rafmacalaba/datause-encoder-data huggingface dataset:
9 hypertension were developed fcr the Dominican Republic by a team of Dominican physicians. These ire used to compare practice patterns and cocts of the ideal procedures with actual diagnosis and treatment to evaluate quality of care as weil as possible unnecessary expenditures.

# IV. QUALITY OF CARE

Quality is difficult to measure. An important gauge of quality is typically the qualifications of attending staff. In the developing country context, the lack or inappropriate mix of manpower and other inputs (e.g., drugs, consumables and so on) is reported to compromise quality; however, no good evidence exists on this issue. Another accepted but indirect measure of quality has been government expenditures on health ot. on particular health facilities. Unfortunately, the allocation and use cf resources is rarely examined, nor is efficiency controlled for, so that this too is an inappropriate quality measure.

In attempting to complement these types of measures and adjust for shortcomings, data were collected in Aybar Hospital to examine: (1) the qualifications of medical manpower deliverLng services, (2) the extent and nature of shortages, (3) the services on which the hospital has actually spent its resources, as compared to what was ordered by physicians, and, (4) for selected diagnoses, the specifics of clinical practices in the hospital and how they compare to accepted clinical norms. The latter comparison is based on norms developed by Dominican physicians for this effort (See Lewis, et al. 1990).

# Medical Staff Time Allocation

Although almost every patient treated at the hospital received some physician attention, very feo inpatient or emergency patients were treate6 by a staff physician or higher level physician. Table 1 summarizes the doctor-patient contacts by physician level. The vast majority of emergency and inpatients were treated by interns and lower level residents (89 percent and 62 percent respectively). Outpatients, however were treated by higher level physicians 86.2 percent of the time.

Among nursing staff, a similar pattern for inpatients and emergency patients emerges, with auxiliary nurses providing over 90 percent of the patient care. In outpatient services, nursing care was minimal--affecting only 111 patients, and provided almost exclusively by auxiliaries.

This pattern of care suggests a low level of service provided to patients, especially in emergency and the wards. Moreover, since most of the attending physicians are students, involvement of staff and chief-of-service physicians would be expected. Other evidence from the study suggests that few interns and residents are under the supervision of experienced physicians. Very little supervision/observation time was recorded for the higher priced physicians who constitute the teaching staff, which means that a limited amount of instruction occurs during patient care (See Lewis et al., 1992).
-.49^

aggregate output, however (equation (5.2)). Direct taxation was not found to be significant and therefore subsequ'ently dropped from equation (5.2). In any case the stropg results obtained for the influence of aggregate economic distortions on agricultural growth as well as its share in the economy, in addition to the fact that the distortions have increased quite substantially from intermediate levels, corroborate the predictions of the Easterly (1990) model which shows that the impact on growth is likely to be larger, the larger the increase in distortions starting from intermediate initial levels.

For both measures of agricultural growth, investment has appreciable and statistically significant effect. The factor reflecting the role of labor was not found to be significant in each of the two specification and was therefore dropped from both equations. The factor representing the effect of capacity utilization as reflected by the rate of growth of intermediate goods' imports, has a perverse but insignificant effect in each of the two specifications. On the other hand, there is some weak evidence that permanent deterioration in the TOT tend to reduce the share of agriculture in aggregate output. Real growth rate in ag.iculture, however, does not seem to be influenced by the exogenous factor (TOT shock) beyond its effect through overvaluation. The evidence - at least for the share of agriculture in the economy - supports the widely held view regarding the exogeniety of growth in SSA (e.g. Ndulu (1990)).'7 This also shows the vulnerability of agriculture in Sudan (and SSA) to permanent worsening of TOT even when domestic corrective measures were taker to accommodate the TOT shock. The evidence on the non-relevance of the effect due to import compression - induced decline in capacity utilization noted for SSA (e.g. Ndulu (1990)), is contrary 27"n fact permanent TOT deterioration would lower .-.quilibrium share of agriculture in the economy. This does not say that growth is determined by external TOT. But here I am implicitly assuming that at this early stage of the development of SSA; the global growth of its economy requires a rising share for agriculture-especially export-oriented agriculture, so that the process of structural transformation towards a more diversified and fast growing economy can be achieved.
completed secondary school, and 22 percent had an undergraduate or graduate degree. These figures reflect the relatively high education level of formal sector workers in Bangladesh relative to the population as a whole. Average language and math scores were 4.74 out of 8 and 5.8 out of 8, respectively, with a standard deviation of 2.62 for language and 2.05 for math. The language and math tests measure a range of primary-level competencies to assess basic cognitive skills. Eighty-nine percent of respondents were permanent employees, 4 percent worked on temporary full-time contracts, and 6.3 percent worked on part-time contracts. Thirty-two percent of respondents were construction and craft workers, 16 percent were clerks, and 13 percent were professionals. Sixty-nine percent of respondents worked for large firms and 67 percent worked in manufacturing. Fourteen percent worked in public administration and 12 percent worked in the education sector.

