rafmacalaba/datause-encoder-data huggingface dataset:
**Table 18: Survey results - Restrictions in cross-border trade in Darjeeling and Nepal**

|Restrictions|Frequ<br>**Darjeeling**|ency<br>**Nepal**|Percentage of<br>indicating c<br>**Darjeeling**|respondents<br>onstraint<br>**Nepal**|
|---|---|---|---|---|
|High Cost|0|0|0|0|
|Lack of information|24|2|92|4|
|Lack of relationship with sellers|25|6|96|11|
|Transport Costs|0|6|0|11|
|Border Crossing Costs|4|14|15|26|
|Delays due to border crossing|4|11|15|20|
|Payment Difficulties|0|4|0|7|
|Other reasons|0|11|0|20|

_Source: Field Survey_ <u>Other constraints that impact trade and competitiveness</u> Various other general constraints are faced by farmers in the three countries. The most severe constraints are summarized below (Table 19).

**Table 19: Major Obstacles Faced in Tea Chains**

||**Bangladesh**|**India**|**Nepal**|
|---|---|---|---|
|Electricity|20|73|75|
|Obtaining licenses||24|27|
|Labor regulations|||97|
|Transport|3||75|
|Skills|3|42|92|
|Corruption|||97|

_Source: Field survey_ Among other constraints noted are:

- **_Packaging_** : This is seen to be extremely poor in the region – it often done in plastic materials and cement bags, leading to poor quality control in the transport process. This is apparently more of a problem on the Indian side of the border, where packaging is determined by the requirements of the auction (set by the Tea Board); in Nepal most packaging requirements are stipulated by the ultimate buyers.

- **_Warehousing_** : In Darjeeling, there is substantial warehousing space available at a low cost due to government provision, while in Nepal farmers often are forced to have their own facilities (and this is under-provisioned). In Darjeeling, however, warehouse quality is often poor (no temperature control) and theft is a problem.

- **_Infrastructure_ :** At Siliguri, most people commented that roads are good except those in the interior. In Darjeeling there are road connectivity problems and water scarcity problems, with no ground water. Electricity is available 24 hours in Siliguri and Darjeeling. In Nepal, on the other hand, there is shortage of power, with “load shedding” power cuts of about 12 hours per day common in the winter months – this is seen as a significant constraint in the sector, both for irrigation and the prospects of processing. In addition, roads are generally in a poor state.

- **_Labor_ :** Both the Indian and the Nepali sectors report problems with labor shortage. In addition, huge wage differences between Indian, Bangladeshi, and Nepalese workers can

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- 11 -

# Table 4 Concentration of credit to related parties

|Institution|As % of<br>total loans<br>Dec. 1982|As % of<br>total loans<br>Feb. 1983|
|---|---|---|
|Banks|||
|De Santiago|42.3|45.8|
|Nacional|25.7|30.1|
|Internacional|22.8|25.9|
|Colocadora Nacional de Valores|23.8|24.4|
|De Chile|18.6|19.7|
|Hipotecario de Fomento Nacional|18.5|18.9|
|Sud Americano|14.8|16.2|
|De A. Edwards|14.9|15.4|
|Credito e Inversiones|11.9|12.0|
|Concepcion|12.2|12.0|
|Del Pacifico|10.0|10.3|
|O'Higgins|9.1|8.9|
|Morgan Finansa|7.0|6.8|
|osorno y La Union|5.6|5.9|
|nd. y de Comercio Exterior|4.0|5.5|
|Del Trabajo|1.6|1.9|
|Finance Companies|||
|Corfinsa S.A.|20.8|22.4|
|Fusa S.A.|22.5|18.0|
|De Interes Social S.A.|15.4|18.0|
|LatinoAmericana De Desarrollo|7.6|7.9|
|Condell S.A.|6.8|6.4|
|Comercial|1.5|1.4|
|Mediterraneo S.A.<br>|0.1<br>|0.1|
|Davens S.A.|0.2|-|

Source: Superintendency of Banks (Informacion Financiera).
6 inputs were virtually unknown. Even inventory systems were lacking. The study collected the information regularly included in patient records in developed countries. This entailed tracking patients as they moved through the hospital and recording the type and quantity of all services, pricing all components of those hospital services, collectinc price data for all delivered services, estimating time costs of medical personnel, and undertaking a measure of all indirect costs.

