provided training.

Nevertheless, interviews with the target population and training center directors prior to the experiment suggested that women in Nigeria may face higher obstacles in obtaining employment in an office setting, not only because of labor market discrimination, but also because of women’s own confidence in seeking work in the formal sector. To test this hypothesis, the initial assessment taken by all applicants also included questions and tasks designed to measure biases related to female labor force participation, female participation in an office job, and female professionalism.

While the government was interested in building the ICT-enabled service sector across all five cities that participated in the experiment, these cities demonstrated very different levels of sector maturity at baseline. Of the approximately 470,000 people across the country employed in the information and communication sector in 2010, 18 percent were in Lagos (Nigeria’s largest city), 3 percent in Kano, 1 percent in Kaduna, 0.8 percent in Enugu, and 0.7 percent in Abuja (National Bureau of Statistics, 2010).

# **Empirical strategy**

# _Data_

The computer-based pre-assessment provided a platform to collect baseline data on the 3,018 applicants of the program. A self-administered questionnaire followed the assessment, with questions on applicants’ socio-economic and demographic backgrounds, education history, and

> 3 While not all occupations classified as _sales_ or _services_ can be considered office jobs, including these as office jobs would bring the percentage to 87.2 percent.

> 4 This figure increases to 73 percent when _sales_ or _services_ responses are included as _professional_ , _technical_ , or _managerial_ .

8