The average entry-level wage was 5,698 Bangladeshi taka (BDT) per month, and the average current wage was BDT 9,377 per month.<sup>11</sup> Combined with an average of five years of job experience, this indicates that wages grew at a rate of BDT 800 per year. Summary statistics for the standardized Rasch scores, as well as the raw scores for each of the eight personality traits, are also presented in Table 3. Both Rasch scale scores and raw scores were standardized to have a mean of 0 and a standard deviation of 1. Six of the eight personality trait scores (or domains) had three items each, while decision-making style had four items and hostile attribution bias had two. Unanswered items were excluded, and as a result, the sample size for each trait varies.<sup>12</sup> It is important to note that these personality traits may not remain constant over time. The literature presents mixed conclusions on the persistence of personality traits. Many researchers agree that while personality traits vary over an individual’s lifetime, they remain sufficiently stable across situations to confirm their continued existence (Cobb-Clark and Schurer, 2012; Borghans _et al_ ., 2008; Heckman and Kautz, 2012; Soto _et al_ ., 2011). There is strong evidence that mean personality

> 11 Wage data were converted to a log scale for subsequent regression analysis. By controlling for tenure, relative prices at the time of entry were accounted for in the entry-level wage model.

> 12 The sample size varies between 5,304 for “conscientiousness” and 4,385 for “decision-making style.” The questions related to decision-making style were placed toward the end of the questionnaire, and some respondents may not have completed the entire set of questions. Unintelligible or clearly false answers were also excluded. The survey contained 752 cases in which the answers of one respondent were identical to those given by at least one other respondent in the same firm. As two or more respondents providing the exact same responses for 24 items is unlikely, these cases were excluded if they occurred within a single firm.

11
# **Annex F – Validation and Precision**

One concern often raised regarding poverty map results is that the poverty estimates may not be precise enough for the purposes to which they are put. One way to assess this is to measure whether there are clear and statistically significant differences between the resulting poverty rates at the district level that can be usefully integrated into the work of the World Bank, counterparts, and other partners. This can be demonstrated graphically by reporting the poverty rates with the associated 95% confidence interval.

Because there are some districts in which there are no survey observations, these must be imputed out of sample when using the FH approach. In this minority of cases, the root mean square errors are larger than average, and should be used with caution. These cases of relatively high imprecision are noted in the district level tables with red stars (*). However, it is also important to note that most cases in this application are in sample do not suffer from this limitation of the FH approach.