# Cost and Data Collection Methodology and Approach

Variable costs were collected using a combination of time and motion studies to examine the nature and cost of medical services, and the resource use and value of ancillary services and other inputs devoted to patient care. Close to census samples were taken since virtually no information on patients, providers or non-personnel inputs existed for the hospital.

**Survey of Patients.** A set of questionnaires and registries was developed and implemented in Aybar Hospital during the period April 17 through 28, 1989. The questionnaires were used to collect information on patient socioeconomic characteristics, patient care time by medical staff, and use of consumaLles, drugs, diagnostic tests and operating theater; registries collected information on services rendered to categories of patients not surveyed with the questionnaires. A set of questions on shortages and their impact on medical decisions was also included to capture the extent of unusable or unavailable equipment, instruments, consumables or drugs.<sup>4</sup> ' All questionnaires were pretested by a team of Dominican physicians. Data collectors were all graduated physicians.

The sampled patients include the following: all patients entering emergency service over a one-week period; a sample of outpatient visits during that week, and those not included in the outpatient survey were inventoried;<sup>5</sup> ' and inpatients admitted to five of the hospital's 18 wards during a two-week period **(3** surgical wards and 2 ophthalmology wards). Inpatients were tracked daily after the termination of the survey until their discharges. Hence, the sample of inpatients was not truncated.

Time allocation of all physicians and nurses providing care to or for patients was included in the emergency, outpatient, and inpatient surveys. Data collectors recorded the amount of medical attention, administration for the patient, and supervision/observation of/by medical staff (to capture teaching and 4/ The Spanish language questionnaires are contained in Appendix A of Lewis **et al.** (1990).

5/ Originally all outpatients were to be surveyed; however, the concentration **of consultations** in the early hours of the day due to physicians' schedule preferences, forced physicians to double and triple up in consultation rooms. Thus, the enumerators surveyed as many patients as possible, and only counted **the overflow** whom they did not have time to include in the survey.
# **Abstract**

The Latvian economy made great strides in recovering from the economic shock of the early transition and the adverse after-effects of the 1998 Russian financial crisis. Nevertheless, Latvia faces serious challenges to its future growth and prosperity despite these impressive achievements and the outward appearance of macroeconomic stability and economic progress.

However, a wide variety of recent studies suggest that the Latvian economy is not particularly competitive and, even more worrisome, they indicate that Latvia is not well positioned to gain ground in the race for global competitiveness, prosperity, and rising standards of living. Most of Latvia’s growth to date has come from one-off gains generated by structural reforms, privatization, and reallocating resources, not inexhaustible reservoirs of growth. Latvian enterprises will be able to sustain economic growth and create high wage jobs only by becoming internationally competitive, innovating, accumulating new knowledge and technology, and finding a high value added niche in the European and global division of labor.

This paper is designed to help Latvian leaders (i) develop and clear diagnosis of the innovation and competitiveness challenges facing Latvia as it prepares to enter the EU and, more importantly (ii) design and implement policies and programs to ensure that Latvia reaps the maximum possible benefits from EU structural funds. Section II analyzes the current structure of Latvia’s production, imports, and exports. Section III utilizes data from a number of competitiveness reports to benchmark Latvia’s current progress against a number of comparator countries and to pinpoint Latvia’s strengths and weaknesses as an innovative economy. Section IV offers a detailed list of potential policies and programs that could improve the competitiveness of Latvian enterprises and the efficiency of the Latvian NIS. The recommendations include specific policies and programs to improve (i) the production of knowledge in Latvia, (ii) the commercialization of technology produced by Latvian scientists, small companies, and research institutes, and perhaps most importantly, (iii) local firms’ capacity to absorb, adapt, and adopt existing knowledge produced outside Latvia for use inside Latvia.

2
these two effects. In contrast, demographics contributed to only 35 percent of Namibia’s poverty reduction.

**Figure 6: South Africa will maintain the highest share of people 65+ in SACU till 2050**

Population share of people 65+ (percent) <!-- Start of picture text --> 11<br>Botswana<br>Lesotho<br>10 Namibia<br>South Africa<br>9 Swaziland<br>Sub-Saharan Africa<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1950 60 70 80 90 2000 10 20 30 40 50<br><!-- End of picture text --> Source: Authors’ estimates Note: Data from UN (2015). The value for Sub-Saharan Africa is a weighted average.

**Figure 7: Demographic change and realizing the demographic dividend can account for 35 to 75 percent of poverty reduction in SACU economies by 2050**

Poverty headcount rate, 2012 (percent) Change in poverty headcount rate, 2012-50 (percentage points) <!-- Start of picture text --> South<br> 60<br>Botswana Lesotho Namibia Africa Swaziland<br>0<br> 50<br>-10<br> 40 (11.6) (13.4)<br>-20 (16.9)<br> 30<br>-30 (26.6)<br> 20<br>-40 (37.9)<br> 10 -50 Realizing demographic dividends<br>Botswana Lesotho Namibia South Swaziland Shifts in household distribution<br>Africa<br>No demographic effects<br><!-- End of picture text --> Source: Panel A is from PovcalNet (2016). Panel B is from authors’ simulation results. Note: The poverty headcount rate is based on $1.90 international poverty line determined with 2011 PPP.

Considering the policy outcomes that could enhance the magnitude of SACU’s demographic dividends, almost all the interventions considered – on educational improvement, savings and investment, and improving the employment ratio – accelerate income per capita growth beyond 17
# Table 3. Average loan size, per capita  w/ Interaction term (MFI-level regression)

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

## - <u>Panel A: Overall Score Nominal (0 100)</u>

|||<br>De<br>Version 1(Ful sampl|<br>pvar: Average loan si<br>es)|ze /per capita G<br>Version 2|<br>NI<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Overall Score Nominal|0.0049|-0.0003|0.0014|0.0052|-0.0004|0.0012|
||[0.178]|[0.697]|[0.472]|[0.166]|[0.588]|[0.561]|
|NBFI dummy * Overall Score Nominal (0-100)|0.0067|0.0032***|0.0021|0.0079|0.0032***|0.0023|
||[0.273]|[0.002]|[0.454]|[0.210]|[0.001]|[0.430]|
|Combined group dummy * Overall Score Nominal (0-100)|0.0063|0.0024**|0.0013|0.0119|0.0039***|0.0006|
||[0.497]|[0.032]|[0.666]|[0.357]|[0.001]|[0.869]|
|NBFI dummy|-0.3001|-0.0963*|0.0138|-0.3419|-0.0973**|-0.0002|
||[0.348]|[0.064]|[0.930]|[0.295]|[0.046]|[0.999]|
|Combined group dummy|-0.0303|0.0206|0.1097|-0.2077|-0.0914|0.1562|
||[0.945]|[0.710]|[0.526]|[0.731]|[0.130]|[0.425]|
||[0.002]|[0.000]|[0.012]|[0.002]|[0.000]|[0.015]|
|Observations|641|641|616|583|583|560|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.272|0.573|0.409|0.283|0.615|0.431|
|Adjusted R-squared|0.25|0.56|0.39|0.258|0.602|0.41|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.0115*|0.00296***|0.00353|0.0131**|0.00279***|0.00354|
||[0.067]|[0.002]|[0.315]|[0.048]|[0.002]|[0.330]|
|Combined group dummy|0.0112|0.00211**|0.00273|0.0172|0.00351***|0.00182|
||[0.197]|[0.041]|[0.413]|[0.157]|[0.002]|[0.649]|

25
Figure A5: M&A Number of Investing Firms 2003-2022 <!-- Start of picture text --> 3500<br>Developing<br>High-income<br>3000<br>2500<br>2000<br>1500<br>1000<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>Number of investing firms<br><!-- End of picture text --> Source: Authors’ analysis on Refinitiv M&A. Note: The analysis relies on M&A investment project unique firm identifiers, without any exclusion from the full sample. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