**Figure 32: Upper and Lower-bound Confidence Intervals, Including Out-of-Sample Predictions**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>fh_55 upper_kaz lower_kaz upper_kgz lower_kgz<br>upper_tjk lower_tjk upper_uzb lower_uzb<br><!-- End of picture text --> Another way to demonstrate performance is to benchmark against “gold standard” results using the Elbers, Lanjouw, and Lanjouw (ELL) method previously published by the World Bank. The following graph demonstrates the comparison visually for 17 previously published maps that used the ELL approach. The results from the Central Asia poverty map perform quite well in this comparison for those cases that are in-sample. The 95% confidence intervals are usually well within those of other
**Table 2: Output means per port authority: 1990-2002**

|**Port Authority**|**Containers**<br>**(tonnes)**|**Liquid bulk**<br>**(tonnes)**|**Other Cargo**<br>**(tonnes)**|**Passangers**<br>**(number)**|
|---|---|---|---|---|
|B. Algeciras|14,209,958|7,356,611|7,722,807|3,800,291|
|Alicante|625,242|385,326|1,610,922|185,242|
|Baleares|1,481,024|2,653,233|5,019,041|3,153,879|
|Barcelona|8,614,954|8,233,942|7,558,250|1,239,828|
|Bilbao|3,233,316|4,076,144|10,581,053|104,084|
|Las Palmas|3,854,494|3,601,112|5,265,526|985,888|
|S.C. Tenerife|2,292,871|7,792,254|4,580,726|3,609,534|
|Valencia|9,214,198|1,513,538|7,861,188|246,620|
|Vigo|964,664|409,051|2,161,053|1,350,561|

Source: Own elaboration from data from Puertos del Estado (some years).

# **5.1.2 The factors**

In order to perform their function as infrastructure service providers, port authorities use three productive factors (see table 3 for a quantitative summary). First, they employ the **labor** factor, approximated by the mean number of employees of port authorities, where not only administrative staff is included but also more specialized technical employees. The nature of the activities performed by this productive factor (supervision of port facilities, control of port operations, port promotion, management of garbage, etc.) makes labor a very important resource.

Another important factor consists of the **berths** necessary for ships’ docking. Berths have been measured in linear meters and we have considered not only the berths owned by port authorities but also by private people (for example, shipyards). Only those berths not reaching a 4-meter depth have been excluded since they are places basically intended for water-sports activities.

Finally, the third factor is the **land area or surface** that has been measured in square meters and includes warehouses, roads and the rest (gardens, buildings, etc.). This area encompasses all port facilities, i.e. property owned by port authorities as well as property assigned under administrative concessions to port companies.

**<u>Table 3: Factor means per port authority: 1990-2002</u>**

|**Port Authority**|**Berths**<br>**(meters)**|**Surface**<br>**(squared meters)**|**Labor**<br>**(employees)**|
|---|---|---|---|
|B. Algeciras|10,239|2,486,245|282|
|Alicante|5,591|1,115,208|153|
|Baleares|16,470|1,545,945|279|
|Barcelona|19,976|7,404,316|487|
|Bilbao|16,473|1,830,255|374|
|Las Palmas|16,335|2,535,582|297|
|S.C. Tenerife|15,361|1,837,463|207|
|Valencia|11,628|4,355,749|369|
|Vigo|9,376|1,834,232|239|

Source: Own elaboration from data from Puertos del Estado (some years).

13
serve as legal witnesses (Hakim and Aziz, 1998). The second category is associated with the enforcement of _purdah_ (the seclusion and hiding of women from men) or the seclusion of women (Amin 1995). All of these restrictions curtail women's decision making about factors that affect their lives: education, employment, and other economic and social activities, as well as medical services.

Efforts to increase women’s social status in Pakistan have achieved some success, particularly efforts to provide education for girls (Hou 2010). Most recently, the Benazir Income Support Program (BISP) was established to provide cash transfers to the female heads of the poor families. This program provides Rs. 1,000 ($12) per month to eligible families, and cash is delivered to the doorstep to ensure that women receive it. The main objective of making women the beneficiaries is to empower them.

# **3. Methods**

# 3.1. Data

The data for this study come from the Pakistan Social and Living Standards Measurement Survey (PSLM) 2005-06, which is a large household survey of more than 15,000 households on a range of social sector issues, including education, health, immunization, pre/postnatal care, family planning, and household consumption. More specifically, a maternal history was recorded for all women between 15 and 49 years old. Details about maternity health care utilization were collected for women who had delivered a child within three years of the date of interview. After excluding cases with missing values, a total sample of 5,061 women (from 4,515 households) is included in this study.