Figure A6: Outward FDI Concentration Ratio, Top Investing Countries <!-- Start of picture text -->
1.6<br>United States<br>Japan<br>Germany<br>1.4 China<br>United Kingdom<br>1.2<br>1<br>.8<br>.6<br>2010 2012 2014 2016 2018 2020 2022<br>Project year<br>Concentration Ratio (5 investing MNEs)<br><!-- End of picture text -->

Note: The analysis relies on greenfield FDI capital expenditure values. Each line denotes the destination country’s outward FDI concentration ratio calculated as the share of the 5 largest outward investors for each year.

20
**<u>Table 8: Labor Market Performance Indicators by Gender</u>**

||**Work**<br>**Pop**<br>**(**|**ing A**<br>**ulatio**<br>**15+)**|**ge**<br>**n**|**Work**<br>**Popul**<br>|**ing A**<br>**ation (**<br>**64)**|**ge**<br>**15-**<br>**Wor**<br>**Popul**|**king A**<br>**ation (6**|**ge**<br>**5+)**|**Part**|**icipatio**<br>**Rate**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|%Δ||Mean|%Δ|Mean|%Δ||Mean|%Δ||
|Male|65.5|0.56|***|61.5|0.52|***<br>3.9|0.02|***|77.3|-2.43|*******|
|Female|66.7|0.13|***|61.9|0.31|***<br>4.8|-0.17|***|47.5|-1.28|*******|
|**Philippines**|**66.1**|**0.35**||**61.7**|**0.42**|**4.4**|**-0.07**||**62.4**|**-1.82**||
||**Emp**|**loyme**|**nt**|**Unem**|**ploym**|**en**<br>**Daily**|**Earnin**|**gs**||||
||Mean|%Δ||Mean|%Δ|Mean|Δ|||||
|Male|72.0|-2.5|***|6.8|0.3|195.5|-7.6|***||||
|Female|44.3|-1.1|**|6.7|-0.3|*** 194.8|-9.3|***||||
|**Philippines**|**58.1**|**-1.8**||**6.8**|**0.1**|**195.3**|**-8.3**|||||

_Source:_ LFS, 2003-2007 _Note:_ Test statistics are shown for significance of change in each indicator during the 2003 – 2007 period: *, **, and *** represent significance at 10 percent, 5 percent and 1 percent, respectively.

**Table 9: Inequality in daily wage distribution by age and gender groups**

**(2003-2007)**

A. Inequality of Daily Wage distribution <u>by Age Group</u>

||**P90/P10 ratio **|**Gini index**|
|---|---|---|
|Age group (15<=age<25)|5.4|0.34|
|Age group (25<=age<50)|5.4|0.35|
|Age group (50<=age<65)|8.0|0.43|
|Age group (65<=age)|6.3|0.46|
|**Philippines**|**6.3**|**0.38**|

B. Inequality of Daily Wage distribution <u>by Gender</u>

||**P90/P10 ratio **|**Gini index**|
|---|---|---|
|Male|4.8|0.35|
|Female|8.3|0.42|
|**Philippines**|**6.3**|**0.38**|

_Source:_ LFS, 2003-2007

# **4. The role of individual and regional characteristics in employment and wages**

# _4.1 Description of the Model_

This study employs the Heckman correction estimation instead of Ordinary Least Squares estimation in order to address the potential problem of selection bias as participation in the labor force is not random. Individuals who choose to work may be different from those who choose not to work – only those whose reservation wage does not exceed the wage offered by the 10
Table 8: Whether Woman Reports Beating is Ever Acceptable

|Dependent variable = 1(R|eports that Dome|stic Violence|is Ever Accept|able)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Work||0.157||-0.022|
|||[0.146]||[0.050]|
|Age First Marriage|-0.011**|-0.008|||
||[0.005]|[0.005]|||
|Work X Age First Marriage||-0.010<br>[0.010]|||
|Education|||-0.011***|-0.013***|
||||[0.004]|[0.004]|
|Work X Education||||0.005<br>[0.007]|
|Observations|1,330|1,318|1,319|1,319|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1._
improved stoves, most of these studies focus on a particular stove and are not able to identify preferences for attributes of stoves in general. There is limited information on preferences and willingness to pay for particular attributes of stoves in general and this information is vital for policy makers working on designing effective stoves and improving stove adoption.