# 3.2. Dependent variables

Four dependent variables are used for this study: prenatal care, institutional birth, skilled birth attendance, and postnatal care. They are all treated as dichotomous variables indicating the use or non-use of the service. Institutional birth is defined as 1 if birth takes place in a government or private hospital or clinic, and 0 otherwise; skilled birth attendance is defined as 1 if the pregnant woman receives assistance from a midwife, trained Dai, doctor, LHW, nurse, or other health professional, and defined as 0 if assistance is received from a family member, relatives, or neighbors.

# 3.3. Construction of indices of women’s decision-making power

There are eight questions in the PSLM regarding household decision making about education, employment, birth control methods, having more children, and household food, clothing, medical treatment, and recreation expenditures. The answers to these questions can be broadly categorized as “woman decides alone,” “household head or husband decides alone,” “household 4
|Home FE|Y|Y|Y|Y|
|---|---|---|---|---|
|Sector FE|Y|Y|Y|Y|
|Observations|2,508|2,508|2,508|2,508|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, *np<0.1 We then consider the interaction of IEAs with cultural distance (Table 6). In Model 10 we interact BIT and PTA with _Com language,_ which is a binary variable that takes the value of 1 if the two countries share a common official language (spoken by at least 20% of the population of the country). Consistent with our expectations, the coefficient of this interaction is negative, although it is only statistical significant in the second stage negative binomial regression. Thus, as these results suggest, the BITs are more effective in promoting investments between pairs of countries that don’t speak the same official language. In this sense, BITs appear to also work to reduce the effects of _cultural_ distance. The coefficients for _PTA*Com language_ , on the other hand, while having the expected sign, do not reach acceptable levels of statistical significance. In Model 11, we use an alternative measure of cultural distance, _Com language (ethno_ ), which is set to 1 if a language is spoken by at least 9% of the population in both countries. The interaction of BIT and common language continues to be negative and significant in the count model only. In addition, when using this alternative measure of common cultural background, _PTA*Com language_ now becomes negative and significant in the first stage logistic model, suggesting PTAs have a greater effect on the odds of investment in pairs of countries that are culturally different than in those that share a language.

**Table 6.** Cultural distance, IEAs, and FDI. Hurdle negative binomial regressions

||(1|0)|(|11)|
|---|---|---|---|---|
|VARIABLES|Logit|NB|Logit|NB|
|_GDPpcj_|0.00684|-0.00877|0.139|0.149|
||(0.161)|(0.223)|(0.136)|(0.247)|
|_Populationj_|0.575***|0.563***|0.534***|0.343**|
||(0.113)|(0.160)|(0.0958)|(0.160)|
|_Distance ij_|-0.0002***|-6.81e-05|-0.0002***|-7.34e-05|
||(4.98e-05)|(5.33e-05)|(4.78e-05)|(6.38e-05)|
|_Trade ij_|5.20e-08*|-1.10e-08|4.98e-08*|-1.91e-09|
||(2.74e-08)|(1.03e-08)|(2.56e-08)|(1.28e-08)|
|_Contiguity ij_|0.599|0.401|0.400|0.208|
||(0.568)|(0.675)|(0.553)|(0.768)|
|_Colony ij_|-0.0199|-0.469|0.896|2.421**|
||(0.799)|(1.172)|(0.706)|(1.103)|
|_Comlang (off)ij_|1.742***|13.78**|||
||(0.472)|(5.570)|||
|_Comlang (ethno)ij_|||1.135***|16.42***|
||||(0.436)|(3.311)|
|_BITij_|1.369***|11.98**|0.910**|14.50***|
||(0.458)|(5.567)|(0.428)|(3.323)|
|_PTAij_|0.523|0.340|0.870***|0.624|
||(0.331)|(0.421)|(0.329)|(0.476)|
|_BIT*Comlang ij_|-0.325|-11.10*|||
||(0.877)|(5.726)|||