In this paper we use a choice experiment survey to elicit preferences for improved stoves and to evaluate the benefits relative to costs of stove adoption taking into account both household level benefits and the potential economic value of payments for reducing deforestation. We conduct the choice experiment in Ethiopia as Ethiopia provides an interesting context for the study of improved stove initiatives with about 96% of the population dependent on biomass for household energy needs (Bizzari 2010, Beyene et al. 2015) and the government currently engaged in effort to increase improved stove adoption. Though we focus on Ethiopia the results and the policy recommendations are applicable broadly to Sub‐Saharan Africa and other developing countries in Asia and South America.

Our results indicate that rural Ethiopian households put significant value on each of the attributes of the improved stove that we consider– durability, cooking time reduction, fuelwood use reduction, smoke reduction and cost. We find that durability has the highest average WTP per unit of each attribute, though because units differ we cannot directly compare the average WTPs across attributes. Among cooking time reduction, smoke reduction and fuelwood reduction (all denominated as percentages), respondents have the highest WTP for fuelwood reduction. Contrary to our expectations, most of the household demographic variables that we believed would influence stove adoption are not significant and robust determinants of household stove adoption. We do find that having previously seen an improved stove increases the respondents’ WTP for improved stoves. Finally, we find that using estimates of fuelwood savings derived from field measurements for the Mirt stove, our choice experiment valuation of fuelwood savings and at a modest carbon price, Mirt stoves would lead to avoided greenhouse gas emissions valued at a significant percentage of the cost of Mirt stoves.

Often, campaigns by international organizations to make improved stoves available focus on an initial wave of distribution for a new design. Our results suggest the need to first ascertain and emphasize the attributes of the stove that are most important to households. With the Mirt 19
contrasts kernel-density estimates for these outcomes and reveals a clear shift to the left for these distributions when focusing on children of illiterate mothers. These are meaningful differences which indicate that the children of illiterate mothers are worse off.

The ECED survey also collected child outcomes on a battery of cognitive tests and examinations of school-curriculum materials. Children were administered tests to assess their ability in terms of Bahasa Indonesia, mathematics and abstract reasoning. Two versions of the overall test were administered: a shorter test for 6 and 7-year-olds and a longer test for 8 and 9-year-olds. In this paper, we use the common set of items that were included in both versions of the test. We standardized the percentage of correct answers for each test to have a mean of 0 and standard deviation of 1, using the age-specific mean and standard deviation of the entire ECED sample.<sup>7</sup> The language test consisted of two sections. The first section (match pictures) evaluated children’s phonological awareness (i.e., whether they can match pictures that start with a given sound) and letter recognition (i.e., whether they can match pictures that start with a given letter). The second section (mention objects) assessed children’s vocabulary skills (i.e., whether they can name the word associated with a given image). The mathematics test included two sections. The first section (summation) evaluated children’s ability to add and subtract (i.e., whether they can add to or subtract away from a set of objects). The second section (order numbers) assessed children’s ability to recognize patterns (i.e., whether they can order one- to two-digit numbers in ascending and descending order). Finally, the abstract reasoning section was modeled on the Ravens Progressive Matrices. Students were presented with an image that was missing a small section and asked to select the missing pieces from six options, based on color, pattern, and orientation.

Like in the case of health outcomes we see a strong positive gradient between child performance on each of these tests and maternal education. Figure 3 reveals a clear positive gradient between scores and maternal education. When looking at raw scores we see that children of literate mothers get rates of correct responses that are approximately 13.6 p.p. (Language), 15.8 p.p. (Math), and 10.7 p.p. (Reasoning) higher than their peers whose mothers cannot read, for example.

> 7Panel A in Table A5 presents descriptive statistics for the raw scores in the working sample (in terms of percentage of correct answers).

9