18
# **4. Savings Determinants in Transition and Non-Transition Economies**

The elimination or at least reduction of price controls after 1989<sup>in EE and after 1991 in the</sup> FSU [De Melo, Denizer and Gelb (1996)] opened a margin of substitution,<sup>eliminating forced</sup> savings [Kornai (1992), Welfe (1989), Quant (1989)], primarily through a reduction<sup>of the real</sup> value of savings through inflation. For the 1990/1-1995 period, the evolution<sup>of savings is thus</sup> arguably best thought off in terms of changes in desired equilibrium savings. Table<sup>4 begins the</sup> exploration of savings in this period by contrasting the savings regression for market economies (underlying the estimation of predicted savings rates reported in table 3) with identical<sup>regressions</sup> estimated for the transition economies. The first column reports a panel regression for<sup>the full</sup> sample, the second column drops the year 1989, the third column reports<sup>results for the final</sup> sample year, and column 4 reports the results for market economies.

**Table 4: Savings Rate Regressions for Transition and Market Economies**

||Transition|Transition|Transition|Market|
|---|---|---|---|---|
||1989-95|1990-95|1995||
|Constant|0.078|0.152|0.683|7.758|
||(0.67)|(1.17)|(2.05)**|(1.00)|
|Dependency Ratio|0.031|-0.032|-0.518|-0.175|
||(0.27)|(0.25)|(1.63)|(2.40)**|
|Urbanization Ratio|0.070|0.010|-0.420|-0.171|
||(0.74)|(0.10)|(1.75)|(3.70)**|
|GDP Growth|-0.213|-0.263|-0.664|0.008|
||(2.41)**|(2.82)**|(1.97)*|(0.05)|
|M2/GDP|0.077|0.076|0.283|-0.077|
||(2.15)**|(1.98)**|(2.23)**|(2.42)**|
|CPI Inflation|-0.001|-0.001|-0.001|0.001|
||(2.79)**|(2.60)**|(0.23)|(0.91)|
|Terms of Trade Change|-0.007|-0.009|0.613|0.119|
||(0.26)|(0.31)|(3.81)**|(1.82)*|
|GDP per capita|1.6E-5|1.37E-5|-4.2E-6|3. lE-5|
||(2.46)**|(1.89)*|(0.28)|(6.57)**|
|War|-0.200|-0.214|-0.229|-0.099|
||(6.03)**|(6.12)**|(3.09)**|(1.42)|
|R2<br>-|0.544|0.55|0.78|0.25|
|Observations|150|131|22|254|
|Time Dummies<br>|N<br>|N<br>|N<br>|N<br>|
|t-statistics in bracket|s. * and **: sig|nificant at the|0% and 5% le|vel.|

7
# Figure 9.A. Economic activity during global recoveries

## A. Output

## B. Output per capita

<!-- Start of picture text --> Index, t = 100 Index, t = 100<br>120 112<br>1975 1982 1975 1982<br>1991 2009 108 1991 2009<br>110 Average Average<br>104<br>100<br>100<br>90 96<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>C. Industrial production D. Trade<br>Index, t = 100 Index, t = 100<br>130 130<br>1975 1982 1975 1982<br>1991 2009 1991 2009<br>120 120<br>Average Average<br>110 110<br>100 100<br>90 90<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>E. Unemployment rate F. Oil consumption<br>Percent Index, t = 100<br>1975 1982<br>8 120<br>1991 2009<br>1975 1982<br>7 Average 115<br>1991 2009<br>6 110 Average<br>5 105<br>4 100<br>3 95<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text --> Sources: British Petroleum; Feenstra, Inklaar, and Timmer (2015); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to the average of the four global recessions identified. Output, output per capita, industrial production, trade, and oil consumption are index numbers equal to 100 in recession years. Aggregates for output, output per capita, and industrial production are market-weighted. For details, see Appendix 2.

41